mobile apps research

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Mobile App Research: The Ultimate Guide for Your App Success

Mobile App Development

 / December 18, 2023

Picture this: You’ve invested your passion and creativity into an innovative app idea, dreaming of millions eagerly clicking “download.” The concept seems promising, but a vital question remains: Does your mobile app idea align with your audience’s needs? Don’t dive into development without clarity on this. Understand your potential users’ desires first. This is where mobile app market research becomes indispensable.

If you crave a thorough guide to embark on mobile app market research, keep reading this article. This guide will enlighten you about the advantages of market research for app development, the significance of choosing the right platforms for your apps, and the latest technologies in the field. Consider this guide your essential partner in mastering mobile app research. Let’s start this enlightening journey together! 

Why Conduct Mobile App Market Research?

Market research is an integral contributor to your app success because it helps reveal what your users crave and what the market truly needs.

In other words, mobile app research helps answer these questions when you learn about your target audience and the market:

• Who are your ideal users? (Age, location, interests, or mobile usage habits)
• What are their needs and pain points? What problems do they face with existing apps?
• What features would they find most valuable? What would make their lives easier or more enjoyable?
• How much are they willing to pay for a solution? What are their spending habits and preferences?
• What platforms do they use most? iOS, Android, or something else?

By answering these questions and many more, you can unlock the following benefits of mobile app research:

Validate App Ideas

Research helps you identify if your app concept is a diamond or a lump of coal. This helps avoid pouring resources into ideas with low market potential and discover hidden gems that users will flock to.

Understand Your Target Audience

Ever tried designing a shirt without knowing your friend’s favorite color or style? It wouldn’t exactly be a hit, right? Similarly, building an app without understanding your target audience is a recipe for disaster. Research paints a vivid picture of your ideal users – their demographics, tech savviness, mobile habits, and even their deepest pain points. This empathy-driven approach ensures your app speaks their language and solves their problems seamlessly.

Analyze Competition

The mobile app landscape is a competitive battlefield, but knowledge is your ultimate weapon. Research unveils the strengths and weaknesses of your competitors, allowing you to learn from the best and carve your unique path. Besides, you can identify untapped market segments, avoid their pitfalls, and create an app so unique and compelling that it leaves them in the dust.

Reduce Risk

Mobile app research serves as a magic shield against costly mistakes. By making informed decisions about features, platforms, and monetization strategies based on real data, you minimize the risk of wasted resources and development dead ends. This ensures your app is set on a path to sustainable success.

A 6-Step Guide to Powerful Mobile App Research

You’ve learned about the importance of market research for your mobile apps. So now, let’s navigate the six actionable steps to conduct mobile app research effectively.

Step 1: Define Your Research Objectives and Questions

Defining your research objectives and questions is the foundation upon which successful mobile app research is built. This step is crucial as it sets the direction for your entire research process. It helps you to focus on what you want to learn and the specific questions you want to answer.

Ask yourself the following questions to help identify your research objectives:

• How big is the market for my app idea ?
• Who are my competitors, and what are their strengths and weaknesses? Can I identify gaps in their offerings or capitalize on their blind spots?
• What are the top frustrations my potential users face in similar apps?
• What features do users value most?
• What pricing model would be most profitable?

By answering these questions, you can formulate clear and concise research objectives. For this reason, you can avoid wasting time and resources on searching irrelevant or unnecessary information. Furthermore, having clear research objectives and questions can help guide your data collection and analysis, ensuring that your research findings are relevant and actionable.

Step 2: Choose Your Research Methods and Tools

Now that you’ve charted your course with clear research objectives, it’s time to grab suitable methods and tools for your research. This step determines how you will collect and analyze the data needed to answer your research questions. Accordingly, choosing the right methods and tools helps you target your research data and increase the reliability of your findings.

There are two main types of research methods you can use: primary research and secondary research.

Primary research involves collecting new data that has not been collected before. For example, surveys, interviews, and observations are all types of primary research. You can use online survey tools, such as SurveyMonkey or Google Forms, to create and distribute surveys to your target audience.

Secondary research, on the other hand, involves using existing data that has been collected by someone else. This can include reports, market research studies, and articles. You can use web search tools, such as Google Trends, Statista, or Pew Research, to find relevant and trustworthy sources of secondary data.

Depending on your objectives and questions, you can use different methods and tools to collect and analyze data. For instance, if you want to understand the size and growth of your market, you might use secondary research to find existing reports and studies. If you want to understand the pain points of your customers, you might use primary research to conduct surveys or interviews.

Step 3: Collect and Organize Your Data

With your research tools in hand, it’s time to embark on excavating your data for mobile app research, by both qualitative and quantitative methods. When collecting data, you should consider various aspects of your target market. These include: 

• Demographics: Age, location, income, and education – these basic details paint a picture of your target audience.
• Psychographics: Values, interests, and lifestyle choices offer deeper insights into user motivations and aspirations.
• Online behavior: Web browsing habits, social media engagement, and even app usage patterns reveal valuable clues about user preferences.
• Mobile app usage habits: How do users interact with apps? What features do they value? Or how often do they use them? These questions help you better understand app usage patterns. Such information is essential for creating an engaging and user-friendly experience later.

After you have collected data, it’s important to record and organize your data in a clear and consistent way. This could involve using spreadsheets, charts, or tables to keep track of your data. Tools like Microsoft Excel or Google Sheets can be particularly helpful for data management and visualization.

Organizing your data effectively can make the subsequent steps of analyzing and interpreting your data much easier. It can help you to see patterns and trends in your data, and it can make it easier to share your findings with others.

Step 4: Analyze and Interpret Your Data

Once you have collected and organized your data, you can start analyzing and interpreting it to find patterns, insights, and answers to your research questions. 

But in the age of data, caution is key. The 2021 Report by Remesh revealed that 56% of agencies are becoming more cautious about how they receive and analyze data. This underscores the importance of responsible data handling in mobile app research.

Data analysis involves looking at your data from different angles and having in-depth insights into these aspects:

• Existing apps in your niche: Understand the landscape, their strengths, weaknesses, and feature sets. This helps you avoid reinventing the wheel and carve out your unique space.
• Market trends and user insights: Stay ahead of the curve by identifying emerging technologies, user behavior shifts, and evolving market demands. This ensures your app remains relevant and competitive.

Remember, analysis isn’t just about dull numbers and texts. It’s about storytelling. You should use data analysis platforms (Power BI, Google Analytics) to transform complex data into interactive dashboards and insightful reports. Besides, consider visualization tools (Graphic_art, Tableau) to create compelling charts, graphs, and diagrams to tell the story hidden within your data. These tools support you in telling a persuasive, thrilling story that all stakeholders can understand.

Step 5: Assess the Risks

Besides opportunities, data analysis helps you spot potential risks linked to your app and the market. Through thorough risk assessments in mobile app research, you are well-equipped to anticipate problems and develop solutions in advance. This way, you can reduce the likelihood of failure when developing and launching the app to market. 

Common Risks Faced by Mobile Apps

During market research, you may discover the following risks the app may encounter:

Market Saturation: Is your niche already overflowing with similar apps? Can you carve out a unique space or offer a differentiated value proposition? Trusted data helps you answer these questions to check the saturation level of the market you plan to jump into. Then determine the demand for your app and prevent you from launching into a sea of red faces (and low downloads).

Competition Intensity: The intensity of competition can impact the success of your app. If there are many apps with similar features, you may need to find unique ways to differentiate your app. So to evaluate this risk, ask yourself:

• Are established players dominating the market? 
• How can you stand out from the crowd and attract your target audience?

Monetization Potential: Assessing the monetization potential of your app is crucial. This involves understanding how your app can generate revenue, whether through in-app purchases, advertising, subscriptions, or other means.

Technical Feasibility: Does your app concept align with current technological limitations? Can you develop it within your budget and timeframe? These questions help you better assess the technical feasibility of your app. Ensure you grasp the technical requirements of your app and have the resources and capabilities to meet these requirements. 

Techniques in Risk Assessment

There are several tools and techniques that can assist risk assessment:

SWOT Analysis: This tool can help you understand the strengths, weaknesses, opportunities, and threats associated with your app.

Competitor Pricing Models: Understanding how your competitors price their apps can give you insights into how to price your own app.

User Acquisition Cost Analysis: This involves understanding how much it costs to acquire a new user for your app. This can help you to determine the profitability of your app.

Developing Mitigation Strategies

Based on your risk assessment, you should develop strategies to mitigate the identified risks. This could involve adjusting your app’s features, changing your marketing strategy, or even reconsidering your entire app concept.

Step 6: Report and Apply Your Findings

After meticulous data analysis and risk assessment, you can report and apply your findings to your mobile app development and marketing strategy. 

Use tools like Microsoft Word or Google Docs to create and share your research report, which should include an executive summary, an introduction, a methodology, a results section, a discussion section, a conclusion, and a list of references. Besides, such tools as Microsoft PowerPoint or Google Slides prove helpful in creating and presenting your research findings to your stakeholders, such as investors, partners, or customers.

Platform Selection: iOS vs. Android and Beyond

The platform selection is an extension of your app research. Mobile app research findings help you not only identify the right development direction for your app but also choose the right platform to reach its full potential. But why does platform selection matter? And what are the key considerations in selecting a platform? Read on to find the answers.

Why Choose the Right Mobile App Platforms?

The platform you choose determines the reach of your app. Each platform comes with its own demographic characteristics, and understanding these can help you tailor your app to your target audience. Moreover, different platforms support different features and have varying development costs and timelines. Therefore, choosing the right platform is crucial for maximizing your reach, minimizing your risks, and setting your app up for success.

Factors to Consider

When selecting a platform, consider the following factors:

Target Audience: Where are your ideal users hanging out? Do they dominate the iOS ecosystem or rule the Android kingdom? Understanding their platform preferences is key to helping you identify which platforms they use the most.

App Features: Does your app require specific functionalities or advanced capabilities? Some platforms, like iOS, are known for their high-end features, while others, like KaiOS, cater to simpler devices. Not to mention some features may only be available on certain platforms. So, make sure the platform you choose supports all the features you want to include in your app.

Budget and Development Time: Building for multiple platforms can be a resource-hungry beast. Consider your budget and development timeline to choose the platform that delivers the most impact on your investment. 

Choosing Your Launchpad: Android vs. iOS and the Niche Alternatives

Android and iOS are the two major players in the mobile app market. Android, with a market share of over 70%, is the most widely used mobile operating system. Its open-source nature allows for high customization, but this can also lead to fragmentation and security issues.

On the other hand, iOS, with a market share of nearly 30%, is known for its uniformity across devices and its stringent app review process, which enhances security and user experience but can also lengthen the app approval time.

While iOS and Android dominate the market, niche platforms like Samsung’s Tizen, KaiOS, Windows, and Linux also exist. These platforms may not have as wide a reach as Android or iOS, but they cater to specific audiences and can offer unique benefits. For instance, KaiOS is popular in emerging markets, and Linux is favored for its open-source nature. Exploring these platforms could open up new opportunities for your app.

However, the ideal platform isn’t always a single choice. Consider a hybrid approach, leveraging cross-platform development tools to reach a wider audience while maintaining a native feel. 

Top 3 Emerging Technologies in Mobile App Market Research

Technology is transforming the landscape of mobile app research. Tech-based solutions offer quicker and more affordable insights at a larger scale. Therefore, they’re increasingly being adopted by organizations, replacing traditional research methodologies. In this section, let’s explore the top four emerging technologies together.

Artificial Intelligence (AI) Integration

AI is increasingly becoming a cornerstone in the field of market research. A significant 80% of industry professionals believe that AI will have a positive impact on market research, primarily due to its potential to enhance data quality. Furthermore, 75% of researchers predict that the data generated through AI will surpass today’s accuracy levels.

These statistics underscore the expanding role of AI in market research tasks. From sentiment analysis and predictive analysis to demand forecasting, AI integration is becoming more prevalent. 

This trend is evident in the growing number of companies introducing AI-powered market research tools to support mobile app research. Tools such as SEMRush Market Explorer, Poll the People, AI Persona Builder, and Kompyte are just a few examples of how AI is being leveraged to revolutionize mobile app market research. These tools not only enhance the efficiency of data collection and analysis but also provide more accurate and actionable insights, paving the way for more informed decision-making in the mobile app market.

Social Listening Platforms

Social listening platforms are potent tools that monitor and analyze conversations and mentions related to specific topics on social media platforms. They aggregate social data and scrutinize it based on distinct attributes or metrics. These tools are a boon for mobile app market research, providing real-time feedback and insights from users.

The social listening landscape is constantly evolving, with new tools and vendors emerging to address specific needs and challenges. Looking ahead to 2024, nearly half of social intelligence professionals plan to focus on new social listening technology . While established players like Brandwatch, Sprinklr, or Talkwalker continue to dominate, the future holds exciting possibilities for even more sophisticated and targeted solutions.

Big Data Analytics

Big data signifies the enormous volume of both structured and unstructured data generated by various sources in our digital world, including social media, e-commerce transactions, and mobile devices. In 2021, it was reported that 46% of organizations utilized big data analytics as a research method.

The advent of big data is revolutionizing the approach to market research in several ways. Particularly, big data analytics , coupled with such advanced analytical techniques as AI/ML, provides access to a significantly larger and more diverse dataset. So you can generate a more accurate and in-depth perspective of consumer behavior and preferences on a scale that was previously unattainable. 

Further, it enables you to conduct research in real-time, equipping brands with insights into consumer behavior and preferences as they occur. 

With these outstanding benefits in market research, the potential of big data analytics continues to be harnessed further in the future.

Market research isn’t just for big companies, but also startups and SMEs! With this guide, you’ve unlocked the secrets to conducting effective market research for your mobile app project. Remember, the best apps solve real problems for real people. So, grab your phone, chat with your friends, and see what makes their lives tick. Use the tools we covered, from surveys to competitor analysis, to build a picture of your perfect user. Then, design an app that makes their day easier, brighter, or simply more fun. 

Mobile app research is your superpower – use it to create something amazing, and remember, Designveloper is always here to help your app dreams take flight!

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How to Conduct Mobile App Research: Best Strategies & Ideas

August 30, 2023, samantha jones.

Overview: Mobile App Research helps determine customer preferences and generate viable options for your firm. Knowing what customers expect from your products and how it improves efficiency benefits you long-term.

Are you an entrepreneur or a large business that is considering a mobile app for business? Mobile App Development is your gateway to success (and TechnBrains is the key). This explosive growth of the mobile app market has made it a lucrative space for businesses. Before you rush into the mobile app development process, you need to do in-depth mobile app research so you can make an informed decision.

Mobile app market research is undoubtedly the most crucial step for any mobile app development project. It serves as the foundation for validating your ideas and jotting down the strategies you would follow. Whether you are building a social media app like Instagram or a vacation rental app like Airbnb , mobile app research is essential. 

In this blog, you will learn about

• Mobile app research
• Importance of Mobile app research
• Best market research strategies for mobile app development

What is Mobile App Market Research?

Mobile app market research is the process of gathering, analyzing, and interpreting data related to the mobile app ecosystem. It involves studying user behavior, market trends, and competition to make informed decisions about app development and marketing strategies. In essence, the compass guides businesses in the vast dimensions of mobile app research.

Types of Mobile App Research

Mobile app research can be categorized into two main types: Primary Research and Secondary Research. Here’s an explanation of each:

Primary Research

Primary research involves the collection of original data and information directly from individuals or sources. It is conducted specifically for the purpose of the research study and provides firsthand insights. In the context of mobile app research, primary research methods can include:

• Creating surveys or questionnaires to gather feedback and opinions from mobile app users or potential users. This can help in understanding user preferences, needs, and satisfaction.
• Conducting one-on-one or group interviews with mobile app users to gain in-depth insights into their experiences, challenges, and suggestions for improvement.
• Observing and collecting data on how users interact with the app. This can help identify usability issues and areas for improvement.
• Bringing together a small group of individuals to discuss and provide feedback on the app. This can uncover collective opinions and ideas.
• Observing users in their natural environment as they use the app. This can provide real-world insights into user behavior.
• Testing early versions or mobile app prototyping with potential users to gather feedback and make necessary adjustments.
• Comparing different versions of the app with real users to determine which one performs better in terms of user engagement, retention, or other key metrics.

Secondary Research

Secondary research involves the use of existing data and information that has been collected by others for different purposes. It does not include the direct collection of new data. In the context of mobile app research, secondary research methods can include:

• Reviewing existing literature, articles, academic papers, and industry reports related to mobile apps. This can provide insights into trends, best practices, and user behavior.
• Studying other similar mobile apps in the market to understand their features, user reviews, and user ratings. This can help identify gaps and opportunities for your app.
• Utilizing market research reports and studies that provide data on the mobile app industry, including market size, growth trends, and user demographics.
• Analyzing user reviews and ratings on app stores (e.g., Apple App Store, Google Play Store) to gain insights into user satisfaction and areas for improvement.
• Monitoring social media platforms for discussions, comments, and feedback related to your app or similar apps. This can help identify user sentiment and issues.
• Analyzing data collected from your own app (if available) to understand user behavior, usage patterns, and areas of improvement.

Both primary and secondary research are valuable for informing mobile app development and optimization. Primary research provides direct insights from users, while secondary research offers a broader industry and market perspective. Combining these research approaches can lead to a more comprehensive understanding of your target audience and the competitive landscape.

The Importance of Mobile App Market Research

In today’s highly competitive digital landscape, simply creating an app and releasing it to the market is not enough. Here is why mobile app research is essential:

Identifying User Needs

Mobile app research empowers businesses to grasp user needs and preferences. Through surveys, interviews, and user feedback analysis, you can get an understanding. It uncovers the pain points, desires, and expectations that drive user satisfaction. This deep understanding is essential for tailoring apps that resonate with users.

Knowing what users truly want allows mobile app developers to create features and functionalities that not only meet but also exceed expectations. It is akin to having a roadmap that guides you through the process of app development, ensuring you are on the right track to providing genuine value to your target audience.

Staying Ahead of Market Trends

Mobile app research enables businesses to adapt and innovate accordingly. For instance, the rise of AR VR app development in mobile apps is a trend that businesses need to consider to stay competitive. 

The landscape of mobile technology is in a state of perpetual flux. New trends, features, and technologies emerge at a rapid pace. Market research serves as the radar that detects these shifts. By staying updated with the latest trends in mobile app development and user behavior, businesses can adapt and innovate accordingly. Without mobile app research, such trends might pass unnoticed, leaving businesses trailing behind their competitors.

Competitor Analysis

Knowing who your competitors are and what they offer can give you a competitive edge. App Market Research helps in identifying gaps in the market that your app can fill. It sheds light on the strengths and weaknesses of rival apps, their user acquisition, and Mobile App Research Strategies.

By analyzing competitor data, businesses can build on unexplored niches or opportunities to outshine rivals in specific areas. It is like having a treasure map that reveals where the hidden gems lie and how to navigate the competitive landscape.

Monetization Strategies

Mobile App Research helps to identify the most effective monetization strategies for your app. Whether it’s through in-app purchases , subscriptions, or ads, understanding what your audience is willing to pay for or tolerate in terms of advertising is key to generating revenue.

Moreover, market research for apps can help optimize pricing strategies, determining the ideal balance between generating revenue and providing value to users. This financial research ensures your app is not only popular but also profitable.

Risk Mitigation

Launching an app without conducting thorough market research is akin to setting sail without a navigational chart. Mobile App Research will assess the risks associated with app development and market entry. It highlights potential pitfalls, market saturation, or unforeseen challenges.

By identifying risks early, we can implement strategies to mitigate them. This could involve refining the app’s features, adjusting marketing tactics, or even reconsidering the timing of the app’s launch. In essence, research provides a safety net that prevents costly mistakes.

User-Centric Design

User experience (UX) and user interface (UI) are critical elements in app development. Our creative UI/UX designer gathers insight that aids in the design process, and you can do it. This includes understanding user preferences for layout, color schemes, navigation, and overall usability.

By aligning design decisions with user expectations, your mobile app research can result in creating apps that are not only functional but also aesthetically pleasing and user-friendly. This fosters positive user experiences, leading to higher user satisfaction and retention rates.

Feedback-Driven Iteration

The journey of app development does not end with the app’s initial release. Continuous improvement is the key to long-term success. Mobile App Market research provides a mechanism for gathering feedback from users, enabling businesses to iterate and enhance their apps.

By listening to user feedback, businesses can identify areas for improvement, fix bugs, and introduce new features that align with user demands. This iterative process not only keeps users engaged but also helps maintain a competitive edge in the market.

Optimized Marketing and User Acquisition

Understanding your target audience through research allows for more precise marketing efforts. Businesses can create tailored marketing campaigns that resonate with their audience’s interests, behaviors, and pain points. This results in more effective user acquisition strategies and higher conversion rates.

Additionally, mobile app research helps in choosing the most suitable marketing channels. It answers questions like, “Where does my target audience spend their time online?” and “What messaging appeals to them the most?” This knowledge streamlines marketing budgets and efforts.

Best Strategies for Mobile App Research

Depending on the creative direction we are heading to, everyone’s mobile app market research will be different. The strategies we are going to explore below are just to make sure that your mobile app research is steering in the right direction: 

Validate Your App Idea 

Before you start building your app, you need to validate your idea. Begin by searching for relevant keywords like “mobile app research” and “app market research” to see if people are talking about a problem your app intends to solve. If there is a buzz, it’s a good sign. Even if there is not, don’t fall back fuel it to reshape your app to make it even better. 

Reach out to people who might use your app. Ask friends, family, or colleagues if they would find your app useful. You can also conduct surveys to get invaluable feedback. To go overboard, You can also create a simple webpage describing your app’s idea and its benefits. Share it on social media and see if people sign up or express interest. This can be a strong indicator of demand.

Identify Your Target Audience

Understanding your future users is a fundamental step. In your mobile app research, you need to get a clear picture of your audience. Create User Personas will help you get through it. It will also be the best app development company to add value to your app idea. Imagine your ideal app user. 

• What do they do? 
• What are their interests? 
• What problems does your app solve for them? 
• Location of the user
• The age bracket that you are targeting

It is important to consider these factors when interacting with others, as they can provide insight into how they may react in different situations. By being aware of these factors, we can communicate more effectively and build stronger relationships based on mutual understanding and respect.

Create detailed personas to guide your app’s design and marketing.

Ask your potential audience about their needs and preferences. Use simple online surveys or conduct informal interviews to gather insights. Divide your potential users into smaller groups with similar characteristics or needs. This will help you tailor your app to different user segments.

Conducting Competitor Analysis for Mobile Apps

Understanding your competition is key to standing out. Search for apps similar to your idea using keywords like “mobile applications market research” and “apps market research.” In your mobile app research, analyze their features. 

Download and use these apps. Take notes on what works well and what does not. Think about how your app can offer something better or different. Read user reviews of competitor apps in the app stores. Pay attention to what users praise and complain about. This can guide your app’s development.

SWOT Analysis

It is a simple but powerful tool for you to get ahead in the game. SWOT analysis stands for 

• Opportunities

Assess your app’s strengths and weaknesses. What can your app do better than others, and where might it fall short? You can also conduct a SWOT Analysis of the Android App . Look at the market and the competition. What opportunities can your app seize? What threats should it be prepared for? Mobile App Research Market research is unquestionably one of the many excellent uses for conducting a SWOT analysis in many business settings. A SWOT analysis ultimately aids in your preparation for mobile app development firm and mobile marketing strategies. It also helps you stay one step ahead of the competition by increasing your awareness of the market and yourself.

Analyze App Store Data

App stores are goldmines of information. Here’s how to extract valuable insights: Read through the user ratings and reviews of apps similar to yours. What do users like and dislike? What problems do they mention that your app could solve? You can also optimize your Google Play Store App Ratings accordingly. Check app rankings in your app store category. What are the top-performing apps doing right? Can your app emulate their success while offering something unique?

Social Media Listening

There are numerous social media listening tools available, such as Hootsuite, Brandwatch, and Mention. These tools help automate the process of monitoring and analyzing social media conversations.

Before you start, clarify your goals. Are you monitoring brand mentions, tracking industry trends, or conducting competitor analysis? Having clear objectives will guide your efforts. Choose keywords, hashtags, and topics that are pertinent to your goals. Use a mix of broad and specific terms to capture a wide range of conversations.

Social media listening isn’t limited to just one platform. You should track discussions on popular platforms like Twitter, Facebook, Instagram, and LinkedIn, as well as niche forums and communities where relevant conversations may occur. Don’t just collect data; analyze it. Look for trends, sentiments, and emerging issues. Use these insights to inform your marketing strategies, content creation, or product improvements.

These Strategies ensure that your mobile app idea is not just a shot in the dark but has a real chance of hitting the bullseye in the market. 

Mobile app research doesn’t have to be overly complex. By following these simplified steps and using keywords like “mobile app research,” “app market research,” and “market research for apps,” you can ensure that your app idea is on the right track. Remember, research is your compass, guiding you towards creating an app that not only meets user needs but also thrives in the competitive app market.

Start Your Mobile App Research Today

In conclusion, mobile app market research is not a mere formality; it is the cornerstone of successful app development and market entry. It equips businesses with the insights needed to navigate the complex mobile app landscape. From understanding user needs to outsmarting competitors and optimizing monetization strategies, research is the compass that ensures businesses do not get lost in the crowded sea of mobile applications. It is a strategic imperative that can spell the difference between app success and obscurity in an intensely competitive marketplace. Contact TechnBrains to get started on the App Development right now. 

 How to perform market research for mobile apps?

Market research for mobile apps is crucial for understanding your target audience and competition. 

