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Firm performance remains at the heart of strategic management. In the quest to refine the field’s contribution, Venkatraman and Ramanujam (1986) argued that reliance upon single measures of firm performance is risky and firm performance should be treated as a multidimensional construct. Subsequently, researchers have examined trends in firm performance measurement ever since. Over a decade since the last examination of this issue, this study aims to add to the ongoing conversation.

The authors investigated 1,972 research papers published in five premier management journals for the years 2015–2019 to determine if multidimensional measurement of firm performance has improved.

The findings suggest that approximately two-thirds of papers that measure firm performance are published using only a single measure of firm performance, and approximately three-fourths do not measure firm performance across multiple dimensions.

This study contributes to the literature by emphasizing the necessity to consider the dimensionality of firm performance, use multiple measures and consistently ground firm performance variables with theory – especially control variables – to keep firm performance as the focus of the strategy field. Evidence and implications are discussed and recommendations for researchers and reviewers are provided.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

This study investigates the significance of trade credit (TC) as an alternative source of funding in financing the growth of financially dependent firms.

Panel data analysis using the difference generalized method of moments (GMM) and fixed-effects ordinary least squares (FE-OLS) is conducted on annual data from publicly listed firms across a number of developing economies. The data cover the period from 2003 to 2019.

The findings indicate that financially dependent firms rely on TC to manage their growth, especially when they have exhausted their debt capacity. This dependence on TC displays a cyclical pattern. As firms enhance their financial position, they tend to scale back their dependence. Nevertheless, firms with significant growth opportunities continue utilizing TC for at least two years after their initial identification as financially dependent.

The author's conclusion highlights that TC can be a valuable and accessible source of funding, especially in developing economies where the real sector may require alternative financing channels. Hence, TC has the potential to play a very significant role in financing corporate growth in these economies.

The current study adds to the existing body of literature by revealing that access to alternative sources of finance is also critical for firms that are dependent on external sources and for firms that have exhausted their financial debt capacity.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

The purpose of this paper is to investigate the impact of changes in credit stock on real and financial indicators in Turkey with a focus on conditional and time-varying dynamics.

In addition to lag-augmented vector autoregression (LA-VAR) based time-varying Granger causality tests, threshold models and a research setting that identifies high/low states of credit growth based on 24-month moving averages are used to explore regime-dependent behavior. For investigating the asymmetric dynamics, the authors use a methodology that identifies good/bad news in credit growth based on 24-month moving averages and standard deviations.

Results strongly suggest that the impact of changes in credit stock induces conditional responses. Moreover, we find evidence for asymmetric responses. In the case of Turkey, efforts to spur growth through credit produce a strong negative byproduct, a depreciation in the exchange rate. The authors also find that changes in credit stock have become more relevant for uncertainties in inflation and exchange rate expectations, particularly in the era after mid-2018 in which credit growth volatility has increased noticeably.

This study provides a comprehensive analysis of time-varying and conditional responses to a change in credit stock in a major emerging economy. Using a moving threshold based only on the available information in the analysis of state-dependency represents a new approach.

The primary objective of this research is to chronicle how the Environmental Protection Agency (EPA) and other United States Federal Government Agencies (USFGA) agencies have played a role in shaping the trajectory of financial reporting for sustainability, with a particular emphasis on triple bottom line (TBL). This exploration extends to other indexes reporting sustainability data encompassed within financial, social and environmental reporting.

This study adopts an illustrative methodology, utilizing data sourced from governmental, business and international organizational documents.

Sustainability accounting predominantly finds its place within the framework of TBL. However, it is crucial to note that sustainability reporting remains voluntary rather than mandatory. Nevertheless, accounting firms and professional accounting societies have embraced it as a supplementary facet of financial accounting reporting.

The research highlights the historical evolution of sustainability within the USFGA and corporate entities. Corporations’ interest in accounting for sustainability performances has significantly contributed to the emergence of voluntary sustainability accounting rules, as embodied by the TBL.

To measure the dynamic features of compassion as an emotional and behavioral construct, the present research used a univariate latent growth modeling (LGM) approach within the structural equation modeling (SEM) framework. The aim was to trace the dynamic development of compassion longitudinally in accounting and business students during a three-credit English course at university.

