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The authors examine the effect of greenhouse gas (ghg) assurance on cost of debt, and the effect of board gender diversity on cost of debt, for an international sample of listed companies.

Utilizing firm-level data and a quantile regression approach, this study examines the effects of greenhouse gas assurance and board diversity on cost of debt by employing an international sample of firms during 2015–2021.

The authors find that in firms with a relatively low cost of debt the external assurance of greenhouse gas emissions and gender diversity could significantly contribute to a reduction of cost of debt. Furthermore, other measures of board diversity that are linked with independent directors and skilled directors seem to contribute to an increase of firms' cost of debt in the lower end of distribution. Drawing from the agency theory, the authors showcase the fact that ghg assurance reduces information asymmetry and therefore agency costs such as borrowing costs and signals to the stakeholders a long-term commitment to excellence.

This study is the first that provides insights on the relationship between ghg assurance, board diversity and cost of debt.

This research aims to examine and understand the rationales and modalities behind the use of disclosure before, during and after a corporate governance scandal involving CPA Australia (CPAA).

Data beyond CPAA's annual reports were collected, such as news articles, media releases, an independent review panel (IRP) report, and the Chief Operating Officer's letter to members. These disclosures were manually coded and analysed through the word counts and word trees in NVivo. This study also relied on Norbert Elias' conceptual tool of power games among networks of actors – figurations – to model the scandal as a power game between the old Board, the press, concerned members, the IRP and the new Board. This study analysed the data to reveal a collective and in fieri power balance that changed with the phases of the scandal.

A mix of voluntary, involuntary, requested and absent disclosures was important in triggering, managing and ending the CPAA scandal. Moreover, communication and disclosure fulfilled a constitutive role since both: mobilised actors, enabled coordination among actors, contributed to pursuing shared goals and influenced power balances. Such a constitutive role was at the heart of the ability of coalitions of figurations to challenge and restore the powerful status quo.

This research introduces to accounting studies the collective and in fieri dimensions of power from figurational theory. Moreover, the research sheds new light on using voluntary, involuntary, requested and absent disclosures before, during and after a corporate crisis.

Acknowledging the importance of both lean thinking (LT) and Industry 4.0 (I4.0) for successful business performance and organisational success, the present study investigates the critical success factors (CSFs) for the concurrent implementation of both approaches, a field not yet significantly explored.

The study conducts two systematic literature reviews (SLRs), one on LT and the other one on I4.0 to map out the CSFs for the effective implementation of each approach. These CSFs are subsequently prioritised with the use of a Delphi Study. Finally, from the set of the common CSFs recognised through the two approaches, a more condensed list is put forward as the first step towards achieving a successful synergy between LT and I4.0.

The study’s findings suggest the most important CSFs and determine their definition in the context of a concurrent implementation of LT and I4.0. This can provide managers and practitioners with the awareness of crucial factors, enabling them to take the necessary steps for planning and implementing both approaches.

A concise set of CSFs for the concurrent implementation of both LT and I4.0 has been identified, which can be viewed as a starting point for providing top executives and managers with useful insights into enhanced business efficiency and performance. This study furthermore contributes to the overall body of knowledge on LT and I4.0.

The scholarly literature that explores a common set of CSFs for the concurrent implementation of LT and I4.0 is limited. This gap significantly enhances the importance of the present research, contributing to a better understanding amongst both academics and practitioners of the key supporting factors for the integration of the two approaches.

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.

In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.

Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.

The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.

The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.

Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events.

The identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed “density peak,” the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach “unexpected mixing ratio (UMR) peak.”

While the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors.

The novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly.

The purpose of this paper is to argue that human resource development (HRD) needs to embrace and include futures and foresight learning (FFL) as a new addition to its field of theorising and practice. The question to consider is: How can FFL become a new feature of HRD? A key part of the authors’ argument is that the inclusion of FFL will enable HRD to add to the success of any organisation and make a vital contribution to the management of people at work.