Determine what you want to achieve with your app and the questions you need to answer through research. Know who your potential users are, their demographics, preferences, and pain points. Study your competitors’ apps. Identify their strengths, weaknesses, and unique features. You can also hire TechnBrains the best app development company in USA to make your dream app into reality.

Why is market research important for mobile app development?

Market research is essential for several reasons:

• It helps in creating apps that cater to users’ needs and preferences, improving user satisfaction.
• Research enables you to identify gaps in the market and develop features that set your app apart from competitors.
• It reduces the risk of investing resources in an app with little demand or potential for success.
• Helps in choosing the right monetization model based on user behaviors and market trends.
• Informs your marketing plan, helping you reach the right audience effectively.
• A well-researched app is more likely to sustain long-term success and adapt to changing market conditions.

 How to know if your mobile app will be successful?

While there are no guarantees, these indicators can suggest potential success. If your research indicates a demand for your app and a lack of strong competitors, it’s a good sign. TechnBrains can provide Consistent growth in user numbers, downloads, or revenue over time is a positive sign. We can build an app that can adapt to changing user needs and industry trends has a better chance of long-term success.

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How to Conduct Mobile App Market Research

Mobile app development is exciting. New entrepreneurs and established businesses alike are always eager to bring their app ideas to life.

But before you rush into development, there are a few things you need to do first.

Mobile app market research is arguably the most important step for any project. It helps validate your ideas and lays the foundation for every other stage of mobile app development. No matter what type of app you’re building, mobile app research cannot be overlooked.

Continue below to learn more about mobile app research, the importance of market research, and the best market research strategies for mobile app development. 

What is Mobile App Market Research?

Mobile app research is the process of understanding your target market, competitors, industry, and current market trends. Market research is the first step of every mobile app development project. 

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Mobile app market research helps validate your app idea and ensures your app is addressing existing market needs. 

Conducting mobile app research requires data collection and analysis. In some cases, you can pull data from existing sources. Other times, you’ll need to conduct unique research on your own. Most of the time, market research is a combination of these two approaches. 

Mobile app research aims to answer the following questions:

• Is there a market need for your application?
• Who is your target market, and what are their preferences?
• What competitors and obstacles are you faced with?
• Does your mobile app have a unique differentiator from the competition?
• Do you have a viable mobile app business model?
• How can you create an optimized marketing strategy?

When you’re sourcing information from existing sources, you’ll want to stick with trustworthy and authoritative references like Statista, Pew Research Center, Gallup, Google Trends, and similar sources. 

You can also refer to our Mobile App Statistics Guide for general information about smartphone users, industry trends, and other data related to mobile technologies.

Original research is typically conducted through focus groups, interviews, customer surveys, and observation.

Why is Mobile App Market Research Important for Mobile App Development?

Every successful mobile app starts with quality market research. 

Without thorough mobile app research, you’re essentially just guessing whether or not people actually want your app. This could prove to be a very expensive guess if you go through the entire mobile app development process only to discover there’s no market need for your product.

You can save a ton of time and money by simply validating the idea first—before you start writing code or hiring a development team. 

Market research also helps you identify competitors and obstacles on your path to success. 

For example, let’s say you have an app idea for a free video sharing platform, making it easy for anyone to share and watch videos from their smartphones. Users can create their own profiles, like videos, follow content creators, and write comments on videos. 

Great idea, right? Well, this idea basically just described YouTube.

Market research would help you identify this, and you’d likely decide not to directly compete with the largest video hosting platform on the planet. 

Instead, your market research could help you pivot your original app idea to target a different market niche. Maybe you still want to create a video hosting application, but you want it to be specifically for online education. 

Market research helps you better understand your customers. You’ll find out who they are, along with their wants and needs. You might think that your target audience wants to use your mobile app. But market research will help validate or disprove your hypothesis. 

Then you can lay the foundation for your mobile app features, brand positioning, and app marketing strategies based on your findings. 

Types of Mobile App Research

It’s much easier to perform market research when you break everything down into categories. 

Market research for mobile app development can be segmented into two phases—primary research and secondary research. We’ll take a closer look at each of these stages below.

Primary Research

The first thing you need to do is conduct in-depth market research on your users. 

Define your target market and find as much information as you can about them. Start with generic information, habits, likes, dislikes, wants, and needs. Then narrow that research even more, specifically related to their behavior with mobile applications.

Your primary research should also consider the latest technology trends and how the current market landscape works with your business model. 

These initial steps are crucial, as this early market research ultimately guides you through the entire mobile app development process.

Your findings here will help you determine whether or not to target certain users. You can also frame certain components of your mobile app based on this early market research.

For example, let’s say you want to create a real estate app. The initial idea might be focused on prospective home buyers. 

But after conducting your primary research, you could discover a lack of market need for this type of product. Maybe the market is too saturated, or maybe the app’s goal didn’t meet the needs of your target audience.

If you’re an expert in the real estate industry, you still might proceed with the app creation. Only you’ll pivot to an app that addresses the needs of real estate agents instead. 

Then the app’s features will be based on meeting the needs of your new target audience.

Secondary Research

The second step is your sales and marketing strategy research. 

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By now, you’ve already validated your idea and decided it’s worth moving forward. You just need to take your existing research and get more information on how you’re going to reach your target audience.

You’ll use this information to create a marketing plan, optimize social media accounts, prepare ad copy, and other things that will ultimately speak to and attract your target market. 

What does your target user base respond to? How can you market your mobile app accordingly to pull on those strings?

Your secondary research will help shape your branding and market positioning within your niche.

For example, let’s say you’re creating a B2B smartphone app designed for other businesses. Rather than simply branding yourself as a project management tool, your secondary research can help define your market positioning as an application for productivity improvement.

This type of positioning would likely be more aligned with the problems your target audience is trying to solve. 

Best Strategies for Mobile App Research

Everyone’s market research will look a little bit different. But there are some proven strategies specific to mobile app development that will help steer you in the right direction:

Mobile App Idea Validation

The first thing you need to do is validate your mobile application idea. 

If there’s not a market need for your app, then there’s no reason to proceed with the remaining steps for market research. You might need to head back to the drawing board and rethink your idea.

Discovering that there is not a market need for your app isn’t a bad thing. Lots of people are disappointed when they hear this news. But you should be happy that you didn’t pour money into a project that wouldn’t deliver a return on your investment. 

Instead, you can take what you’ve learned during the validation stages and use that to frame new ideas. 

To validate a mobile app idea, you need to get out there and start talking to people.

Run customer focus groups. Conduct interviews. Send out surveys. 

Do everything you can to get as much information as possible about your idea and how it does or doesn’t meet the needs of prospective users. 

Identify Your Target Audience

Narrowing down your app’s target market must be another primary area of focus for your market research.

All too often, we hear things like, “my app is for everyone” or “my app is great for women of all ages.” That’s not specific enough.

You need to define more specific characteristics, like:

• Marital status
• Highest level of education
• Personality traits
• Lifestyle choices

With mobile app development, you also need to see if your target audience prefers some mobile devices over others. 

For example, if you discover that 90% of your target market uses iPhones, you may not necessarily create an Android app for the Google Play Store right now. You could focus your efforts specifically on Apple devices and make an iOS app.

When you’re doing market research on your target audience, it’s important to remember that you’re not excluding other users or other people who might be interested in your app. But your primary focus and efforts will be surrounding your target market. 

Conducting Competitor Analysis for Mobile Apps

Researching the competition is an absolute must for mobile app development.

It’s rare to see mobile apps today that are 100% unique. There’s a good chance that at least a few other apps on the market do something similar to yours.

Your job is to identify those competitors. Find out what they’re doing well, and find out where their apps fall short.

You can use this information to your advantage and create an app that fills the gap with your competitor’s shortcomings.

Even if you think those players have a head start because they already have apps available for download, you have a faster path to development by avoiding the same mistakes that they did.

Eventually, you need to figure out how you’re going to stand out from the competition. 

What’s your differentiation factor? Why should someone download your app over another app that does the same thing?

This could be related to your marketing strategy, feature differences, different target users, or all three. 

SWOT Analysis

Many of you might already be familiar with a SWOT analysis. It’s an acronym that stands for:

• Opportunities

There are dozens of great use cases for running a SWOT analysis in different business situations, and market research is definitely one of them. 

Get together with your team and assess all of your findings from both the primary and secondary research. Now write out everything that comes to mind for each of these categories.

• What is your advantage?
• Where are you vulnerable?
• Are there specific niches, users, or features that are open for a new market need?
• What competitors or external market factors could hurt your app?

At the end of the day, a SWOT analysis helps prepare you for mobile app development and your mobile marketing strategies. By becoming more aware of yourself and the market as a whole, it also helps you stay ahead of the competition.

Final Thoughts

Mobile app research is key to the success of mobile apps.

If you need help with market research, schedule a free consultation with BuildFire. 

As part of our full-service app development solution, we assist with this part of the process. We’ll help you run a competitive analysis, so you have a better understanding of your market. Our team will also provide expert guidance with app strategy sessions to ensure your app is meeting your business goals.

More from the blog

33 creative ways to promote your app for free (app marketing strategy), the 5 ways to build a mobile app, what to expect from a mobile app development platform, 8 steps you must take before starting mobile app development, how much does mobile app development really cost, 5 hidden costs of software you need to anticipate after you launch, the definitive guide to mobile app design, mobile app download and usage statistics (2021).

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Why is Market Research Vital for your Future Mobile App?

Novikova Darya

Tech Researcher

Jumping into the development process without understanding the needs and wants of your target audience is like shooting in the dark. If you are uncertain about where to start mobile app development, detailed app market research is a foolproof step to take.

• Why shouldn’t you skip this step?
• What kind of data is needed?
• And, most importantly, how to use gathered information to your advantage?

This post will shed light on the essentials of mobile app research and explain how your business can make informed decisions about product development strategies to deliver maximum value.

What is app market research?

Mobile app research is an integral part of the discovery phase of mobile app development. In fact, it is something that no startup owner should take lightly if they are looking to break into the mobile app industry.

At its core, app market research is a concept that helps businesses identify trends and user preferences, and reduce the risks of creating a product that fails to hit customer needs. With this knowledge in hand, you'll be able to make informed decisions about the right direction to move on.

Why should you start your project with a discovery phase?

Why is app market research so important.

According to Business of Apps, the number of applications available across iOS and Android platforms has reached over 7 million , and it’s not the limit. The massive outspread is expected to continue and lead to an annual growth rate of 7.77% with a projected market volume of US$614.40bn by 2026 . We bet you don't want to get lost among them, do you?

Here are the benefits that such research brings:

Stand out among your rivals

Granular mobile app research provides insights into where competitors are succeeding or failing in their efforts, allowing businesses to adjust their strategies accordingly. With the right data and analysis, companies can craft apps that stand out from the competition while still being attractive for users to download and use them.

Increase your chances for a successful launch

Besides, market research will increase your chances of success when releasing an application. You will know the right targeting, optimal launch time, possible obstacles, and ways to eliminate them. In other words, if you’re serious about stepping into this highly competitive space – do your homework first!

Reduce the risk of wasting resources

By conducting deep research before investing financial resources or time into a project, you can significantly reduce your risk of wasting both. Plus, you ensure that all efforts are focused on creating an app that will be successful in today's competitive market.

Mobile app research milestones

Though mobile app research may vary from one industry to another, there’re some obligatory milestones you should cover to move your mobile app development in the right direction. Here’re the most crucial pieces in mobile app market research

1. Target audience identification

Understanding your target audience should be one of the first steps when conducting mobile app market research. By defining a clear portrait of your target audience, you can ensure that your mobile app is tailored to the needs of those specific individuals. This will help you create an effective marketing strategy and develop features that are most likely to be appreciated by your intended users.

Try to be as specific as possible and consider such parameters as age, gender, demographics, location, specialization, income, personal interests, and preferences, among other traits. Another crucial point is to learn the types of devices they prefer to utilize so that you can tailor the design and features of your app to fit their expectations.

Ultimately, it’s an excellent idea to find out the social media platforms your target audience uses. This insight will help you maximize engagement with potential customers and provide valuable feedback from existing ones.

From our experience this step is one of the most important ones to develop a fully-functioning mobile app or an MVP. You have to precisely define who your users are to craft the most relevant user journeys and develop the features they need: no less, no more.

Let’s say you are creating a food delivery app and your target users are young people who want fast deliveries and more fast food. Then you should focus on including more fast food restaurants as your partners and make the process of ordering as fast and intuitive as possible.

On the other hand, if you see that most of your customers are middle-aged individuals who have cars and are focusing on a healthy lifestyle, you may need to look for more healthy food restaurants to be featured and show the takeaway option along with the delivery.

Those are just hypothetical examples, but depending on the target audience its realization differs. That is why, you should perform a very deep audience investigation in order to succeed.

“Analysis of the target audience is not just an important stage in the application research, it is the key to success and the full implementation of the idea.

Sometimes it seems to us that we perfectly understand the needs of the audience, trying to imagine ourselves in their place. However, this idea is nothing more than an illusory sketch, an imagination far from reality.

In order to be in demand with the audience, you need to clearly understand and know it in detail, based on real analysis data.”

— Waleriya Bagnyuk-Yurkantovich, Business Analyst at SolveIt

2. Competitor analysis

The mobile app market is a rapidly changing landscape, and if you want to stay ahead of the competition, keep sight of your rivals. Conducting mobile app market research is the best way to ensure that you have a competitive edge over other similar products and can provide users with an experience that surpasses what they might already be familiar with. You can also leverage tools like web scraping API to gather data and insights from the competitive landscape in real-time.

By understanding your competitors' tactics, you can identify areas for improvement. As a way to give your app a solid competitive advantage, try to figure out user needs that haven't been met yet and address them more efficiently.

Finally, conducting competitor analysis will help you develop better marketing strategies as well as get to know how much time and resources should be allocated toward development efforts. This will ensure that all aspects of launching a successful application are taken into account.

Real-world example

For example, market analysis for one of our clients’ projects revealed the key USP to implement. The client was looking to develop a new scalable app for job search and redesign its interface. It was critical to find something that would help the client to get ahead of the other market players.

Thanks to the competitor analysis it was decided to show salary for every vacancy in the application for one simple reason: no other job search app in the region had it. As a result, the app became the best business application in the App Store already after the first week after the release. Isn’t it a remarkable result?

3. SWOT analysis

SWOT analysis is a field-proven concept for mobile app research that reveals the strengths, weaknesses, opportunities, and threats of your idea or product before investing in development. To take into account all factors from both inside and outside of your organization, you need to assemble a team of experts from all areas and levels. That way, you can get the most comprehensive picture possible.

• Strengths : What are your core competencies? What do you do better than anyone else?
• Weaknesses : Are there any areas where performance could be improved?
• Opportunities : Which markets could be exploited with your app? What are the ways of entering into new markets?
• Threats : How vulnerable are you to external factors such as competitors entering the market with superior offerings, changes in customer preferences, and so on?

Once everyone has contributed their insights, you will uncover some blind points as well as have an accurate representation of the current situation and potential future prospects. And who knows – it might just open up some exciting possibilities!

4. Mobile app idea validation

Take validation of a mobile app idea like a test drive before you buy a new car. It helps you determine if the audience will be interested in what you have to offer. By testing out different ideas, you can identify potential problems before they become costly mistakes and refine your concept into something customers actually want to use.

How to validate your app idea? Make sure to get first-hand feedback from your target audience. Use surveys, interviews, or any other available options to learn what they really think of your app.

However, you should be prepared for a negative outcome either. We understand that realizing that there is no market need for your mobile app is a bitter pill to swallow. But even if your findings reveal the lack of user interest, always look at the bright side of things – you save both time and money by not investing in something that wouldn't be profitable anyway.

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Market research methods.

Considering the milestones mentioned above, it's time to dive into the nitty-gritty details of how to collect data in order to make your predictions accurate and comprehensive. We’ve collected four methods that you may use as part of your journey toward success:

Statistical research

Since figures always speak for themselves, real-time statistics can become a great source of insights for your mobile app market research. Valuable information on the current industry trends helps to determine what strategies are most effective for launching an application that users will adore. With statistical data, you can identify which platforms, demographics, or countries would be most beneficial to target with your app.

• Statista is an invaluable resource for anyone looking to get their hands on the latest figures and understand market trends across 170 industries in more than 150 countries.
• Sensor Tower is a powerful tool that provides insights into the mobile app economy, including app store optimization (ASO) data and key metrics to track app performance in real time.
• data.ai , formerly App Annie, is a platform that opens access to detailed mobile app analytics about downloads, revenue, usage trends, and more.
• McKinsey & Company is the trusted advisor that brings act-based insights and signature reports on main industries, including mobile app development.
• Business of Apps is a valuable resource that provides insightful podcasts and articles that include stats about the leaders of the global app industry.

Surveys are a great way to learn how users feel about your product and how you can improve it as long as you ask the right questions. With platforms like SurveyMonkey or Typeform, crafting a questionnaire is as easy as pie, but don’t forget that surveys come with a few caveats.

You need to be careful in crafting the right questions and make sure they’re not too narrow or too open-ended. One-world answers won't give you much useful information either while long surveys pose the risk that people lose interest mid-way through.

When it comes to collecting feedback for your mobile app research, make sure you survey unbiased people who have no connection with you or those close to you. Otherwise, their opinions will likely be biased one way or another. Also, remember that negative feedback can sometimes provide more illuminating discoveries than positive comments, so use this knowledge wisely when interpreting survey results.

Customer interviews

Conducting user interviews is a valuable mobile app research technique that should be used throughout your app development journey. Interviews with potential customers open up the reasons why users make their decision whether they want your app to be installed on their device or not. Additionally, they help you identify potential issues that could arise during the development process and validate ideas before committing them to code.

User interviews can be conducted over call or video, giving you access to users from all around the world as well as those living close by. As an alternative to a personal interview, you can also assemble a focus group. This allows you to get feedback from different perspectives which can help to shape the product accordingly.

Social media listening

If you’ve never leveraged social media listening as part of your marketing strategy, it’s high time to begin. Put shortly, this tactic is aimed at monitoring social media to spot mentions of your product or company. But how to apply the power of social media listening for mobile app market research?

Given that almost half the world's population uses social media on a regular basis, it becomes a goldmine of useful knowledge. First, identify the key topics related to your business or industry that people are talking about on social media platforms. Then set up a monitoring system so you can track conversations around those topics in real time. Finally, analyze the data from these conversations to gain valuable insights into customer sentiment towards products similar to yours in order to stay ahead of the game and create more effective campaigns.

How to apply the findings to your mobile app?

The app market research is not the endpoint in the discovery phase of app development. Further, you need to compare all your findings to the assumptions that you had at the beginning of this journey and reshape your strategy in line with new data. Besides, the preparatory steps also include developing a design concept, conducting technical analysis, and formulating the value proposition.

If you are new to this matter, SolveIt experts will be happy to assist with discovery phase services as our team has obtained vast experience in tailoring mobile app ideas to your business needs.

When all the gaps are filled and the overall vision is formed, you can get down to mobile app development. We recommend securing yourself and starting with MVP development first, instead of building a full-fledged mobile app right away.

Turn your idea into a successful MVP

Investing only in basic features will allow you to test the waters before committing more time and financial resources to further development. With the focus on core features, developers can create an MVP faster than they could develop and launch an all-inclusive mobile application. Plus, it’s a witty opportunity to get user feedback quickly so that any necessary changes or improvements can be made sooner in the process.

Skipping market research for mobile app development is like gambling with your company's resources – you may win big or lose even bigger! Without understanding the market and its needs, companies risk wasting significant resources on an idea that is not viable in the long run.

So if you're thinking about developing an app for your business, take your time to do thorough mobile app research, or leave this task to a reliable mobile app service provider.

Don’t know where to start with your app idea? SolveIt will help!

Get a free consultation from SolveIt experts!

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Mobile App Market Research: How To Conduct Effective Research

| july 5, 2021.

Before you develop even a single line of code on your next app, I’d like to illustrate why adding a lot more firepower to your market research recon mission is so critically important.

Apps are popular and it’s a rapidly expanding market, right? Well, yes and no. You see, the flip side of that coin means that the competition is swarming the market at breakneck speeds with no signs of letting up–and to be honest, by the time you’re reading this, those numbers are probably already laughable, because of this rapid growth.

Put another way, our grossly over-populated sea of apps is going to continue to flood over at an exponential rate. And this can present a bit of a hurdle for the current appreneur who’s thinking about how to get their app to stand out from the other millions of apps out there on Apple’s app store and Android’s Google Play.

So, what’s an app developer to do? Market research. And lots of it. At every step of your app’s lifecycle.

Let’s start with the first stage of Market Research…

Phase 1: Idea Exploration

Okay, so you’ve got an idea for an app you think will become the next Angry Birds meets Instagram meets Draw Something , right? So let’s check out the market viability and see if your amazing new app idea holds any water.  It’s time to break out your trusty smartphone and conduct the kickoff of your market research.

Start by searching out competitors to see who the players are. This first piece of data is a doozy. If the developers of a similar app are one of the big boys like Zynga, Rovio, EA or similar, you might just want to tuck that idea away for a while. At least until you have a Kajillion dollar marketing and advertising budget (give or take) to go head and head with those 800 pound gorillas. However, if your only competition is just a handful of indie developers, that won’t present the same uphill battle for you, and you can feel free to proceed with your idea.

The next step is to download a handful of the competitor’s apps and examine them with a fine-toothed comb in order to find the areas upon which you can improve and differentiate your own app.

Then, you’ll need to drill down even further and check out the reviews. Sure, take a gander at the 5 star reviews, but make sure to pay even closer attention to the 1 and 2 star reviews. Ultimately, those people are your new unsuspecting, unpaid focus group. They’ll provide you great insight, and I’ll bet you a dollar to a donut that as soon as your app hits the market they’ll jump ship and hop on board with your better new and improved version of the app.

And last, but certainly not least, make sure you check out and analyze the icon, screenshots, graphics and user interface (UI). You can even study the apps you didn’t download, as chances are they’ve uploaded up to 5 screenshots, which should give you a pretty good indication of the look and feel of the app. You’ll then know where the bar has been set, and you’ll know how to proceed when fleshing out your app’s icon, screenshots, graphics and UI.

Now that you’re finished exploring the competition, take out a notepad and jot down all of the things you think you can do better, record all of the gripes and complaints from dissatisfied users, and make notes on how you can set yourself apart graphically. Then, it’s time to validate your idea.

Phase 2:  Market Validation

The next market research and development tool to consider is the App Charts. App stores allow you to see which apps are in the top percentage of apps.

So, what should you be looking for? Simple. Check and see where your competitors are on the charts. Are they top 25? 50? Are they lost in the great app store abyss? They are all things to consider when researching whether your app idea will be a success or not.

For argument’s sake, let’s say you found an app that’s similar to your new app idea that’s ranking top 25 in your particular category, and it isn’t from one of the big studios. Well tip of the cap to you, you’re one step closer to validating your idea.

However, at the end of the day, creating apps is a business. And as with any successful business, economics plays a key roll. So the next questions that are begging to be asked about your new app idea are, “Will it make money? And if so, how much?”

Well, only one way to find out… time for more market research 😉

  Phase 3:  Revenue Projection

During a recent podcast interview I did with Taylor Pierce, author of the brilliant book Appreneur  (which I believe should be the official handbook of the app development business), I asked him the following seemingly complex question, “Is there any way to predict potential revenue for an app idea to determine whether or not it’s worth moving forward on?” And with a slight cheshire grin on his face, like he was about to reveal the holy grail of insider secrets, Taylor answered with, “yeah, just go into forums and ask.”

Wait a minute? That’s this über successful appreneur’s revenue projection tactic? It seemed too simple to be worth its weight. But just as Leonardo da Vinci so famously said, “Simplicity is the ultimate form of sophistication.”  If it works, why make it more complicated. No software to learn, no scripts to run, no analytics to study. Nope, just some folks to talk to. Easy peasy. Taylor then revealed to me his secrets of how exactly to do it… and now I’m going to pass them along to you (I don’t think he’ll mind).

Say for instance the competitor’s app that you’re thinking about creating is hanging around the top 25 spot pretty consistently for a certain category, all you need to do is get on a good development forum like Reddit’s r/iosprogramming subreddit (if you’re an iOS developer) and ask around what people in the same category ranked in the top 25 are making every day. If they come back and tell you they generate around 500 sales per day, then you know to some level of degree that your similar “new and improved app” can do as well (if not better) than they are doing.

Also, while you’re in the forums, you might as well ask other categories what they are also doing in sales. That way, when you come up with your next genius idea in another category, you’ve already got the revenue projection research done!

Okay, now your app idea officially has the green light and it’s time to pull the trigger on development. While the development process is underway it’s no time to relax, there’s still more research that can be done in tandem.

Phase 4:  App Store SEO Preparation

One of the things I love so much about app marketing is that there is a direct set of carry-over principles from the online marketing world, where I’ve spent a good portion of my adult life. In particular, the fundamentals of Search Engine Optimization (SEO). This is one of the most under-utilized and over-looked powerful marketing strategies that can literally make or break an app. Keyword research plays an enormous role in every app’s success, but to do it right takes the right tools and strategies.

Think about this fact for a second, 80% of all apps that are downloaded are found by simple keyword searches. What this means to you as a developer, is that even though there’s a mountain of apps to compete with out there, the majority of them are found by people doing a simple search – not because of other fancy, more expensive marketing tactics.

Take for example that fact that there are roughly eighteen million flashlight apps in the App Store (give or take). Some of them rank among the top 25 in the App Store categories, but the lion’s share don’t. Why? Because some developers respect the fact that discoverability is key. And since so much of this planet’s smartphone users find their apps by simply searching keywords inside the App Store, the proof’s in the pudding that great keywords out of the gate will give your app big organic download numbers. And them’s be my favorite kind of downloads… FREE!  But that’s just because I’m a cheap son-of-a-gun.

