Search results

This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance characteristics enhance the relationship between sustainability compensation and firms’ non-financial performance and to expand the domain of the impact of sustainability on non-financial performance.

This analysis is based on a sample of companies listed on the Milan Italian Stock Exchange from the Financial Times Milan Stock Exchange Index over the 2016–2020 period. Regression analysis was used by using data retrieved from the Refinitiv Eikon database and the sample firms’ remuneration reports.

The findings of this paper show that embedding sustainability in executive compensation positively affects firms’ non-financial performance. The results of this paper also reveal that specific corporate governance features can improve the impact of sustainability on non-financial performance.

This analysis is limited to Italian firms included in the Financial Times Milan Stock Exchange Index; however, the findings are highly significant.

The findings provide regulators with useful insights for considering the integration of sustainability goals into executive remuneration. Another implication is that policymakers should require – at least – listed firms to fulfil specific corporate governance structural requirements. Finally, the findings can provide investors and financial analysts with a greater awareness of the role played by executive remuneration in the long-term value-creation process.

This paper contributes to addressing the relationship among sustainability, remuneration and non-financial disclosure, drawing on the stakeholder–agency theoretical framework and focusing on Italian firms. This issue has received limited attention with controversial results in the literature.

The purpose of this study is to assess the interaction effect of government non-financial support and firms' regulatory compliance on firms' innovativeness. Firms' regulatory compliance with environmental and safety issues has been suggested as one of the reasons why firms innovate. Such compliance provides legitimacy, improves reputation and corporate image, and enhances customer loyalty and competitive advantages, which influence firm innovativeness. However, regulatory compliance is costly and with limited resources, the role of government support is crucial as a moderator, to help firms become more compliant and influence their innovativeness.

The study uses data from the World Bank Enterprise Innovation Survey for seven countries in Sub-Saharan Africa.

Regulatory compliance has a positive and significant effect on firm innovativeness. Increased use of government non-financial support enhances the level of firm regulatory compliance and the effect of regulatory compliance on firm innovativeness.

The study contributes to the literature on compliance and firm innovativeness in Africa by showing how the positive effect of regulatory compliance on firm innovativeness is stronger when firms benefit from government non-financial support.

This study investigates the impact of business strategy on earnings management practices for financial and non-financial firms in Oman. To assess the research objective, 430 firm-year observations from 2015 to 2019 were employed in the study. Using regression analysis, the findings suggest that differentiation strategy positively affects earnings management in financial sector firms. In addition, cost leadership strategy positively affects earnings management in non-financial sector firms. This indicates that business strategy is associated with company leaders managing their earnings while they are trying to survive through competition. These findings are useful for regulators, as they can introduce mechanisms to curb earnings management practices and instil more faith in investors.

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 research paper is to establish the impact of process and service innovation on non-financial and financial performance in the tourism sector, as well as to explain the mediating role of organizational learning.

A quantitative approach was used, based on partial least squares–structural equation modeling (PLS-SEM), Smart PLS 4.0 software. The sample consisted of 426 establishments in the tourism sector in Zone I, Ecuador. Online surveys were employed to collect data, yielding 118 responses, to assess the impact of innovation on business performance from 2020 to 2022.

Process and service innovation positively and significantly affect non-financial and financial performance, respectively. Organizational learning mediates the relationship between process innovation and disservice. The relationship between non-financial and financial performance could not be confirmed. The model explains 53.10% of non-financial performance and 26.10% of financial performance.

The relationship between innovation and performance in the tourism sector has been studied in several developed economies, while little has been studied in developing countries. This work contributes to an empirical discussion including a mediating variable in the innovation–performance relationship in the Latin American context.

El propósito de esta investigación es establecer el impacto de la innovación de procesos y servicios en el desempeño no financiero y financiero en el sector turístico, así como explicar el papel mediador del aprendizaje organizacional.

Se utilizó un enfoque cuantitativo, basado en el software PLS SE M, Smart PLS 4.0. La muestra estuvo constituida por 426 establecimientos del sector turístico de la Zona I, Ecuador. Se aplicaron encuestas en línea para recopilar datos, que arrojaron 118 respuestas, para evaluar el impacto de la innovación en el desempeño empresarial de 2020 a 2022.

La innovación de proceso y de servicio afecta positiva y significativa al desempeño no financiero y financiero, respectivamente. El aprendizaje organizacional media la relación entre innovación de proceso y des servicio. No se pudo confirmar la relación entre desempeño no financiero y financiero. El modelo explica el 53.10% del desempeño no financiero y el 26.10 del desempeño financiero.

