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School of Information Systems and Management Theses and Dissertations

Theses/dissertations from 2021 2021.

Designing Targeted Mobile Advertising Campaigns , Kimia Keshanian

Informing Complexity: The Business Case for Managing Digital Twins of Complex Process Facilities as a Valuable Asset , William Randell McNair

Designing a Health Coach-Augmented mHealth System for the Secondary Prevention of Coronary Heart Disease , Avijit Sengupta

Impact of Health IT on Practice of Medicine , Deepti Singh

Theses/Dissertations from 2020 2020

Complexities of Data, Tasks and Workflows in Health IT Management , Gaurav Jetley

Understanding the Complex Ethical Landscape of Artificial Intelligence Adoptions , Chrissann R. Ruehle

Theses/Dissertations from 2019 2019

Evaluating Conversation Agent Impact on Student Experience in a Distance Education Course , Grover Walters

Theses/Dissertations from 2018 2018

An Effectual Approach for the Development of Novel Applications on Digital Platforms , Onkar Shamrao Malgonde

Analytics for Novel Consumer Insights (A Three Essay Dissertation) , Utkarsh Shrivastava

Theses/Dissertations from 2017 2017

Essays on Crowdfunding: Exploring the Funding and Post-funding Phases and Outcomes , Onochie Fan-Osuala

Theses/Dissertations from 2016 2016

An Information and Communication Technologies for Development (ICT4D) Decision Framework for Building an Information Economy in Developing Countries: The Case of Palestine , Hasan Nuseibeh

Blind and Visually Impaired Users Adaptation to Web Environments: A Qualitative Study , Raneem Saqr

Theses/Dissertations from 2015 2015

Predictive Analytics of Organizational Decisions and the Role of Rationality , Arash Barfar

Analytics and Healthcare Costs (A Three Essay Dissertation) , Lina Bouayad

Information Technology & Sustainability: An Empirical Study of the Value of the Building Automation System , Daphne Marie Simmonds

Theses/Dissertations from 2014 2014

The Use and Effectiveness of Online Social Media in Volunteer Organizations , Amy J. Connolly

Inter-Organizational Social Network Information Systems: Diagnosing and Design , Matthew T. Mullarkey

Informing Systems, Interventions, and Innovations , William Francis Murphy Jr.

Theses/Dissertations from 2013 2013

Do Personality Tests have a place in Academic Preparation of Undergradute Hospitality Students , Gunce Malan

Applications of Agent Based Approaches in Business: A Three Essay Dissertation , Shankar Prawesh

Theses/Dissertations from 2012 2012

Economic Culture and Trading Behaviors in Information Markets , Khalid Nasser Alhayyan

TagLine: Information Extraction for Semi-Structured Text Elements In Medical Progress Notes , Dezon K. Finch

Learning and Relevance in Information Retrieval: A Study in the Application of Exploration and User Knowledge to Enhance Performance , Harvey Stuart Hyman

Theses/Dissertations from 2010 2010

Contribution to and Use of Online Knowledge Repositories: The Role of Governance Mechanisms , Varol O. Kayhan

Understanding Organizational Adoption Theories Through the Adoption of a Disruptive Innovation: Five Cases of Open Source Software , Delmer Nagy

Organizational Information Markets: Conceptual Foundation and an Approach for Software Project Risk Management , Areej M. Yassin

Theses/Dissertations from 2009 2009

Social Shopping , Rebecca Anderson

Goal Attainment On Long Tail Web Sites: An Information Foraging Approach , James A. Mccart

Theses/Dissertations from 2008 2008

The Impact of Computer Mediated Communication Systems Monitoring on Organizational Communications Content , Carolyn F. Holton

Theses/Dissertations from 2007 2007

Privacy in Database Designs: A Role Based Approach , Gary A. Poe

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Home > BIT > Masters Theses

Business and Information Technology Masters Theses

Theses from 2023 2023.

