Skip to main content
SpringerLink
Log in

Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Contest-based intermediary crowdsourcing represents a powerful new business model for generating ideas or solutions by engaging the crowd through an online competition. Prior research has examined motivating factors such as increased monetary reward or demotivating factors such as project requirement ambiguity. However, problematic issues related to crowd contest fitness have received little attention, particularly with regard to crowd strategic decision-making and contest outcomes that are critical for success of crowdsourcing platforms as well as implementation of crowdsourcing models in organizations. Using Expectation-Confirmation Theory (ECT), we take a different approach that focuses on contest level outcomes by developing a model to explain contest duration and performance. We postulate these contest outcomes are a function of managing crowdsourcing participant contest-fitness expectations and disconfirmation, particularly during the bidding process. Our empirical results show that contest fitness expectations and disconfirmation have an overall positive effect on contest performance. This study contributes to theory by demonstrating the adaptability of ECT literature to the online crowdsourcing domain at the level of the project contest. For practice, important insights regarding strategic decision making and understanding how crowd contest-fitness are observed for enhancing outcomes related to platform viability and successful organizational implementation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143.

    Google Scholar 

  • Bayus, B. L. (2013). Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management Science, 59(1), 226–244.

    Google Scholar 

  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351.

    Google Scholar 

  • Bhattacherjee, A., & Premkumar, G. (2004a). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.

    Google Scholar 

  • Bhattacherjee, A., & Premkumar, G. (2004b). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.

    Google Scholar 

  • Bolton, G., Loebbecke, C., & Ockenfels, A. (2008). Does competition promote trust and trustworthiness in online trading? An experimental study. Journal of Management Information Systems, 25(2), 145–170.

    Google Scholar 

  • Boudreau, K. J., Lacetera, N., & Lakhani, K. R. (2011). Incentives and problem uncertainty in innovation contests: An empirical analysis. Management Science, 57(5), 843–863.

    Google Scholar 

  • Boudreau, K. J., & Lakhani, K. R. (2013). Using the crowd as an innovation partner. Harvard business review, 91(4), 60–69.

  • Brown, S. A., Venkatesh, V., & Goyal, S. (2012). Expectation confirmation in technology use. Information Systems Research, 23(2), 474–487.

    Google Scholar 

  • Bullinger, A. C., Neyer, A.-K., Rass, M., & Moeslein, K. M. (2010). Community-based innovation contests: Where competition meets cooperation: Community-based InnovationContests. Creativity and Innovation Management, 19(3), 290–303.

    Google Scholar 

  • Chanal, V., & Caron-Fasan, M.-L. (2010). The difficulties involved in developing business models open to innovation communities: The case of a crowdsourcing platform. M@n@gement, 13(4), 318–340.

    Google Scholar 

  • Chawla, S., Hartline, J. D., & Sivan, B. (2015). Optimal crowdsourcing contests. Games and Economic Behavior., 13, 80–96.

    Google Scholar 

  • Cheung, C. M. K., & Lee, M. K. O. (2009). Understanding the sustainability of a virtual community: Model development and empirical test. Journal of Information Science, 35(3), 279–298.

    Google Scholar 

  • Chiu, C.-M., Liang, T.-P., & Turban, E. (2014). What can crowdsourcing do for decision support? Decision Support Systems, 65, 40–49.

    Google Scholar 

  • Djelassi, S., & Decoopman, I. (2013). Customers’ participation in product development through crowdsourcing: Issues and implications. Industrial Marketing Management, 42(5), 683–692.

    Google Scholar 

  • Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? — An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.

    Google Scholar 

  • Geiger, D., & Schader, M. (2014). Personalized task recommendation in crowdsourcing information systems — Current state of the art. Decision Support Systems, 65, 3–16.

    Google Scholar 

  • Giles, M. (2018). Crowdsourcing the hunt for software bugs is a booming business—And a risky one. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/611892/crowdsourcing-the-hunt-for-software-bugs-is-a-booming-businessand-a-risky-one/.Accessed 10 Sept 2018

  • Gräuler, M., Freundlieb, M., Ortwerth, K., & Teuteberg, F. (2013). Understanding the beliefs, actions and outcomes of sustainability reporting: An experimental approach. Information Systems Frontiers, 15(5), 779–797.

    Google Scholar 

  • Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & William, C. (1995). Multivariate data analysis with readings. New Jersey: Prentice Hall.

    Google Scholar 

  • Haruvy, E., & Leszczyc, P. T. L. P. (2010). The impact of online auction duration. Decision Analysis, 7(1), 99–106.

    Google Scholar 

  • Hastie, R., & Dawes, R. M. (2010). Rational choice in an uncertain world: The psychology of judgment and decision-making. Sage.

  • Hayes, A. F. (2012). My macros and code for SPSS and SAS. Retrieved from URL: http://afhayes.com/spss-sas-andmplus-macros-and-code.html. Accessed 10 Sept 2018

  • Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis. New York: Guilford Press.

    Google Scholar 

  • Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24(10), 1918–1927.

    Google Scholar 

  • Javadi Khasraghi, H., & Aghaie, A. (2014). Crowdsourcing contests: Understanding the effect of competitors’ participation history on their performance. Behaviour & Information Technology, 33(12), 1383–1395.

    Google Scholar 

  • Kajino, H., Arai, H., & Kashima, H. (2014). Preserving worker privacy in crowdsourcing. Data Mining and Knowledge Discovery, 28(5–6), 1314–1335.

    Google Scholar 

  • Kauffman, R. J., & Wood, C. A. (2006). Doing their bidding: An empirical examination of factors that affect a buyer’s utility in internet auctions. Information Technology and Management, 7(3), 171–190.

