Evidence for The Power of Implicit Response Measures
Cloud Army’s Reactor Implicit Apps are based
on tests originally designed by academic
psychologists to more accurately understand
attitudes and perceptions. Implicit or Priming
tests such as the Evaluative Decision Task(1)
and the Implicit Association Test(2) are the
inspiration for our apps and have proved their
worth in a large number of published academic
studies.
Over the last few decades, many peer-reviewed scientific papers have demonstrated the validity, reliability and predictability of Implicit measures.
For example, a paper published in 2012(3) examined findings from 167 academic studies wrote that: “Sequential priming measures have incremental predictive validity over explicit measures.”, “A comprehensive examination of all available data suggests that priming tasks, as a class, are indeed valid measures.” And that “Priming measures were associated with controllable and uncontrollable behaviors, and with socially sensitive and socially innocuous behaviors.”
Another paper(4) that reviewed 122 published studies, this time specifically on the Implicit Association Test (IAT), found that the Implicit measures added ability to predict behavior to what could be predicted by self-report measures alone, and that the IAT was particularly effective at predicting behavior on socially sensitive topics (i.e. topics that people don’t usually like talking about).
Here are some more examples of published research that has demonstrated the predictability of Implicit measures in specific areas:
Predicting consumer choice across categories (FMCG, services & durable goods) and across cultures
In a study across 5 countries and 15 consumer categories, researchers found: “Implicit attitudes result in a higher number of significant effects than their explicit counterparts when used to explain behavioral intentions, brand closeness and brand usage.”(5)
Predicting consumer choice preferences for food, soft drinks and fast food restaurants
An implicit response test “Increased the prediction of behavior relative to explicit attitude measures alone”.(6)
Predicting preference for diet food products
An implicit response test measuring attitudes towards low calorie foods showed a preference for them amongst those who are currently dieting.(7)
Alcohol consumption
Responses to words related to alcohol in an implicit response test were correlated with levels of consumers’ consumption of alcohol in everyday life.(8)
Predicting choices of healthy vs. unhealthy food
Implicit tests measuring attitudes towards fruit and chocolate accurately predicted whether people subsequently bought these foods, whereas an explicit measure failed to predict this.(9)
In addition to working with validated Implicit tests from the published, scientific literature, CloudArmy continuously run research to optimize the power of our apps. Reactor is also designed to make it easy to create benchmarked databases of scores for your own particular category of interest, enabling you to gain extra context to your results.
Over the last few decades, many peer-reviewed scientific papers have demonstrated the validity, reliability and predictability of Implicit measures.
For example, a paper published in 2012(3) examined findings from 167 academic studies wrote that: “Sequential priming measures have incremental predictive validity over explicit measures.”, “A comprehensive examination of all available data suggests that priming tasks, as a class, are indeed valid measures.” And that “Priming measures were associated with controllable and uncontrollable behaviors, and with socially sensitive and socially innocuous behaviors.”
Another paper(4) that reviewed 122 published studies, this time specifically on the Implicit Association Test (IAT), found that the Implicit measures added ability to predict behavior to what could be predicted by self-report measures alone, and that the IAT was particularly effective at predicting behavior on socially sensitive topics (i.e. topics that people don’t usually like talking about).
Here are some more examples of published research that has demonstrated the predictability of Implicit measures in specific areas:
Predicting consumer choice across categories (FMCG, services & durable goods) and across cultures
In a study across 5 countries and 15 consumer categories, researchers found: “Implicit attitudes result in a higher number of significant effects than their explicit counterparts when used to explain behavioral intentions, brand closeness and brand usage.”(5)
Predicting consumer choice preferences for food, soft drinks and fast food restaurants
An implicit response test “Increased the prediction of behavior relative to explicit attitude measures alone”.(6)
Predicting preference for diet food products
An implicit response test measuring attitudes towards low calorie foods showed a preference for them amongst those who are currently dieting.(7)
Alcohol consumption
Responses to words related to alcohol in an implicit response test were correlated with levels of consumers’ consumption of alcohol in everyday life.(8)
Predicting choices of healthy vs. unhealthy food
Implicit tests measuring attitudes towards fruit and chocolate accurately predicted whether people subsequently bought these foods, whereas an explicit measure failed to predict this.(9)
In addition to working with validated Implicit tests from the published, scientific literature, CloudArmy continuously run research to optimize the power of our apps. Reactor is also designed to make it easy to create benchmarked databases of scores for your own particular category of interest, enabling you to gain extra context to your results.
