Investigating User Perceptions of Persuasive Design Elements that Influence Perceived Credibility

Given the prevalent use of technology for human activities, the determinants of user perceptions of the credibility of technological systems must be understood. This study investigated how user perceptions of persuasive elements that are incorporated in Academic Social Networking Sites (ASNSs) influence the Perceived Credibility of these sites. Specifically, the effects of Personalization, Reduction, Tailoring, Self-Monitoring, Rehearsal, Simulation and Tunneling on Perceived Credibility are examined. An online survey was adopted to examine the research model. Partial Least Square techniques were used to assess the significance of the determinants on Perceived Credibility. Results indicate that Tailoring positively influences Perceived Credibility whereas Personalization has a negative significant effect. Implications for theoretical insights into Perceived Credibility and its determinants and design implication are discussed.</p

Credibility in Persuasive Systems:A Systematic Review

Credibility of systems is important in Human-Computer Interaction (HCI) and when designing persuasive systems. Still, the role of credibility in the design of persuasive systems remains unclear. To date, there has not been a systematic review examining the concept of credibility in persuasive systems. Therefore, this study presents a systematic literature review of primary empirical studies published from 2011 to 2020 that examined credibility within the context of persuasive systems. A total of 41 publications were reviewed. Overall, the results highlight the trends of credibility research, the theoretical frameworks that have been used to examine credibility, the research methods used in credibility studies, as well as the antecedents, and consequents of credibility in persuasive systems’ context. Majority of the reviewed studies pursued a correlational research approach as opposed to testing theories through experimental studies. While giving little attention to user characteristics and how they influence the relationship between credibility and its antecedents, existing studies have also barely examined the influence of system features as antecedents. Based on these findings, we argue that existing theoretical frameworks do not provide adequate directions for implementing credibility features in system design. Future research should therefore conduct more experimental studies and provide directions for how credibility features can be implemented in system design

Investigating User Perceptions of Persuasive Design Elements that Influence Perceived Credibility

Given the prevalent use of technology for human activities, the determinants of user perceptions of the credibility of technological systems must be understood. This study investigated how user perceptions of persuasive elements that are incorporated in Academic Social Networking Sites (ASNSs) influence the Perceived Credibility of these sites. Specifically, the effects of Personalization, Reduction, Tailoring, Self-Monitoring, Rehearsal, Simulation and Tunneling on Perceived Credibility are examined. An online survey was adopted to examine the research model. Partial Least Square techniques were used to assess the significance of the determinants on Perceived Credibility. Results indicate that Tailoring positively influences Perceived Credibility whereas Personalization has a negative significant effect. Implications for theoretical insights into Perceived Credibility and its determinants and design implication are discussed

Credibility in Persuasive Systems: A Systematic Review

Credibility of systems is important in Human-Computer Interaction (HCI) and when designing persuasive systems. Still, the role of credibility in the design of persuasive systems remains unclear. To date, there has not been a systematic review examining the concept of credibility in persuasive systems. Therefore, this study presents a systematic literature review of primary empirical studies published from 2011 to 2020 that examined credibility within the context of persuasive systems. A total of 41 publications were reviewed. Overall, the results highlight the trends of credibility research, the theoretical frameworks that have been used to examine credibility, the research methods used in credibility studies, as well as the antecedents, and consequents of credibility in persuasive systems’ context. Majority of the reviewed studies pursued a correlational research approach as opposed to testing theories through experimental studies. While giving little attention to user characteristics and how they influence the relationship between credibility and its antecedents, existing studies have also barely examined the influence of system features as antecedents. Based on these findings, we argue that existing theoretical frameworks do not provide adequate directions for implementing credibility features in system design. Future research should therefore conduct more experimental studies and provide directions for how credibility features can be implemented in system design

Supporting to be Credible: Investigating Perceived Social Support as a Determinant of Perceived Credibility

Technological systems can be equipped with persuasive design principles that influence user perceptions. For instance, earlier research showed that Perceived Social Support can influence user perceptions such as effectiveness and knowledge sharing intentions. However, to our knowledge, how Perceived Social Support affects Perceived Credibility has not been investigated. This study investigates the influence of Perceived Social Support on Perceived Credibility. A survey questionnaire was employed to gather user perceptions of social support and credibility in the context of Academic Social Networking Sites (ASNSs). Analysis using Partial Least Square Structural Equation Modeling (PLS-SEM) confirmed Perceived Social Support as a determinant of Perceived Credibility. Also, Dialogue Support and Primary Task Support were identified to be predictors of Perceived Social Support. The study recommends that designers improve the social support features (e.g., through integrating machine learning and data mining techniques)