    Google Scholar 

  • Kennedy, P. (1998). A guide to econometrics (4th ed.). Cambridge: MIT Press.

    Google Scholar 

  • Kidwell, R. E. (2010). Loafing in the 21st century: Enhanced opportunities—And remedies—For withholding job effort in the new workplace. Business Horizons, 53(6), 543–552.

    Google Scholar 

  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.

    Google Scholar 

  • Kimbarovsky, R., & Samson, M. (2008). crowdSPRING, LLC. Retrieved from www.crowdspring.com.

  • Klein, S., & Teubner, R. A. (2000). Web-based procurement new roles for intermediaries. Information Systems Frontiers, 2(1), 19–30.

    Google Scholar 

  • Kuo, M.-J. (2007). How does an online game based learning environment promote students’ intrinsic motivation for learning natural science and how does it affect their learning outcomes? Digital Game and Intelligent Toy Enhanced Learning, 2007. DIGI℡‘07. The First IEEE International Workshop On, 135–142.

  • Lankton, N. K., McKnight, D. H., Wright, R. T., & Thatcher, J. B. (2016). Research note—Using expectation disconfirmation theory and polynomial modeling to understand Trust in Technology. Information Systems Research, 27(1), 197–213.

    Google Scholar 

  • Leimeister, J. M., Huber, M., Bretschneider, U., & Krcmar, H. (2009). Leveraging crowdsourcing: Activation-supporting components for IT-based ideas competition. Journal of Management Information Systems, 26(1), 197–224.

    Google Scholar 

  • Lin, T.-C., & Huang, C.-C. (2010). Withholding effort in knowledge contribution: The role of social exchange and social cognitive on project teams. Information & Management, 47(3), 188–196.

    Google Scholar 

  • Liu, T. X., Yang, J., Adamic, L. A., & Chen, Y. (2014). Crowdsourcing with all-pay auctions: A field experiment on taskcn. Management Science, 60(8), 2020–2037.

    Google Scholar 

  • McKinney, V., Yoon, K., & Zahedi, F. “Mariam.” (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296–315.

  • Mithas, S., & Jones, J. L. (2009). Do auction parameters affect buyer surplus in E-auctions for procurement? Production and Operations Management, 16(4), 455–470.

    Google Scholar 

  • Oliver, R. L. (1977). Effect of expectation and disconfirmation on Postexposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480–486.

    Google Scholar 

  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469.

    Google Scholar 

  • Pe-Than, E. P. P., Goh, D. H.-L., & Lee, C. S. (2017). Does it matter how you play? The effects of collaboration and competition among players of human computation games. Journal of the Association for Information Science and Technology, 68(8), 1823–1835.

    Google Scholar 

  • Robert, L. P., & Romero, D. M. (2017). The influence of diversity and experience on the effects of crowd size. Journal of the Association for Information Science and Technology, 68(2), 321–332.

    Google Scholar 

  • Satzger, B., Psaier, H., Schall, D., & Dustdar, S. (2013). Auction-based crowdsourcing supporting skill management. Information Systems, 38(4), 547–560. https://doi.org/10.1016/j.is.2012.09.003.

    Article  Google Scholar 

  • Schenk, E., & Claude, G. (2011). Towards a characterization of crowdsourcing practices. Journal of Innovation Economics & Management, 1, 93–107.

    Google Scholar 

  • Schonfeld, E. (2008, May 12). When Crowdsourcing Fails: Cambrian House Headed to the Deadpool. CrunchBase. Retrieved from https://techcrunch.com/2008/05/12/when-crowdsourcing-fails-cambrian-house-headed-to-the-deadpool/. Accessed 11 July 2017

  • Simon, H. A. (1978). Rationality as process and as product of thought. The American Economic Review, 1–16.

  • Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146.

    Google Scholar 

  • Taylor, P. (2017). March. In Crowdsourcing service providers Retrieved from http://clients1.ibisworld.com/reports/us/industry/ataglance.aspx?entid=5672. Accessed 17 July 2018

  • Venkatesh, & Goyal. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34(2), 281.

    Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J.-H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context: Context, expectations and IS continuance. Information Systems Journal, 21(6), 527–555.

    Google Scholar 

  • Xiao, L. (2014). Effects of rationale awareness in online ideation crowdsourcing tasks: Journal of the American Society for Information Science and Technology. Journal of the Association for Information Science and Technology, 65(8), 1707–1720.

    Google Scholar 

  • Yu, J., Zo, H., Kee Choi, M., & P. Ciganek, A. (2013). User acceptance of location-based social networking services: An extended perspective of perceived value. Online Information Review, 37(5), 711–730. https://doi.org/10.1108/OIR-12-2011-0202

  • Zheng, H. (2014). Antecedents Of Solution Quality In Crowdsourcing: The Sponsor’s Perspective. 15(3), 14.

  • Zheng, H., Li, D., & Hou, W. (2011). Task design, motivation, and participation in crowdsourcing contests. International Journal of Electronic Commerce, 15(4), 57–88. https://doi.org/10.2753/JEC1086-4415150402.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems, The University of Texas Rio Grande Valley, 1201 W. University Dr., Edinburg, TX, 78539, USA

    Emmanuel W. Ayaburi

  2. Department of Computer Information Systems, Louisiana Tech University, Ruston, LA, USA

    Jaeung Lee & Michele Maasberg

Authors
  1. Emmanuel W. Ayaburi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaeung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Maasberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emmanuel W. Ayaburi.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayaburi, E.W., Lee, J. & Maasberg, M. Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective. Inf Syst Front 22, 1227–1240 (2020). https://doi.org/10.1007/s10796-019-09926-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-019-09926-w

Keywords

Search

Navigation