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Determinants of entrepreneurial knowledge and information sharing in professional virtual learning communities created using mobile messaging apps

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Abstract

There is very limited knowledge about the factors that influence entrepreneurial knowledge and information sharing in professional virtual learning communities created using mobile messaging apps. To address this gap, a survey was conducted on 165 members of agricultural entrepreneurship virtual communities created using Viber and WhatsApp. Qualitative data were also gathered through interviews with 30 virtual community members, and were analyzed to provide further understanding of the quantitative findings. Structural equation modeling was used to evaluate the model and the significance of relationships between the constructs. Results indicated that subjective norms affect perceived ease of use, and perceived ease of use directly and indirectly (through perceived usefulness) affect knowledge and information sharing intentions. In addition, perceived ease of use and perceived usefulness indirectly (through the intentions) affect knowledge and information sharing. The findings of qualitative data analysis provided support for the influence of perceived ease of use and perceived usefulness on sharing behavior. The qualitative results also revealed that subjective norms, virtual community norms and rules, and leadership play roles in encouraging or discouraging community members to participate in knowledge sharing activities. This study confirmed the validity of the technology acceptance (TAM) model for explaining knowledge and information sharing within professional virtual learning communities on mobile messaging apps, particularly when the model includes subjective norms. The current study contributes to the literature on knowledge sharing in virtual communities by focusing on virtual learning communities created using mobile messaging apps. This research also provides practical implications to improve knowledge sharing in professional virtual learning communities on mobile messaging apps.

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Data availability

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

Abbreviations

TAM:

technology acceptance model

TRA:

Theory of Reasoned Action

TRI:

Technology Readiness Index

SET:

Social Exchange Theory

References

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior Hamman Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Arbuckle, J. L. (2007). Amos 16.0 user's guide. Chicago: SPSS, Inc..

    Google Scholar 

  • Borrero, J. D., Shumaila, Y. Y., Javed, U., & Page, K. L. (2014). Expressive participation in internet social movements: testing the moderating effect of technology readiness and sex on student SNS use. Computers in Human Behavior, 30, 39–49.

    Article  Google Scholar 

  • Bourhis, A., Dube, L., & Jacob, R. (2005). The success of virtual communities of practice: the leadership factor. Electronic Journal of Knowledge Management, 3(1), 23–34.

    Google Scholar 

  • Chen, C. J., & Hung, S. W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 47, 226–236.

    Article  Google Scholar 

  • Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: an extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.

    Article  Google Scholar 

  • Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information and Management, 45(5), 458–465.

    Article  Google Scholar 

  • Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: the usefulness of electronic weak ties for technical advice. Organization Science, 7(2), 119–135.

    Article  Google Scholar 

  • Davenport, T. H., & Prusak, L. (1998). Working knowledge: how organizations manage what they know. Boston: Harvard Business School Press.

    Google Scholar 

  • Da Silva, C., Baker, D., Shepherd, A. W., Jenane, G., & Miranda-da-Cruz, S. (Eds.). (2009). Agro-industries for development. Rome: FAO and UNIDO.

    Google Scholar 

  • Davidsson, P. (2016). Researching entrepreneurship: conceptualization and design (2nd ed.). Switzerland: Springer International Publishing.

    Book  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. Management Information Systems Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Dias, C. S., Rodrigues, R. G., & Ferreira, J. J. (2019). What’s new in the research on agricultural entrepreneurship? Journal of Rural Studies, 65, 99–115.

    Article  Google Scholar 

  • Donath, J. S. (1999). Identity and deception in the virtual community. In M. Smith & P. Kollock (Eds.), Communities in cyberspace (pp. 29–59). New York: Routledge.

    Google Scholar 

  • Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.

    Article  Google Scholar 

  • Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.

    Article  Google Scholar 

  • Gefen, D., & Ridings, C. M. (2002). Implementation team responsiveness and user evaluation of customer relationship management: a quasi-experimental design study of social exchange theory. Journal of Management Information Systems, 19(1), 47–70.

