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The aim of this study is to understand a family firm's choice of related-party transaction (RPT) types and analyze their value impacts to separate the abusive from benign RPTs.

It uses a 10-year panel of BSE-listed 378 family (and 200 non-family) firms. The fixed effects, logit and difference-in-difference (DID) models help examine value effects, propensity and persistence of harmful RPTs.

Loans/guarantees (irrespective of counterparties) destroy firm value. Capital asset RPTs decrease the firm value but enhance value when undertaken with holding parties. Operating RPTs increase firm value and profitability. They improve asset utilization and reduce discretionary expenses (especially when made with controlled entities). Family firms have larger loans/guarantees and capital asset volumes but have smaller operating RPTs than non-family firms. They are less likely to undertake loans/guarantees (and even operating RPTs) and more capital RPTs vis-à-vis non-family firms. Family firms persist with dubious loans/guarantees but hold back beneficial operating RPTs, despite RPTs being in investor cross-hairs amid the Satyam scam.

Rent extractability and counterparty incentives supplement each other. (1) The higher extractability of related-party loans and guarantees (RPLGs) dominates the lower extraction incentives of controlled parties. (2) Holding parties' bringing assets, providing a growth engine and adding value dominate their higher extraction incentives (3) The big gains to the operational efficiency come from operating RPTs with controlled parties, generally operating companies in the family house. (4) Dubious RPTs seem more integral to family firms' choices than non-family firms. (5) Counterparty incentives behind the divergent use of RPTs deserve more research attention. Future studies can give more attention to how family characteristics affect divergent motives behind RPTs.

First, the study does not single out family firms for dubious use of all RPTs. Second, investors, auditors or creditors must pay close attention to RPLGs as a special expropriation mechanism. Third, operating RPTs (and capital RPTs with holding parties) benefit family firms. However, solid procedural safeguards are necessary. Overall, results may help clarify the dilemma Indian regulators face in balancing the abusive and business sides of RPTs.

The study fills the gap by arguing why some RPTs may be dubious or benign and then shows how RPTs' misuse depends on counterparty types. It shows operating RPTs enhance operating efficiencies on several dimensions and that benefits may vary with counterparty types. It also presents the first evidence that family firms favor dubious RPTs more and efficient RPTs less than non-family firms.

The purpose of this paper is to investigate the relationship between CEOs' inside debt holdings (pension benefits and deferred compensation) and the operating leverage of the firms they manage, with the aim to examine whether CEO incentives play a role in corporate risk-taking.

The authors investigate the relation between CEO inside debt holdings (CIDH) (pension benefits and deferred compensation) and the operating leverage (DOL) of the firms they manage. Using a sample of 11,145 US firm-year observations over the period 2006–2017, the authors find a strong negative association between CIDH and DOL. Additional analyses reveal that the relationship between CIDH and DOL is more pronounced in firms with heightened agency issues, powerful CEOs and for CEOs with stronger professional networks. The results are robust to various sensitivity and endogeneity tests.

The authors find strong evidence confirming the expected negative association between CEO inside debt and DOL suggesting that firms with higher inside debt tend to maintain lower levels of operating leverage. These findings continue to hold with the alternative measure for the inside debt and operating leverage, and across a range of tests designed to rule out the possibility that the primary findings are in any way driven by potential endogeneity. In addition, the findings demonstrate that the presence of manager-shareholder agency conflicts can strengthen the inside debt–DOL relationship suggesting the strong role of inside debt in reducing firm risk.

Findings in this paper have implications for design of compensation structures so that corporate boards can establish incentives as a tool for risk management. A limitation of this study is that it is focused on one market, i.e. US listed companies, so the findings may not be applicable on a global scale.

To the best of the authors’ knowledge, this is the first study that links firm-level management of operating leverage through design of CEO inside debt incentives (two obvious choices for risk-reduction at the CEOs’ disposal include reducing financial risk through reduction of firm leverage and reducing operating risk through reduction of operating leverage). While use of firm leverage as an instrument of choice has been explored in the past, use of operating leverage to achieve risk reduction when CEO possess high inside holding, has received very little attention.

The study aims at examining the relationship between the forms of misclassification practices, namely expense shifting and revenue shifting. In particular, the study aims at identifying the form of shifting that has been preferred by firms to meet the industry average profitability.

Core earnings and operating revenue expectation models are used to measure expense shifting and revenue shifting, respectively. The panel fixed-effects models are used to control for unobserved heterogeneity across industries and time.

Based on a sample of Bombay Stock Exchange-listed firms, the author finds that firms prefer expense shifting over revenue shifting to meet industry average profitability, implying that firms choose the shifting tool based on the relative advantage. Further, the findings deduced from the empirical results demonstrate that firm life cycle and mandatory adoption of International Financial Reporting Standards (IFRS) moderates the relationship between shifting forms and industry average profitability. However, the negative impact of IFRS on shifting practices is found to be less pronounced among BigN audit firms.

The study is among the pioneering attempt to document the substitution relationship between shifting forms. It is the first study that examines a form of classification shifting, where gross profit and core earnings both change as an effect of misclassification.

This paper aims to investigate the impact of a firm’s pension insurance contributions (PIC) on its financialization (investment in risky assets) using a sample of Chinese firms.

The authors use a multiple regression model to conduct the analysis.

The findings suggest that a firm’s PIC increases its financialization. Additional analysis suggests that firms with higher PIC are more likely to have lower operating profit and higher financial risk. In addition, the impact of PIC on financialization is more salient when a firm faces high industry competitiveness, holds more cash, has high labor costs and labor intensity or is non-state owned.

