The aim of this study is to identify and empirically validate the factors that increase students' engagement in remote work-integrated learning (WIL). The technology interactivity model was considered an appropriate theoretical foundation for proposing the conceptual model in this study. Four factors i.e. interactivity, customisation, active control, and synchronicity were derived as key predictors of student's engagement. This was also extended by considering two factors from social identity theory: social identity and personal identity. The necessary data was collected using an online questionnaire with a purposive sample of students at different levels and from different educational backgrounds. Statistical findings largely approved the impact of social identity, interactivity, customisation, and active control on the students' engagement with remote WIL. Results supported the moderating effects of telepresence and social presence on the relationships between the key independent factors: interactivity, customisation, social identity and engagement.

This study explores how digital nudging techniques impact pro-environmental intentions and behaviors among tourists. Using data from 320 participants across two field-based studies, it examines both direct and indirect effects of nudging. Results show that while nudging effectively promotes pro-environmental intentions and behaviors, the link between intention and actual behavior is weak. This study contributes original insights by integrating behavioral economics with tourism research, and by empirically validating the persistence of the intention-behavior gap in real-world travel settings. Besides, research offers practical insights for tourism stakeholders aiming to encourage sustainable practices and contributes to the emerging literature on digital nudging by integrating perspectives from nudging theory and information systems. It highlights the importance of bridging the intention-behavior gap in sustainable tourism.

The aim of this study is to identify and empirically validate the factors that increase students’ engagement in remote work-integrated learning (WIL). The technology interactivity model was considered an appropriate theoretical foundation for proposing the conceptual model in this study. Four factors i.e. interactivity, customisation, active control, and synchronicity were derived as key predictors of student’s engagement. This was also extended by considering two factors from social identity theory: social identity and personal identity. The necessary data was collected using an online questionnaire with a purposive sample of students at different levels and from different educational backgrounds. Statistical findings largely approved the impact of social identity, interactivity, customisation, and active control on the students’ engagement with remote WIL. Results supported the moderating effects of telepresence and social presence on the relationships between the key independent factors: interactivity, customisation, social identity and engagement.

Purpose The crowdfunding concept and activities have recently been the focus of attention of many researchers and practitioners over different business contexts. However, there is a dearth of literature considering the main aspects of e-equity crowdfunding activities and their impact on the innovation performance for entrepreneurial business. Therefore, this study aims to explore how entrepreneurs' engagement in e-crowdfunding activities could enhance both knowledge acquisition and innovation performance. Design/methodology/approach The conceptual model will be proposed based on three main theoretical perspectives: relationship marketing orientation (RMO); Kirzner's alertness theory; and the DeLone and McLean model of information systems. The data of the current study were collected using an online questionnaire from a sample of 500 entrepreneurs who have actively engaged in e-crowdfunding in Saudi Arabia. Findings The statistical results of structural equation modelling (SEM) approved the impacting role of RMO, entrepreneurial alertness, system quality and service quality on the entrepreneurs' engagement in e-equity crowdfunding, which in turn, predicts both knowledge acquisition and innovation performance. Research limitations/implications There are several limitations which could be addressed in future studies, for example, this study has only considered one form of crowdfunding (equity based crowdfunding) and due to its nature these findings would not be easily generalized to other kinds of crowdfunding (i.e. donation-based crowdfunding; rewards-based crowdfunding; and debt-based crowdfunding). Future studies could consider these kinds of crowdfunding activities. Originality/value This study has contributed to the understanding of e-equity crowdfunding in several aspects. For example, this study presents results that assist both researchers and practitioners in the Middle East and Saudi Arabia to develop an in-depth knowledge of e-equity crowdfunding by considering new dimensions such as RMO and information system success factors.

