Not Too Much and Not Too Little: Information Processing for a Good Purchase Decision.

When deciding on an online purchase, consumers often face a plethora of information. Yet, individuals consumers differ greatly in the amount of information they are willing and able to acquire and process before making purchasing decisions. Extensively processing all available information does not necessarily promote good decisions. Instead, the empirical evidence suggests that reviewing too much information or too many choice alternatives can impair decision quality. Using simulated contract conclusion scenarios, we identify distinctive types of information processing styles and find that certain search and selection strategies predict the quality of the final choice. Participants (N = 363) chose a cellular service contract in a web-based environment that closely resembled actual online settings in the country of study. Using information processing data obtained with tracking software, we identify three consumer segments differing along two dimensions - the extent dimension, referring to the overall effort invested in information processing, and the focus dimension, referring to the degree to which someone focuses on the best available options. The three subgroups of respondents can be characterized as follows: (1) consumers with a low-effort and low-focus information processing strategy (n = 137); (2) consumers with a moderate-effort and high-focus information processing strategy (n = 124); and (3) consumers with high-effort and low-focus information processing strategy (n = 102). The three groups differed not only in their information processing but also in the quality of their decisions. In line with the assumption of ecological rationality, most successful search strategies were not exhaustive, but instead involved the focused selection and processing of a medium amount of information. Implications for effective consumer information are provided.

When your goals inspire my goals: the role of effort, personal value, and inference in goal contagion.

Just observing other people can influence what we do. Under certain conditions, it inspires us to strive for the same goal as the other person. Such goal contagion occurs, because one first automatically infers the goal and then adopts it for oneself. In a series of three experiments (overall N = 840 university students), we investigated personal goal value and the observed person's effort as moderators of goal contagion, which is mediated by goal inference. In all three experiments, participants read a brief story about a student who either wants to earn money (target goal) or to do an internship (control) and expects to show much or little effort. In Studies 1a and b, goal inference was the dependent variable, whereas in Study 2, we considered the full moderated-mediation model and measured how strongly participants pursue the goal to earn money. We aimed at locating the moderators within this two-step process. We hypothesized that high effort increases goal inference, whereas personal goal value strengthens the relationship between goal inference and goal adoption. Across experiments, we did find evidence for explicit and spontaneous, but not for implicit goal inference. Furthermore, participants did not pursue to earn money to a different degree across conditions and different degrees of goal value. Taken together, neither the moderated-mediation process nor the basic goal contagion effect was supported. Results are discussed in the light of other published studies on goal contagion and the current Replication Crisis.

Improving Self-Control: The Influence of Role Models on Intertemporal Choices.

The ability to delay rewards is one of the most useful qualities one may wish to develop. People who possess this quality achieve more successful careers, display better interpersonal skills and are less vulnerable to psychopathology, obesity or addictions. In the present online studies, we investigated the extent to which delay-of-reward behaviors in female participants can be improved by observing others mastering it. We developed an intertemporal choice (IC) paradigm in which participants had to make fictitious choices between sooner smaller rewards and later bigger ones (e.g., $150 in 1 week vs. $170 in 4 weeks). In Study 1 (N = 186), we found that participants who delayed more had higher socioeconomic statuses and were less likely to procrastinate, smoke or develop obesity. In Study 2 (N = 178), we exposed female participants to a role model who, faced with ICs, chose most of the time the delayed option. Results showed that, although participants were only asked to memorize the model's decisions, they tended to choose the delayed option more often after than before exposure to the model. In Study 3 (N = 148), we found that the direction of the influence depended on the model's behavior: our female participants delayed more after having seen a high delay than a low delay model. In Study 4 (N = 370), we confirmed the effect of modeling on ICs in comparison to a control condition and showed that this effect was still significant 3 months after exposure to the model. Altogether, these results speak in favor of a high efficacy of modeling to develop self-control in women.

Inspired to Lend a Hand? Attempts to Elicit Prosocial Behavior Through Goal Contagion.

Helping often occurs in a broader social context. Every day, people observe others who require help, but also others who provide help. Research on goal contagion suggests that observing other people's goal-directed behavior (like helping) activates the same goal in the observer. Thus, merely observing a prosocial act could inspire people to act on the same goal. This effect should be even stronger, the more the observer's disposition makes him or her value the goal. In the case of prosocial goals, we looked at the observer's social value orientation (SVO) as a moderator of the process. In three studies (N = 126, N = 162, and N = 371), we tested the hypothesis that prosocial observations (vs. control) will trigger more subsequent casual prosocial behavior the more the observer is prosocially oriented. In line with the original research, we used texts as stimulus material in Study 1 and short video clips in Study 2 and 3. In Study 1 and 2, SVO was measured directly before the manipulation was induced and in Study 3 even a week prior to the actual experiment. Additionally, we included a second control condition video clip in Study 3, which did not depict human beings. Despite thoroughly developed stimulus material and methods, we found no support for an effect of the interaction, nor of the prosocial observation, but some support for an effect of SVO on casual helping behavior in Study 1 and 2. A mini meta-analysis revealed an effect equivalent to zero for goal contagion and a small, but robust SVO effect across studies. The main implication for the theory of goal contagion is that prosocial goals might not be as contagious as other goals addressed in the literature. We suggest a meta-analytic review of the literature to identify suitable goals and moderators for the goal contagion process.

Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression.

BACKGROUND: Mobile technology gives researchers unimagined opportunities to design new interventions to increase physical activity. Unfortunately, it is still unclear which elements are useful to initiate and maintain behavior change. OBJECTIVE: In this meta-analysis, we investigated randomized controlled trials of physical activity interventions that were delivered via mobile phone. We analyzed which elements contributed to intervention success. METHODS: After searching four databases and science networks for eligible studies, we entered 50 studies with N=5997 participants into a random-effects meta-analysis, controlling for baseline group differences. We also calculated meta-regressions with the most frequently used behavior change techniques (behavioral goals, general information, self-monitoring, information on where and when, and instructions on how to) as moderators. RESULTS: We found a small overall effect of the Hedges g=0.29, (95% CI 0.20 to 0.37) which reduced to g=0.22 after correcting for publication bias. In the moderator analyses, behavioral goals and self-monitoring each led to more intervention success. Interventions that used neither behavioral goals nor self-monitoring had a negligible effect of g=0.01, whereas utilizing either technique increased effectiveness by Δg=0.31, but combining them did not provide additional benefits (Δg=0.36). CONCLUSIONS: Overall, mHealth interventions to increase physical activity have a small to moderate effect. However, including behavioral goals or self-monitoring can lead to greater intervention success. More research is needed to look at more behavior change techniques and their interactions. Reporting interventions in trial registrations and articles need to be structured and thorough to gain accurate insights. This can be achieved by basing the design or reporting of interventions on taxonomies of behavior change.