SENTIMENT STRENGTH AND TOPIC RECOGNITION IN SENTIMENT ANALYSIS , Esi A.R. Adeborna

Theses from 2022 2022

Smart living with artificial intelligence -- potential impact of artificial intelligence on mental well-being , Weiyu Wang

Theses from 2021 2021

Computer gamification: Addressing symptoms of inattentive ADHD in children , Shavonne Victoria Halterman

Factors affecting COVID-19 pandemic , Linrui Han

Values of trust in AI in autonomous driving vehicles , Ru Lian

A systematic analysis of eye-tracking metrics for sleepiness , Debasis Roy

A meta-analysis: Gamification in education , Yalin Wang

Theses from 2020 2020

Values of artificial intelligence in marketing , Yingrui Xi

Theses from 2019 2019

Predictive modeling of webpage aesthetics , Ang Chen

The impact of artificial intelligence on higher education , Yizhi Ma

Impact of framing and base size of computer security risk information on user behavior , Xinhui Zhan

Theses from 2018 2018

Classification of EEG signals of user states in gaming using machine learning , Chandana Mallapragada

Impact of probable and guaranteed monetary value on cybersecurity behavior of users , Santhosh Kumar Ravindran

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study , Tejaswini Yelamanchili

Theses from 2017 2017

AN EXPERIMENTAL COMPARISON OF TWO MACHINE LEARNING APPROACHES TO INVESTIGATE GENDER DIFFERENCES IN EMOTION CLASSIFICATION , Wangchuchu Zhao Han

Impact of framing and priming on users' behavior in cybersecurity , Kavya Sharma

Sentiment analytics: Lexicons construction and analysis , Bo Yuan

Theses from 2016 2016

Factors influencing the adoption of smart wearable devices , Apurva Adapa

Effect of cooperation on players' immersion and enjoyment , Lakshmi Sushma Daggubati

Using eye-tracking to understand user behavior in deception detection system interaction , Prashanth Kumar Lakkapragada

Understanding user experience in gaming , Sri Chaitanya Sanaboina

Effect of timing and source of online product recommendations: An eye-tracking study , Qing Zeng

Bridging the gap between supply chain and consumer experience , Cui Zou

Theses from 2015 2015

Online diagnosis of diabetes with Twitter data , Farheen Ali

Theses from 2013 2013

Assessment of an enterprise employee portal using dashboard monitoring system: a case study , Aditi Mishra

Shopping alone online vs. co-browsing: a physiological and perceptual comparison , Ceren Topaloglu

Theses from 2012 2012

Under pressure: a psychophysiological analysis of the effect of temporal constraints on information processing and decision making , Nagaratna Krishna Sneha Pochinapeddi

Theses from 2011 2011

Improving student learning outcomes through personal response systems (clickers) , Ran Cai

Comprehensive evaluation of a computer based learning system , Arun Sharma Chintalapati

Assess commercialization feasibility of algae-based bio-fuel with Dashboard and Scorecard technologies , Madhur Chopra

Eyes don't lie: understanding users' first impressions on website design using eye tracking , Sirjana Dahal

Analyzing dashboard performance in educational, non-profit sectors , Rutuja B. Honnatti

Effect of web atmospheric cues on users' emotional responses in ecommerce , Tanvi Joginapelly

Social network analysis of the video bloggers' community in YouTube , Anusha Mogallapu

Applying text timing in corporate spin-off disclosure statement analysis: understanding the main concerns and recommendation of appropriate term weights , Aravindh Sekar

Longitudinal evaluation of a learning system for teaching geographical information system within the context of a geotechnical problem , Aparna Sukhavasi

Theses from 2010 2010

Developing a model for evaluating software tools in education , Vedant Jain

A pilot study in an application of text mining to learning system evaluation , Nitsawan Katerattanakul

Impact of management status, gender, and level of project involvement on corporate governance in a role-based enterprise portal environment , Daniel Augusto Pereira Brás Sequeira

Theses from 2009 2009

The effects of technology on literacy(s) , Thomas Glen Petersen

Integration of a GIS learning system into civil engineering curricula: an evaluation , Basanta Tandon

