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This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

Small and medium-sized enterprises (SMEs) are currently showing an increasingly open innovation (OI) approach. Public policies supporting the adoption of OI by SMEs are becoming a priority for policymakers. Therefore, the aim of this article is to contribute to the literature by mapping scholars' policy recommendations for implementing OI among SMEs.

The authors conducted a systematic review of the literature (SRL) on the topic to achieve this purpose. A total of 99 academic articles were selected from the Web of Science and Scopus databases to suggest the main scholars' policy recommendations to implement OI among SMEs.

Results indicated that scholars' policy recommendations for OI adoption in SMEs can be organized into: research and development (R&D), networking, collaboration, knowledge and intellectual property rights (IPR), ecosystem, managerial capabilities, funding and incentives and sustainability policies.

Only relevant articles about this topic have been included due to the reliance on the interpretations of the authors. The analysis of the literature revealed that the authors did not always distinguish policies dedicated to SMEs and those dedicated to large companies. Moreover, policies are not matched according to each OI dimensions (e.g. inbound, outbound and coupled OI).

The article uses a systematic literature review method that combines qualitative and quantitative analyses. This method contributes to theoretical development of OI policies dedicated, in particular to SMEs. This paper also provides policymakers and researchers with insights on the scope of OI policies that could support economic growth.

Social media users can now create, exchange, modify and consume socially generated experiences which can enhance social influence toward mobile banking (MB). This study aims to provide understanding of how social actor interactions through social networking platforms (SNPs) can create social influence for MB adoption and present a research framework that can help to understand which social actors have higher social influence toward MB adoption in conventional and Islamic banks.

SNP users have different levels of perceptions and experiences about the usability and credibility of MB. Therefore, their experiences are subjective realties which can generate socially constructed knowledge. To understand these subjective realties, a social constructivist approach is adopted. Data were collected from interviews with 60 individuals from diverse occupational backgrounds.

Identification element of social influence explained that the shared reviews and recommendations of opinion leaders, industry experts, celebrities and friends were highly positive for conventional banks; therefore, there is high word-of-mouth for MB of conventional banks. Internalization of social influence highlighted that people are more likely to accept the wisdom of the crowd and close friends, which can generate their engagement and connection with MB. Finally, the compliance factor of social influence explained that people can only adopt MB when they perceive high usability and credibility.

This study has provided understanding to the marketers of how social actors on SNPs can play a role in the creation, exchange, modification and consumption of socially generated influence that can impact the MB adoption intention for conventional and Islamic banks.

Although many theories and models have been presented about the marketing strategies and antecedents of MB adoption, the extensive use of SNPs has changed marketing strategies. For example, this study has found that social media users are highly influenced by the social reviews and recommendations they receive from their close friends. Therefore, socially generated influence on SNPs can create an adoption intention toward MB.

The objective of this study is to investigate the effect of consumption values (functional, social, epistemic, and emotional value) on mobile banking (m-banking) usage intention and intention to recommend m-banking services in the context of the Bangladeshi banking sector. Furthermore, this study examines how perceived security moderates the effects of consumption values on m-banking usage intention and recommendation intention.

A self-structured questionnaire had been used to obtain survey data from a sample of 237 m-banking users in Bangladesh using a convenience sampling method. Data were analyzed by structural equation modeling based on AMOS 23.0.

The findings show that all the dimensions of consumption values (functional, epistemic, and emotional value) except social value have a significant impact on mobile banking usage intention. In turn, mobile banking usage intention has a substantial direct effect on users' recommendation intention. Furthermore, the results also demonstrate that perceived security moderates the effects of functional values on m-banking usage intention as well as m-banking usage intention on recommendation intention.

These research findings offer insightful guidelines to bank marketers to increase m-banking usage intention and recommendation intention by emphasizing the functional, epistemic, and emotional value of m-banking services as well as safeguarding consumers' perceived security in m-banking transactions.

This study contributes to the body of knowledge by measuring the effects of consumption values dimensions on the m-banking usage intention and recommendation intention which were hardly investigated particularly in the m-banking context. To date, this study is the first attempt to examine how perceived security moderates the effects of consumption values dimensions on the m-banking usage intention as well as m-banking usage intention on recommendation intention. The findings contribute to a theoretical understanding of the importance of consumption values in consumer decision-making, particularly in the rarely studied domain of mobile banking.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

Interest in battery energy storage systems (BESS) is high, and technologies such as Li-ion (and other advanced chemistry) batteries in specific use cases are already economically viable. In this paper, the authors build further on the authors' previously published paper1 to estimate the potential positive impact that accelerated adoption of Li-ion batteries for stationary storage per the authors' identified already economically viable use cases, can have both on India's macro-economy and current account deficit as well as in helping meaningfully accelerate circular economy and Sustainable Development Goals (SDG) benefits of green economy transition.

The authors identified key challenges for development of BESS ecosystem and applied quantitative and qualitative assessment methodology for rapid adoption of BESS in India. The authors' study was validated through interviews with stakeholders and the authors summarize applicable findings for emerging countries such as India to encourage faster, wider adoption of energy storage.

The authors' study provides key policy recommendations to achieve a better balance in policy focus—not only for electronic vehicles (EVs) and utility-scale storage, but also for stationary behind-the-meter storage through key policy measures including placing a CESS on diesel generators (DGs), differential tariffs, encouraging advanced battery imports as a way to reduce crude oil imports, green financing and investments in de-carbonized energy breakthrough technologies (e.g. gravity-based energy storage systems). The authors recommend key technology priorities and strategic business rationale for private sector efforts by developing competitive advantages for non-battery hardware and software and expanding into emerging markets, with potential US$15–20+bn enterprise value.

While the dominant discourse focuses on EVs and utility scale applications of storage, the authors' paper shows the larger near term opportunity for impact is in stationary storage that too in end-user adoption use cases.

Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).

Using the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).

The results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.

This study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

Despite extensive academic research related to digital technologies (DT), their integration into architecture, engineering and construction (AEC) projects lags in practice. This paper aims to discover DT deployment patterns and emerging trends in real-life AEC projects.

A case study methodology was adopted, including individual case analyses and comparative multiple-case analyses.

The results revealed the temporal distribution of DT in practical AEC projects, specific DT products/software, major project types integrated with digital solutions, DT application areas and project stages and associated project performance. Three distinct patterns in DT adoption have been observed, reflecting the evolution of DT applications, the progression from single to multiple DT integration and alignment with emerging industry requirements. The DT adoption behavior in the studied cases has been examined using the technology-organization-environment-human (TOE + H) framework. Further, eight emerging trend streams for future DT adoption were identified, with “leveraging the diverse features of certain mature DT” being a shared recognition of all studied companies.

This research offers actionable insights for AEC companies, facilitating the development of customized DT implementation roadmaps aligned with organizational needs. Policymakers, industry associations and DT suppliers may leverage these findings for informed decision-making, collaborative educational initiatives and product/service customization.

This research provides empirical evidence of applicable products/software, application areas and project performance. The examination of the TOE + H framework offers a holistic understanding of the collective influences on DT adoption. The identification of emerging trends addresses the evolving demands of the AEC industry in the digital era.