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The purpose of this study is to (1) investigate how IFRS 16 affects firms’ risk in Egypt and (2) examine the moderating role of managerial overconfidence on this relation.

This study uses data from the annual reports of 38 Egyptian firms from 2014 to 2022. This study employs the generalized method of moments (GMM) and the three-stage least squares (3SLS) as estimation techniques.

The results show that IFRS 16 positively affects Egyptian firm risk, while managerial overconfidence reduces this positive effect.

This study has some limitations. First, the sample size was relatively small. Second, our analysis did not incorporate other metrics of managerial overconfidence owing to the unavailability of relevant data in Egypt.

This study assists stakeholders and regulators in realising the implications of IFRS 16 on a firm’s risk, especially in emerging markets. Also, it enables managers to identify and assess lease-related risks more accurately to assist in developing appropriate risk mitigation strategies and optimizing lease-related decision-making processes. Furthermore, it aids in enhancing comprehension and knowledge of the interplay between managerial behaviour and firm outcomes.

Grounded in agency theory, this study reveals novel empirical insights into the impact of IFRS 16 on firm risk, especially in the context of emerging markets. Utilizing behavioural decision theory and upper echelons theory, it examines the previously unexplored influence of managerial overconfidence on this relationship.

This study investigates the impact of tax planning, both independently and in conjunction with earnings management, on the persistence of earnings and its various components.

In this study, tax planning refers to corporate strategies aimed at minimizing taxes, while earnings management involves manipulating reported earnings through accounting accruals. The analysis uses a dataset of US companies from 1989 to 2016 and includes a series of regression tests.

The study finds that firms implementing aggressive tax strategies exhibit lower persistence in cash flows from operations and earnings. Furthermore, companies using both aggressive tax planning and earnings management techniques show the lowest persistence in total accruals, cash flows from operations and reported earnings.

Our sample of US firms limits generalizability. Future research could explore the international impacts of tax planning and earnings management on earnings quality and include post-2016 data for insights on the 2018 tax cuts and COVID-19. Investigating other earnings quality measures and their influence on investors and analysts could enhance performance assessment.

This research identifies key factors influencing the interpretation of financial statements, offering valuable insights for regulators, auditors, tax authorities, financial analysts and other users with significant practical and social implications.

This study contributes to prior research by highlighting the need to investigate the real effects of tax avoidance and extends prior research by examining the impact of high levels of tax planning, along with aggressive earnings management, on earnings persistence.

This study introduces a multi-vaccine multi-echelon supply chain (MVMS) framework designed to ensure sustainable vaccine distribution during outbreaks. The framework aims to minimize the total costs of vaccine distribution and reduce greenhouse gas (GHG) emissions to mitigate environmental impacts while maximizing job opportunities within the network.

Our proposed appraoch employs a multi-objective mixed-integer linear programming model.

The findings indicate that incorporating uncertainties related to demand and inspection errors significantly facilitates timely responses to unexpected shortages, fulfills the requirements of healthcare facilities, and enhances the supply chain’s resilience against future uncertainties. This study also explores managerial implications and suggests avenues for future research to further advance this field.

Existing literature on MVMS often relies on simplifying assumptions of perfect vaccines and primarily focuses on demand uncertainty. However, real-world supply chains are typically marked by imperfections, disruptions, and a variety of uncertainties beyond demand. In this work, we address several sources of parameter uncertainty, including demand variability, inspection errors, vaccine waste, and defective treatments rates to enhance the robustness of our model.

Using Hirschman's Exit–Voice–Loyalty–Neglect (EVLN) framework, this study examines the dual-moderating role of the big five personality traits in shaping workers' behavioral responses to psychological contract breach. Building from calls for research on individual differences in psychological contract dynamics, the current study applies the theory of purposeful work behavior to delineate how the higher-order goals prescribed by one's personality jointly guide interpretation processes in forming emotional and behavioral responses. In doing so, we map how certain big five traits shape felt violation and EVLN responses following breach events, while others seem to only moderate emotional or EVLN responses.

A scenario-based experimental study asked participants (N = 610) about their reactions to a breach event. We tested a dual moderated mediation model, in which agreeableness, extraversion, conscientiousness and neuroticism moderated the intensity of felt violation, and the likelihood of each EVLN behavior following from felt violation.

