The more, the better? The effect of feedback and user's past successes on idea implementation in open innovation communities
Qian Liu
China Center for Internet Economy Research, The Central University of Finance and Economics, Beijing, China
Search for more papers by this authorZhengfa Yang
Information School, The Central University of Finance and Economics, Beijing, China
Search for more papers by this authorXiaofang Cai
College of Business, Southern University of Science and Technology, Shenzhen, China
Search for more papers by this authorCorresponding Author
Qianzhou Du
Department of Marketing and E-commerce, Nanjing University, Nanjing, China
Correspondence
Qianzhou Du, Department of Marketing and E-commerce, Nanjing University, Nanjing, China.
Email: [email protected]
Search for more papers by this authorWeiguo Fan
Department of Business Analytics, University of Iowa, Iowa City, Iowa, USA
Search for more papers by this authorQian Liu
China Center for Internet Economy Research, The Central University of Finance and Economics, Beijing, China
Search for more papers by this authorZhengfa Yang
Information School, The Central University of Finance and Economics, Beijing, China
Search for more papers by this authorXiaofang Cai
College of Business, Southern University of Science and Technology, Shenzhen, China
Search for more papers by this authorCorresponding Author
Qianzhou Du
Department of Marketing and E-commerce, Nanjing University, Nanjing, China
Correspondence
Qianzhou Du, Department of Marketing and E-commerce, Nanjing University, Nanjing, China.
Email: [email protected]
Search for more papers by this authorWeiguo Fan
Department of Business Analytics, University of Iowa, Iowa City, Iowa, USA
Search for more papers by this authorFunding information: National Natural Science Foundation of China, Grant/Award Number: 71702206; National Natural Science Foundation of China, Grant/Award Number: 71872149; Beijing Outstanding Young Scientist Program, Grant/Award Number: 01201910034034
Abstract
Establishing open innovation communities has evolved as an important product innovation and development strategy for companies. Yet, the success of such communities relies on the successful implementation of many user-submitted ideas. Although extant literature has examined the impact of user experience and idea characteristics on idea implementation, little is known from the information input perspective, for example, feedback. Based on the information overload theory and knowledge content framework, we propose that the amount and types of feedback content have different effects on the likelihood of subsequent idea implementation, and such effects depend on the level of users' success experience. We tested the research model using a panel logistic model with the data of MIUI Forum. The study results revealed that the amount of feedback has an inverted U-shaped effect on idea implementation, and such effect is moderated by a user's past success. Moreover, the type of feedback content (cost and benefit-related feedback and functionality-related feedback) positively affects idea implementation, and a user's past success positively moderated the above effects. Finally, we discuss the theoretical and practical implications, limitations of our research, and suggestions for future research.
REFERENCES
- Adamczyk, S., Bullinger, A. C., & Möslein, K. M. (2012). Innovation contests: a review, classification and outlook. Creativity and Innovation Management, 21(4), 335–360.
- Barron, F., & Harrington, D. M. (1981). Creativity, intelligence, and personality. Annual Review of Psychology, 32(1), 439–476.
- Batkovskiy, A. M., Konovalova, A. V., Semenova, E. G., Trofimets, V. Y., & Fomina, A. V. (2015). Risks of development and implementation of innovative projects. Mediterranean Journal of Social Sciences, 6(4), 243.
- Bawden, D., & Robinson, L. (2009). The dark side of information: overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191.
- Bayus, B. L. (2010). Crowdsourcing and individual creativity over time: the detrimental effects of past success. SSRN Electronic Journal, 14(3), 128–162.
- Bayus, B. L. (2013). Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community. Management Science, 59(1), 226–244. https://doi.org/10.1287/mnsc.1120.1599
- Benselin, J. C., & Ragsdell, G. (2016). Information overload: the differences that age makes. Journal of Librarianship and Information Science, 48(3), 284–297.
- Berg, J. M. (2016). Balancing on the creative highwire: forecasting the success of novel ideas in organizations. Administrative Science Quarterly, 61(3), 433–468.
- Bowers, J., & Khorakian, A. (2014). Integrating risk management in the innovation project. European Journal of Innovation Management, 17(1), 25–40.
