Winner Determination of Open Innovation Contests in Online Markets

Online innovation contests have been used by more and more firms for idea seeking and problem solving. Most studies of contests take the perspective of innovation seekers, and little is known about solvers’ strategies and responses. However, contest performance also relies on understanding solver responses. This paper provides insights to these questions. Specifically, we show that past experience of a solver is a good predictor of his future winning probability and that winners are more likely to be those who submit early or later during the submission period as opposed to those submit in the middle. We also find that “strategic waiting” (to submit solutions) is associated with higher winning probability. Furthermore, we show that different contests appear to attract solvers with different expertise, which invalids the common assumption of fixed solver expertise distribution across projects in previous literature. This finding has strategic implications to the design of contest parameters

Comparing Strategies for Winning Expert-rated and Crowd-rated Crowdsourcing Contests: First Findings

Many studies have been done on expert-rated crowdsourcing contests but few have examined crowd-rated contests in which winners are determined by the voting of the crowd. Due to the different rating mechanisms, determinants for winning may be different under two types of contests. Based on previous studies, we identify three types of winning determinants: expertise, submission timing, and social capital. Our initial investigation, based on 91 entries of two contests in Zooppa, supports that those variables play different roles in winning crowd-rated contests than in winning expert-rated contests. Specifically, past winning experience in crowd-rated contests predicts future success in crowd-rated contests, while past winning experience in expert-rated contests predicts future success in expert-rated contests. We discover a U-shaped relationship between the submission time and winning in both types of contests. Social capital elevates the probability of winning a crowd-rated contest only if the social capital is sufficiently high

Making Task Recommendations in Crowdsourcing Contests

Crowdsourcing contests have emerged as an innovative way for firms to solve business problems by acquiring ideas from participants external to the firm. To facilitate such contests a number of crowdsourcing platforms have emerged in recent years. A crowdsourcing platform provides a two-sided marketplace with one set of members (seekers) posting tasks, and another set of members (solvers) working on these tasks and submitting solutions. As crowdsourcing platforms attract more seekers and solvers, the number of tasks that are open at any time can become quite large. Consequently, solvers search only a limited number of tasks before deciding which one(s) to participate in, often examining only those tasks that appear on the first couple of pages of the task listings. This kind of search behavior has potentially detrimental implications for all parties involved: (i) solvers typically end up participating in tasks they are less likely to win relative some other tasks, (ii) seekers receive solutions of poorer quality compared to a situation where solvers are able to find tasks that they are more likely to win, and (iii) when seekers are not satisfied with the outcome, they may decide to leave the platform; therefore, the platform could lose revenues in the short term and market share in the long term. To counteract these concerns, platforms can provide recommendations to solvers in order to reduce their search costs for identifying the most preferable tasks. This research proposes a methodology to develop a system that can recommend tasks to solvers who wish to participate in crowdsourcing contests. A unique aspect of this environment is that it involves competition among solvers. The proposed approach explicitly models the competition that a solver would face in each open task. The approach makes recommendations based on the probability of the solver winning an open task. A multinomial logit model has been developed to estimate these winning probabilities. We have validated our approach using data from a real crowdsourcing platform

Problem Specification in Crowdsourcing Contests: A Natural Experiment

Problem specification is a key aspect in crowdsourcing contests through which seekers convey their requirements and taste for the desired submissions. Hence, it is important to understand how problem specification should be framed to achieve better crowdsourcing contest outcomes. In this empirical study, we investigate the effects of a relatively more structured problem specification on contest quantity, solver quantity, and idea quality. We leverage a natural experiment set up on a major crowdsourcing contest platform where the problem specification of logo design contests changed from open-ended to structured. Our results show that the specification change impacts both seekers and solvers. Specifically, the number of contests increases after the change but solver quantity and idea quality in the respective contests tend to be lower. We discuss the theoretical and practical contributions of this research

Econometric Analysis of Pricing and Operational Strategies

This dissertation contains three essays. The first essay, entitled Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry, uses a detailed dataset of the U.S. auto industry to examine the relationship between production flexibility and responsive pricing. Our analysis shows that deploying production flexibility is associated with a reduction in observed discounts and with an increase in plant utilization. Our results allow quantifying some of the benefits of production flexibility. The second essay, entitled An Empirical Analysis of Reputation in Online Service Marketplaces, uses a detailed dataset from a leading online intermediary for software development services to empirically examine the role of reputation on choices and prices in service marketplaces. We find that buyers trade off reputation and price and are willing to accept higher bids posted by more reputable bidders. Sellers primarily use a superior reputation to increase their probability of being selected, as opposed to increasing their prices. Our analysis shows that the numerical reputation score has a smaller effect in situations where there exists a previous relationship between buyer and seller, when the seller has certified his or her skills, when the seller is local, or in situations that prompt higher interpersonal trust. The third essay, entitled The Effects of Product Line Breadth: Evidence from the Automotive Industry, studies the effects of product line breadth on market shares and costs, using data from the U.S. automotive industry. Our results show a positive association between product line breadth and market share and production costs. Beyond the effects on production costs, we study the effect of product line breadth on mismatch costs, which arise from demand uncertainty, and we find that product line breadth has a substantial impact on average discounts and inventories. Our results also show that platform strategies can reduce production costs and that a broader product line can provide a hedge against changes in demand conditions

Competitive market innovation contests and social capital: diametrically opposed, or inherently linked?