Comparison of dashboard-based and balanced scorecard-based corporate performance management system , Lin Zhu

Theses from 2007 2007

Toward improving performance measurement in public sector organizations , Matthew Thomas Kutz

An exploratory study of the videoblogger's community , John Warmbrodt

Theses from 2006 2006

A framework of text mining approach for sentiment analysis of news articles using information agents , Balasubramanian Guruswamy

An exploration of collaborative 3D visualization processing in engineering education and supporting tools , Erik Schwartz

A usability and performance analysis of multiple monitor displays and multitasking , Jacob M. Truemper

Theses from 2005 2005

Exploring cooperative learning in an initial computer programming course using Visual Basic , Christopher Duane Brown

Mental models in airborne landmine detection , John William Brown

A conceptual framework of using collaborative filtering algorithms to enhance keyword search , Shreyas S. Deokule

Multi-user search engine (MUSE): supporting collaborative information seeking and retrieval , Rashmi Krishnappa

Social networks: role of personalization and interaction in virtual community for enhancing business , Nisha Maguluru

Collaborative information seeking: A study of a patient care team in the emergency department , Patricia Ruma Spence

Theses from 2004 2004

Iterative usability testing in the development of a learning technology system for teaching geographic information systems within a civil engineering curriculum , John McKenna Sullivan

Learning in affectively intense virtual environments , Lawrence M. Wilfred

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Home > CCSE > IT > MSIT

Master of Science in Information Technology Theses

Information Technology (IT) focuses on meeting the needs of users within an organizational and societal context through the selection, creation, application, integration and administration of computing technologies. To succinctly put it - IT uses technology to apply information to meet business needs.

Information Technology is the term used to describe the convergence of computer science, management and information systems. The main distinction between IT and the other computing disciplines (e.g., computer science, software engineering, etc.) is that IT professionals must know how to apply computing technology and to build a business case for that application. It emphasizes the management and performance of information technology planning, development, implementation and operation, and development of the infrastructure to support the processes necessary to achieve organizational objectives.

The Masters of Science in Information Technology degree is designed to enhance career options for a broad mix of students. The program is geared for those students interested in pursuing a career in the management and performance of information systems planning, development, implementation and operation.

This collection was started with the graduates of the Fall 2015 semester, after the consolidation between Kennesaw State University and Southern Polytechnic State University was final (July 2015).

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Theses/dissertations from 2023 2023.

SCREENSAFEFUTURE: A PARENT-EMPATHETIC AND PRACTICAL MHEALTH APPLICATION FOR TODDLERS' BRAIN DEVELOPMENT ADDRESSING SCREEN-ADDICTION CHALLENGES , Nafisa Anjum

Analysis of the Adherence of mHealth Applications to HIPAA Technical Safeguards , Bilash Saha

Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications , Aiasha Siddika

Theses/Dissertations from 2022 2022

A Literature Review On Privacy and Security in Virtual Reality and Augmented Reality , Yunus Gumbo

A Maturity Model of Data Modeling in Self-Service Business Intelligence Software , Anna Kurenkov

Blockchain-based Medical Image Sharing and Critical-result Notification , Jiyoun Randolph

Theses/Dissertations from 2021 2021

Deep Learning for Human MicroRNA Precursor Prediction: A Systematic Literature Review , Martina ojonah

Non-Invasive Monitoring of Human Hygiene using Vibration Sensor and Classifier , Shashank Trivedi, Maria Valero, Hossain Shahriar, and Liang Zhao

Theses/Dissertations from 2020 2020

Distributed Denial of Service Attack Detection , Travis Blue

A Framework to Detect Presentation Attacks , Laeticia Etienne

Using Multiple Data Sources for Customer Satisfaction Analyzing: A Sentimental Approach , Jhanvi Vyas

Theses/Dissertations from 2019 2019

An Architecture for Blockchain-based Collaborative Signature-based Intrusion Detection System , Daniel Laufenberg

