NDLI: Personalizing Social Influence Strategies in a Q&A Social Network

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Personalizing Social Influence Strategies in a Q&A Social Network

Content Provider	ACM Digital Library
Author	Adaji, Ifeoma Vassileva, Julita
Abstract	Research has shown that persuasive technologies are more effective when they are personalized. Persuasive strategies work differently for various people; hence a one size fits all approach may not bring about the desired change in behavior or attitude. This paper contributes to personalization in question and answer (Q&A) social networks by exploring the possibility of personalizing social influence strategies based on the computer programming skill level and the highest level of education of users. In particular, this paper explores the susceptibility of users in Stack Overflow, a Q&A social network, to social support influence strategies for novice and expert computer programmers. In addition, we explore if first degree holders respond to the social support influence strategies the same way graduate degree holders do. Using a sample size of 282 Stack Overflow users, we constructed four models using Partial Least Squares Structural Equation Modelling (PLS-SEM) and carried out multi-group analysis between these models. The results of our analysis show that social facilitation significantly influences cooperation for novice programmers, but not for expert programmers. In addition, social learning does not significantly influence the persuasiveness of the system for expert programmers compared to users who are novice in computer programming. For the users grouped according to their highest level of education, social learning influenced cooperation among the graduate degree holders and competition influenced the graduate degree holders to continue using the system. The result of this study can provide useful guidelines to social network developers that can be used in implementing personalized influence strategies in Q&A social communities.
Starting Page	215
Ending Page	220
Page Count	6
File Format	PDF
ISBN	9781450350679
DOI	10.1145/3099023.3099057
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-07-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Influence strategies Social support
Content Type	Text
Resource Type	Article

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