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지식경영연구 = Knowledge Management Research, v.21 no.3, 2020년, pp.67 - 87
강민형 (아주대학교 경영대학 이비즈니스학과)
AI-Helper
Maintaining active users who repeatedly share high-quality knowledge is critical for the success of online Q&A sites. This study suggests two paths that lead to active users' continuous knowledge sharing: 1) elaborated decision process, represented by continuance intention, and 2) automated cognitiv...
| 핵심어 | 질문 | 논문에서 추출한 답변 |
|---|---|---|
| 온라인 Q&A 사이트에 양질의 답변을 많이 등록하는 핵심 사용자들을 동기부여 하기 위한 제도에는 어떤 것이 있는가? | 따라서 이들이 지식공유를 지속하도록 하는 것은 온라인 Q&A 사이트를 성공적으로 운영하는 데 필수적인 조건이 된다(Fang, Chencheng and Zhang 2019). 사이트 운영회사들은 양질의 답변을 많이 등록하는 핵심 사용자들을 동기부여 하기 위해 ‘파워지식인’(네이버 지식인), ‘Top Writers’(Quora.com), ‘Leaderboard’(Yahoo! Answers)와 같은 제도를 운영하고 있다. | |
| 핵심 사용자의 지식공유 지속행위를 유발하는 두가지 경로는? | 0 트렌드의 대표적 사례로, 고품질의 지식을 반복적으로 공유하는 핵심 사용자들이 지속적으로 지식을 공유하도록 하는 것이 사이트의 성패를 결정짓는다. 본 연구는 핵심 사용자의 지식공유 지속행위를 유발하는 두가지 경로, 즉 지속의도로 대표되는 정교한 의사결정 과정(elaborate decision process)와 과거행위로 대표되는 자동화된 인지적 과정(automated cognitive process)을 제안하였다. 네이버 지식인의 핵심 사용자 337인의 주관적 의도 데이터와 객관적 온라인 행동 데이터를 수집한 뒤, 지속의도와 과거행위의 직접효과 및 둘 간의 조절효과를 검증해 보았다. | |
| 콕스비례위험 회귀분석과 음이항 회귀분석을 사용하여 지속의도와 과거행위가 지속행위의 두가지 유형에 미치는 효과는 어떻게 나타났는가? | 종속변수로 이전 연구에서 주로 사용되었던 지식공유 빈도를 측정하는 것과 더불어, 특정 기간 이상 답변활동이 없을 경우 지식공유를 중단한 것으로 판단하는 지식공유 지속여부를 측정하였다. 콕스비례위험 회귀분석과 음이항 회귀분석을 사용하여 지속의도와 과거행위가 지속행위의 두가지 유형에 미치는 효과를 살펴본 결과, 지식공유 지속여부에는 과거행위만 유의한 영향력을 보였으며, 지식공유 빈도에는 지속의도와 과거행위 모두 유의한 영향력을 보였다. 또한, 과거행위가 지속의도의 지식공유 빈도에 대한 영향력을 부정적으로 조절하는 것까지 확인할 수 있었다. |
김재희 (2016). R을 이용한 생존분석 기초. 파주: 자유아카데미.
지은정, 박상준, 안소연, 이민정 (2018). 안과영역에서 생존분석 방법론 적용. Journal of Retina, 3(1), 1-12.
Aarts, H., Verplanken, B., & Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage Publication, Inc.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control. Springer.
Barnes, S. J. (2011). Understanding use continuance in virtual worlds: Empirical test of a research model. Information & Management, 48(8), 313-319.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
Bland, J. M., & Altman, D. G. (2004). The logrank test. BMJ, (328), 1073.
Bornfeld, B., & Rafaeli, S. (2019). When interaction is valuable: Feedback, churn and survival on community question and answer sites: The case of stack exchange. Proceedings of the 52nd Hawaii International Conference on System Sciences.
Cameron, A. C., & Trivedi, P. K. (1990). Regression-based tests for overdispersion in the poisson model. Journal of Econometrics, 46(3), 347-364.
Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data. Cambridge, UK, Cambridge University Press.
Cheung, C. M., Lee, M. K., & Lee, Z. W. (2013). Understanding the continuance intention of knowledge sharing in online communities of practice through the post-knowledge-sharing evaluation processes. Journal of the American Society for Information Science and Technology, 64(7), 1357-1374.
Chiu, C. M., Wang, E. T., Shih, F. J., & Fan, Y. W. (2011). Understanding knowledge sharing in virtual communities: An integration of expectancy disconfirmation and justice theories. Online Information Review, 35(1), 134-153.
Ciuca, V., & Matei, M. (2010). Survival analysis for the unemployment duration. Proceedings of the 5th WSEAS International Conference on Economy and Management Transformation.
Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429-1464.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-202.
Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to poisson regression and its alternatives. Journal of Personality Assessment, 91(2), 121-136.
Danner, U. N., Aarts, H., & De Vries, N. K. (2008). Habit Vs. intention in the prediction of future behaviour: The role of frequency, context stability and mental accessibility of past behaviour. British Journal of Social Psychology, 47(2), 245-265.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
De Guinea, A. O., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. Mis Quarterly, 33(3), 433-444.
Deng, Y., Hou, J., Ma, X., & Cai, S. (2013). A dual model of entertainment-based and community-based mechanisms to explore continued participation in online entertainment communities. Cyberpsychology, Behavior, and Social Networking, 16(5), 378-384.
