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Defining, and understanding commitment to, activity streaks

  • Original Empirical Research
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Abstract

To encourage and facilitate consumer engagement, marketers may incorporate streaks into their social media platforms, apps, loyalty programs, and other marketing tools. To build an understanding of the phenomenon, the current research examines activity streaks. With a theories-in-use approach, we first define an activity streak. We then differentiate activity streaks from other forms of patterned behavior, including winning and lucky streaks, habits, and collections. With survey, experimental, and field data, we investigate factors that impact one’s commitment to an activity streak. We find that streak commitment is a positive function of individual differences, including the activity’s identity relevance to the actor, beliefs about self-improvement resulting from the activity, and the actor’s personal need for structure, and marketer-controlled elements, including structuring the streak as a daily (versus cumulative) task, presenting the streak as a short-term (versus indefinite) goal, and allowing the actor to keep the streak private (versus sharing with others). These findings lead to a deeper understanding of streaks, provide practical implications for marketers, and highlight opportunities for future research.

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Notes

  1. Zeithaml et al. (2020) recommend one-on-one interviews with 15–25 theory holders. TSR provides insight from a much larger number of theory holders. The publication spans over 20 years and 80 issues. We focus on 20 issues (five full years, V16N1-V20N4), which contain 1844 runner profiles. Archived issues of TSR are available online at https://www.runeveryday.com/registry_archives.php.

  2. For a Snapstreak, Snapchat requires both Snapchatters to send a photo, not a chat, back and forth to each other (performance parameter) within a 24-h window (temporal parameter).

  3. Surveyed streak runners indicated after how many days they felt they had a legitimate running streak (Question 5 in Web Appendix A). The modal response was 365 (41.0% of respondents), corresponding to USRSA’s official rules. Even among USRSA members, responses ranged from one day to 1000 days to “I never thought about it.”.

  4. Surveyed streak runners indicated whether they could imagine a reason for which they missed a day running but still considered the streak active (Question 13 in Web Appendix A). While a majority (94.1%) of respondents answered “no,” 5.9% of respondents could imagine reasons for which they missed a day but still considered the streak to be alive. Reasons included, in the words of one respondent, “something beyond my control,” such as health problems, childbirth, travel, and natural disasters.

  5. Among surveyed streak runners, 98.4% of respondents indicated that the streak itself served to motivate their daily runs to some degree (Question 6b in Web Appendix A). Respondents estimated the percentage of days for which they did not want to run but did so just to keep the streak alive (Question 7 in Web Appendix A). The average response was 14.0%, with 92.7% of respondents providing responses greater than 0.

  6. Among surveyed streak runners, 13.8% of respondents indicated that their streaks were not a conscious decision but emerged over time (Question 4 in Web Appendix A). A specific goal other than the streak itself provided at least some motivation to most (65.2%) respondents (Question 6a in Web Appendix A).

  7. Web Appendix A presents the results of a cluster analysis of the motivation measures from the streak runner survey (Question 6). As shown in Table A2, four of the five identified clusters (81.3% of respondents) were highly motivated by the streak itself. As described in Web Appendix A, the cluster that was not motivated by the streak (representing 18.7% of respondents) displayed characteristics that align with running as a habit.

  8. While this effect may occur for a given activity, it may not occur when comparing across activities. Some tasks, such as running, lend themselves to discrete performance parameters (e.g., running at least one mile) and periodic schedules (e.g., daily). Other tasks, such as weight loss, lend themselves to cumulative performance (e.g., losing at least five pounds a week). Future research could examine potential interactions among type of task, periodicity, and cumulative versus discrete behavior. We thank an anonymous reviewer for highlighting this possibility.

  9. Thirty respondents (7.0%) provided questionable responses, as indicated by response patterns, incoherent responses to the open-ended attention check, or extremely fast responding. Because results did not qualitatively differ when these respondents were included or excluded from the analysis, we present results based on the entire sample.

  10. Thirty-seven respondents displayed low reliability in their rankings, as indicated by a regression r-square value less than .5. Although it is common to remove respondents who exhibit such low levels of reliability in their rankings, the results did not qualitatively differ when these respondents were included or excluded from the analysis. Consequently, we present results based on the entire sample.

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  1. Department of Marketing, Wilbur O. and Ann Powers College of Business, Clemson University, Clemson, SC, USA

    Danny Weathers & T. Andrew Poehlman

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Weathers, D., Poehlman, T.A. Defining, and understanding commitment to, activity streaks. J. of the Acad. Mark. Sci. 52, 531–553 (2024). https://doi.org/10.1007/s11747-023-00944-4

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