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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This study aims to examine the herding behavior of socially responsible exchange traded funds (SR ETFs) in comparison to conventional ETFs during the COVID-19 pandemic.

To test for herding behavior, the authors use the cross-sectional absolute deviation and a quadratic market model.

During the pandemic, investments in socially responsible financial products grew rapidly. And investors in the popular SR ETFs herd during this special period, while holders of conventional ETFs did not.

Investors in socially responsible investments must do their own research and make their own financial decisions, rather than follow the crowd, especially during extreme events like the COVID-19 pandemic.

The evidence shows that, during the pandemic, socially responsible ETFs behaved in line with theoretical predictions of herding, that is, herding is more significant during extreme market conditions.

This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

The purpose of this study is to examine the benefits and challenges of using logistics service providers (LSPs) for small and medium-sized enterprises (SMEs) and large corporations.

This study uses the focus group interview method with eight LSP companies of varying sizes in the UAE. The aim is to obtain detailed insights into the advantages and obstacles associated with using LSPs.

The interview results reveal differences in the services and payment options offered by large and small-medium-sized LSPs, leading to varying impacts on SMEs. Additionally, both large and small-medium-sized LSPs encounter challenges in delivering services to SMEs, including sustaining logistic excellence in a competitive market, meeting the high customer expectations from SMEs and large enterprises, difficulty in recruiting skilled and competent employees and high costs of business technology.

This study is limited to the UAE context and the perspectives of eight LSP companies. The findings may not be generalizable to other regions or industries.

The findings of this research provide valuable insights for SMEs and large corporations considering the use of LSPs. Understanding the benefits and challenges associated with outsourcing logistics services can assist in making informed decisions and developing effective collaborations with LSPs.

This research contributes to the existing literature by specifically examining the benefits and challenges of using LSPs for SMEs and large corporations. This study provides a comprehensive analysis of the factors that influence logistics outsourcing decisions and highlights the unique challenges faced by LSPs and SMEs in the context of the UAE.

The purpose of this paper is to investigate whether audit committee characteristics, in particular audit committee size, audit committee activity and audit committee gender diversity, are associated with financial performance in Egyptian banks. The second purpose of this paper is to explore the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance.

A multiple regression analysis is used to estimate the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance of a sample of Egyptian banks during the period between 2018 and 2022.

The results indicate that audit committee size has a negative and insignificant effect impact on return on assets (ROA) and return on equity (ROE), respectively. The results also indicate that the audit committee gender diversity has a significant positive impact on ROA and ROE, respectively. Regarding audit committee activity, the number of board meetings has a negative and insignificant effect on ROA and ROE, respectively. Regarding gender diversity as a moderating variable, in general there is a positive effect of gender diversity on the relationship between audit committee characteristics and financial performance.

The study was limited to 20 banks in one country, but it sets the tone for future empirical research on this subject matter. The study also relied on one moderating variable, which is board gender diversity. This study provides an avenue for future research in the area of corporate governance and financial performance in other emerging countries, especially other African countries.

This study provides useful insights for managers and policymakers to better understand which audit committee characteristics can best encourage a company to improve financial performance. Furthermore, regulators should ensure that banks strictly adhere to corporate governance principles to build a strong banking industry capable of achieving economic development.

Banks will benefit equally from valuable qualities across demographic groupings in society by having females on the audit committee and appropriate audit committee meetings. Additionally, if audit committee members are correctly selected, banks with more females in audit committee and suitable audit committee meetings can successfully contribute to strengthening financial performance and social welfare of diverse segments of society. A culture of good banking governance must emerge to improve bank financial stability and, as a result, greater stability and economic growth.

To the best of the authors’ knowledge, the study is, perhaps, the first to examine the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance in Egyptian banks. This study adds to the literature by investigating such an issue in a developing economy that operates in a different context than those in developed countries.

This paper aims to propose and test a corporate entrepreneurship strategy (CES) model in small- and medium-sized enterprises (SMEs) with international activity located in Andalusia (Spain) – a peripheral region with high levels of inequality in the European Union (EU).

A quantitative analysis has been carried out with data from 101 SMEs to contrast and analyze the proposed CES model. The sample data were obtained through questionnaire-guided interviews with chief executive officers. Data processing has been done using partial least squares-path modeling, a variance-based technique for structural equation modeling.

The results of this study show the positive effect of environmental conditions on the development of CES actions in Andalusian SMEs (Spain) and the positive influence of CES on the results of SMEs’ international activity. In turn, environmental conditions do not directly affect the international activity.

Although previous works address the relationship between corporate entrepreneurship (CE) and international enterprise activity, to the best of the authors’ knowledge, this work is original in testing a CES model (including CE and the entrepreneurial strategic vision) in SMEs in a region that has one of the lowest levels of development in the EU. The results have important implications for SMEs and policymakers and could be extrapolated to other emerging economies.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

This study proposes an analysis of the performance of companies that have assumed the responsibility of facilitating the digitalization of economic activities. Because of their potential to accelerate digitization, these companies have been financially supported. The monitoring of the performances recorded by these companies, including the evaluation of the impact of different determining factors, meets both the needs of the financiers (concerned with the evaluation of the efficiency of the use of nonreimbursable financing) and the needs of continuous improvement of the activities of the companies in the field.

The study assesses performance dynamics and the impact of its determinants. The model allows achieving a simplified vision of performance and its determinants, supporting decision-makers in the management process. The construction of an estimation model based on the multiple regression method was considered. Robustness tests were performed on the results, using parametric and nonparametric tests.

The results of the analysis at the level of the extended sample indicated that, during the analyzed period, the economic and commercial performances decreased, and significant influences in this respect include the financing structure, sales dynamics and volume of receivables. The analysis at the level of the restricted sample confirmed these interdependencies and provided additional evidence of the impact of other determinants.

The study contributes both to performance research and to the assessment of the prospects for accelerating digitalization in support of economic activities. Since the empirical research was carried out on a sample of Romanian companies that provide services in information technology, which accessed nonreimbursable financing, the representativeness of the results is limited to this sector. For the analyzed sample, the study provides support for improving performance.

The results of the study prove to be useful from a microeconomic and macroeconomic perspective as well, as they provide evidence on the performance of companies that have implemented information and communication technology (ICT) projects and on the efficiency of the use of non-reimbursable funding dedicated to business support.

The study fills the literature gap regarding the performance of companies that have developed ICT projects and received grant funding for the implementation of these projects. The literature review indicated that there are few studies conducted on these companies, which did not include Romanian companies.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.