    Article  Google Scholar 

  • Giddens, J., Fogg, L., & Carlson-Sabelli, L. (2010). Learning and engagement with a virtual community by undergraduate nursing students. Nursing Outlook, 58, 261–267.

    Article  Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. Management Information Systems Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  • Gongla, P., & Rizzuto, C. R. (2001). Evolving communities of practice. IBM global services experience. IBM Systems Journal, 40(4), 842–862.

    Article  Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: a global perspective (7th ed.). New Jersey: Pearson Prentice Hall.

    Google Scholar 

  • He, W., & Wei, K. K. (2008). What drives continued knowledge sharing? An investigation of knowledge contribution and seeking beliefs. Decision Support Systems, 46(4), 826–838.

    Article  Google Scholar 

  • Hidayanto, A., & Setyady, S. (2014). Impact of collaborative tools utilization on group performance in university students. Turkish Online Journal Education Technology, 13(2), 88–98.

    Google Scholar 

  • Ho, R. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Florida: Chapman & Hall/CRC, Boca Raton.

    Book  Google Scholar 

  • Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: a meta-analytic review. PLoS Medicine, 7(7), 1–20.

    Article  Google Scholar 

  • Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63, 597–606.

    Article  Google Scholar 

  • Hsiao, C., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social apps: satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355.

    Article  Google Scholar 

  • Hsu, C., & Lu, H. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information Management, 41(7), 835–868.

    Article  Google Scholar 

  • Hsu, M. H., Ju, T. L., Chang, C. M., & Yen, C. H. (2007). Knowledge sharing behavior in virtual communities: the relationship between trust, self-efficacy, and outcome expectations. International Journal of Human-Computer Studies, 65(2), 153–169.

    Article  Google Scholar 

  • Hu, P. J., Clark, T. H., & Ma, W. W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information Management, 41, 227–241.

    Article  Google Scholar 

  • Hung, S. W., & Cheng, J. M. (2013). Are you ready for knowledge sharing? An empirical study of virtual communities. Computers & Education, 62, 8–17.

    Article  Google Scholar 

  • Hung, S. W., Lai, H. M., & Chou, Y. C. (2015). Knowledge-sharing intention in professional virtual communities: a comparison between posters and lurkers. The Journal of the Association for Information Science and Technology, 66(12), 2494–2510.

    Article  Google Scholar 

  • Igbaria, M., Iivari, J., & Maragahh, H. (1995). Why do individuals use computer technology? A finish case study. Information Management, 29, 227–238.

    Article  Google Scholar 

  • Jimenez-Zarco, A. I., Gnzalez-Gonzalez, I., Saigi, F., & Torrent-Sellens, J. (2015). The co-learning process in healthcare professionals: assessing user satisfaction in virtual communities of practice. Computers in Human Behavior, 51, 1303–1313.

    Article  Google Scholar 

  • Jin, C. H. (2014). Adoption of e-boo among college students: the perspective of an integrated TAM. Computers in Human Behavior, 41, 471–477.

    Article  Google Scholar 

  • Jolaee, A., Nor, K. M., Khani, N., & Yousoff, R. M. (2014). Factors affecting knowledge sharing intention among academic staff. International Journal of Educational Management, 28(4), 413–431.

    Google Scholar 

  • Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. Management Information Systems Quarterly, 30(4), 781–804.

    Article  Google Scholar 

  • Karubanga, G., Kibwika, P., Okry, F., & Sseguya, H. (2017). How farmer videos trigger social learning to enhance innovation among smallholder rice farmers in Uganda. Cogent Food & Agriculture, 3, 1–16.

    Article  Google Scholar 

  • Koh, J., & Kim, Y. G. (2004). Knowledge sharing in virtual communities: an e-business perspective. Expert Systems with Applications, 26, 155–166.