If you can spend just about 3-minutes a day tracking and tweaking your keywords, you’ll slingshot past the 99% of developers that don’t. Simply continue to track your keywords, research others, tweak, and repeat.

App Store Optimization (ASO) is one step you can’t afford to skip or only give little thought to. ASO includes your app’s keyword rich name, title and description. Technically, full ASO also includes your graphics, UI and reviews, but that’s for another day.

A couple notable tools to mention are Appcod.es   – The Swiss Army Knife for App Store Optimization, and SearchMan.com.  Mind you, these are not just market research tools, but also tracking tools and marketing weapons as well. After all, good SEO research ultimately leads to great App Store Optimization. These tools help you feel less like a blindfolded monkey throwing darts, and more like an App Store Optimization pro!

By utilizing these tools, you’ll be able to plug in some keywords and find out quickly where you’ll rank for them and how many results there are for each, which should give you a good sense of which ones to use and not use. A good rule of thumb is that if the keywords you’re thinking about using aren’t going to put you into the first 50 spots on the charts, then move along little doggie and choose new words. There are 25 spots on each page, so if your words won’t put you into contention for the top 50, you’re dramatically decreasing your chances of being found. Similarly, think of this as doing a search online. When is the last time you’ve made it all the way to page 3 while Googling something? Well, searchers on the App Store operate no differently.

Okay, development of your app is complete, and your keywords have been chosen and are locked and loaded. It’s finally time to hit the big read button and deploy that bad boy… Almost.  

Phase 5:  Monitoring & On-Going Optimization

It’s important to note that some of the research tactics from phase 4 will slide over into phase 5. However, the distinct difference is that in this phase, you’re going to actually release your app into the wild and let it go head to head with the competition on the App Stores. But before you do that, you’ll need to do a bit of stalking of your competition first (but not in a creepy “checking out your ex’s Facebook page” kind of way). You’ll need to track their search results and category rankings like a hawk to see which keywords are working like gangbusters for them and which ones to avoid like the plague–and then swipe the winners and insert them into your own keyword mix.  After all, copying is one of the highest forms of flattery, and a great way to leverage existing knowledge.

Once live, you’ll need to take a regular snapshot of where you’re starting to rank, and pay attention to your position changes on each of your individual keywords. Search Man can give you this kind of data quickly, such as word X jumped to this position and word Y dropped to that position. And when you take this information and pair it with your app’s category rankings, you’ll be able to get a good indication of how your app is trending. By utilizing Search Man’s keyword comparisons, keyword suggestions and popular keyword query features, you can really start to optimize your efforts and take things to the next level. By seeing which keywords to keep or replace based on current performance, competition, popularity, you’ll be able to fix keyword under-utilization, analyze your keyword effectiveness and optimize them, thus setting your app up for maximum exposure in the app store.

More market intel that’s useful to know are things like where your traffic’s coming from and how your app is doing in various app stores across the globe. Learning your customer’s whereabouts, and knowing where you app is popular are both important pieces of information, as they allow you to beef up your marketing in those areas, and kill it in areas that aren’t so hot for you–saving you time, money and energy.

One more thing to monitor is your own app’s user reviews. Remember how this feature helped you find all of the holes in your competitor’s apps? Well, by paying close attention to your own app’s user feedback, you’ll gather great intel to integrate into your app’s next update. Distimo can help collect those reviews for you too, instead of you having to spend your valuable time scouring multiple app stores one at a time.

And last, but certainly not least, pay attention to revenue. This is an obvious indicator if changes need to be had. If your revenue drops, make changes.

The long and short of it is, if you’re planning on making an honest go of this whole app development thing, you’ll need to really pay close attention as to how your keywords are doing. You’ll want to always be monitoring those words to see how you can climb to the top. But be warned, while getting to the top is one thing, staying there is a whole other ball of wax.

While this has been an overview of some really powerful market research weapons, there’s more than one way to skin a cat. I merely scratched the surface of available tools out there, and just as creative app developers will always find ways to out-innovate each other when it comes to marketing and discoverability of their apps, so to will the creators of these tools we use to optimize and monitor them. So try the tools out from this article, and try out some others, and pay attention to the new innovative ones hitting the market. And remember to always stay on top of  industry trends and to keep current on what app marketing techniques are working today from the appreneurs and app developers in the trenches who are actually making money at this game for a living. The point is, play around a bit and find out what works best for you when it comes to marketing research, then implement it.

When you pair all of these tools together, you’ll be able to look at the market, see how your app did, see how it’s doing, and most importantly, predict with a decent level of confidence where it’s going to go. And when you fold into the mix some marketing fundamentals and strategies, you’ll be able to amplify your results exponentially.

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How to conduct mobile app market research

Last updated

1 April 2024

Reviewed by

Market analysis template

Save time, highlight crucial insights, and drive strategic decision-making

• What is mobile app market research?

Market research for mobile apps helps you identify your target audience and lets you know what your potential customers want in an app. You can use it to get the answers to questions such as:

Is there a significant market for your app?

Who are your ideal customers?

What kind of competition exists for your app?

What sets your app apart from the competition?

What features do your customers value most?

Will the app be free? Or will you charge users for it?

• Why is mobile app market research important?

Market research for mobile apps is essential for the reasons below:

It lets you learn about your customers. Without knowing your customer’s needs and preferences, you are mostly relying on guesswork. Mobile app developers may have advanced knowledge of their industry but aren’t necessarily in touch with their audience’s current needs. Market research lets you close this gap.

It helps you minimize risks. Releasing a new product always involves risks, but you can increase your chances of success with market research. You can avoid wasting time and investing funds into projects that lack sufficient interest from potential users.

It makes you aware of your competition. No one releases apps in a vacuum. You’ll almost certainly have competitors who offer products or services in a similar space—no matter how original your idea may be. Market research tells you where you fit in the marketplace and what you need to do to set yourself apart.

It informs you of the latest trends. The marketplace changes rapidly due to factors such as the economy, technological advances, and changing consumer tastes. One purpose of market research is to help you stay current so that your products remain relevant.

• Aspects of mobile app market research

There are several aspects of mobile app market research:

Identifying your purpose: every app has a purpose, whether it’s to solve a problem or entertain the user. The first step is to identify the app’s purpose and who will be motivated to use it.

Collecting and analyzing data. All apps generate user data (e.g., session length and areas of engagement) that can be analyzed to identify their strengths and weaknesses. You can use these insights to create strategies to improve the app.

Creating a strategy based on your findings

Pitching your app to the relevant stakeholders

• Types of mobile app market research

There are two main types of market research: primary and secondary.

Primary research

Primary research involves collecting original data using methods such as:

Focus groups

Online, phone, or in-person interviews

Questionnaires

A/B testing

Some of these methods can be done either online or in person. The advantage of primary research is that it’s up-to-date and specifically targeted to your needs. The downside is that it can be time-consuming and costly.

Secondary research

Secondary research involves gathering pre-existing research. You might find this research from websites, data collection agencies, or print publications.

This type of research is inexpensive to gather and often easy to find. On the other hand, it may be outdated and not targeted precisely. You might not be able to find answers to very specific questions and how they relate to your unique user base.

Your budget and time constraints will determine how much of your research is primary and how much is secondary. If you rely mostly on secondary research, be aware that this approach may be too general to apply to your specific product.

What does mobile app market research aim to achieve?

Market research for a mobile app may have several goals, such as:

Learning if there’s sufficient demand for an app

Identifying the right audience

Providing evidence of the app’s value to investors and stakeholders

Learning what features your app needs to make it useful

Understanding who your competition is

How do I find users for user research?

You first need to identify your target audience so that you recruit users who will be relevant to your research.

Be sure to qualify potential testers or interviewees in any outreach you do. For example, you might advertise with wording like: “If you are a college student in the United States who enjoys streaming music, you can earn a $25 gift certificate in exchange for an hour of your time.”

You can find users in a number of ways, including:

Using a recruitment agency that finds users for you (the simplest but costliest option)

Contacting existing customers via phone, email, or social media

Using paid ads on Google, Facebook, or other platforms

Providing incentives - depending on your budget, you can offer cash, gift certificates, or products/services from your business

Asking for referrals - when you find users, ask them to refer others who may be interested

Guerilla testing, where you set up in a public place such as a busy street or college campus, or at an event, and approach participants (note that you might need to pay a fee or get permission for some locations)

• Best strategies for mobile app research

Let’s look at some effective strategies for conducting market research for mobile apps.

Mobile app idea validation

Not all ideas are marketable. Your idea for an app might be interesting and creative, but market research will help you decide whether or not it’s worth pursuing.

Here are some points your development team should consider to validate an idea for an app:

Identify the app’s purpose

With so many apps to choose from, users will only download a new one if it serves a purpose.

Many apps are designed to solve a problem or make a task easier, such as making a purchase or an appointment. Others are more for entertainment, which can also be valuable for building your brand and increasing customer loyalty .

Ask yourself if you would use this app. Pose this question to fellow team members.

Compare it to similar apps

If there are already apps that perform very similar functions, you need to consider why users would choose your app instead of them.

There’s usually an advantage to being first or early in the marketplace. A new app should have some unique characteristics that set it apart. However, an app doesn’t have to be completely unique to be useful and popular. For example, a small business such as a spa or gym might create its own app that allows members to set appointments. The function will be very similar to other apps, but it’s unique to that business.

On a larger scale, social media apps operate in a very similar way but still appeal to users who want to access a particular platform, such as Facebook, TikTok, and Twitter. On the other hand, an app that isn’t connected to a well-known brand needs to differentiate itself in some way. For example, if your app just counts steps, it will be competing with many other similar apps that millions of people already use.

Identify your target audience

To identify your app’s target audience, you’ll need to consider factors such as:

Age range - does the app appeal to people of a particular age or in a certain life stage?

Sex - do you expect more men to use it or more women?

Income, education, and professional status - even if the app is free or low-cost, it may appeal to people with certain levels of education or within certain income brackets.

Lifestyle - what are the user’s interests, hobbies, and routines?

New or existing customer - an app can be a good way to attract new customers. It often helps if the app has features that can be used by non-customers.

SWOT analysis

SWOT analysis means looking at the strengths, weaknesses, opportunities, and threats your app has or is likely to face.

For an app that hasn’t yet been released, this is partly speculative. However, you can use your business’s existing features as the basis for conducting a SWOT analysis for an app.

Strengths - look at your business’s strengths, such as areas of the market where it’s doing well. You may be able to incorporate app features based on your best-selling products.

Weaknesses - consider aspects of your business, as well as the app, that need attention. In some cases, an app can help improve aspects of the business.

Opportunities - look for ways to channel strengths and weaknesses into opportunities. For example, you may be able to use the app to attract younger or more tech-savvy customers.

Threats - may include competitors, economic conditions, or disruptive technologies that are on the horizon.

SWOT analysis template

Competitor analysis.

Take a close look at apps that compete with yours. Go beyond carrying out an assessment—include research into the company itself. Apps are generally integrated with other channels, such as websites and social media pages, so you want to observe everything your competitors are doing.

When analyzing a competitor’s app, you can look into the following areas:

Look at how the app is ranking on app stores.

Look at reviews of the app—positive and negative reviews can provide valuable insights. For example, they might point out a flaw or issue with a competitor’s app that you can use to inform your strategy and help you offer an alternative.

Check out their website’s SEO using a tool such as SEMRush, SpyFu, or Ahrefs.

Research the company’s social media presence, including video channels, podcasts, and any other channels where they are active.

Perform a SWOT analysis of the competitor, similar to the one you do for your own app.

Competitor analysis templates

Create a buyer persona.

Once you have identified your target audience and created a buyer persona, it’s time to consider how to tailor your app to their needs.

After identifying your audience’s general characteristics, get more specific by creating a user persona . You can create more than one.

For example, if you are creating a fitness app, your user persona might be: “Sue is a 31-year-old professional woman who goes to the gym three times per week and wants to track her workouts and calorie intake.”

App store optimization (ASO) keywords provide helpful clues to what your audience is searching for. It’s largely about how to optimize listings for existing apps, but you can also use ASO in the planning stages. There are both free and paid ASO research tools, such as the App Store Keyword Suggestion Tool .

Social media listening

Social listening (or monitoring) is another useful tool for understanding your audience. When creating buyer personas , consider what social media sites they are likely to use. Monitor these sites, paying attention to posts, stories, groups, and other content that’s relevant to your app.

• Pitching your strategy

If you need to pitch a mobile app, you need to give a clear and concise summary of why an investor or other stakeholder should support your project. Here are some points to emphasize:

Define the app’s purpose and benefits—you need to be able to explain the app’s main purpose (such as the problem it solves) in a couple of sentences.

Why it’s a market fit—describe the nature and size of the market, your target audience, the expected number of users, and the projected growth of the market in the future. Solid data is especially crucial for this part of your pitch.

Monetization—is your app going to be free or paid? According to Statista, around 94% of apps in Apple’s App Store and 97% of Android apps are free. There are several ways for free apps to make money, such as ads and in-app purchases. Stakeholders will want to know about your plans for monetizing the app.

• Revamp your app as necessary

Almost all software and applications are regularly updated and improved over time. Mobile apps are no exception. You may need to redesign aspects of an app even before it hits the marketplace based on user feedback and other research.

Don’t think of a revamp as a failure. It’s an essential part of the process.

It’s always best to consider revamping rather than scrapping an entire project. Tweaking or adding certain features can take your app from not being market-ready to being a big success.

Conducting ongoing market research can help you identify appropriate changes.

• How to conduct market research for a mobile app

Here’s a summary of the steps for doing market research for a mobile app:

Know your target audience

Gather relevant data using primary and/or secondary research

Research your competition

Do a SWOT analysis

Pitch your strategy when you’re ready

Redesign and revamp your strategy and app as necessary

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Open Access

Peer-reviewed

Research Article

The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia

Roles Investigation, Methodology, Writing – review & editing

Affiliation Alliance for Research in Exercise, Nutrition and Activity, UniSA Allied Health and Human Performance, University of South Australia, Adelaide, Australia

Roles Writing – review & editing

Affiliation Deakin University, Geelong, Australia, Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences

Roles Data curation, Formal analysis, Software, Writing – review & editing

Affiliation Royal Melbourne Hospital, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

Affiliations NIHR Imperial Patient Safety Translational Research Centre, Imperial College of London, London, United Kingdom, Centre for Health Technology and Services Research, Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, Porto, Portugal

Affiliation Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia

Affiliations Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia, Department of Cardiology, Westmead Hospital, Sydney, Australia

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

¶ ‡ These authors are joint senior authors on this work.

Affiliations Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia, Western Sydney Primary Health Network, Sydney, Australia

Affiliations Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia, Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia

• Huong Ly Tong, 
• Carol Maher, 
• Kate Parker, 
• Tien Dung Pham, 
• Ana Luisa Neves, 
• Benjamin Riordan, 
• Clara K. Chow, 
• Liliana Laranjo, 
• Juan C. Quiroz

• Published: August 18, 2022
• https://doi.org/10.1371/journal.pdig.0000087
• Peer Review
• Reader Comments

To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups.

An online cross-sectional survey was conducted during June–September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher’s exact tests. Three open-ended questions were included to elicit participants’ views; thematic analysis was conducted.

Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18–44 (46.1%), more people aged 60+ (74.5%) and more people aged 45–60 (57.6%) used a COVID-19 related app ( P < .001). Qualitative data suggest people viewed technologies (especially social media) as a ‘double-edged sword’: helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19.

Conclusions

Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.

Author summary

Technologies such as mobile apps or fitness trackers may play a key role in supporting healthy behaviors and deliver public health interventions during the COVID-19 pandemic. We conducted an international survey that asked people about their health behaviors, and their use of technologies before and during the pandemic. Sixty percent reported using a mobile app for health purposes; 38% used a fitness tracker. People who used mobile apps and fitness trackers during the pandemic were more active than people who did not. Women were more likely to use health apps than men, and people aged 45+ were more likely to use COVID-19 apps than people under 45. Differences in app usage based on sex and age indicate that tailored technologies are needed to support different groups. Participants revealed that they had to adapt their use of mobile apps to fit their needs during the highly restricted circumstances caused by COVID-19. Altogether, our findings provide new insights into how mobile apps and devices can deliver health support remotely during a pandemic, and highlight the need for these technologies to adapt to support people’s changing needs.

Citation: Tong HL, Maher C, Parker K, Pham TD, Neves AL, Riordan B, et al. (2022) The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey. PLOS Digit Health 1(8): e0000087. https://doi.org/10.1371/journal.pdig.0000087

Editor: Laura M. König, University of Bayreuth: Universitat Bayreuth, GERMANY

Received: December 25, 2021; Accepted: July 14, 2022; Published: August 18, 2022

Copyright: © 2022 Tong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data that support the findings of this study are openly available at https://osf.io/wa5p8/?view_only=06a70c1321114dfc8f45bd4e1affca4b .

Funding: HLT was supported by the International Macquarie University Research Excellence Scholarship (iMQRES) (Macquarie University funded Scholarship – No. 2018148) and the Australian Government Research Training Program Scholarship. CM is supported by a Medical Research Future Fund Investigator Grant (APP1193862). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coronavirus disease 2019 (COVID-19) and subsequent public health measures have drastically impacted lifestyles worldwide and have had adverse effects on health behaviors [ 1 – 6 ]. Several cross-sectional surveys of adults in Australia, the US and UK have reported negative changes in health behaviors and mental health during the pandemic, including reduced physical activity [ 3 , 4 ], unhealthy eating habits and lower diet quality [ 3 , 4 ], increased alcohol consumption [ 1 ], and higher prevalence of anxiety and depression symptoms [ 1 , 2 , 6 ]. In addition to self-reported changes, studies using objective smartphone-based data also showed a decline in daily step count worldwide [ 5 , 7 ]. During the pandemic, the World Health Organization highlighted the importance of maintaining healthy behaviors in the fight against COVID-19 [ 8 ]. With restrictions on face-to-face clinical consultations and the strain on health care systems in delivering patient care, mobile devices were increasingly harnessed to remotely deliver health care support [ 9 , 10 ].

Mobile devices such as mobile apps and fitness trackers [ 11 ] can be leveraged to deliver behavior change interventions and might play a role in supporting healthy behaviors during the pandemic. Specifically, mobile apps and fitness trackers can incorporate behavior change techniques (i.e., the active component of an intervention designed to regulate behavior change [ 12 ]) that are known to be effective in changing behaviors. Systematic reviews have found that behavior change techniques such as goal setting and self-monitoring of behavior are effective at improving physical activity and diet outcomes [ 13 , 14 ]. Mobile apps or fitness trackers can deliver these behavior change techniques, such as by enabling users to set their own goals, or to self-monitor some behaviors, as demonstrated in prior reviews [ 15 , 16 ]. During the pandemic, mobile apps and fitness trackers can offer unique benefits, by allowing people to access health support remotely and engage in virtual activities (e.g., livestreamed exercise class), in replacement of disrupted in-person activities. Evidence from systematic reviews suggests that under pre-pandemic or ‘normal’ conditions, mobile apps and fitness trackers can improve physical activity [ 17 – 21 ], diet [ 17 , 22 ], sleep [ 23 ], reduce smoking and alcohol intake [ 22 , 24 , 25 ], and help manage mental health [ 17 , 26 ]. However, little is known about the use of these technologies for health behaviors during the COVID-19 pandemic, and the association between using mobile apps and fitness trackers, and healthy behaviors.

A few studies have examined the use of digital technologies for physical activity and mental health during the pandemic. Specifically, a study of Google Trends showed an increase in searches for physical activity and exercise in Australia, the US and the UK [ 27 ]. An analysis of App store data in the US showed an increase in downloads of mental health apps [ 28 ]. Cross-sectional surveys found that the use of digital platforms (e.g., streaming services, mobile apps) was associated with higher physical activity levels [ 29 – 31 ]. While this evidence is promising, the scope was limited to physical activity and mental health and did not explore other behaviors (e.g., diet, smoking, alcohol intake) that are important to maintain good health during the pandemic. Moreover, existing research has not examined the use of fitness trackers, which have been known to have a positive impact on health behaviors [ 18 , 20 , 21 ]. Thus, there remain gaps in understanding how a range of mobile devices were being used for physical and mental wellbeing during the pandemic, and the association between usage and health behaviors.

In addition to supporting healthy behaviors, mobile devices have also been leveraged to deliver public health interventions during the pandemic. Specifically, mobile apps have been developed for COVID-19 purposes, such as to support contact tracing [ 9 ], self-management of symptoms, or home monitoring [ 32 – 34 ]. Despite rapid growth in the number of COVID-19 mobile apps, little is known about their adoption, with preliminary evidence suggesting that specific subgroups (e.g., older people) are more likely to adopt such apps [ 35 ]. It is important to better understand how different subgroups might adopt COVID-19 apps, to inform public health strategies and policy makers in their response to the pandemic.

To address these gaps, we conducted a cross-sectional survey to examine use of mobile apps and fitness trackers to support health behaviors (i.e., self-reported physical activity, diet, sleep, smoking, alcohol consumption), mental wellbeing, and public health interventions (e.g., COVID-19 apps) during the pandemic.

The secondary aims of the study were to examine:

• Whether using mobile apps and/or fitness trackers was associated with healthy behaviors,
• What was the adoption of COVID-19 related apps (i.e., mobile apps designed specifically for COVID-19), and
• Whether specific subgroups showed a higher use of COVID-19 related apps and mobile apps and fitness trackers for health-related purposes.

Study design

This study is a cross-sectional survey that examined the use of mobile apps and fitness trackers for health behaviors and public health interventions during the COVID-19 pandemic. The reporting adheres to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guideline for cross-sectional studies [ 36 ] ( S1 Appendix ). Ethical approval was granted by Macquarie University’s Human Research Ethics Committee (Approval number: 52020674017063). All participants provided electronic written consent prior to participation ( S2 Appendix ).

Settings and participants

An anonymous online survey was hosted on the Qualtrics platform [ 37 ]. The study was advertised via various channels, including social media (Facebook, Twitter, LinkedIn, Instagram, Reddit), public posters (e.g., at parks, libraries, university campus), and research institute networks (e.g., email lists, university website). In our social media advertisements, we also asked people to share the study with their networks (e.g., re-tweet on Twitter), in order to expand the geographical scope of the study. Study recruitment was self-selected, i.e., interested individuals could click on the survey link, upon which they were provided with the study information and provided an electronic written consent prior to participation. Eligible study participants were adults aged over 18 years who were proficient in English. We followed published heuristics for sampling for behavioral research and aimed to recruit at least 500 participants into the study [ 38 ]. The survey was open from start of June to end of September 2020 to achieve the targeted sample size.

During the data collection period (June–September 2020), the World Health Organization assessed the global risk of COVID-19 to be very high [ 39 ]. The number of infected cases globally increased from over 10 million [ 40 ] to 32.7 million [ 41 ] during this period, with vastly different infection rates amongst countries. Public health policies across countries varied considerably with respect to lifestyle restrictions such as lockdown measures, travel restrictions, and mask mandates [ 42 , 43 ]. It is worth noting that during June–September 2020, a few countries had started to ease lifestyle restrictions (e.g., Australia, UK, Canada) [ 43 ].

Survey development and measures

Existing COVID-19 surveys [ 44 – 46 ] were reviewed to inform the wording and structure of the present survey. Subsequently, a draft survey was prepared and reviewed independently in three rounds to establish face validity. Specifically, in round one, a draft survey was prepared by the first author and reviewed by a clinician and a computer science expert, with revisions made accordingly. In round two, the survey was sent out to three experts in digital health and behavioral research for feedback, and revised accordingly. Finally, the revision made in round two was reviewed again by a clinician prior to being finalized. A copy of the Qualtrics survey can be found in S2 Appendix .

Demographic characteristics.

Participants reported their age (years), gender (female, male, other, prefer not to say), highest level of education completed (primary school, high school, vocational training, bachelor’s degree, postgraduate degree), country of residence, and whether they had medical conditions that required regular medical care or medication (yes, no).

Health behaviors.

Health behaviors including physical activity, diet, smoking and alcohol consumption during the pandemic were self-reported. Participants were asked how many minutes of moderate-to-vigorous physical activity they completed each week. Participants were considered to have adhered to the recommended levels of aerobic physical activity if they self-reported at least 150 minutes of moderate-to-vigorous physical activity in a week, based on the World Health Organization’s guidelines [ 47 ].

Participants self-reported daily servings of vegetables and fruits. Participants were considered to have adhered the recommended intake of vegetables and fruits if they self-reported consuming at least five servings of vegetables and fruits in a day, based on the World Health Organization’s recommendation [ 48 ]. Participants also reported the number of standard drinks they typically have in a week, their smoking status (yes, no) and number of cigarettes smoked in a day. Examples of moderate-to-vigorous physical activity, fruit and vegetable servings, and standard alcoholic drink servings were provided.

The use of mobile apps and fitness trackers for health behaviors.

The survey contained 20 questions about participants’ usage of mobile apps (including health apps, general apps, and social media apps) and fitness trackers to support health-related purposes before and during the COVID-19 pandemic. In the survey, health-related purposes were defined as staying active, eating healthily, sleeping better, reducing/stopping smoking and alcohol drinking, and managing mental wellbeing, and it was specified that the focus was not on chronic disease management (e.g., monitor blood glucose, medication reminders). Usage status during the pandemic was classified into three groups: current users, past users and never-users, based on existing literature [ 30 , 31 , 49 ]. The definition of usage status is provided in Box 1 . Additionally, participants were asked to indicate the extent to which they agreed with the usefulness of technologies in supporting different health behaviors. These items were measured using a five-point Likert scale, ranging from strongly disagree to strongly agree. The survey also contained three optional, open-ended questions to collect qualitative data on how participants used mobile apps, fitness trackers, and other technologies to support health behaviors and mental wellbeing during the COVID-19 pandemic.

