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A gap between research and practice is commonly perceived throughout accounting academia. However, empirical evidence on the magnitude of this detachment remains scarce. The authors provide new evidence to the ongoing debate by introducing a novel topic-based approach to capture the research-practice gap and quantify its extent. They also explore regional differences in the research-practice gap.

The authors apply the unsupervised machine learning approach Latent Dirichlet allocation (LDA) to compare the topical composition of 2,251 articles from six premier research, practice and bridging journals from the USA and Europe between 2009 and 2019. The authors extend the existing methods of summarizing literature and develop metrics that allow researchers to evaluate the research-practice gap. The authors conduct a plethora of additional analyses to corroborate the findings.

The results substantiate a pronounced topic-related research-practice gap in accounting literature and document its statistical significance. Moreover, the authors uncover that this gap is more pronounced in the USA than in Europe, highlighting the importance of institutional differences between academic communities.

The authors objectify the debate about the extent of a research-practice gap and stimulate further discussions about explanations and consequences.

To the best of the authors' knowledge, this is the first paper to deploy a rigorous machine learning approach to measure a topic-based research-practice gap in the accounting literature. Additionally, the authors provide theoretical rationales for the extent and regional differences in the research-practice gap.

The demand for halal food products is increasing globally. However, fraudulent activities in halal products and certification are also rising. One strategy to ensure halal integrity in the food supply chain is applying halal blockchain technology. However, to date, a few studies have assessed the factors and variables that facilitate or hinder the adoption of this technology. Thus, this study aims to assess the significant factors and variables affecting the adoption of halal blockchain technology.

A Delphi-based approach, using semi-structured interviews, was conducted with three food companies (chicken slaughterhouses, milk processing plants and frozen food companies). The cognitive best–worst method determines the significant factors and variables to prioritise halal blockchain adoption decisions.

The results showed that the most significant factors were coercive pressure and halal strategy. Nineteen variables were identified to establish a valid hierarchical structure for halal blockchain adoption in the Indonesian food industry. The five significant variables assessed through the best–worst method were demand, regulator, supply side, sustainability of the company’s existence and main customers.

The proposed halal blockchain decision structure can assist food companies in deciding whether to adopt the technology.

This study proposes 19 variables that establish a valid hierarchical structure of halal blockchain adoption for the Indonesian food industry.

The main focus is to provide a non-similar solution for the magnetohydrodynamic (MHD) flow of Casson fluid over a curved stretching surface through the novel technique of the artificial intelligence (AI)-based Lavenberg–Marquardt scheme of an artificial neural network (ANN). The effects of joule heating, viscous dissipation and non-linear thermal radiation are discussed in relation to the thermal behavior of Casson fluid.

The non-linear coupled boundary layer equations are transformed into a non-linear dimensionless Partial Differential Equation (PDE) by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ordinary differential equations (ODEs). The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

The results indicate that the non-linear radiation parameter increases the fluid temperature. The Casson parameter reduces the fluid velocity as well as the temperature. The mean squared error (MSE), regression plot, error histogram, error analysis of skin friction, and local Nusselt number are presented. Furthermore, the regression values of skin friction and local Nusselt number are obtained as 0.99993 and 0.99997, respectively. The ANN predicted values of skin friction and the local Nusselt number show stability and convergence with high accuracy.

AI-based ANNs have not been applied to non-similar solutions of curved stretching surfaces with Casson fluid model, with viscous dissipation. Moreover, the authors of this study employed Levenberg–Marquardt supervised learning to investigate the non-similar solution of the MHD Casson fluid model over a curved stretching surface with non-linear thermal radiation and joule heating. The governing boundary layer equations are transformed into a non-linear, dimensionless PDE by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ODEs. The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

