Numbers and Units Affect Goal Pursuit Organization and Motivation

Abstract

Goals are one of the most ubiquitous drivers of behavior. Despite the wealth of research on goal pursuit, less is known about how individuals organize their goal pursuit in the first place. This manuscript represents one of the first studies to provide insight into quantitative goal organization, proposing that the unit/numerical value in which a goal is described influences goal pursuit organization. Specifying a superordinate goal in units with larger numbers (e.g., studying for an examination for 120 min per week), rather than with smaller numbers (2 hr per week), leads to a goal pursuit structure that consists of more, but smaller subgoals. We also find that units with larger compared to smaller numbers tend to have a positive effect on goal motivation (i.e., more likely to start the goal earlier and to finish it). Finally, this positive effect on goal motivation is attenuated when consumers focus is on the number of subgoals left (rather than completed) while pursuing the overarching goal. We believe that changing units may be an easy‐to‐implement nudge for anyone (e.g., marketers, managers, public policymakers, behavioral therapists) who wants to increase the likelihood that individuals use a particular goal pursuit structure.

Beautify the blurry self: Low self‐concept clarity increases appearance management

“…Participants in the low (vs. high) self‐concept clarity condition showed greater interest in cosmetic procedures ( M low = 2.53, SD = 1.76 vs. M high = 2.12, SD = 1.67; t [353] = 2.25, p = 0.025, d = 0.24). On closer inspection, we found that interest in cosmetic procedures was positively skewed (skewness = 1.15, which was greater the threshold for high skewness of 1; Lembregts & Pena‐Marin, 2021): 38% of participants in the low self‐concept clarity condition and 54% of participants in the high self‐concept clarity condition indicated zero interest in cosmetic procedures (i.e., interest in cosmetic procedures index = 1; χ 2 = 8.07, p = 0.004). Although we did not preregister transforming the dependent variable, to achieve the normality assumption in linear regression for subsequent mediation analysis, we log transformed interest in cosmetic procedures (Cohen et al, 2013).…”

Consumer-Driven Climate Mitigation: Exploring Barriers and Solutions in Studying Higher Mitigation Potential Behaviors

“…For example, consumers (and researchers) may see adopting a vegetarian or vegan lifestyle as a big challenge and consequently fail to reduce their meat consumption. However, the literature on consumer goals (Fishbach & Dhar, 2006;Gal & Mcshane, 2012;Lembregts & Pena-Marin, 2021) suggests that unpacking such an intimidating task into smaller steps would help with this starting problem (e.g., by reducing meat consumption to once or twice a week or shifting to chicken products).…”