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A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

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 investigate the effects of peers on corporate payout policies in one of the largest emerging markets – India. It also examines the motives for mimicking payout decisions.

The sample is composed of 3,024 non-financial and non-government firms listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for the period 1995 to 2020. To encounter the endogeneity problem, the instrumental variable technique based on peer firms' idiosyncratic risk is used to estimate the effects of peers on firms' payout policy. To define peer reference groups, the authors use the basic industry classification of the firms.

The results indicate a significant positive impact of peers on firms' dividend policies in India. A firm with all dividend-paying peers is more likely to declare dividends than the one with no dividend-paying peers. Further, peer effects are found to be more pronounced amongst larger and older firms, thus supporting the rivalry theory of mimicking.

To the best of the authors' knowledge, the present study is the first of its kind that attempts to understand peer effects on payout decisions in an emerging market India, that offers a unique institutional setting. Moreover, the authors extend the existing literature by investigating the peer effects on a firm's payout policies considering various firm-level characteristics, such as growth opportunity, cash holding, financial constraint and profitability, which previous studies have not taken into consideration. These results provide additional insights into the heterogeneity and motives behind peer effects.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

The mounting focus on environmental, social and governance (ESG) factors in business has sparked substantial curiosity in understanding the nexus between ESG and the companies’ strategic decisions. This study aims to investigate the influence of firms’ ESG disclosure scores on their dividend payout. Furthermore, it examines the nuanced dynamics of this relationship by exploring the moderating role of the country’s investor protection regulations and regulatory enforcement.

This study uses pooled ordinary least square regression with year, industry and country effects. It analyzes a balanced panel data set of 192 non-financial firms drawn from the primary equity indices of BRICS nations. This study examined the data of six years spanning 2015–2020.

The findings discover a significantly positive relationship between ESG scores and dividend payout ratio, conveying that firms with higher ESG scores allocate more of their profits as dividends. Furthermore, the finding reveals that country-level robust investor protection and effective regulatory enforcement mechanisms undermine the positive association between ESG ratings and payouts of dividends, suggesting that the ESG disclosure of firms operating in a setting characterized by enhanced investor safeguards and stricter regulatory oversight will exert less influence on their dividend decisions.

To the best of the authors’ knowledge, this is the first study to concentrate on the ESG–dividend nexus in the BRICS countries. Furthermore, this study used each country’s investor protection index and regulatory enforcement scores to comprehend the influence of country-level legal frameworks in shaping the relationship between ESG and dividend decisions, thus adding value to the existing literature on corporate sustainability.

This study aims to investigate how organizational culture (OC) and transformational leadership (TL) affect corporate social responsibility (CSR) performance (environmental performance and social performance) and financial performance (FP) in the context of the Italian manufacturing sector. Grounded in resource-based view theory, this study explores how these factors influence sustainable firm performance.

Data gathered from 260 employees were analyzed to examine the multidimensional aspects of CSR, encompassing social and environmental sustainability.

The findings highlight the pressing need for sustainable firm performance in the existing environment, supporting the hypothesis that firms achieve sustainable and FP through the recognition of TL and OC. Moreover, a positive and significant relationship between CSR performance and FP was established, underscoring the strategic importance of integrating CSR initiatives into core business practices. This study offers valuable insights for both academia and firms, providing theoretical and practical implications that underscore the importance of cultivating a robust OC to drive performance enhancements.

This study is novel because it is one of the first, to the best of the author’s knowledge, to analyze the relationships between TL, OC and performance components associated with CSR.

Financial inclusion in Peru has been addressed through coverage, quality of financial services, movement of transactions, and service points. The purpose of this chapter is to evaluate for the department of Ancash, Peru, the link between financial inclusion and its socioeconomic factors. Socioeconomic variables and financial inclusion of the Ancash department of the National Household Survey are taken as indicators, later contrasted through the logit model, with the financial inclusion variable being the explained variable.

There is evidence of positive and negative relationships between financial inclusion and socioeconomic variables; these are important components for planning financial inclusion. Raising the levels of formal employment, the educational level and considering the area of residence would be a strategy to generate a dynamic of inclusion in the department of Ancash.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

Innovative work behaviour (IWB) has long been advocated as a crucial resource for organisations. Evidence that work characteristics stimulate the adoption of IWB is widespread. Yet, the relationship between knowledge characteristics and IWB has often been overlooked. This study aims to address this gap by examining this relationship.

Building on an integrative vision of innovation, this study analyses the effects of combinations in work characteristics on IWB through a configurational approach. Job autonomy, complexity, problem solving, specialisation and demand for constant learning were examined as determinants of IWB using fuzzy-set qualitative comparative analysis.

Based on a sample of 214 Belgium employees, the results highlight seven configurations of work characteristics to elicit high levels of IWB. For six of them, problem solving appears as a needed condition.

Presented findings offer insights for organisations aiming at evolving in a competitive context to generate optimal conditions for promoting employee innovation.

While most studies have tested the influence of work characteristics independently, this research investigates the joint influence of work characteristics and identifies how combinations of multiple variables lead to IWB.