Box 1: Classification based on technology usage during the pandemic*

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https://doi.org/10.1371/journal.pdig.0000087.t001

COVID-19 related apps

The survey included two questions about whether people used COVID-19 related apps (i.e., mobile apps created specifically for use during the COVID-19 pandemic), and for what purposes (e.g., for contact tracing, symptom checking).

Data analysis

Quantitative data were analyzed using R version 4.0.4 [ 50 – 52 ]. Descriptive statistics, including frequencies and percentages, were generated for categorical variables; means and standard deviations (SD) were generated for continuous variables. Two logistic regression models were used to examine the association between 1) the use of mobile apps and fitness trackers and adherence to aerobic physical activity guidelines, and 2) the use of mobile apps and adherence to fruit and vegetable consumption guidelines. Specifically, one logistic regression model included adherence to aerobic physical activity guidelines as the outcome variable, and the independent variables were current use of mobile apps or fitness trackers, whether participants used an app or tracker before COVID-19 (as a proxy for interest in technology before COVID-19), and whether participants started using a new app or tracker since COVID-19. Another model included adherence to fruit and vegetable consumption guidelines as the outcome variable, and the independent variables were current use of mobile apps, whether participants used a mobile app before COVID-19, and whether participants started using a new app since COVID-19. Both models were adjusted for factors selected a priori, including age, gender, education, and the existence of current medical conditions. Odds ratios (OR) and 95% confidence intervals (CI) were reported. Post-hoc sensitivity analyses were conducted to include only Australia-based participants, given the large proportion of this group in the sample.

Subgroup analyses were conducted to explore to explore whether age and gender subgroups were more likely to use mobile apps for health-related purposes or COVID-19 related apps. These subgroups were chosen based on the literature, as previous cross-sectional surveys have found that app usage might differ by age and gender [ 30 , 35 ]. Specifically, Thomas et al found that COVID-19 app downloads appeared to increase with age, with the 65+ age group having the highest proportion of downloads [ 35 ]. Additionally, Parker et al also found that more women than men used digital platforms for their physical activity during the pandemic [ 30 ]. Chi-square tests were used for categorical data. When the assumption of chi-square test was violated, Fisher’s exact test was used instead. The significance level for all statistical tests was set at P < .05, two-tailed.

Qualitative data (from free-text responses) were analyzed using thematic analysis [ 53 ] in NVivo 12 [ 54 ] to explore the different ways people used technologies to maintain health and wellbeing during the pandemic. Integration of results was conducted after quantitative and qualitative analyses were completed, through embedding of the data. Integration is presented throughout the Discussion section.

Sample description

While 554 people consented to participation, two were under 18, and thus, were not eligible. In total, 552 participants (mean age 38±13.6 years, 76.6% women) were included in data analysis. Responses were recorded from 32 countries, with most participants (382/549, 69.6%) living in Australia. The majority (359/552, 65%) had completed a postgraduate degree, and 71.1% (385/541) reported having no current medical condition requiring regular care or medication. The self-reported average weekly time spent in moderate-to-vigorous physical activity was 164 (SD 152) minutes. The average vegetable and fruit consumption reported by participants were 2.7 and 1.7 daily servings, respectively. Most of the sample (525/541, 97%) were non-smokers. The average alcohol consumption was reported as 3 drinks per week. The sociodemographic and health characteristics of the study sample are presented in Table 1 .

https://doi.org/10.1371/journal.pdig.0000087.t002

Technology use for health behaviors and mental wellbeing during COVID-19

Mobile apps..

Regarding participants’ app usage habits, 59.9% (302/504) were currently using apps for health purposes during the pandemic (i.e., current users) ( Table 2 ). Amongst the current app users, 77.8% (235/302) consistently used mobile apps for their health before COVID-19. A greater proportion of women were current app users than men (64.0% vs 46.8%, P = .004, S4 Appendix provides more details on subgroup analyses). The most popular apps used for health purposes during the pandemic were general and social media apps (e.g., Zoom, Facebook, Youtube), which were not purposedly built to promote health behavior change ( Table 2 ).

https://doi.org/10.1371/journal.pdig.0000087.t003

Compared to pre-pandemic times, nearly half (192/401, 47.8%) used mobile apps more frequently for health purposes during the COVID-19 pandemic ( Table 2 ). Forty percent (164/401, 40.9%) started using a new mobile app for health-related purposes since the outbreak of COVID-19.

During the COVID-19 pandemic, the most reported health purpose of app usage was to stay active (248/298, 83%) ( Table 2 ). Amongst those who used apps for physical activity, the majority used them to track activity levels (196/246, 79.7%), or to follow an exercise video (148/246, 60.1%) ( Table 2 ). Over two-third of participants (203/298, 68.1%) used mobile apps for more than one health purpose during the COVID-19 pandemic. Compared to men, a greater proportion of women used mobile apps to stay active (48% vs 36.7%, P = .02) and to connect with other people (22.7% vs 9.2%, P = .004, S4 Appendix ).

Regarding the perceived usefulness of mobile apps for health, 59.4% (232/390) of participants agreed that mobile apps helped them incorporate more activity in their days; 43.5% (167/384) agreed that mobile apps helped them manage their mental wellbeing. Compared to men, a greater proportion of women found mobile apps helpful for managing their mental wellbeing (80.6% vs 63.2%, P = .04, S4 Appendix ).

Fitness trackers.

Over a third of participants (188/492, 38.2%) were current users of fitness trackers, 19.3% (95/492) were past users, and 42.7% (210/492) had never used fitness trackers for their health. The median length of usage for current and past users was 2 years (range 1 month—10 years). Forty-eight percent of responders (237/492, 48.1%) mentioned that they had used fitness trackers before the pandemic. Amongst those who used trackers before the pandemic, the most popular trackers used pre-COVID were Fitbit, and Apple Watch. Since the COVID-19 outbreak, 5.1% of respondents (25/492) started using a new fitness tracker.

During the pandemic, the most common reasons for using fitness trackers were to track different measurements (e.g., distance run or walked, heart rate), and to receive reminders to move. Over half (147/274, 53.6%) agreed that fitness trackers helped them incorporate more activity in their daily lives.

The association between technology usage and healthy behaviors.

People who currently used a mobile app or fitness tracker during the pandemic had almost two times the odds of meeting aerobic physical activity guidelines (OR = 1.91, 95% CI 1.07 to 3.46) compared to non-users ( Table 3 ). Whether participants used mobile apps or fitness trackers before COVID-19, and whether participants started using a new app or tracker since COVID-19 were also statistically associated with meeting aerobic physical activity guidelines. Specifically, people who started using a new app or tracker since COVID-19 had 1.7 times the odds of meeting aerobic physical activity guidelines than people who did not (OR = 1.66, 95% CI 1.06 to 2.61) ( Table 3 ). People who had used mobile apps or trackers before COVID-19 had more than 2 times the odds of meeting aerobic physical activity guidelines than non-users (OR = 2.32, 95% CI 1.36 to 4.02). Mobile app usage was not associated with meeting fruit and vegetables consumption guidelines (OR = 0.97, 95% CI 0.53 to 1.76) ( Table 3 ).

https://doi.org/10.1371/journal.pdig.0000087.t004

Given the large proportion of Australia-based participants in our sample, we conducted a sensitivity analysis with this subgroup ( S5 Appendix ). The sensitivity analysis showed that current app or tracker usage was no longer statistically associated with meeting aerobic physical activity guidelines (OR = 1.63, 95% CI 0.79 to 3.43). Age, whether participants used an app or tracker before COVID-19, and whether participants started using a new app or tracker since COVID-19 were statistically associated with meeting aerobic physical activity guidelines. Mobile app usage was also not associated with meeting fruit and vegetable consumption guidelines in this subgroup (OR = 1.08, 95% CI 0.52 to 2.27).

COVID-19 related apps.

Less than half of the participants (235/508, 46.3%) used a COVID-19 related app. Of those that used COVID-19 related apps, most used country-specific apps (e.g., COVIDSafe in Australia). The main purpose of using COVID-19 related apps was to support contact tracing. Twelve percent (59/508, 11.6%) used COVID-19 related apps for more than one purpose, most often to support contact tracing and get COVID-19 information.

Use of COVID-19 related apps differed by age and whether they were currently using mobile apps for their health. Compared to people aged 18–44, a larger proportion of people aged 60+ (74.5% versus 46.1%) and a larger proportion of people aged 45–60 (57.6% versus 46.1%) used a COVID-19 related app ( P < .001, S4 Appendix ). Compared to never-users, a greater proportion of current users (50.3% vs 35.3%) and past users (47.6% vs 35.3%) of mobile apps for health used COVID-19 related apps ( P = .034, S4 Appendix ).

Qualitative results.

The most common and central themes from the responses to open-ended questions are described below and comprised: maintaining a sense of normalcy and social connections; technologies as a double-edged sword; desired features of technology. S6 Appendix includes demographic details of the subset of participants who answered each of the open-ended questions.

Maintaining a sense of normalcy and social connections.

Participants mentioned that during the pandemic, mobile devices has allowed them to maintain a routine despite the disruption caused by COVID-19, and maintain a sense of normalcy, which in turn gave them motivation to exercise ( Table 4 , quotes 1–2). Additionally, most participants mentioned that technologies helped them stay socially connected with their family and friends, which alleviated some emotional stress and allowed them to share their fitness progress ( Table 4 , quote 3–4).

https://doi.org/10.1371/journal.pdig.0000087.t005

Technologies as a double-edged sword.

Participants cited both positive and negative effects from the use of technologies, especially social media, during the COVID-19 pandemic. On one hand, social media allowed people to stay updated with COVID-19 news ( Table 4 , quote 5). On the other hand, participants also mentioned that the high volume of COVID-19 news could cause information overload and emotional stress ( Table 4 , quote 6). Similarly, when talking about fitness trackers, some participants indicated negative emotions associated with self-monitoring, as their physical activity had declined due to COVID-19 circumstances ( Table 4 , quote 7).

Desired features of technology.

There were two subthemes within the area of desired features of technology: adaptability and gamification. Participants mentioned that while technologies had been helpful, one key thing missing was the adaptability of technologies to the unprecedented circumstances caused by COVID-19 ( Table 4 , quote 8). Consequently, several mentioned that they took the initiative to repurpose existing health apps to serve their needs during COVID-19 pandemic ( Table 4 , quotes 9–10). Many participants across different ages also valued gamification features of technologies (e.g., competition, exercise challenges, exercise role-playing games), which helped them to incorporate fitness into their life with an element of fun and enjoyment ( Table 4 , quotes 11–12).

Principal results

Our study found that 60% of participants used mobile apps and 38% used fitness trackers for health behaviors during June–September 2020. People who used mobile apps or fitness trackers during the pandemic were more likely to self-report meeting recommended levels of aerobic physical activity than non-users. A greater proportion of women used apps for their health during the pandemic than men. Additionally, 46% of respondents self-reported using COVID-19 apps. Specific subgroups such as people aged 45+ and current or past users of mobile apps for health purposes were more likely to use COVID-19 related apps. We note that these subgroup analyses based on age and gender are exploratory in nature and should be confirmed in future research. The generalizability of our quantitative findings is limited, given our sample of highly educated individuals who might have been more health-conscious, and had better access and more inclined to use technologies. Qualitative findings complemented quantitative findings by showing while mobile devices helped maintain a sense of normalcy, there were potential negative effects of using technologies (e.g., stress and information overload from seeing COVID-19 information on social media, guilt when seeing low activity levels), which might have impacted users’ motivation and continued use of mobile devices. Our participants highlighted the need for technologies to adapt to changing circumstances.

Impact of mobile devices on health behaviors

Our results are consistent with existing literature showing that users of mobile apps and other digital technologies seem to be more active than non-users during the pandemic [ 29 – 31 , 55 ]. Uniquely, by adjusting our model to variables related to ‘previous use of mobile devices before COVID’ and ‘adoption of new apps or trackers during the outbreak’, we found these were associated with adherence to physical activity guidelines. It is possible that the physical activity benefits observed in our study are influenced by an overrepresentation in our sample of health-conscious and tech-adopting people. Future research is needed to understand how mobile devices can extend its reach and benefit other groups beyond the typical highly motivated and ‘worried-well’ adopters [ 56 ]. A sensitivity analysis including only Australia-based participants found that current mobile app or tracker usage was not associated with adherence to physical activity guidelines. It is possible that the smaller sample size made it difficult to detect the difference. Given the inconsistency between the primary and sensitivity analyses, the potential physical activity benefits associated with mobile devices observed in our findings should be interpreted with caution, and future research is needed to ascertain the potential impact of mobile devices on health behaviors.

Our qualitative data highlight the need for mobile apps and fitness trackers to adapt quickly to the changing circumstances of human lives, especially in health crises like COVID-19. Given the disruption to normal routines and closure of exercise and health facilities, people might need additional, or different types of support to maintain healthy behaviors, which is difficult to accommodate by mobile apps and devices based on static algorithms. With recent development in artificial intelligence and machine learning, mobile apps and devices can collect information about its users (including users’ behaviors, context or preferences) to continuously adapt their content, timing and delivery, and personalize their support to suit the person’s needs [ 57 , 58 ].

Differences in app usage between genders

Findings suggested that a greater proportion of women used mobile apps during the pandemic than men. Specifically, women were more likely to use apps to support physical activity and to connect with others, and more likely to report apps as useful for mental health. It is worth noting that this gender difference is based on a subgroup analysis and is exploratory in nature. However, we also note that our finding is in line with previous research reporting higher use of digital platforms for physical activity amongst women [ 30 ]. There are several possible explanations for this observed gender difference. Research has shown that during the pandemic, women reported increased overeating [ 4 ] and less physical activity than men [ 59 ], and heightened stress from taking on more caring or home-schooling responsibilities [ 1 , 59 – 62 ]. Thus, women might have needed additional support and turned to mobile devices to support their wellbeing. Another possible explanation is linked to the type of health activities that can be accommodated in health apps. Research has suggested that women were more likely to engage in directed activities (e.g., exercise classes [ 63 , 64 ]), which could be delivered online more easily, compared to competitive sports usually done by men [ 63 ]. Future research is needed to explore how the adoption of mobile devices might differ by gender and how to design health interventions to reduce the existing gender differences in adoption.

Adoption and usage of COVID-19 related apps

Only 46.3% of our participants used a COVID-19 related app. Previous research has reported uptake ranging from 20% [ 65 , 66 ] to 40% [ 35 , 67 ] amongst European countries and Australia. Given that the most common purpose is contact tracing, this low uptake is concerning as digital tracing apps rely on a high adoption rate to work effectively [ 9 ]. Research has suggested that the reasons for low uptake are mainly privacy and functionality concerns (e.g., battery drain, apps not working as intended) [ 35 ]. This indicates the need to improve the functionality of digital tracing apps, as well as public health communication regarding the privacy protections of tracking technologies [ 68 ]. Our study found a greater proportion of people aged 60+, and people aged 45–60 used COVID-19 related apps compared to those less than 45 years. This is in line with previous research which suggests that the higher uptake in older adults might be related to concerns about their vulnerability to COVID-19 [ 35 ]. This trend highlights the need for public health communication to also target younger populations to ensure a high adoption rate in this subgroup. It is worth noting that since 2021, some countries (e.g., Australia) have made ‘signing-into’ venues mandatory, usually through a ‘check-in’ function in government apps to support contact tracing. Thus, since the completion of this study, it is likely that the use of these government apps for COVID-19 purposes have increased. Furthermore, given the exploratory nature of this subgroup analysis, future research is needed to confirm potential age differences in COVID-19 app uptake.

Strengths and limitations

A strength of our study is the mixed-methods design, including qualitative, open-ended questions, which allowed us to acquire a deeper exploration of users’ perspectives. However, the results must be interpreted considering some limitations. While face validity was established through multiple co-authors independently reviewing the survey draft, the survey questions were not formally assessed for criterion or content validity, and the survey was not pilot tested. Health behaviors were assessed through self-report. We assessed the impact of technologies on only aerobic physical activity and the intake of fruits and vegetables. To enable a more comprehensive analysis on the link between technologies and physical activity and diet, future research should collect data on other types of activity (e.g., muscle strengthening exercises) and food groups (e.g., salt or sugar intake). We were not able to examine the link between technologies usage and alcohol intake and smoking because only a small percentage of our sample used technologies for these purposes. While our sampling was worldwide, the majority of participants resided in Australia. As a large proportion of participants were women, and had high level of education, this might bias our findings and affect the generalizability to other population groups. Previous surveys have reported a similarly high participation rate from women and people with higher education levels [ 1 , 3 , 4 , 30 ]. The survey was conducted online and proficiency in English was required, which might have precluded participation from non-English speaking individuals and those lacking access to the Internet. Finally, our findings are also impacted by common limitations of survey research—self-reported answers and self-selection sampling method. This might have led to sampling bias, social desirability bias, or recall bias, which affect the generalizability of the findings and the reliability of the responses.

Implications

Mobile apps and fitness trackers seem promising in promoting physical activity during the COVID-19 outbreak. Potential improvements on these technologies from users’ perspectives should focus on personalization and adaptability, such as allowing for higher customization of content delivered and a better ability to support people’s changing needs. This is in line with previous research which suggests that personalization can increase user engagement with mobile devices [ 69 ]. By leveraging recent advances in big data and artificial intelligence [ 58 ], mobile devices may be able to provide more in-time, personalized support to users. Future research is needed to investigate whether the engagement with health apps and devices is sustained post-COVID, and robust clinical trials are needed to ascertain their objective benefits for preventative health, including physical activity and other health behaviors.

Our findings may be influenced by the large proportion of highly educated individuals who might be more health-conscious and have access to technologies more easily than other population groups. Previous research has described this phenomenon as the “digital divide” [ 70 , 71 ], which can widen existing social inequalities. The benefits of mobile apps and devices would be limited if they can only reach high socioeconomic status groups. Thus, efforts must be made to bridge this gap in technology adoption, such as through increasing access, promoting collaborative and inclusive design, and improving digital literacy [ 70 , 71 ].

Our study found a positive impact of mobile apps and fitness trackers on physical activity during the pandemic, in a sample of likely health-conscious and technology-inclined individuals. Qualitative data revealed the lack of flexibility of mobile apps and devices and highlighted the need for these technologies to adapt quickly to changes in life circumstances. Future research should assess the use of mobile apps and fitness trackers post-COVID, and whether these technologies provide objective benefits to health behaviors.

Supporting information

S1 appendix. strobe checklist..

https://doi.org/10.1371/journal.pdig.0000087.s001

S2 Appendix. Survey.

https://doi.org/10.1371/journal.pdig.0000087.s002

S3 Appendix. Country of residence breakdown by the number of responses and %.

https://doi.org/10.1371/journal.pdig.0000087.s003

S4 Appendix. Subgroup analyses.

https://doi.org/10.1371/journal.pdig.0000087.s004

S5 Appendix. Sensitivity analyses in the Australia sub-sample.

https://doi.org/10.1371/journal.pdig.0000087.s005

S6 Appendix. Demographic information of participants who responded to open-ended questions.

https://doi.org/10.1371/journal.pdig.0000087.s006

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• Published: 09 August 2021

The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage

• David Manuel Duarte Oliveira   ORCID: orcid.org/0000-0002-8763-6997 1 ,
• Luís Pedro 1 &
• Carlos Santos 1  

Smart Learning Environments volume  8 , Article number:  14 ( 2021 ) Cite this article

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This paper was developed within the scope of a PhD thesis that intends to characterize the use of mobile applications by the students of the University of Aveiro during class time. The main purpose of this paper is to present the results of an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this paper we present the context of the pilot, its technological settings, the analysed cases and the discussion and conclusions carried out to gather mobile applications usage data logs from students of an undergraduate degree on Communication Technologies.

Our study gathered data from 77 participants, taking theoretical classes in the Department of Communication and Arts at the University of Aveiro. The research was based on the Grounded Theory method approach aiming to analyse the logs from the access points of the University. With the collected data, a profile of the use of mobile devices during classes was drawn.

The preliminary findings suggest that the use of apps during the theoretical classes of the Department of Communication and Art is quite high and that the most used apps are Social networks like Facebook and Instagram. During this pilot the accesses during theoretical classes corresponded to approximately 11,177 accesses per student. We also concluded that the students agree that accessing applications can distract them during these classes and that they have a misperception about their use of online applications during classes, as the usage time is, in fact, more intensive than what participants reported.

Introduction

The use of mobile devices by higher education students has grown in the last years (GMI, 2019 ). Technological advancements are also pushing society with consequent rapidly changing environments. Higher Education Institutions (HEIs) are not exempted from these technological changes and advancements, and it is compulsory that they follow this technological evolution so that the teaching-learning process is improved and enriched.

HEI’s are trying to integrate digital devices such as mobile phones and tablets, and informal learning situations, assuming that the use of these technologies may result in a different learning approach and increase students’ motivation and proficiency (Aagaard, 2015 ).

In a study by Magda, & Aslanian ( 2018 ), students report that they access course documents and communicate with the faculty via their mobile devices, such as smartphones. Over 40% say they perform searches for reports and access institutions E-Learning systems via mobile devices (Magda, & Aslanian, 2018 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education. However, teachers believe students use their gadgets less than they actually do, and mobile devices also challenge teaching practices. Students use devices for off-task (Jesse, 2015 ) or parallel activities and there may be inaccurate references to their actual use of mobile devices.

Mobile device users have very different usage habits of their devices and their applications, and it is important to study and characterize these behaviours in different contexts, as explained below. The reports that usually support these studies are made with questions directed to the users themselves asking them questions about the apps they have on the devices and the reasons for using them. However, Gerpott & Thomas ( 2014 ) argue that other types of studies are needed to properly support this type of research.

Studies are usually conducted in organizations, based on the opinion of the participants, and cannot be replicated and generalized, for example, regarding the use of the internet or mobile applications by the general public, because these devices, unlike desktop devices, can be used anywhere and at any time (Gerpott & Thomas, 2014 ).

Furthermore, in mobile contexts, it becomes difficult for people to remember what they have used, because mobile applications can be used for various tasks, in various contexts, whether professional or personal, and the variety of applications, the use made, the periods of use are usually so wide and differentiated, that it can become difficult for users to refer which services or applications they have used, under which circumstances and how often. (Boase & Ling, 2013 ).

Thus, it is relevant, for several areas and especially for this research area, to have studies that cross-reference reported usage with actual usage. One of the ways to achieve this is with the use of logs of the use of mobile devices and applications, as mentioned by De Reuver & Bouwman ( 2015 ):

Using this approach this pilot study aims to create and validate a methodology:

i) to show the profile of these users,

ii) to reveal the kind of applications they use in the classroom and when they are in the institutions,

iii) and also, to compare the users’ perceptions with the real use of mobile applications.

Knowing the real usage and the usage students mention may provide valuable insights to teachers and HEIs and use this data for decision making about institutional applications to support students and teachers in their teaching and learning activities. Such information can also bring insights on the integration of M-Learning strategies, promoting interaction, communication, access to courses and the completion of assignments using students’ devices.

The central focus of this study is, therefore, to show preliminary results of the use of applications by students in class time during theoretical classes, through logs collected during class time.

The paper is divided into five parts. In the first part, relevant theoretical considerations are addressed, having in mind the current state of the art in terms of the literature and empirical work in this field. The second part outlines the study methodology. In the third part, the technological setting is highlighted. The cases and the results of the data analysis are described in the fourth part. Lastly, the results are interpreted, connected and crossed with the preliminary considerations.

Literature review

The massive use of mobile devices has created new forms of social interaction, significantly reducing the spatial difficulties that could exist, and today people can be reached and connected anytime and anywhere (Monteiro et al., 2017 ). This also applies to the school environment, where students bring small devices (smartphones, tablets and e-book readers) with them, which, thanks to easy access to an Internet connection, keep them permanently connected, even during classes.

In HEIs there is also a growing tendency among members of the academic community to use mobile devices in their daily activities (Oliveira et al., 2017 ), and students expect these devices to be an integral part of their academic tasks, too (Dobbin et al., 2011 ). A great number of users take advantage of mobile devices to search information and, since they do not always have computers available, these devices allow them an easy access to academic and institutional information (Vicente, 2013 ).

One of the challenges educational institutions face today has to do with the ubiquitous character of these devices and with finding ways in which they can be useful for learning, thus approaching a new educational paradigm: Mobile Learning (M-Learning) (Ryu & Parsons, 2008 ).

M-learning allows learning to take place in multiple places, in several ways and when the learner wants to learn. As learning does not necessarily have to occur within school buildings and schedules, M-Learning reduces the limitations of learning confined to the classroom (Sharples, M., Corlett, D. & Westmancott,  2002 ), leading UNESCO to consider that M-Learning, in fact, increases the reach of education and may promote equality in education (UNESCO, 2013 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education and, therefore, it becomes increasingly relevant to rethink learning spaces in a more open perspective, both physically and methodologically, namely through mobile learning that places the student at the centre of the learning process.

Quite often studies that intend to determine the use of mobile applications focus on general questions, but the most common ones are related to the frequency and duration of the use of these devices, for example, questions such as “how many SMS or calls are made?” or “how often do you use the device?”

In fact, instruments like questionnaires are widely used in this type of studies. However, since mobile devices are completely integrated in our daily life and we use them quite extensively, it is difficult to retain and define with plausible accuracy the actual use that we make of them.

It is therefore relevant to effectively understand how these students use these devices, more specifically the applications installed on them. To this end, most studies have been based on designs that are focused on the users’ perceptions and based are on these reports.

