Elsevier

Decision Support Systems

Volume 84, April 2016, Pages 28-40
Decision Support Systems

Which ideas are more likely to be implemented in online user innovation communities? An empirical analysis

https://doi.org/10.1016/j.dss.2016.01.004Get rights and content

Highlights

  • We study how the likelihood of idea implementation is affected in user innovation communities.

  • Our model is validated using logistic regression analysis on secondary data of 19,964 user ideas.

  • The results show significant impacts of the characteristics of its contributor as well as the characteristics of a submitted idea and its presentation.

  • We also identify important differences in their effects for hybrid versus professional user innovation communities.

Abstract

Online user innovation communities are increasingly being deployed by firms to garner innovation ideas from customers or users. However, very few ideas from such communities are successful in getting selected for implementation by the host firm. Given the limited understanding of the phenomenon, this study examines the determinants of firms' implementation of customers' ideas from user innovation communities. Drawing on theories of message persuasion and cognitive overload, we develop a conceptual model to explain how the likelihood of idea implementation is affected by the characteristics of its contributor as well as the characteristics of a submitted idea and its presentation. Specifically, we study the effects of the contributor's prior participation and prior implementation rate, as well as the idea's popularity, length, and supporting evidence on the idea's implementation likelihood. Our model is validated through logistic regression on a secondary dataset of 19,964 user ideas collected from two large user innovation websites, Salesforce.com IdeaExchange and Dell IdeaStorm. The results show significant impacts of these characteristics on idea implementation likelihood and also reveal important differences in their effects for hybrid (i.e., Dell IdeaStorm) versus professional (i.e., Salesforce.com IdeaExchange) user innovation communities.

Introduction

Innovation is a critical activity for sustaining firms' competitiveness in the market [7]. As a result, firms continue to invest in the development of new products, services, and processes. However, managers are concerned about how to encourage innovation while reducing its costs and risks. An approach to mitigate the risks and cost of innovation is to involve customers or users in the process [65], [70]. For instance, in a study conducted at 3 M, innovations from users were found to generate more sales than traditional market research techniques [39]. By involving customers in the process of innovating, firms may benefit from lower development costs and enhanced customer acceptance of the innovations [64]. To formalize this approach, online user innovation communities are increasingly being deployed by firms to source for users' innovation ideas and preferences. As examples, Salesforce.com, Dell, and Starbucks have been pioneers in launching user innovation communities. By implementing ideas from its users, Dell introduced new options for its personal computers, such as installing Linux as an operating system [13]. Salesforce.com enhanced its customer relationship management (CRM) software by building new features adopted from its user innovation community, such as a mobile platform CRM. Starbucks introduced the customer idea of splash sticks poked into a hole on the top of its to-go cups to prevent the beverage from spilling out.1

Despite the potential value of sourcing innovation ideas from users, companies face challenges in setting up these communities, assessing a large number of submitted ideas, and obtaining valuable ideas from them [57]. Many firms do not have clear criteria to assess the submitted ideas and suffer from a lack of manpower and systematic processes to evaluate them [13], [57]. At the same time, users also face challenges in getting their ideas implemented by host firms after investing their time and intellectual capital to generate them. With the typically low percentage of user ideas that are chosen for implementation,2 users would want to know how they could improve the likelihood of their ideas being selected. With prior research on online user innovation communities mainly focusing on identifying users' motivations for contributing ideas [4], [23], [24], [35], [37], [38], there is limited study of the factors that influence firms' implementation of user ideas. Among the few studies in this area, Di Gangi and Wasko [14] employed the diffusion of innovations (DOI) theory to examine how the inherent characteristics of user ideas affect their implementation in Dell IdeaStorm. However, they found that idea characteristics such as relative advantage and compatibility were difficult for firms to predict and therefore did not influence idea implementation. Focusing on idea contributors, Bayus [4] and Huang et al. [29] used a learning perspective to explore how users' idea contribution behavior and their implementation rate change over time, rather than examining the implementation likelihood of individual ideas. Further, most prior studies used data from a single user innovation website, e.g., Dell IdeaStorm [4], [14], [29]. This could limit the generalizability of their findings, as the differences across communities, e.g., [7], are not considered. Overall, our review indicates a lack of understanding of the factors influencing the implementation likelihood of an idea, and that too across different user innovation communities.

The practical and theoretical issues mentioned above motivate us to holistically (by including both idea and contributor characteristics) examine the antecedents of idea implementation likelihood in user innovation communities by employing alternative theories that could explain the phenomenon. In reality, a user innovation community is typically characterized by low review capacity of the firm, i.e., there is insufficient manpower to review the numerous user ideas in detail when a large number of ideas are submitted every day [11], [57]. In such a context, we propose that user ideas will be selected for implementation if they are persuasive and, at the same time, their presentation does not cognitively overload readers (i.e., both firm's reviewers and other community members). Accordingly, we develop a model based on theories of persuasion [46], [49], [50], [51] and cognitive overload [32] to explain the likelihood of user idea implementation. Through the model, we aim to answer three fundamental questions: (1) What user/contributor characteristics influence the implementation likelihood of their ideas by firms? (2) What characteristics of the idea and its presentation influence their implementation likelihood by firms? and (3) How do the effects of idea presentation characteristics differ across the type of user innovation community (i.e., professional communities with corporate members vs. hybrid communities with both corporate and individual members)? The model is tested with secondary data on 19,964 user ideas collected from two large user innovation communities, Salesforce.com IdeaExchange (a professional community) and Dell IdeaStorm (a hybrid community).