- Chan, K. W., Li, S. Y., & Zhu, J. J. (2015). Fostering customer ideation in crowdsourcing community: the role of peer-to-peer and peer-to-firm interactions. Journal of Interactive Marketing, 31(3), 42–62.
- Chan, K. W., Li, S. Y., & Zhu, J. J. (2018). Good to be novel? understanding how idea feasibility affects idea adoption decision making in crowdsourcing. Journal of Interactive Marketing, 43, 52–68.
- Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
- Cross, R., & Sproull, L. (2004). More than an answer: information relationships for actionable knowledge. Organization Science, 15(4), 446–462.
- Davidson, T., & De Stobbeleir, K. E. (2011). Shaping environments conducive to creativity: the role of feedback, autonomy and self-concordance. Academy of Management Proceedings, 2011(1), 1–6.
- Di Gangi, P. M., & Wasko, M. (2009). Steal my idea! organizational adoption of user innovations from a user innovation community: a case study of Dell IdeaStorm. Decision Support Systems, 48(1), 303–312.
- Di Gangi, P. M., Wasko, M. M., & Hooker, R. E. (2010). Getting customers' ideas to work for you: learning from dell how to succeed with online user innovation communities. Mis Quarterly Executive, 9(4), 213–228. https://aisel.aisnet.org/misqe/vol9/iss4/4
- Fan, Z.-P., Suo, W.-L., & Feng, B. (2012). Identifying risk factors of IT outsourcing using interdependent information: an extended DEMATEL method. Expert Systems with Applications, 39(3), 3832–3840.
- Fleming, L., Mingo, S., & Chen, D. (2007). Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52(3), 443–475.
- Füller, J., Hutter, K., Hautz, J., & Matzler, K. (2014). User roles and contributions in innovation-contest communities. Journal of Management Information Systems, 31(1), 273–308.
- George, G. M., & Zhou, J. (2007). Dual tuning in a supportive context: joint contributions of positive mood, negative mood, and supervisory behaviors to employee creativity. Academy of Management Journal, 50(3), 605–622.
- George, J. M., & Zhou, J. (2001). When openness to experience and conscientiousness are related to creative behavior: an interactional approach. Journal of Applied Psychology, 86(3), 513–524.
- Greyson, D. (2018). Information triangulation: a complex and agentic everyday information practice. Journal of the Association for Information Science and Technology, 69(7), 869–878.
- Haans, R. F., Pieters, C., & He, Z. L. (2016). Thinking about U: theorizing and testing U-and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177–1195.
- Hargittai, E., Neuman, W. R., & Curry, O. (2012). Taming the information tide: perceptions of information overload in the American home. The Information Society, 28(3), 161–173.
- Harrison, S. H., & Rouse, E. D. (2015). An inductive study of feedback interactions over the course of creative projects. Academy of Management Journal, 58(2), 375–404.
- Hon, A. H. Y., Chan, W. W. H., & Lu, L. (2013). Overcoming work-related stress and promoting employee creativity in hotel industry: the role of task feedback from supervisor. International Journal of Hospitality Management, 33, 416–424.
- Hoornaert, S., Ballings, M., & Malthouse, E. C. (2017). Identifying new product ideas: waiting for the wisdom of the crowd or screening ideas in real time[J]. Journal of Product Innovation Management, 34(5), 580–597.
- Jalonen, H. (2012). The uncertainty of innovation: a systematic review of the literature. Journal of Management Research, 4(1), 1.
- 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 and Management, 52(7), 840–849.
- Kirschbaum, R. (2005). Open innovation in practice. Research-Technology Management, 48(4), 24–28.
- Koltay, T. (2017). The bright side of information: ways of mitigating information overload. Journal of Documentation, 73, 767–775.
- Li, M., Kankanhalli, A., & Kim, S. H. (2016). Which ideas are more likely to be implemented in online user innovation communities? an empirical analysis. Decision Support Systems, 84, 28–40.
- Liu, Q., Du, Q., Hong, Y., & Fan, W. (2018). What kind of user-generated ideas are more likely to be implemented? evidence from an open innovation community. Paper presented at the Thirty Ninth International Conference on Information Systems, San Francisco.
