Zemack-Rugar et al. (2012), “The Response-To-Failure Scale: Predicting Behavior Following Initial Self-Control Failure”, Journal of Marketing Research, 49(6), 996-1014. 

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Zemack-Rugar, Yael, Canan Corus, and David Brinberg (2019a), “If at First You Do Succeed, Do You Try, Try Again? Developing the Persistence-Licensing Response Measure to Understand, Predict, and Modify Behavior Following Subgoal Success,” Journal of Marketing Research, 56 (2), 324-344. 

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Overview: 

In these two articles I examined how subgoal failure/success affected subsequent goal persistence. For example, how exceeding one's budget affected subsequent spending, or how succeeding at a fitness test affected subsequent persistence at exercise. Building on dynamic goal theories (Bagozzi and Dholakia 1999; Carver and Scheier 2001) I proposed and showed that behavior following subgoal failure/success was driven by consumers’ cognitive and emotional responses to that failure/success, and that these responses were consistent over time and across domains, constituting an individual difference or tendency. I developed and validated the Response-to-Failure Scale and the Persistence-Licensing Response Measure, which (respectively) captured these responses to subgoal failure/success. I demonstrated that these scales predicted post-failure (or post-success) behavior better than existing related measures, and I applied insights from the measures to generate interventions that improved consumer persistence. 

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As a blind peer reviewer noted: “I’m impressed by the Sisyphean work that the authors have done; scale development is an immensely laborious task that most people avoid like the plague. And scale development about something interesting—as is the case here—is even rarer.”

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Theoretical and applied contributions:

• Although prior work examined the effects of consumers’ cognitive and emotional responses to subgoal failure one-at-a-time, theoretical models suggest that multiple responses are likely experienced concurrently. My work accounted for this concurrent experience, providing a more complete theoretical account of consumers’ post-failure behavior.     

• My work was the first to suggest and show that these responses can be driven by chronic individual differences. I created and validated measures that captured these differences. 

• I applied these constructs to identify theoretical and empirical differences between self-regulation in the absence of subgoal failure/success, and sequential self-regulation, following sub-goal failure/success. Such differences had been theorized (Baumeister, Heatherton, and Tice 1994), but had not been empirically shown.

• I applied these constructs to identify differences between subgoal failure and subgoal success, which were largely treated as mirror images. For example, I showed that as compared to post-success behavior, post-failure behavior was more strongly affected by consumers' emotional responses.

• A wide array of products, such as dieting apps, gym memberships, tutoring programs, and savings plans are intended to help consumers persist at their long-term goals. Increasing persistence at such goals can create a win-win: consumers can benefit from greater success on key goals, and marketers can benefit from consumers’ satisfaction and loyalty. My measures provide marketers with an effective segmentation tool that can help them identify vulnerable consumers (e.g., those likely to fail/drop out of long-term goal programs).

• These tools can be used to design and target marketing communications. For example, my data suggests that consumers with low Persistence-Licensing Response scores should receive few communications about progress/success, as these tend to demotivate them; the opposite of true of consumers with high scale scores.

• I demonstrated how insights from the scale can be used to increase consumer persistence, for example, by increasing their long-term focus, or changing products' reward structure.

• Both measures were developed in key goal domains (e.g., eating, spending). However, a template is provided for marketers (and researchers) to develop additional ad-hoc measures.

 

This work has been cited in a variety of fields, including medicine (Edlind et al., 2018), finance (Peltier et al., 2016), psychology (Prinsen, Evers, and deRidder 2016; Worrell et al. 2016), and marketing (Haws et al. 2016a; Fernbach et al. 2015; Salerno et al., 2015). Further, this work has served as a foundation for other scale work in top-tier journals (Haws et al., 2016b; Hsee et al. 2015; Weathers and Siemens 2018). This work has also received exposure in the popular press (e.g., Business Insider).