The impact of individual differences on influence strategies

Alkış, Nurcan; Temizel, Tuğba Taşkaya

doi:10.1016/j.paid.2015.07.037

Cited by 64 publications

(42 citation statements)

References 24 publications

(29 reference statements)

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“…Similar results were obtained in a study by Adnan et al [1] where an application was developed to persuade users to study more using persuasion strategies that were tailored to users' personalities: different personalities indeed preferred different (persuasive) study behaviors. Similar results were also obtained by Alkış and Temizel [3], where they showed significant relations between personality traits and persuasive strategies.…”

Section: Introductionsupporting

confidence: 87%

“…Overall, Agreeableness was related to the most processes (six in total). This is somewhat similar to [3], where Alkış and Temizel found Agreeableness to be the most susceptible to their six persuasion strategies (i.e., authority, reciprocation, scarcity, liking, commitment, and consensus). On the other hand, they found Openness to Experience to be the least susceptible to these strategies, which is not reflected in our results.…”

Section: Personalitysupporting

confidence: 68%

“…At the end of the survey, we asked participants information about when they would prefer to receive messages such as these, how many of such messages they would prefer to receive weekly, and whether they had experience with smartphones and exercise apps. Moreover, we measured people's susceptibility to persuasion [24], and their current stage of change 3 [39], perceived experiences with processes of change 3 [36], self-efficacy 3 [6], and decisional balance [35] for exercise 3 . To measure participants' personality, we used the 50-item IPIP representation of the revised version of Costa and McCrae's [9] NEO Personality Inventory 4 which posed 50 statements (e.g., "Make plans and stick to them.").…”

Section: Methodsmentioning

confidence: 99%

“…Some recent studies investigate how systems could tailor the strategies adopted to people's individual preferences for certain strategies [24,29]. One way this can be done is by building profiles of the users that account for factors that are relevant for tailoring strategies [25], for example by selecting strategies which fit their personality [3].…”

Section: Introductionmentioning

confidence: 99%

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A word of advice: how to tailor motivational text messages based on behavior change theory to personality and gender

Vries

Truong

Zaga

et al. 2017

Pers Ubiquit Comput

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Developing systems that motivate people to change their behaviors, such as an exercise application for the smartphone, is challenging. One solution is to implement motivational strategies from existing behavior change theory and tailor these strategies to preferences based on personal characteristics, like personality and gender. We operationalized strategies by collecting representative motivational text messages and aligning the messages to ten theory-based behavior change strategies. We conducted an online survey with 350 participants, where the participants rated 50 of our text messages (each aligned to one of the ten strategies) on how motivating they found them. Results show that differences in personality and gender relate to significant differences in the evaluations of nine out of ten strategies. Eight out of ten strategies were perceived as either more or less motivating in relation to scores on the personality traits Openness, Extraversion, and Agreeableness. Four strategies were perceived as more motivating by men than by women. These findings show that personality and gender influence how motivational strategies are perceived. We conclude that our theory-based behavior change strategies can be more motivating by tailoring them to personality and gender of users of behavior change systems.

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Section: Introductionsupporting

confidence: 87%

Section: Personalitysupporting

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A word of advice: how to tailor motivational text messages based on behavior change theory to personality and gender

Vries

Truong

Zaga

et al. 2017

Pers Ubiquit Comput

View full text Add to dashboard Cite

show abstract

“…ey work to gather various kinds of data to build their recommendations. As a general classi cation, data used by RSs can refer to three kinds of entities: (1) items that are recommended to users, (2) users that will receive the recommendations, and (3) transactions (i.e., recorded interactions between items and users) [59]. ere are many di erent ways of modelling users, which will depend on the application domain and the recommendation technique.…”

Section: Related Work 21 Recommender Systemsmentioning

confidence: 99%

Recommender Systems for Personalized Gamification

Tondello

Orji

Nacke

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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Gami cation has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve be er results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., so ware tools and technologies to recommend suggestions to users that they might enjoy). is framework contributes to understanding and building e ective persuasive and gameful applications by describing the di erent building blocks of a recommender system (users, items, and transactions) in a personalized gami cation context.

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