The suggested method ensures the measurement invariance over time, deals with the first order latent variable, traces its growth and takes into account the measurement errors. This longitudinal analytical method was used to explore the initial state and the growth of compassion in four points of time during a language course. The data were collected from 60 adult accounting and business students in four time phases using Sprecher and Fehr's Compassionate Love Scale and were analyzed in Mplus 8.4 with univariate LGM.

The model fit was accepted and the invariance of the latent factor was confirmed over time. The negative covariance between intercept and slope (second-order latent variables) suggested that lower initial scores in L2 learners' compassion show a faster increase in compassion over time as the mean of slope is larger than that of the intercept. L2 learners who started off at a higher level of compassion showed a slower change in compassion over time. This can be at least partly explained by the teacher's motivating role or learners' compassion but needs to be further explored in complementary qualitative phases for deeper insights.

In the present research, awareness was raised of the developmental nature of compassion as an emotional and behavioral construct essential to the accounting and business profession. The great strength of this research lies in the dynamic approach to the compassion construct and the LGM used to capture the temporal growth of compassion and how it evolved through the L2 course.

Technology is widely recognised to be revolutionising the accounting profession, allowing accountants to focus on professional skills and technical knowledge that deliver value for organisational success. Despite the known benefits, it is reported that accountants are not fully leveraging the potential value of certain technologies. To understand why, this study aims to draw on the technology adoption model (TAM) and investigates accounting professionals’ perceptions towards technology, and how these may influence adoption at work.

The study gathered online survey data from 585 accounting managers from organisations of varying sizes and in different sectors in Australia and parts of Southeast Asia. Qualitative data were thematically analysed, and quantitative data were analysed using both descriptive and multivariate techniques.

The study highlighted the pivotal role of staff perceptions on the importance and ease of using technology on the uptake and successful usage. Findings emphasised important opportunities for organisations to educate accounting staff on the value of technology and optimise their confidence and skills through training and support initiatives, particularly smaller businesses. Marked differences in the orientation towards technology among Australian and Southeast Asian participants illuminate how national work culture and practice can influence technology adoption.

The study makes a practical contribution by advancing the understanding of the relative importance and value of certain technologies in different regions and organisation types in the accounting profession. It extends the theoretical understanding of the role of TAM’s core elements to the accounting context, exploring staff’s notions of perceived usefulness and perceived ease of use from the manager’s perspective.

National cultures significantly explain cross-country differences in the relation between asset growth and stock returns. Motivated by the notion that managers in individualistic and low uncertainty-avoiding cultures have a higher tendency to overinvest, this study aims to show that the negative relation between asset growth and stock returns is stronger in countries with such cultural features. Once the researchers control for cultural dimensions, proxies associated with the q-theory, limits-to-arbitrage, corporate governance, investor protection and accounting quality provide no incremental power for the relation between asset growth and stock returns across countries. Evidence of this study highlights the importance of the overinvestment hypothesis in explaining the asset growth anomaly around the world.

The purpose of this study is to examine the relationship between pollutant emissions, financial development and IFRS in developed and developing countries between 1998 and 2022.

Data were obtained from World Development Indicators and World Governance Indicators of the World Bank.

Using FGLS and GMM estimators, the results provide evidence that financial development has a significant positive impact on a variety of pollutant emissions. However, this positive impact is moderated by IFRS for the overall sample and country income groups.

Governments and regulatory organizations should support companies’ investments in clean energy and technologies to slow down environmental degradation. Tax credits and subsidies may be helpful to achieve this goal. Also, governments may encourage companies to cooperate with universities and research institutions to develop environment-friendly production and distribution methods to reduce pollution. Although stakeholders may obtain information about environmental issues in financial statements that are prepared in accordance with IFRS, there is a need for standardization of their contents.

Greenhouse gases are major contributors to climate change and global warming. In addition to private costs borne by producers, the production and consumption of products have social costs arising from pollution that affects air, water, and soil. Pollution adversely affects people's physiological and psychological health, which decreases labor productivity, thereby leading to a decrease in economic growth.

According to the author’s knowledge, this is the first study that examines the impact of IFRS on the relationship between financial development and pollutant emissions.