knowledge sharing in online environments a qualitative case study

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Knowledge sharing in online environments: A qualitative case study

National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616

School of Library and Information Science, Indiana University, 1320 East 10th St. Bloomington, IN 47405 USA

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Journal of the American Society for Information Science and Technology

This study expands the perspective of knowledge sharing by categorizing the different types of knowledge that individuals shared with one another and examining the patterns of motivators and barriers of knowledge sharing across three online environments pertaining to the following professional practices—advanced nursing practice, Web development, and literacy education. The patterns indicate the different possible combinations of motivators or barriers that may exist in individuals. Data were gathered through online observations and semistructured interviews with 54 participants. The cross-case analysis shows that the most common type of knowledge shared across all three environments was practical knowledge. Overall, seven motivators were found. Analysis also suggests that the most common combination of motivators for knowledge sharing was collectivism and reciprocity. A total of eight barriers were identified. The most common combination of barriers varied in each online environment. Discussions as to how the types of professional practices may contribute to the different results are provided, along with implications and future possible research directions. © 2007 Wiley Periodicals, Inc.

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Computing methodologies

Modeling and simulation

Simulation theory

Systems theory

Human-centered computing

Human computer interaction (HCI)

Mathematics of computing

Information theory

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• Published: 1 December 2007

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• communication patterns
• computer mediated communication
• information dissemination
• scholarly communication

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Knowledge Sharing In Online Environments : A Qualitative Case Study

Journal of the American Society for Information Science and Technology (Print) . 2007, Vol 58, Num 14, pp 2310-2324, 15 p ; ref : 1 p.1/4

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• Published: 28 September 2023

Research on the development and innovation of online education based on digital knowledge sharing community

• Xi Huang 1 ,
• Hongwei Li 2 ,
• Lirong Huang 3 &
• Tao Jiang 4  

BMC Psychology volume  11 , Article number:  295 ( 2023 ) Cite this article

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Digital knowledge sharing (DKS) communities have emerged as a promising approach to support learning and innovation in online higher education. These communities facilitate the exchange of knowledge, resources, and ideas among educators, students, and experts, creating opportunities for collaboration, innovation, and lifelong learning. However, the impact and role of DKS communities in online education are not well understood, and further research is needed to explore their potential benefits and challenges.

This multi-objective qualitative study aims to investigate the impact and role of DKS communities in online higher education, identifying the factors that promote student success and the implications for the development of online education. The study collected data from 20 informants who have experienced teaching online during and after the pandemic. Data were collected through in-depth interviews and analyzed using thematic analysis. The informants were selected through theoretical sampling.

Methodology

To explore the impact and role of DKS communities in online higher education, this study employed a multi-objective qualitative research method. Data were collected through in-depth interviews conducted with 20 informants who possessed experience in teaching online during and after the pandemic. The informants were selected through theoretical sampling to ensure diverse perspectives and insights. The collected data were subsequently analyzed using thematic analysis, allowing for the identification of key themes and patterns.

The findings of this study provide valuable insights into the impact and role of DKS communities in online higher education. These insights encompass various aspects, including the benefits and challenges of DKS in online education, the factors that contribute to student success, and the implications for the ongoing development and innovation of online education.

Conclusions

In conclusion, this multi-objective qualitative study sheds light on the significance of DKS communities in online higher education. It underscores their potential to enhance collaboration, innovation, and lifelong learning. The findings also emphasize the importance of addressing challenges and fostering an inclusive and supportive online learning environment. These insights inform best practices and contribute to the continuous development and innovation of online education, particularly in the post-pandemic educational landscape.

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Introduction

Education for sustainable development is a vital aspect of achieving the Sustainable Development Goals proposed by the United Nations Economic and Social Development Organization (UNESCO) and adopted by institutions worldwide [ 1 ]. Education is seen as an essential means of creating awareness and promoting sustainable development by encouraging individuals to adopt environmentally friendly behaviors. The concept of Higher Education for Sustainable Development (HESD) has been widely discussed in recent years [ 2 ]. Higher education institutions, such as universities and technical training colleges, have gradually become essential platforms for promoting sustainable development in the 21st century [ 3 ].

Digitalization, on the other hand, has silently revolutionized the way humans live. Almost all fields of knowledge are benefiting from digitalization, including education, healthcare, business, and entertainment [ 4 ]. Digitalization has provided a more efficient way of knowledge sharing, allowing individuals to access and share information quickly and easily. This has enabled education to be more widespread and accessible to individuals worldwide, creating opportunities for people to improve their knowledge and skills [ 5 ]. Institutions of higher education have been at the forefront of digitalization, transforming the way instructors develop courses and disseminate research findings. Digital networks such as 5G are gradually rolling out worldwide, enabling faster and more reliable communication, which has transformed many industries, including the industrial sector. Universities and technical training colleges have played a significant role in advancing UNESCO’s Sustainable Development Goals, with many initiatives launched worldwide to promote its further development [ 1 ].

According to Elmassah et al. [ 4 ] higher education has traditionally been the primary platform for generating, developing, and promoting knowledge. In recent years, countries such as China, India, Thailand, Vietnam, Nigeria, and Kenya have successfully applied digitalization and used higher education to promote sustainable development [ 6 , 7 ]. Digitalization has been a powerful tool for sharing knowledge, as noted by Gregson et al. [ 8 ]. With the advent of the internet and higher technology, institutions of higher education can share knowledge generated by experts with new generations of learners [ 9 ]. As Funk [ 10 ] suggests, “sharing knowledge is power,” and digitalization provides an effective means of achieving this.

Digital technology is transforming how learners understand and interpret new knowledge, as well as impacting the motivation of academics to share their research findings. The construction industry [ 11 , 12 ] and the information technology field [ 12 ] are examples of how digitalization is changing the way people work. This is due to the increased flow of information made possible by digitalization, which enables better coordination between independent units and individuals. In higher education institutions, digitization has had a significant impact, with several initiatives launched worldwide to promote its further development [ 13 ]. Digital platforms provide new structures for better knowledge sharing and continuous innovation, as noted by Arfi et al. [ 14 ].

Technology can be used to create Digital Knowledge Sharing (DKS) communities (DKSCs). These communities enable collaboration and networking among learners and educators, facilitate personalized learning, incorporate gamification to enhance the learning experience, leverage artificial intelligence and machine learning to provide adaptive learning experiences, and require quality control to ensure the accuracy and reliability of learning resources [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Digital knowledge sharing in online education has several benefits, including accessibility, flexibility, cost-effectiveness, customization, and interactive learning [ 22 , 23 ]. This approach has the potential to transform traditional education by providing students with a flexible, accessible, and engaging learning experience.

Digitalization is portrayed as an omnipresent force that has revolutionized various sectors, including education [ 15 ]. The literature underscores the advantages of digital technology in facilitating knowledge sharing, making education more accessible, and fostering global connectivity. Nonetheless, it is crucial to acknowledge the digital divide that persists globally, wherein not all individuals have equitable access to digital resources and technologies. Therefore, the assertion that digitalization universally enhances accessibility should be tempered with an awareness of existing disparities [ 16 , 17 ].

Grounded in the belief that collaborative learning, dissemination of best practices, and technological innovation are central components of educational progress, this research seeks to explore how DKS communities act as catalysts for continual improvement in the digital education landscape. Drawing from theories of educational technology, social learning, and innovation diffusion, the study aims to elucidate the multifaceted ways in which these communities enhance the online learning experience, foster critical thinking, and promote a culture of lifelong education [ 17 ]. By investigating the interplay between technology, collaborative learning, and innovation within the context of online education, this study endeavors to contribute to the theoretical foundations underpinning the advancement of digital learning environments [ 17 ].

Furthermore, the literature review highlights the transformative potential of digital technology in higher education, particularly in terms of knowledge dissemination and collaboration among learners and educators. It suggests that digital platforms can facilitate continuous innovation and personalized learning experiences [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, the review does not thoroughly address the challenges and drawbacks of this digital transformation. Issues related to digital literacy, data privacy, and the quality of online education resources require careful consideration. Additionally, the potential homogenization of education through digitalization, where diverse perspectives may be marginalized, warrants scrutiny.

The concept of DKS communities is introduced as a means to harness the power of digital technology in education. These communities are presented as innovative solutions for collaboration, gamification, and adaptive learning. While the potential benefits of DKSCs are intriguing, the review does not offer a comprehensive examination of the effectiveness of such communities in practice. Are DKS communities accessible to all students, and do they effectively enhance learning outcomes? These questions remain unanswered.

In conclusion, the literature review provides a compelling narrative about the transformative potential of digitalization in higher education for sustainable development. However, it is important to approach this paradigm shift with critical scrutiny. Equitable access, digital literacy, quality assurance, and the preservation of diverse educational experiences are paramount considerations in the era of digital education. The promising fusion of education and digitalization should be tempered with a commitment to addressing the challenges that arise in this evolving landscape.

As we stand at the intersection of technology and education, it becomes increasingly evident that our ability to harness these innovations will have far-reaching implications for the future of learning. In this context, understanding the intricate relationship between cognitive control, relational aggression, and emerging learning technologies among sportsmen adults is not just academically valuable but also relevant to the broader discourse on how technology is shaping the educational landscape. By delving into this subject matter, we aim to contribute to the ongoing dialogue on educational innovation, shedding light on the ways in which emerging technologies impact cognitive processes and social dynamics within the context of sports education. Specifically, there is a need to understand the challenges and opportunities of using these communities to develop and innovate online education. This study aims to contribute to the growing body of research on the development and innovation of online education based on digital knowledge sharing. This research can inform educators and policymakers on how to best leverage DKS communities to enhance the quality and effectiveness of online education in the post-pandemic era. Therefore, this research aims to investigate the following research questions:

What are the benefits and challenges of digital knowledge sharing in online education?

What is the role of DKS communities in the development and innovation of online education?

How do DKS communities impact student learning and engagement in online education?

What factors contribute to the effectiveness of DKS communities in promoting student success?

Research method

Research design.

The research design for this study was a qualitative research method using a phenomenological approach. “Phenomenology is particularly useful in research studies that aim to explore subjective experiences and perceptions of participants. It allows the researcher to gain a deep understanding of the lived experiences of the participants, and to uncover the meaning and essence of those experiences [ 24 ]. The study aimed to explore the experiences of university professors in higher education in China regarding the role and contribution of DKS communities to online education. The phenomenological approach allowed the researcher to understand the subjective experiences and perceptions of the informants regarding the use of DKS communities in online education.

The informants for this study were 20 university professors of higher education in China who were selected through theoretical sampling. The selection criteria for the informants were that they have experience in teaching online courses and have used DKS communities in their teaching. The rationale for sample size was data saturation which occurred when the 20 th informant was interviewed. The informants were selected from Tsinghua University, Peking University, Fudan University, Nanjing University, and Wuhan University. The average age of the group was 30 years old with a standard deviation of 5 years, while the average number of years of teaching experience was 12 with a standard deviation of 5 years. The informants who participated in the study did so willingly and were aware of the purpose of the research. They were provided with information about the study and provided their informed consent before being interviewed. The confidentiality and privacy of the informants and the data collected were ensured throughout the research process. The informants were assured that their personal information and responses would be kept confidential and that their identities would not be revealed in any publications or reports resulting from the study. All data collected during the study was stored securely and only accessed by the research team. These measures were taken to ensure that the informants felt comfortable sharing their experiences and perceptions and to protect their privacy and confidentiality.

Instrumentation

The primary instrument for data collection in this study was semi-structured and focused interviews. The semi-structured interviews allowed the researcher to explore the informants’ experiences and perceptions regarding the use of digital knowledge-sharing communities in online education in a flexible and open-ended manner. The focus interviews allowed the researcher to probe more deeply into specific topics or issues related to the use of digital knowledge-sharing communities in online education (See Additional file 1 : Appendix A).

Data collection procedure

The informants who participated in the study were recruited through theoretical sampling, which involved identifying potential participants who met the selection criteria and inviting them to participate in the study. The selection criteria included having experience in teaching online courses and using digital knowledge-sharing communities in their teaching. The informants were selected from several universities, including Tsinghua, Peking, Fudan, Nanjing, and Wuhan. Once the informants were recruited, they were given information about the study and provided their informed consent before being interviewed. The interviews were conducted either face-to-face or online, depending on the availability and preference of the informants. For the face-to-face interviews, the researcher arranged to meet with the informants at their universities or other convenient locations. For the online interviews, the researcher used video conferencing applications such as Skype or Zoom.