This study aims to identify and empirically examine the influence of the main factors related to the content quality of generative conversational AI agents on decision-making efficiency. Additionally, this study explores the ramifications of decision-making efficiency facilitated by generative conversational AI agents in organisational innovation performance. This study proposes a model based on the information quality model as well as other factors, such as novelty seeking and ethical concerns. Data from this study was collected using online questionnaires from a purposive sample size of 228 employees in business organisations. Based on Structural Equation Modelling (SEM) analyses using AMOS, the results support the significant impact of information quality (intrinsic information quality, contextual information quality, representational information quality, and accessibility of information quality) on decision-making efficiency. The results also support the significant impact of novelty seeking and ethical concerns on decision-making efficiency. Decision-making efficiency was also found to have a significant positive impact on innovation performance. This empirical study makes a considerable contribution as it is among the first to expand the current understanding of the effective use of generative conversational AI agents in managerial practices (i.e. decision-making and innovation).

Purpose There is always a need to discover how a paradox between a customer’s desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers’ continued intention (CIN) to use contactless payment. Design /methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods. Findings Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN. Research limitations/implications There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment. Originality/value This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers’ CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia).

Artificial intelligence (AI) is a highly effective solution for enhancing decision-making efficiency and optimising the functional performance of organisations. However, there have been limited attempts to assess the consequences of implementing AI systems on the quality and efficiency of decision-making. This study proposes and empirically examines an extended model covering all aspects that would shape the successful adoption of AI by decision-makers while investigating how the successful adoption of AI enhances the efficiency of the decision-making process. This study also intends to test the validity of the integrated AI acceptance-avoidance model (IAAAM) proposed by Cao et al. (2021) using the Middle East context (i.e. Saudi Arabia). The extended model of the current study was based on the IAAAM and IS professional distinctiveness (ISPD). Two quantitative studies were conducted to achieve the research objectives. The first study was conducted to validate the IAAAM using a purposive sample of employees (non-adopters of AI applications). The second study tested the proposed model using a purposive sample of employees (actual adopters). The structural equation modelling (SEM) results of the first study (non-adopters) supported the validity of the IAAAM in Saudi Arabia. Factors (performance expectancy (PE), facilitating conditions (FC), personal well-being concern (PWC), perceived threat (PT), and attitudes (ATT)) had a significant impact on either ATT or the intention to use AI. The SEM results of actual adopters supported the impact of PE, EE, FC, PWC, and ATT on either ATT or the adoption of AI (AoAI). As an external factor, the ISPD was the most significant predictor of AoAI. The AoAI was confirmed to strongly predict decision-making efficiency, which, in turn, contributes to functional performance. This study enriches the current understanding of the main factors that contribute to the successful implementation of AI systems, offering an in-depth understanding of both AI adopters and non-adopters. It identifies factors important to non-users to enhance future adoption, whereas current AI users focus on improving decision-making quality with the AI assistance.

This study introduces an innovative automated model, the Scientists and Researchers Classification Model (SRCM), which employs data mining and machine-learning techniques to classify, rank, and evaluate scientists and researchers in university settings. The SRCM is designed to foster an environment conducive to creativity, innovation, and collaboration among academics to augment universities’ research capabilities and competitiveness. The model's development roadmap, depicted in Figure 1, comprises four pivotal stages: preparation, empowerment strategies, university-recognised research ID, and evaluation and re-enhancement. The SRCM implementation is structured across three layers: input, data mining and ranking, and recommendations and assessments. An extensive literature review identifies ten principal procedures further evaluated by experts. This study utilises Interpretive Structural Modelling (ISM) to analyse these procedures’ interactions and hierarchical relationships, revealing a high degree of interdependence and complexity within the SRCM framework. Key procedures with significant influence include determining the input data sources and collecting comprehensive lists of university scientists and researchers. Despite its innovative approach, SRCM faces challenges, such as data quality, ethical considerations, and adaptability to diverse academic contexts. Future developments in data collection methodologies, and addressing privacy issues, will enhance the long-term effectiveness of SRCM in academic environments. This study contributes to the theoretical understanding of academic evaluation systems and offers practical insights for universities that aim to implement sophisticated data-centric classification models. For example, by implementing data-centric models, universities can objectively assess faculty performance for promotion or tenure. These models enable comprehensive evaluations based on publication records, citation counts, and teaching evaluations, fostering a culture of excellence and guiding faculty development initiatives. Despite its limitations, SRCM has emerged as a promising tool for transforming higher education institutions’ academic management and evaluation processes.

