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The present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.

To address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.

The findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.

The paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.

Warehouses are under pressure to operate as efficiently as possible. In pursuit of attaining high efficiency in the order picking process, the warehouse manager must take several planning decisions, typically supported by a central planning system. However, highly centralised work erodes the autonomy of warehouse workers, interfering with worker well-being and productivity. This study holistically explores the impact of a work system with more decision autonomy for order pickers.

We conduct a unique field experiment in a real-world warehouse and use a within-subjects design to compare two work systems, one with worker autonomy and one without. 18 permanent employees participate in our study, in which we measure both psychosocial and physical well-being as well as productivity. Post-experimental interviews are conducted to delve deeper into the observed effects.

Our study illustrates that involving order pickers in operational decisions can benefit their job satisfaction and motivation without compromising productivity. Although we fail to find significance at the conventional level (α = 0.05), we do find marginally significant effects of our treatment on physical well-being aspects. Furthermore, our intervention invoked a highly positive user experience.

We show that slightly loosening tight process control results in organisational and individual benefits without endangering smooth operational flows. The warehouse in this paper acknowledged this and decided to permanently work according to this philosophy.

This study is the first to holistically explore the effects of a participatory work setting in a real-world warehouse.

The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.

This study examines the relationship between the writing anxiety experienced by English second language learners and their intention to employ ChatGPT for their academic writing as an automated writing evaluation tool. This research integrates writing anxiety as an additional variable to understand how much writing anxiety affects the perceived usefulness of ChatGPT as an automated writing evaluation tool, perceived ease of use of ChatGPT, and attitude towards using ChatGPT as an automated writing evaluation tool for their academic writing with the technology acceptance model (TAM) as a theoretical framework.

This is a cross-sectional study, with SEM PLS to analysis data collected from 639 undergraduate students.

This study found that writing anxiety significantly affects perceived ease of use of ChatGPT as an automated writing evaluation tool, and attitude towards using ChatGPT. Altogether they both influence students’ intention to use the ChatGPT as an automated writing evaluation tool.

This study contributes to the understanding of students intention to use ChatGPT as an automated writing evaluation tool when they suffer from writing anxiety.

This paper aims to study the role of artificial intelligence (AI) agents in creating a climate of inclusion for people with special needs in the higher education sector (HES).

A qualitative methodology is used in this research that is mainly based on semistructured interviews conducted with the top ten universities in Lebanon with deans, information technology managers, professors and administrative officers.

This paper highlights findings related to the current status of the higher education institutions (HEIs) in Lebanon vis-à-vis their accreditation and quality assurance processes in accommodating and creating a climate of inclusion for people with special needs. The results show the important role of AI agents in aiding HEI in creating such a climate of inclusion for people with special needs.

The study sheds light on existing gaps in the literature related to creating a climate of inclusion for people with special needs in HEI. Additionally, there is yet a lack of research that focuses on the role of AI technology in general and AI agents in particular in fostering a climate of inclusion for people with special needs within the HES.

Prior research has extensively examined how bringing technology from work into the non-work life domain creates conflict, yet the reverse pathway has rarely been studied. The purpose of this study is to bridge this gap and examine how the non-work use of smartphones in the workplace affects work–life conflict.

Drawing from three literature streams: technostress, work–life conflict and role boundary theory, the authors theorise on how limiting employees' ability to integrate the personal life domain into work, by means of technology use policy, contributes to stress and work–life conflict. To test this model, the authors employ a natural experiment in a company that changed its policy from fully restricting to open smartphone access for non-work purposes in the workplace. The insights gained from the experiment were explored further through qualitative interviews.

Work–life conflict declines when a ban on using smartphones for non-work purposes in the workplace is revoked. This study's results show that the relationship between smartphone use in the workplace and work–life conflict is mediated by sensed stress. Additionally, a post-hoc analysis reveals that work performance was unchanged when the smartphone ban was revoked.

First, this study advances the authors' understanding of how smartphone use policies in the workplace spill over to affect non-work life. Second, this work contributes to the technostress literature by revealing how, in specific situations, engagement with ICT can reduce distress and strain.

This study aims to analyze the implications of the recent European Union Regulation 2023/1542 on the circular economy and stakeholder strategies within the electric vehicle lithium-ion battery (EV-LIB) sector. It aims to explain the policy intentions, recommend practical strategies for stakeholders and examine how the new regulation exerts pressure on stakeholders to transition from older directives to more sustainable practices and operational standards, while also highlighting policy gaps.

The research employs a dual-method approach, combining text analysis of EU legislation with semi-structured interviews of industry stakeholders. This methodology allows for a comprehensive understanding of the regulatory impacts by integrating legislative intent with practical, on-the-ground insights from key players in the EV-LIB sector.

Our findings show that the three aggregated dimensions of operational sustainability, R&D and new technologies and collaborative dynamics are the key dynamics underlying the intended outcomes. The findings also highlight the policy’s historical development, the stakeholder categories, the implications for each and practical recommendations in responding to the policy requirements. Additionally, the findings identify policy gaps, such as weak incentives and broad economic operator classifications, with examples from international markets. The regulation creates proactive stakeholders driving innovation and collaboration and reactive ones adapting to changes, where static implicit implications may affect their viability by imposing unequal burdens.

To the best of the authors’ knowledge, this paper is the first to analyze the new EU Regulation 2023/1542, offering novel insights into the strategic responses required by stakeholders to adapt to the regulatory pressures. By focusing on the latest regulatory framework and its practical implications, the study bridges the gap between policy and practice, providing valuable guidance for industry players navigating the evolving regulatory environment.

• (1)

  EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

• (2)

  Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

• (3)

  R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

• (4)

  Emergence of Proactive vs Reactive stakeholder dynamics.

• (5)

  The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

Emergence of Proactive vs Reactive stakeholder dynamics.

The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.