This paper firstly considers some of the debates surrounding the meaning of HRD. The authors suggest that instability of the time serves to disturb any comforts that have been created in HRD and that there is a need to consider how there might be different futures for what we still call HRD in research, practice and praxis. This paper then considers how FFL might become one possibility for expanding the existing boundaries of HRD. The authors characterise futures and foresight as a learning process, which provides new but complementary features to what is already considered as HRD. This paper will show how FFL can lead to organisation's success and the way this can be achieved.

There is a wide variety of meanings of the term HRD; however, HRD is still cast as a “weakened profession” which has to play a subservient role to others in the workplace. Over the last 15 years, the expansion of the meaning of HRD has been seen as evidence of its evolving and emerging nature and development based on a co-creation with other disciplines. This creates a space for FFL, defined as an ongoing learning process to find predictable, probable, possible and/or a variety of long-term futures. FFL embraces three key processes of scanning, futuring and reconfiguring, all of which contain a high potential for participants and others to learn as they proceed, providing outcomes at each stage. FFL has been shown to enhance organisation performance and success and HRD interventions can play a key part in implementation. This represents a significant opportunity for the HRD profession to move from weakness towards strength.

For HRD researchers, while FFL is not yet on its radar, the authors would argue that the uncertainties of the future require that more attention be given to what might lie ahead. Indeed, HRD researchers need to ask the question: What is the future of HRD research? In addition, if the authors’ call for FFL to be included in the practice of HRD, such practice will itself provide new pathways for HRD research. Further research questions might include: To what extent is FFL practiced in organisations and what role do HRD practitioners play in delivery? How does FFL impact on organisation behaviour and outcomes? What new products and services emerge from FFL? What new skills are required to deliver FFL? Can FFL enhance the status of HRD practitioners in the work place and its role in decision-making? and How can the HRD profession develop as a hybrid profession with respect to machine learning (ML)/artificial intelligence (AI)?

FFL produces outcomes that have importance for strategy, HRD practitioner can learn to facilitate FFL by action learning and in leadership development programmes. FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. FFL offers those involved in HRD a significant opportunity to transfer ideas into practice that have an impact on organisation sustainability. HRD can play a significant role in the design and delivery of ML and AI projects.

This paper concludes with a call for embracing FFL as a challenging but important addition to how we talk about learning at work. The authors argue that FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. At its centre, FFL involves learning by people, groups, organisations and machines and this has to be of concern to HRD.

This paper aims to explore how the owners of less competitive micro-firms (MFs) perceive the “crisis–innovation–change management” triangle. It examines whether their understanding of these overarching entrepreneurship theory principles is inadequate compared to the relevant scientific literature.

This qualitative analysis follows principles based on the inductive method and grounded theory, thickly describing the results from research conducted in a sample of 38 tertiary-sector MFs in the Eastern Macedonia and Thrace region – one of the least developed and competitive areas across Europe. It triangulates the data with 11 respective small firms.

MF owners perceive the crisis as an ostensibly exogenous phenomenon, innovation as something quasi-unattainable – although vaguely significant – and change management as a relatively unknown process. This understanding lies somewhat distant from the extant literature that examines the structural nature of crises, the innovational power to exit profound restructurings and the rebalancing requisite for building new overall organizational methods to survive this internal–external transformation. In essence, the triangle crisis–innovation–change management is a blind spot for the examined MF owners as they ignore its significance as an adaptation mechanism – contrary to several direct competitors.

Based on the reluctance of these individuals to cultivate their systematic business knowledge, it seems unrealistic that they would seek to pay the necessary high price for business consulting in the future. An ideal solution would be to build public entrepreneurship clinics to provide these less dynamic and adaptable organizations with free preliminary or in-depth counseling. The Institute of Local Development-Innovation could aim to provide free consulting services to reinforce organizational physiology by coordinating different socioeconomic actors.

To the best of our knowledge, this empirical research is one of the first to test the comprehension of weaker MFs – less competitive and developed in organizational terms – to the triangle crisis–innovation–change management.