The interviews were semi-structured and focused on the research questions, with the aim of exploring the experiences and perceptions of the informants regarding the role and contribution of digital knowledge-sharing communities to online education. The interviews were audio-recorded with the consent of the informants and later transcribed verbatim for analysis. In addition to the audio recordings, the researcher also took field notes during the interviews to capture non-verbal cues and contextual information.

Throughout the data collection process, the privacy and confidentiality of the informants and the data collected were ensured. The informants were assured that their personal information and responses would be kept confidential and that their identities would not be revealed in any publications or reports resulting from the study. All data collected during the study was stored securely and only accessed by the research team. These measures were taken to protect the privacy and confidentiality of the informants and to ensure that they felt comfortable sharing their experiences and perceptions.

Data analysis

The data were analyzed using MAXQDA software (version 2022), following the recommendation of Creswell [ 24 ]. The unit of analysis in this study was the sentence, and the researcher focused on manifest content rather than latent content. The qualitative data were collected, analyzed, and reported in English. An inductive approach to content analysis was employed, as no preexisting theory or framework guided the generation of codes, categories, and themes [ 25 ]. Sutton and Austin [ 26 ] proposed a five-step process for qualitative data analysis, which was followed in this study. Firstly, the data were cleaned by addressing linguistic errors, ambiguities, inaccuracies, and repetitions. Secondly, the researcher read the data multiple times and developed open codes. Thirdly, the open codes were categorized as relevant axial codes or subtopics. Fourthly, the axial codes and subtopics were grouped under higher-order selective codes and general themes. Finally, a detailed report was prepared to document the completed data analysis process and its interpretation. The frequency of generated codes, topics, and categories was reported, and the results were visually presented using the MAXMAP properties of MAXQDA, creating visual representations. To ensure the credibility of the analytical process, 20% of the generated codes were randomly selected and re-coded by a second coder with sufficient knowledge and experience in qualitative research. Specifically, 80 codes were created, and 20 of them were sent to the second coder. Upon coding, the second coder disagreed with the first coder on one code, resulting in an intercoder agreement coefficient of 96%. The two coders discussed and resolved any disagreements by making the necessary changes, ensuring the completion of the qualitative data analysis process.

Research quality

In order to uphold the integrity of the research conducted in this study, the researcher implemented various methodologies, such as member checking, peer debriefing, and reflexivity. Member checking is a method used to validate and authenticate data by consulting with the individuals who provided the information. This process aids in ensuring that the data accurately represents their experiences and perspectives. Peer debriefing is a process that entails soliciting feedback and input from fellow researchers or subject matter experts in order to enhance the credibility and reliability of the research findings. Reflexivity encompasses the critical examination of the researcher’s biases, assumptions, and values, and their potential impact on the research process and outcomes. This practice serves to enhance the study’s validity and reliability.

The findings of the study are presented based on the order of the research questions.

Research questions 1

The first research question aimed at exploring the benefits and challenges of digital knowledge sharing in online education. interviews with the informants were analyzed and 7 benefits and 10 main challenges were extracted. Below are explanations for each extracted theme using at least two citations, along with two quotations from informants for each theme:

Benefits of DKS communities

Detailed analysis of the interviews with the informants revealed that DKS communities have several benefits and challenges, each is explained and exemplified as follows.

Improved student engagement

Improved student engagement is a benefit of digital knowledge sharing in online education. DKS communities provide a platform for students to interact and collaborate with each other, which can improve their engagement and motivation to learn. According to one informant, “DKS communities help keep students engaged in the course material, which can lead to better learning outcomes” (Informant 1). Another informant stated, “When students are able to participate in online discussions and share their own ideas and perspectives, they become more invested in the learning process” (Informant 2).

Enhanced learning outcomes

Using DKS communities in online education can enhance learning outcomes by providing students with access to a wider range of resources and perspectives. According to one informant, “DKS communities can help students develop critical thinking skills by exposing them to diverse perspectives and encouraging them to challenge their own assumptions” (Informant 3). Another informant stated, “Using digital tools like online discussion forums and collaborative documents can help students engage more deeply with the material and apply what they’ve learned in new ways” (Informant 4).

Flexibility and accessibility

Flexibility and accessibility are benefits of digital knowledge sharing in online education. Online education allows for greater flexibility in terms of when and where learning takes place, which can be especially beneficial for students who have other commitments such as work or family responsibilities. According to one informant, “Online courses provide flexibility for students who might not be able to attend traditional in-person classes due to other commitments” (Informant 5). Another informant stated, “DKS communities make education more accessible to students who might not have access to traditional educational resources, which can help level the playing field for students from different backgrounds” (Informant 6).

Improved student outcomes

Digital knowledge sharing in online education can lead to improved student outcomes such as better grades, higher retention rates, and increased satisfaction with the learning experience. According to one informant, “Digital knowledge sharing can lead to better learning outcomes because students are able to access a wider range of resources and engage with different perspectives” (Informant 13). Another informant stated, “Online education can be especially beneficial for students who might struggle with traditional classroom settings, as it can provide a more personalized and flexible learning experience” (Informant 14).

Increased collaboration

DKS communities can facilitate increased collaboration among students and instructors. According to one informant, “Online discussion forums and collaborative documents can allow students to work together on projects and assignments, which can enhance their understanding of the material and improve their communication skills” (Informant 15). Another informant stated, “Digital knowledge sharing can create a sense of community among students who might not have had the opportunity to interact with each other otherwise” (Informant 16).

Personalization of learning:

Digital knowledge sharing in online education can enable a more personalized learning experience for students. According to one informant, “Online courses can allow students to work at their own pace and focus on the areas where they need the most help, which can lead to better learning outcomes” (Informant 11). Another informant stated, “Digital knowledge sharing can allow instructors to provide more targeted feedback to individual students, which can help them improve their understanding of the material” (Informant 18).

Greater feedback and assessment opportunities:

DKS communities can provide greater opportunities for feedback and assessment. According to one informant, “Online quizzes and assessments can provide immediate feedback to students, which can help them identify areas where they need to improve” (Informant 19). Another informant stated, “Digital knowledge sharing can allow instructors to provide more frequent and detailed feedback to students, which can help them stay on track and improve their performance” (Informant 20). Despite the benefits of the DKS communities, the informants mentioned some challenges, which are exemplified and explained as follows.

Challenges of DKS communities

Informant have stated some challenges of DKS communities, which are explained and exemplified as follows.

Technical difficulties

Technical difficulties are a challenge of digital knowledge sharing in online education. Students and instructors may encounter technical difficulties such as internet connectivity issues or software glitches, which can disrupt the learning process and create frustration for students and instructors alike. According to one informant, “Technical difficulties can be a major barrier to effective online learning, and can lead to students feeling frustrated and disengaged from the course material” (Informant 7). Another informant stated, “Instructors need to be prepared to troubleshoot technical issues and provide support to students who are experiencing difficulties with the digital tools” (Informant 8).

Maintaining academic integrity

Maintaining academic integrity is a challenge of digital knowledge sharing in online education. DKS communities can create challenges around maintaining academic integrity, as students may be tempted to plagiarize or share answers with each other. According to one informant, “Ensuring academic integrity in DKS communities requires a concerted effort from instructors and students to communicate expectations and uphold ethical standards” (Informant 9). Another informant stated, “Instructors need to be vigilant about monitoring student behavior in DKS communities to ensure that academic dishonesty is not taking place” (Informant 10).

Digital divide

The digital divide is a challenge of digital knowledge sharing in online education. Not all students have equal access to technology and internet connectivity, which can create a digital divide in online education. According to one informant, “The digital divide can exacerbate existing inequalities in education and limit opportunities for students who lack access to the necessary technology and resources” (Informant 11). Another informant stated, “Institutions need to be mindful of the digital divide and take steps to ensure that all students have access to the technology and resources necessary to participate in online learning” (Informant 12).

Social isolation and lack of interaction

DKS communities can create a sense of social isolation and limit opportunities for interaction among students and instructors. According to one informant, “Online education can be a lonely experience for students who are used to traditional classroom settings, as they may miss out on the social interactions that are a key part of the learning experience” (Informant 17). Another informant stated, “Instructors need to be intentional about creating opportunities for interaction and collaboration among students in online courses” (Informant 20).

Time management and self-discipline

Digital knowledge sharing in online education can require strong time management and self-discipline skills, which can be challenging for some students. According to one informant, “Online courses require a high degree of self-discipline and time management skills, as students are often responsible for setting their own schedules and managing their own learning” (Informant 10). Another informant stated, “Instructors can help students develop these skills by providing clear expectations and deadlines, and by encouraging them to set goals and prioritize their workload” (Informant 4).

Limited access to hands-on learning experiences:

Digital knowledge sharing in online education can limit opportunities for hands-on learning experiences, which can be a challenge for students in certain fields of study. According to one informant, “Online education may not be suitable for certain fields such as science and engineering, where hands-on learning experiences are an important part of the curriculum” (Informant 3). Another informant stated, “Instructors need to be creative in finding ways to provide hands-on learning experiences in online courses, such as through virtual simulations or online labs” (Informant 14).

Quality control

The challenge of DKS communities is ensuring the quality and accuracy of the content being shared. With so much information available, it can be difficult to sift through it all and ensure that learners are accessing high-quality and reliable resources.” (Informant 14). Similarly, informant 18 has stated that, “in the era of DKS Communities, the abundance of information poses a formidable challenge: the assurance of content quality and reliability. Amidst the vast digital landscape, the task of curating high-quality resources becomes imperative to safeguard the learning journey.” (Informant 11).

Intellectual property

The issue of intellectual property is a complex one in the context of digital knowledge-sharing communities. It is important to ensure that content is properly attributed and that copyright laws are respected, but this can be difficult to enforce in online environments. (Informant 16). In addition, informant 15 stated, “In the realm of DKS communities, the intricacies of intellectual property come to the forefront. Balancing the imperative of proper attribution and the adherence to copyright laws with the challenges of enforcement in the vast online domain presents a multifaceted dilemma.”

Cultural differences

Cultural differences can present a challenge for digital knowledge-sharing communities, particularly when it comes to language barriers. It is important to ensure that these communities are inclusive of all cultures and languages and that learners have access to resources that are culturally relevant to them. This finding is in line with quotations from informant 9 who stated, “cultural diversity emerges as a compelling challenge, often manifesting through the formidable barriers of language. The imperative lies in fostering inclusive platforms that transcend cultural boundaries, granting learners access to resources imbued with cultural relevance.” Informant 5 also stated, “the harmonious coexistence of diverse cultures within digital knowledge-sharing communities highlights the importance of dismantling language barriers. Ensuring inclusivity through culturally relevant resources stands as an essential endeavor to bridge the gap in global education.”

Research question 2

The second research question addressed the role of DKS communities in the development and innovation of online education. Interviews with the informants were analyzed and 7 themes were extracted, which are explained and exemplified as follows.

Facilitation of collaboration and innovation

DKS communities can facilitate collaboration and knowledge exchange among students and instructors, which can lead to the development of innovative approaches to teaching and learning in online education. According to one informant, “DKS communities can help to create a culture of collaboration and experimentation, where ideas can be shared and refined in real-time” (Informant 1). Another informant stated, “Collaboration is a key component of online education, and DKS communities can provide a platform for students and instructors to work together on projects and assignments, which can lead to the development of innovative solutions” (Informant 2).

Encouragement of innovation

DKS communities can encourage innovation in online education by providing a space for experimentation and exploration of new teaching methods and technologies. According to one informant, “DKS communities can encourage instructors to experiment with new technologies and teaching methods, which can lead to the development of more engaging and effective online courses” (Informant 3). Another informant stated, “Innovation in online education can lead to improved learning outcomes and greater student engagement, and DKS communities can play a key role in supporting this innovation” (Informant 4).

Dissemination of best practices

DKS communities can serve as a platform for disseminating knowledge about best practices and successful approaches to online education. According to one informant, “Sharing knowledge and experiences through digital platforms can help to build a community of practice around online education, and can lead to the development of new insights and approaches” (Informant 5). Another informant stated, “DKS communities can provide a way for instructors to learn from each other and to stay up-to-date on the latest trends and developments in online education” (Informant 6).