Theses/Dissertations from 2018 2018

Why Certified IoT Devices Remain Untrustworthy , Donald Privitera

Theses/Dissertations from 2016 2016

The Paradox of Social Media Security: A Study of IT Students’ Perceptions versus Behavior on Using Facebook , Zahra Y. Alqubaiti

A Framework for Hybrid Intrusion Detection Systems , Robert N. Bronte

Perceived Effectiveness of E-Learning for Technology Instruction in Public Library Staff Development Programs: A Survey Based on the Technology Acceptance Model , Julia Huprich

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Master Thesis Information Technology and Management -Cloud Computing Evaluation - How it Differs to Traditional IT Outsourcing

Related Papers

International Journal of Computers Communications & Control

Raluca Brandabur

The purpose of this paper is to provide an evolutionary perspective of cloud computing (CC) by integrating two previously disparate literatures: CC and information technology outsourcing (ITO). We review the literature and develop a framework that highlights the demand for the CC service, benefits, risks, as well as risk mitigation strategies that are likely to influence the success of the service. CC success in organisations and as a technology overall is a function of (i) the outsourcing decision and supplier selection, (ii) contractual and relational governance, and (iii) industry standards and legal framework. Whereas CC clients have little control over standards and/or the legal framework, they are able to influence other factors to maximize the benefits while limiting the risks. This paper provides guidelines for (potential) cloud computing users with respect to the outsourcing decision, vendor selection, service-level-agreements, and other issues that need to be addressed when opting for CC services. We contribute to the literature by providing an evolutionary and holistic view of CC that draws on the extensive literature and theory of ITO. We conclude the paper with a number of research paths that future researchers can follow to advance the knowledge in this field.

Deekshant Jeerakun

Build to Order: The Road to the

Jens K Roehrich

With the continuing increase of competitive pressures in the automobile industry, the acceleration of cost and price pressures and the omnipresent need for im-provement of engineering productivity managers have to constantly ensure the company’s survival in the market. This perspective emphasises the need for man-agers to consider outsourcing in order to sustain a company’s competitive ad-vantage. The use of external assembly service providers in the automotive industry is widespread and is embraced in the build to order concept. Outsourcing capacity brings with it the risk of outsourcing competency. Managers in the customer-conscious automotive market have to thoroughly understand the concept and the associated risks in order to benefit from the outsourcing practice. Therefore, this chapter critically evaluates the benefits and risks associated with outsourcing core and supporting activities in the automotive industry. Moreover, the study draws on different theoretical positions delivering a rigorous description and comparison of the theoretical outsourcing standpoints. The research study is underpinned by empirical evidence from the automotive industry and concludes with a set of managerial implications to facilitate well-grounded outsourcing decisions within the automotive industry.

Noel Carroll

Ashik Subedi

john egbuch

Cloud computing source: en.wikipedia.org/ wiki/Cloud_computing Cloud computing metaphor: For a user, the network elements representing the provider-rendered services are invisible, as if obscured by a cloud. Cloud computing is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services), [1][2] which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centers. [3] It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over a network. Advocates claim that cloud computing allows companies to avoid upfront infrastructure costs, and focus on projects that differentiate their businesses instead of on infrastructure. [4] Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable business demand. [4][5][6] Cloud providers typically use a "pay as you go" model. This will lead to unexpectedly high charges if administrators do not adapt to the cloud pricing model. [7] The present availability of high-capacity networks, low-cost computers and storage devices as well as the widespread adoption of hardware virtualization, service-oriented architecture, and autonomic and utility computing have led to a growth in cloud computing. [8][9][10] Companies can scale up as computing needs increase and then scale down again as demands decrease. Cloud computing has become a highly demanded service or utility due to the advantages of high computing power, cheap cost of services, high performance, scalability, accessibility as well as availability. Some cloud vendors are experiencing growth rates of 50% per year, [11] but being still in a stage of infancy, it has pitfalls that need to be addressed to make cloud computing services more reliable and user friendly. [12][13]

Journal of Business Diversity

Himanshu Shee

This study aims to investigate the outsourcing relationships between firms in Australia and China by using a problem-solution approach, and find out practical solutions. Empirical data from Australian firms were collected by a questionnaire survey and interviews. The survey data were analysed by descriptive statistics and correlation to find the most common problems and related solutions, and then eight case studies were compiled. The results agree that outsourcing can achieve many significant benefits, though it presents a variety of new risks. Solutions found include increasing communication levels, trust and management control.

