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In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

The purpose of this study was to examine the experiences of multiple campus teams as they engaged in the assessment of their science, technology, engineering and mathematics (STEM) mentoring ecosystems within a peer assessment dialogue exercise.

This project utilized a qualitative multicase study method involving six campus teams, drawing upon completed inventory and visual mapping artefacts, session observations and debriefing interviews. The campuses included research universities, small colleges and minority-serving institutions (MSIs) across the United States of America. The authors analysed which features of the peer assessment dialogue exercise scaffolded participants' learning about ecosystem synergies and threats.

The results illustrated the benefit of instructor modelling, intra-team process time and multiple rounds of peer assessment. Participants gained new insights into their own campuses and an increased sense of possibility by dialoguing with peer campuses.

This project involved teams from a small set of institutions, relying on observational and self-reported debriefing data. Future research could centre perspectives of institutional leaders.

The authors recommend dedicating time to the institutional assessment of mentoring ecosystems. Investing in a campus-wide mentoring infrastructure could align with campus equity goals.

In contrast to studies that have focussed solely on programmatic outcomes of mentoring, this study explored strategies to strengthen institutional mentoring ecosystems in higher education, with a focus on peer assessment, dialogue and learning exercises.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The aim of this study was to conduct a comprehensive investigation into declining and emerging occupations and job titles and to develop a national career progression pathway for the tourism and hospitality (T&H) sector.

Anchored on the Social Cognitive Career Theory, this study used face to face in-depth interviews of 33 industry stakeholders: policymakers, trade association, training providers and beneficiaries (T&H).

The finding reveals that only the “watchman” occupation was identified as the declining job while majority of the emerging jobs were more related to information technology and environmental occupations (website designers, digital marketers, data analysts, hygienists, and safety and hazard experts).

The findings provide a valuable signal for the growing number of jobs in security services, hygiene and information technology-oriented occupations, which the Ministry of Tourism, Arts and Culture including practitioners including HR directors and general managers should respond timely to and to these growing needs in order to remain competitive in the sector.

This is the first study in context that responded to a call by industry players to fill in a practical knowledge gap in examining declining and emerging jobs and job titles in the T&H sector. The study provides vocational insights into mapping the entry level requirements for the jobs allied with occupations in the national technical and vocational educational training qualifications framework of Ghana at the national level.

The purpose of this study is to chart the development of Makerspaces in higher education (MIHE), by building a map of existing research work in the field. Based on a corpus of 183 manuscripts, published between January 2014 and April 2021, it sets out to describe the range of topics covered under the umbrella of MIHE and provide a holistic view of the field.

The approach adopted in this research includes development of the 2014–2021 MIHE corpus; literature overview and initial coding scheme development; refinement of the initial coding scheme with the help of a focus group and construction of the MIHE map version 1.0; refinement of the MIHE map version 1.0 following a systematic approach of content analysis and development of the MIHE map version 2.0; evaluation of the proposed structure and inclusiveness of all categories in the MIHE map version 2.0 using card-sorting technique; and, finally, development of the MIHE map version 3.0.

The research trends in the categories of the MIHE map are discussed, as well as possible future directions in the field.

This paper provides a holistic view of the field of MIHE guiding both junior MIHE researchers to place themselves in the field, and policymakers and decision-makers who attempt to evaluate the current and future scholar activity in the field. Finally, it caters for more experienced researchers to focus on certain underinvestigated domains.

This study aims to explore how the boundary between science and society can be addressed to support the transformation of higher education towards sustainable development (HESD) in the sense of the whole institution approach. It analyses students’ learning experiences in self-led sustainability projects conducted outside formal curricula to highlight their potential contribution to HESD. The students’ projects are conceived as learning spaces in “sustainability-oriented ecologies of learning” (Wals, 2020) in which five learning dimensions can be examined.

Using an iterative, grounded-theory-inspired qualitative approach and sensitising concepts, 13 in-depth semi-structured interviews were conducted exploring students’ learning experiences. Interviews were categorised in MAXQDA and analysed against a literature review.

Results revealed that students’ experiences of non-formal learning in self-led projects triggered deep learning and change agency. Trust, social cohesion, empowerment and self-efficacy were both results and conditions of learning. Students’ learnings are classified according to higher education institutions’ (HEIs) sustainability agendas, providing systematised insights for HEIs regarding their accommodative, reformative or transformative (Sterling, 2021) path to sustainable development.

The education for sustainable development (ESD) debate focuses mainly on ESD competences in formal settings. Few studies explore students’ learnings where formal and non-formal learning meet. This article investigates a space where students interact with different actors from society while remaining rooted in their HEIs. When acting as “change agents” in this hybrid context, students can also become “boundary agents” helping their HEIs move the sustainability agenda forward towards a whole institution approach. Learning from students’ learnings is thus proposed as a lever for transformation.

This study aims to support researchers and practitioners in finding suitable instruments for future research studies and organizational quality assessments.

Employees’ success of learning at work is strongly influenced by the quality of the workplace learning environment. In the recent decades growing effort has been given to the development of surveys to measure the quality of workplace learning, resulting in a large number of available survey instruments. This study conceptually draws on a 3-P model and uses a qualitative metasynthesis to collect and categorize n = 94 surveys that intend to measure the quality of workplace learning (WPL).

The results underline that research on WPL environments is a highly interdisciplinary endeavor, where every discipline enriches the field by a new perspective and own foci. Overall, this study finds a focus on learning culture and working conditions, on social and functional inclusion of the learner and on support and feedback during training. Products of WPL such as professional competences or career aspirations play a minor role.

With the integration of quality measurement instruments from various research studies, this study produces an interactive online instrument map that gives a broad, yet organized overview of available quality measures in the WPL field.

Recent years have seen a meteoric rise in the study of narcissism in entrepreneurship, although little consolidation has occurred in this area. The purpose of this paper is the development of an integrative framework to harmonise the academic discussion and serve as a structured foundation for future research.

The authors conducted an artificial intelligence-aided, structured literature review focused on content analysis of concepts and contexts to map out current findings and research gaps in startup narcissism research.

According to the findings of this study, narcissistic tendencies have the potential to positively influence startup success early on in an entrepreneur's journey, but after a certain point in the process, the influence of narcissism on success becomes predominantly negative.

The research field is currently not very harmonised regarding research measures, research subjects and key research terms. Further research must use a standardised approach to add value to the research body.

Narcissism is a two-sided sword for founders. In the early stages of a company, many of the founder’s tasks can benefit from narcissistic tendencies. In the later stages of a company, that might shift to overwhelmingly negative effects of narcissism.

Methodically, this study is the first one to establish an artificial intelligence component to add value to the results of a review paper to the best of the authors’ knowledge. The results of this study provide a clear framework of entrepreneurial intention, entrepreneurial activity and entrepreneurial performance to give researchers the opportunity of a more differentiated way of organising work.