Skip to main content

Advertisement

Springer Nature Link
Log in

Identifying contributory domain experts in online innovation communities

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

Conventional approaches for identifying domain experts focus only on their level of expertise and fail to consider their innovation potential. Thus, we propose a more comprehensive method by considering the types of innovation tasks and their corresponding knowledge domains. With a set of novel and effective metrics, the proposed method is able to assess the knowledge quality and innovation potential of each participating user. We evaluate our method with a real-world dataset collected from a popular online innovation community. The results indicate that the proposed method is efficient and scalable for contributory domain expert identification with different innovation tasks and different knowledge domains. This work expands expert identification research by providing both a new theoretical angle and new technical solution for quantifying the value of users.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Von Hippel, E. (1986). Lead users: A source of novel product concepts. Management science, 32(7), 791–805. https://doi.org/10.1287/mnsc.32.7.791

    Article  Google Scholar 

  2. Eisenberg, I. (2011). Lead-user research for breakthrough innovation. Research-Technology Management, 54(1), 50–58. https://doi.org/10.1080/08956308.2011.11657673

    Article  Google Scholar 

  3. Faisal, M. S., Daud, A., Akram, A. U., et al. (2019). Expert ranking techniques for online rated forums. Computers in Human Behavior, 100, 168–176. https://doi.org/10.1016/j.chb.2018.06.013

    Article  Google Scholar 

  4. Dehghan, M., Biabani, M., & Abin, A. A. (2019). Temporal expert profiling: With an application to T-shaped expert finding. Information Processing & Management, 56(3), 1067–1079. https://doi.org/10.1016/j.ipm.2019.02.017

    Article  Google Scholar 

  5. Yang, Z. J., Lin, J., & Yang, Y. S. (2021). Identification of network behavioral characteristics of high-expertise users in interactive innovation: The case of forum autohome. Asia Pacific Management Review, 26(1), 11–22. https://doi.org/10.1016/j.apmrv.2020.06.002

    Article  Google Scholar 

  6. Chen, Z. S., Zhang, X., Rodríguez, R. M., Pedrycz, W., & Martínez, L. (2021). Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III. Automation in Construction, 125, 103578. https://doi.org/10.1016/j.autcon.2021.103578

    Article  Google Scholar 

  7. Zhou, X., Liang, X., Du, X., et al. (2017). Structure based user identification across social networks. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1178–1191. https://doi.org/10.1109/tkde.2017.2784430

    Article  Google Scholar 

  8. Cao, J., Yang, Y., Cao, B., et al. (2019). Local experts finding using user comments in location-based social networks. Transactions on Emerging Telecommunications Technologies, 30(9), e3600. https://doi.org/10.1002/ett.3600

    Article  Google Scholar 

  9. Hevner, A., March, S. T., Park, J., et al. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625

    Article  Google Scholar 

  10. Baskerville, R., Baiyere, A., Gregor, S., Hevner, A., & Rossi, M. (2018). Design science research contributions: Finding a balance between artifact and theory. Journal of the Association for Information Systems, 19(5), 358–376. https://doi.org/10.17705/1jais.00495

    Article  Google Scholar 

  11. Ye, H. J., & Kankanhalli, A. (2018). User service innovation on mobile phone platforms: Investigating impacts of lead userness, toolkit support, and design autonomy. MIS Quarterly, 42(1), 165–188. https://doi.org/10.25300/MISQ/2018/12361

    Article  Google Scholar 

  12. Le, T. D. (2018). Influence of WOM and content type on online engagement in consumption communities: The information flow from discussion forums to Facebook. Online Information Review, 42(2), 161–175. https://doi.org/10.1108/OIR-09-2016-0246

    Article  Google Scholar 

  13. Li, Z., et al. (2021). Knowledge topic-structure exploration for online innovative knowledge acquisition. IEEE Transactions on Engineering Management, 68(6), 1880–1894. https://doi.org/10.1109/TEM.2019.2940074

    Article  Google Scholar 

  14. Yang, L., et al. (2017). Identifying opinion leaders in social networks with topic limitation. Cluster Computing, 20(3), 2403–2413. https://doi.org/10.1007/s10586-017-0732-8

    Article  Google Scholar 

  15. Roper, S., & Love, J. H. (2018). Knowledge context, learning and innovation: An integrating framework. Industry and Innovation, 25(4), 339–364. https://doi.org/10.1080/13662716.2017.1414744