    Article  Google Scholar 

  • Krejcie, R., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610.

    Article  Google Scholar 

  • Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: an extended expectation–confirmation model in web–based services. Electronic Commerce Research and Applications, 10(3), 342–357.

    Article  Google Scholar 

  • Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information Management, 40, 191–204.

    Article  Google Scholar 

  • Leng, G. S., Lada, S., Muhammad, M. Z., Ibrahim, A. A. H. A., & Amboala, T. (2011). An exploration of social networking sites (SNS) adoption in Malaysia using Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB) And Intrinsic Motivation. Journal of Internet Banking and Commerce, 16(2), 1–27.

    Google Scholar 

  • Lin, H. C. (2014). An investigation of the effects of cultural differences on physicians’ perceptions of information technology acceptance as they related to knowledge management systems. Computers in Human Behavior, 38, 368–380.

    Article  Google Scholar 

  • Lin, M. J. J., Hung, S. W., & Chen, C. J. (2009). Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior, 25(4), 929–939.

    Article  Google Scholar 

  • Lin, C. H., Shih, H. Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: the TRAM model. Psychology & Marketing, 24(7), 641–657.

    Article  Google Scholar 

  • Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review, 23(2), 242–266.

    Article  Google Scholar 

  • Nammir, D. S. S., Marane, B. M. R., & Ali, A. M. (2012). Determine the role of customer engagement on relationship quality and relationship performance. European Journal of Business and Management, 4(11), 27–36.

    Google Scholar 

  • Nasri, W., & Charfeddine, L. (2012). Factors affecting the adoption of Internet banking in Tunisia: an integration theory of acceptance model and theory of planned behavior. Journal of High Technology Management Research, 23, 1–14.

    Article  Google Scholar 

  • Naudiyal, N., Arunachalam, K., & Kumar, U. (2019). The future of mountain agriculture amidst continual farm-exit, livelihood diversification and outmigration in the central Himalayan villages. Journal of Mountain Science, 16(4), 755–768.

    Article  Google Scholar 

  • Nistor, N., Baltes, B., Dacalu, M., Mihaila, D., & Smeaton, & Trausan-Matu, S. (2014). Participation in virtual academic communities of practice under the influence of technology acceptance and community factors. A learning analytics application. Computers in Human Behavior, 34, 339–344.

    Article  Google Scholar 

  • Ozgen, E., & Minsky, B. D. (2007). Opportunity recognition in rural entrepreneurship in developing countries. International Journal of Entrepreneurship, 11(2007), 49–73.

    Google Scholar 

  • Papadopoulos, T., Stamati, T., & Nopparuch, P. (2013). Exploring the determinants of knowledge sharing via employee weblogs. International Journal of Information Management, 33(1), 133–146.

    Article  Google Scholar 

  • Parasuraman, A. (2000). Technology readiness index (TRI): a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.

    Article  Google Scholar 

  • Parasuraman, A., & Colby, C. L. (2001). Techno-ready marketing: how and why your customers adopt technology. New York: Free Press.

    Google Scholar 

  • Park, J. H., Gu, B., Lung, A. C. M., & Konana, P. (2014). An investigation of information sharing and seeking behaviours in online investment communities. Computers in Human Behavior, 31, 1–12.

    Article  Google Scholar 

  • Park, J., & Gabbard, J. L. (2018). Factors that affect scientists’ knowledge sharing behavior in health and life sciences research communities: differences between explicit and implicit knowledge. Computers in Human Behavior, 78, 326–355.

    Article  Google Scholar 

  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69–103.

    Google Scholar 

  • Pi, S. M., Chou, C. H., & Liao, H. L. (2013). A study of Facebook groups members’ knowledge sharing. Computers in Human Behavior, 29, 1971–1979.

