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Despite the global resolves to curtail fossil fuel consumption (FFC) in favour of clean energies, several countries continue to rely on carbon-intensive sources in meeting their energy demands. Financial constraints and limited knowledge with regards to green energy sources constitute major setbacks to the energy transition process. This study therefore aims to examine the effects of financial development and human capital on energy consumption.

The empirical analysis is based on the system generalised method of moments (SGMM) for a panel of 134 countries from 1996 to 2019. The SGMM estimates conducted on the basis of three measures of energy consumption, notably fossil fuel, renewable energy as well as total energy consumption (TEC), provide divergent results.

While financial development significantly reduces FFC, its effect is positive though non-significant with regards to renewable energy consumption. Conversely, financial development has a positive and significant effect on TEC. Moreover, the results reveal that human capital development has an enhancing though non-significant effect on the energy transition process. In addition, the results reveal that resource rents have an enhancing effect on the energy transition process. However, when natural resources rents are disaggregated into various components (oil, coal, mineral, natural gas and forest rents), the effects on energy transition are divergent. Although our findings are consistent when the global panel is split into developed and developing economies, the results are divergent across geographical regions. Contingent on these findings, actionable policy implications are discussed.

The study complements extant literature by assessing nexuses between financial development, human capital and energy transition from a global perspective.

The employer brand is a crucial intangible asset for companies as it enhances the employer–employee relationship, leading to improved employee performance and overall company outcomes. This paper aims to investigate the contribution of the employer brand to the financial results of companies in southern Europe.

The sample consists of 266 companies operating in southern European countries during the year 2020. Secondary data on employer brand attributes, assessed from the perspective of current employees, were collected from the Glassdoor platform. Financial indicators were obtained from the companies' annual financial reports. The research hypotheses were tested using regression analysis.

The results of the regression analysis support the notion that the employer brand contributes to profitability indicators and management effectiveness indicators of southern European companies. However, the study did not find evidence supporting the contribution of the employer brand to market indicators and financial structure indicators of the observed companies.

This study is one of the first empirical investigations to assess the role of the employer brand as a human capital tool for enhancing the financial performance of companies in southern Europe. The study examines employer brand attributes from the perspective of current employees, who actively participate in shaping the employer brand and the company's image. In contrast to prior research, this study incorporates a more extensive set of financial indicators, categorized into four groups: profitability indicators, management effectiveness indicators, market indicators and financial structure indicators.

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.

Focusing on the Agri-Food and Beverage sector, the paper investigates the direct effect of worldwide mandatory non-financial disclosure on several financial dimensions as well as its moderating effects on the relationship between sustainability and financial performance.

The authors performed fixed-effect regressions on a sample of 180 global listed companies, considering a period of eight years. The authors also tested the moderating effects of non-financial disclosure regulation on the relationship between sustainability and financial performance.

The authors found a positive direct impact of mandatory non-financial disclosure on Operating Return on Asset, Return on Equity and Return on Sales. The analysis also highlighted the negative moderating effects of non-financial reporting regulation on the relationship between sustainability issues and financial performance. As for the Cost of Debt, the authors found mixed results.

This study considers a short-term perspective focusing on a limited sample composed of companies playing a key role in the global agri-food system.

The paper identifies which financial performance dimensions are positively or negatively affected by mandatory non-financial disclosure. Accordingly, managers can rearrange corporate activities to deal with further reporting normative requirements concurrently preserving financial performances and fostering corporate sustainability.

This study recommends fostering mandatory non-financial disclosure to increase corporate transparency fostering the sustainability transition of the Agri-Food and Beverage industry.

The paper highlights global mandatory non-financial disclosure effects on financial performance considering a sector that is cross-cutting impactful on plural sustainability issues.

The importance of sustainable supply chain management practices (SSCMPs) in the financial performance of firms is increasing significantly. However, the influence of SSCMPs on financial performance can vary across sectors and contexts. This research aims to provide a comprehensive understanding of the influence of SSCMPs on the financial sustainability of small and medium-sized enterprises (SMEs) in the food processing industry. For this, the influence of sustainable environment practices, customer and supplier relationships, social SCMPs and lean supply chain on the financial sustainability of food processing SMEs is studied.

A questionnaire survey was conducted to collect data from 56 food processing SMEs in Mbeya, Tanzania. The study employed a cross-sectional research design and a census approach to capture data from all eligible SMEs in the target population. Exploratory factor analysis (EFA) and multiple linear regression (MLR) were utilized as the primary data analysis techniques.

The findings of the study revealed a positive and significant influence of various SSCMPs on the financial sustainability of food processing SMEs. Specifically, sustainable environment management practices (β = 0.147, p = 0.000), supplier relationship management (SRM) (β = 0.715, p = 0.001), customer relationship management (CRM) (β = 0.894, p = 0.016), social SCMP (β = 0.901, p = 0.005) and lean supply chain practices (β = 0.675, p = 0.003) were all found to have a significant effect on the financial sustainability of the surveyed food processing SMEs.

The study recommends the need to plan and integrate SSCMPs in firms’ operation processes, promote collaboration and networking and offer capacity-building initiatives that equip food processing SMEs with the necessary skills and knowledge to implement SSCMPs effectively. These will nurture effective adoption of SSCMP, leading to improved operations, environmental performance, financial sustainability and long-term viability of the sector.

While SSCMPs have gained attention in the literature, the specific focus on its impact on financial sustainability in the context of food processing SMEs adds originality to this research. Industry stakeholders and policymakers can utilize the findings of this study to develop supportive policies and programs that promote sustainable supply chain practices and enhance financial sustainability in the food processing sector.

