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The purpose of the study is to assess if a policy of female inclusive education should be complemented with a policy of female ownership of bank accounts to fight female unemployment. The study therefore examines how female ownership of bank accounts moderates the incidence of female education on female unemployment.

The focus is on 44 sub-Saharan African (SSA) countries for the period 2004–2018 and the empirical evidence is based on interactive quantile regressions. The interactions are tailored such that female ownership of bank accounts influences the effect of female inclusive education on female unemployment.

From the empirical findings it is evident that female ownership of bank accounts does not effectively moderate female education in order to reduce female unemployment unless complementary policies are considered. The complementary policies should be in view of boosting the interaction between female education and female bank account ownership in increasing employment opportunities for the female gender and by extension, reducing female unemployment. The invalidity of the moderating effect is robust to the inclusion of more elements in the conditioning information set as well as accounting for other dimensions of endogeneity such as simultaneity and the unobserved heterogeneity. Policy implications are discussed.

This study contributes to the extant literature by assessing how female ownership of bank accounts complements female inclusive education to reduce female unemployment.

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.

This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

In this fast-changing world, digitization has become crucial to organizations, allowing decision-makers to alter corporate processes. Companies with a higher corporate social responsibility (CSR) level not only help encourage employees to focus on their goals, but they also show that they take their social responsibility seriously, which is increasingly important in today’s digital economy. So, this study aims to examine the relationship between digital transformation and CSR disclosure of Chinese A-share companies. Furthermore, this research investigates the moderating impact of governance heterogeneity, including CEO power and corporate internal control (INT) mechanisms.

This study used fixed effect estimation with robust standard errors to examine the relationship between digital transformation and CSR disclosure and the moderating effect of governance heterogeneity among Chinese A-share companies from 2010 to 2020. The whole sample consists of 17,266 firms, including 5,038 state-owned enterprise (SOE) company records and 12,228 non-SOE records. The whole sample data is collected from the China Stock Market and Accounting Research, the Chinese Research Data Services and the WIND databases.

The regression results lead us to three conclusions after classifying the sample into non-SOE and SOE groups. First, Chinese A-share businesses with greater levels of digitalization have lower CSR disclosures. Both SOE and non-SOE are consistent with these findings. Second, increasing CEO authority creates a more centralized company decision-making structure (Breuer et al., 2022; Freire, 2019), which improves the negative association between digitalization and CSR disclosure. These conclusions, however, also apply to non-SOE. Finally, INT reinforces the association between corporate digitization and CSR disclosure, which is especially obvious in SOEs. These findings are robust to alternative HEXUN CSR disclosure index. Heterogeneity analysis shows that the negative relationship between corporate digitalization and CSR disclosures is more pronounced in bigger, highly levered and highly financialized firms.

Digitalization and CSR disclosure are well studied, but few have examined their interactions from a governance heterogeneity perspective in China. Practitioners and policymakers may use these insights to help business owners implement suitable digital policies for firm development from diverse business perspectives.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

This study is devoted to examining the peer effects of engineering enterprises’ internationalization from the viewpoint of industry subdivision and how information and competition alter peer effects. Furthermore, the heterogeneity of peer effects is analyzed based on manager characteristics.

In this study, multiple regression analysis was conducted on a sample of 38 Chinese engineering enterprises listed in the Engineering News-Record’s top 250 international contractors over the period of 2013–2021. This study collected the paired data from the enterprise annual reports and the China Stock Market & Accounting Research database.

The results reveal that (1) there exist peer effects within the subdivided industry of the engineering field; the quality of information disclosure of peer enterprises and degree of market competition moderate the peer effects; (2) the peer effects of internationalization are more pronounced in engineering enterprises with managers who have lower ability, hold greater power or are older.

The findings of this study contribute to understanding the peer effect in the process of internationalization of engineering enterprises, and help enterprises to effectively supervise the irrational behavior of top managers, so as to develop better internationalization strategies.

The results extend peer effects to the subdivision industry of the engineering field. Furthermore, this study also enriches the relevant research on peer effects among enterprises by empirically supporting the moderating role of information and competition as well as analyzing the heterogeneity of the peer effects from the perspective of manager characteristics.