    Article  Google Scholar 

  • Priyadarshi, S. (2002). Reforming global trade in agriculture: a developing country perspective. Environment and development: Trade Retrieved December 15, 2005, from www.ceip.org/files/pdf/TED_2.pdf.

    Google Scholar 

  • Punnoose, A. C. (2012). Determinants of intention to use elearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11, 301–337.

    Article  Google Scholar 

  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

    Google Scholar 

  • Rolls, K., Hansen, M., Jackson, D., & Elliott, D. (2016). How health professionals use social media to create virtual communities: an integrative review. Journal of Medical Internet Research, 18(6), e166. https://doi.org/10.2196/jmir.5312.

    Article  Google Scholar 

  • Sobrero, P. M. (2008). Essential components for successful virtual learning communities. Journal of Extension, 46(4) Retrieved from https://joe.org/joe/2008august/a1.php.

  • Sohrabi Safa, N., & Von Solms, R. (2016). An information security knowledge sharing model in organizations. Computers in Human Behavior, 57, 442–451.

    Article  Google Scholar 

  • Tamjidyamcholo, A., Bin Baba, M. S., Mohd Shuib, N. L., & Rohani, V. A. (2014). Evaluation model for knowledge sharing in information security professional virtual community. Computers & Security, 43, 19–34.

    Article  Google Scholar 

  • Tang, Y., & Hew, K. F. (2017). Is mobile instant messaging (MIM) useful in education? Examining its technological, pedagogical, and social affordances. Educational Research Review, 21, 85–104.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

    Article  Google Scholar 

  • Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In H. E. Howe (Ed.), Nebraska symposium on motivation (pp. 195–259). Lincoln: University of Nebraska Press.

    Google Scholar 

  • Van Mele, P., Wanvoeke, J., & Zossou, E. (2010). Enhancing rural learning, linkages and institutions: the rice videos in Africa. Development in Practice, 20, 414–421.

    Article  Google Scholar 

  • Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., Davis, G. B., Davis, F. D., & Morris, M. G. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Victorino, L., Karniouchina, E., & Verma, R. (2009). Exploring the use of the abbreviated Technology Readiness Index for hotel customer segmentation. Cornell Hospitality Quarterly, 50(3), 342–359.

    Article  Google Scholar 

  • Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44, 206–215.

    Article  Google Scholar 

  • Wang, D., Xiang, Z., & Fesenmaier, D. R. (2014). Adapting to the mobile world: a model of smartphone use. Annals of Tourism Research, 48, 11–26.

    Article  Google Scholar 

  • Wasko, M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. Management Information Systems Quarterly, 29(1), 34–56.

    Article  Google Scholar 

  • Xia, M., Zhang, Y., & Zhang, C. (2017). A TAM-based approach to explore the effect of online experience on destination image: a smartphone user’s perspective. Journal of Destination Marketing & Management., 8, 259–270. https://doi.org/10.1016/j.jdmm.2017.05.002.

    Article  Google Scholar 

  • Zamani, N., & Mohammadi, M. (2018). Entrepreneurial learning as experienced by agricultural graduate entrepreneurs. Higher Education, 76(2), 301–316.

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge Dr. Dariush Hayati, Dr. Kurosh Rezaei-Moghaddam, Dr. Ghasem Salimi, and Dr. Mehdi Mohammadi for their invaluable comments and suggestions throughout the research project. We also appreciate the comments from the reviewers on a prior draft of this manuscript, and thank all research participants.

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  1. Department of Agricultural Extension and Education, Shiraz University, Shiraz, Iran

    Naser Zamani, Fatemeh Kazemi & Ehsan Masoomi

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  1. Naser Zamani
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Correspondence to Ehsan Masoomi.

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Zamani, N., Kazemi, F. & Masoomi, E. Determinants of entrepreneurial knowledge and information sharing in professional virtual learning communities created using mobile messaging apps. J Glob Entrepr Res 11, 113–127 (2021). https://doi.org/10.1007/s40497-021-00275-0

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