Support for lifelong learning

DKS communities can play a role in supporting lifelong learning by providing access to a wide range of learning resources and opportunities. According to one informant, “DKS communities can provide a platform for individuals to continue learning throughout their lives and can help to bridge the gap between formal education and informal learning” (Informant 9).

Enhancement of critical thinking

DKS communities can enhance critical thinking skills by exposing students to diverse perspectives and encouraging them to engage in discussions and debates. According to one informant, “DKS communities can help to develop critical thinking skills by exposing students to a variety of viewpoints and challenging them to think deeply about complex issues” (Informant 10).

Promotion of student agency

DKS communities can promote student agency by giving them more control over their learning and encouraging them to take an active role in shaping their educational experiences. According to one informant, “DKS communities can give students a sense of ownership over their learning, and can help to foster a sense of autonomy and independence” (Informant 14).

Development of digital literacy

DKS communities can help to develop digital literacy skills by providing opportunities for students to engage with digital tools and platforms. According to one informant, “DKS communities can help to develop digital literacy skills by giving students the opportunity to interact with a variety of digital tools and platforms, and by encouraging them to experiment and explore” (Informant 18).

Research question 3

The third research question aimed at exploring how DKS communities impact student learning and engagement in online education. Findings revealed that digital knowledge sharing (DKS) communities can have a significant impact on student learning and engagement in online education. Here are some potential ways that DKS communities can impact student learning and engagement:

Increased access to resources

DKS communities can provide students with access to a wide range of learning resources, including articles, videos, podcasts, and other multimedia. This can help to increase the diversity of perspectives and ideas that students are exposed to, enhancing the quality and effectiveness of their learning. For instance, informant 7 stated, “Being part of the DKS community has given me access to a wide range of resources that I wouldn’t have found on my own. It has helped me to deepen my understanding of the course material and to see things from different perspectives.”

Peer support and collaboration

DKS communities can provide opportunities for peer support and collaboration, which can help to promote student engagement and motivation. Students can ask questions, share insights, and work together on projects and assignments, fostering a sense of community and connection. This finding can be supported by a quotation from informant 2 who stated, “The DKS community has been a great way to meet new people and to work together on projects. It’s helped me to feel more connected to the course and to stay motivated.”

Diverse perspectives

DKS communities can expose students to diverse perspectives and ideas, which can help to broaden their understanding and deepen their critical thinking skills. Students can engage in discussions and debates with others who have different backgrounds and experiences, leading to a more well-rounded learning experience. The finding is supported by quotation from informant 7, who stated, “The DKS community has exposed me to a wide range of perspectives and ideas that I never would have encountered otherwise. It’s helped me to broaden my understanding of the course material and to think more critically about the issues.”

Active learning

DKS communities can promote active learning by providing opportunities for students to engage with course material in meaningful ways. Students can apply their learning to real-world scenarios, participate in simulations or case studies, and engage in hands-on activities that promote deeper understanding of course concepts. As an example, informant 5 stated, “The DKS community has been a great way to engage with the course material in a more meaningful way. It’s helped me to stay motivated and to feel like I’m making progress.”

Flexibility and personalization

DKS communities can provide flexibility and personalization in the online learning experience, giving students more control over their learning and allowing them to tailor their experience to their individual needs and preferences. This can help to increase engagement and motivation, as well as promote a sense of ownership and responsibility for learning. As an example, informant 9 stated, “The DKS community has been a great way to personalize my learning experience. I’ve been able to explore topics that interest me and to find resources that are relevant to my goals.”

Research question 4

Research question 4 aimed at exploring the factors which contribute to the effectiveness of DKS communities in promoting student success. The theme analysis of the interviews with the informants showed 5 factors contribute to the effectiveness of DKS communities, each is explained as follows:

Active participation by students:

The data analysis revealed that active participation by students is a key factor that contributes to the effectiveness of DKS communities in promoting student success. Students who actively participate in these communities tend to have a better understanding of the course material and are more engaged in their learning. For instance, informant 8 argued, “Students who participate in the community are more likely to have a better understanding of the course material and are more engaged in their learning.”

Clear expectations and guidelines

The analysis also found that establishing clear expectations and guidelines for DKS community participation can help students understand what is expected of them and how they can contribute to the community. This can lead to greater engagement and participation by students, which in turn can contribute to their success. One of the informants stated. “I give clear guidelines and expectations to my students, and I encourage them to interact with each other and share their knowledge.”

Supportive and collaborative learning environment:

The findings suggest that a supportive and collaborative learning environment is another factor that contributes to the effectiveness of DKS communities in promoting student success. Students who feel supported and encouraged by their peers and instructors in these communities tend to be more motivated and engaged in their learning. For instance, informant 5 stated, “The DKS community is a place where students can learn from each other, share their experiences, and support each other.”

Relevance and usefulness of the DKS community

The analysis also revealed that the relevance and usefulness of the DKS community is important in promoting student success. Students are more likely to participate in these communities when they see the relevance and usefulness of the community in relation to their course goals and learning outcomes. To exemplify the theme, the following quotation is used, “The DKS community is a valuable resource for students to ask questions, share knowledge, and learn from each other.” (Informant 3).

Flexibility and adaptability of the DKS community:

Finally, the data analysis showed that the flexibility and adaptability of the DKS community is another important factor in promoting student success. DKS communities that are flexible and adaptable to the changing needs and preferences of students tend to be more effective in promoting student success. For instance, informant 19 stated, “I try to be flexible and adapt the community to the changing needs and preferences of my students.”

This multi-objective qualitative study at exploring the impact and role of DKS communities in innovation in online higher education. A qualitative research method was used and the interviews with 20 informants were analyzed. With regard to the first objective, the informants mentioned 7 benefits and 9 challenges of using DKS communities in the development and innovation of online education based on a digital knowledge-sharing community. The findings highlight the potential benefits and challenges of using DKS communities for developing and innovating online education, as well as the ways in which these communities can contribute to higher education sustainability. Recent research has supported these findings and provided further insights into the benefits and challenges of using DKS communities in education. For example, a study by Ansari, et al. [ 27 ] found that DKS communities can enhance collaborative learning and knowledge-sharing among students, leading to better learning outcomes. Similarly, a study by Cheng, et al., [ 28 ] found that DKS communities can improve teacher collaboration and professional development, leading to more effective teaching practices.

The findings related to the challenges are also consistent with the findings of some researchers. For example, a study by Nugroho, et al. [ 29 ] found that quality control remains a challenge for digital knowledge-sharing communities, with a need for better tools and strategies for evaluating the quality of shared resources. Additionally, a study by Zhao, et al., [ 30 ] found that the digital divide remains a major barrier to accessing digital knowledge-sharing communities, particularly for learners in rural areas or with limited access to technology. To address these challenges, recent research has proposed various solutions and strategies. For example, Similarly, a study by Thi Minh Ly, et al. [ 31 ] proposed a model for bridging the digital divide, which involves providing learners with access to digital infrastructure and training in digital literacy skills. Similarly, a study by Shawar et al. [ 32 ] proposed a framework for quality assurance in digital knowledge-sharing communities, which includes guidelines for content evaluation and quality control.

With regard to the second objective, the analysis of the interviews with informants in this study revealed seven themes related to the role of DKS communities in the development and innovation of online education. These themes include facilitation of collaboration and innovation, encouragement of innovation, dissemination of best practices, support for lifelong learning, enhancement of critical thinking, promotion of student agency, and development of digital literacy. The findings of this study are consistent with previous studies that have examined the role of DKS communities in the development and innovation of online education. For example, Dabbagh and Kitsantas [ 33 ] found that personal learning environments, which are similar to digital knowledge-sharing communities, can facilitate collaboration and knowledge exchange among learners and can enhance critical thinking and digital literacy skills. Similarly, Barab, et al. [ 34 ] found that online communities of practice can support professional development and knowledge sharing among educators.

The finding that DKS communities can encourage innovation is also consistent with previous studies. Siemens and Tittenberger [ 35 ] argued that emerging technologies, such as digital knowledge-sharing communities, can support innovation in education by providing a platform for experimentation and exploration of new teaching methods and technologies. Moreover, the finding that DKS communities can disseminate knowledge about best practices and successful approaches to online education is also consistent with previous studies. Palloff and Pratt [ 21 ] argued that online communities of practice can serve as a platform for sharing knowledge and experiences, leading to the development of new insights and approaches. Similarly, Garrison, et al. [ 36 ] found that computer conferencing can support knowledge sharing and dissemination among learners and educators. Furthermore, the finding that DKS communities can support lifelong learning is also consistent with previous studies. Warschauer and Matuchniak [ 37 ] argued that digital technologies can support lifelong learning by providing access to a wide range of learning resources and opportunities.

In addition, that DKS communities can enhance critical thinking skills is also consistent with previous studies. Hrastinski [ 38 ] argued that online learning can support critical thinking skills by providing opportunities for collaborative learning and discussion among learners. Similarly, the findings are consistent with Wang, et al., [ 39 ] found that social media, which are similar to digital knowledge-sharing communities, can promote student agency by giving students more control over their learning and encouraging them to take an active role in shaping their educational experiences. Finally, the finding that DKS communities can develop digital literacy skills is also aligned with previous studies. Siemans and Tittenberger [ 35 ] and Garrison, et al. [ 36 ] argued that emerging technologies, such as digital knowledge-sharing communities, can support the development of digital literacy skills by providing opportunities for learners to engage with digital tools and platforms.

The third objective was to delve into the impact of DKS on students learning in higher education. The findings of this study are consistent with previous studies that have examined the impact of DKS communities on student learning and engagement in online education. The first potential impact, increased access to resources, is supported by the findings of Palloff andPratt [ 21 ] and Garrison, et al. [ 36 ] who argued that digital technologies can support lifelong learning by providing access to a wide range of learning resources and opportunities. The second potential impact, peer support, and collaboration is supported by the findings of Warschauer, and Matuchniak [ 37 ] who found that online communities of practice can support professional development and knowledge sharing among educators. The third potential impact, diverse perspectives, is supported by the findings of Hrastinski [ 38 ] who argued that online learning can support critical thinking skills by providing opportunities for collaborative learning and discussion among learners. The fourth potential impact, active learning, is supported by the findings of Dabbagh and Kitsantas [ 33 ] who found that personal learning environments can enhance critical thinking and digital literacy skills. The fifth potential impact, flexibility, and personalization, is supported by the findings of Wang, et al., [ 39 ] who found that social media can promote student agency by giving students more control over their learning and encouraging them to take an active role in shaping their educational experiences.

With regard to the last question findings, it can be argued that the findings of this study are consistent with previous research on the importance of student participation in online learning communities. A study by Warschauer and Matuchniak [ 37 ] found that students who actively participated in online learning communities had higher levels of engagement and were more likely to succeed in their courses. Similarly, a study by Rovai and Jordan [ 40 ] found that students who were highly involved in online learning communities had higher levels of satisfaction and academic success.

The next finding was the importance of clear expectations and guidelines for online learning communities which has also been identified in previous research. For example, Richardson [ 41 ] found that providing clear guidelines and expectations for online learning communities can help students feel more comfortable and engaged in the learning process. Additionally, Palloff and Pratt [ 21 ] found that clear guidelines and expectations can help students understand how to participate in online learning communities and contribute to their success.

The significance of a supportive and collaborative learning environment has also been supported by previous research. A study by Garrison, et al., [ 36 ] found that students who felt supported and encouraged by their peers and instructors in online learning communities were more likely to be engaged and successful in their courses. Additionally, a study by Shea et al., [ 42 ] found that a sense of community and support was a key factor in promoting student success in online learning environments. Moreover, the relevance and usefulness of online learning communities have also been identified as an important factor in promoting student success. Similarly, a study by Ertl [ 43 ] found that students who perceived online learning communities as valuable were more likely to engage in collaborative learning and be successful in their courses. Finally, the importance of flexibility and adaptability in online learning communities has also been supported by previous research. A study by Shea, et al., [ 42 ] found that flexibility and adaptability were important factors in promoting student success in online learning environments. Additionally, a study by Garrison, et al., [ 36 ] found that online learning communities that were flexible and adaptable to the needs and preferences of students were more effective in promoting student success. Finally, Swan and Shih [ 44 ] found that students who perceived online learning communities as relevant and useful were more likely to participate and be successful in their courses.