Thus, it was important to understand if what users report using corresponds to what they actually use, and if this use does not occur for distraction or entertainment, for example.

Considering the above, some studies have focused on the validity of the use of these instruments. One of these first studies, carried out by Parslow et al. ( 2003 ), aimed at determining the number of calls made and received in the days, weeks or months preceding the date of the questionnaire, and their duration. The answers were compared with the logs of the operators and it was concluded that self-report questionnaires do not always represent the actual pattern of use.

Finally, in self-report instruments, which refer to questions of daily activity on mobile devices, this activity may not represent a general pattern of activity, since from individual to individual the patterns of daily use may vary considerably and thus reflect a very irregular use.

In a study by Boase & Ling ( 2013 ), the authors mentioned that about 40% of studies on mobile device use, based on articles published in journals (41 articles between 2003 and 2010), are based on questionnaires.

The questions asked aim to estimate how long or what type of use they have made of their devices on a daily basis, and sometimes aim to know about time periods of several days. In most of these studies, 40% of papers use at least one measure of frequency of use and 27% a measure of duration of use that users make. Another factor that is mentioned is that users do not always report their usage completely accurately. On the other hand, the same study mentions that users may over or under report the use they make for reasons of sociability (Boase & Ling, 2013 ).

Given the moderate correlation between self-report instruments and data from records or logs (Boase & Ling, 2013 ), the author considers that researchers can significantly improve the results if they use, together with other instruments, data from logs to make their studies more accurate and rigorous. Another suggestion would be the use of mobile applications that record these usage behaviours (Raento et al., 2009 ).

Indeed, this kind of instrument is widely used in this type of studies. However, given that mobile devices are fully integrated into our daily lives and we use them quite extensively, it becomes difficult to retain and define with plausible accuracy the use we make of them. In addition to the factors mentioned in the previous paragraph, it is important that these types of studies are validated with other methods, such as the use of logs, as presented in this study. The logs in this study refer to the capture records of the mobile device traffic made by the students.

This article therefore aims to present preliminary results with an approach that uses cross-checking of log data with questionnaire results.

Methodology

This article intends to present and discuss preliminary results of a study that aims to characterize the use of mobile applications at the University of Aveiro through collected logs, crossing its results with questionnaires answered by students during the classes, and also with an observation grid with data from the analysed class and questions to teachers related to what teachers recommend regarding the use of mobile phones during class time.

The research question that motivated this article is: which digital applications/services are most frequently used on mobile devices by the students of the University of Aveiro during their classes?

The study was composed of 40 students, that answered the questionnaires.

The research was based on the Grounded Theory method aiming to analyse the logs from the access points of the University. With the collected data, a usage profile of mobile devices during classes was drawn.

Figure  1 presents a diagrammatic representation of the created methodological process.

General diagram of the study

Therefore 3 instruments were used for the data collection: a questionnaire, an observation grid and logs collected through mobile traffic in the wi-fi network of the university.

The questionnaire allowed for a quantitative assessment of the profile of the participants and collected data on the use that participants claimed to make of their mobile devices. The observation grid served as a guide for the implementation of the study, allowing to record data on the classes where the collections took place and to verify whether certain items were present, such as permission to use mobile devices or planning to use them by teachers. The observation grid would also serve to make the link between use and content in class, but in this pilot, it was not possible to make this link between the class content and the usage of mobile applications, because the author could not observe the applications used by students.

The database containing the usage records enabled the analysis of the logs, resulting in the quantification and verification of the type of activity that each (anonymous) participant made of their device.

The 3 instruments used aimed to i) determine which application(s) students were really using during the classes, through the analysis of the data logs collected from the Wi-Fi network of the University; ii) identify the participants’ representations of their activities by means of several questions regarding mobile usage during class time; iii) observe students’ behaviour and focus via an observation grid that was used by the researcher/observer when he was attending the classes.

The group who participated in this pilot study was selected in accordance with the professors and classes available, so it is considered a convenience sample. The group was constituted by students of undergraduate classes from the Communication and Arts Department of the University of Aveiro.

Table  1 summarizes the schedule of the pilots carried out, the curricular units where they took place, their duration and the instruments used. For ease of management, all the pilots took place in the same department of the University.

The Table  2 summarizes the collected data from questionnaires and logs.

This pilot aimed to build an approach to data analysis, close to the Grounded Theory methodology, in which a provisional theory is built based on the observed and analysed data (Alves et al., 2017 ; Long et al., 1993 ). The data collected in this pilot will serve to define a more complete methodology to be used in a larger study.

This chapter is divided into three parts: context, technological setting and cases analysed. In the context part, the classes which are part of the study will be described, relating the answers from the questionnaires with the teachers’ recommendations about the use of mobile devices. In the technological scenario section, it is intended to describe the technological background underlying the collection process of the logs and in the last part, analysed cases, the objective was to validate if the data to be collected matched the outlined objectives.

In the questionnaire, the questions were divided into two main groups: aspects related to the participant’s profile and aspects directly related to the use of the applications. Aspects related to participants were intended to characterize them. Regarding the use of applications, we aimed to find out the students’ perception of the applications they use in their daily routine, inside and outside of the classroom, and how they do it. Data were collected using a Google Forms form and processed using Microsoft Excel.

In this subchapter, through the data collected from the students’ answers to the questionnaires, and by crossing this information with the data collected from the teachers in the observation grid, we try to describe the context of the pilot.

All of the teachers stated that they allowed their students to use mobile phones during class time, but that they did not plan that use. They also stated that in most part of the classes several students use their mobile phones and apps to search for class related materials. The teachers also showed curiosity about knowing, with more detail, the mobile phone use their students actually have.

In the three classes analysed (Aesthetics, Scriptwriting and Music in History and Culture), when asked about the possibility of using mobile applications as a pedagogical complementary resource 43%, 47% and 55% of students fully agreed that these should be used. In these three classes, 31%, 44%, and 67% of students showed a more moderate opinion: they agreed (but not in such an assertive way) that these should be used.

Another conclusion is that most of the students used a smartphone (88,9%, 75%, 52%) during class time, but many of them also used a computer (66,7%, 100%, 84%). The percentage use of tablets is much lower (11,1%, 0%, 15%).

In the analysed scenario, the majority of the students used the android operating system and 94% also agreed that mobile applications could help to manage the academic tasks, except in the case of the “Aesthetics Curricular Unit”.

When it comes to the time of use, per week, in classes, 53%, 58%, and 22% of the students answered they used these devices between 4 to 5 days a week and 15%, 40% and 70% said they used them between 1 to 3 days a week.

Students were also asked about how frequently they accessed mobile applications during class time and, in all, 77% of the respondents reported accessing apps at least between 1 to 5 times per class. About 20% referred they accessed apps from 6 to 10 times per class.

As for the purposes of accessing apps during classes, most students mentioned categories related i) to support the class / to research (70%, 100%, 77,8%), ii) to access institutional platforms (47.4%, 66.7%, 89, 9%), iii) to access to information (47.4%, 50%, 66.7%) and iv) to work (36.8%, 50%, 44.4%).

Interestingly, the categories communication (52.6%, 41.7%, 22.3%), collaboration (10.5%, 16.7%, 0%), access to institutional services (5.3%, 0% 0%) and “I do not use them” (10.5%, 0%, 0%) presented very low percentages, namely the last one.

When questioned about the use of mobile devices that did not include academic reasons, many students referred to the categories “to be linked/connected” or “to be updated” (42.1%, 66.7%, 33.3%), “to communicate” (57.7% 75.7%, 66.7%), “to share and access content” (31.6%, 58.3%, 33.3%), but few mentioned “for entertainment” (26.3%, 16.7%, 22.2%), “as a habit or routine” (10.5%, 41.7%, 11.1%) and “I do not use them” (10.5%, 0%, 11.1%).

When asked about which mobile applications are most used in an academic context, the most relevant category was “to research / to study” (73.7%, 58.3%, 89.9%), “to check the calendar” (31.6%, 25%, 66.7% %) and “to surf the web” (47.4%, 50%, 55.6%). Again, categories such as “to work” (36.8%, 33.3%, 33.3%), “to take notes” (26,2%, 33.3%, 55.6%) and “to create content” (31.6%, 25%, 11.1%) presented relatively low percentages. It should also be noted that the respondents presented answers that created categories which were not expected such as “to watch films” (10.5%, 8.3%, 0%), “to listen to music” (31.6%, 33.3%, 33.3%), “to take photos” (10.5%, 0%, 0%) and “to play games” (5.3%, 0%, 0%) All the students said that they used applications during classes in at least one of the categories. In fact, in the three courses no one stated “not to use them” (0% in all).

When asked about the teachers’ permission to use the mobile devices in the classroom, most of the students said that teachers allowed free use (52.6%, 100%, 77.8%). Only a few stated that teachers allowed using them specifically when planned (41, 1%, 0%, 22.2%). The respondents of one course stated that teachers did not allow the use of devices (Aesthetics - 5.3%). Finally, when asked about the usefulness of integrating mobile applications in class, there was an overwhelming majority of respondents (100%, 78,9%, 100%) saying they believed that such integration could be enriching and useful.

Below is presented a table describing the most used mobile apps during class activities. It should be noted that only the two answers with the greatest relevance for each category were considered.

Table  3 systematizes what the results have been showing until now: there is an important part of students that use mobile phones during their classes and, even when teachers advise them not to use them, they ignore the recommendations and use them anyway. The main purposes stated were: to be in contact with others through social networking but also to access different kinds of information in browsers. Moreover, the classes where the use of devices is not recommended by the teachers seems to be the one where some applications are most used.

Technological setting

In this section we intend to describe the technological background underlying the process of collecting the logs. The first goal was to register and capture logs from the wi-fi network of the university, which consists of a wireless network that users can access using their universal user credentials.

In order to do that a meeting was scheduled with the university’s technology services, as our main concern was the anonymization of the data collected in order (i) to confer more neutrality to the data treatment, and (ii) to comply with European data protection legislation. Another issue for discussion was the need of powerful machines so that they could process the large amount of data collected.

In this meeting the necessary steps were agreed in order to guarantee the users’ privacy, the authorization of the university’s central services to do the study and the registration method of the logs. The overall procedure demanded several experiences of data collection to fine-tune the final pilot, which works as the basis capture setting for all the main study.

The Wi-Fi traffic capture software (Wireshark) was selected to work both with Android and IOS devices and it was possible to understand the functionalities of the software.

The pilot also helped to understand and solve additional problems that appeared during the previous tests, related to the anonymization of the users’ data. It was necessary to ensure that the users’ personal data were not identifiable, which was a commitment: in fact, only HTTPS Footnote 1 traffic was captured, being all the other information encrypted.

After the first tests, an initial data collection pilot took place in a classroom context. A specific capture system was created to allow the capture of mobile application logs used only by a certain group of students, from a designated Curricular Unit. A specific scenario was set up to ensure that only those students communicating through the IP Footnote 2 defined for the scenario and during that class time were considered and treated under the scope of this study:

If the traffic of the concerned student is communicating through one of the APs (Access Points) covering the room, then the device will be assigned a “Room network” IP;

If the student’s traffic is not communicating through one of the APs covering the room, then the device will be assigned a “Non Room network” IP;

If the student traffic does not belong to the group to be analysed and the device in question is communicating through one of the APs covering the room, then the device will be assigned an IP from a “normal eduroam network”;

In the final steps we resolved the IP’s in Wireshark (software used for the capture) and the unsolved IP’s where filtered in a PHP Footnote 3 script, through the gethostbyaddr method where the unsolved ones are incrementally added.

Finally, using an IP list, we performed a comparison to resolve any unresolved names;

This step allowed to fine tune the process and to make the final test.

Analysed cases

After performing these tests, a scenario for this final pilot was set up to validate if the data to be collected matched the outlined objectives. In this final pilot, logs were collected in a classroom so that the scenario was as close to the desired collection as possible. In this pilot, it was possible to verify that the collected data fulfilled the requirements. At this point, in addition to the HTTPS traffic packets, the packets referring to DNS Footnote 4 traffic were also included. This option made the HTTPS traffic more easily understandable. Furthermore, the researcher could conclude that all authenticated devices belonged to separate accounts.

The results show that the pre-tests/pilots and the final pilot turned out very well and in a very reliable way since they allowed to verify the main problems that could occur and helped to certify that the traffic anonymity condition was respected. In fact, only the HTTPS was considered, and all other communication was encrypted with no risk of corruption of private data. Moreover, this option had an important justification: the fact that HTTPS traffic could be more easily understandable and the fact that it allowed certifying that all the authenticated devices of the wireless network belonged to separate accounts.

To process and create output visualization of the data, the choice was an integrated solution, both for the processing stage and for creating visualisations. Given the variety of tools available, several were tried out and Tableau Software® (Tableau Prep® and Tableau Desktop®) was chosen. Tableau Software is an interactive data processing and visualisation tool that belongs to the Salesforce company and, although it is paid software, it allows for an academic licence that was used in this project.

This solution, besides allowing working with a large amount of data, also allows for a very interactive data treatment and visualisation. This software also allows the importation of data from various sources, which in the case of this study was also an advantage.

This solution allowed us to work with large amounts of data but it also allowed for a very interactive data treatment and visualization. In the case of Tableau Prep, the file with the logs was imported in a CSV format Footnote 5 and treated iteratively in a dynamic way, being refined to the desired data in a second stage. As an example, we can mention the separation of the field “time duration” in hours, minutes and seconds fields; all the IPs were converted to a generic name “student”; all the destinations visited by the students were grouped in main categories, as for instance “Facebook”, as each application had numerous distinct destinations.

About 30 changes in data treatment and in data flow “cleaning” were performed, which were, later, exported to Tableau Desktop. Each file imported to Tableau Prep, in addition to the changes applied to the previous file, was refined with more changes, in an iterative process.

After treating the data on Tableau prep the generated data flow was imported to Tableau Desktop so that dynamic data visualizations were created. At this stage, dimensions, measurements, and filters were created according to the desired data visualization. The software has the big advantage of creating dynamic visualizations of the logs’ data which allows for a different and richer perspective on the data obtained, in order to deepen further studies about the same topic.

Discussion and conclusions

This paper aimed to describe the process of a pilot to carry out a larger study where we wanted to cross-reference actual usage data (logs) of mobile applications in the classroom with data from student questionnaires. In this article we also present the main results of this pilot, both from the point of view of the process of the pilot and from the point of view of the data of use of mobile applications by students in the classroom.

From the preliminary data analysis of this pilot, we can infer that the most used apps are Facebook, Google and Instagram, as we can see in Fig.  2 and Fig.  3 , although some variations between the attendees of the courses were registered when it comes to other apps. For example, in the case of the Design course, there are alternative apps being used such as YouTube or Vimeo.

General use of applications in Scriptwriting class

General use of applications in Aesthetics classe

Another noticeable preliminary result is that students use Facebook more at the beginning of classes and Instagram is used more at the end, as we can see in Fig.  4 and Fig.  5 .

Use of Facebook per hour in Scriptwriting class

Use of Instagram per hour in Scriptwriting class

In addition, the developed model was used in the main study with a bigger convenience sampling approach, which may provide a more accurate representation of the population of mobile-phone-users in the study field.

The visualizations created in a dynamic way during this study showed that the use of logs as a complementary data provider to other instruments, such as questionnaires, can be an added value for this research field.

On the other hand, this pilot contradicts (sometimes slightly, others considerably) the results of the questionnaires answered by the students and whose logs were collected and analysed. Logs show that:

there is a common use of mobile applications during the classes;

the purpose of the access is different: participants report that they use mobile applications mostly for academic reasons, but it can be noted that there is a general use of other mobile applications such as social networks and Youtube;

the usage time is much longer than what participants reported;

the frequency is also different: students stated that they use mobile applications in classes only 1–3 days a week, but we found that, in the analysed classes, there is an almost constant use of them, and finally

students report that they do not use social networks much in class, but the use is, in fact, massive.

The students’ perception of the “use of mobile devices and applications during lessons”, and as already mentioned, during a teaching activity - 70% of the students refer using the applications between 1 to 5 times, 22% between 6 to 10 times and 4% more than 10 times. It should also be noted, as previously mentioned, that only 4% mention not using them. With regards to the use during the week, 56% of the students refer using them between 4 to 5 days per week and 39% between 1 to 3 days per week. There is also a relatively low percentage of students mentioning that they use the devices during class more than ten times (4%).

However, analysis of the logs shows that this use appears to be much more intensive. We performed a calculation based on the average number of accesses, from which we removed 40% of potential automatic accesses and divided by the average number of accesses each application had in the initial test. The results present 6.6 accesses to the device per class/student in the class with the fewest accesses, and for the highest case, 313 accesses to the device per class/student.

This result is reinforced by results from other studies, such as the Mobile Survey Report, which states that students make regular use of laptops and smartphones during lessons (Seilhamer et al., 2018 ).

These conclusions lead us to some very serious insights on this subject. Apparently, even older students have a misperception of their use of online applications during classes. There is a serious discrepancy and incongruency between the behaviours that they claim to adopt and those they actually engage in during the classes. There are authors, who argue for the need for other types of studies that support this type of approach (Gerpott & Thomas, 2014 ), because the perception reported by users may not correspond to the actual use. It means that this gap deserves a deeper reflection. Why does it happen? Are students not motivated in higher education? Is the world offered online more interesting than the one in the physical campus? We will try to answer these questions in the main study.

Availability of data and materials

Some of the visualizations created are publicly available at https://public.tableau.com/profile/davidoliveiraua

HTTPS It is a protocol used for secure communication over a computer network, and is widely used on the Internet

IP is the s a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication

PHP is a general-purpose scripting language especially suited to web development

DNS is naming system for computers, services, or other resources connected to the Internet

Unformatted file where values are separated by commas

Abbreviations

Higher Education Institutions

Access Points

Hypertext Transfer Protocol Secure

Internet Protocol

Hypertext Preprocessor

Domain Name System

Comma-separated values

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DO wrote the introduction and discussion, and saw to the article structure, wrote the method section and conducted the data analysis. LP and CS conducted the literature review. All authors contributed to the discussion and conclusion sections, and the overall flow of the article. All authors read and approved the final manuscript.

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Oliveira, D.M.D., Pedro, L. & Santos, C. The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage. Smart Learn. Environ. 8 , 14 (2021). https://doi.org/10.1186/s40561-021-00159-6

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In the flywheel's early stages, mobile apps are powerful tools for increasing visibility and attracting new customers. Through app store optimization (ASO) and targeted push notifications, businesses can cut through the noise and reach potential customers directly on their most used personal device—their mobile phones.

Samsung Issues Critical Update For Millions Of Galaxy Users

Drake kendrick lamar feud timeline another trespasser caught at drake s toronto home police say, the best queen mattresses that promote more restful nights.

Apps genuinely shine by offering a faster, more responsive mobile experience. They provide personalized content, streamlined booking processes and instant communication, elevating the customer experience.

Retention And Referrals

Mobile apps can significantly enhance customer retention and encourage word-of-mouth referrals by offering tailored rewards and making it easier for satisfied customers to leave a review or share their experience with a friend.

The Future Is Mobile

As we envision the future of local business growth, it's clear that mobile apps will play a pivotal role. Businesses without websites today find themselves at a significant disadvantage, but those without mobile apps are poised to face similar challenges soon. The consumer preference for mobile apps over mobile websites is a trend that's only gaining momentum. This preference is rooted in the convenience, speed and personalized experience that apps provide—a trifecta that mobile websites struggle to match.

For local businesses, the message is clear: Adopting a mobile app is not just about keeping pace with current trends but positioning oneself for future success. As the digital landscape continues to evolve, the businesses that will thrive are those that recognize the shifting preferences of their customers and adapt accordingly.

Small businesses often grapple with several key concerns when contemplating the development of a mobile app. Chief among these is the daunting specter of cost. Understandably, smaller enterprises with limited budgets may hesitate due to the perceived financial burden associated with app development. From the initial investment required for coding and design to ongoing maintenance costs and marketing expenses, the financial commitment can appear prohibitive. A mobile app helps with retention marketing, making customers more loyal and more likely to refer friends. While the upfront costs may seem daunting, businesses can view app development as a strategic investment in customer engagement and brand visibility, potentially leading to increased sales and loyalty over time.

Moreover, the challenge of limited resources and expertise presents a significant barrier for small businesses eyeing the mobile app landscape. Without in-house developers or designers versed in app development, the prospect of outsourcing these tasks can be daunting both in terms of cost and time. By tapping into development agencies that specialize in mobile apps, small enterprises can access the necessary expertise without overextending their capabilities or budgets, thereby making the prospect of launching a mobile app more feasible.

Looking Ahead

As we stand on the cusp of this digital evolution, the imperative for local businesses to develop and integrate mobile apps into their marketing strategy has never been more critical. The companies that invest in mobile app development today are not just setting themselves up for success in the present—they're paving the way for sustained growth and competitiveness in the digital future.

More prominent brands and franchises have already implemented mobile apps in their retention strategy. In a world where consumer preferences increasingly lean toward mobile app interactions, those who fail to adapt risk being left behind. For local businesses aiming to flourish in the coming years, embracing mobile apps is not just a strategy; it's a necessity for survival.

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The power of App Inventor: Democratizing possibilities for mobile applications

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In June 2007, Apple unveiled the first iPhone. But the company made a strategic decision about iPhone software: its new App Store would be a walled garden. An iPhone user wouldn’t be able to install applications that Apple itself hadn’t vetted, at least not without breaking Apple’s terms of service.

That business decision, however, left educators out in the cold. They had no way to bring mobile software development — about to become part of everyday life — into the classroom. How could a young student code, futz with, and share apps if they couldn’t get it into the App Store?

MIT professor Hal Abelson was on sabbatical at Google at the time, when the company was deciding how to respond to Apple’s gambit to corner the mobile hardware and software market. Abelson recognized the restrictions Apple was placing on young developers; Google recognized the market need for an open-source alternative operating system — what became Android. Both saw the opportunity that became App Inventor.

“Google started the Android project sort of in reaction to the iPhone,” Abelson says. “And I was there, looking at what we did at MIT with education-focused software like Logo and Scratch , and said ‘what a cool thing it would be if kids could make mobile apps also.’”

Google software engineer Mark Friedman volunteered to work with Abelson on what became “Young Android,” soon renamed Google App Inventor. Like Scratch, App Inventor is a block-based language, allowing programmers to visually snap together pre-made “blocks” of code rather than need to learn specialized programming syntax.

Friedman describes it as novel for the time, particularly for mobile development, to make it as easy as possible to build simple mobile apps. “That meant a web-based app,” he says, “where everything was online and no external tools were required, with a simple programming model, drag-and-drop user interface designing, and blocks-based visual programming.” Thus an app someone programmed in a web interface could be installed on an Android device.

App Inventor scratched an itch. Boosted by the explosion in smartphone adoption and the fact App Inventor is free (and eventually open source), soon more than 70,000 teachers were using it with hundreds of thousands of students, with Google providing the backend infrastructure to keep it going.

“I remember answering a question from my manager at Google who asked how many users I thought we'd get in the first year,” Friedman says. “I thought it would be about 15,000 — and I remember thinking that might be too optimistic. I was ultimately off by a factor of 10–20.” Friedman was quick to credit more than their choices about the app. “I think that it's fair to say that while some of that growth was due to the quality of the tool, I don't think you can discount the effect of it being from Google and of the effect of Hal Abelson's reputation and network.”

Some early apps took App Inventor in ambitious, unexpected directions, such as “Discardious,” developed by teenage girls in Nigeria. Discardious helped business owners and individuals dispose of waste in communities where disposal was unreliable or too cumbersome.

But even before apps like Discardious came along, the team knew Google’s support wouldn’t be open-ended. No one wanted to cut teachers off from a tool they were thriving with, so around 2010, Google and Abelson agreed to transfer App Inventor to MIT. The transition meant major staff contributions to recreate App Inventor without Google’s proprietary software but MIT needing to work with Google to continue to provide the network resources to keep App Inventor free for the world.

With such a large user base, however, that left Abelson “worried the whole thing was going to collapse” without Google’s direct participation.

Friedman agrees. “I would have to say that I had my fears. App Inventor has a pretty complicated technical implementation, involving multiple programming languages, libraries and frameworks, and by the end of its time at Google we had a team of about 10 people working on it.”

Yet not only did Google provide significant funding to aid the transfer, but, Friedman says of the transfer’s ultimate success, “Hal would be in charge and he had fairly extensive knowledge of the system and, of course, had great passion for the vision and the product.”

MIT enterprise architect Jeffrey Schiller, who built the Institute’s computer network and became its manager in 1984, was another key part in sustaining App Inventor after its transition, helping introduce technical features fundamental to its accessibility and long-term success. He led the integration of the platform into web browsers, the addition of WiFi support rather than needing to connect phones and computers via USB, and the laying of groundwork for technical support of older phones because, as Schiller says, “many of our users cannot rush out and purchase the latest and most expensive devices.”

These collaborations and contributions over time resulted in App Inventor’s greatest resource: its user base. As it grew, and with support from community managers, volunteer know-how grew with it. Now, more than a decade since its launch, App Inventor recently crossed several major milestones, the most remarkable being the creation of its 100 millionth project and registration of its 20 millionth user. Young developers continue to make incredible applications, boosted now by the advantages of AI. College students created “ Brazilian XôDengue ” as a way for users to use phone cameras to identify mosquito larvae that may be carrying the dengue virus. High school students recently developed “ Calmify ,” a journaling app that uses AI for emotion detection. And a mother in Kuwait wanted something to help manage the often-overwhelming experience of new motherhood when returning to work, so she built the chatbot “ PAM (Personal Advisor to Mothers) ” as a non-judgmental space to talk through the challenges.

