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Expert Identification via Virtual Stock Markets: Finding Lead Users in Consumer Product Markets

Expert Identification via Virtual Stock Markets: Finding Lead Users in Consumer Product Markets. Martin Spann, Holger Ernst, Bernd Skiera, Jan Henrik Soll. DIMACS Workshop on Markets as Predictive Devices Rutgers University, February 3 rd 2005. Objective of the Presentation.

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Expert Identification via Virtual Stock Markets: Finding Lead Users in Consumer Product Markets

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  1. Expert Identification via Virtual Stock Markets: Finding Lead Users in Consumer Product Markets Martin Spann, Holger Ernst, Bernd Skiera, Jan Henrik Soll DIMACS Workshop on Markets as Predictive Devices Rutgers University, February 3rd 2005

  2. Objective of the Presentation • Application of virtual stock markets to marketing research • Outline the rationale for virtual stock markets as an efficient tool for expert identification • Present results of an empirical study

  3. Marketing Research Applications • Business Forecasting • Company revenues and product sales • Market shares • Return of strategic investments (e.g. technology) • New Product Development • Evaluation of Product Concepts • Identification of lead users

  4. Information Source VSM Traders' individual portfolios Stock Prices Expert Identification ForecastingAlternatives Forecasting Events in near Future • Chen/Plott (2002) • Forsythe et al. (1992) • Forsythe/Rietz/Ross (1999) • Pennock et al. (2000) • Spann/Skiera (2003) • Wolfers/Zitzewitz (2004) • Chan et al. (2002) • Hanson (1992) • This study Marketing Research via VSM

  5. Lead User Concept • von Hippel (1986): Lead Users • face future product needs months or even years earlier than normal customers • often try to find solutions to these needs by themselves • Analysis of lead users can detect these future needs and obtain new product ideas to satisfy these needs

  6. Rationale for Virtual Stock Markets as a Tool for Lead User Identification • Idea: Participant's performance at a virtual stock market is an indicator of knowledge about event to be predicted Two effects permit identification: • Self-selection effect Attraction of participants who display higher involvement with the product • Performance effect • Successful participants are more knowledgeable, because they detect and exploit inefficient prices • Inefficient prices = incorrect predictions

  7. Empirical Study • Goal: Analyze feasibility of VSMs for lead user identification • Methodology: Analyze participants at an VSM according to performance and lead user characteristics • Application to success forecasting of movies: • Relevant for producers and exhibitors • Each movie is a new product with high failure rates • Movies increasingly rely on branding (e.g. sequels) • Value chain: theaters, rental, sale and merchandize

  8. Design of Empirical Study:Two Phases • Setup of VSM for the prediction of the box-office success of movies in Germany • Forecast of number of movie visitors • 6 rounds with total number of 350 participants • 70 movies (release between May and October 2001) • Participant's performance: Mean Portfolio increase in active rounds • Online-survey for lead user characteristics (after end of VSM; lottery of gift vouchers as incentive): • Opinion leadership • Expertise • Expected Benefit • Survey response rate of 29.2% (n=102)

  9. Proportion of Lead Users among Traders • Identification of lead users by threshold levels (=sample mean) of each factor: opinion leadership, expertise and expected benefit • Result: 20.6% (=21) of respondents fulfill required level of the three lead user criteria

  10. Are Top Traders more likely to be Lead Users? (1/2) • Factor scores of top and bottom traders

  11. Are Top Traders more likely to be Lead Users? (2/2) • Significant relationship between performance and frequency of lead users

  12. How differ successful and non successful Lead Users? • Factor scores of top and bottom performing lead users

  13. How do Lead Users achieve higher Performance? • Hypothesis: Lead users exploit every perceived price inefficiency  conduct significantly more orders and trades than non lead users: • No. of trades per active round: 47.71 for lead users (20.90 for non lead users) • No. of orders per active round: 45.00 for lead users (19.35 for non lead users) • Differences significant at 1% level (t-test for independent samples and ANOVA)

  14. Discussion and Limitations • Not all lead users perform well at VSM • not all lead users can translate assessment of unmet needs into success forecast of product • VSM selects those lead users with better market understanding  most desired ones to integrate into new product development process • Only one product category • Limited availability of benchmark studies (proportion of lead users in consumer markets)

  15. Managerial Implications • Results show feasibility of VSM for efficient lead user identification • Possible double benefit of VSM: forecasting and lead user identification • Identified lead users can be used for in-depth studies: • Interviews • Idea generation • Concept testing

  16. Contact • Martin SpannSchool of Business and EconomicsJohann Wolfgang Goethe-University60054 Frankfurt am Main (Germany)Phone:  +49-(0)69-798-22380Fax: +49-(0)69-798-28973 E-Mail: spann@spann.de www.virtualstockmarkets.com

  17. Items to Measure Lead User Characteristics

  18. Dimensions of Lead User Characteristics • Exploratory FA • Satisfactory results of confirmatory FA:GFI = .88, CFI = .89, RMSEA = .13

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