- Liu, Q., Du, Q., Hong, Y., Fan, W., & Wu, S. (2020). User idea implementation in open innovation communities: evidence from a new product development crowdsourcing community. Information Systems Journal, 30(5), 899–927.
- Liu, S., Xia, F., Zhang, J., & Wang, L. (2016). How crowdsourcing risks affect performance: an exploratory model. Management Decision, 54(9), 2235–2255.
- Lusch, R. F., & Vargo, S. L. (2006). Service-dominant logic: reactions, reflections and refinements. Marketing Theory, 6(3), 281–288.
- Ma, J., Lu, Y., & Gupta, S. (2019). User innovation evaluation: empirical evidence from an online game community. Decision Support Systems, 17, 113–123.
- Messner, C., & Wänke, M. (2011). Unconscious information processing reduces information overload and increases product satisfaction. Journal of Consumer Psychology, 21(1), 9–13.
- Mishra, J., Allen, D., & Pearman, A. (2015). Information seeking, use, and decision making. Journal of the Association for Information Science and Technology, 66(4), 662–673.
- Modaresnezhad, M., Iyer, L., Palvia, P., & Taras, V. (2020). Information Technology (IT) enabled crowdsourcing: a conceptual framework. Information Processing & Management, 57(2), 102135.
- Ogink, T., & Dong, J. Q. (2017). Stimulating innovation by user feedback on social media: the case of an online user innovation community. Technological Forecasting and Social Change, 144, 295–302.
- Pee, L. G., Pan, S. L., Li, M., & Jia, S. (2020). Social informatics of information value cocreation: a case study of xiaomi's online user community. Journal of the Association for Information Science and Technology, 71(4), 409–422.
- Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015.
- Perry-Smith, J. E. (2014). Social network ties beyond nonredundancy: an experimental investigation of the effect of knowledge content and tie strength on creativity. Journal of Applied Psychology, 99(5), 831–846.
- Poetz, M. K., & Schreier, M. (2012). The value of crowdsourcing: can users really compete with professionals in generating new product ideas? Journal of Product Innovation Management, 29(2), 245–256.
- Ramachandran, V., & Gopal, A. (2010). Managers' judgments of performance in IT services outsourcing. Journal of Management Information Systems, 26(4), 181–218.
- Schemmann, B., Chappin, M. M. H., & Herrmann, A. M. (2017). The right kind of people: characteristics of successful ideators' online behaviour. Creativity and Innovation Management, 26(3), 277–290.
- Schlagwein, D., & Andersen, N. (2014). Organizational learning with crowdsourcing: the revelatory case of LEGO. Journal of the Association for Information Systems, 15(11), 754–778.
- Sijbom, R. B. L., Anseel, F., Crommelinck, M., Beuckelaer, A. D., & Stobbeleir, K. E. M. D. (2017). Why seeking feedback from diverse sources may not be sufficient for stimulating creativity: the role of performance dynamism and creative time pressure. Journal of Organizational Behavior, 39(3), 355–368.
- Stobbeleir, K. E. M. D., Ashford, S. J., & Buyens, D. (2011). Self-regulation of creativity at work: the role of feedback-seeking behavior in creative performance. Academy of Management Journal, 54(4), 811–831.
- Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.
- Turner, R., Ledwith, A., & Kelly, J. (2012). Project management in small to medium-sized enterprises: tailoring the practices to the size of company. Management Decision, 50(5), 942–957.
- Wang, X., Schneider, C., & Valacich, J. S. (2015). Enhancing creativity in group collaboration: how performance targets and feedback shape perceptions and idea generation performance. Computers in Human Behavior, 42, 187–195.
- Wooldridge, J. M. (2016). Introductory econometrics: A modern approach. Nelson Education.
- Wooten, J. O., & Ulrich, K. T. (2017). Idea generation and the role of feedback: evidence from field experiments with innovation tournaments. Production and Operations Management, 26(1), 80–99.
- Zhou, J. (1998). Feedback valence, feedback style, task autonomy, and achievement orientation: interactive effects on creative performance. Journal of Applied Psychology, 83(2), 261–276.
- Zhou, J. (2008). Promoting creativity through feedback. In Handbook of organizational creativity (pp. 125–145). New York, NY: Erlbaum.