The antecedents and determinants of entrepreneurial capabilities and competencies remain one of the incontestable questions that drive the exploitation and discovery of effective financial and digital opportunities. In the present paper, we propose a conceptual model based on Kirzner's alertness theory [entrepreneurial alertness] and rely on two factors [entrepreneurial orientation and marketing orientation] as key accelerators of entrepreneurial financial alertness. We assume that entrepreneurial financial alertness (EFA) might have a direct impact on entrepreneurial finance-based digital transformation (EFDT), which in turn, is expected to predict both innovation entrepreneurial finance (IEF) and SMEs' entrepreneurial performance (SMEEP). Structural equation modeling (SEM) was employed using data collected from a purposive sample size of 214 Jordanian entrepreneurs. Our findings largely support the impact of EFA on EFDT. EFDT was also supported having a significant impact on both IEF and SMEEP. Our study has many implications for both researchers and practitioners in the area of entrepreneurial finance-based digital transformation. The study has great added-value by proposing and examining a solid theoretical foundation covering the most influential factors that drive digital entrepreneurial transformation as such transformation stands as an emerging and pressing issue, not fully tackled by prior studies.

This study aims to propose and investigate the key factors that predict the Utilization of Green Internet of Things (U-GIoT) applications by business organizations. This study will also look at the effect of using GIoT applications on energy efficiency and carbon footprint reduction, which in turn, contributes to the sustainable green performance. Therefore, value-belief-norm theory (VBN) was chosen as the theoretical basis for this study's conceptual model. Two constructs derived from VBN (i.e. biospheric value and ecological worldview) are suggested as key predictors for the use of GIoT applications. The conceptual model is extended by considering the role of green marketing orientation (GMO); green energy awareness; and energy knowledge/technical capabilities. The current model also suggests that the utilization of GIoT applications would impact both energy efficiency and carbon footprint reduction. Online questionnaires are used to gather data from a purposive sample (n = 500) of managers and employees at different levels in different service organizations. Statistical results show strong evidence demonstrating the significant effect of biospheric value; ecological worldview; green marketing orientation (GMO); and energy knowledge/technical capabilities with GIoT. This study presents a valuable contribution that helps researchers; policy-makers, and practitioners to identify and understand the most important antecedents and consequences of U-GIoT. It is also worth noting for future studies looking at different applications of pro-environmental behaviour such as sustainable practices, green mobility and transportation, paperless work; recycling; reducing waste levels; and smart green solar. Policy-makers, decision-makers, practitioners and specialists in green energy systems (i.e. GIoT) will be able to use this information for future improvement.

Purpose Society's concerns about environmental degradation have tightened competitive pressure and brought new challenges to small firms. Against this backdrop, this study develops a decision model to determine a suitable configuration for entrepreneurial orientation to help small firms manage circular economy challenges and improve their performance. Design/methodology/approach This study used a multi-study and multi-method approach. Study 1, through qualitative in-depth interviews, identified a portfolio of circular economy challenges and entrepreneurial-orientation components. Study 2 applied the quality function deployment technique to determine the most important components of entrepreneurial orientation. Study 3 adopted a fuzzy set qualitative comparative analysis to determine the best configuration for challenges and components. Findings The findings reveal a set of challenges and identify the salient need to combine the negation of these challenges with the components of entrepreneurial orientation; this combination will improve the performance of small firms. The research extends the current knowledge of managing circular economy challenges and offers decision-makers insights into improving their resilience. Originality/value The use of the dynamic capability view, together with the multi-study and multi-method approach, may lead to an appropriate reconfiguration of entrepreneurial orientation, which, to date, has received limited empirical attention in the small-business-management discipline.

