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The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

As of now, the digitization of corporate finance presents a paradigm shift in business strategy, innovation, financing and managerial capability around the globe. However, the prevailing finance scholarly works hardly document the impact of the digitalization of corporate finance on firm performance with global evidence and analysis. Hence, the contemporary debate on whether firm performance is genuinely stimulated because of the digitalization of corporate finance or not has been a pressing issue in the relevant literature. Therefore, the purpose of this study is to identify a data-driven, concise response to an unaddressed finance issue if the performance of high-digitalized firms (HDFs) outperforms that of their counterpart peers for wealth maximization.

The first stage test models examine the firm performance of relatively high-digitalized firms as opposed to low-digitalized firms based on the system GMM. The second stage test of the probabilistic (logit) model infers that the probability of being HDFs explores because of better performance. Then, the authors execute robust checks based on the different quantile regressions and Z-score-based system GMM. In addition, the authors recheck and present the test results of the fixed effect and random effect to capture time-invariant individual heterogeneity. Finally, the supplementary test findings of firms’ credit strength by using Altman five- and four-factor Z-score models are presented.

By using cross-country panel analysis as 15 years’ test bed for HDFs and low digitalized firms (LDFs), the test results indicate that the overall firm performance of a digitalized firm is significantly better than that of a non-digitalized firm. The global evidence documents that HDFs are exposed to higher values and are financially more persistent as compared to their counterparts. The finding is remarkably concomitant across several possible subsample analysis, such as country–industry–size–period analysis.

This study can be remarkably effective in encouraging managers, policymakers and investors to acknowledge the need for adopting the required digitalization. Overall, this original study addresses a core research gap in the corporate finance literature and remarkably provides further direction to rethink the assumptions of firm digitalization on additive value and thereby identify optimal decisions for wealth maximization. The findings also imply that investors require an additional risk premium if they invest in relatively LDFs, which have relatively lower market value and weaker firm performance.

From an investors point of view, the academic novelty contributes to an innovative and unsettled issue on the impact of digitization of corporate finance on firm performance because there is a new question of high or low digitization of corporate finance in the global market. Hence, this academic novelty contributes to sharing global evidence of the digitalization of corporate finance and its effect on firm performances. In addition, an intensive critical review analysis is conducted based on the most recent and relevant scholarly works published in the top-tier journals of finance and business stream to fix the hypothesis. Overall, this study addresses a core research gap in the corporate finance literature; notably provides further direction to rethink firm digitalization; and thereby identifies optimal decisions for shareholders’ wealth maximization.

This study aims to study the impact of earnings quality on firms’ financial performance.

An unbalanced panel data of 237 small- and medium-sized Portuguese companies from the mold industry, using 2010–2018 yearly data was analyzed. While most studies focus only on earnings management when assessing earnings quality, in this study six proxies for earnings quality are used, namely, accruals quality (a proxy for earnings management), earnings persistence, earnings predictability, earnings smoothness, earnings timeliness and earnings conservatism. Moreover, two proxies of financial performance are considered, the return on assets and the economic value added. An econometric model was estimated using either a fixed-effects or a random-effects specification to account for the individual firm-specific effects and ensure heteroscedasticity corrected estimates.

The results show that managers must be concerned with the quality of reported earnings, as it can affect positively firms’ financial performance, especially regarding accruals quality. Persistence, predictability, smoothness, timeliness and conservatism are shown not to exert significant influence on financial performance in the sample.

This work contributes not only as a literature review on these thematic but also to firms’ managers and stakeholders, who have information that helps them select strategies that guarantee earnings quality and improve firms’ financial performance.

This study proposed an econometric model that studies the relationship between earnings quality (using several proxies for it) and financial performance that can be applied to all companies.

The purpose of this study is to determine the moderating effect of group affiliation (GA) while examining the relationship of corporate governance (CG) with firm performance (FP) and firm risk-taking (FRT).

The study employed a sample of 100 non-financial firms, selected randomly from the top 500 companies listed on the Bombay Stock Exchange (BSE) based on their market capitalisation for 2013–2022. The random effects and fixed effect models are employed for the analysis. Furthermore, the generalised estimating equations (GEE) population-averaged model is used for added robustness.

The results reveal that while strong CG improves FP, GA modifies the effect of CG on FP. Both GA and CG have beneficial effects, but their synergy is insignificant. However, in the context of CG and FRT, the study unveils that a strong CG is associated with a reduction in FRT, and this relationship is more pronounced for standalone firms.

To the best of the authors' knowledge, the present study is a maiden attempt to investigate the moderating effect of GA while examining the relationship of CG with FP and FRT.

This study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.

This paper conceptualizes timely concepts supported by research spanning multiple domains.

This research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.

This viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

In this study, the authors examine the relationships between market-based regulations and corporate carbon disclosure and carbon performance. The authors also investigate whether these relationships vary across emission-intensive and non-emission intensive industries.

