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Whose Crystal Ball to Choose? Individual Difference in the Generalizability of Concept Testing.

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Title: Whose Crystal Ball to Choose? Individual Difference in the Generalizability of Concept Testing.
Authors: Peng, Ling1 (AUTHOR), Finn, Adam2 (AUTHOR)
Source: Journal of Product Innovation Management. Sep2010, Vol. 27 Issue 5, p690-704. 15p. 7 Charts.
Subjects: New product development, Generalizability theory, Product management, Individual differences, Personality, Technological innovations, Manufacturing processes, Industrial research
Abstract: The product development literature has identified several individual characteristics that could influence how subjects respond to new products in concept tests. Few of these characteristics have been thoroughly investigated. The purpose of this research is to examine whether a number of personality traits (1) do influence concept evaluation scores and (2) can be used to identify respondents who provide substantially higher-quality data in concept testing and whether the answers to these questions change for major versus minor innovations. The data quality of the concept testing data is defined using the generalizability theory, which provides a decision-specific G-coefficient. Higher quality means a G-coefficient closer to 1 for a particular managerial decision. A Web-based study to concept test 10 appliance innovations on multiple occasions was conducted among 105 panelists from the Institute for Online Consumer Studies (IOCS). During the concept testing, respondents' innovativeness, change-seeking tendency, and propensity to exert cognitive effort were also measured. The results showed that the respondent characteristics influence the mean evaluation of the concepts and the psychometric quality of the concept testing data: (1) there is a significant linear relationship between concept scores and all of the innovativeness scales and change-seeking measures; (2) the effect of innovativeness on concept testing outcomes is even more substantial for major innovations than for minor innovations; (3) the study provides evidence that the quality of concept testing data provided by respondents varies substantially with their innovativeness, whereas the differences are more modest when scaling just minor innovations; (5) there are also strong effects on data quality for the Need to Evaluate scale used to capture cognitive effort characteristics; and (6) there is little effect of segmenting on social desirability on data quality. Managerially, the current results indicate that a product manager wanting to concept test a pool of appliance concepts can benefit from screening for the respondents who will provide higher-quality concept testing data. For example, respondents who are high on domain-specific innovativeness provide the highest-quality concept testing data for both minor and major innovations. The effects of traits are stronger for major innovations, supporting the claim that subject selection is a more critical issue in concept testing of major innovations. Product managers can improve the quality of their concept testing data without an increase in cost by screening the subjects they use in concept testing. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Product Innovation Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Whose Crystal Ball to Choose? Individual Difference in the Generalizability of Concept Testing.
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  Data: The product development literature has identified several individual characteristics that could influence how subjects respond to new products in concept tests. Few of these characteristics have been thoroughly investigated. The purpose of this research is to examine whether a number of personality traits (1) do influence concept evaluation scores and (2) can be used to identify respondents who provide substantially higher-quality data in concept testing and whether the answers to these questions change for major versus minor innovations. The data quality of the concept testing data is defined using the generalizability theory, which provides a decision-specific G-coefficient. Higher quality means a G-coefficient closer to 1 for a particular managerial decision. A Web-based study to concept test 10 appliance innovations on multiple occasions was conducted among 105 panelists from the Institute for Online Consumer Studies (IOCS). During the concept testing, respondents' innovativeness, change-seeking tendency, and propensity to exert cognitive effort were also measured. The results showed that the respondent characteristics influence the mean evaluation of the concepts and the psychometric quality of the concept testing data: (1) there is a significant linear relationship between concept scores and all of the innovativeness scales and change-seeking measures; (2) the effect of innovativeness on concept testing outcomes is even more substantial for major innovations than for minor innovations; (3) the study provides evidence that the quality of concept testing data provided by respondents varies substantially with their innovativeness, whereas the differences are more modest when scaling just minor innovations; (5) there are also strong effects on data quality for the Need to Evaluate scale used to capture cognitive effort characteristics; and (6) there is little effect of segmenting on social desirability on data quality. Managerially, the current results indicate that a product manager wanting to concept test a pool of appliance concepts can benefit from screening for the respondents who will provide higher-quality concept testing data. For example, respondents who are high on domain-specific innovativeness provide the highest-quality concept testing data for both minor and major innovations. The effects of traits are stronger for major innovations, supporting the claim that subject selection is a more critical issue in concept testing of major innovations. Product managers can improve the quality of their concept testing data without an increase in cost by screening the subjects they use in concept testing. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Product Innovation Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/j.1540-5885.2010.00745.x
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        Text: English
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        Type: general
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      – SubjectFull: Product management
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      – SubjectFull: Individual differences
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      – SubjectFull: Personality
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      – SubjectFull: Technological innovations
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      – SubjectFull: Manufacturing processes
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      – SubjectFull: Industrial research
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      – TitleFull: Whose Crystal Ball to Choose? Individual Difference in the Generalizability of Concept Testing.
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              M: 09
              Text: Sep2010
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              Y: 2010
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