We found evidence for the dual moderating effect of agreeableness on voice responses to breach. Additionally, neuroticism strengthened felt violation following breach, and extraversion weakened endorsement of neglecting work to cope with felt violation. Our results suggest that certain traits are particularly important for individual differences in emotional responses to breach (e.g. neuroticism), while others are important for shaping differences in behavior (e.g. extraversion). Additionally, results shed light on the importance of taking a person-by-situation perspective in understanding work behavior, such that extraversion, while conceptualized as general emotional tendencies, does not significantly influence felt violation in breach contexts.

While previous research has looked at how personality traits moderate either the breach–felt violation relation or the breach–EVLN relation, research has yet to test the moderating effect of personality simultaneously. Excluding one or the other overlooks important individual differences in the process, as interpretation processes guiding emotional and behavioral responses happen concurrently. In doing so, we examine responses to a specific breach event (rather than general breach perceptions), as this better aligns with the conceptualizations of breach (as specific occasions of broken promises) and felt violation (as an emotional state).

This paper aims to illustrate a possible combination of critical success factors (CSFs) that best enhance firm performance and compare configurations of CSFs in ISO-9001-2015-certified and non-ISO firms in Bangladesh.

The study sample comprises individuals from the service and manufacturing sectors and ISO-9001-2015-certified and non-ISO firms. We have collected data from 229 respondents from 134 firms, including 64.5% from ISO-certified and the remaining 35.5% from non-ISO-certified categories. In this exploratory study, we performed a fuzzy-set qualitative comparative analysis (fsQCA) to identify the combinations of success factors that enhance performance.

The results of this study capture the causal complexity surrounding the CSFs and firm performance. We found six configurations for ISO firms and five for non-ISO firms that enhance firm performance. Besides, the necessary conditions for firm performance of ISO firms slightly differ from those of non-ISO firms. Thus, the empirical results highlight the importance of equifinality and complementary relationships between conditions relating to quality management systems to increase firm performance. A significant difference was found between ISO-9001:2015-certified firms and non-ISO firms.

A significant limitation arises from the sample’s inclusion of only Bangladeshi firms; a cross-cultural study could add new dimensions to the configurations. Understanding how these CSFs vary and need a longitudinal approach is essential.

Our configurational approach provides new insights into the complex dynamics of eight CSFs with a holistic approach. Instead of focusing on the additive linear net effects of CSFs on firm performance, we explain how the CSFs of TQM implementation combine into multiple combinations. By doing so, we show multiple equifinal pathways to firm performance.

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

In this research, arbitrage opportunity is tested between the yield rates computed by the NSS model, and the computed forward rates between conventional and Islamic finance to see any arbitrage opportunity. The research questions are the conventional and Islamic finance yields at the same level and equal to each other to avoid arbitrage? Whether conventional and Islamic forward rates differ significantly and thus create any arbitrage opportunity. This study aims to find the presence or absence of arbitrage between conventional and Islamic finance yield rates.

The NSS model is the latest model in calculating yield and forward rates. In the method the error level is minimized so expected yield rate and given yield rate both converged (Vahidin and Anastasios, 2020). When they converged it gives the researchers all six months’ yield rates. For the Nelson Siegal method, all the six months’ yield rates are available and these yield rates can be used to compute the forward rates.

The authors concluded there is a significant difference between the conventional yield rate and the Islamic yield rate. It suggests that because there are significant differences, its suggest arbitrage is possible. So anyone interested in making a guaranteed profit. The conventional yield rates are lower; hence, anyone can borrow from the conventional finance system and invest the money in the Islamic financial system because investments are getting higher rates of income in the form of yield rate in Islamic Finance. So, one can make money because of this difference. Statistically, it is possible to make money, but practically, the authors observed the difference, however it is very meager. The arbitrage opportunity between Islamic finance and conventional finance will not affect the economy because the significant difference is too small. The disturbance in the arbitrage opportunity due to the values is very meager and insignificant.

This research does not address the derivative contracts’ role in risk management; future researchers could take up this as another research.

