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Who wins in crowd innovation contests?
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Author: Christian Landau, EBS Business School
Author: Thomas Mack, EBS Business School
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Open innovation researchers are interested in understanding which participants in participatory settings such as innovation contests make the “best” contributions. Drawing on the componential model of creativity and data generated in innovation contests by car manufacturer Porsche and food company Ritter Sport, we analyze the effect of participants’ creativity, domain knowledge, and several motivational factors on the innovativeness of their contributions. Except for intrinsic and reputation-based motivation, we find all of these factors affect the innovativeness of an idea. However, regarding generating incremental or radical innovations, some factors have opposing roles. In particular, our results showed that extrinsic and internalized extrinsic motivation have opposite influences, which calls for fine-grained analyses of motivational factors. Our findings imply that innovation managers must decide which type of innovations they intend to generate and then design contests to attract participants with the required characteristics.
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Search Terms: open innovation | user innovation | contest
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Paper is No Longer Available Online: Please contact the author(s).
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Design myopia and vicarious learning from good versus bad examples in creative design competitions
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Author: Christoph Riedl, Northeastern U.
Author: Victor P. Seidel, Babson College
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High-quality creative design work creates tremendous value for organizations, but how do individual designers learn to produce better designs? Learning through direct experience often presumes evaluation of performance that is objective and immediate; in creative design work that evaluation is social and often temporarily displaced, providing hard-to- interpret signals for learning. We investigate how individual designers learn from direct experience and learn vicariously from indirect experience—evaluating both good and bad examples of others. Our analysis draws on data from a ten-year panel of almost 180,000 design submissions and 150 million evaluations. We find that in the absence of any vicarious learning, individuals experience “design myopia” resulting in successively lower quality designs before reaching a positive learning rate. Furthermore, we find that while individuals learn from evaluating the good examples of others, they fail to learn from evaluating bad examples. However, individuals’ can overcome their inability to learn from bad examples through prior experience in evaluating good examples. We also find that experience helps designers not only to gain high evaluation from consumers but also to understand the “black box” of how designs are chosen by firms for production.
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Search Terms: Learning | Design | Online communities
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Paper is No Longer Available Online: Please contact the author(s).
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An empirical taxonomy of crowdsourcing intermediaries
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Author: Niklas Leicht, U. of St. Gallen
Author: David Durward, U. of Kassel
Author: Philipp Haas, U. of St. Gallen
Author: Shkodran Zogaj, Kassel U.
Author: Ivo Blohm, U. of St. Gallen
Author: Jan Marco Leimeister, U. of St. Gallen / U. of Kassel
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Crowdsourcing has drawn much attention from researchers in the past. Thus, there are already attempts to conceptualize and classify the phenomenon. All of the existing work has their merits; however they lack an overviewing perspective or meta-characteristic. They are conceptual in nature, lack theoretical grounding, and – most importantly – are not empirically validated. Hence, we develop an empirical taxonomy of crowdsourcing intermediaries embedded in the theory of two-sided markets. Collecting data from 100 intermediaries and performing cluster analysis, we identify five archetypes of crowdsourcing intermediaries: Micro-tasking, knowledge work, design competition, testing and validation as well as innovation. The taxonomy establishes a systematic and comprehensive overview of crowdsourcing intermediaries and thereby provides a better understanding of the basic types of crowdsourcing and its core functions. For practice, we provide decision support for crowdsourcers as well as crowdsourcees on which platform to be active on.
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Search Terms: Crowdsourcing | cluster analysis | empirical taxonomy
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Paper is No Longer Available Online: Please contact the author(s).
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Minimizing the Dilemma Caused by Mixed Online Customer Ratings in Online Communities
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Author: Pradeep Kumar Ponnamma Divakaran, ESC Rennes School of Business
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Mixed or neutral ratings are those online customer ratings (OCR) that are neither positive nor negative, and such mixed ratings are given by those customers who are neither satisfied nor unsatisfied by their product experience. It is unclear whether such mixed ratings have any effect on other potential consumers’ adoption intentions and adoption rate. Prior studies have focused only on extreme ratings (positive or negative) but not on mixed or neutral ratings. Mixed ratings create dilemmas or uncertainty among potential consumers regarding whether to purchase the product. This study argues that when consumers are faced with dilemmas or uncertainty, they look for additional independent sources of information (such as film critics' ratings [FCR] in the case of movies) that may help to attenuate uncertainty. Moreover, brand equity also plays a major role in consumer decisions in such situations. Analysing OCR data collected from a movie-based online community, the findings show that for movies that received mixed OCR, FCR influences the consumer adoption rate only for weak or unknown brands but not for strong brands. For example, the results show that for movies with neutral OCR, positive FCR and weak brand equity lead to a higher adoption rate, whereas movies with negative FCR and weak brand equity result in a poor adoption rate because stronger brands provide more credible signals of quality than weaker brands, as they are more susceptible to the loss of established brand equity. Moreover, unknown brands by themselves create uncertainty (which is aggravated further by neutral OCR), and hence, any additional information such as FCRs will help reduce uncertainty. For strong brands with neutral OCR, positive FCR does not help reduce consumer dilemmas, while negative FCR will help potential customers to make decisions easily (i.e., not to purchase). Implications for community, marketing and brand managers are discussed.
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Search Terms: Online customer ratings and reviews | brand equity | adoption intention
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Paper is No Longer Available Online: Please contact the author(s).
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