Competitive market innovation contest platforms are increasingly used by businesses to identify new products or services to offer their customer base; yet, the degree to which social capital has been explored within these online communities remains scarce. While there is ample support for the presence of social capital within other forms of virtual communities to facilitate knowledge sharing, competitive markets represent a unique setting given the inherent competitive nature of their contest solvers. This has led to a distinct lack of prior research exploring this area, especially as previous studies have chosen to focus instead on social capital vis-à-vis solver motivation rather than a standalone theory. We investigate six competitive markets from the perspective of their experts to explore how the three dimensions of social capital have a role within this setting: (1) the structural dimension (involving social ties), (2) the relational dimension (involving trust, reciprocity and self-identity), and (3) the cognitive dimension (involving shared language and shared vision). Through this study, we present a theoretical model of both the emergent themes and the net impacts of social capital within competitive markets, and discuss its implications for both IS research and practice

INFORMATION TRANSPARENCY AND USER BEHAVIOR IN EMERGING ONLINE MARKETPLACES: EMPIRICAL STUDIES OF SOCIAL MEDIA AND OPEN INNOVATION MARKETS

Web 2.0 and social media have significantly increased the amount of information available to users not only about firms and their offerings, but also about the activities of other individuals in their networks and markets. It is widely acknowledged that this increased availability of information is likely to influence a user's behavior and choices. However, there are very few systematic studies of how such increased information transparency influences user behavior in emerging marketplaces. My dissertation seeks to examine the impact of increased information transparency - particularly, information about other individuals - in two emerging platforms. The first essay in my dissertation compares online "social" marketing on Facebook with "non-social" marketing and examines their relative impacts on the likelihood of adoption, usage and diffusion of an "App". While social marketing - wherein a user gets to see which of her other friends have also "liked" the product being marketed- is one of the fastest growing online marketing formats, there are hardly any studies that have examined the value of the social aspect of such marketing. I find that social marketing is associated with increased app adoption, usage, and diffusion as compared to non-social marketing. The study also uncovers interesting tradeoffs between the effects of different types of "social" information on user behavior outcomes. The second essay examines the behavior of contestants in an open innovation design marketplace, wherein firms seek solutions from a crowd through an online contest. The study examines how the availability of information about other contestants as well as the availability of feedback information provided to others by the contest holder, impacts a focal contestant's behavior and outcomes. I find that contestants adopt different strategic behaviors that increase their odds of winning the contest under the different information-transparency regimes. The findings have interesting implications for the design of online contests and crowdsourcing markets. Overall, my dissertation provides a deeper understanding of how the visibility of different types of information in online platforms impacts individual behaviors and outcomes

IT-enabled innovation contest platforms: an exploration of the impacts and mechanisms of social capital

IT-enabled innovation contest platforms are quickly growing in prominence due their provision of a cost effective, yet far reaching method of allowing organisations to connect with a global network of innovation solvers. Borne of the open innovation movement, this phenomenon and the research surrounding it have emerged suddenly and proliferated rapidly. Although conceptual support for the relevance of social capital as an antecedent of innovation seems compelling, there is a distinct lack of research to support this in existing literature. The result is that little is understood by scholars and practitioners in terms of its influence in the overall contest setting. This research study explores the heretofore unexplored influences of social capital toward these contests. Empirical data was gathered through two rounds of data collection, a pilot study of Trend Micro along with case studies of 15 separate IT-enabled innovation contest platforms. Through this analysis, three theoretical models emerged from the findings, which in turn formed: i. A preliminary theory of social capital for innovation contest platforms ii A preliminary theory of social capital for competitive markets iii A preliminary theory of social capital for collaborative communities The study contributes to IS theory and practice in several ways. Firstly, it provides the first investigation of social capital theory within the innovation contest domain. Through the research strategy implemented, social capital theory is revealed to be a valid and appropriate theoretical lens that can be implemented by future researchers. Secondly, this investigation provides a solid foundation for further investigations, and the academic community is encouraged to validate and refine the theorisations presented herein. Thirdly, the findings serve to identify the strategic value of social capital constructs, while also presenting the mechanisms used to facilitate their development. Fourthly, the findings of this study highlight the need for an understanding of appropriate management strategies towards social capital within the innovation contest platforms