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This study aims to examine how knowledge sharing contributes to organizations’ ambidexterity, their overall performance and the role of knowledge quality in this relationship. Knowledge sharing is conceptualized based on tacit and explicit dimensions, and ambidexterity is viewed as comprising exploitative and explorative capabilities.

This study uses a cross-sectional survey-based research design and structural equation modeling to test the proposed model of knowledge sharing and knowledge quality in organizational ambidexterity and the related hypotheses.

The results indicate that tacit knowledge sharing has a significant, direct impact on the exploitative and explorative capabilities of the organization and indirectly impacts both dimensions of ambidexterity (i.e. exploitative and explorative) through knowledge quality. In contrast, explicit knowledge sharing does not have a significant impact on knowledge quality and affects only the exploitative extent of ambidexterity. Both exploitative and explorative capabilities significantly impact organizational performance.

To the best of the authors’ knowledge, this study is the first study to empirically examine the role of knowledge quality in the context of knowledge sharing for ambidexterity, especially within the context of organizations in the United Arab Emirates.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Due to the rapid increase in global warming and environmental disasters, destination management and tourists' environmental awareness have increased. This increase in environmental awareness has led destinations to prioritize green practices that reduce environmental pollution. Moreover, in recent years, with the rapid development of technology, artificial intelligence technology has also been used in applications that reduce environmental pollution in destinations. This is because environmentally friendly products and services offered by destinations have started to have an impact on tourists' travel choices. Additionally, tourists' awareness and loyalty towards environmentally friendly destinations have started to increase, resulting in the formation of brand value for destinations. Therefore, green practices and AI technologies play a role in the formation of consumer-based destination green brand value.

The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team psychological empowerment and information systems development (ISD) team performance.

A survey approach was employed to collect time-lagged, multi-source data for testing the proposed model of this study (N = 514 responses from 88 teams). PROCESS macro was used to analyze the data to generate empirical results.

The results suggest that instrumental AI use indirectly influences ISD team performance by enhancing team psychological empowerment. Additionally, it moderates the effects of team-level LMX on team psychological empowerment and ISD team performance. Furthermore, the results demonstrate that the interaction effect of LMX and instrumental AI use on ISD team performance is mediated by team psychological empowerment.

While research on ISD consistently demonstrates that teams, data, and technology collectively contribute to the success of these projects. What is less known, however, is how the exchange relationship between ISD teams and their leader, as well as technological factors, contribute to ISD projects. This study draws on LMX theory to propose how team-level LMX and the instrumental use of AI by team members influence team psychological empowerment and ISD team performance. The study puts forth a mediated moderation model to develop a set of hypotheses. It offers valuable contributions to AI and LMX, along with implications for ISD team management.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

This chapter proposes to understand the prospects of smart technologies that can transform tourism destinations and instigate regenerative development process. Bio-based resource consumption and technology-driven practices aimed for better sustainable development have been the need of the era. This study emphasizes the theory of regenerative tourism, which attempts to preserve and improve a destination's natural and cultural resources while contributing to the socio-economic development of the host communities. It examines how transformational technologies, like smart infrastructure, big data analytics and renewable energy systems, could assist the tourism industry achieve the transition to a green economy. This chapter illustrates the benefits and problems of integrating such technologies into the tourism infrastructure of a destination. Additionally, it highlights the necessity of cooperation among stakeholders and policymakers and examines the possible environmental, social and economic implications of using a regenerative approach to tourism. The results of this study contribute to the expanding body of knowledge on the development of sustainable tourism and shed light on the transformative potential of technology in creating a more sustainable and resilient future.

Though current literature has started to recognize the significant role that online faith-holders play in the context of brand reputation crises, extant research lacks a theoretical framework to explain the process in which online faith-holders endure the harm in brand reputation while collectively rebuilding the reputation. We propose and test a dual-challenge model for a more systematic understanding of faith-holder communities in brand reputation crises.

Focusing on collective-level communication activities, we quantitatively compared the volume, valence and variance of a faith-holder community’s communication (441,611 posts by 3,228 fans over 14 days) before and after a brand reputation crisis.

Our longitudinal data demonstrated that the crisis was a significant threat to group sentiment and cohesion. Nevertheless, the community was highly resilient and adaptive. Their emotions quickly recovered, and they promptly restored group cohesion and coordinated crisis response efforts after the crisis.

This study challenges the traditional assumption that online users are independent, static and reactive during brand crises. Instead, it conceptualizes online faith-holder community as a connected, proactive and dynamically adaptive group in crisis situations. This dual-challenge model highlights the importance of internally fostering collective resilience while externally coordinating crisis responses in a faith-holder community.

The objective of this study was to elucidate the intentions of university teachers regarding the utilization of ChatGPT for instructional purposes.

In this cross-sectional quantitative research, data were collected through an online survey tool from 493 university teachers across Pakistan.

The findings revealed that positive attitudes and a sense of perceived behavioral control had a positive impact on teachers' adoption of ChatGPT for instructional purposes. Conversely, subjective norms exhibited a significant negative influence. The results underscore that teachers are inclined to embrace ChatGPT for instructional cause due to their recognition of its educational utility. However, it does not appear that their social environment, which includes their coworkers and managers, has a significant impact on how they decide what to do.

The findings bear implications for devising relevant policies that support AI integration in curricula and assessments and teachers’ professional development (PD) programs. There is a need for formulating guidelines at the universities and the policy tiers to make the ChatGPT use more relevant. Future research should strive to generate insights toward AI use in the areas of curriculum, assessment and teachers’ PD.

The study adds to the relatively new literature on the integration of ChatGPT in higher education. This study’s findings contribute to the body of knowledge related to AI’s pedagogical use and set future directions to consider factors influencing meaningful and responsible use of AI in teaching and learning.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.