In conclusion, this multi-objective qualitative study has shed light on the impact and role of digital knowledge-sharing (DKS) communities in the development and innovation of online higher education. The study identified several benefits and challenges of using DKS communities in online education, as well as the ways in which these communities can contribute to higher education sustainability. The findings also revealed the role of DKS communities in facilitating collaboration and innovation, encouraging innovation, disseminating best practices, supporting lifelong learning, enhancing critical thinking, promoting student agency, and developing digital literacy. Moreover, the study highlighted the potential impact of DKS communities on student learning, including increased access to resources, peer support and collaboration, diverse perspectives, active learning, flexibility, and personalization. The study also identified important factors that promote student success in online learning communities, such as clear expectations and guidelines, supportive and collaborative learning environments, relevance and usefulness, and flexibility and adaptability. These findings have important implications for the development of online education and the use of DKS communities in higher education.

Despite the valuable insights provided by this multi-objective qualitative study, there are limitations that must be considered. First, the sample size of 20 informants may not be representative of the larger population, limiting the generalizability of the findings. Further studies with larger sample sizes are needed to confirm the results and provide more comprehensive insights into the impact and role of DKS communities in online higher education. Second, the use of a qualitative research approach may introduce researcher bias and limit the generalizability of the findings. Combining qualitative and quantitative methods could provide a more comprehensive understanding of the impact of DKS communities on online education. Finally, the study was conducted in a specific context, and the findings may not be applicable to other contexts. Future studies should consider contextual factors such as cultural differences and institutional policies to provide a more comprehensive understanding of the impact of DKS communities on online education. To address these limitations, future studies could employ quantitative research designs to provide more objective and generalizable results. Additionally, longitudinal studies could investigate the long-term impact of DKS communities on online education, providing insights into the sustainability of these communities. Comparative studies could also be conducted to compare the impact of DKS communities with other models of online education, identifying the strengths and weaknesses of DKS communities and providing insights into how to optimize their impact on online education. By addressing these limitations and exploring these suggestions, future studies can further advance our understanding of the impact and role of DKS communities in online higher education, informing best practices and contributing to the ongoing development and innovation of online education.

Implications

First and foremost, it underscores the transformative potential of DKS communities in enhancing online education through the facilitation of collaboration, dissemination of best practices, and encouragement of innovation. These communities are poised to act as powerful catalysts for the continuous improvement of digital education. Secondly, the study places a spotlight on the paramount importance of addressing challenges such as quality control and bridging the digital divide to safeguard the long-term sustainability of DKS communities. Effective proactive strategies and purpose-built tools are imperative to unlock their full potential. Thirdly, DKS communities emerge as champions of lifelong learning, expanding horizons by broadening access to a wealth of diverse resources. Institutions stand to harness these communities to cater to a more extensive demographic of learners, thereby nurturing a culture of lifelong education. Fourthly, within the realm of online education, DKS communities assume a pivotal role in elevating critical thinking skills among students while fostering a sense of empowerment. The creation of environments that stimulate critical thought becomes indispensable in this context. Fifthly, the study underscores the necessity of establishing clear guidelines and expectations within DKS communities, serving as the linchpin for maximizing student engagement and success. The provision of structured and meticulously defined online learning environments emerges as the cornerstone of this endeavor.

Availability of data and materials

The data would be available upon request from the corresponding author (email:[email protected]).

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Xi Huang drafted the manuscript. Tao Jiang approved the draft. Xi Huang and Hongwei Li collected data and completed the draft. Xi Huang, Hongwei Li, Lirong Huang, and Tao Jiang read the manuscript and verified the content and findings.

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Huang, X., Li, H., Huang, L. et al. Research on the development and innovation of online education based on digital knowledge sharing community. BMC Psychol 11 , 295 (2023). https://doi.org/10.1186/s40359-023-01337-6

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VINE Journal of Information and Knowledge Management Systems

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Issue publication date: 7 March 2023

The purpose of this study is to look at the attitudes of the employees in terms of sharing knowledge during COVID-19 in an online environment and the various difficulties associated and to analyze knowledge sharing (KS) in a virtual office setting, using the conservation of resources theory.

Design/methodology/approach

A qualitative approach was used by conducting face-to-face interviews online through GoogleMeet, Skype and Zoom. A total of 34 interviews from 14 multinational companies (or their subsidiaries), in a supervisory role, were conducted for the study. A thematic analysis was conducted to analyze the responses.

During a crisis, the tendency of employees to share knowledge at the individual, team and organizational level increases and is interlinked. The results of this study suggest that during the initial phases of lockdown, the creativity levels among employees were high; however, as the work from the office got postponed because of extended lockdowns, the creativity level of employees saw a dip. Furthermore, the findings of this study also highlighted that KS in remotely located teams was found to be dependent on the extent to which the team members knew each other, such that known teams were in a better position to share knowledge than a newly formed team with unknown or less known members.

Research limitations/implications

This study has 34 respondents which is an acceptable number for a qualitative inquiry. However, the number of industries could be increased for generalization purposes. Responses were collected from a group of knowledge workers who were willing to correspond digitally, using social media channels of the authors, such as Linkedin. Responses collected personally could provide different results.

Practical implications

This study provides insights into visible change in organizational processes. The conceptual model developed in this study has several implications which will help chief knowledge officers to understand why the various individual, team and organizational factors lead to KS, particularly with respect to COVID-19.

Originality/value

This study has explored a contemporary phenomenon – KS during the ongoing COVID-19 pandemic, in an online environment. This study depicts the extant literature on knowledge management during a pandemic, thus bridging the scholarly gap. This study tried to bring in a broader perspective by selecting respondents across continents, domains and varied age groups. Fourth, most studies analyzing KS/knowledge hiding in the extant literature, especially during the pandemic, have followed a quantitative approach. This study followed a qualitative approach to gain insights into the KS of the firm and the thoughts and practicalities behind it.

• Knowledge sharing
• Conservation of resources (COR) theory
• Qualitative approach
• Thematic analysis
• Virtual environment

Pradhan, S. , Bashir, M. and Singh, S. (2023), "The impact of a pandemic on knowledge sharing behavior: a COR perspective", VINE Journal of Information and Knowledge Management Systems , Vol. 53 No. 2, pp. 271-291. https://doi.org/10.1108/VJIKMS-02-2022-0064

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• Published: 16 October 2023

Knowledge sharing and innovation performance: a case study on the impact of organizational culture, structural capital, human resource management practices, and relational capital of real estate agents

• Chung-Chang Lee   ORCID: orcid.org/0000-0001-9984-2002 1 ,
• Wen-Chih Yeh 2 ,
• Zheng Yu 3 &
• Yuan-Chen Luo 1  

Humanities and Social Sciences Communications volume  10 , Article number:  707 ( 2023 ) Cite this article

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• Business and management
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This study focused on the factors that influence innovation performance in housing agents. Based on a worldwide literature review on the topic of innovation performance, we defined relational capital, knowledge sharing at the individual level, and organizational culture, structural capital, and human resource management practices at the organizational level to carry out the analysis using hierarchical linear modeling. The survey subjects were housing agents in Kaohsiung City, Taiwan. A total of 1130 questionnaires were distributed to 113 agencies. Of a total of 444 collected surveys, 40 unanswered questionnaires were invalid and three with fewer than three answers were eliminated. The final number of valid questionnaires was 401. The response rate of effective questionnaires was 35.49%. The results show that organizational culture can indirectly affect innovation performance through knowledge sharing, indicating that there is a partial mediating effect. Structural capital can indirectly affect innovation performance through knowledge sharing, demonstrating a complete mediating effect. Relational capital can indirectly affect innovation performance through knowledge sharing, having a partial mediating effect. Human resource management practices did not have a confounding effect on innovation performance.

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Michael Yao-Ping Peng, Li Zhang, … Yuan He

Introduction

Innovation performance reflects an organization’s ability to transform innovation inputs into outputs and to acquire achievements and outcomes through the innovation process. Wang and Lee ( 2018 ) regarded innovation strategies as applying innovation to maximize an enterprise’s value. West and Anderson ( 1996 ) pointed out that innovation is crucial to social or organizational development and advancement. Innovation performance includes employee growth, team cohesion, effective internal communication, and continuous improvements in other related performances. In the technology sector, Ma et al. ( 2023 ) showed that innovation performance can be better generated when companies proactively accept external information and engage in intra-organizational knowledge transfer with the acquired information. Taking a service-oriented approach, Yiu et al. ( 2020 ) suggested that innovation performance can be enhanced through mutual learning, in which knowledge sharing and transfer occur between partners within an organization.

These arguments highlight the importance of innovation performance in various industries.

Most of the research on innovation performance in the real estate agency industry is centered on the financial and service aspects (Hameed et al., 2021 ; Rajapathirana and Hui, 2018 ). The financial aspect is measured through various factors, including performance-based bonuses, business performance, the number of transactions, and organizational financial status (see Yu and Liu, 2004 ; Lee and You, 2007 ; Meslec et al., 2020 ). Real estate agents aim toward achieving strong individual performances for the sake of their own bonuses (Mallik and Harker, 2004 ; Bradler et al., 2019 ; Manzoor et al., 2021 ; Lee et al., 2023 ). The service aspect is measured through factors such as research satisfaction and service quality (see Wu, 1999 ; Wang, 2004 ; Ullah and Sepasgozar, 2019 ; Yeh et al., 2020 ).

In the real estate agency industry, direct sales business operators may adopt the competition strategy of branch cooperation, as the internal systems and organization inside a branch are closely associated. Organizational culture includes the values, forms, and traditions conveyed by the organization to all its members (Ouchi, 1981 ). Narver and Slater ( 1990 ) suggested that organizational culture can be measured through the three components of market orientation: customer orientation, competitor orientation, and interdepartmental coordination. Lee and Sheng ( 2022 ) suggested that shared beliefs, expectations, values, norms, and routine tasks influence the relationship and methods of cooperation between organizational members, effectively creating different organizational values. Since the real estate agency industry is a part of the service industry, having a customer orientation is conducive to improving the quality of interactions between employees and customers (Bitner et al., 1994 ; Bowen and Schneider, 1985 ). Interdepartmental coordination is also associated with the human resources within the organization. Competition among enterprises has intensified in response to globalization, which highlights the importance of human resource management. In this 21st-century global economy driven by services, knowledge, technology, innovation, and globalization, human resource management (HRM) remains among the main models of competition management in local and foreign companies as well as emerging or established markets (Thite, 2015 ). Dessler ( 2000 ) stressed the importance of HRM for an enterprise. The functions of HRM include talent recruitment and selection, promotion and allocation, training and development, remuneration and benefits, labor relations, employment security, and labor safety. Well-executed HRM practices allow employees to improve organizational cohesion, teamwork, and organizational climate through self-directed work teams, inter-team cooperation, decision-making authorization, trustworthy organizational communications, and flexible management (Evans and Davis, 2005 ). To accurately and effectively allocate and leverage human resources, the real estate agency industry must rely on productive HRM practices (Wu, 2007 ). Therefore, adopting suitable HRM practices effectively improves elements such as employee training and development procedures (periodically arranging suitable internal and external training programs), performance evaluation (firm-specific evaluation criteria), and compensation and benefit packages (merit pay). Consequently, employees effectively enhance their innovative behavior at the individual and organizational levels through knowledge sharing, thus reducing their turnover (Hsu et al., 2021 ).

This study seeks to examine whether HRM evokes employees’ enthusiasm toward their jobs through motivational human resource activities such as human resource planning, training and development, remuneration and benefits, and employee relations, thereby increasing knowledge sharing and innovation performance within the organization. Additionally, it investigates whether the value generated from intangible intra-organizational assets and knowledge is key to an enterprise’s success. Such intangible assets and knowledge-creation mechanisms are collectively known as structural capital (Chen, 2007 ). Excellent structural capital not only improves an organization’s value but is also conducive to its development and enables it to gain continuous competitive advantages that can be converted into higher performances (Bontis et al., 2000 ; De Pablos, 2004 ). Structural capital includes an organization’s internal creativity and encompasses the development of new products, trade secrets, patents, and so on, which are also referred to as innovation capital. An organization’s internal infrastructure, including its management fad, corporate culture, management procedures, and information systems, is also known as process capital and represents major assets of structural capital (Saswat, 2018 ).