App Inventor’s long-term sustainability now rests with the App Inventor Foundation, created in 2022 to grow its resources and further drive its adoption. It is led by executive director Natalie Lao.

In a letter to the App Inventor community, Lao highlighted the foundation’s commitment to equitable access to educational resources, which for App Inventor required a rapid shift toward AI education — but in a way that upholds App Inventor’s core values to be “a free, open-source, easy-to-use platform” for mobile devices. “Our mission is to not only democratize access to technology,” Lao wrote, “but also foster a culture of innovation and digital literacy.”

Within MIT, App Inventor today falls under the umbrella of the MIT RAISE Initiative — Responsible AI for Social Empowerment and Education, run by Dean for Digital Learning Cynthia Breazeal, Professor Eric Klopfer, and Abelson. Together they are able to integrate App Inventor into ever-broader communities, events, and funding streams, leading to opportunities like this summer’s inaugural AI and Education Summit on July 24-26. The summit will include awards for winners of a Global AI Hackathon , whose roughly 180 submissions used App Inventor to create AI tools in two tracks: Climate & Sustainability and Health & Wellness. Tying together another of RAISE’s major projects, participants were encouraged to draw from Day of AI curricula, including its newest courses on data science and climate change .

“Over the past year, there's been an enormous mushrooming in the possibilities for mobile apps through the integration of AI,” says Abelson. “The opportunity for App Inventor and MIT is to democratize those new possibilities for young people — and for everyone — as an enhanced source of power and creativity.”

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Google built some of the first social apps for Android, including Twitter and others

Here’s a tidbit of startup history that may not be widely known outside of the tech firms themselves: The first versions of popular Android apps, like Twitter, were built by Google itself. That revelation came about via a new podcast with Twitter’s former senior director of product management, Sara Beykpour, now the co-founder of the AI news startup Particle.

In a podcast hosted by Lightspeed partner Michael Mignano, Beykpour reminisces about her role in Twitter’s history. She explains how she began working at Twitter in 2009, initially as a tools engineer, when the company employed only around 75 people. Later, Beykpour moved to work on mobile at Twitter around the time when other third-party apps were growing in popularity on other platforms, like BlackBerry and iOS. One of those, Loren Brichter’s Tweetie , was even acquired by Twitter to form the basis of its first official iOS app.

As for Twitter’s Android app, that came from Google, Beykpour said.

The Twitter for Android client was “a demo app that Google had created and gave to us,” she said on the podcast. “They did that with all the popular social apps at the time: Foursquare … Twitter … they all looked the same in those early days because Google wrote them all.”

Mignano interjected, “Wait, so back up; explain this. So Google wanted companies to adopt Android, so they build you apps?”

“Yes, exactly,” Beykpour responded.

Twitter then took the Android app that Google built and continued to develop it. Beykpour was the second Android engineer at the company, she said.

In fact, Google had detailed its work on the Android Twitter client in a 2010 blog post , but much of the press coverage at the time didn’t credit the app to Google’s work, making this a forgotten bit of internet history. In Google’s post, the company explains how they implemented early Android best practices within the Twitter app. Beykpour told TechCrunch that the post’s author, Virgil Dobjanschi, was the main software engineer.

“If we had questions, we were supposed to ask him,” she recalls.

Beykpour shared other stories about Twitter’s early days, too. For instance, she worked on Twitter’s video app, Vine, (after returning to Twitter from a stint at Secret ), and had been under pressure to launch Vine on Android before Instagram launched its video product. She met that deadline by launching Vine roughly two weeks before Instagram Video, she said.

The latter “significantly” affected Vine’s numbers, and, in Beykpour’s opinion, was what led to the popular app’s demise.

“That was the day the writing was on the wall,” she said, even though it took years to eventually shut Vine down.

At Twitter, Beykpour led the shutdown of Vine’s product — an app still so well-liked that even new Twitter/X owner Elon Musk keeps teasing about bringing it back. But Beykpour thinks Twitter made the right decision with Vine, noting the app wasn’t growing and was expensive to run. She admits that others may see it differently, perhaps arguing that Vine was under-resourced or didn’t have leadership’s backing. But ultimately, the closure came down to Vine’s impact on Twitter’s bottom line.

Beykpour also shared an interesting anecdote about working on Periscope. She joined the startup right as it was acquired by Twitter , and after leaving Secret. She remembers having to officially rejoin Twitter under a fake name to keep the acquisition under wraps for a time.

At Twitter, she also talked about the difficulty in getting resources to develop products and features for power users, like journalists.

“Twitter really struggled to define its user,” she said, because it “used a lot of traditional OKRs and metrics.” But the fact was that “only a fraction of people tweet,” and “of the fraction of the people that are tweeting, a subset of those are responsible for the content that everyone actually wants to see,” was something that Beykpour says was difficult to measure.

Now at Particle , her experience building Twitter is informing strategy for the AI news app, which has the goal of connecting people with the news they care about that is going on around them.

“Particle is a re-imaging of how you intake your daily news,” Beykpour says on the podcast. The app aims to provide a multi-perspective view of news while also providing access to high-quality journalism. The startup is looking to find another way to monetize reporting beyond ads, subscriptions or micropayments. However, the specifics of how Particle will do this are still in discussion. The startup is currently talking with potential publisher partners on how to compensate them for their work.

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What impacts learning effectiveness of a mobile learning app focused on first-year students?

• Original Article
• Open access
• Published: 26 July 2023
• Volume 21 , pages 629–673, ( 2023 )

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• Florian Johannsen   ORCID: orcid.org/0000-0003-3175-6954 1 ,
• Martin Knipp 2 ,
• Thomas Loy 2 ,
• Milad Mirbabaie 3 ,
• Nicholas R. J. Möllmann 4 ,
• Johannes Voshaar 2 &
• Jochen Zimmermann 2  

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In recent years, the application of digital technologies for learning purposes is increasingly discussed as smartphones have become an integral part of students’ everyday life. These technologies are particularly promising in the so-called “transition-in” phase of the student lifecycle when first-year students start to develop a student identity and integrate into the university environment. At that stage, most premature dropouts are observed, presumably due to a lack of self-organization or self-responsibility. Considering this, a mobile app to tackle insufficient student experiences, support learning strategies, and foster self-organization in the “transition-in” phase was developed. The research at hand proposes a generalizable success model for mobile apps with a focus on first-year students, which is based on the IS success model (Delone and McLean in Inf Syst Res 3(1):60–95, 1992) and analyzes those factors that influence student satisfaction with such an app, the intention to reuse the app, and—foremost—students’ learning effectiveness. The results indicate that learning effectiveness is determined both by the perceived user satisfaction and users’ intention to reuse, which are particularly influenced by perceived enjoyment but also system and information quality. Finally, design principles are derived to develop similar mobile solutions.

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Avoid common mistakes on your manuscript.

1 Introduction

For some time now, the European labor market is facing a severe lack of skilled professionals (Peichl et al. 2022 ). In 2022 alone, 29% of companies in the European Union (EU) reported problems in finding suitable personnel, which is an all-time high considering the development in recent decades (Peichl et al. 2022 ). The situation is agitated in Germany, with 50% of enterprises seriously suffering from the shortage of specialists (ifo Institut 2022 ; Peichl et al. 2022 ). Consequently, more than 770,000 vacant positions for the entire economy will not be adequately occupied in 2023 (cf. Statista 2023 ). In this context, a high student dropout rate is seen as a serious problem in meeting the economy’s demand for qualified workers in the upcoming years (cf. Ahlers and Quispe Villalobos 2022 ; Behr et al. 2021 ; Heublein 2014 ). While in Germany, 14.7% of Bachelor students do not finish their studies, this number is even higher in other EU countries like the Netherlands (28.3%) or Italy (34.1%) (Behr et al. 2020 ; Schnepf 2014 ). For education policies, such student dropout rates imply not only inefficiently used resources for higher education but also high educational costs for students not achieving the aspired educational goals (Baars and Arnold 2014 ; Behr et al. 2021 ). At the same time, dissatisfaction and negative psychological long-term effects are observed for corresponding students, which may paralyze them when searching for alternative pathways to gain a foothold in the labor market (Behr et al. 2021 ; Ibrahim et al. 2013 ; Roso-Bas et al. 2016 ).

In terms of time, most student dropouts happen during the first year of studies (Isleib et al. 2019 ; Neugebauer et al. 2019 ; Opazo et al. 2021 ). According to the student lifecycle (Lizzio 2011 ), which describes the evolution from a prospective to a commencing, continuing, and finally graduating student, first-year students find themselves in the “transition-in” phase. In this phase, self-organized research-based learning (cf. Huber et al. 2009 ) or self-responsibility, which are essential for a successful transition from the highly-structured school environment into the university system, are often perceived as challenging (cf. Zehetmeier et al. 2014 ). Moreover, an inadequate student experience and psychological factors—such as inefficient learning strategies or insufficient intrinsic motivation—are identified as additional reasons for early dropout (cf. Blüthmann et al. 2011 ; Heinze 2018 ; Neugebauer et al. 2019 ). Therefore, “remedial support early in the curriculum” (Baars and Arnold 2014 , p. 106) is necessary to reach students who are at risk of dropping out prematurely.

Parallel to this, universities also experience a change in students’ way of consuming and processing information, organizing their daily routines, socializing or communicating with one another (Musik and Bogner 2019 ), which is mainly triggered by technological progress (Cho et al. 2021 ; Gómez-Galán et al. 2020 ; Gupta et al. 2021 ; Youssef et al. 2021 ). Consequently, the impact of new technologies on students’ learning behaviors or interactions with lecturers is increasingly discussed in higher education (Ronzhina et al. 2021 ; Sultana 2020 ). By now, it is widely recognized that digital technologies may support behaviorist, constructivist, collaborative, situated, and informal/lifelong learning (e.g., Criollo-C et al. 2021 ; Goksu 2021 ; Gupta et al. 2021 ). Thus, in the recent past, special attention was given to learning management systems (LMS), which are “web-based software platforms that provide an interactive online learning environment and automate the administration, organization, delivery, and reporting of educational content and learner outcomes” (Turnbull et al. 2020 , p. 1). The functionalities of today’s open-source (e.g., Moodle) or proprietary LMS solutions (e.g., Blackboard, WebCT of the University of Columbia) are diverse and range from course management to communication tools and progress tracking abilities amongst others (Al-Sharhan et al. 2020 ; Koh and Kan 2021 ). Although several studies have shown a positive effect of LMS usage on students’ learning performance (e.g., Leontyeva 2018 ; Msomi and Bansilal 2019 ; Oguguo et al. 2021 ), there are concerns that the new generation of “information consumers”—who are now entering the university system—will refrain from using LMS if these systems have not been optimized for mobile devices or just serve the provision of course materials (cf. Koh and Kan 2021 ; Turnbull et al. 2020 ).

As a consequence, higher education gradually focuses on the ubiquity and great acceptance of mobile phones (cf. Al-Bashayreh et al. 2022 ; Author self-citation 2; Beatson et al. 2020 ), which have become an integral part of students’ daily lives to establish and maintain social networks (Criollo-C et al. 2021 ; Diacopoulos and Crompton 2020 ; Goksu 2021 ). The COVID-19 pandemic even accelerated this development, as higher education was challenged to find alternative teaching options and students primarily interacted electronically with fellow students and instructors (e.g., Al-Bashayreh et al. 2022 ). Several studies show a positive effect of smartphone-based learning on student performance for various courses (e.g., accounting, psychology, etc.; cf. Beatson et al. 2020 ; Diliberto-Macaluso and Hughes 2016 ; Voshaar et al. 2023 ). In particular, research outlines the supportive impact of gamification on learning effectiveness (Pechenkina et al. 2017 ; Voshaar et al. 2023 ).

Against this backdrop, this research addresses the necessity for abovementioned “remedial support” (Baars and Arnold 2014 , p. 106) at initial stages of the student lifecycle to prevent first-year students from dropping out early and considers their affinity towards mobile phones equally. So, we focus on designing a mobile app for first-year students who are just about to develop a student identity and integrate into the student world (Lizzio 2011 ; Matheson 2018 ; Msomi and Bansilal 2019 ). We claim that a mobile app may be a suitable solution to tackle insufficient student experiences and support learning strategies as well as self-organization in the “transition-in” phase of the student lifecycle. We built a corresponding mobile app using a Design Science Research (DSR) approach, and the results of a first evaluation at the University of Bremen (Germany) encouraged us to pursue the project and develop the app further (cf. Johannsen et al. 2021 ). Further, in this research, a success model for mobile apps for first-year students in the “transition-in” phase, which is based on the IS success model of Delone and McLean ( 2003 ), is proposed and those factors that influence student satisfaction with the app, the intention to reuse the app, and students’ learning effectiveness are analyzed. This prepares the ground for formulating design principles for mobile app development afterwards. Accordingly, we pose the following research questions:

Which factors contribute to the success of a mobile app to support first-year students in the “transition-in” phase in terms of learning effectiveness, user satisfaction, and intention to reuse the app?

What design principles can be derived for a mobile app to support first-year students during the “transition-in” phase?

The contributions of this research are threefold: First, a self-developed mobile learning app with the aim to support students in the “transition-in” phase by improving learning strategies and self-organization abilities as well as promoting the perceived student experience is introduced. Thereby, we contribute to the ongoing search for (technological) solutions to prevent early student dropouts. Second, factors positively affecting user satisfaction, intention to reuse the app, and students’ learning effectiveness are identified with the help of a success model for mobile apps and data collected in an introductory accounting course at the University of Bremen (Germany). Based on that, mobile app functionalities can be assessed more purposefully regarding their relevance for first-year students, which complements the existing body of knowledge regarding student app design (e.g., Almaiah et al. 2022 ; Laine and Lindberg 2020 ). Third, the findings are used to formulate design principles (cf. Gregor and Hevner 2013 ) for mobile apps to support students in the “transition-in” phase, which are largely missing for apps that focus on this particular stage of the student lifecycle yet. Other institutions may reference these propositions to create beneficial mobile solutions for first-year students who strive to adapt to the university environment.

The structure of this paper is as follows: Section  2 provides an overview of mobile apps for higher education, the student lifecycle, and a self-developed mobile learning app to tackle challenges in the “transition-in” phase. Section  3 introduces the research model and describes the data collection. Afterwards, the results are presented (Sect.  4 ) and discussed (Sect.  5 ). The paper concludes with a summary and an outlook.

2 Conceptual basics and related work

2.1 the use of mobile apps in higher education.

The use of mobile apps in higher education teaching—such as “learning management applications”, “vodcasts and podcasts”, “language learning applications”, “game-based learning applications” or “collaborative learning applications” (Goundar and Kumar 2022 )—is discussed lively in the literature (e.g., Beatson et al. 2020 ; Gupta et al. 2021 ; Liu and Guo 2017 ; Ronzhina et al. 2021 ; Voshaar et al. 2023 ). In the following, we summarize related work on mobile learning apps in terms of potentials and challenges, technical and organizational issues, associated theories, as well as future developments. This should give readers a better overview of this mature research area.

In general, the potential of mobile student apps to support learning effectiveness is well-analyzed for various classroom and course examples (cf. Castek and Beach 2013 ). For instance, Larkin ( 2015 ) evaluates apps to foster the building of mathematical knowledge, while Diliberto-Macaluso and Hughes ( 2016 ) show that mobile apps may help psychology students achieve their learning objectives. Hence, apps can help to develop students’ self-regulation and deep thinking abilities or support them in labeling, summarizing, and discovering new knowledge amongst others (cf. Diliberto-Macaluso and Hughes 2016 ; Larkin 2015 ). In medical education , the benefits of mobile apps to offer an “enjoyable learning experience” are pointed out by Morris et al. ( 2016 ) for a neuroanatomy course. Mohapatra et al. ( 2015 ) present an overview of apps that are judged to be beneficial for medical education in general, with a particular focus on their ability to manage information from one or more sources to foster communication and support effective time management. Steel ( 2012 ) focuses on language students in particular and discusses the potential of mobile apps for this group, e.g., in terms of vocabulary acquisition. An overview of corresponding apps for language students is given by Gangaiamaran and Pasupathi ( 2017 ). In accounting and management , Beatson et al. ( 2020 ) and Voshaar et al. ( 2023 ) find out that students’ behavioral engagement with the help of mobile apps and gamification elements is positively associated with exam results. Seow and Wong ( 2016 ) introduce the so-called “Accounting Challenge (ACE)” app, which helps to keep up students’ motivation in studying accounting through gamification as well.

On the contrary, there are challenges of using mobile apps for student education. As Goundar and Kumar ( 2022 ) point out, the literature to date has a strong focus on “solution papers”, which introduce fully developed mobile applications that are supposed to improve learning performance. However, a discussion as to what degree singular app functionalities affect students’ cognitive knowledge processing or an explication of the implications for learning theories often come up short (e.g., Damyanov and Tsankov 2018 ). Along these lines, Mehdipour and Zerehkafi ( 2013 ) provide technical as well as social and educational challenges for mobile learning scenarios. These include content security and copyright issues, accessibility and cost barriers for end-users, or the lack of a learning theory for the mobile age in general, to mention just a few (cf. Mehdipour and Zerehkafi 2013 ). Furthermore, digital technologies may not adequately reproduce the emotional side of interactive learning, so attention should be given to the right balance between digital and human educational interactions (Montiel et al. 2020 ). A classification scheme for mobile learning challenges according to “management and institutional challenges”, “design challenges”, “technical challenges”, “evaluation challenges”, and “cultural/social challenges” is introduced by Damyanov and Tsankov ( 2018 ). In summary, education institutions need to establish a clear mobile learning policy, offer pedagogical support, consider the hardware capabilities of mobile devices, provide a suitable technical infrastructure, and deal with the cultural differences concerning perceptions and attitudes towards digital technologies (cf. Damyanov and Tsankov 2018 ).

From a technical perspective , the requirements on mobile learning environments, the core functionalities of apps to assure their practicability for educational purposes, and engineering processes for app realization are particularly important. In this context, Zhu et al. ( 2015 ) propose a design framework for mobile augmented reality education in healthcare. Further, Clayton and Murphy ( 2016 ) analyze mobile apps’ peer-learning and -teaching capabilities for conducting collaborative video design projects. The establishment of a content delivery infrastructure for educational material and suggestions on integrating mobile apps is done by Khaddage et al. ( 2011 ). Vázquez-Cano ( 2014 ) focuses on the mandatory capabilities of smartphones to support distance learning, while Pechenkina et al. ( 2017 ) identify the potential of gamification elements to increase student engagement, retention, and achievement. Finally, Papanikolaou and Mavromoustakos ( 2006 ) introduce critical success factors for learning app engineering processes, while Kumar and Mohite ( 2018 ) suggest approaches for testing their usability.

From an organizational perspective , the factors for successfully adopting digital technologies in higher education institutions are discussed (e.g., Chuchu and Ndoro 2019 ). It is accentuated that mobile learning initiatives are not limited to purchasing and deploying digital technologies but require a holistic consideration of diverse factors related to people, technology, or pedagogy (Krotov 2015 ). Thereby, principal factors that may impact user satisfaction, the intention to use, and the actual usage of mobile applications in higher education are examined (e.g., Almaiah and Alismaiel 2019 ; Chuchu and Ndoro 2019 ). As an example, Almaiah and Alismaiel ( 2019 ) focus on Jordanian universities and analyze two apps—one that provides student services (e.g., a timetable) and another one enabling “open virtual classes”—in light of the abovementioned factors. Thereby, so-called “quality factors” and “individual factors” that have been adapted from Delone and McLean ( 1992 ) and Davis ( 1989 ) seem to have a positive effect. Besides, also the variable “intention to use” was examined for this specific student group (cf. Almaiah and Al Mulhem 2019 ). Further, Chuchu and Ndoro ( 2019 ) present indicators that the “perceived usefulness” and “perceived ease-of-use” of a mobile learning app are central factors in creating a positive attitude among the target group and in ensuring its acceptance. An overview of critical success factors for mobile learning in organizations is provided by Krotov ( 2015 ). This study integrates the perspectives “organization” (e.g., executive involvement), “people” (e.g., personal innovativeness), “pedagogy” (e.g., quality of content provided), and “technology” (e.g., quality of mobile system) to arrive at a list of success factors from a socio-technical perspective (cf. Krotov 2015 ).

Considering the complex process of establishing mobile education technologies in organizations, a pedagogical and educational requirements model was proposed by Sarrab et al. ( 2018 ), which supports when searching for a suitable solution to deliver content for mobile learning. Besides, the role of mobile apps in facilitating the inclusion of students with handicaps into the university environment is a subject of investigation. For instance, Ok et al. ( 2016 ) introduce an evaluation scheme to purposefully select apps for students with learning disabilities. Moreover, people with developmental disabilities can benefit enormously from mobile apps, which hold true for educational, communication, and leisure purposes, helping them connect with their environment (Stephenson and Limbrick 2015 ). In addition, Bravou and Drigas ( 2019 ) reflect the suitability of mobile devices and apps for students with sensory, physical, and cognitive disabilities. In this respect, a comprehensive literature review on digital technologies for people with learning or cognitive disabilities was performed by Williams and Shekhar ( 2019 ).

Researchers are also engaged in theory building (cf. Hevner and Chatterjee 2010 ) to guide the purposeful usage of mobile apps in higher education. However, a widely accepted mobile learning theory has not yet been established (cf. Bernacki et al. 2020 ; Curum and Khedo 2021 ). Therefore, Park ( 2011 ) refers to the transactional distance theory (cf. Moore 1991 ), which defines “distance” as a pedagogical concept, and combines this theory with applications of digital technologies to arrive at a “pedagogical framework of mobile learning”. The framework distinguishes between four types of mobile learning depending on whether a (1) high or (2) low transactional distance is given and (3) an individualized or (4) socialized activity is to be solved. Thereby, the transactional distance is defined as the psychological gap between the learner and the instructor, whereas the activity type (i.e., individualized or socialized) assesses the importance of social aspects for a particular learning environment (Park 2011 ). Another mobile learning framework was introduced by Motiwalla ( 2007 ), who proposes to integrate the concepts “mobile connectivity” and “e-learning” for being able to delineate application requirements for mobile learning. Furthermore, a meta-framework to guide the establishment of mobile learning frameworks can be found in Liu et al. ( 2008 ). This meta-framework is, for instance, referenced by Nordin et al. ( 2010 ) as a theoretical base to create a lifelong, continuing learning framework.

Future developments of mobile learning apps will essentially emphasize the integration of Artificial Intelligence (AI) with learning environments (cf. Alzahrani et al. 2021 ; Chong 2019 ; Diaz et al. 2015 ; Kabudi et al. 2021 ). The purpose is to improve students’ learning performance via personalization of learning, facilitate the evaluation of student knowledge, or systematically assess learner requirements (Kabudi et al. 2021 ). Besides, the use of virtual reality (VR) and augmented reality (AR) to progress students’ learning experiences is intensively discussed (e.g., Fradika and Surjono 2018 ; Nicolaidou et al. 2021 ). For instance, Nicolaidou et al. ( 2021 ) show that a VR learning environment can positively affect vocabulary acquisition and learners’ experience when studying foreign languages. Further, the readiness of students to adapt VR technology to achieve learning goals is high (Ismail and Hashim 2020 ). Moreover, the use of chatbots and conversational agents is also rising (Hwang and Chang 2021 ; Liu et al. 2020 ; Smutny and Schreiberova 2020 ). Chatbots can serve as efficient information retrieval tools for specific domains to facilitate learning (cf. Liu et al. 2020 ). In this context, various platform-specific chatbots for learning (e.g., for the Facebook Messenger platform) at different maturity levels have been developed in recent years (cf. Smutny and Schreiberova 2020 ). Having said that, chatbots for education are primarily found for language courses as well as the disciplines of “engineering” and “computers”, while topics like “arts” or “mathematics” are less accentuated (Hwang and Chang 2021 ). Thus, chatbots may not be suitable for all types of courses alike, especially in case students’ hands-on competencies (i.e., arts) or computations and problem-solving skills (i.e., mathematics) are to be promoted. While the effectiveness of chatbots for learning purposes is usually measured by pre-/post-test questionnaires, profound insights on chatbots’ impact on behavioral aspects of the student learning process are still elusive (Hwang and Chang 2021 ).

To conclude this overview, our study aims to analyze factors contributing to the success of a mobile app, which was designed to meet the needs of first-year students in the “transition-in” phase of the student lifecycle. A particular interest is in the ability to positively impact their learning effectiveness, user satisfaction, and intention to reuse the app. To the best of our knowledge, a corresponding study concerning this stage of the student lifecycle has not been done yet. We provide insights that can help establish a mobile learning theory in the “transition-in” phase.

2.2 The student lifecycle and the “transition-in” phase

Throughout university life, students experience an evolution of their “student identity”, which goes along with a shift of priorities and agendas (Lizzio 2011 ). As mentioned above, our research focuses on “commencing” students who are just about to become acquainted with the university system and have an increased interest in opportunities for social interaction, active engagement, and early formative feedback (Matheson 2018 ). Generally, various propositions regarding the development stages of students exist (cf. Burnett 2007 ; Morgan 2013 ) that primarily differ in their conception of student transition (Gale and Parker 2014 ). A widely acknowledged proposition for an integrative framework was introduced by Lizzio ( 2011 ), which is depicted in Fig.  1 and differentiates between four major stages. Whereas future students (“transition-towards”) are engaged in finding an appropriate study program and university, commencing students (“transition-in”) work on the integration into the student world (Lizzio 2011 ). In the “transition-through” phase, continuing students work on developing graduate attributes and seek challenges by authentic curricula and assessments (Lizzio 2011 ; Matheson 2018 ; Msomi and Bansilal 2019 ). Finally, the “transition-up, out & back” stage addresses students that are graduating or returning for postgraduate studies to further strengthen their skills for employability (Lizzio 2011 ; Matheson 2018 ).