Fang, C., & Zhang, J. (2019). Users' continued participation behavior in social q&a communities: A motivation perspective. Computers in Human Behavior, (92), 87-109.
Fang, Y. H., & Chiu, C. M. (2010). In justice we trust: Exploring knowledge-sharing continuance intentions in virtual communities of practice. Computers in Human Behavior, 26(2), 235-246.
Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention, and behavior: An introduction to theory and research, reading. MA, Addison-Wesley Publishing Company.
Gazan, R. (2011). Social Q&A. Journal of the American Society for Information Science and Technology, 62(12), 2301-2312.
Guan, T., Wang, L., Jin, J., & Song, X. (2018). Knowledge Contribution behavior in online Q&A communities: An empirical investigation. Computers in Human Behavior, (81), 137-147.
Hairs, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Printice Hall.
Hartley, S. L., Barker, E. T., Seltzer, M. M., Floyd, F., Greenberg, J., Orsmond, G., & Bolt, D. (2010). The relative risk and timing of divorce in families of children with an autism spectrum disorder. Journal of Family Psychology, 24(4), 449.
Hashim, K. F., & Tan, F. B. (2015). The mediating role of trust and commitment on members' continuous knowledge sharing intention: A commitment-trust theory perspective. International Journal of Information Management, 35(2), 145-151.
Hong, S., Thong, J. Y., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819-1834.
Hung, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: An extended expectation-confirmation model of information system use. International Journal of Mobile Communications, 5(4), 409-422.
Jin, J., Li, Y., Zhong, X., & Zhai, L. (2015). Why users contribute knowledge to online communities: An empirical study of an online social Q&A community. Information & Management, 52 (7), 840-849.
Jin, X. L., Zhou, Z., Lee, M. K. O., & Cheung, C. M. K. (2013). Why users keep answering questions in online question answering communities: A theoretical and empirical investigation. International Journal of Information Management, 33(1), 93-104.
Kang, M. (2018). Active users' knowledge-sharing continuance on social Q&A sites: Motivators and hygiene factors. Aslib Journal of Information Management, 70(2), 214-232.
Khansa, L., Ma, X., Liginlal, D., & Kim, S. S. (2015). Understanding members' active participation in online question-and-answer communities: A theory and empirical analysis. Journal of Management Information Systems, 32(2), 162-203.
Kim, B. (2011). Understanding antecedents of continuance intention in social-networking services. Cyberpsychology, Behavior, and Social Networking, 14(4), 199-205.
Kim, B., Kang, M., & Jo, H. (2014). Determinants of postadoption behaviors of mobile communications applications: A dual-model perspective. International Journal of Human-Computer Interaction, 30(7), 547-559.
Kim, S. S. (2009). The integrative framework of technology use: An extension and test. Mis Quarterly, 33(3), 513-537.
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418-432.
Li, Z., Zhou, S., Choubey, S., & Sievenpiper, C. (2007). Failure event prediction using the cox proportional hazard model driven by frequent failure signatures. IIE Transactions, 39(3), 303-315.
Lim, S., Xue, L., Yen, C. C., Chang, L., Chan, H. C., Tai, B. C., Duh, H. B. L., & Choolani, M. (2011). A study on Singaporean women's acceptance of using mobile phones to seek health information. International Journal of Medical Informatics, 80(12), e189-e202.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737.
Mirkovski, K., Jia, Y., Liu, L., & Chen, K. (2018). Understanding microblogging continuance intention: The directed social network perspective. Information Technology & People, 31(1), 215-238.
Nunnally, J. C. (1978). Psychometric theory. New York, NY, McGraw-Hill.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54-74.
Paroutis, S., & Al Saleh, A. (2009). Determinants of knowledge sharing using web 2.0 technologies. Journal of Knowledge Management, 13(4), 52-63.
Pudipeddi, J. S., Akoglu, L., & Tong, H. (2014). User churn in focused question answering sites: Characterizations and prediction. Proceedings of the 23rd International Conference on World Wide Web, Seoul, South Korea.
R Core Team (2020). R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing. Vol.
Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696.
Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decisions, and habits as determinants of repeated behavior. In A. R. Pratkanis, S. J. Breckler & A. G. Greenwald (Eds.), Attitude structure and function. London, United Kingdom, Psychology Press.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.
Triandis, H. C. (1980). Value attitude and interpersonal behavior, Nebraska Symposium on Motivation, 1979: Belief, attitude and value. In H. E. Howe & M. Page (Eds.), Nebraska symposium on motivation. Lincoln, NE, University of Nebraska Press.
Tsai, H. T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities: Cognitive, emotional, and social influences. Mis Quarterly, 38(1), 143-164.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang, X., Zhao, K., & Street, N. (2017). Analyzing and predicting user participations in online health communities: A social support perspective. Journal of Medical Internet Research, 19(4), e130.
Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21(6), 680-698.
Yang, J., Wei, X., Ackerman, M. S., & Adamic, L. A. (2010). Activity lifespan: An analysis of user survival patterns in online knowledge sharing communities. Fourth International AAAI Conference on Weblogs and Social Media.
Zhang, Y., Fang, Y., Wei, K. K., & Chen, H. (2010). Exploring the role of psychological safety in promoting the intention to continue sharing knowledge in virtual communities. International Journal of Information Management, 30(5), 425-436.
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524.
Zhou, T. (2018). Understanding online knowledge community user continuance: A social cognitive theory perspective. Data Technologies and Applications, 52(3), 445-458.
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