Engaging consumers through artificially intelligent technologies

While consumer engagement (CE) in the context of artificially intelligent (AI-based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature-based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach to synthesize the AI-based CE literature. Our review yields three major themes of AI-based CE, including (i) Increasingly accurate service provision through AI-based CE; (ii) Capacity of AI-based CE to (co)create consumer-perceived value, and (iii) AI-based CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI-based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI-based CE consequences of consumer-based, firm-based, and human-AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AI-based CE) and practice (e.g., by reducing consumer-perceived costs of their brand/firm interactions)

Consumer engagement with self-driving cars: a theory of planned behavior-informed perspective

Purpose This study investigates the mediating role of consumer engagement (CE) in the relationship between perceived behavioral control (PBC) and purchase intent and the moderating role of perceived safety in the relationship between PBC and CE in the self-driving car (SDC) context. Design/methodology/approach To test the model, a sample of 368 consumers was deployed using partial least-squares structural equation modeling (PLS-SEM). Findings The findings reveal that consumers' SDC engagement mediates the relationship between PBC and their intent to purchase an SDC. Consumer-perceived SDC safety also moderates the association of PBC/engagement. Originality/value While prior research has examined consumer-based drivers of SDC adoption, understanding of consumers' SDC engagement-related dynamics and outcomes lags behind. Addressing this gap, we propose and test a model that explores consumers' SDC engagement vis-à-vis its drivers (perceived SDC safety/behavioral control) and outcomes (SDC purchase intent)

Why switch? The role of customer variety-seeking and engagement in driving service switching intention

PurposeThis study aims to examine the role of service customers’ variety-seeking and engagement in driving their service switching intention. The authors also explore the moderating role of customer relationship proneness in this association.Design/methodology/approachTo test the hypotheses, the authors deployed a sample of 227 service customers, whose data was analyzed by using partial least squares structural equation modeling.FindingsThe findings show that customer engagement mediates the relationship of customer variety-seeking and their service switching intention, as hypothesized. Moreover, customer relationship proneness weakens the negative effect of engagement on customers’ service switching intention.Originality/valueThough scholarly acumen of customer engagement is rapidly developing, little remains known regarding its theoretical interface with customer variety-seeking and switching intention. Addressing this gap, the authors test a model exploring the mediating role of customer engagement in the association of customer variety-seeking and switching intention, and the potentially moderating role of customer relationship proneness in the association of customer engagement and service switching intention

Engaging consumers through artificially intelligent technologies : Systematic review, conceptual model, and further research

While consumer engagement (CE) in the context of artificially intelligent (AI-based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature-based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach to synthesize the AI-based CE literature. Our review yields three major themes of AI-based CE, including (i) Increasingly accurate service provision through AI-based CE; (ii) Capacity of AI-based CE to (co)create consumer-perceived value, and (iii) AI-based CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI-based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI-based CE consequences of consumer-based, firm-based, and human-AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AI-based CE) and practice (e.g., by reducing consumer-perceived costs of their brand/firm interactions)

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

While consumer engagement (CE) in the context of artificially intelligent (AI-based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature-based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach to synthesize the AI-based CE literature. Our review yields three major themes of AI-based CE, including (i) Increasingly accurate service provision through AI-based CE; (ii) Capacity of AI-based CE to (co)create consumer-perceived value, and (iii) AI-based CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI-based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI-based CE consequences of consumer-based, firm-based, and human-AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AI-based CE) and practice (e.g., by reducing consumer-perceived costs of their brand/firm interactions)

Hallmarks and potential pitfalls of customer- and consumer engagement scales : a systematic review

Multiple scales measuring a customer's, or consumer's, engagement (CE) with a brand or specific brand elements (e.g., advertising/social media content) have been proposed in the literature, offering researchers different options to gauge CE. However, the myriad proposed operationalizations can yield confusion among scholars regarding how to best capture CE, exposing a growing issue for CE research. Addressing this issue, we take stock of major scales measuring a CE with a brand or specific brand elements. To achieve this objective, we performed a systematic review of major CE scale development articles (2005 to January 2023) using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach. We systematically evaluated these scales in terms of their respective CE conceptualization, dimensionality, itemization, and underlying theoretical perspective. We also identify potential scale-related risks, or pitfalls, exposing important insight for CE researchers. Overall, the results suggest the existence of theoretical contamination in specific CE measures (e.g., through the inclusion of related concepts in the proposed CE definition), compromising their theoretical rigor and raising a need for scholars to verify the theoretical underpinnings of their adopted CE scales