The student lifecycle according to Lizzio ( 2011 )

Against this background, most premature dropouts are observed in the “transition-in” phase (Chen 2012 ; Isleib et al. 2019 ; Neugebauer et al. 2019 ). An empirical study focused on German higher education institutions (60 universities and universities of applied sciences) identified a lack of social and academic integration as a major reason for premature dropouts (Isleib et al. 2019 ). More specifically, differences in the perception of study requirements were observed for dropout and non-dropout first-year students. These observations are generally also confirmed for other countries (cf. Chen 2012 ; Kehm et al. 2019 ; Xenos et al. 2002 ; Zvoch 2006 ). In terms of academic integration, many first-year students obviously struggle with self-organizing their studies and balancing the time slots for attending courses, obtaining credits, and preparing or post-processing lectures (Schulmeister 2007 ). In such cases, the danger of not meeting the academic standard and the probability of an early dropout increases significantly (Kehm et al. 2019 ). Consequently, the importance of promoting student retention and student achievements has been identified as a crucial responsibility of higher education institutions at that point (Matheson 2018 ; Sheader and Richardson 2006 ).

In the “Student Adjustment Model” of Menzies and Baron ( 2014 ), the stages experienced by first-year students when entering the university system are specified more in-depth, which helps to gain a deeper understanding of the overall “transition-in” phase. Hence, upon arrival, a sense of excitement can be observed among first-year students, which comes to a halt after some weeks when first negative experiences in the new environment have been made—a phase called the “party’s over stage” (Menzies and Baron 2014 ). Now students need to realistically assess their capabilities, identify gaps (e.g., self-organization skills) and carefully reflect on the institutional requirements (Matheson 2018 ). Here, universities can support by providing informative student feedback and curricula that offer opportunities for social networking and learning, or teaching methods that foster active learning and encourage student engagement (Matheson 2018 ; Whittaker and Brown 2012 ). Afterwards, students are able to enter the so-called “healthy adjustment stage” (Menzies and Baron 2014 ).

Furthermore, the base competencies of first-year students have been a subject of investigation (cf. Krumrei-Mancuso et al. 2013 ; Zehetmeier et al. 2014 ), which provides valuable insights into the academic skills of young people that contemporaneously enter the university system. Whereas some studies focus on special types of competencies—like digital (e.g., Reddy et al. 2020 ) or leadership competencies (e.g., Smart et al. 2002 )—a more extensive investigation at a German higher education institute, comprising 18 competency types in total, was performed by Zehetmeier et al. ( 2014 ). Concerning first-year students, deficiencies in self-organization, accurateness, perseverance, intrinsic motivation, or self-criticism were described (Zehetmeier et al. 2014 ). Based on these findings, universities should develop solutions that can account for diverse student backgrounds, tackle insufficient experiences, and support individual learning and self-organizational strategies to achieve academic success.

2.3 Overview of a mobile app to support students in the “transition-in” phase

Considering this, we argue that a mobile app may be a suitable solution to tackle the abovementioned challenges (e.g., insufficient student experiences, lack of self-organization, etc.) in the “transition-in” phase of the student lifecycle. To provide a general overview, Table 1 gives a brief selection of campus apps that come to use at German universities. Of course, campus apps can be found internationally (e.g., UC San Diego mobile app) (e.g., Almaiah and Alismaiel 2019 ; Holotescu et al. 2018 ).

According to the mobile learning app ontology of Notari and Hielscher ( 2016 ), the majority of mobile campus applications are designed as “learning and teaching support apps” with diverging functionalities and purposes. As a common denominator, almost all of them provide campus maps, an overview of cafeteria offerings, official timetables, or event directories, while some of them (e.g., apps of the University of Arts Bremen, University of Hohenheim) also enable access to learning content or the registration for exams. However, communication functionalities are rare since students usually evade to commercial apps like Facebook and Instagram to share information with their peers (cf. Statista 2020b ).

Besides, a broad range of commercial apps supports users in organizing their daily lives, e.g., for scheduling daily routines and tasks (e.g., “24me”, “Todoist”), structuring brainstorming ideas (e.g., “MindNode”), or tracking fitness activities (e.g., “FitNotes”, “MyFitnessPal”). Such mobile apps are these days usually well-integrated into young peoples’ lives (Goodyear et al. 2019 ; Statista 2020a ). However, this does not necessarily hold true for campus apps that run danger of not being further developed as soon as students lose interest or their commitment to using the app (Potgieter 2015 ). That may be especially true if a campus app provides (redundant) content already readily available elsewhere (e.g., the university’s homepage or LMS).

In light of the above explanations, we aimed to provide a mobile app that supports first-year students academically during the “transition-in” stage of the student lifecycle and is easily and practically integrable into everyday student life to ensure long-term student acceptance and commitment. The app was designed to combine functionalities of commercial apps to organize daily routines (e.g., “24me”, etc.) with university-related content, functionalities (e.g., timetables, define tasks and goals, etc.), and gamification elements. The design and development of the app are also described in a prior work in more detail (cf. Johannsen et al. 2021 ).

Our app’s target user group is business and economics students at the University of Bremen (Germany). We initially focus on this narrow group because requirements can be specified more precisely, and the authors are well acquainted with study-related challenges of this relatively homogeneous user group. Though, adapting the app to the needs of other departments and universities is generally possible. The app was developed in a DSR project (Baskerville et al. 2018 ; Peffers et al. 2007 ) with the goal of supporting students’ experience, learning strategies, and self-organization in the “transition-in” phase. Considering this, our artifact is based on three meta requirements (cf. Gregor and Hevner 2013 ) to address the student factors “student experience”, “learning strategies”, and “self-organization”, which are of utmost importance to successfully tackle the transition into the university environment (cf. Blüthmann et al. 2011 ; Heinze 2018 ; Neugebauer et al. 2019 ) (Fig.  2 ). In DSR, meta requirements define “what the system is for” (Gregor and Jones 2007 , p. 325) and outline the purpose and scope of the type of artifact to be developed (Gregor and Jones 2007 ; Schmid et al. 2022 ) in our case a mobile solution for first-year students. Referring to the abovementioned student factors that have been derived from the literature (see Sect.  2 ), the following meta requirements were formulated:

MR 1: The mobile app should support students’ experience.

MR 2: The mobile app should improve students’ learning strategies.

MR 3: The mobile app should support students’ self-organization.

Overview of design requirements

Thereby, the term “student experience” subsumes “all experiences of an individual student” while being in the “identity as a ‘student’” including all “facets of the university” (e.g., administrative processes, IT support etc.), which “contribute” to the “personal development” as a learner (Baird and Gordon 2009 , p. 194).

To specify the meta requirements and arrive at design requirements for the app, (I) user stories, (II) market research, (III) user requirements, and (IV) user journeys were used (Schilling 2016 ). In this context, also second- and third-year undergraduates (N = 54), who are still well familiar with the challenges experienced at the beginning of their studies were surveyed. Finally, we came up with eight major design requirements classified into the categories “course attendance/reminders” (Fig.  2 —DR 1-2), “support of study phases” (DR 3-5), and “technical requirements” (DR 6-8) to support students’ experience, learning strategies, and self-organization (cf. Fig.  2 , Johannsen et al. 2021 ).

The architecture of the app consists of a front- and a back-end. The frontend was developed with the help of the IONIC Framework ( https://ionicframework.com/ ), which works based on Angular ( https://angular.io/ ) (Green and Seshadri 2013 ). Further, the back-end was realized via the Spring Framework ( https://spring.io/ ) and the Spring Boot solution (cf. Walls 2016 ). Figure  3 shows exemplary screenshots. So, a new course is added to a student’s timetable (Screenshot 1), and sample functionalities for this course—derived from the design requirements—are shown, such as the conduction of quizzes (Screenshot 2) or the comparison with a peer group (Screenshot 3). The app can be classified as type 2 (i.e., high transactional distance and individualized mobile learning activity) in the “pedagogical framework of mobile learning” of Park ( 2011 ). Hence, this type allows a high degree of flexibility and portability, enabling students to integrate it flexibly into their mobile lifestyle (Park 2011 ).

Screenshots of the application prototype

With respect to the challenges in the “transition-in” phase (see Sect.  2.2 ) and the meta requirements, various functions that support students’ learning strategies, experience, and self-organization are offered by the app. For instance, the features of tracking learning time along with an overview of exam dates and events largely foster students’ self-organization abilities (cf. Zehetmeier et al. 2014 ). This is further supported by push notifications or newsfeeds about new learning content as well as a calendar function with reminders for lectures and important academic dates contributing to student experience (e.g., Staddon and Standish 2012 ; Trotter and Roberts 2006 ). Gamification is used to enrich students’ learning strategies (e.g., performance tests via quizzes), while they can work on exercises independent of time and place.

3 Research design

In the following, we present the research model, the hypotheses, and the data collection. We heavily rely on the IS success model of Delone and McLean ( 1992 ), which is a commonly referenced model to measure the success of information systems, and has been referenced in many studies in the field of technology-supported education (cf. Almaiah and Alismaiel 2019 ; Aparicio et al. 2017 ; Cidral et al. 2018 ; Dorobat 2014 ; Holsapple and Lee‐Post 2006 ; Huang et al. 2015 ; Kruger-Ross and Waters 2013 ; Wang et al. 2019b ). Hence, it arguably is one of the most widely applied models in this field (Almaiah and Alismaiel 2019 ).

When it comes to user acceptance of technologies also the TAM (Technology Acceptance Model) approach is discussed in literature (e.g., Davis et al. 1989 ; Liu and Guo 2017 ; Mohammadi 2015 ). According to TAM, the factors influencing the acceptance and usage of technologies can be categorized into the clusters “external variables”, “perceived usefulness”, and “perceived ease of use” (Davis 1989 ; Davis et al. 1989 ). Nevertheless, the model is criticized since it mainly focuses on individuals’ perception of technology, while the context in a business, university, or organizational setting (e.g., policy, IT guidelines) is neglected (Ajibade 2018 ).

In this light, the IS success model is particularly suitable for our research for several reasons: First, its quality dimensions can be easily aligned with web-based applications (cf. Delone and McLean 2003 ; Efiloğlu Kurt 2019 ), which are dominant in e-learning environments to foster students’ learning activities (Freeze et al. 2010 ; Muhammad et al. 2020 ). Our mobile app (see Sect.  2.3 ) represents a corresponding solution to support student learning, whereby the querying of database information (e.g., timetables), the login-logics, or the provision of content (e.g., training questions) are enabled by a backend server, while the data is sent to the frontend by help of the JSON and HTTP standard. Further, the mobile app considered in this study can be allocated to the “communication and system phenomenon” (Freeze et al. 2010 ) of e-learning solutions, for which not only the quality of the system is of interest but also the communication with a “service provider”, who creates study-relevant content, provides advice, or resolves problems (cf. Aparicio et al. 2017 ). The IS success model explicitly covers these aspects by corresponding constructs and, thus, represents the base of our research model introduced hereafter, whereas the quality dimensions are adapted to the study context as proposed by Delone and McLean ( 1992 ).

Second, although the model has already been intensively used in education research for years, the focus of this study is on a mobile app that was designed for the “transition-in” phase in particular and represents an instance of a “type 2 app” according to the “pedagogical framework of mobile learning” of Park ( 2011 ). In literature, there is a lack of knowledge regarding the success factors for apps of this type with a special focus on first-year students. Considering this, using a widely established success model and quality dimensions is promising to prepare the ground for further developments of similar solutions. Hence, results attained in other studies with the help of the IS success model may not necessarily be confirmed for the type of mobile app investigated herein. Furthermore, the way to compare the impact of the IS success model across studies is paved and, hence, the relevance of singular dimensions of IS success for various types of apps directed at different stages of the student lifecycle may be assessed more profoundly in the next steps.

Third, for being able to derive design principles that allow the creation of similar instances of artifacts that belong to the identical class (cf. Kruse et al. 2016 ; Sein et al. 2011 ), the use of the widely accepted IS success model is promising. This is because its success dimensions have already been broadly recognized and therefore represent a solid base for defining verifiable and comprehensible design principles. These may be extended in future steps as soon as the knowledge about beneficial mobile app development for the focused application field evolves along with additional insights about beneficial success dimensions for learning effectiveness. The proposed research model, its variables, and our hypotheses are introduced in the following section.

3.1 Theoretical model and hypotheses development

3.1.1 system quality.

According to Delone and McLean ( 1992 ), system quality is a central success factor for IS. The variable describes the desired characteristics of the information system to produce the required information (Urbach et al. 2009 ). Thereby, Wang et al. ( 2019b ) have shown that the system quality of paid mobile learning apps has a positive impact on “user satisfaction” and the “intention to (re-)use”. Similar results for mobile learning apps at Jordanian universities were introduced by Almaiah and Alismaiel ( 2019 ). Though, a study on an e-learning system in Brazil was less clear about the beneficial role of system quality (cf. Cidral et al. 2018 ). Moreover, Aparicio et al. ( 2017 ) investigated “grit” as a determinant of “e-learning system success” and confirmed the supportive effect of system quality on “user satisfaction”. A positive effect on “user satisfaction” was also shown by Chiu et al. ( 2016 ) for a “cloud e-bookcase system” for libraries, whereas Huang et al. ( 2015 ) identified a positive impact on both, “intention to (re-)use” and “user satisfaction” for a mobile library service system. Considering literature and the preferences of business students concerning mobile applications (e.g., Kouser et al. 2014 ), an app’s ease of use (Wang et al. 2019b ), its structuredness (Cidral et al. 2018 ; Urbach and Müller 2012 ), an easy-navigation (Kouser et al. 2014 ), and the ability to efficiently retrieve relevant information (Wang et al. 2019a ) are highly appreciated by the target group in terms of system quality (Aparicio et al. 2017 ; Urbach et al. 2010 ). Hence, we hypothesize:

H1a: System quality will have a positive effect on first-year students’ intention to reuse the app.

H1b: System quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.2 Service quality

The construct “service quality” refers to the overall support for users offered by a service provider (Delone and McLean 2003 ). In terms of “e-learning”, Aparicio et al. ( 2017 ) emphasize the importance of the willingness and readiness of the support staff to resolve students’ difficulties at any time because this positively influences the intention to use the system. This positive effect was also confirmed in earlier studies (e.g., Chiu et al. 2016 ; Huang et al. 2015 , among others). Generally, “service quality” may be interpreted from different angles and refer to concepts such as assurance, empathy, or flexibility—just to mention a few (Urbach and Müller 2012 ). In alignment with the propositions of Aparicio et al. ( 2017 ) and Urbach et al. ( 2010 ), we see the willingness of the service personnel to provide support upon request immediately, the personal attention offered to students, the timeliness of the service response as well as the competence and knowledge of the service personnel as central factors for the app’s success. Considering this, we claim:

H2a: Service quality will have a positive effect on first-year students’ intention to reuse the app.

H2b: Service quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.3 Information quality

Information quality addresses the system output or the information that is produced by a system (Delone and McLean 1992 ). According to Almarashdeh et al. ( 2010 ), information quality is the most crucial factor when determining the success of educational technology systems (Almaiah and Alismaiel 2019 ). Hence, the positive impact of information quality on the “intention to (re-)use” and “user satisfaction” is confirmed by manifold studies that focus on e-learning or mobile learning systems (e.g., Aparicio et al. 2017 ; Cidral et al. 2018 ; Wang et al. 2019b ). However, there are also studies in which information quality played a subordinate role for the acceptance of a system (cf. Chiu et al. 2016 ). Once more, the construct “information quality” can be reflected from various perspectives such as data accuracy, adequacy, or completeness (cf. Klier 2008 ; Urbach and Müller 2012 ). For our app, we determine information quality based on the reliability and understandability of the information provided and its usefulness and relevance for the target group (cf. Aparicio et al. 2017 ; Urbach et al. 2010 ).

H3a: Information quality will have a positive effect on first-year students’ intention to reuse the app.

H3b: Information quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.4 Perceived enjoyment

Davis et al. ( 1992 ) summarize enjoyment in the information systems context as “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (p. 1113). Against this background, the construct of “perceived enjoyment” is increasingly getting attention when it comes to the measurement of IS success (cf. Kim et al. 2007 ; Wang et al. 2019b ). Therefore, it is suggested that technology adoption is more likely in cases where users experience immediate pleasure or joy through mere use (Kim et al. 2007 ). Since the positive influence of perceived enjoyment on users’ attitudes is well examined in the mobile services and mobile commerce context (cf. Tseng and Lo 2011 ; Wang and Li 2012 ; Wang et al. 2019b ), it is increasingly discussed in terms of e-learning technologies, as well (cf. Balog and Pribeanu 2010 ; Hussein 2018 ; Khalid 2014 ). Hence, we also assume a positive effect on “user satisfaction” and “intention to (re-)use”. In this regard, gamification elements may purposefully impact the hedonic motivation to engage with mobile apps and, as a positive side-effect, impact users’ perceived enjoyment (cf. Beatson et al. 2020 ; Pechenkina et al. 2017 ; Wang et al. 2019b ).

Generally, gamification is seen as a means to overcome a lack of motivation among students to deal with study-related content (cf. Kiryakova et al. 2014 ). Thereby, principles such as “freedom to fail”, “rapid feedback”, “progression”, or “storytelling” play a decisive role for the successful application of gamification elements in learning environments (Stott and Neustaedter 2013 ). Hence, specific mechanisms that are traditionally used in game design (e.g., Laine and Lindberg 2020 ) to increase user engagement and hedonic motivation have found their way into modern pedagogy, although their purposeful selection should be made in regards to the target group (Stott and Neustaedter 2013 ). For the design of mobile education apps, corresponding principles need to be purposefully transferred to corresponding design requirements (cf. Herrington et al. 2009 ; Laine and Lindberg 2020 ). Section  2.3 presents the design requirements of our mobile app (see also Fig.  3 ), whereas these are taken up in Sect.  5.2 once again and reflected against the findings of the study.

In summary, we determine perceived enjoyment based on the fun and enjoyment experienced by app users (cf. Kim et al. 2007 ; Wang et al. 2019b ) and the abilities of entertaining and playful features to enhance users’ learning experience and structure their learning efforts (cf. Suki and Suki 2007 ).

H4a: Perceived enjoyment will have a positive effect on first-year students’ intention to reuse the app.

H4b: Perceived enjoyment will have a positive effect on first-year students’ satisfaction with the app.

3.1.5 Intention to reuse, perceived user satisfaction, and learning effectiveness

“Intention to use” is specified as users’ intent to perform a defined behavior (Davis 1989 ). The construct is acknowledged to be strongly associated with the acceptance of an information system (Almaiah and Alismaiel 2019 ) and it largely depends on the users’ attitude towards the system (Agrebi and Jallais 2015 ). However, there is a distinct difference between “intention to use” and actual “use”, because the former represents an attitude, whereas the latter concept describes a concrete behavior (Delone and McLean 2003 ). To resolve the closed-loop relationships between user satisfaction, intention to use, and use in the original IS success model (Wang et al. 2019b ), “intention to reuse” is commonly proposed as a worthwhile measure (Delone and McLean 2003 ; Wang 2008 ). In line with the proposition of Wang ( 2008 ), “intention to reuse” thus represents the favorable student attitude towards our app in this study.

In addition, “perceived user satisfaction” helps to measure the successful interaction of users with the IS (Delone and McLean 1992 ). Generally, user satisfaction can be interpreted as “the extent to which users believe the information system available to them meets their information requirements” (Ives et al. 1983 , p. 785). Thereby, perceived user satisfaction leads to an increasing “intention to reuse” in the post-use situation (Wang 2008 ).

Either way, the major purpose of mobile learning technologies is to increase knowledge acquisition (cf. Wang et al. 2019b ) and, hence, improve learning outcomes (cf. Noesgaard and Ørngreen 2015 ). Generally, the beneficial individual or organizational impact of IS, which is supposed to be measured by the IS success model may occur in many ways (e.g., awareness/recall, competitive advantage, etc.) (cf. Delone and McLean 2003 ; Urbach and Müller 2012 ). Considering this, there is a lively discussion on how to operationalize the individual benefits of using e-learning technologies that cumulate in better knowledge acquisition and learning outcomes in the end (e.g., Chiu et al. 2016 ; Noesgaard and Ørngreen 2015 ; Wang et al. 2019b ; Zhang et al. 2006 ). In that context, “learning effectiveness” (cf. Noesgaard and Ørngreen 2015 ) has become a commonly accepted measure to assess the success of technology-assisted learning for individuals (Smith et al. 2006 ; Wang et al. 2019b ; Zhang et al. 2006 ). The variable builds on the recognition that effective learning asks for learners’ engagement, motivation, awareness, and an individualized learning process, which can be enabled by offering access to content randomly or repeatedly on demand for instance (Zhang et al. 2006 ). This, in turn, promotes learning skills (e.g., enhanced problem-solving or critical thinking abilities; Zhang et al. 2006 ) and leads to an improved understanding of study-related content, which can be recollected any time (cf. Chiu et al. 2016 ; Gable et al. 2008 ; Wang et al. 2019b ). Hence, improved knowledge acquisition and learning outcomes emerge from a general point of view.

To properly address these considerations, the literature proposes to ask for students’ perceptions of learning performance, efficiency, motivation (cf. Liaw 2008 ), awareness, and recollection of study-related information (Gable et al. 2008 ) along with their understanding of the course content (cf. Chiu et al. 2016 ). Accordingly, these aspects determine the items of our questionnaire to assess the variable “learning effectiveness” (see Appendix).

As evident from the above explanations, a rather broad spectrum of factors (e.g., awareness, motivation, etc.) is required to describe “learning effectiveness” comprehensively. Nevertheless, the variable allows students to carefully reflect on the achieved individual (net) benefits (cf. Delone and McLean 2003 ) when using a mobile learning app to cope with the challenges of the “transition-in” phase. Therefore, the variable is used hereafter to measure students’ (net) benefits since we believe that other variables that have been proposed in the context of the IS success model (e.g., recall, job simplification, etc.) (cf. Urbach and Müller 2012 ) would not comply with the multidimensionality of first-year students’ learning success in the “transition-in” phase and may not be adequately transferred to our context.

We formulate the following hypotheses:

H5: The perceived user satisfaction will have a positive effect on first-year students’ intention to reuse the app.

H6: The intention to reuse will have a positive effect on first-year students’ learning effectiveness.

H7: The perceived user satisfaction will have a positive effect on first-year students’ learning effectiveness.

Figure  4 summarizes the proposed research model, variables, and hypotheses.

Proposed research model

3.2 Design of the questionnaire

We developed a questionnaire based on the abovementioned established and validated scales from previous studies and modified them accordingly for the mobile learning context to test our hypotheses. As previously described, the constructs of “system quality”, “service quality”, and “information quality” were adapted from Aparicio et al. ( 2016 ) and Urbach et al. ( 2010 ) and are all measured by four underlying items. “Perceived enjoyment” consists of five items, three being adapted from Kim et al. ( 2007 ) and Wang et al. ( 2019b ), and two used by Suki and Suki ( 2007 ). Three items were adapted from Wang et al. ( 2019b ) and Wang ( 2008 ) and are complemented by one item each from Chiu et al. ( 2016 ) and Sun et al. ( 2008 ) to measure the construct “intention to reuse”. “Perceived user satisfaction” was measured by four underlying items used by Liaw ( 2008 ). Finally, we adopted three items from a previous study of e-learning effectiveness from Liaw ( 2008 ) and added one item each from Chiu et al. ( 2016 ) and Gable et al. ( 2008 ) in order to measure “learning effectiveness”.

Initially, we developed the survey in English, in accordance with prior research, and then translated it to German through a professional translation service in order to ensure a low-threshold participation opportunity and, thus, a high number of participants. Subsequently, a different professional translator translated it back into English to ensure conversion correspondence (Brislin 1970 ). As previously described, the constructs were unanimously measured with four or five items each. All items were assessed on a seven-point Likert scale (from 1 = “strongly disagree” to 7 = “strongly agree”). Table  4 in the Appendix presents the final survey consisting of the mentioned items used in our research model.

3.3 Data collection and sample selection

The mobile learning app was initially implemented in a mandatory introductory accounting course in the Winter semester 2020/21. Because of the COVID-19 pandemic, the social distancing requirements, and the sudden closures of university campuses, all lectures were held digitally in an asynchronous format via screencasts. Complementing the lectures, students could participate in synchronous, live tutorials and submit exercise sheets. Additionally, preparatory courses were also offered synchronously via Zoom. For our study, we invited all students who used the mobile learning app at some point in the Winter semester 2020/21 to participate in the online survey conducted during the final week of teaching (i.e., before the final exam) and administered on the university’s LMS. We did not offer any additional (e.g., monetary or extra course credit) incentives for participating, and the students were informed of the research purpose and their voluntary participation in the study. Even if they took part in the survey, they had the possibility to refuse to answer any question. Subsequently, one member of the research team, who was not involved with the empirical analysis, merged and pseudonymized the data from students’ questionnaires with data from several other sources, including students’ demographics being collected through another survey in the first week of the semester, students’ course attendance during the semester, and the academic performance data. More specifically, the students’ attendance at tutorials and workshops has been manually evaluated via Zoom participation protocols. Finally, the central examination office provided the student’s exam performance. In our analysis, we only use the final pseudonymized dataset, which does not allow identification of individual students.

