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This study aims to conduct a systematic literature review and bibliometric analysis on the topic of digital labour. The study also identifies the future research directions for the topic.

In total, 118 research papers were identified and reviewed from 11 established research databases and A*, A and B category journals from the ABDC journal list. The papers covered a timespan between 2006 and 2023. Bibliometric analysis was conducted to identify key research hotspots.

The emergent themes and associated sub-themes related to digital labour were identified from the literature. The paper found three significant themes that include digital labour platform, gig economy and productivity. This study also acts as a platform to initiate further research in this field for academicians, scholars, industry practitioners and policymakers. The future research scope in the topic is also presented.

The present study is unique in its nature as it approaches the topic of digital labour from all relevant perspectives.

This study aims to conduct a comprehensive analysis of the current body of existing literature on chatbots and online shopping. Additionally, this study identifies and emphasize the future research agenda and emerging trends within this domain.

A thorough investigation was conducted on a set of 147 publications sourced from the Scopus database spanning the years 2016 to 2023 by using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The analysis included bibliometric techniques through VOSviewer, including science mapping and performance analysis of the literature under investigation.

The findings of the study indicate a systematic impression of prevailing scientific research on integration of Chatbot in online shopping. A majority of publications were contributed by developing countries specifically Asian regions. There has been a notable rise in research collaborations over the course of time. Further, themes were identified through keyword co-occurrence for exploration of future trends in the domain.

This study identifies and analyzes the patterns in the existing literature on chatbot and online shopping, with the objective of enhancing e-retailers comprehension of this particular topic area. The research findings hold significance for both researchers and organizations in their efforts to enhance strategy design.

This study uses bibliometric analysis to examine the literature on chatbots and online shopping, aiming to develop a systematic comprehension of the research field. This study makes a valuable contribution to the current scholarly discourse and provides support for future scholars in their investigations.

This study aims to recognize the role of information system (IS) model on young adults’ experience of housing and real estate chatbots. This model of IS takes into account the quality of information, the quality of system and the quality of service.

This study uses a sample frame for analysis which comprises young adult population in India, i.e. between the ages of 18 and 35. A questionnaire consisting of five components was used to collect information in a structured manner. The 386 responses thus collected were analysed using the structural equation model.

It was found that there is a significant influence of the quality of information, quality of system and quality of service on young adults’ experience of housing and real estate chatbots. The findings also showed that there is moderation role of effort expectancy between the quality parameters and young adults’ user experience of housing and real estate chatbots.

This study focusses exclusively on the young adults from various parts of India. Future research can consider larger population categories across age groups and across sectors employing chatbots.

This study will enable in-depth understanding of IS model – quality dimensions’ relation with the user experience. In particular, housing and real estate organisations will profit from the expanded usage of artificial intelligence through chatbots for user correspondence and communication.

To the best of the authors’ knowledge, this study is first of its kind, as it investigates how IS model – quality dimensions affect the young adults’ experience of housing and real estate chatbots in India. This study also ventures into identifying the moderation role of effort expectancy between the quality dimensions as per IS model and young adults’ experience of housing and real estate chatbots. This study will be useful for the stakeholders of housing and real estate industry.

The experience of leaving the real world and entering a virtual service environment makes many individuals happy. This study heeds the call by multiple researchers to conceptualize, interpret and illustrate the impact of the perceived service experience in the metaverse in a holistic way. In particular, this study aims to understand how the consumption of experiences is perceived in a metaversal space.

The authors analyze mega virtual live events with famous artists broadcast in virtual worlds. The authors take a big data approach and include two studies to gain insight into the online public audience’s perceptions and experiences in the metaverse. In the first study, the authors analyze text from YouTube with Leximancer. In the second study, the authors go one step further to refine the conceptual model from Study 1. The authors scrutinize additional Facebook comments using seeded Latent Dirichlet Allocation (LDA).

The findings reveal that the meta service experience (MEX) encompasses four dimensions: immersion, metascape, immediacy and hedonism.

This research provides important guidance not only for consumer behavior scholars but also for service marketers and event planners. The study proposes research opportunities to advance service experience research in the metaverse.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.