Skip to main content
Log in

Which questions are valuable in online Q&A communities? A question’s position in a knowledge network matters

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Online Q&A communities serve as important channels for diffusing knowledge. Questions, the main content in these forums, are not well investigated in the research literature. This study explores why some questions attract more attention in this context by emphasizing the knowledge network embedded in the questions. We examine how the characteristics of the question tags impact the popularity of the questions by collecting data from a programming-related Q&A site—Stack Overflow. We collected data for ten years, spanning the years 2008 to 2017, and included 6,833,276 users and 34,857,917 questions and answers in the analyses. The empirical analyses indicated that the frequency of the question tag is positively related to the popularity of the question. Additionally, the tag distance of the questions is curvilinearly related to the popularity of the questions in an inverted U-shape. Our study contributes to research discussions on how knowledge is diffused in an online community and further examines how these types of online forums efficiently promote knowledge sharing through the presentation of user’s questions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.

  • Allamanis, M., & Sutton, C. (2013). Why, when, and what: Analyzing stack overflow questions by topic, type, and code. In 2013 10th Working conference on mining software repositories (MSR) (pp. 53–56). IEEE.

  • Arai, K., & Handayani, A. N. (2013). Predicting quality of answer in collaborative Q/A community. International Journal of Advanced Research in Artificial Intelligence, 2(3), 21–25.

    Article  Google Scholar 

  • Badar, K., Hite, J. M., & Ashraf, N. (2015). Knowledge network centrality, formal rank and research performance: Evidence for curvilinear and interaction effects. Scientometrics, 105(3), 1553–1576.

    Article  Google Scholar 

  • Barua, A., Thomas, S. W., & Hassan, A. E. (2014). What are developers talking about? An analysis of topics and trends in stack overflow. Empirical Software Engineering, 19(3), 619–654.

    Article  Google Scholar 

  • Bhat, V., Gokhale, A., Jadhav, R., Pudipeddi, J., & Akoglu, L. (2015). Effects of tag usage on question response time. Social Network Analysis and Mining, 5(1), 24.

    Article  Google Scholar 

  • Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic organizational climate. MIS Quarterly, 29(1), 87–111.

    Article  Google Scholar 

  • Burgelman, R. A. (1991). Intraorganizational ecology of strategy making and organizational adaptation: Theory and field research. Organization Science, 2(3), 239–262.

    Article  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(4), 226–236.

    Article  Google Scholar 

  • Chiu, C. M., Hsu, M. H., & Wang, E. T. G. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872–1888.

    Article  Google Scholar 

  • Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.

    Google Scholar 

  • Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ, 2(4), 169–187.

    Google Scholar 

  • Dahlander, L., & Piezunka, H. (2014). Open to suggestions: How organizations elicit suggestions through proactive and reactive attention. Research Policy, 43(5), 812–827.

    Article  Google Scholar 

  • Dutton, J. E., & Ashford, S. J. (1993). Selling issues to top management. Academy of Management Review, 18, 397–428.

    Article  Google Scholar 

  • Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal, 21(3), 345–367.

    Article  Google Scholar 

  • Emerson, R. M. (1976). Social exchange theory. Annuals Review of Sociology, 2(15), 335–362.

    Article  Google Scholar 

  • Espinosa, J. A., Slaughter, S. A., Kraut, R. E., & Herbsleb, J. D. (2007). Familiarity, complexity, and team performance in geographically distributed software development. Organization Science, 18, 613–630.

    Article  Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Article  MathSciNet  Google Scholar 

  • Haans, R. F. J., Pieters, C., & He, Z. (2016). Thinking about U: Theorizing and testing U-and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177–1195.

    Article  Google Scholar 

  • Haas, M. R., Criscuolo, P., & George, G. (2015). Which problems to solve? Online knowledge sharing and attention allocation in organizations. Academy of Management Journal, 58(3), 680–711.

    Article  Google Scholar 

  • Hansen, M. T., & Haas, M. R. (2001). Competing for attention in knowledge markets: Electronic document dissemination in a management consulting company. Administrative Science Quarterly, 46(1), 1–28.