Published in June 2020, the European Union (EU) Taxonomy Regulation is an important tool for the reorientation of capital flows toward sustainability, establishing a classification system that enables investors to identify green economic activities. Confronted by the reporting demands of this regulation, companies are caught in a sustainability economic revolution. This study seeks primarily to understand firms’ responses to the EU taxonomy, and whether they recognize value creation opportunities by aligning market and nonmarket strategies with the taxonomy goals. For that, we conducted expert interviews and adopted a conceptual framework based on institutional theory, dynamic capabilities view and nonmarket strategy research. Our findings indicate that most firms respond reactively, while firms with sustainability-driven business models tend to respond in an anticipatory way, and firms with high greenhouse gas (GHG) emissions and low taxonomy eligibility in a defensive way. We also find evidence for mimetic isomorphism related to the influence of consulting and auditing services. Further, high levels of uncertainty, ambiguity and lack of clarity has a great impact on firms’ responses and motives. Finally, this study highlights the EU taxonomy considering a paradigmatic shift toward sustainability, which is not recognized by most firms. To this end, we find that most companies have not identified opportunities arising from nonmarket integration and, rather, see the taxonomy only as an extra regulation to be compliant with. Hence, we argue that it is crucial that firms contextualize the taxonomy within its larger institutional paradigmatic shift to capture the importance of going beyond mere compliance.

This article aims to analyze the strategic work of Polish entrepreneurs in the furniture industry following the political changes in 1989. The authors examined how these entrepreneurs transitioned from local craftsmen or importers into leaders of international manufacturing companies and how their strategizing contributed to the unprecedented growth of the Polish furniture sector.

The authors examined extant data, specifically biographical interviews conducted with 11 prominent leaders in the Polish furniture industry (Hryniewicki, 2015, 2018). They analyzed within a theoretical framework that integrates J.C. Spender’s theory of strategic management with Barry Johnson’s concept of polarity management. Polarity is a way of understanding and managing interdependent, opposing pairs of values or perspectives that give rise to conflict.

The analysis reveals key patterns of strategic challenges at the level of human agency, history and sense-making. The authors identified four key polarities: life and business, knowledge presence and absence, concordance and discordance, and instrumental and non-instrumental sense-making.

The polarity concept illuminates the interplay of agency and determinism in strategic decision-making, offering valuable insights for methodology and a deeper understanding of Poland’s furniture industry.

This study aims to examine the impact of coworking spaces (CWS) on the wellbeing and entrepreneurial satisfaction of remotely working micro-entrepreneurs, highlighting the role of these spaces beyond conventional work locations. It specifically investigates how CWS foster a supportive ecosystem for micro-entrepreneurs, supporting their professional journey.

Employing a mixed-methodological approach, this study uses nearest-neighbor matching and multiple regressions based on quantitative surveys from a representative sample of micro-entrepreneurs, including those working from CWS, and a qualitative focus group with frequent CWS users. This comprehensive approach allows for a nuanced exploration of the impacts of CWS on micro-entrepreneurs' wellbeing and satisfaction.

The study finds that micro-entrepreneurs experience significant benefits from utilizing CWS, most notably in terms of wellbeing and work–life balance. These spaces provide valuable support through community building, networking opportunities and structured work environments. The benefits of CWS are particularly pronounced for women and micro-entrepreneurs with young children, aiding in boundary management and mitigating the challenges of social isolation.

We provide clear implications for remotely working micro-entrepreneurs, especially women with young children at home, on why to use CWS as their primary locations. The findings also offer insights for policymakers and CWS providers, underlining the importance of designing CWS environments that meet micro-entrepreneurs' diverse needs.

This study contributes to the existing literature by providing a detailed examination of the utilization of CWS among micro-entrepreneurs and the impact of CWS on their wellbeing and entrepreneurial satisfaction.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

The primary objective of this study is to investigate the ethical implications of deploying AI-enabled chatbots in the healthcare sector. In addition, the research underscores trust and reliability as critical factors in addressing the ethical challenges associated with these chatbots.

This study takes a qualitative approach, conducting 13 semi-structured interviews with a diverse range of participants, including patients, healthcare professionals, academic researchers, ethicists, and legal experts. This broad spectrum of perspectives ensures a comprehensive understanding of the ethical implications of AI-enabled chatbots in healthcare. The rich exploratory data gathered from these interviews is then analysed using thematic analysis.

The findings of this study are highly significant in the context of AI-enabled healthcare chatbots. They highlight four major themes: developing trust, ensuring reliability, ethical considerations, and potential ethical implications. The interconnectedness of these themes forms a coherent narrative, highlighting the pivotal role of trust and reliability in mitigating ethical issues.

This study contributes to the existing literature on AI-enabled healthcare chatbots. It not only reveals potential ethical concerns associated with these technologies, such as data security, patient privacy, bias, and accountability, but it also places a significant emphasis on trust and reliability as critical elements that can boost user confidence and engagement in using AI-enabled chatbots for healthcare advice.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.