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This study intends to investigate how an employee's proactive personality and a supervisor's idiosyncratic deals (i-deals) relate to their subordinates' affective commitment (AC) and occupational well-being (OWB), in light of the mediating role of subordinates' i-deals, using proactive motivation theory and the job demand–resource (JD-R) model as theoretical foundations.

The study consisted of 342 employees working in the hospitality industry. To examine the proposed model, the researchers used the structural equation modelling approach and bootstrapping method in AMOS.

The results affirmed the influence of subordinates' proactiveness on AC and OWB, but no direct influence of supervisors' prior i-deals on subordinates' AC and OWB was established. When investigating the mediational role of subordinates' i-deals, a partial mediation effect was found between subordinates' proactive personality with AC and OWB, whereas full mediation was established between supervisors' i-deals and subordinates' AC and OWB.

These findings shed light on how i-deals improve AC and OWB for both groups of supervisors and subordinates. In an era of increasing competition amongst organizations operating within the hospitality industry, i-deals serve as a human resource strategy to recruit, develop and retain talented individuals.

The novelty of this research lies in its specific investigation of the combined influence of proactive personality as an individual factor and supervisors' i-deals as an organizational factor on subordinates' i-deals within the context of the hospitality industry. Furthermore, it aims to analyse the potential impact of these factors on AC and OWB.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

This paper aims to analyze the thematic content of research addressing the relation between board of directors (BoD) and business transformation (BT) to obtain better understanding of status and to derive future areas of study.

This paper reviews literature through a bibliometric analysis based on co-occurrence of articles published in Web of Science Core Collection ™ (WoS) between 1990 and 2022, identifying key concepts, setting network of relations and identifying the strategic importance of clusters of concepts. Findings and implications are discussed, future lines of research are presented and limitations are noted.

Thematic research on boards addressing transformation shifted from the analysis of individuals' traits to an organizational approach with majority of research centered on the role of boards under different theories and the consequences of strategic changes on firm's performance. Further research is around gender diversity, sustainability and the moderating role of ownership structure and business culture.

Some limitations are also noted. This analysis considered articles indexed by WoS for Q1+Q2 publications as source of literature, while including others such as Scopus would increase knowledge base. Also, to identify main streams of research, the authors considered keywords with cumulative occurrence spanning from 30% to 40% while increasing this percentage would add terms that might improve precision to the connections among keywords. Other techniques could have been used such as co-citation or bibliographic coupling, although the authors find these as better suited to investigate the basic structure behind the foundational knowledge of the topic while the authors’ intention was to understand the positioning of study fields regarding the degree of research progress.

This paper presents some practical implications for future researchers. Those who wish to leverage previous evidence to address new research questions might look into principal themes covering BoD dynamics and composition to exert CG, and the relation between strategic decisions and performance measured by different variables. Those who wish to position their research as new findings to shed light on dilemmas, might find opportunities in the fields of climate change-sustainability, R&D for growth and innovation under the perspective of intangible assets.

This paper, is the first to the best of the authors’ knowledge, to identify research clusters for the intersection of boards and transformation and to determine their stage of development.

本文旨在分析探討董事會與業務轉型之間的關係的學術研究的專題內容，以能對有關課題的研究狀況有更深入的了解，並擬從分析中取得未來可供研究的範疇。

本文透過科學計量分析法來進行文獻探討。方法乃基於在1990年至2022年期間在Web of Science Core Collection 刊載的學術論文的共現分析而進行; 透過這個研究方法，研究人員建立了聯繫的網絡，並確認了各個概念群組的策略重要性。在本文中，研究結果和研究結果帶來的啟示會被討論，未來的研究領域和方針也會得到說明，研究的局限也會被認定和記錄下來。

探討董事會而又涉及業務轉型的專題研究，由當初集中探討董事個人的特質、轉移到現在研究整體的組織理念和處事取向，而就後者來說，大部份的研究都集中於在不同的理論框架裡董事會所扮演的角色，以及因策略上的改變而為公司的業績帶來的影響。進一步的學術研究都是圍繞著性別多元化、可持續性、所有權結構所扮演的緩和角色和商業文化的研究。

盡我們所知，本文乃為首篇學術論文，去鑑定關於董事會與業務轉型之間的關聯的研究集群，也是首篇學術論文，去確定這些研究集群的發展階段。

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.