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This study analyses the effects of firm efficiency on firm performance and how controlling shareholders moderate the link between the two variables.

This study employs data envelopment analysis to estimate firm efficiency and the panel regression method to assess the hypothesised relationships among 1,295 firm-year observations of publicly listed firms in Malaysia from 2015 to 2019.

The results indicate that firm efficiency (technical efficiency, pure technical efficiency and scale efficiency) has mixed relationships with firm performance (return on assets, market-to-book ratio and operating cash flows), all of which are being moderated by controlling shareholdings.

This study highlights the importance of assessing firm efficiency as the key success factor for improving firm performance. Industrial managers should manage efficiently their resources or operating costs in achieving their corporate financial goals. Moreover, this study notes the presence of controlling shareholders, who can be either self-interested or company goal aligned.

This study suggests becoming efficient in transforming inputs into outputs is a prerequisite before investigating accrual-based and cash-based firm performance measures, and the presence of controlling shareholders matters in these regards.

This paper analyses and assesses the effect of firm-specific determinants (FSDs) on supply-chain performance (SCP) and export performance (EP). It examines SCP’s influence on EP and its mediating effect on the relationship between FSD and EP.

This paper develops a theoretical framework based on the resource-based view (RBV) and dynamic-capability theory to understand SCP’s role in the FSD-EP link while empirically validating using the Indian automobile industry segments (IAIS) data. The sample frame comprises all listed firms in IAIS between the financial year 2010-11 and 2021–22, with continuous data availability throughout the considered timeline. The paper employs factor analysis for dimension reduction, a panel-data-fixed-effect model to analyze the relationships, bootstrap to test the mediation effect and focus-group discussion for validating the results obtained through statistical analyses.

FSD directly influences SCP’s efficiency aspect and EP. Distribution efficiency and inventory efficiency characteristics of SCP directly impact EP and completely mediate the relationship between FSD and EP.

This study provides significant insights into how firms can increase EP by focusing on firm-specific and SCP-related factors. To improve EP, firms should concentrate on enhancing distribution and inventory efficiencies. Firms must focus on critical firm-level factors like age, size and raw-material import capability to increase their ability to solve SC-specific barriers and improve SCP, resulting in enhanced exports.

This study investigates the impacts of FSD on SCP and EP and examines the mediating effect of SCP on the relationship between FSD and EP. Such a mediating role of SCP has rarely been probed in the literature.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

Despite the increasing interest in the role of supply chain concentration (SCC) in improving performance, its influence on firms' sustainability performance remains unexplored, as do the underlying mechanisms of this relationship. Drawing on resource dependence theory, the authors investigate the relationship between SCC and manufacturing firms' sustainability performance and the moderating roles of operational slack and information transparency.

The authors use secondary data from 3,581 manufacturing firms listed on the Shanghai and Shenzhen A-share stock markets from 2006 to 2020 to conduct an empirical analysis using panel data regression models.

Manufacturing firms' SCC is negatively related to sustainability performance until it reaches a certain point, where SCC positively affects sustainability performance, presenting a U-shaped relationship. In addition, operational slack represented by a quick ratio moderates the relationship between SCC and sustainability performance by flattening the curve. Operational slack represented by receivable turnover ratio moderates the relationship between SCC and sustainability performance by steepening the curve and shifting the turning point left. Information transparency strengthens the effect of SCC on the sustainability performance by steepening the curve.

This investigation provides a comprehensive view of the SCC– sustainability performance relationship.

The aim of this study is to review the literature on how intellectual capital (IC) contributes to the decarbonization efforts of firms. It explores how carbon accounting can measure the components of IC in decarbonization efforts to balance profitability with environmental and social goals, particularly in promoting decent work and economic growth (Sustainable Development Goal [SDG] 8 and its targets [2, 5, 6, 8]). Moreover, it emphasises the importance of multi-stakeholder partnerships for sharing knowledge, expertise, technology, and financial resources (SDG17-Target 17.G) to meet SDG8.

As a consolidated methodological approach, a systematic literature review (SLR) was used in this study to fill the existing research gaps in sustainability accounting. To consolidate and clarify scholarly research on IC towards decarbonization, 149 English articles published in the Scopus database and Google Scholar between 1990 and 2024 were reviewed.

The results highlight that the current research does not sufficiently cover the intersection of carbon accounting and IC in the analysis of decarbonization practices. Stakeholders and regulatory bodies are increasingly pressuring firms to implement development-focused policies in line with SDG8 and its targets, requiring the integration of IC and its measures in decarbonization processes, supported by SDG17-Target 17.G. This integration is useful for creating business models that balance profitability and social and environmental responsibilities.

The integration of social dimension to design sustainable business models for emission reduction and provide a decent work environment by focusing on SDG17-Target 17.G has rarely been investigated in terms of theory and practice. Through carbon accounting, IC can be a key source of SDG8-Targets 8.[2, 5, 6, 8] and SDG17-Target 17.G. Historically, these major issues are not easily aligned with accounting research or decarbonization processes.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

How does business model design play a role in enabling manufacturing firms’ services? This study aims to investigate the impact of two distinct types of business model design, namely, efficiency-centered business model design (EBMD) and novelty-centered business model design (NBMD), and their effects in balanced and imbalanced configurations, on two types of services: product- and customer-oriented services.

Using matched survey data of 390 top managers and objective performance data of 195 Chinese manufacturing firms, this study uses hierarchical regression, polynomial regression and response surface analysis to test the hypotheses.

The results show that while EBMD positively affects product-oriented services, NBMD positively affects customer-oriented services. Both types of services exert a significant influence on firm performance. Furthermore, the degree of product- and customer-oriented services increases with an increasing effort level with a balance between EBMD and NBMD. Asymmetrical, imbalanced configuration effects reveal that the degree of product-oriented services is higher when the EBMD effort exceeds the NBMD effort, and the degree of customer-oriented services is higher when the NBMD effort exceeds the EBMD effort.

This study enriches the understanding of designing business models to facilitate service growth in manufacturing firms, ultimately benefiting firm performance. In addition, exploring balanced and imbalanced configurations of EBMD and NBMD offers new insights into business model dual design research.

This study aims to develop and test a framework of the antecedents to and performance outcomes of exporters’ use of different services offered by Internet-based Business-to-Business (I-B2B) platforms.

We test the model based on a unique survey dataset of 350 Chinese exporters who subscribed to Alibaba.com, a major I-B2B platform.

Drawing on the signaling theory, export and I-B2B platform literature, we develop a typology of exporters’ use of services offered by I-B2B platforms. We find that the extent to which firms have cost efficiency advantages, adopt an export diversity strategy, operate under high levels of psychic distance and experience high levels of domestic regulatory uncertainty are all positively related to exporters’ use of I-B2B platform services. The use of those services is either positively or negatively related to export success depending on the services in question. The magnitudes of these performance relationships are contingent on the exporters’ transparency strategies.

This is the first study to examine the antecedents to and consequences of exporters’ use of the services offered by I-B2B platforms.

This study examines how corporate litigation, both securities-related and not, is affected by hedge fund (HF) activism.

We use a difference-in-differences (DiD) method, along with propensity score matching and firm fixed effects and a comparison of HF and non-HF activists for identification.

We find that companies that are targeted by HFs face operation-related lawsuits, mainly from stakeholders or competitors. This effect does not seem to be caused by targets' higher tendency to settle the cases. Our evidence shows that HF activists increase firm value for the target firms that are prone to litigation.

Therefore, our evidence supports the idea that the higher operation litigation risks are unintended consequences of improving firm efficiency through cost savings or restructuring of target firms by the activists.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.