Using the lens of crisis innovation and strategic alignment, this study explores how a segment of the restaurant sector that may be less agile than others—Michelin-starred restaurants—perceives and aligns with the challenges brought about by the COVID-19-pandemic.

The study collected data from 19 Michelin-starred restaurants in Spain using a qualitative interview method. The data were analyzed qualitatively and organized thematically.

Four key categories of strategic challenges were identified: human resources, uncertainty, control and economic challenges. In response, chefs displayed both behavioral and organizational strategies. Those organizational strategies were new human resource management, reorganization, product and service innovation and marketing. While the new human resource management actions adopted to align with the human resource challenges identified, a misalignment remains between some of the other strategic actions, such as product and service innovation, marketing and economic and uncertainty challenges.

The findings offer new insight into Michelin-starred restaurant chefs' challenges and (mis)alignment strategies, an area that has been understudied in the current literature on innovative responses in the hospitality sector post-pandemic.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

The purpose of this study is to examine the dynamic relationship between intellectual capital (IC) and its components on financial performance of banks within the selected eight countries of Association of Southeast Asian Nations (ASEAN).

The study utilizes the balanced panel data of 37 publicly listed banks from eight leading ASEAN economies for the period of 2017–2021. In this sense, the authors applied the Ante Pulic's typology, i.e. value-added intellectual coefficient (VAIC™) to evaluate the efficiency of intangible and tangible assets. While, investigating the dynamic nature of relationship, the authors employed the generalized system method of moments because of its power to account for the problem of endogeneity and heteroscedasticity.

The results of the study demonstrate that banks in ASEAN countries shed a varied degree of a spotlight on VAIC™ and its components to create value. The findings revealed that structural capital efficiency is significantly associated with earning per share (EPS), return on assets (ROA) and return on equity (ROE), compared to human capital efficiency (HCE) and capital employed efficiency of ASEAN banks. These results endorse the importance of resource- and knowledge-based views of organizations to leverage the financial performance of banks. However, contrary to theoretical expectations, this study found no positive relationship between HCE with ROA and ROE. Whereas, the relationship of VAIC™ is positive and significant with EPS and ROE but it remains statistically very marginal.

There are some inherent limitations in this study that could be opportunities for future research. The current study uses the VAIC™ typology, but future researchers can use the modified value-added intellectual coefficient (MVAIC) or triangulation approach to enhance the validity and reliability of the study. Additionally, future research can investigate the similarities and differences among countries in terms of their cultural backgrounds and regulatory frameworks regarding the disclosure of intangibles. Furthermore, future research can increase the length and sample size of the study to enhance its generalizability.

The robust empirical findings extend the academic debate on IC by unveiling the dynamic nature of relationship between IC and financial performance in context of ASEAN banking sector. The findings provide plausible recommendations for policy makers (managers, regulators and stakeholders) to understand how to increase the IC efficiently, especially human capital as a source to evaluate the firms’ ability in determining value-added and financial performance. Further, findings of this study also suggest that how can policy makers get the benefit by investing more on structural capital as a valuable strategic source to guarantee the optimal performance returns.

Prior studies on IC have been country- and firm-specific, utilizing cross-sectional research designs. However, this research contributes to the limited literature by investigating the dynamic nature of the relationship between IC and financial performance of banks in the context of ASEAN countries using micro-panel data.

This paper examines the impact of financial inclusion on poverty through access to mobile money in developing economies.

The authors employ the principal component analysis to construct an index of financial inclusion using demand and supply indicators, including mobile accounts. The authors use the two-step system GMM estimator for the analysis because of its efficiency and robustness in addressing heteroscedasticity and autocorrelation.

The main finding is that financial inclusion generally increased and significantly reduces poverty in the sample period. Furthermore, income inequality worsens poverty.

This study has few limitations. First, the empirical analysis of the study is restricted to macroeconomic factors only because of limited Household Finance Survey data set and time availability. Second, the study is limited to developing countries and the results cannot be generalized.

Financial inclusion is a significant policy tool for poverty reduction. There is the need to enhance strategies that further improve financial inclusion by expanding and improving the use of mobile money accounts.

The paper sheds light on how developing countries can harness financial inclusion to reduce poverty.

The paper differs from the previous studies in two ways. Firstly, mobile money account is included in the computation of financial inclusion index over the sample period. It also determines the impact of financial inclusion on poverty for short-run and long-run periods.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0690

The purpose of this paper is to describe how first-line managers (FLMs) in home care (HC) reason about the opportunities and obstacles to lead the work according to the individual’s needs and goals.

In this participatory appreciative action reflection project, eight managers within one Swedish municipality were interviewed. The data were analysed using a thematic analysis.

The results showed a polarization between two different systems that FLMs struggle to balance when attempting to lead HC that adapts to the needs and goals of individuals. One system was represented by the possibilities of a humane system, with human capital in the form of the individual, older persons and the co-workers in HC. The second system was represented by obstacles in the form of the economic needs of the organization in which the individual receiving HC often felt forgotten. In this system, the organization’s needs and goals governed, with FLMs needing to adapt to the cost-effectiveness principle and keep a balanced budget. The managers had to balance an ethical conflict of values between the human value and needs-solidarity principles, with that of the cost-effectiveness principle.

The FLMs lack the opportunity to lead HC according to the needs and goals of the individuals receiving HC. There is a need for consensus and a value-based leadership model based on ethical principles such as the principles of human value and needs-solidarity to lead the HC according to the individual’s needs and goals.