In terms of theoretical contributions, this study is novel in (1) examining the factors leading to the implementation of user ideas based on persuasion and cognitive overload perspectives, (2) considering both contributor and idea (including presentation) factors as antecedents, and (3) comparing the differential effects of idea presentation factors across two types of user innovation communities. Further, by answering the above questions, this study suggests a number of practical implications for management. For firms that are aiming to launch online user innovation communities, our findings from these successful communities can provide guidelines on what kind of user ideas are being implemented, how to filter ideas and assess them, especially when there are a large number of ideas and limited resources or capacity to process them. It can also help firms to identify the contributors who may potentially submit valuable ideas and respond to their ideas quickly in order to incentivize them. Based on our findings about the characteristics of implemented ideas and their presentation, online user innovation communities can provide their members with guidelines on how to position and present their ideas. For users, adopting these guidelines could help them draw more attention to their ideas from firms' reviewers and other members, than those contributors not following the guidelines. Last, based on the differences in effects of idea presentation characteristics found across the two types of user innovation communities, the guidelines for idea presentation could be modified for each type of community.

Section snippets

Conceptual background

We first review the related studies on user innovation communities to indicate the gap in the literature that our study seeks to address. We then introduce the message persuasion perspective that helps us to identify contributor, idea, and presentation factors that make the idea posting persuasive. We subsequently apply cognitive overload concepts to explain the relation between idea's presentation characteristics and its implementation likelihood.

Model and hypotheses

As described above, we derive the antecedents of idea implementation likelihood in a user innovation community based on the message persuasion and cognitive overload perspectives. Under conditions of low review capacity in such contexts, we propose that the implementation likelihood of a submitted idea will be affected by its contributor, idea, and presentation characteristics. These characteristics identified in the previous section are observable by reviewers and other members in the user

Data collection

We chose two online user innovation communities, i.e., Salesforce.com IdeaExchange and Dell IdeaStorm for our study, which represent a professional community and a hybrid community, respectively. The two communities were chosen for their popularity and the publicly available data on the activities of members and the adopting firms in the communities. Additionally, both communities run on the same platform since Dell adopted the platform solution provided by Salesforce.com. That is, both

Estimation results

Table 3 shows the estimation results for our study. Column (1) contains the coefficient estimates when only control variables are included. Column (2) adds the main effects of the independent variables. Column (3) shows the estimates with the quadratic effects of supporting evidence added, while Column (4) adds the moderating effect of community type. The pseudo R-squared value, which explains the variance of idea implementation likelihood caused by the antecedents, is 13.23% in Column (2) with

Implications and contribution

Despite an increasing number of user innovation communities being established by firms in recent years, theoretical explanations of a firm's likelihood of implementing a specific user idea have been scant. Motivated thus, this study set out to address three fundamental questions: (1) What user/contributor characteristics influence the implementation likelihood of their ideas by firms? (2) What characteristics of the idea and its presentation influence their implementation likelihood by firms?

Conclusion

Our work sheds light on how to better exploit the potential of user innovation communities. Deriving from theories of message persuasion and cognitive overload, we develop a conceptual model to explain the likelihood of innovation idea implementation based on the characteristics of the submitted idea and its presentation as well as the characteristics of its contributor. Specifically, the contributor's prior participation and prior implementation rate, as well as the idea's popularity, length,

Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5A8016480). This research was partially supported by the Singapore Ministry of Education Academic Research Fund (R-253-000-091-112).

Mingguo Li was a graduate student at Tepper School of Business, Carnegie Mellon University. He received his Master of Engineering degree from Ecole Polytechnique, France, and MS in Information Systems from National University of Singapore. He has been working in financial technology industry after graduation.

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  • Cited by (0)

    Mingguo Li was a graduate student at Tepper School of Business, Carnegie Mellon University. He received his Master of Engineering degree from Ecole Polytechnique, France, and MS in Information Systems from National University of Singapore. He has been working in financial technology industry after graduation.

    Atreyi Kankanhalli is an associate professor in the Department of Information Systems at the National University of Singapore (NUS). She obtained her B. Tech. from the Indian Institute of Technology Delhi, M.S. from the Rensselaer Polytechnic Institute, New York, and Ph.D. from NUS. Her research interests are in knowledge management, virtual teams and communities, and IT-enabled innovation in service sectors e.g., e-government and healthcare. Her work has appeared in premium outlets including MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Journal of the American Society for Information Science and Technology, IEEE Trans. on Engineering Management, ACM Transactions on IS, International Journal of Human Computer Studies, and the Proceedings of the International Conference on Information Systems. She serves or has served on several IS conference committees and on the editorial boards of MIS Quarterly, Information Systems Research, IEEE Trans. on Engineering Management, and the Journal of AIS among others. Professor Kankanhalli's work has received a number of awards including the ACM SIGMIS ICIS 2003 Best Doctoral Dissertation Award and the IBM Faculty Award.

    Seung Hyun Kim is an associate professor of information systems at the School of Business, Yonsei University. He received his Ph.D. and M.S. from Carnegie Mellon University, and his bachelor's degrees from Yonsei University. His primary research interests include economics of information security, knowledge management, and customer relationship management. His work has been published or is forthcoming in leading academic journals including Information Systems Research, MIS Quarterly, and Communications of the ACM.

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