The study sample consists of the world's 500 largest companies across most major industries over a recent five-year period. Country-specific random effect multiple regression analysis is used to test empirical models that predict relationships between market-based regulations and carbon disclosure and carbon performance.

Results indicate that market-based regulations significantly and positively affect corporate carbon performance. However, market-based regulations do not significantly affect corporate carbon disclosure. This study also finds that the association between regulatory pressures and carbon disclosure and carbon performance varies across emission-intensive and non-emission-intensive industries.

The findings of this study have key implications for policymakers, practitioners and future researchers in terms of understanding the factors that drive businesses to increase their carbon performance and disclosure. The study sample consists of only large firms, and future researchers can undertake similar studies with small and medium-sized firms.

The results of this study are expected to help business managers to identify the benefits of adopting market-based regulations. Regulators can use this study’s results to evaluate if market-based regulations effectively improve corporate carbon performance and disclosure. Furthermore, stakeholders may use this study to evaluate and improve their businesses' reporting of carbon disclosure and performance.

In contrast to current literature that has used command and control regulations as a proxy for regulation, this study uses market-based regulations as a proxy for climate change regulations. In addition, this study uses a more comprehensive measure of carbon disclosure and carbon performance compared to the previous studies. It also uses global multi-sector data from carbon disclosure project (CDP) in contrast to most current studies that use national data from annual reports of sample firms of specific sectors.

In the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

Using data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

The authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.

Literature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

This study aims to investigate the impact of financial performance on carbon emission disclosure.

The study uses ordinary least squares (OLS) multiple regression analysis on a sample of 177 Financial Times Stock Exchange 350 index (FTSE-350) non-financial firms to test the impact of market (Tobin’s Q) and accounting (return on equity) financial performance indicators on carbon emission disclosure.

The results show that the financial performance market indicator has a significant positive impact on carbon emission disclosure. The accounting indicator illustrates similar results except for Scope 3, where the results are insignificant. This study may help firms understand how financial performance affects carbon emission disclosure, particularly by showing that high-performing firms are motivated to maintain strong environmental practices and enhance carbon emission awareness.

This paper enhances stakeholders’ understanding of how firms’ environmental policies align with their financial objectives, thereby expanding knowledge in carbon accounting.

By focussing on the anticipated emotional reactions of volunteers and drawing on theories of motivation and identity, this study investigates how volunteers react to different options of the entrepreneurial model of Amigos for Children Foundation (ACF). The paper proposes a hypothetical model for volunteer’s emotional reactions to potential business model changes. We suggest the relative importance of intrinsic motivational factors, professional identity and attitudes towards business organisations as mediating variables. ACF works exclusively with university students as volunteers, so their specific characteristics may limit some of the conclusions and propositions of this qualitative research, but public policy consequences of supporting similar entrepreneurial transitions can be generally relevant.

Based on the qualitative analysis of semi-structured interviews with volunteers of ACF, a Hungarian non-profit organisation, we explore the challenges of transitioning into a social enterprise.

Previous research showed controversial results about the impact of pay on the motivations of volunteers. For a non-profit organisation that would like to utilise the competencies of its volunteers, introducing a market-based service may mean additional financial resources and the potential loss of human resources. Understanding the moderating factors of volunteers' reactions might help build better theories for managing the non-profit-social enterprise transition and designing public policies to support scaling up the impact of successful social purpose organisations.

For practitioners, the research underlines the importance of participatory mechanisms in volunteer management. By managing transitions better, non-profit organisations can expand their social impact by acquiring more financial resources through market-based activities closely related to their original activities and keeping their volunteers. The study elucidates the relevance of the crowding-out effect and indicates some hypothetical moderating variables influencing its potential degree.

While there is an increased demand from various corporate stakeholders on the need for public companies to have risk management frameworks as well as a stand-alone risk management committee to mitigate risks and simultaneously improve performance, this study investigates the effects of the risk management committee attributes on firm performance, and the role of board size is highlighted on this relationship in Malaysian listed companies.

Both accounting- and market-based performance measures have been used for measuring performance. A dynamic model using the generalized method of moments (GMM) has been employed to control for potential endogeneity, simultaneity and unobserved heterogeneity.

The findings reveal that risk management committee attributes such as size, independence and meetings negatively affect book-based performance measures and positively affect market-based performance measures. Moreover, board size positively moderates the risk management committee attributes and performance relationship. The study embraces the predictions of agency theory and resource dependence theory.

The findings are practically significant for Bursa Malaysia, Securities Commission Malaysia to assess the compliance of the Corporate Governance Code (MCCG, 2017) and for academia to further explore significant relationships in other emerging economies.

The paper contributes to multiple aspects: first, it studies the impact of risk management committee attributes on firm performance; second, it investigates the moderating effect of board size on RMC–performance relationship; in the end, the study employs dynamic modeling for estimation process to avoid dynamic endogeneity considered a main econometric problem for CG–performance relationships.