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This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.

Online retailers are pioneering the incentivization of customers to generate more product reviews by rewarding them. However, little is known about the impact of reward types on customers' review behavior, including review frequency and sentiment. To address this gap, we investigated the effects of different reward types on customers' review behavior and how these rewards influence customers' review behavior.

We collected secondary data and empirically tested the hypothesis by analyzing the change in reward policy. Regression and two-stage Heckman models were applied to investigate the effects, with the latter used to control potential selection issues.

The results revealed that monetary rewards can stimulate customers to generate more positive product reviews. Furthermore, the reward amount has a negative moderating effect on the aforementioned relationship. Additionally, customer tenure negatively moderates the relationship between monetary rewards and review behavior.

This study contributes to the understanding of user-generated content motivation and provides managerial implications for reward programs.

Many brands claim they were born by coincidence, yet the effects and contingencies of this communication strategy are little understood by extant marketing research on unexpected events. This study aims to investigate how consumers react to brand communications portraying a coincidental vs planned origin.

This research presents five experimental studies embedding coincidental brand origins into different types of marketing communications (i.e. crowdfunding campaigns, visual ads and brand biographies).

This research finds that coincidental brand origins increase persuasion (measured as money pledged to a crowdfunding campaign, overall brand equity and purchase intention) but only for consumers high in need for cognition (NFC). This effect is mediated by processing enjoyment, as the intrinsic need for thinking that characterizes high NFC consumers is satisfied by the opportunity to process the coincidence. Further to process, the authors show that explicitly providing an explanation for the coincidence makes the effect disappear, as this deprives high-NFC consumers of the opportunity to autonomously engage in and enjoy the cognitive process.

Brand managers able to leverage coincidences in their storytelling efforts should target high-NFC consumers and should not provide an explanation for the coincidences.

This research advances the limited literature on how consumers react to coincidences in a marketing context, the understanding of how brand communication strategies persuade consumers through information processing and the NFC literature.

Campaigns to promote the COVID-19 vaccination messages to vaccine-hesitant consumers in the late stages of the pandemic are often met with resistance. This study aims to explore a way to leverage positive emotions induced from entertainment media consumption to promote vaccination messages to this audience group.

An online experiment was conducted with vaccine-hesitant consumers (N = 409). Participants viewed personally relevant entertainment music videos or mundane videos and vaccinated messages embedded in user-generated comments.

Data revealed that feelings of inspiration and nostalgia induced from entertainment media consumption increased vaccination intentions via increased risk perceptions and reduced anti-vaccination attitudes.

Social marketers should consider leveraging the combined effect of entertainment media-induced positive emotions and user-generated comments to motivate behavioral change among vaccine-hesitant individuals in the late stages of the COVID-19 pandemic.

The present study adds to social marketing literature by showing mechanisms that positive emotions induced from entertainment social media consumption might lead to health behavioral change.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

The deployment of artificial intelligence (AI) technologies in travel and tourism has received much attention in the wake of the pandemic. While societal adoption of AI has accelerated, it also raises some trust challenges. Literature on trust in AI is scant, especially regarding the vulnerabilities faced by different stakeholders to inform policy and practice. This work proposes a framework to understand the use of AI technologies from the perspectives of institutional and the self to understand the formation of trust in the mandated use of AI-based technologies in travelers.

An empirical investigation using partial least squares-structural equation modeling was employed on responses from 209 users. This paper considered factors related to the self (perceptions of self-threat, privacy empowerment, trust propensity) and institution (regulatory protection, corporate privacy responsibility) to understand the formation of trust in AI use for travelers.

Results showed that self-threat, trust propensity and regulatory protection influence trust in users on AI use. Privacy empowerment and corporate responsibility do not.

Insights from the past studies on AI in travel and tourism are limited. This study advances current literature on affordance and reactance theories to provide a better understanding of what makes travelers trust the mandated use of AI technologies. This work also demonstrates the paradoxical effects of self and institution on technologies and their relationship to trust. For practice, this study offers insights for enhancing adoption via developing trust.

The purpose of this paper is to explore how gamification may be used for project stakeholder engagement.

The paper presents the results of a systematic literature review of extant research concerning the gamification of projects. Based on this, an agenda for future studies is outlined.

Extant research on the gamification of projects is scarce and scattered among various disciplines, but the engineering fields dominate. The research performed does indicate that gamification may be used for involving stakeholders in projects, primarily by promoting learning, but also by engaging them, motivating action and solving problems.

In several cases, extant research display poor quality in research design and a lack in cross-disciplinary perspectives, which means that more research is needed. The users’ perspective is often lacking. Furthermore, the ideas gamification might be “hidden” within other technologies.

The findings of this research may assist project management practitioners in the endeavor of adopting gamification principles to better involve stakeholders.

The study fills a gap in summarizing the research on how gamification may be used to promote project stakeholder engagement. Based on this, it proposes a research agenda for future research on the use of gamification to promote project stakeholder engagement.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

This study aims to investigate how artificial intelligence (AI)-powered smart home devices affect young consumers' requirements for convenience, support, security and monitoring, as well as their ability to advance environmental sustainability. This study also examines the variables that impact users' motivation to use AI-powered smart home devices, such as perceived value, ease of use, social presence, identity, technology security and the moderating impact of trust.

The responses from residents of Sarawak, Malaysia, were collected through online questionnaires. This study aimed to examine the perceptions of millennials and zillennials towards their trust and adoption of AI-powered devices. This study used a quantitative approach, and the relationships among the study constructs were analysed using partial least squares - structural equation modelling.

The present study found that perceived usefulness, ease of use and social presence were the main motivators among actual and potential users of smart home devices, especially in determining their intentions to use and actual usage. Additionally, there was a moderating effect of trust on the relationship between perceived ease of use, social presence, social identity and intention to use AI-powered devices in smart homes.

To the best of the authors’ knowledge, this is one of the first studies to examine the factors influencing smart technology adoption. This study provided meaningful insights on the development of strategies for the key stakeholders to enhance the adoption and usage of AI-powered smart home devices in Sarawak, one of the promising Borneo states. Additionally, this study contributed to the growing body of knowledge on the associations between technology acceptance model dimensions, intention and actual usage of smart technology, with the moderating impact of trust.

The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.