La relación entre innovación y rendimiento en el sector turístico se ha estudiado en varias economías desarrolladas, mientras que en los países en desarrollo se ha estudiado poco. Este trabajo contribuye a una discusión empírica incluyendo una variable mediadora en la relación innovación-desempeño en contexto latinoamericano.

This study highlights the influence of non-financial sustainability reporting and firm reputation (FR) on the China Stock Exchange. The study is based on the components of sustainability reporting that influence FR.

A simple ordinary least squares (OLS) regression model is initially run to test the hypotheses. Advanced econometric methods are used to detect the presence of heteroskedasticity. The study utilizes fixed-effect, two-stage least squares (2SLS) and two-step generalized method of moments (GMM) regression models to address endogeneity issues.

Findings suggest that NFSR has a negative influence on FR. Conversely, environmental, social and governance (ESG) sustainability reporting exhibited positive associations with a FR in fixed-effect, 2SLS and GMM results.

This study has limitations, and data collection is restricted to the period from January 2018 to June 2023, limiting the scope of findings due to data constraints. Brand equity measurement is considered only one aspect of a company's activities, and other methods can also be considered for measuring brand equity. Another limitation is a standardized method for measuring NFSR. While this study used the Arianpoor and Salehi (2021) model to measure sustainability reporting in the Chinese market, future research could explore different methods.

The findings of this study have important practical implications for corporate management, highlighting reputation challenges and the strategic importance of sustainability. Managers are encouraged to use NFSR strategically to enhance their reputation and corporate strategy.

The social implications highlight ownership and regulatory structures, promoting enhanced sustainability reporting in China's business culture. This insight informs policymakers, businesses and stakeholders regarding the importance of sustainability reporting, guiding decisions on corporate reputation and sustainability regulations.

The research indicates the importance of context-specific sustainability reporting for enhancing reputation. It provides insights into sustainability's impact on a company's reputation, promoting responsible practices for a sustainable global economy. To the best of the authors' knowledge, this is the first research that utilizes the NFSR frameworks and a sample of firms in China to discuss sustainability reporting with different guidelines.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

The study explores how the intention to export affects financing and non-financing variables for small and medium-sized enterprises (SMEs) in a low-income country (LIC). The objectives of this study are (1) to discern between regional and global exporting and (2) to evaluate its policymaking implications.

Primary survey data were collected from 330 Rwandan SMEs and were analysed using ordered logistic models as an application of the expectation-maximisation iterating algorithm, which was tested for robustness using a sampling model variation.

The results show that alternative sources of finance are the predominant choice to finance the intention to export within and outside Africa. As the scope of export intentions broadened from regional to global, there was a shift in preferences from less formal to more formal lending technologies, moving from methods like factoring to lines of credit. Moreover, reliance on bank officers became more significant, with increasing marginal effects. Finally, the study determined that government financing schemes were not relevant for SMEs pursuing either regional or global exporting.

Whilst alternative sources of finance predominate the export intentions of Rwandan SMEs, establishing a robust banking relationship becomes crucial for global exporting. Despite this implication, the intention to export should prompt more transparent communication regarding government financial support programmes. There is an opportunity for increased usage of relationship lending to customise support for SMEs involved in exporting, benefiting both the private and public sectors.

This study accentuates how export distance alters SME financing priorities. The results also contribute to understanding how the value of relationship lending changes when less familiar markets (i.e. global exporting) are the objective. Moreover, the study offers a new perspective on how institutional voids affect entrepreneurial financing decisions in LICs.

This study aims to examine how different combinations of firm determinants enhance environmental reporting (ER) in New Zealand.

This study collects data from annual and sustainability reports of 145 listed companies in New Zealand. This study uses content analysis to examine the extent of ER and then the fuzzy set qualitative comparative analysis (FsQCA) to determine the configurations of determinants of reporting.

The findings reveal ten configurations of determinants showing that ER relies on the existence or non-existence of other firm determinants such as firm size, profitability, ownership and presence of an environment committee (EC). Among ten configurations, ER*∼ROE (ROE denotes return on equity; firms with no profitability but with ECs) stands out, indicating that ER is strongly influenced by the presence of an EC when no profitability exists.

The configuration analysis in this study extends the current ER literature.

The findings provide insight into the management to look for new paths when they make environmental-related strategies based on the existence and non-existence of firm determinants. The findings also support policymakers considering multiple combinations of criteria when mandating ER to promote better climate risk reporting in New Zealand.

Previous studies on determinants of ER mainly use regression analysis to analyse their data. In contrast, the current study uses configuration analysis.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.