Moreover, real estate agent services are closely associated with the people involved. Of the personal relationship factors, relational capital is among the most important traits that real estate agents should possess. There are four types of intellectual capital: human capital, innovation capital, organizational capital, and relational capital (Tseng and Goo, 2005 ). In particular, relational capital refers to relationships that involve interpersonal trust and mutual identification (Cabrera and Cabrera, 2005 ). Numerous researchers have emphasized the important role of relational capital-associated factors in an organization’s business performance (Kogut and Zander, 1996 ; Uzzi, 1996 ; Saswat, 2018 ). Employees’ willingness to share knowledge is also based on the beliefs and behaviors associated with strong social interactions (Kim et al., 2013 ). Knowledge sharing is the mutual learning and understanding promoted through interaction and conversation between people (Lin and Wang, 2005 ). Knowledge sharing is crucial for externalizing individual knowledge within the organization, ensuring that employees who require such knowledge can effectively execute their work tasks. In other words, knowledge sharing is the transmission of knowledge to others anywhere, anytime (Wu and Lin, 2007 ). Several barriers also exist to knowledge sharing and organizational innovation. Firstly, an employee who feels that knowledge sharing is tedious and time-consuming may choose to hide knowledge so that they can save time and focus on their own tasks. Next, some knowledge is essentially confidential or sensitive information (such as personal connections or the knowledge to perform a task), and an employee can retain their competitive advantage by hiding this knowledge. Lastly, employees may worry that others will be skeptical of or criticize them for the knowledge that they share (Chen and Chen, 2022 ). Based on these arguments, we posit that individual relational capital is a key factor that affects personal knowledge sharing and innovation performance.

This study focused on organizational culture among real estate agents. Organizational culture can be described as a meaningful system with complex and profound effects. The complex and profound effects of organizational culture are mainly displayed through morals and values (Ke and Wei, 2008 ). Organizational culture is the sharing of values or beliefs to regulate the behaviors of organizational members (Geiger, 2017 ). For an enterprise or organization, human resources are an important internal resource while structural capital is a key internal element that creates value (Robbins, 2006 ). Previously, few studies within Taiwan and abroad have investigated the organizational level variables of organizational culture, structural capital, and HRM practices collectively. Unlike previous studies, this study examined the influence of these variables on knowledge sharing and innovation performance. In essence, relational capital, as part of intellectual capital, emphasizes connections with the external environment (Bontis, 1999 ). In this study, we categorized relational capital, knowledge sharing, and innovation performance as individual-level variables and collectively examined them alongside the aforementioned organizational-level variables. The goal was to investigate whether these two levels of variables positively affect innovation performance. Additionally, we considered HRM practices as a confounding variable that affects the influence of knowledge sharing on innovation performance.

Real estate agencies in Taiwan are commonplace. Countless real estate agency branches can be found nationwide, where many frontline real estate agents carve out their careers. In the past, the real estate industry in Taiwan was notorious for its sales tactics that often resulted in disputes. After the promulgation of the Real Estate Broking Management Act in 1999, the industry became professionally institutionalized. Due to the impacts of economic downturns, real estate agencies in recent years have turned to marketing their own brands. The industry adopted an atypical compensation scheme, based on commission. The industry is also known for its long work hours, challenging tasks, and high turnover. Thus, the means to enhance its innovation performance has gained much interest in academia and industry, most of which is directed at the organization’s internal business and management models. The influences of organizational culture, structural capital, HRM practices, relational capital, and knowledge sharing on innovation performance can shed light on the intra-organizational modes of operation of a real estate company, thus enabling research on and evaluation of the innovation performance of different industries.

Literature review and research hypotheses

Thanks to technological advancements, millennials (the demographic cohort currently aged 29 to 35 years) prefer to acquire consumer-related information from online platforms and visit physical stores after receiving marketing information online (Chang et al., 2023 ). As the popularity of artificial intelligence (AI) and the platform economy grows, the real estate agency industry has developed its own strategy, called property technology (PropTech), in response to technological advancements. PropTech refers to the consolidation of technology and real estate, whereby various emerging information and communications technologies are introduced into various fields of the real estate industry, enhancing the business efficiency of the overall industry and opening up new opportunities for innovative developments (Kuo, 2022 ). Lin ( 2021 ) identified several impacts of PropTech on the real estate agency industry: 1. Enhancing the efficiency of real estate transactions by increasing the convenience of acquiring information by sellers and buyers; 2. Providing new information rapidly and promoting transactions, such as generating empathetic responses through virtual reality settings in online platforms; 3. Unbundling real estate agents’ work tasks, in which traditional full-service tasks are split into several smaller ones, such as assigning dedicated personnel to assist house sellers or handling the company’s online business. This strategy provides a new stage for knowledge sharing and innovation in the business. This study will analyze the relationships between organizational culture and other internal factors in the industry.

The essence of organizational innovation is the means to effectively and adequately foster an excellent organizational culture that positively and significantly influences its performance (Daft, 2004 ; Lemon and Sahota, 2004 ). Hurley and Hult ( 1998 ) found that an organizational culture rooted in innovation can provide the organizational resources to help the organization leverage innovation to their advantage for progress. Organizational innovation is a part of organizational culture and is the precursor to innovation. Shahzad et al. ( 2017 ) revealed that organizational innovation performance is supported and influenced by organizational culture. Deal and Kennedy ( 1984 ) pointed out that a well-performing enterprise must have an excellent organizational culture as it is the main reason behind organizational innovation performance. Srisathan et al. ( 2020 ) examined the influence of organizational culture on open innovation performance using a sample of 300 Thai and Chinese small and medium-sized enterprises (SMEs). They demonstrated the significant influence of organizational culture on innovation performance concerning marketing, operations, customer orientation, and capital management. Aboramadan et al. ( 2020 ) contended that organizational culture positively influences market innovation and technology innovation. Srisathan et al. ( 2020 ) argued that organizational culture positively influences innovation performance through organizational sustainability. We propose H1 as follows:

H1: Organizational culture has a significant and positive influence on innovation performance .

Edvinsson and Malone ( 1997 ) described structural capital as an intangible organizational asset that cannot be taken away by employees when they resign. Furthermore, structural capital reflects an organization’s ability to function as one and is made up of organizational capital, process capital, and innovation capital. De Pablos ( 2004 ) observed that structural capital improves organizational value. Lin et al. ( 2011 ) conceptualized structural capital as an organization’s capacity to solve problems and create value in its general systems and procedures. Consequently, structural capital improves an organization’s competitiveness and innovation performance. Structural capital also reflects the mechanisms and capabilities within an organization that allow it to integrate and utilize all of its resource production procedures. Organizations need to apply for legal protection and patents for the components of structural capital, such as manufacturing processes, trade secrets, and business secrets. The core of structural capital is the common knowledge that is retained in the organization after an employee begins their tenure (Grasenick and Low, 2004 ; Roos et al., 1997 ). Ji et al. ( 2017 ) argued that structural capital positively affects innovation performance directly and indirectly (through intellectual capital). When examining the associations between structural capital and performance in the Mexican and Peruvian public administrations, Pedraza et al. ( 2022 ) found that structural capital is an intangible asset for public and private organizations because it positively and significantly affects organizational resources, capacities, and innovation performance. Therefore, organizations must establish their internal structural capital management strategies to improve innovation performance at the individual and organizational levels.

On this basis, structural capital is conducive to an enterprise’s innovation performance. We propose H2 as follows:

H2: Structural capital has a significant and positive influence on innovation performance

Relational capital refers to an organization’s establishment, maintenance, and development of relationships with its customers, suppliers, and partners (Molyneux, 1998 ). Bontis ( 1998 ) suggested that customer-based relationship capital represents the potential ability of an organization to own external intangible assets and is embedded within the organization’s external customer relationships. Tu ( 2009 ) demonstrated that relational capital positively influences knowledge integration, which in turn positively and significantly influences innovation performance. Nonaka and Takeuchi ( 1995 ) contend that knowledge innovation stems from interpersonal interactions; exchanges between organizational members promote the creation of innovative knowledge and thereby trigger innovation performance. Onofrei et al. ( 2020 ) studied the influence of relational capital on innovation performance in supply chains using a sample of 557 manufacturing plants across 10 countries. The results showed that suppliers and customers who build strong relational capital effectively enhanced the company’s innovation performance, which is also the best way to maintain one’s competitive advantage in the global supply chain. Onofrei et al. ( 2020 ) found that relational capital positively affects innovation performance. Duan et al. ( 2023 ) suggested that relational capital positively affects innovation performance through trust, reciprocity, and transparency. We propose H3 as follows:

H3: Relational capital has a significant and positive influence on innovation performance

Calantonea et al. ( 2002 ) argued that when an organization creates an environment that is highly conducive to learning, its innovativeness and innovation performance can be improved through the active knowledge interaction processes. Lin ( 2007 ) revealed that an organization can further achieve innovation through knowledge sharing after it acquires the necessary information. Bavik et al. ( 2018 ) posited that through knowledge sharing, employees are provided the relevant information to help them achieve individual innovation. The results of the study by Perry-Smith and Shalley ( 2003 ) suggested that information exchange and knowledge sharing between team members are positively associated with innovation performance. Shi et al. ( 2022 ) investigated the effects of knowledge sharing, collaborative innovation, and building information modeling (BIM) application on innovation performance in the construction supply chain by creating and validating the rationality of a relationship model entailing all four factors. The relationships between the factors not only were useful for understanding the role of knowledge sharing in collaborative innovation in the construction supply chain but also had positive effects on developing BIM functions. Wang and Hu ( 2020 ) agreed that knowledge sharing positively influences innovation performance. Hanifah et al. ( 2022 ) highlighted that knowledge sharing has a significant impact on firm innovation performance. We propose H4 as follows:

H4: Knowledge sharing has a significant and positive influence on innovation performance

According to Tushman and O’Reilly ( 1996 ), an enterprise should foster an innovative organizational culture, as the values of cultural factors affect behavior, which in turn affects knowledge creation and sharing. McDermott and O’Dell (2001) examined organizational culture and knowledge sharing and found that the core values of an organization must be closely associated with knowledge sharing. An organization would create its own culture of knowledge sharing and convert it into a tangible asset alongside its business objectives. Svelby and Simons ( 2002 ) stressed that embodying organizational culture during its creation process is conducive to knowledge sharing. Caruso ( 2017 ) suggested that knowledge sharing is the sharing of information, techniques, and professionalism between organizational members; it is a valuable intangible asset and is affected by organizational culture. Earl and Scott ( 1999 ) showed that creating a culture that is conducive to the promotion of knowledge sharing within the organization improves knowledge acquisition skills at the individual and aggregate levels, thus significantly increasing knowledge value. Gooderham et al. ( 2022 ) used the ability, motivation, and opportunity (AMO) approach to examine how organizational culture and national culture affect knowledge sharing in multinational enterprises. The research encompassed 11 countries and regions in northern, central, and eastern Europe and southeast Asia. A questionnaire was administered to 11,484 people employed in 1235 departments. The results showed that organizational and natural cultures were both important factors for understanding knowledge sharing due to their positive influences. Knowledge sharing is conducive to understanding the intrinsic motivations of employees, and managers can broaden its range through organizational culture, thus promoting long-term organizational development. We propose H5 as follows:

H5: Organizational culture has a significant and positive influence on knowledge sharing

Joia ( 2000 ) pointed out that structural capital comprises the structure and strategies necessary for an organization to function, and its influence is realized through the organization’s internal operations. Bontis ( 1999 ) classified intellectual capital as human resource capital, structural capital, and relational capital, and investigated ways to associate between internal organizational knowledge and the external environment. Yli-Renko et al. ( 2001 ) suggested that individual members who hold advantageous positions in their organizations can help their organizations accumulate knowledge and assets through knowledge sharing. This imperceptibly improves the knowledge and competence of other employees and leaders, and their sharing behaviors become conducive to organizational growth. Kim and Shim ( 2018 ) found that the density of social capital, including structural capital, has a positive influence on knowledge sharing between small and medium enterprise employees. In a study on the influence of social capital on knowledge sharing in online user communities, Yan et al. ( 2019 ) highlighted a significant bidirectional relationship between social capital (structural, cognitive, and relational) and knowledge sharing, mainly manifested in the knowledge sharing behaviors of core participants in the community. Structural capital positively influences knowledge sharing through expansion and conversion. We propose H6 as follows:

