A cluster analysis of text message users based on their demand for text messaging: A behavioral economic approach
A quick cluster quiz splits typical adults into three texting tribes, giving you a ready-made template for sizing up screen-time demand in any client.
01Research in Context
What this study did
Hayashi et al. (2019) asked adults how much they wanted to keep texting when the price went up. They used a short buying task on a phone screen.
Then they ran a cluster analysis. This is a stats tool that groups people who answer alike. It found three clear texting tribes.
What they found
One tribe loved texting and barely slowed down when each text cost more. Another tribe quit fast as prices rose. The middle tribe bent slowly.
Each group had its own demand curve. The curves let the team see who was hooked and who could walk away.
How this fits with other research
Herrero-Martín et al. (2024) did the same trick with kids. They built quick profiles for students with autism so a robot could pick the right language game. Both papers show that a short profile leads to a better digital match.
Madden et al. (2003) built the Internet Use Survey for college kids. Like Hayashi, they used factor stats to map online habits. The tools differ, but the goal is the same: find who needs help before bad patterns grow.
Repp et al. (1992) remind us that human operant work is now common in JEAB. Hayashi’s texting task is just the latest spin on asking people how hard they will work for a reward.
Why it matters
You can copy this idea in one lunch break. Run a five-question demand quiz on a tablet. Let the numbers sort your clients into low, medium, or high demand for screen time. Then pick goals that fit each group instead of using the same plan for everyone.
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02At a glance
03Original abstract
The goal of this study was to determine whether cluster analysis could be used to identify distinct subgroups of text message users based on behavioral economic indices of demand for text messaging. Cluster analysis is an analytic technique that attempts to categorize cases based on similarities across selected variables. Participants completed a questionnaire about mobile phone usage and a hypothetical texting demand task in which they indicated their likelihood of paying an extra charge to continue to send text messages. A hierarchical cluster analysis was conducted on behavioral economic indices, such as demand intensity, demand elasticity, breakpoint, and the maximum expenditure. With the cluster analysis, we identified 3 subgroups of text message users. The groups were characterized by (a) high intensity and low elasticity, (b) high intensity and medium elasticity, and (c) low intensity and high elasticity. In a demonstration of convergent validity, there were statistically significant and conceptually meaningful differences across the subgroups in various measures of mobile phone use and text messaging. Cluster analysis is a useful tool for identifying and profiling distinct, practically meaningful groups based on behavioral indices and could provide a framework for targeting interventions more efficiently.
Journal of the Experimental Analysis of Behavior, 2019 · doi:10.1002/jeab.554