This paper aims to explore the relationship between prudence in risk attitudes and patience of time preference of Chinese apple growers regarding off-farm cold storage of production and marketing in non-harvest seasons. The authors also consider the effect of farmer participation in cooperative-like organizations known as Farm Bases (FBs).

The authors use multiple list methods and elicitation strategies to measure Chinese apple farmers' risk attitudes and time preferences. Because these farmers can either sell their apples immediately to supermarkets or intermediaries or place them in storage, the authors assess correlations between their storage decisions and their preferences regarding risk and time. The authors also differentiate risks involving gains and losses and empirically examine individual risk attitudes in different scenarios.

Marketing decisions are moderately associated with risk attitudes but not time preference. Farmers with memberships in local farmer cooperatives are likely to speculate more in cold storage. Thus, risk aversion behavioral and psychological motives affect farmers' decision-making of cold storage and intertemporal marketing activities. However, membership in cooperatives does not always result in improved income and welfare for farmers.

The research confirms that behavioral factors may strongly drive vulnerable smallholder farmers to speculate into storage even under seasonal and uncertain marketing volatility. There is the need to think deeper about the rationale of promoting cooperatives and other agricultural forms, because imposing these without careful consideration can have negative impacts.

Do risk and time preferences affect the decision of farmers to utilize storage facilities? This question is important because it is not clear if and how risk preferences affect the tradeoff between consuming today and saving for tomorrow, especially for farmers in developing countries.

Exploiting a unique measure of hostile takeover exposure principally based on the staggered adoption of state legislations, the authors investigate how external audit quality is influenced by the discipline of the takeover market. External auditors and the takeover market both function as important instruments of external corporate governance.

The authors execute a standard regression analysis and run a variety of robustness checks to minimize endogeneity, namely, propensity score matching (PSM), entropy balancing, an instrumental-variable analysis, Generalized method of moment (GMM) dynamic panel data analysis and Lewbel's (2012) heteroscedastic identification.

The authors’ immense sample spans half a century, encompassing nearly 180,000 observations and 17 takeover-related state legislations, one of the largest samples in the literature in this area. The authors’ results suggest that firms with more takeover exposure are significantly less likely to use Big N auditors. Therefore, a more active takeover market results in poorer external audit quality, corroborating the substitution hypothesis. The discipline of the takeover market substitutes for the necessity for a high-quality external auditor. Specifically, a rise in takeover susceptibility by one standard deviation lowers the probability of using a Big N auditor by 4.29%.

The authors’ study is the first to examine the effect of the takeover over market on audit quality using a novel measure of hostile takeover susceptibility mainly based on the staggered implementation of state legislation. Because the enactment of state legislation is beyond the control of any firm individually, it is plausibly exogenous. The authors’ results therefore probably reflect a causal influence rather than merely a correlation.

This study aims to examine the impact of corporate governance (CG) on anti-corruption disclosure (A-CD), paying particular attention to the FTSE 100. Notably, it examines how board and audit committees’ characteristics affect the quantity and quality of anti-corruption disclosure.

Data from FTSE 100 firms, spanning the period from 2014 to 2020, were analysed using the regression of the Poisson fixed effect and GEE analyses.

The findings show that gender diversity, audit committee expertise and the independence of the audit committee are positively associated with both quantity and quality of anti-corruption disclosure. Notably, no statistically significant relationships were identified between anti-corruption disclosure and factors such as board size, role duality or board meetings.

The findings provide valuable insights for decision-makers and regulatory bodies, shedding light on the elements that compel UK companies to enhance their anti-corruption disclosure and governance protocols to alleviate corruption and propel efforts towards ethical behaviour.

This study makes a notable contribution to the sparse body of evidence by examining the influence of board and audit committee attributes on anti-corruption disclosure subsequent to the implementation of the UK Bribery Act in 2010. Specifically, to the best of the authors’ knowledge, this study assesses for the first time the impact of board and audit committee mechanisms on both the quantity and quality of anti-corruption disclosure.