H6: Structural capital has a significant and positive influence on knowledge sharing

McFadyen and Cannella ( 2004 ) pointed out that the strength of the relations in relational capital influences knowledge sharing and innovation. Hooff and Huysman ( 2009 ) revealed that relational capital positively influences knowledge sharing. Lai ( 2013 ) found that the stronger the relationships (the higher the relational capital), the more likely employees are to exhibit cooperative behaviors and promote knowledge sharing. Kim et al. ( 2013 ) demonstrated a strong association between relational social capital and knowledge sharing. Allameh ( 2018 ) described the three dimensions of social capital as structural, relational, and perceptional social capital, all of which positively affect knowledge sharing. Qiao and Wang ( 2021 ) opined that relational capital concurrently positively influences explicit knowledge sharing and tacit knowledge sharing. Hanifah et al. ( 2022 ) examined relational capital, knowledge sharing, and innovation performance in the Malaysian manufacturing sector. They identified internal and external relational capital as determinants of innovation performance against the backdrop of the competitiveness and survivalist challenges of the manufacturing sector. Knowledge sharing also mediated innovation performance, and relational capital positively influenced knowledge sharing. We propose H7 as follows:

H7: Relational capital has a significant and positive influence on knowledge sharing

Valle et al. ( 2000 ) argued that human resource training should be in line with corporate strategies in order to achieve optimal organizational performance. An enterprise that adopts innovative strategies and design training programs that correspond to these innovative strategies can enhance their innovation performance. Enterprises can improve employees’ knowledge, skills, and competence by managing specific human resources, thus improving their employees’ contributions to the organization and further enhancing innovation performance (Valle et al., 2000 ; Youndt and Snell, 2004 ). According to research, human resource management practices include providing authorization to employees, encouraging employee engagement, and enhancing organizational innovation (Garaus et al. 2016 ). Human resource management activities play a key role in improving market share, individual activeness, and service innovation (Anderson et al. 2014 ; Ardito and Messeni, 2017 ). Using a sample of 129 companies, Papa et al. (2020) examined the effects of knowledge acquisition, employee retention, and HRM practices on innovation performance. The results showed that companies are under immense pressure due to increasing innovation models and means of knowledge acquisition. Leaders who can promptly adapt to such external changes can consolidate the HRM practices of their company, thereby reducing employee turnover and promoting innovation performance. We propose H8 as follows:

H8: Human resource management practices have a significant and positive influence on innovation performance

To enhance employees’ knowledge, skills, and competence, enterprises can leverage the managerial strengths of specified human resources, thereby strengthening employees’ contributions to the organization and subsequently to organizational performance (Sanz-Valle et al., 1999 ; Youndt and Snell, 2004 ). Schneider and Reichers ( 1983 ) mentioned that positive interactions between organizational members create an environment conducive to information sharing within the organization. This environment allows high-performing employees to enhance their leadership skills, thus improving organizational performance through employees’ awareness of knowledge sharing. A team’s ability to showcase their performance or achieve knowledge sharing and innovation depends on the degree to which their organization’s human resource management effectively stimulates team operations (McHugh, 1997 ). Papa et al. ( 2020 ) demonstrated the positive effects of knowledge sharing on innovation performance, while HRM enhances the relationship between knowledge sharing and innovation performance. Regarding the positive effects of knowledge sharing on innovation performance, studies have also shown that the influence of knowledge sharing on innovation performance is moderated by the HRM practices adopted (Kim and Park, 2017 ; Jada Mukhopadhyay and Titiyal, 2019 ). Haq et al. ( 2021 ) examined the influence of HRM practices on knowledge sharing and innovation performance in 213 manufacturing plants in China. The results showed that HRM practices indeed influence knowledge sharing, and knowledge sharing directly influences innovation performance. Supplier knowledge sharing complements intra-organizational knowledge sharing, and HRM practices interfere with the relationship between knowledge sharing and innovation performance. We propose H9 as follows:

H9: The influence of knowledge sharing on innovation performance is moderated by HRM practices

Regarding mediation effects, we have proposed three hypotheses about knowledge sharing as a mediator variable. Firstly, we explained why knowledge sharing was assigned as a mediator variable, followed by proposing the three hypotheses. Bagherzadeh et al. ( 2019 ) examined the influence of outside-in open innovation (OI) on innovation performance while considering the mediating roles of knowledge sharing and innovation strategy. The results revealed that knowledge sharing and innovation strategy fully mediated the relationship between outside-in OI and innovation performance. Hanifah et al. ( 2022 ) studied the influences of intellectual capital and entrepreneurial orientation on innovation performance in SMEs, with knowledge sharing as a mediator. The results showed that human capital, as well as external relational capital, had a positive correlation with both knowledge sharing and innovation performance mediated by knowledge sharing. Hanifah et al. ( 2022 ) studied relational capital, knowledge sharing, and innovation performance in the Malaysian manufacturing sector. They showed that internal and external relational capital were determinants of innovation performance, while knowledge sharing mediated the influence of innovation performance, and relational capital positively influenced knowledge sharing.

The means of creating appropriate and effective organizational culture underpins organizational innovation. Research has demonstrated the positive and significant influence of organizational culture on organizational innovation and innovation performance (Daft, 2004 ; Lemon and Sahota, 2004 ). Shahzad et al. ( 2017 ) revealed that organizational innovation performance is supported and influenced by organizational culture. Sveiby and Simons ( 2002 ) stressed that realizing organizational culture while establishing it is conducive to knowledge sharing. Caruso ( 2017 ) agreed that organizational culture influences knowledge sharing. Bavik et al. ( 2018 ) suggested that employees can acquire the necessary knowledge through knowledge sharing and thus achieve personal innovation. Perry-Smith and Shalley ( 2003 ) revealed that information exchange and knowledge sharing between team members positively influence innovation performance. We propose H10 as follows:

H10: Knowledge sharing mediates the influence of organizational culture on innovation performance.

De Pablos ( 2004 ) demonstrated that good structural capital empowers organizational value. Lin et al. ( 2011 ) defined structural capital as the ability to resolve organizational problems and create value in the organization’s system and procedures as a whole, thus enhancing organizational competitiveness and firm innovation performance. Yli-Renko et al. ( 2001 ) contended that members who hold advantageous positions in the organization’s structural capital framework can contribute to its accumulation of knowledge assets by leveraging sharing environments where leaders and subordinates share knowledge and capabilities. Kim and Shim ( 2018 ) showed that the density of social capital, which includes structural capital, positively influenced knowledge sharing among SME employees. Lastly, knowledge sharing positively and significantly influenced innovation performance. We propose H11 as follows:

H11: Knowledge sharing mediates the influence of structural capital on innovation performance.

Nonaka and Takeuchi ( 1995 ) suggested that interpersonal interactions and exchanges between organizational members generate innovative knowledge and subsequently promote innovation performance. Tu’s (2009) empirical results showed that relational capital positively influences knowledge integration abilities, which positively and significantly influences innovation performance. Moreover, the empirical study by Kim et al. ( 2013 ) demonstrated a strong link between relational social capital and knowledge donation. Allameh ( 2018 ) identified the structural, relational, and cognitive dimensions of social capital, all of which positively influence knowledge sharing. Lastly, knowledge sharing positively and significantly influences innovation performance. We propose H12 as follows:

H12: Knowledge sharing mediates the influence of relational capital on innovation performance.

Study framework

We designed a study framework consisting of a hierarchical linear model for analysis and estimation as shown in Fig. 1 . The main reason for using hierarchical linear modeling is because traditional single-level regression analysis is prone to bias. Wen and Chiou ( 2009 ) pointed out that in the traditional approach, organizational level and individual level variables are placed into a single regression model, which likely violates the assumption of independence. The standard error of the estimated regression coefficient analyzed through traditional regression analysis is also excessively small and may reject the null hypothesis, resulting in type 1 error inflation. Therefore, hierarchical linear modeling was used for data analysis in this study with the goal of demonstrating the relationships between all the organizational level and individual level variables, as well as the interactions between different levels.

The study framework.

The empirical model

Analytical strategies and levels.

In hierarchical linear mediation analyses, several configurations exist such as 1 → 1 → 1, 2 → 1 → 1, and 2 → 2 → 1 (Krull and MacKinnon, 1999 ). The mediating effect models in this study were the 1 → 1 → 1 and 2 → 1 → 1 configurations. These three numbers represent the independent variable, mediator variables, and outcome variables, respectively. The mediating effect models in this study were (1) organizational culture → knowledge sharing → innovation performance; (2) structural capital → knowledge sharing → innovation performance; and (3) relational capital → knowledge sharing → innovation performance.

In this study, the individual variables (innovation performance and knowledge sharing) were assigned as outcome variables. First, prior to conducting a hierarchical linear analysis, a null model must be used to check for significant differences in the individual innovation performance (PERFORMANCE) and knowledge sharing (KNOWLEDGE), as well as to estimate the amount of between-branch variance that constitutes the total variance in the individual innovation performance and knowledge sharing. The model settings are shown in Eqs. ( 1 ) to ( 2 ):

where PERFORMANCE ij represents the individual innovation performance of the i th person in the j th branch; β 0 j represents the mean innovation performance of the j th branch; r ij indicates the within-group error, with a mean of 0; the variance σ 2 is independent, homogenous, and normally distributed; γ 00 represents the total mean score of the individual innovation performance; u oj represents the difference in the mean individual innovation performance and the total mean score of the individual innovation performance of each branch; u oj is the between-group error, which is independent and has a mean of 0; τ 00 is the variance and is independent, homogenous, and normally distributed; and r ij and u oj are assumed to be independent of each other. We further examined the ICC of the null model ( ICC  =  τ 00 / τ 00  +  σ 2 ) to determine the necessity to perform HLM analysis. Heck and Thomas ( 2009 ) suggested that HLM can be used for estimation and analysis when the ICC is greater than or equal to 0.05. The same settings were applied to the knowledge sharing (KNOWLEDGE) null model, and shall not be elaborated on further.

Hierarchical linear mediation model

Based on the construction of the hierarchical linear mediation model, random effects were used to set the Level1 intercept. The mediation models were: (1) Organizational culture → knowledge sharing → innovation performance; (2) Structural capital → knowledge sharing → innovation performance; (3) Relational capital → knowledge sharing → innovation performance. Regarding mediation effect testing, the ordered regression coefficient test proposed by Baron and Kenny ( 1986 ) is a popular method. This study followed Baron and Kenny’s ( 1986 ) three-step test method in which the first step was to test the influence of the independent variables on the dependent variables, namely the influences of organizational culture ( CULTURE ), structural capital ( STRUCTURE ), and relational capital ( RELATION ) on innovation performance ( PERFORMANCE ), as shown in Eqs. ( 3 ) through ( 5 ). The second step was to test the influence of the independent variables on the mediator variables, namely the influences of organizational culture ( CULTURE ), structural capital ( STRUCTURE ), and relational capital ( RELATION ) on knowledge sharing ( KNOWLEDGE ). Lastly, the other variables were included in the model, and the influences of organizational culture ( CULTURE ), structural capital ( STRUCTURE ), HRM practices ( RESOURCE ), relational capital ( RELATION ), and knowledge sharing ( KNOWLEDGE on innovation performance ( PERFORMANCE ) were estimated, as shown in Eqs. ( 9 ) to ( 12 ). Sex ( SEX ), job tenure ( EXP ), and business model ( MANAGE ) were set as control variables. The first step is as follows:

where β 0 j is the Level1 intercept; β 1 j ~ β 3 j represent the coefficients of the Level1 independent variables; γ 00 is the total mean innovation performance; γ 01 is the coefficient of organizational culture ( CULTURE ); γ 02 is the coefficient of structural capital ( STRUCTURE ); μ 0 j is the between-group error, which is independent and has a mean of 0; and τ 00 is the variance and is independent, homogenous, and normally distributed. Fixed effects were applied to Eq. ( 5 ), without a random error. The estimations for Eqs. ( 3 ) through ( 5 ) are presented in Model 1 in Table 3 . If γ 10 , γ 01 , or γ 02 was significant, then the second step was used for estimation.