    Article  Google Scholar 

  • Hsu, M. H., Ju, T. L., Yen, C. H., & Chang, C. M. (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 

  • Jeon, J., Croft, W. B., Lee, J. H., & Park, S. (2006). A framework to predict the quality of answers with non-textual features. In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 228–235).

  • Jeppesen, L. B., & Frederiksen, L. (2006). Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organization Science, 17(1), 45–63.

    Article  Google Scholar 

  • Jiang, S., & Chen, H. (2019). Examining patterns of scientific knowledge diffusion based on knowledge cyber infrastructure: A multi-dimensional network approach. Scientometrics, 121(3), 1599–1617.

    Article  Google Scholar 

  • Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15(2), 194–210.

    Article  Google Scholar 

  • Kane, G. C., Johnson, J., & Majchrzak, A. (2014). Emergent life cycle: The tension between knowledge change and knowledge retention in open online coproduction communities. Management Science, 60(12), 3026–3048.

    Article  Google Scholar 

  • Kiron, D., Palmer, D., Phillips, A. N., & Kruschwitz, N. (2012). Social business: What are companies really doing? MIT Sloan Management Review, 53(4), 1.

    Google Scholar 

  • Kotha, R., George, G., & Srikanth, K. (2013). Bridging the mutual knowledge gap: Coordination and the commercialization of university science. Academy of Management Journal, 56(2), 498–524.

    Article  Google Scholar 

  • Krätke, S. (2010). Regional knowledge networks: A network analysis approach to the interlinking of knowledge resources. European Urban and Regional Studies, 17(1), 83–97.

    Article  Google Scholar 

  • Larsen, K. (2008). Knowledge network hubs and measures of research impact, science structure, and publication output in nanostructured solar cell research. Scientometrics, 74(1), 123–142.

    Article  Google Scholar 

  • Leonardi, P. M. (2015). Ambient awareness and knowledge acquisition: Using social media to learn ‘who knows what’and ‘who knows whom.’ MIS Quarterly, 39(4), 747–762.

    Article  Google Scholar 

  • Leskovec, J., Huttenlocher, D., & Kleinberg, J. (2010). Governance in social media: A case study of the Wikipedia promotion process. In Fourth International AAAI Conference on Weblogs and Social Media (pp. 98–105).

  • Li, H., Shankar, R., & Stallaert, J. (2020). Invested or indebted: Ex-ante and ex-post reciprocity in online knowledge sharing communities. ACM Transactions on Management Information Systems (TMIS), 11(1), 1–26.

    Article  Google Scholar 

  • Li, Q., Maggitti, P. G., Smith, K. G., Tesluk, P. E., & Katila, R. (2013). Top management attention to innovation: The role of search selection and intensity in new product introductions. Academy of Management Journal, 56(3), 893–916.

    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 

  • Lind, J. T., & Mehlum, H. (2010). With or without U? The appropriate test for a U-shaped relationship. Oxford Bulletin of Economics and Statistics, 72(1), 109–118.

    Article  Google Scholar 

  • Mitsuhashi, H., & Greve, H. R. (2009). A matching theory of alliance formation and organizational success: Complementarity and compatibility. Academy of Management Journal, 52, 975–995.

    Article  Google Scholar 

  • Ocasio, W. (1997). Towards an attention-based view of the firm. Strategic Management Journal, 18, 187–206.

    Article  Google Scholar 

  • Oktay, H., Taylor, B. J., & Jensen, D. D. (2010). Causal discovery in social media using quasi-experimental designs. In Proceedings of the first workshop on social media analytics (pp. 1–9).

  • Pal, A., Farzan, R., Konstan, J. A., & Kraut, R. E. (2011). Early detection of potential experts in question answering communities. In International conference on user modeling, adaptation, and personalization (pp. 231–242). Springer.

  • Ponzanelli, L., Bavota, G., Di Penta, M., Oliveto, R., & Lanza, M. (2014). Mining StackOverflow to turn the IDE into a self-confident programming prompter. In Proceedings of the 11th working conference on mining software repositories (pp. 102–111).

  • Quigley, N. R., Tesluk, P. E., Locke, E. A., & Bartol, K. M. (2007). A multilevel investigation of the motivational mechanisms underlying knowledge sharing and performance. Organization Science, 18(1), 71–88.