    Article  Google Scholar 

  16. Füller, J., et al. (2014). User roles and contributions in innovation-contest communities. Journal of Management Information Systems, 31(1), 273–308. https://doi.org/10.2753/MIS0742-1222310111

    Article  Google Scholar 

  17. Deng, S., Jiang, Y., Li, H., & Liu, Y. (2020). Who contributes what? Scrutinizing the activity data of 4.2 million Zhihu users via immersion scores. Information Processing & Management, 57(5), 102274. https://doi.org/10.1016/j.ipm.2020.102274

    Article  Google Scholar 

  18. Wang, Z. and P. Zhang. (2016). Examining user roles in social Q&A: the case of health topics in Zhihu. com. Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology. American Society for Information Science. https://doi.org/10.1002/pra2.2016.14505301103

  19. Akar, E., & Mardikyan, S. (2018). User roles and contribution patterns in online communities: A managerial perspective. SAGE Open, 8(3), 1–19. https://doi.org/10.1177/2158244018794773

    Article  Google Scholar 

  20. Guo, W., et al. (2017). User roles and contributions during the new product development process in collaborative innovation communities. Applied Ergonomics, 63, 106–114. https://doi.org/10.1016/j.apergo.2017.04.013

    Article  Google Scholar 

  21. Çiçek, M., & Eren-Erdogmus, I. (2013). Social media marketing: Exploring the user typology in Turkey. International Journal of Technology Marketing, 8(3), 254–271. https://doi.org/10.1504/IJTMKT.2013.055343

    Article  Google Scholar 

  22. Waheed, M., & Kaur, K. (2016). Knowledge quality: A review and a revised conceptual model. Information Development, 32(3), 271–284. https://doi.org/10.1177/0266666914539694

    Article  Google Scholar 

  23. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. https://doi.org/10.1287/orsc.5.1.14

    Article  Google Scholar 

  24. Waheed, M., Kaur, K., & Qazi, A. (2016). Students’ perspective on knowledge quality in eLearning context: A qualitative assessment. Internet Research, 26(1), 120–145. https://doi.org/10.1108/IntR-08-2014-0199

    Article  Google Scholar 

  25. Yagci, I. A., & Das, S. (2018). Measuring design-level information quality in online reviews. Electronic Commerce Research and Applications, 30, 102–110. https://doi.org/10.1016/j.elerap.2018.05.010

    Article  Google Scholar 

  26. Kyoon Yoo, D. (2014). Substructures of perceived knowledge quality and interactions with knowledge sharing and innovativeness: A sensemaking perspective. Journal of Knowledge Management, 18(3), 523–537. https://doi.org/10.1108/JKM-09-2013-0362

    Article  Google Scholar 

  27. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  28. Geva, H., Oestreicher-Singer, G., & Saar-Tsechansky, M. (2019). Using retweets when shaping our online persona: Topic modeling approach. MIS Quarterly, 43(2), 501–524.

    Article  Google Scholar 

  29. Zhong, N., & Schweidel, D. A. (2020). Capturing changes in social media content: A multiple latent changepoint topic model. Marketing Science, 39(4), 827–846. https://doi.org/10.1287/mksc.2019.1212

    Article  Google Scholar 

  30. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101

    Article  Google Scholar 

  31. Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3–4), 181–201. https://doi.org/10.1016/0004-3702(73)90011-8

    Article  Google Scholar 

  32. Goel, V., & Pirolli, P. (1992). The structure of design problem spaces. Cognitive Science, 16(3), 395–429. https://doi.org/10.1016/0364-0213(92)90038-V

    Article  Google Scholar 

  33. Kudaravalli, S., Faraj, S., & Johnson, S. L. (2017). A configural approach to coordinating expertise in software development teams. MIS Quarterly, 41(1), 43–64.