Big data and predictive analytics (BDPA) techniques have been deployed in several areas of research to enhance individuals' quality of living and business performance. The emergence of big data has made recycling and waste management easier and more efficient. The growth in worldwide food waste has led to vital economic, social, and environmental effects, and has gained the interest of researchers. Although previous studies have explored the influence of big data on industrial performance, this issue has not been explored in the context of recycling and waste management in the food industry. In addition, no studies have explored the influence of BDPA on the performance and competitive advantage of the food waste and the recycling industry. Specifically, the impact of big data on environmental and economic performance has received little attention. This research develops a new model based on the resource-based view, technology-organization-environment, and human organization technology theories to address the gap in this research area. Partial least squares structural equation modeling is used to analyze the data. The findings reveal that both the human factor, represented by employee knowledge, and environmental factor, represented by competitive pressure, are essential drivers for evaluating the BDPA adoption by waste and recycling organizations. In addition, the impact of BDPA adoption on competitive advantage, environmental performance, and economic performance are significant. The results indicate that BDPA capability enhances an organization's competitive advantage by enhancing its environmental and economic performance. This study presents decision-makers with important insights into the imperative factors that influence the competitive advantage of food waste and recycling organizations within the market.

Social media message strategy is critical in determining how customers will engage with B2B firms on social media platforms. The present study examined the role of message source (i.e., firm-generated vs. employee-generated) and message content (i.e., emojis and objective information) in determining social media engagement. Four experiments were conducted to test the proposed relationships. The study findings revealed that employee-generated content leads to higher social media engagement (i.e., intentions and behaviors) than firm-generated content. Content-based trust and engagement-based trust were found to be the underlying mechanisms by which message source impacts social media engagement. Furthermore, we observed that, for an employee-generated message, including emojis has a greater impact on customer engagement than when they are included in a firm-generated message. Finally, no evidence was found concerning the effectiveness of incorporating objective information in social media messages on customer engagement. These findings have marked implications for B2B marketers in developing effective social media message strategies.

PurposeDeepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content.Design/methodology/approachThe study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3.FindingsThe findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs.Originality/valueThe study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.

This paper employed an integrated model for examining behavioral intention to adopt blockchain technology in the supply chain management of manufacturing industries in Bangladesh. The proposed conceptual model was empirically tested using data collected from 189 supply chain managers working in manufacturing organizations in Bangladesh. The findings suggest that perceived usefulness, trading partners' pressure, and competitive pressure are the most important determinant of behavioral intention.

The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human–computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

Chatbots incorporate various behavioral and psychological marketing elements to satisfy customers at various stages of their purchase journey. This research follows the foundations of the Elaboration Likelihood Model (ELM) and examines how cognitive and peripheral cues impact experiential dimensions, leading to chatbot user recommendation intentions. The study introduced warmth and competence as mediating variables in both the purchase and postpurchase stages, utilizing a robust explanatory sequential mixed-method research design. The researchers tested and validated the proposed conceptual model using a 3 x 3 factorial design, collecting 354 responses in the purchase stage and 286 responses in the postpurchase stage. In the second stage, they conducted in-depth qualitative interviews (Study 2) to gain further insights into the validity of the experimental research (Study 1). The results obtained from Study 1 revealed that "cognitive cues" and "competence" significantly influence recommendation intentions among chatbot users. On the other hand, "peripheral cues" and warmth significantly contribute to positive experiences encountered during the purchase stage. The researchers further identified 69 thematic codes through exploratory research, providing a deeper understanding of the variables. Theoretically, this study extends the ELM by introducing new dimensions to human-machine interactions at the heart of digital transformation. From a managerial standpoint, the study emphasizes the significance of adding a "humanness" element in chatbot development to create more engaging and positive customer experiences actively.

Purpose This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation. Design/methodology/approach The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data. Findings The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business. Research limitations/implications The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship. Originality/value Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.