The students were asked to answer the questionnaire according to their user experience throughout the semester. Thereby and due to the requirements of the IS success model, we were ex-ante limited to the population of 367 students who used the app during the semester and participated in the final exam to draw our sample. Our initial sample consists of 131 students who participated in our survey regarding their user experience. Out of the initial sample, we exclude 10 observations due to missing values in their survey responses and 1 without any variation in the responses. Hence, we received 120 usable responses, bringing our usable response rate to 91.60%. Further, we exclude 7 students because of missing values in their demographics, resulting in a final sample of 113 students who participated in the final exam Footnote 1 of the mandatory introductory accounting course and used the mobile learning app for learning purposes. Accordingly, our sample represents 30.79% of the underlying population that could be used for a study of this type. Footnote 2 The final sample comprises 63 female and 50 male students, with the overwhelming majority (94.69%) being 25 years old and younger. Table  2 presents the summarized descriptive statistics for the final sample of 113 students. Footnote 3

3.4 PLS-SEM approach

Our research model was evaluated using PLS-SEM as the most favorable method to validate multistage models with complex relationships, interdependencies, constructs, and indicators (Hair et al. 2011 ; Sarstedt et al. 2016 , 2021 ). We thereby followed recent recommendations as suggested by Hair et al. ( 2019 ) and Sarstedt et al. ( 2021 ). The minimum sample size was ascertained by multiplying the total number of constructs by ten (Hair et al. 2011 ; Marcoulides et al. 2009 ) and met with our 113 participants. The construct indicators in our model represent reflective measurements caused by latent variables (Churchill Jr 1979 ). We used SmartPLS (v. 3.3.2; Ringle et al. 2015 ) and applied a path weighting scheme with 300 iterations with \({10}^{-7}\) as the stop criterion. Bootstrapping was done via two-tailed bias-corrected and accelerated (BCa) confidence interval method with 4,999 subsamples followed by blindfolding with an omission distance of 7 (Henseler et al. 2016 ).

Initially, we ensured that the indicator loadings are above the threshold of 0.708. Slightly weaker indicators were only kept if they contribute to content validity and are relevant on the grounds of measurement theory (Hair et al. 2011 ). Internal consistency was given with Cronbach’s alpha, composite reliability, and Rho_A with values greater than 0.7 (Diamantopoulos et al. 2012 ; Dijkstra and Henseler 2015 ; Drolet and Morrison 2001 ; Hair et al. 2019 ). Convergent validity was measured via average variance extracted (AVE) with values greater than 0.5 (i.e., at least half the variance of the construct’s items is explained; Fornell and Larcker 1981 ; Hair et al. 2019 ; Henseler et al. 2016 ). Table  5 in the Appendix presents the detailed results of the reliability and validity measurements. The Fornell-Larcker criterion (Fornell and Larcker 1981 ) was examined to assess the discriminant validity, which can be assumed as the square root of AVE is greater than any inter-factor correlation (see Table  7 in the Appendix; Fornell and Larcker 1981 ). Common method bias (CMB) was examined via Harman’s one-factor test for a full collinearity assessment approach. The values for the variance inflation factors (VIF) were below the threshold of 3.30 (see Table  8 in the Appendix; Kock 2015 ). Finally, we analyzed cross-loadings to rule out misassigned indicators (Henseler et al. 2016 ).

Statistical significance was provided with p-values lower than or equal to 0.05 and t-statistics greater than 1.96 (Greenland et al. 2016 ). Cohen’s f 2 indicates statistical relevance, where effect sizes are considered small, 0.02 < f 2  ≤ 0.15; medium, 0.15 < f 2  ≤ 0.35; or large, f 2  > 0.35 (Cohen 1988 ). The exploratory power of the model was measured using R 2 , which ranges between 0 and 1 where higher values indicate greater explanatory power (Hair et al. 2011 ; Reinartz et al. 2009 ). The Stone-Geisser Q 2 measure was calculated to support explanatory significance, that is, explaining how well the data could be (artificially) reproduced by the research model (Geisser 1974 ; Stone 1974 ). We achieved predictive accuracy with results above 0 where values greater than 0, 0.25, and 0.5 are considered as small, medium, and large effect sizes, respectively (Hair et al. 2019 ). Goodness of fit (GoF) is assessed using AVE and the adjusted R 2 . Our value of 0.78 is above the threshold of 0.36 (Wetzels et al. 2009 ), which indicates a valid model. We finally controlled our model using the participants’ age, grade, courses of study, and current semester, which we unanimously found not to be significant for the research question at hand. The final results of our evaluation are presented in Fig.  5 , and Table  3 provides an overview of the results for the hypotheses.

Research model with results (N = 113). * p  ≤ 0.05; ** p  ≤ 0.01; *** p  ≤ 0.001; n.s. = not significant

5 Discussion and benefits for research and practice

5.1 factors that contribute to the success of a mobile app to support first-year students in the “transition-in” phase in higher education.

As mentioned, our app’s key user group are students who have just entered the university system. This focus on the “transition-in” phase of the student lifecycle differentiates our study from prior literature in this field (e.g., Almaiah and Al Mulhem 2019 ; Cidral et al. 2018 ; Wang et al. 2019a ). Furthermore, we focus on students at a German university and, hence, in a typical Continental European higher education setting. The main differences between the Continental European and the Anglo-Saxon model of higher education arise primarily through universities’ funding and tuition fees. The Continental European model is characterized by state sponsorship of universities and free or very low tuition (e.g., Jongbloed 2004 ). As a result of this greatly reduced financial burden, students from disadvantaged backgrounds can also participate in higher education, and, therefore, the student population might be more (economically) diverse (Lenzen 2015 ). At the same time, limited state funding results in large lectures and a high student-lecturer ratio, which probably disadvantages students in need of greater guidance and, thus, increases the need for technological learning solutions.

At first, the research indicates a positive effect of system quality on perceived user satisfaction (H1b), a finding in line with prior results (e.g., Almaiah and Alismaiel 2019 ; Aparicio et al. 2017 ). As expected, factors like ease of use, easy navigation, structuredness, and the ability to efficiently retrieve relevant information help to increase user satisfaction. However, this effect could not be observed regarding the impact of system quality on the intention to reuse the app (H1a). A possible explanation for this finding, which is in line with prior literature (Aparicio et al. 2017 ; Chiu et al. 2016 ) is that students tend to use a system independently from its perceived quality, in case a university has committed to this particular system. Transferred to our context, the developed mobile app is the only solution of its kind. As such, first-year students obviously use it—regardless of their perception of system quality—as a means to (potentially) increase their learning performance. Nevertheless, further analysis of further factors seems promising to better understand the relationship between system quality and the intention to reuse the app (cf. Almaiah and Al Mulhem 2019 ).

Second, contrary to the proposed expectation in hypotheses H2a and H2b, service quality neither significantly impacts the intention to reuse nor perceived user satisfaction (see Table  3 or Fig.  5 ). Thus, service quality seems to be a minor issue in evaluating the mobile app’s benefits in terms of learning effectiveness. This is also reasonable as we noticed in the field that app users rarely contact the administrative or teaching staff for help with respect to mobile app usage. Likewise, previous research finds evidence that service quality is relatively less important in the context of knowledge-orientated information system success (e.g., Wang et al. 2019b ; Wu and Wang 2006 ).

Third, information quality positively impacts the perceived user satisfaction in our study (H3b). Accordingly, the reliability, understandability, and relevance of the information provided by the app for students in the “transition-in” phase are greatly appreciated by our target group. However, no significant impact on the intention to reuse could be observed (H3a). A similar finding is presented by Chiu et al. ( 2016 ). As a potential explanation, students may believe that using the app is decisive for a successful start of studies, and therefore, they do not draw the presented information into question. This assumption aligns with the observation that students’ critical thinking abilities are only starting to take shape in their first year at university and significantly increase in the subsequent semesters (cf. Ralston and Bays 2015 ; Wallace and Jefferson 2015 ). Hence, analyzing students from more advanced semesters might lead to different results.

Fourth, the effects of perceived enjoyment on both the intention to reuse (0.407) and user satisfaction (0.346) are significantly positive and greater than the impact of system quality, service quality, and information quality. These findings support our hypotheses H4a and H4b. Therefore, since perceived enjoyment is the only construct that influences both intention to reuse as well as perceived user satisfaction, it can be stated that the enjoyment and joy-related aspects are of utmost importance to promote learning effectiveness in our research setting. This result is quite striking and provides further evidence on the value of gamification in higher education, a field of research which is still quite in its infancy.

Fifth, we find support that perceived user satisfaction mediates the effects of system quality, information quality, and perceived enjoyment on the intention to reuse (H5). This is also reasonable since it might be more likely that students intend to reuse the app when their level of satisfaction is high. Additionally, this mediating effect might be why the service quality (H2a) and the information quality (H3a) are not directly associated with the intention to reuse. We also find evidence that intention to reuse significantly positively affects learning effectiveness (H6). In other words, the greater the likelihood to reuse the app, the greater the app’s positive effect on students’ learning effectiveness.

Lastly, perceived user satisfaction plays the most important role in determining students’ learning effectiveness, supporting our hypothesis H7. Since user satisfaction influences learning effectiveness, both directly and indirectly as mediated by the intention to reuse, it has the most significant total impact on learning effectiveness (0.448 + (0.445 × 0.426) = 0.6376). Moreover, it also has the most substantial direct effect (0.448), while the intention to reuse has a slightly lower impact (0.426).

5.2 Linking the factors to design requirements and derivation of design principles

As described above, system quality, information quality, and perceived enjoyment positively affect perceived user satisfaction, while the latter also promote the intention to reuse. Furthermore, perceived user satisfaction and intention to reuse have a supportive influence on students’ learning effectiveness. In this light, we now compare and contrast the design requirements and their realization as app functionalities with the influencing factors established before (see Fig.  6 ). By that, we contribute to discussing how digital technologies can be purposefully designed to support student retention in higher education. Hence, our artifact and the design requirements may serve as propositions for developing similar student apps specifically for the “transition-in” phase. Furthermore, based on the findings and the instantiated artifact, we derive design principles (cf. Gregor and Jones 2007 ) for mobile app creation in this field.

Linkage to design requirements

First, considering information quality, the option to self-track one’s course attendance and analyze this data against the course offerings during the semester (DR 1), the functionality to allow pop-up messages as reminders for lectures and academic events (DR 2) as well as the availability of training and exam-oriented exercises to control the learning process (DR 3) have proven useful to provide understandable, interesting, and reliable information to first-year students. Thus, regarding DR 1, the user may navigate to a site called “course overview” after login, showing the portfolio of courses the user has registered for. There, the app provides the option to confirm attendance for lectures. On a more detailed level, the user can create a time tracker for each course on demand, which allows for tracking attendance and self-study periods. Concerning DR 2, students are notified via push messages if an important event (e.g., lecture, exam registration deadline) in a course for which they have registered is about to take place. As a further option, students can export the course or event dates to their personal smartphone calendars. Via the site “exercises”, exam-oriented exercises are offered to check one’s personal learning process (DR 3). At this point, the training content also comprises learning videos and course scripts, which can be accessed at any time, enabling students to adjust the pace of learning at their own discretion.

The information quality offered to students with the help of the abovementioned functionalities (see Fig.  6 ) supports them in planning their participation in academic events and, thus, integrating into the daily student life (student factor “student experience”). Furthermore, they can improve their self-organization and adjust their learning strategies in accordance with the results that were scored for the exam-oriented exercises for instance (student factors “self-organization” and “learning strategies”).

To increase users’ perceived enjoyment, gamification elements like quizzes (DR 4) and competitions with the peer group are available (DR 5). This is meaningful since a lack of motivation and engagement to participate in the learning process has been identified as a major challenge in contemporaneous education (Hassan et al. 2021 ; Kiryakova et al. 2014 ). In order to reach a higher level of commitment and motivation among students, we propose the functionalities DR 4 and DR 5. Therefore, the game design mechanisms “freedom to fail” and “rapid feedback” (Stott and Neustaedter 2013 ; Thakur et al. 2020 ) were purposefully transferred to our learning app. We consider both mechanisms as helpful for first-year students to reduce mental barriers to interact with fellow students and teachers. The principles can be ideally addressed by quizzes, which give students direct feedback, even beyond the classroom, and disassemble psychological barriers to “fail” or to give wrong answers (cf. Alberti et al. 2019 ; Gordon et al. 2021 ). Furthermore, rewarding students’ efforts immediately (e.g., by credits) is a recognized way to increase motivation (Kiryakova et al. 2014 ). Hence, during app usage, students may collect credits through various actions (e.g., quizzes, attending courses, correctly solving exam-oriented exercises, etc.), determining their ranking in a playful competition with their peer group. Further, the training exercises can also be offered in the form of an interactive survey during the lecture (i.e., a “clicker” functionality) to test students’ current knowledge and support their “progression” in becoming familiar with the course content (cf. Stott and Neustaedter 2013 ).

Against this background, we propose the described functionalities to increase students’ perceived enjoyment (when dealing with subject-related content) as means to address the student factors “learning strategies” and “self-organization” (see Fig.  6 and Sect.  2.3 ).

Additionally, we suggest the design of the solution as a hybrid app (DR 6), which differentiates between a front- and backend (DR 7) and transfers data with the help of HTTP and JSON (DR 8) as a way to effectively produce the required information and, hence, contribute to system quality in our setting (cf. Urbach et al. 2009 ). The design as a hybrid app allows us to offer the app for both common mobile phone platforms (i.e., iOS and Android), whereby the development efforts were less than for corresponding native apps (e.g., Schilling 2016 ). The app’s architecture enables easy maintenance, further development, as well as the addition of further services. Finally, the data (e.g., exam-oriented exercises, quiz questions, etc.) are stored in a database, which strongly facilitates content management and the provision of new content once users log in as “lecturers”. Figure  7 provides our proposition for the architecture.

General architecture of the app

Based on our findings, we propose the following four design principles to facilitate the design of related instances of the artifact (cf. Kruse et al. 2016 ; Sein et al. 2011 ), namely mobile solutions to support students in the “transition-in” phase. Generally, design principles build on the knowledge that is gained when developing and using a specific instance of an artifact and are formulated when reflecting upon the results from a more generic perspective (Kruse et al. 2016 ). Concretely, we propose the following design principles:

Principle of fostering course attendance management : In order to support students’ self-organization, a mobile app should offer the option to systematically plan and track one’s course attendance. This would help to structure the time at university and give an overview of the time spent in courses.

Principle of using self-learning control functionalities : To control the learning progress, mechanisms to evaluate one’s subject-related knowledge must be provided. For that purpose, various propositions have been made in the literature, like quizzes, practice tasks, open-ended questions, or criterion tests, amongst others (cf. Chou and Feng 2019 ; Pauli et al. 2020 ). That way, students may begin to critically reflect upon their learning strategies and move from a surface learning approach to deep or strategic learning efforts (Lau and Lim 2015 ).

Principle of assuring a widespread availability : To guarantee the availability of the mobile solution for a wide range of students, it needs to be executable on different platforms (e.g., iOS, Android) and devices (smartphones, Tablet-PCs). Further, active promotion is required to make students aware of the availability of the solution. Moreover, mobile phones have truly become ubiquitous in the student age group.

Principle of easy content management : The content offered for the students (e.g., quizzes, training questions, videos, etc.) should be easy to manage. This calls for an architecture that decouples the front- from the backend and uses a database for content storage. By that, the provision of new materials or a revision of existing content is tremendously fostered.

These design principles have been formulated based on a concrete instance of an artifact, considering the findings of this study. They can purposefully complement existing design principles, which are directed at the creation of innovative learning environments (cf. Herrington et al. 2009 ), the design of mobile course material (cf. Ally 2005 ), or making use of gamification elements (cf. Laine and Lindberg 2020 ). However, our design principles are different from existing propositions in the field of mobile learning (e.g., Palalas and Wark 2017 ) since we focus on support during the “transition-in” phase and the corresponding challenges. As such, the suggestions may be referenced and consolidated with further design principles to cover all stages of the student lifecycle in future research, contributing to an even better understanding of mobile learning in higher education.

5.3 Benefits for research

As mobile phones are widely available among students and the field of higher education becomes aware of the potential to use them for learning purposes through the development and usage of mobile learning apps, it is crucial that researchers and practitioners develop a better understanding of what makes learning apps successful and—equally important—how to measure their success in the first place. Considering this, our study contributes to research in the following ways.

First, the mobile app to support students in the “transition-in” phase was developed with the help of a DSR procedure (cf. Peffers et al. 2007 ), as outlined in Sect.  2.3 . The app represents the artifact (i.e., outcome) of the Design Science (DS) effort and, hence, a “human-made object” (Goldkuhl and Karlsson 2020 , p. 1241) to solve a practical problem (March and Smith 1995 ). However, besides the artifact itself also its contribution to theory should be clearly highlighted in DSR (Baskerville et al. 2018 ). This contribution to scientific knowledge is addressed by Hevner’s “rigor cycle” (Hevner 2007 ) and a mandatory element from the perspective of the “design theory school of thought” (Baskerville et al. 2018 , p. 359). Thereby, the IT-artifact (i.e., the mobile app) of our DS effort was built in previous work (see Sect.  2.3 ) and can be seen as an instance of a “type 2 app” according to the “pedagogical framework of mobile learning” (Park 2011 ) for students of an accounting course. In this paper, the app’s contribution to the scientific knowledge base is analyzed by identifying the factors that impact a student’s learning effectiveness, user satisfaction, and intention to reuse the app and linking these to design requirements (see Sects.  4 and 5 ). Furthermore, we present design principles that offer DS researchers “actionable knowledge useful in building new versions of similar artifacts” (Kruse et al. 2022 , p. 1236). Accordingly, these insights may extend the “pedagogical framework of mobile learning” (Park 2011 ) by laying the foundation for success factors and design propositions that determine the acceptance of mobile apps among students in the “transition-in” phase.

Additionally, this research supports previous findings on the employment of system success models in the field of mobile learning. In this respect, we confirm the model of Wang ( 2008 ), which suggests that user satisfaction has a direct as well as indirect impact on other net benefits (e.g., learning effectiveness) through the mediation of intention to reuse. Another implication that can be drawn from this result is that the perceived user satisfaction and the intention to reuse are prerequisites for students’ learning effectiveness. Following Wang et al. ( 2019b ), who implemented perceived enjoyment in the context of fee-based mobile learning, we now use the perceived enjoyment in our proposed success model for free mobile learning apps. Therefore, this study benefits future research by providing a validated IS success model for free mobile learning apps by combining perceived enjoyment and learning effectiveness with the established IS success model.

However, deviating from the traditional IS success model, we found service quality to have no significant impact on perceived user satisfaction and intention to reuse (see Fig.  5 ). Since this can be explained, as shown in Sect.  5.1 , it can be stated that service quality is relatively less important in the context of knowledge-oriented IS success, which is in line with previous research (e.g., Wang et al. 2019b ; Wu and Wang 2006 ) as well as rather good news for higher education organizations which mainly expend their resources on teaching staff instead of administrative personnel, which could provide user support for such apps.

In summary, the empirical results emphasize the importance of extending the traditional IS success model by other dimensions like perceived enjoyment when assessing mobile learning app success. Accordingly, future research can rely on this multidimensional approach, compare it to existing models, or examine the influence of the included constructs on mobile learning system success.

5.4 Benefits for practice

This research also provides several implications and benefits for practice. First, the results show that an app, which was collaboratively developed with the target groups (i.e., students and educators) and adapted to the particular needs of first-year students, can positively influence students’ learning effectiveness. This highlights the value of the design and development procedure of the app following a DSR approach (cf. Peffers et al. 2007 ) with several iterative steps.

Second, according to the employed and validated model, learning effectiveness is considered a more effective measure of mobile learning app success than the other six variables. In this regard, learning effectiveness should develop if the model components of system quality, service quality, information quality, perceived enjoyment, intention to reuse, and perceived user satisfaction are appropriately managed. Thus, to support learning effectiveness, an implication for developers of mobile learning apps is to focus on high system quality, information quality, and, foremost, an enjoyable learning experience. To this end, design requirements and principles have been proposed that allow the creation of similar artifacts. More detailed, our results show clear evidence that the impact of students’ perceived enjoyment on user satisfaction, intention to reuse, and learning effectiveness is substantially greater than the total effect of system quality, service quality, and information quality. This calls for strongly emphasizing gamification elements for mobile apps in corresponding DSR efforts.

Third, the four components of system quality, service quality, information quality, and perceived enjoyment have both direct and indirect effects. They all directly influence students’ perceived user satisfaction and the intention to reuse. The perceived user satisfaction, in turn, affects the intention to reuse as well as the learning effectiveness. Therefore, the intention to reuse and learning effectiveness are also influenced indirectly. The findings of this study suggest perceived user satisfaction has the most significant impact on intention to reuse and learning effectiveness. Moreover, perceived user satisfaction has the most substantial direct and total effect on students' learning effectiveness (see Fig.  4 ). Thus, the importance of student’s satisfaction with the learning app in improving their learning effectiveness is emphasized. However, the findings also suggest that developers as well as educators must track changes in both the perceived user satisfaction and intention to reuse, as user satisfaction does not totally mediate the impact of intention to reuse on students’ learning effectiveness.

To summarize, this study helps practitioners like developers and educators to identify the factors that make mobile learning applications more successful. The empirical findings encourage developers to consider the constructs of system quality, information quality, perceived enjoyment, perceived user satisfaction, intention to reuse, and learning effectiveness when designing their products. Moreover, the importance of students’ enjoyment and satisfaction while using the app for their learning outcomes is emphasized. Besides the implications for developers, this aspect is also a fundamental implication for educators, as it requests and motivates them to deliver learning content entertainingly to help their students succeed. Together, both aspects could help reduce early dropout rates among students and, thus, contribute to fighting the current shortage of skilled workforce and help meet the economy’s demand for qualified workers in the next decades.

6 Limitations and further research

This study deals with the analysis of factors that contribute to the success of a mobile app to support first-year students in the “transition-in” phase in view of learning effectiveness, user satisfaction, and intention to reuse the app. Furthermore, the factors are linked to design requirements and the derived design principles. Our study covers one semester during the COVID-19 pandemic and thus includes only digital courses, which should be considered when interpreting the results. Nevertheless, if this circumstance has any impact, we expect it to be in favor of our results rather than against them. Presumably, the impact of our learning app would have been even stronger in the pre-pandemic period than in the online teaching period. This is because the app functionalities purposefully complement the attendance of face-to-face lectures. In a pure online semester, the “value” provided by the app, which is still positive for users, may be less than in the era of traditional teaching. Though, this proposition needs to be explored in more detail in future studies.

Besides providing several benefits for both research and practice, this study has some noteworthy limitations. First, the discussed findings and the implications drawn are limited to a specific context of an app adapted to first-year students’ particular needs in a mandatory introductory accounting course at the University of Bremen (Germany). However, in terms of the topics, structure, and practicalities, the course setting is similar to most foundational undergraduate courses in Continental European study programs in business and economics. Second, since we rely on self-reported data to examine the mobile learning app’s success, this may introduce the risk of common method and response bias. Having said that, as we assured participants of the confidentiality of their responses and offered no monetary rewards or other incentives for participation, we assume that the risk of systematically biased responses is minimal. Third, we employ a cross-sectional approach, which causes possible feedback links from learning effectiveness to perceived user satisfaction and the intention to reuse could not be considered in this study. Finally, our research model largely builds on the initial elements of the IS success model. This was done to receive design principles that are based on widely accepted elements for success, which may positively affect the general acceptance of such principles in the DSR community. Moreover, to the best of our knowledge, corresponding studies based on the IS success model have not been done for “type 2 apps” according to the “pedagogical framework of mobile learning” of Park ( 2011 ).

In future research, a longitudinal design to take these possible feedback links into account and, thus, enhance the understanding of the causality and interrelationships of the research elements in the context of mobile learning app success will be performed. Going forward, the app will be continuously developed further and is planned to be fully integrated into the entire undergraduate curriculum at our faculty. In this course, the integration of AI-based conversational agents to further improve students’ learning experience will be investigated more closely. Particularly their impact on students’ learning behavior is to be considered since the literature on accounting education as well as information systems lacks theoretical foundations in this respect. Moreover, the research model will be extended by additional elements in the next step to identify additional influencing factors that may positively affect student performance (e.g., base competencies or grit; cf. Aparicio et al. 2017 ; Zehetmeier et al. 2014 ).

Exam performance is coded according to the German grade scale from 1.0 (best) through 5.0 (fail).

In order to ensure the representativeness of our drawn sample, we conducted two-tailed t-tests for differences in means between the group of students included in the sample and the underlying population of app users. The results indicate that the characteristics are essentially similarly distributed. The only documented significant differences are in the share of students in Business Studies and Engineering and Management, in the share of (almost) always and (almost) never attending students, as well as in the share of students with a very good exam performance. However, the significance level is only slightly pronounced ( p  < 0.1) for most differences. We present a comparison between the sample and the underlying population with the conducted t-tests in Table 9 (Appendix).

The high proportion of never and rarely attending students is likely due to the conitions of COVID-19 induced online teaching. In order not to disadvantage any students, we provided the recordings of the zoom sessions of tutorials and workshops afterwards. However, we were not able to assess which students accessed the recordings. Moreover, the distribution of the exam performance in our sample, which documents a high level of insufficient performance and thus failure, is in line with both the exam performance of the whole population of the course and the distribution of the exam performance in previous cohorts.

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Johannsen, F., Knipp, M., Loy, T. et al. What impacts learning effectiveness of a mobile learning app focused on first-year students?. Inf Syst E-Bus Manage 21 , 629–673 (2023). https://doi.org/10.1007/s10257-023-00644-0

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Received : 26 July 2022

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Published : 26 July 2023

Issue Date : September 2023

DOI : https://doi.org/10.1007/s10257-023-00644-0

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