The estimations for Eqs. ( 6 ) through ( 8 ) are presented in Model 2 in Table 3 . If γ 10 , γ 01 , or γ 02 was significant, then the third step was used for estimation.

The estimations for Eqs. ( 9 ) through ( 12 ) are presented in Model 3 in Table 3 . If γ 20 was not significant, then there were no mediation effects; if γ 20 was significant alongside any of γ 10 , γ 01 , γ 02 , or γ 03 , then there were partial mediation effects; if γ 20 was significant, but γ 10 , γ 01 , γ 02 , or γ 03 , were not, then there were complete mediation effects.

Questionnaire design

The questionnaire in this study consisted of two sections. The first section covered the participants’ basic information, including sex, age, tenure in the real estate agency, and job position. The second section covered items related to the three organizational-level variables (organizational culture, structural capital, and HRM practices) and the three individual-level variables (structural capital, knowledge sharing, and innovation performance).

The items pertaining to organizational culture were designed according to the studies by Schein ( 1993 ), Wilkins and Ouchi ( 1983 ). Organizational culture consists of three sub-dimensions: artifacts, espoused values, and basic assumptions. According to Schein ( 1993 ), artifacts are all the concrete observations of a company, such as language, style, ceremonies, and office settings; espoused values are the common beliefs, ethics, and behavioral norms shared by the organization, which consist of organizational strategies, objectives, philosophies, and values; basic assumptions refer to the unconscious beliefs that organizational members hold and are the original source of values and organizational action that profoundly influence how organizational members perceive, think about, and interact with the world. Each sub-dimension consists of three items, for a total of nine items. Next, the items pertaining to structural capital were designed according to the studies by Edvinsson and Malone ( 1997 ) and Jaw ( 2004 ). Structural capital consists of three sub-dimensions: organizational capital, innovation capital, and process capital. Organizational capital refers to a company’s investments in systems and instruments that enhance the transfer of knowledge inside the organization as well as improve the means to supply and disseminate knowledge. This capital reflects an organization’s ability to systematize, synthesize, and arrange itself and the systems for enhancing production. Innovation capital refers to an organization’s capacity to innovate and protect trade rights, intellectual property, and other intangible assets and its ability to develop and expedite the launch of new products and services. Process capital includes work procedures, special methods, and employee programs for expanding or enhancing product manufacturing or service efficiency. The above sub-dimensions consist of three, three, and two items, respectively, for a total of eight items. The items pertaining to HRM practices were designed according to the studies by Bae et al. ( 1998 ), and Sun et al. ( 2007 ). HRM practices consist of human resource planning, training and development, and remuneration and benefits, and each sub-dimension includes two or three items, for a total of eight items. Finally, the items pertaining to relational capital were designed according to the studies by Sarkar et al. ( 2001 ). Relational capital consists of mutual trust, commitment, and information exchange, and each sub-dimension includes two or three items, for a total of eight items.

The items pertaining to knowledge sharing were designed according to the studies by Spencer ( 2003 ); Hendrinks ( 1999 ); Bock and Kim ( 2002 ); Bock et al. ( 2005 ); Betz ( 1987 ); Subramanian and Nilakanta ( 1996 ); Becerra-Fernandez and Sabherwal ( 2001 ); Nonaka and Takeuchi ( 1995 ). Knowledge sharing was divided into three sub-dimensions: knowledge sharer, knowledge recipient, and knowledge sharing intentions, each consisting of two or three items, for a total of eight items. The items pertaining to innovation performance were designed according to the studies by Amabile ( 1988 ); Drejer ( 2004 ); and Bilderbeek et al. ( 1998 ). Innovation performance consisted of stimulating innovation and service innovation, which included three and two items, respectively, for a total of five items. All items were measured on a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). Please refer to Table 1 for the detailed questionnaire items.

Cronbach’s α is currently the most common method of measuring the reliability of each dimension. A Cronbach’s α of greater than 0.8 indicates high reliability (Hair et al., 2011 ), which was the case for all dimensions in this study. As a measure of construct validity, the factor loading of each item in this study was significant, thus validating the construct validity of the scale. We further performed measurements using convergent validity, which is based on the factor loading of each item in each dimension. According to Hair et al. ( 2006 ), a good convergent validity should be greater than 0.5, which was the case in our study.

Data collection, descriptive statistics, and data treatment

Data collection.

Convenience sampling was adopted in this study to survey real estate agents from seven real estate agency chains in Kaohsiung City: Sinyi Realty, HandB Housing, Taiching Realty, Taiwan Realty, Yung Ching Realty, CTBC Real Estate, and U-Trust Realty. The surveyed area consisted of the commercial hubs of Sanmin, Zuoying, Lingya, Gushan, and Xinxing districts. The questionnaire was administered in person to the participants before mid-May 2021, and then via mail after mid-May 2021 because of the COVID-19 pandemic situation. The survey period lasted from May 1 to July 31, 2021. A total of 1130 questionnaires were distributed (530 in person, 600 via mail), and 444 were recovered (115 from Sanmin district, 104 from Zuoying district, 66 from Lingya district, 58 from Gushan district, and 101 from Xinxing district). 40 invalid questionnaires were removed for missing items or no response to sex and tenure. The Level 2 variables were in units of branches, and the variance data were the aggregate of the Level 1 individual data. To ensure representativeness, three questionnaires were removed because their branches had returned less than three responses. This left a total of 401 valid questionnaires, or an effective response rate of 35.49%.

Armstrong and Overton ( 1977 ) proposed the non-response bias test process for examining whether significant differences exist in the response rate in the options for sex, marital status, and education level, which was used in each of the two batches of recovered samples. The non-response bias reflects the consistency between the distribution of the actual recovered samples and the population data structure. We split the 401 recovered samples into two groups based on the code number; the first group consisted of 310 responses recovered in person, and the second consisted of 91 responses recovered via mail. We then tested the differences in the demographic backgrounds (sex, marital status, and education level) of the two groups, but found no significant differences. Thus, no serious non-response bias was found in the questionnaire.

Descriptive statistics

In the valid sample, men accounted for 53.9% (216 participants) of the responses while women accounted for 46.1% (185 participants) of the responses. The largest group of participants (29.5%, 116) were in the 30–40 years age group, followed by those in the 41–50 years age group (27.2%, 107). Regarding marital status, unmarried participants accounted for 47.9% (190) while married participants accounted for 47.6% (189) of the responses. Regarding tenure, the largest percentage of the participants had been working for between one and five years (42.1%, 169), followed by those working for less than a year (23.7%, 95). Regarding income, the largest percentage of participants (22.5%, 88) earned between NT$460,000–600,000, followed by those who earned less than NT$300,000 (18.9%, 74). Regarding job positions, the majority (82.3%, 326) of the participants were salespersons, while agents constituted 4.5% (18). Regarding education level, the majority of the participants had received university or two/four-year technical college educations (57.2%, 224), while those who received a senior (vocational) high school education or less made up 21.4% of participants (84). The majority (74.6%, 299) of the participants were working in franchise office branches, followed by those working in direct sales offices (25.4%, 102).

Data processing

Control variables.

In a regression analysis, the influence of control variables such as sex, tenure, and business model must be considered. Gender differences reflect physiological differences and can affect which employees are assigned different work tasks, which may influence their innovation performance. Therefore, we used sex as a control variable in the regression model. Many companies nowadays desire to achieve higher innovation performance. Most employees with longer tenures are older and are less responsive toward accepting new things; on the other hand, employees with shorter tenures are mostly fresh graduates or younger employees with less work experience. They tend to be more enthusiastic about their jobs since they have just entered the workforce and are more likely to develop new ideas; therefore, they have better innovation performance than employees with longer tenures. For this reason, we used tenure as a control variable in the regression model. The real estate industry in Taiwan consists of direct sales and franchise stores. the former is directly operated by the headquarters of a real estate company, and the employed agents and salespersons are dispatched to these direct sales offices after receiving training at the headquarters. The headquarters is responsible for guaranteeing the resources and service contents at each branch office. On the other hand, franchise stores consist of independent branch offices that need to pay a regular franchise fee to the headquarters in exchange for resources such as the headquarters’ brand image, educational training, or advertising. Each franchise is equivalent to a standalone company that must bear its own losses, and its business system and service contents differ as well. Since the innovation performance of both business models may differ, we also used the business model as a control variable in the regression model.

Aggregation issues

In this study, organizational culture, structural capital, and HRM practices were assigned as Level 2 variables. The data was a shared construct since it was collected from each real estate agent. In addressing the treatment of shared construct data, Klein et al. ( 1994 ) indicated that prior to conducting a multi-level analysis, it is necessary to examine the appropriateness of consolidating individual variables to the aggregate level. We used the intraclass correlation test (ICC(1)) approach proposed by James ( 1982 ) and the reliability of the mean test (ICC(2)) approach proposed by Bliese ( 1998 ) to examine the between-group differences. An ICC(1) greater than 0.5 indicates aggregation within organizational members, and the mean is the score of the organizational variable (Bliese, 2000 ; Heck and Thomas, 2009 ); an ICC(2) greater than 0.7 indicates a high reliability for using the group mean of individual data as a contextual variable, and that significant differences exist between the mean of each group (Dixon and Cunningham, 2006 ). The formulas are shown below:

where MS b is the between-group difference, MS w is the within-group difference, N g is the arithmetic mean of the group size.

There were 55 branches in Level 2, and the calculated ICC (1) of organizational culture and structural capital was 0.998 (>0.5), indicating that the individual variables of structural capital and organizational culture can be integrated into the aggregate level. The ICC (1)s of organizational culture and structural capital were both 0.999 (>0.07), which shows that the group means of individual organizational culture and structural capital are highly reliable contextual variable indicators with significant between-group heterogeneity. The ICC (2) of HRM practices was 0.999 (>0.07), which shows that HRM practices are a highly reliable contextual variable indicator with a significant between-group heterogeneity.

We recovered 401 questionnaires from 55 branches. We then used the within-group interrater reliability r wg (James et al., 1984 , 1993 ) to determine the within-group agreement, which reflects the degree of agreement of an individual in a particular population toward a particular variable (Bliese, 2000 ). The within-group agreement is present when r wg,j exceeds 0.7 (James et al., 1984 ). The formula is as follows:

where J is the number of questionnaire items; r wg ( j ) is the within-group agreement coefficient for judges’ mean scores based on the j th item; \(sx_j^2\) is the mean of the observed variances on the j th item; and \(\sigma _E^2\) is the expected variance of a hypothesized null distribution.

The results showed that the mean r wg ( j ) of the 55 branches in relation to organizational culture, structural capital, and HRM practices was 0.973, 0.966, and 0.950, respectively, and all were larger than 0.7. This shows that the within-group agreements were present in the variables of organizational culture, structural capital, and HRM practices, and were strongly correlated. Thus, the organizational members were in agreement regarding organizational culture, structural capital, and HRM practices. Therefore, our consolidation of organizational culture, structural capital, and HRM practices as organizational-level variables was adequate.

Empirical results

Prior to HLM analysis, we needed to examine whether significant differences exist between the individual innovation performance and knowledge sharing between branches, and we also had to estimate the proportion by which the total variance of innovation performance and knowledge sharing is shaped through the differences between branches.

As shown in Table 2 , the estimated variance of the random effects of personal innovation performance was 0.086 and was significant at the 1% level. This shows that significant differences exist in the individual innovation performance at each branch. The intraclass correlation was 0.208 (=0.086/(0.086 + 0.328)), which means that 20.8% of the variance of individual innovation performance consisted of the interclass (between branches) differences, while 79.2% of the variance consisted of intraclass (within a branch) differences. Next, the estimated variance of the random effects of knowledge sharing was 0.068 and was significant at the 1% level. This shows that significant differences exist in the levels of knowledge sharing at each branch. The intraclass correlation was 0.189 (=0.068/(0.068 + 0.292)), which means that 18.9% of the variance of knowledge sharing can be explained by the differences between branches, while 81.1% of the variance can be explained by the differences between agents. Therefore, we further applied HLM for analysis and estimation.