The paper adds to the growing literature on the effect of social insurance on corporate policies. The findings complement those related to the relationship between defined contributions and defined benefits retirement plans and corporate policies.

The study contributes to the debate on the merits of financialization. The literature is mixed on the pros and cons of financialization. The results suggest that financialization has an adverse effect on a firm’s performance and risk in the lens of increased PIC.

China has seen a trend of financialization arising from the rapid economic development in the past decade. Moreover, the PIC premiums in China are not trivial. Thus, the significant cost of PIC and the financialization trend suggest that the answer to the research question is timely and meaningful.

This study aims to examine the role of economic political uncertainty (EPU) on various corporate policies, namely, cash reserves, investment, capital structure and operating activities of Indian listed firms.

To assess the influence of policy-related uncertainties, the author uses the India-specific EPU news-based index constructed by Baker et al. (2016) as a proxy for policy uncertainties. This study uses data from listed Indian firms spanning the period 2003 to 2019. The author uses panel regression models with firm-fixed effects to analyze the impact of EPU on corporate policies, including cash reserves, leverage and CAPEX, while considering key control variables.

In response to heightened EPU, firms tend to increase their cash reserves, curtail their investment activities and favour secured financing options. Notably, this study aligns with the “real options” framework, demonstrating that firms with substantial investment irreversibility significantly reduce their capital expenditures during periods of elevated EPU. Additionally, the results reveal that rising EPU corresponds to heightened borrowing costs and increased operating expenses for firms.

In contrast to prior research that predominantly investigated the impact of EPU on the decisions of listed firms in developed markets, this study delves into the role of EPU on corporate policies among listed firms in India. This focus is particularly relevant, given the significant policy changes that have transpired in the Indian business landscape in recent years.

This study aims to empirically examine how blockchain, one of the emerging Industry 4.0 technologies, can combat climate change by improving their green innovation performance, particularly under conditions of policy uncertainty.

This study utilizes the difference-in-difference-in-difference (DDD) method to explore the effect of blockchain on enterprises' green innovation performance. The analysis is based on data from Chinese-listed enterprises spanning the period from 2013 to 2021.

First, the adoption of blockchain in enterprises registered in areas designated as low-carbon pilot cities can significantly improve their green innovation performance. Second, the enhancement of green innovation efficiency emerges as the primary driving force behind the adoption of blockchain, thereby leading to improved green innovation performance. Lastly, it is observed that blockchain adoption has a greater positive impact on improving green efficiency in private enterprises compared to state-owned enterprises in China.

For managers, the findings can provide valuable insights to help them better prepare for the challenges and opportunities presented by the era of Industry 4.0. For policymakers, this study offers valuable insights into the interaction between new technologies in Industry 4.0 and the performance of green innovation, thereby aiding in the formulation of effective policies.

This study contributes to bridging the existing gap between the adoption of new technologies, such as blockchain, and their potential impact on climate change. Moreover, this research enriches practitioners' understanding of how new technologies in the era of Industry 4.0 can be applied to address significant challenges like climate change.

This paper aims to examine the relationship between economic freedom and Islamic rural banks' efficiency in the case of Indonesia.

The study covers 40 Islamic rural banks in 34 Indonesian regions from 2014 to 2020. Tobit regression is utilized to expose the impact of economic freedom on the efficiency of Islamic rural banks, and nonparametric frontier data envelopment analysis is used to acquire banks' technical efficiency.

The findings reveal that overall economic freedom has a strong favorable impact on the efficiency of Islamic rural banks. The study’s breakdown components suggest that business freedom, government spending and investment freedom are favorable indicators, whereas government integrity and tax burden are negative indicators, and all indicators agree with previous studies.

This research can serve as a guideline for Islamic rural bank management in terms of maintaining financial efficiency. The government should think about the ramifications of financial sector liberalization and reforms, according to these findings. When financial intermediaries operate in a less constrained environment, they are more likely to pursue competitive practices that increase their operating rate and other efficiency metrics. Finally, academics might utilize this information to investigate the economic flexibility of Islamic rural banks.

The novelty of this study is in using data envelopment analysis and Tobit regression to identify economic freedom and Islamic rural banks' efficiency. To the best of the authors' knowledge, the study of the role of economic freedom in Islamic rural bank's efficiency is limited, particularly in the context of Indonesia.

This paper aims to examine the effect of carbon emissions on audit fees. The authors hypothesize that firms in cities with higher carbon emission levels have lower reporting transparency, higher return volatility or are subject to higher reputation risk, causing them to be charged higher audit fees for auditing services.

The authors use panel data of 25,960 firm-year observations from a sample of Chinese firms. The carbon emission data for each Chinese city are obtained from the China Emission Accounts and Datasets for Emerging Economies. This paper adopts a multiple regression model to study the impact of carbon emissions on audit fees.

The authors find that firms located in cities with higher carbon emission levels and firms with more carbon emissions are charged, on average, a higher audit fee. This audit fee effect of carbon risk is transmitted by lessened information transparency and elevated financial risk within these firms. This paper shows that auditors consider carbon risk in their audit fee decisions and other factors that could influence audit risk and effort.

This study draws a connection between carbon emissions and audit fees. It is especially relevant due to the increasing importance of environmental factors in the audit risk assessment. In addition, the findings suggest that a firm implementing a proactive environmental strategy benefits the economy and decreases the costs to the firm for services such as auditing.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.