This research will be beneficial for financial institutions, especially institutional investors. Besides, this research will help the regulators and investment bankers in assisting where and future losses especially bond portfolios in conventional finance and Islamic finance. This study will also contribute and help the asset manager of mutual funds in the mutual fund industries.

In effect, this research will strengthen the financial system, capital market and bond market, derivative contracts such as options contracts, futures contracts, swap contracts and forward contracts will use computed forward rates for assessing future losses (value at risk [VaR]) and to hedge them (Balakrishnan, 2020).

As this topic is rarely studied it will increase the literature present in this domain.

The study aims to assess the effectiveness of prevailing methods for quantifying physical climate risks. Its goal is to evaluate their utility in guiding financial decision-making within the real estate industry. Whilst climate risk has become a pivotal consideration in transaction and regulatory compliance, the existing tools for risk quantification frequently encounter criticism for their perceived lack of transparency and comparability.

We utilise a sequential exploratory mixed-methods analysis to integrate qualitative aspects of underlying tool characteristics with quantitative result divergence. In our qualitative analysis, we conduct interviews with companies providing risk quantification tools. We task these providers with quantifying the physical risk of a fictive pan-European real estate portfolio. Our approach involves an in-depth comparative analysis, hypothesis tests and regression to discern patterns in the variability of the results.

We observe significant variations in the quantification of physical risk for the pan-European portfolio, indicating limited utility for decision-making. The results highlight that variability is influenced by both the location of assets and the hazard. Identified reasons for discrepancies include differences in regional databases and models, variations in downscaling and corresponding scope, disparities in the definition of scores and systematic uncertainties.

The study assists market participants in comprehending both the quantification process and the implications associated with using tools for financial decision-making.

To our knowledge, this study presents the initial robust empirical evidence of variability in quantification outputs for physical risk within the real estate industry, coupled with an exploration of their underlying reasons.

Artificial intelligence (AI) radically transforms organizations, yet ethical AI’s effect on employee innovation remains understudied. Therefore, this study aims to explore whether responsible artificial intelligence (RAI) enhances high-tech employees’ innovative work behavior (IWB) through creative self-efficacy (CSE) and employee mental health and well-being (EMHWB). The study further examines how leaders’ RAI symbolization (LRAIS) moderates RAI’s effect.

Through structural equation modeling, 441 responses of high-tech firms’ employees from Pakistan were utilized for hypotheses testing via SmartPLS-4.

The results revealed that second-order RAI enhances employees’ IWB. The effect was supported directly and indirectly through CSE and EMHWB. Findings also showed that LRAIS significantly moderates RAI’s influence on CSE, on the one hand, and EMHWB, on the other.

High-tech firms’ managers can fix AI-outlook issues that impair their employees’ IWB by prioritizing an ethical AI design involving actions like AI control mechanisms, bias checks and algorithmic audits. Similarly, these managers should facilitate RAI discussions and targeted trainings focusing on employees’ cognitive development and well-being. Likewise, RAI embracement programs and evaluations for leadership positions could be incorporated into high-tech firms.

This study advances the mainstream AI literature and addresses a notable gap concerning RAI’s influence on employees’ IWB while grounding in social cognitive theory. Moreover, this study unveils how CSE and EMHWB affect IWB within RAI milieus. Additionally, through signaling theory, it underscores the significance of LRAIS in amplifying the direct association between RAI, CSE, and EMHWB within high-tech firms in emerging markets.

The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels, information asymmetry and default risk, through which digital transformation can influence bond credit spreads.

We use the bond issuance data of Chinese listed companies over the period 2008–2020. Corporate digital transformation of these companies is measured with textual analysis of the management discussion and analysis part of annual reports. We employ a panel regression model to estimate the effect of digital transformation on bond credit spreads.

We find robust evidence that companies with higher digital transformation experience lower bond credit spreads. We further observe that credit spread reduction is higher for firms that are smaller, non-state-owned, have lower credit ratings and have less analyst coverage. We also find evidence that digital transformation reduces credit spreads by reducing the information asymmetry between firms and investors with enhanced information transformation mechanisms and lowering corporate default risk by strengthening operating efficiency.

To the best of our knowledge, this study is the first attempt to understand the impact of corporate digital transformation on bond credit spreads. Our findings help to understand the effect of digital transformation on firms’ credit worthiness and access to capital.