According to Table 3 and Fig. 2 , the estimation results of Model 1 showed that the estimated coefficient of relational capital was 0.643 and was significant at the 1% level. The estimated coefficient of organizational culture was 0.556 and was significant at the 1% level. The estimated coefficient of structural capital was 0.381 and was significant at the 5% level. These results showed that the three-step mediation effects testing had passed the first step. The estimation results of Model 2 showed that the estimated coefficient of organizational culture was 0.327 and was significant at the 1% level. The empirical results support H5 . The estimated coefficient of structural capital was 0.504 and was significant at the 1% level. The empirical results support H6 . The empirical results support H7 . These results indicated that the three-step mediation effects testing had passed the second step.

The empirical results.

The estimation results of Model 3 showed that the estimated coefficient of knowledge sharing was 0.580 and was significant at a 1% level. The empirical results support H4 . The estimated coefficient of organizational culture was 0.605 and was significant at a 1% level. The empirical results support H1 . The results support H1, H4, and H5, as well as H10 . The estimated coefficient of structural capital was 0.04 but did not attain a significant level. The empirical results do not support H2 . Good structural capital creates organizational value (De Pablos, 2004 ). The empirical results support H2, H4, and H6, as well as H11 . The estimated coefficient of relational capital was 0.250 and was significant at a 1% level. The empirical results support H3 . The empirical results support H3, H4, and H7, as well as H12 .

The estimated coefficient of HRM practices was 0.317 and was significant at a 5% level. The empirical results support H8 . The estimated coefficient of the influence of knowledge sharing on innovation performance through the moderating variable of HRM practices was −0.048 and did not attain a significant level. This shows that HRM practices do not moderate the influence of knowledge sharing on innovation performance. The empirical results do not suppor t H9 .

Theoretical implications

This showed that the stronger the real estate agents’ understanding of organizational culture, the better their understanding of knowledge sharing. The empirical results suppor t H5 and validate Sveiby and Simons’ ( 2002 ) study, which showed that an organizational culture characterized by trust and cooperation increases knowledge sharing and innovation performance. In addition, the process of implementing organizational culture is conducive to the indication of intra-organizational knowledge sharing. This indicates that the real estate agents’ knowledge sharing is significantly influenced by their strong understanding of structural capital. The empirical results support H6 . This finding also demonstrates that organizational members create partnerships rooted in mutual respect through long-term relationships or friendships, as well as that the trust-based structural capital shaped by this cooperative climate promotes organizational members’ willingness to share knowledge (Granovetter, 1992 ). This indicates that real estate agents with a stronger understanding of relational capital have a stronger understanding of knowledge sharing as well. The empirical results support H7 . This finding supports Lai’s ( 2013 ) argument that the higher the relational capital, the more likely organizational members are to engage in cooperation and the more likely they are to share knowledge. A stronger and closer relational capital increases the depth, breadth, and efficiency of knowledge sharing (Lane and Lubatkin, 1998 ).

The empirical results support H4. This finding is in line with Lin’s ( 2007 ) demonstration of the relationship between knowledge sharing and innovation performance. The study revealed that knowledge sharing is essential for information acquisition and, subsequently, innovation. This further indicates that the stronger the real estate agents’ understanding of knowledge sharing, the better their innovation performance. Indeed, knowledge sharing has mediating effects. This indicates that real estate agents’ understanding of organizational culture significantly influences their innovation performance. The empirical results suppo rt H1 . Huang ( 2018 ) showed that a good organizational culture is a determinant of innovation performance. The cultures shaped by an organization play an important role in their innovation performance; organizational culture has significant and direct effects on innovation performance. The results support H1, H4, and H5, as well as H10 . Alavi and Leidner ( 2001 ) highlighted that organizational culture is an important factor affecting knowledge management and organizational learning, is a determinant of organizational value, and promotes knowledge sharing and innovation. Fernandez et al. ( 2011 ) revealed that organizational culture enhances service innovation through knowledge sharing between colleagues. The key to building a strong and proactive organizational culture lies within the knowledge sharing and knowledge management behaviors between colleagues. This increases the likelihood that an organization will create innovative strategies (Al-Refaie, 2015 ). Organizational culture indirectly influences innovation performance through knowledge sharing and has a partial mediating effect.

The empirical results do not suppor t H2 . Good structural capital creates organizational value (De Pablos, 2004 ). Employees with a poorer perception toward their organization’s structural capital are incapable of significantly increasing the innovation performance of the organization. Our results revealed that structural capital has no significant or direct influence on innovation performance. This reflects the reality of the real estate industry since all agents are constantly competing, whether or not they are in the same organization. Consequently, they remain passive or are not attracted to the internal culture and vision of their organization or developing new skills. Regarding the enhancement of their personal and professional skills, each company has its own regulations on employee training. Some large and renowned brands provide internal and external training programs to their employees gratis, whereas smaller and independent brands operate on an out-of-pocket policy. Under such circumstances, structural capital fails to ideally influence innovation performance. The empirical results support H4 and H6, as well as H11 . De Pablos ( 2004 ) wrote that structural capital consolidates individual and group knowledge to generate organizational knowledge during the learning process. An employee who is more willing to share knowledge would gain a higher level of personal achievement. Our results showed that structural capital indirectly influences innovation performance through knowledge sharing, with complete mediation effects. This indicates that real estate agents with a stronger understanding of relational capital have a higher innovation performance. The empirical results suppor t H3 and show that relational capital has a direct influence on innovation performance. The empirical results support H3, H4, and H7, as well as H12 . Nahapiet and Hoshal ( 1998 ) suggested that relational capital promotes innovation in knowledge sharing through the exchange of intangible assets. Tu ( 2009 ) indicated that relational capital serves as a medium for knowledge flow; the knowledge advantage created through knowledge sharing and consolidation enhances the mutual trust, commitment, and bilateral communication between partners, thus increasing their innovation performance. Our results indicate that relational capital indirectly influences innovation performance through knowledge sharing, and the mediation effects were partial.

This suggests that real estate agents with a stronger understanding of HRM practices have a higher innovation performance. The empirical results suppor t H8 . Lazear ( 1996 ) pointed out that employees who express a higher interest in HRM practices and strategies understand more about their organization and innovation performance. The findings therefore suggest that the positive effects of employee recruitment, selection, training, human resource planning, remuneration scheme design, and employee engagement activities can improve an organization’s market performance, overall performance, and innovation (Hartog and Verburg, 2004 ; Andries and Czarnitzki, 2014 ). This shows that HRM practices do not moderate the influence of knowledge sharing on innovation performance. The empirical results do not suppo rt H9 . Previous studies have shown that HRM practices promote knowledge sharing on innovation performance (Lazzarotti et al., 2015 ). Knowledge-sharing activities among employees must be modified through HRM approaches such as designing training programs, reward systems, work teams, etc., so as to increase the willingness of employees to share their knowledge and experiences with others and thereby influence the individual innovation behaviors of employees and improve their innovation performance and creativity (Cano and Cano, 2006 ). Since the real estate agents had a weak understanding of HRM practices. This is because, in reality, many real estate agents do not have a base salary and must depend on making successful transactions by communicating and coordinating with their clients. Therefore, they tend to neglect the HRM practices in their organization. Our empirical results fail to support the aforementioned arguments.

Managerial implications

Our empirical results demonstrated that the indirect influence of organizational culture on innovation performance was partially mediated by knowledge sharing, the indirect influence of structural capital on innovation performance was fully mediated by knowledge sharing, and the indirect influence of relational capital on innovation performance was partially mediated by knowledge sharing. Real estate agents with a more positive perception of HRM practices showed better innovation performance.

First, managers should actively foster an organizational culture to enhance employees’ innovation performance. Measures include encouraging harmonious and friendly interactions between colleagues, creating a productive workplace climate, ensuring fair and equal treatment of all employees, emphasizing interpersonal relations, and establishing robust and comprehensive company policies.

Next, employees’ innovation performance can be enhanced by accumulating structural capital, such as allocating adequate funds and time to encourage employees to acquire new knowledge, establishing all-inclusive HR training programs, using various approaches to help employees develop their innovative capacity, and providing high-quality services that meet customer demands.

Employees can also improve their innovation performance by accumulating structural capital, such as establishing mutual trust between colleagues, treating one another with integrity, sharing knowledge, communicating frequently, and exchanging informal and formal information.

Lastly, HRM practices can be used to improve employees’ innovation performance. This includes setting well-defined career paths in the organization, assisting employees in applying their training contents into practice, giving compensation based on an employee’s contributions, and emphasizing impartiality.

Conclusions and recommendations

This study applied hierarchical linear modeling to explore the influences of organizational culture, structural capital, human resource management practices, relational capital, and knowledge sharing on the innovation performance of real estate agents. Organizational culture, structural capital, and human resource management practices were assigned as organizational-level variables while relational capital, knowledge sharing, and innovation performance were assigned as individual-level variables. First, we studied the influences of organizational culture, structural capital, and relational capital on innovation performance; afterward, we used knowledge sharing as a mediator variable to examine how it is influenced by organizational culture, structural capital, and relational capital. Lastly, we explored the influences of organizational culture, structural capital, human resource management practices, relational capital, and knowledge sharing on innovation performance, as well as whether human resource management practices moderated the influence of knowledge sharing on innovation performance. After testing the null model of the hierarchical linear model, we found that innovation performance and knowledge sharing differed significantly across the office branches, indicating that hierarchical linear modeling was suitable for analysis.

Based on the empirical results, organizational culture indirectly influences innovation performance through knowledge sharing. In other words, organizational culture has a partial mediating effect on innovation performance. Structural capital directly influences innovation performance through knowledge sharing, with a complete mediating effect. Relational capital indirectly influences innovation performance through knowledge sharing, with a partial mediating effect. The stronger the real estate agents’ understanding of human resource management practices, the higher their innovation performance. Human resource management practices did not moderate the influence of knowledge sharing on innovation performance, and our empirical results were not supported. From a theoretical perspective, the impacts of organizational culture in higher education on job satisfaction have been studied previously (see Islamy et al.,, 2020 ), although the model merely consisted of organizational culture, job satisfaction, and knowledge sharing. Moreover, Kutieshat and Farmanesh ( 2022 ) only considered the exogenous variable of new HRM practices when studying innovation performance. Our study expands and enhances the completeness of this theoretical framework by adding the variables of structural capital and relational capital, thus achieving better empirical support. Concerning the research subjects, studies on innovation in the real estate industry (see Benefield et al., 2019 ) have mostly observed the effects of real estate agencies and technology based on housing prices. On the other hand, our study employed latent variables and performed measurements from a psychological level, thereby broadening the research on the real estate industry.

This study was administered to participants in Kaohsiung City, Taiwan; therefore, the results cannot be extrapolated beyond the range of the study area. The control variables in the hierarchical linear model only included sex, tenure, and business model; job position was excluded, and the questionnaire was not directed at supervisors. Therefore, we were unable to explore whether job positions or supervisors’ opinions toward the organization or individual employees differed. Furthermore, we only focused on the human resource planning, training and development, and remuneration and benefits sub-dimensions of human resource management practices. We recommend future studies to explore the other sub-dimensions of human resource management practices, such as performance evaluation and non-financial remuneration schemes. Due to manuscript length restrictions and time and monetary constraints, we did not examine the behaviors of house buyers. As a result of technological advancements and social developments, our lifestyles and behaviors are profoundly influenced by science and technology, which may also alter the preferences of house buyers. Therefore, we suggest that future studies can focus on the impacts of technology (such as AI) on house-buyers’ behaviors or how AI moderates the relationship between knowledge sharing and innovation performance.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Conceptualization: C-CL and W-CY; Methodology: C-CL; Validation: ZY; Formal analysis: Y-CL and W-CY; Investigation: W-CY; Writing—review & editing: C-CL and Y-CL. Y-CL is the representative of Hong-Ni Tang, Yi-Shan Yao, and Yu-Qing Su.

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Lee, CC., Yeh, WC., Yu, Z. et al. Knowledge sharing and innovation performance: a case study on the impact of organizational culture, structural capital, human resource management practices, and relational capital of real estate agents. Humanit Soc Sci Commun 10 , 707 (2023). https://doi.org/10.1057/s41599-023-02185-w

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Published : 16 October 2023

DOI : https://doi.org/10.1057/s41599-023-02185-w

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