    Article  Google Scholar 

  • Reagans, R. (2011). Close encounters: Analyzing how social similarity and propinquity contribute to strong network connections. Organization Science, 22, 835–849.

    Article  Google Scholar 

  • Roth, C., & Bourgine, P. (2006). Lattice-based dynamic and overlapping taxonomies: The case of epistemic communities. Scientometrics, 69(2), 429–447.

    Article  Google Scholar 

  • Simon, H. A. (1965). Administrative behavior. New York: The Macmillan Company.

    Google Scholar 

  • Stanley, C., & Byrne, M. D. (2013). Predicting tags for stackoverflow posts. In Proceedings of ICCM (Vol. 2013).

  • Stephan, P. E., & Robert, K. (2004). Merton’s perspective on priority and the provision of the public good knowledge. Scientometrics, 60(1), 81–87.

    Article  Google Scholar 

  • Stirling, A. (1994). Diversity and ignorance in electricity supply investment: Addressing the solution rather than the problem. Energy Policy, 22(3), 195–216.

    Article  Google Scholar 

  • Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719.

    Article  Google Scholar 

  • Sutanto, J., Jiang, Q., & Tan, C-H. (2021). The contingent role of interproject connectedness in cultivating open source software projects. The Journal of Strategic Information Systems, 30(1), 101598. https://doi.org/10.1016/j.jsis.2020.101598.

    Article  Google Scholar 

  • Treude, C., Barzilay, O., & Storey, M.-A. (2011). How do programmers ask and answer questions on the web? (NIER track). In Proceedings of the 33rd international conference on software engineering (pp. 804–807).

  • Treude, C., Figueira Filho, F., Cleary, B., & Storey, M. A. (2012). Programming in a socially networked world: The evolution of the social programmer. The Future of Collaborative Software Development, 1–3.

  • Tsai, H. T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities: Cognitive, emotional, and social influences. MIS Quarterly, 38(1), 143–164.

    Article  Google Scholar 

  • Vissa, B. (2011). A matching theory of entrepreneurs’ tie formation intentions and initiation of economic exchange. Academy of Management Journal, 54, 137–158.

    Article  Google Scholar 

  • Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57.

    Article  Google Scholar 

  • Wu, M. L., Kang, L., Shi, Y. N., Zhao, J. L., & Liang, L. (2018). Why people are involved in and committed to online knowledge-sharing communities: An expectancy-value perspective. Journal of Global Information Management, 27(2), 78–101.

    Article  Google Scholar 

  • Wu, M., Xu, X. J., Kang, L., Zhao, J. L., & Liang, L. (2019). Encouraging people to embrace feedback-seeking in online learning: An investigation of informational and relational drivers. Internet Research, 29(4), 749–771.

    Article  Google Scholar 

  • Wu, M., Kang, L., Shi, Y., Zhao, J. L., & Liang, L. (2019a). Why people are involved in and committed to online knowledge-sharing communities: An expectancy-value perspective. Journal of Global Information Management, 27(2), 78–101.

    Article  Google Scholar 

  • Wu, M., Xu, X., Kang, L., Zhao, J. L., & Liang, L. (2019b). Encouraging people to embrace feedback-seeking in online learning. Internet Research, 29(4), 749–771.

    Article  Google Scholar 

  • Zhang, W., Du, W., Bian, Y., Peng, C-H., & Jiang, Q. (2020). Seeing is not always believing: An exploratory study of clickbait in WeChat. Internet Research, 30(3), 1043–1058.

    Article  Google Scholar 

Download references

Acknowledgements

This work was jointly supported by the Social Science Foundation of Jiangsu Province (No. 20TQC003), the National Natural Science Foundation of China (NSFC No. 71704078 and No.72072087), the National Social Science Fund of China (No. 18CTQ019).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Si Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Y., Chen, S. & Kang, L. Which questions are valuable in online Q&A communities? A question’s position in a knowledge network matters. Scientometrics 126, 8239–8258 (2021). https://doi.org/10.1007/s11192-021-04135-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-021-04135-2

Keywords

Navigation