    Article  Google Scholar 

  34. Mannucci, P. V., & Yong, K. (2018). The differential impact of knowledge depth and knowledge breadth on creativity over individual careers. Academy of Management Journal, 61(5), 1741–1763. https://doi.org/10.5465/amj.2016.0529

    Article  Google Scholar 

  35. Carlo, J. L., Lyytinen, K., & Rose, G. M. (2012). A knowledge-based model of radical innovation in small software firms. MIS Quarterly. https://doi.org/10.2307/41703484

    Article  Google Scholar 

  36. Fu, H., & Oh, S. (2019). Quality assessment of answers with user-identified criteria and data-driven features in social Q&A. Information Processing & Management, 56(1), 14–28. https://doi.org/10.1016/j.ipm.2018.08.007

    Article  Google Scholar 

  37. Fairchild, C. (2007). Building the authentic celebrity: The “Idol” phenomenon in the attention economy. Popular Music and Society, 30(3), 355–375. https://doi.org/10.1080/03007760600835306

    Article  Google Scholar 

  38. Ho-Dac, N. N. (2020). The value of online user generated content in product development. Journal of Business Research, 112, 136–146. https://doi.org/10.1016/j.jbusres.2020.02.030

    Article  Google Scholar 

  39. Gruber, M., Harhoff, D., & Hoisl, K. (2013). Knowledge recombination across technological boundaries: scientists vs. engineers. Management Science, 59(4), 837–851. https://doi.org/10.1287/mnsc.1120.1572

    Article  Google Scholar 

  40. Gong, J., & Liu, L. (2010). Representing and measuring experts’ knowledge based on knowledge network. Studies in Science of Science, 28(10), 1521–1529.

    Google Scholar 

  41. Chen, W., & Hao, X. (2011). An optimal combination weights method considering both subjective and objective weight information in power quality evaluation. In J. Lee (Ed.), Advanced electrical and electronics engineering (pp. 97–105). Springer.

    Chapter  Google Scholar 

  42. Liao, X. (2015). Knowledge discovering and analyzing methods on user innovation communities based on model construction of multiple knowledge networks. (Doctoral dissertation)

Download references

Funding

We gratefully acknowledge the funding support from the National Natural Science Foundation of China (Grant 72101060, 72071083 and 72171089), the Soft Science Research Program of Guangdong Province (Grant 2020A1010020038), and the Natural Science Foundation of Guangdong Province (Grant 2021A1515012003 and 2019A1515011370).

Author information

Authors and Affiliations

  1. School of Management, Guangdong University of Technology, Laboratory Building, No. 161 Yinglong Road, Tianhe District, Guangzhou, 510520, Guangdong, People’s Republic of China

    Hongting Tang

  2. School of Business Administration, South China University of Technology, Shantou Alumni Building, No. 381 Wushan Road, Tianhe District, Guangzhou, 510640, Guangdong, People’s Republic of China

    Xiaoying Xu, Zhihong Li & Rui Qin

Authors
  1. Hongting Tang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoying Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhihong Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Rui Qin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiaoying Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix Experiment Script (Translated from Chinese)

Appendix Experiment Script (Translated from Chinese)

Experiments on User Knowledge Assessment

Dear Enthusiasts:

Hello! Your participation in this experiment is welcome.

The attached pages contain the post information of 10 users that we randomly selected from the MIUI community. Please carefully read the content of these posts for at least 20 min. After you finish reading the posts, please mark the relevant questions by digit according to what you have read.

Basic information: (The collected data are strictly utilized in this study. Please complete the following questions. Thank you for your cooperation and support!).

  1. 1.

    Name: ________________, Gender: ________________, Age: ________________.

  2. 2.

    Length of time using Xiaomi smartphone: ______; Length of time in MIUI system: ______;

  3. 3.

    Are you familiar with the MIUI community? _______.

  4. 4.

    Completely Unfamiliar; B. Unfamiliar; C. Neutral; D. Familiar; E. Very familiar.

For the following questions, please select a reasonable numerical value for each user’s knowledge according to your reading situation. The number "ww1" means "strongly dissatisfied", and the number "7" means "strongly agree". Please refer to the attached page for details of the target knowledge area.

Users

Questions

Please select

Strongly disagree

Disagree

Slightly disagree

Neutral

Slightly agree

Agree

Strongly agree

Uxx

The quality and quantity of posts posted by this user is very high

1

2

3

4

5

6

7

The post posted by this user is very relevant to the target knowledge area

1

2

3

4

5

6

7

There is also much content in the post posted by this user that is not related to the target knowledge domain

1

2

3

4

5

6

7

…

…

       

If you have any suggestions about this questionnaire or the experiment settings, please write them down in the space below:

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, H., Xu, X., Li, Z. et al. Identifying contributory domain experts in online innovation communities. Electron Commer Res 23, 2759–2787 (2023). https://doi.org/10.1007/s10660-022-09561-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-022-09561-9

Keywords