References: The Evidence
(1) Fazio, R.H. and Williams, C.J., 1986. Attitude accessibility as a moderator of the attitude– perception and attitude–behavior relations: An investigation of the 1984 presidential election. Journal of personality and social psychology, 51(3), p.505.
(2) Greenwald, A.G., McGhee, D.E. and Schwartz, J.L., 1998. Measuring individual differences in implicit cognition: the implicit association test. Journal of personality and social psychology, 74(6), p.1464.
(3) Cameron, C.D., Brown-Iannuzzi, J.L. and Payne, B.K., 2012. Sequential priming measures of implicit social cognition: A meta-analysis of associations with behavior and explicit attitudes. Personality and Social Psychology Review, 16(4), pp.330-350.
(4) Greenwald, A.G., Poehlman, T.A., Uhlmann, E.L. and Banaji, M.R., 2009. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. Journal of personality and social psychology, 97(1), p.17.
(5) Vriens, M. and Martins Alves, A., 2017. Modeling the implicit brand: capturing the hidden drivers. Journal of Product & Brand Management, 26(6), pp.600-615.
(6) Maison, D., Greenwald, A.G. and Bruin, R.H., 2004. Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes, and behavior. Journal of consumer psychology, 14(4), pp.405-415.
(7) Maison, D., Greenwald, A.G. and Bruin, R., 2001. The Implicit Association Test as a measure of implicit consumer attitudes. Polish Psychological Bulletin, 32(1), pp. 61-79.
(8) Wiers, R.W., Van Woerden, N., Smulders, F.T. and De Jong, P.J., 2002. Implicit and explicit alcohol-related cognitions in heavy and light drinkers. Journal of abnormal psychology, 111(4), p.648.
(9) Prestwich, A., Hurling, R. and Baker, S., 2011. Implicit shopping: Attitudinal determinants of the purchasing of healthy and unhealthy foods. Psychology & health, 26(7), pp.875-885.
(1) Fazio, R.H. and Williams, C.J., 1986. Attitude accessibility as a moderator of the attitude– perception and attitude–behavior relations: An investigation of the 1984 presidential election. Journal of personality and social psychology, 51(3), p.505.
(2) Greenwald, A.G., McGhee, D.E. and Schwartz, J.L., 1998. Measuring individual differences in implicit cognition: the implicit association test. Journal of personality and social psychology, 74(6), p.1464.
(3) Cameron, C.D., Brown-Iannuzzi, J.L. and Payne, B.K., 2012. Sequential priming measures of implicit social cognition: A meta-analysis of associations with behavior and explicit attitudes. Personality and Social Psychology Review, 16(4), pp.330-350.
(4) Greenwald, A.G., Poehlman, T.A., Uhlmann, E.L. and Banaji, M.R., 2009. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. Journal of personality and social psychology, 97(1), p.17.
(5) Vriens, M. and Martins Alves, A., 2017. Modeling the implicit brand: capturing the hidden drivers. Journal of Product & Brand Management, 26(6), pp.600-615.
(6) Maison, D., Greenwald, A.G. and Bruin, R.H., 2004. Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes, and behavior. Journal of consumer psychology, 14(4), pp.405-415.
(7) Maison, D., Greenwald, A.G. and Bruin, R., 2001. The Implicit Association Test as a measure of implicit consumer attitudes. Polish Psychological Bulletin, 32(1), pp. 61-79.
(8) Wiers, R.W., Van Woerden, N., Smulders, F.T. and De Jong, P.J., 2002. Implicit and explicit alcohol-related cognitions in heavy and light drinkers. Journal of abnormal psychology, 111(4), p.648.
(9) Prestwich, A., Hurling, R. and Baker, S., 2011. Implicit shopping: Attitudinal determinants of the purchasing of healthy and unhealthy foods. Psychology & health, 26(7), pp.875-885.
