Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis

Bawack, Ransome Epie; Wamba, Samuel Fosso; Carillo, Kévin

doi:10.1016/j.ijinfomgt.2021.102309

Cited by 126 publications

(85 citation statements)

References 154 publications

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“…Mediators are factors that explain the process through which two factors are related, for example, antecedents and consequences. Among the mediators that exemplify why or how antecedents exert impact on consequences in conversational commerce include anthropomorphism or human likeness (Whang & Im, 2021), desire (Ramadan, 2021), parasocial interaction (Han & Yang, 2018), perceived control (Poushneh, 2021b), performance expectancy (Lv et al, 2021), privacy concern (Bawack et al, 2021), and trust (Pitardi & Marriot, 2021).…”

Section: Towards a Conceptual Framework For Conversational Commerce (...mentioning

confidence: 99%

“…Consequences are factors that result from the influence of antecedents, mediators, and moderators. Noteworthily, the consequences in conversational commerce manifest through a process involving attitudinal acceptance (Fernandes & Oliveira, 2021); intentions such as behavioral intention (Vimalkumar et al, 2021), continuance intention (Ashfaq et al, 2021), purchase intention (McLean et al, 2021), and word-of-mouth intention (Hernandez-Ortega & Ferreira, 2021); actual use behavior (Moriuchi, 2021); experience (Bawack et al, 2021); satisfaction (Loureiro et al, 2021); and post-usage behavior such as addiction (Ramadan, 2021), continuance (Poushneh, 2021b), and loyalty (Hasan et al, 2021).…”

Section: Towards a Conceptual Framework For Conversational Commerce (...mentioning

confidence: 99%

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Alexa, what do we know about conversational commerce? Insights from a systematic literature review

Lim

Kumar

Verma

et al. 2022

Psychology and Marketing

149

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Conversational agents are systems with human‐like features that can be deployed in service settings and are fast emerging as the future of online commerce. Considering the importance and proliferation of conversational commerce, it is opportune to take stock of the field's research from a bird's eye view to guide understanding of its current and future progress. In this regard, the goal of this study is to review the performance and intellectual structure of conversational commerce. To do so, this study conducts a comprehensive and systematic review of 722 publications on conversational commerce using the Scientific Procedures and Rationales for Systematic Literature Reviews protocol and a collection of bibliometric analysis techniques consisting of performance analysis and science mapping (e.g., content analysis, keyword co‐occurrence analysis, and bibliographic coupling). In doing so, this study reveals the performance (e.g., publication and citation trends, and top sources, publications, and authors) and the major theories and themes in the intellectual structure of the field. The study concludes with a conceptual framework depicting the constructs (e.g., antecedents, mediators, moderators, consequences, and enablers) of employing conversational agents for commerce and service delivery, as well as suggestions for its future research.

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Section: Towards a Conceptual Framework For Conversational Commerce (...mentioning

confidence: 99%

Section: Towards a Conceptual Framework For Conversational Commerce (...mentioning

confidence: 99%

Alexa, what do we know about conversational commerce? Insights from a systematic literature review

Lim

Kumar

Verma

et al. 2022

Psychology and Marketing

149

View full text Add to dashboard Cite

show abstract

“…Minsky (1968, p. v) defines AI as the "the science of making machines do things that would require intelligence if done by men". The emergence of voice assistants has enabled organisations to develop systems and processes where human interaction with AI has become the norm (Bawack et al, 2021;Hu et al, 2021). The first voice assistant titled "Shoebox" was introduced by IBM at the Seattle world fair in 1962.…”

Section: Introductionmentioning

confidence: 99%

Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

et al. 2021

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This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

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“…Also, the Big Five predict materialism (the belief about the relevance of possessions in life), shopping-center selection, and excessive as well as compulsive buying [16][17][18][19]. Moreover, the Big Five affected privacy concerns and trust in smart speaker manufacturers which, in turn, influenced a customer's experience of voice shopping performance [20]. Lastly, customers with a higher level of empathic concern reported greater satisfaction with online service providers and were more likely to help other online customers [21].…”

Section: Introductionmentioning

confidence: 99%

Influence of Personality Traits on Choice of Retail Purchasing Channel: Literature Review and Research Agenda

Hermes

Riedl

2021

JTAER

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Consumers can choose to buy products through various retail channels (e.g., online or in-store), resulting in a need for retailers to provide a well-integrated shopping experience. However, individual factors, such as a customer’s personality, can influence how a channel is perceived and, as such, can alter their subsequent behavior. Thus, it is critical for retailers to better understand personality-related influences and how these can affect a customer’s purchase channel decision. Against this background, the purpose of this structured review is to analyze the extant literature on the influences of personality traits on purchasing channel decisions. After extensive initial screening, 24 papers published in 2003–2021 were included in the analysis and synthesis phase of this literature review. The results show how personality traits (including Big Five factors and more fine-grained factors like playfulness) influence the choice of retail purchasing channels. Among other results, we find that online shopping intentions have been studied most as an outcome variable and that, in contrast to people high in openness to experience, people high in agreeableness are less likely to shop online. While we synthesize findings in the domains of mobile commerce, social commerce, mall shopping, and augmented and virtual reality as well, little research has compared the effects of personality traits on multiple channels. Based on our findings, we discuss managerial implications as well as directions for future research which are described in the form of a research agenda.

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Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis

Cited by 126 publications

References 154 publications

Alexa, what do we know about conversational commerce? Insights from a systematic literature review

Alexa, what do we know about conversational commerce? Insights from a systematic literature review

Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

Influence of Personality Traits on Choice of Retail Purchasing Channel: Literature Review and Research Agenda

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