American Journal of Computer Science and Information Technology (AJCSIT)

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A.I. Has a Measurement Problem

Which A.I. system writes the best computer code or generates the most realistic image? Right now, there’s no easy way to answer those questions.

Credit... Davide Comai

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By Kevin Roose

Reporting from San Francisco

• April 15, 2024

There’s a problem with leading artificial intelligence tools like ChatGPT, Gemini and Claude: We don’t really know how smart they are.

That’s because, unlike companies that make cars or drugs or baby formula, A.I. companies aren’t required to submit their products for testing before releasing them to the public. There’s no Good Housekeeping seal for A.I. chatbots, and few independent groups are putting these tools through their paces in a rigorous way.

Instead, we’re left to rely on the claims of A.I. companies, which often use vague, fuzzy phrases like “ improved capabilities ” to describe how their models differ from one version to the next. And while there are some standard tests given to A.I. models to assess how good they are at, say, math or logical reasoning, many experts have doubts about how reliable those tests really are.

This might sound like a petty gripe. But I’ve become convinced that a lack of good measurement and evaluation for A.I. systems is a major problem.

For starters, without reliable information about A.I. products, how are people supposed to know what to do with them?

I can’t count the number of times I’ve been asked in the past year, by a friend or a colleague, which A.I. tool they should use for a certain task. Does ChatGPT or Gemini write better Python code? Is DALL-E 3 or Midjourney better at generating realistic images of people?

I usually just shrug in response. Even as someone who writes about A.I. for a living and tests new tools constantly, I’ve found it maddeningly hard to keep track of the relative strengths and weaknesses of various A.I. products. Most tech companies don’t publish user manuals or detailed release notes for their A.I. products. And the models are updated so frequently that a chatbot that struggles with a task one day might mysteriously excel at it the next.

Shoddy measurement also creates a safety risk. Without better tests for A.I. models, it’s hard to know which capabilities are improving faster than expected, or which products might pose real threats of harm.

In this year’s A.I. Index — a big annual report put out by Stanford University’s Institute for Human-Centered Artificial Intelligence — the authors describe poor measurement as one of the biggest challenges facing A.I. researchers.

“The lack of standardized evaluation makes it extremely challenging to systematically compare the limitations and risks of various A.I. models,” the report’s editor in chief, Nestor Maslej, told me.

For years, the most popular method for measuring artificial intelligence was the so-called Turing Test — an exercise proposed in 1950 by the mathematician Alan Turing, which tests whether a computer program can fool a person into mistaking its responses for a human’s.

But today’s A.I. systems can pass the Turing Test with flying colors, and researchers have had to come up with new, harder evaluations.

One of the most common tests given to A.I. models today — the SAT for chatbots, essentially — is a test known as Massive Multitask Language Understanding, or MMLU.

The MMLU, which was released in 2020, consists of a collection of roughly 16,000 multiple-choice questions covering dozens of academic subjects, ranging from abstract algebra to law and medicine. It’s supposed to be a kind of general intelligence test — the more of these questions a chatbot answers correctly, the smarter it is.

It has become the gold standard for A.I. companies competing for dominance. (When Google released its most advanced A.I. model, Gemini Ultra, earlier this year, it boasted that it had scored 90 percent on the MMLU — the highest score ever recorded.)

Dan Hendrycks, an A.I. safety researcher who helped develop the MMLU while in graduate school at the University of California, Berkeley, told me that the test was never supposed to be used for bragging rights. He was alarmed by how quickly A.I. systems were improving, and wanted to encourage researchers to take it more seriously.

