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In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

This paper aims to explore how open innovation communities contribute to the adoption of building information modelling (BIM) in the construction industry.

The paper presents a cross-case analysis of two construction communities, buildingSMART Norway and the BIM Vietnam Community. Data were collected based on 21 semi-structured interviews conducted with industry experts actively engaged in these two communities. The theoretical basis for the study was open innovation and the institutional intervention model, which delineates institutional actions related to the adoption of new information technology.

The findings show both similarities and differences in the way in which the communities contribute to industrial practice. Both communities use similar knowledge channels and repositories but apply different approaches to innovation creation and diffusion. In addition, trust can support BIM innovation in the community context.

The comparison of buildingSMART Norway and the BIM Vietnam Community in accelerating BIM innovation allows for exploring how open innovation communities support BIM adoption in the construction industry. The findings provide insights for construction communities into creating and diffusing BIM innovation. In addition, the examples of gaining benefits from community innovation activities are useful for construction firms and practitioners.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

The recent Covid-19 crisis has exposed the limitations of inventory leanness (i.e. keeping fewer inventories than expected), leading its followers to question whether it is the end of inventory leanness. This study aims to answer that question from a financial perspective.

This study considers 2019, 2020 and 2021 as the pre-, during- and post-Covid periods, respectively, and compares the financial performance and risks of firms that followed a lean inventory strategy (lean firms) to those that do not (non-lean firms). The sample is drawn from manufacturing firms in the USA, and the data are analyzed using univariate tools (such as a t-test) and multivariate regressions.

The results show that the financial performance of lean firms was better than that of non-lean firms under normal operating conditions in 2019, which continued to sustain during the crisis and post-crisis operating conditions in 2020 and 2021, respectively. Lean firms were also less risky than non-lean firms, except for in 2020, where they were equally risky.

A financial perspective suggests that managers of lean firms who might be thinking of changing over to a non-lean or more conservative strategy in the post-Covid era in relation to their firms' level of inventories do not need to do so unless otherwise required.

This is the very first study that shows the implications of inventory leanness for firms across three operating conditions: pre-crisis (normal business condition), crisis (abnormal business condition) and post-crisis (sub-normal business condition).

The global food industry is faced with the dilemma of finding a balance between food wastage and food shortage. Approximately one-third of food produced globally goes to waste, while about 800 million people suffer from undernourishment. Given this context, the purpose of this study is to investigate the unresolved challenges related to enhancing transparency associated with products of high perishability and low shelf life.

The authors conducted 25 interviews with global agri-food supply chains (AFSCs) experts to ask what impedes the progress of the current technologies, such as blockchain, to enable transparency and traceability (T&T) in AFSCs.

The findings indicate barriers at the individual, firm and supply chain levels. Based on these barriers, the authors propose an interconnected framework to explain technologically-driven T&T and guide on barrier removal from AFSCs. The authors conclude that by applying technology (i.e. blockchain) the authors can resolve the tension of supporting T&T in AFSCs. This can enable the efficient and transparent tracking of goods, reduction of food waste and loss, as well as promotion of the use of recyclable packaging and further sustainable practices and materials, all of which are aligned with a range of UN Sustainable Development Goals (2, 8, 10 and 12). Moreover, the authors see that some factors are interrelated. Based on these factors, the authors build an interconnected framework to guide on barrier removal from AFSCs. Managers in AFSC would find the findings especially relevant.

Drawing on industrial network theory and signalling theory, the authors propose an interconnected framework for explaining barriers (challenges) and potential solutions (opportunities) to T&T in AFSCs. This framework is developed by examining the interconnections of barriers at micro, meso and macro levels and applying signalling theory to explain how solutions address these barriers. The specific contributions of this study are: the list of barriers that impede the implementation of technological solutions for T&T in AFSCs; and a three-stage framework that explains how to remove the barriers for T&T. The study is limited by the focus on blockchain, which calls for future research once the next decentralised technology becomes available.

This research aims at arriving at a broad scope of the lessons learnt after two years of the coronavirus disease 2019 (COVID-19) pandemic outbreak by analysing the catalyst and inhibiting factors within three aspects of the tourism sector: destination crisis management, tourist behaviour and tourism industry trends.

The methodology of this paper involves semi-structured interviews with high-ranking European travel agents as the agents represent the intermediates between the tourism offer and demand.

Data obtained from travel agents disclosed the factors that catalysed and inhibited the destination, the behaviour of tourists and the tourism industry trends. By contrasting data with previous literature, constructing an overview of the positive and negative outcomes of the pandemic in the tourism sector is possible.

Governments, destination marketing and management organisations and tourism and hospitality organisations could learn from the lessons of COVID-19 outbreak to cope better with future disruptive events affecting the tourism industry.

The paper is novel as it is the first overview that attempts to synthesise the lessons from the COVID-19 pandemic in the tourism sector by analysing tourism sector's three dimensions: the destination, the tourists and the industry.