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This study aims to explore the quality of corporate governance disclosure under an “apply and explain” regime in the context of an emerging economy (Mauritius), following a transition from the traditional “comply or explain” approach within the national code of corporate governance.

The research relies on a content analysis of corporate governance disclosure in 86 annual reports of companies listed on the Stock Exchange of Mauritius for the financial periods 2018–2019 and 2019–2020, one-way analysis of variance tests and draws on the typology of corporate governance explanations developed by Shrives and Brennan (2015), focusing on specificity, location and comprehensiveness dimensions. This paper draws on legitimacy theory and the concepts of substantive and symbolic disclosures to guide the interpretation of the findings.

From a specificity point of view, the disclosure index revealed significant variations, with the highest score being four times the lowest score. With regards to location and comprehensiveness, only around half of companies are making optimum use of a corporate governance report and providing explanations by principles. This paper also illustrated how some firms provided symbolic disclosures. Overall, there are disparities in the application of the code by companies, reflected in a blend of substantive and symbolic disclosures to maintain their legitimacy.

This study examines “apply and explain” disclosures in a emerging economy in contrast to the “comply or explain” approach studied so far in the literature. Merely professing a “well intended” shift to the “apply and explain” approach does not necessarily lead to improvements in the quality of corporate governance disclosures. Companies, governance professionals and regulatory bodies could formulate disclosure guidance to better underpin the implications of the “apply and explain” approach.

Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.

In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.

The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.

A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.

Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.

This research intends to undertake a rigorous bibliometric analysis of product quality research trends and patterns, map the intellectual and social structure of the field, identify the predominant themes and propose a transition plan for future work in this discipline.

The procedure was carried out in a step-by-step manner. Following a specified search string, Scopus retrieved 1454 journal articles from the previous 39 years (1984–2022). To comprehend the field’s base, various techniques of performance analysis and science mapping were employed using RStudio and VOSviewer.

In light of the results, both the volume and influence of product quality studies have surged over the past four decades, with most works appearing in prestigious academic journals. A number of cross-country product quality collaborations took place in nations with little geographic, historical, or cultural proximity. Using co-citation analysis, five distinct subfields were identified within the literature on product quality. Finally, the use of co-word analysis helps understand the field’s underlying themes and concludes with a set of potential research avenues built on a content analysis of the articles from each of the five subfields.

To the authors' understanding, this study represents a pioneering effort to examine the research field on product quality using bibliometric analysis. Notwithstanding being rooted in scientometrics, this research’s outcomes are instructive for practitioners, academics and aspiring researchers in the field.

Large, influential and profitable young adults are being targeted for fast fashion that negatively impacts the environment. The transition from a fast to an environmentally friendly slow fashion is a challenging process and culturally dependent. The process starts with slow fashion idea adoption. Thus, the authors modified an information acceptance model (IACM) to examine information characteristics (idea/information quality, credibility, usefulness, source credibility) and consumer factors (need for idea and attitudes) impacting intentions to adopt the slow fashion idea in Canada, South Africa (individualists) and China (collectivists).

Cross-sectional data were collected from South African (n = 197), Chinese (n = 304) and Canadian (n = 227) young adults (18–35 years old) at universities in metropolitan cities. Partial least squares structural equation modeling was used to analyze the data.

The results show that while most information characteristics and consumer factors are vital for slow fashion attitudes and intention formation, information quality and trust in the sources were a problem in individualistic cultures as opposed to the collectivist culture. This finding confirms the greater tendency of collectivists to trust disseminated information on environmental issues. In all cultures, attitudes impacted idea adoption intentions. On testing IACM, the multigroup analyses showed no significant differences between young adults in the individualistic cultures. Attitudes mediated most relationships and were highly explained by IACM (South Africa, 49.6%; China, 74.5%; and Canada, 64.5%).

In emerging and developed markets, this study informs environmentalists and green fashion brands of information characteristics that can create positive attitudes and slow fashion idea adoption intentions among influential young adults.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

The institutional conditions of primary care provision remain understudied in low- and middle-income countries. This study analyzes how primary care doctors cope with medical uncertainty in municipal clinics in urban India. As street-level bureaucrats, the municipal doctors occupy two roles simultaneously: medical professional and state agent. They operate under conditions that characterize health systems in low-resource contexts globally: inadequate state investment, weak regulation and low societal trust. The study investigates how, in these conditions, the doctors respond to clinical risk, specifically related to noncommunicable diseases (NCDs).

The analysis draws on year-long ethnographic fieldwork in Pune (2013–14), a city of three million, including 30 semi-structured interviews with municipal doctors.

Interpreting their municipal mandate to exclude NCDs and reasoning their medical expertise as insufficient to treat NCDs, the doctors routinely referred NCD cases. They expressed concerns about violence from patients, negative media attention and unsupportive municipal authorities should anything go wrong clinically.

The study contextualizes street-level service-delivery in weak institutional conditions. Whereas street-level workers may commonly standardize practices to reduce workload, here the doctors routinized NCD care to avoid the sociopolitical consequences of clinical uncertainty. Modalities of the welfare state and medical care in India – manifest in weak municipal capacity and healthcare regulation – appear to compel restraint in service-delivery. The analysis highlights how norms and social relations may shape primary care provision and quality.

International entrepreneurship and marketing research reports the impact of effectual decision-making logic on small and medium-sized enterprises (SMEs) international performance. How the effectual logic of decision-making enhances the overall performance of SMEs in international business-to-business markets remains a puzzle in the field. The purpose of this study is to investigate the concept of networking capability as an important SME capability translating effectual decision-making into international performance.

The authors examine the model presented in this study using quantitative data from 153 founders or managers in charge of international business at SMEs throughout New Zealand. The authors also used 142 open-ended responses to provide post hoc exploratory analysis.

The findings of this study suggest that networking capability is a mechanism through which the logic of decision-making enhances the international performance of SMEs.

This study bridges between international marketing and entrepreneurship by investigating how the networking capability of internationalizing SMEs translates their founders’/managers’ effectual logic into a successful performance in international business-to-business markets.

Common literature review methods such as systematic review and narrative review are poorly suited to the investigation of complex management phenomena. Systematic reviews are highly driven by protocol and procedure, and are oft-criticized as reductive and poorly equipped to examine the interaction between phenomena and context, nonlinear processes, and empirical outcomes that are less predictable. Narrative reviews, on the other hand, are pluralistic and iterative and thus better suited to descriptions of the complex and unpredictable; however, they tend to lack methodological transparency, trustworthiness, and pragmatism in application. The “realist synthesis” approach to literature review can be seen as the middle-ground between these two common methods, offering both methodological rigor alongside flexibility and nuance. Realist synthesis takes an explanatory frame, with a focus on unearthing the theorized causal mechanisms at play beneath a phenomenon of interest.