Mr. Hendrycks said that while he thought MMLU “probably has another year or two of shelf life,” it will soon need to be replaced by different, harder tests. A.I. systems are getting too smart for the tests we have now, and it’s getting more difficult to design new ones.

“All of these benchmarks are wrong, but some are useful,” he said. “Some of them can serve some utility for a fixed amount of time, but at some point, there’s so much pressure put on it that it reaches its breaking point.”

There are dozens of other tests out there — with names like TruthfulQA and HellaSwag — that are meant to capture other facets of A.I. performance. But just as the SAT captures only part of a student’s intellect and ability, these tests are capable of measuring only a narrow slice of an A.I. system’s power.

And none of them are designed to answer the more subjective questions many users have, such as: Is this chatbot fun to talk to? Is it better for automating routine office work, or creative brainstorming? How strict are its safety guardrails?

(The New York Times has sued OpenAI, the maker of ChatGPT, and its partner, Microsoft, on claims of copyright infringement involving artificial intelligence systems that generate text.)

There may also be problems with the tests themselves. Several researchers I spoke to warned that the process for administering benchmark tests like MMLU varies slightly from company to company, and that various models’ scores might not be directly comparable.

There is a problem known as “data contamination,” when the questions and answers for benchmark tests are included in an A.I. model’s training data, essentially allowing it to cheat. And there is no independent testing or auditing process for these models, meaning that A.I. companies are essentially grading their own homework.

In short, A.I. measurement is a mess — a tangle of sloppy tests, apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I. developers themselves grasping in the dark.

“Despite the appearance of science, most developers really judge models based on vibes or instinct,” said Nathan Benaich, an A.I. investor with Air Street Capital. “That might be fine for the moment, but as these models grow in power and social relevance, it won’t suffice.”

The solution here is likely a combination of public and private efforts.

Governments can, and should, come up with robust testing programs that measure both the raw capabilities and the safety risks of A.I. models, and they should fund grants and research projects aimed at coming up with new, high-quality evaluations. (In its executive order on A.I. last year, the White House directed several federal agencies, including the National Institute of Standards and Technology, to create and oversee new ways of evaluating A.I. systems.)

Some progress is also emerging out of academia. Last year, Stanford researchers introduced a new test for A.I. image models that uses human evaluators, rather than automated tests, to determine how capable a model is. And a group of researchers from the University of California, Berkeley, recently started Chatbot Arena , a popular leaderboard that pits anonymous, randomized A.I. models against one another and asks users to vote on the best model.

A.I. companies can also help by committing to work with third-party evaluators and auditors to test their models, by making new models more widely available to researchers and by being more transparent when their models are updated. And in the media, I hope some kind of Wirecutter-style publication will eventually emerge to take on the task of reviewing new A.I. products in a rigorous and trustworthy way.

Researchers at Anthropic, the A.I. company, wrote in a blog post last year that “effective A.I. governance depends on our ability to meaningfully evaluate A.I. systems.”

I agree. Artificial intelligence is too important a technology to be evaluated on the basis of vibes. Until we get better ways of measuring these tools, we won’t know how to use them, or whether their progress should be celebrated or feared.

Kevin Roose is a Times technology columnist and a host of the podcast " Hard Fork ." More about Kevin Roose

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Microsoft said that it would make a $1.5 billion investment in G42 , an A.I. giant in the United Arab Emirates, in a deal largely orchestrated by the Biden administration to box out China.

Instagram is testing a program that offers its top influencers the ability to interact with their followers over direct messages using an A.I. chatbot .

The Age of A.I.

Could A.I. change India’s elections? Avatars are addressing voters by name, in whichever of India’s many languages they speak. Experts see potential for misuse  in a country already rife with disinformation.

Which A.I. system writes the best computer code or generates the most realistic image? Right now, there’s no easy way to answer those questions, our technology columnist writes .

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