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This paper seeks to report a case study that focuses on identifying what may be considered a typical or generic RL process flow as well as the key strategic issues that a firm may use for competitive advantage.

The research involves mainly interviews and plant visits to three companies, all of which manage some RL activities.

Highlighted are what type of RL process flow can be considered as generic, the type of technology innovation and IT a firm needs in order to operate an effective RL system and how these are integrated across the supply chain, the resource commitment (personnel, financial, upper‐level management) that a company needs to make to support successful RL efforts, and finally, the values firms derive from RL and the key performance indicators to measure these values for the RL operations.

A typical returns flow process is provided that can guide managers efficiently on their RL activities. Strategic activities are also presented that characterize successful practices in the RL industry.

A quantitative study sought to better understand responsible leadership (RL) style in the private security sector. The authors analyzed RL’s mediating role in the relationship between rules climate (RC) and employees' perceived organizational support (POS).

The data were collected through an online questionnaire administered to full-time private guards in Ankara (Turkey). A mediation model was proposed, and research hypotheses were tested with structural equation modeling (SEM).

The results indicate a significant positive relationship between the company’s RC and managers' RL, and a positive significant relationship between managers' RL and employees' POS. RL mediates the relationship between company’s RC and employees' POS.

The theoretical framework draws on conservation of resources (COR) theory to consider RL an effective resource. The organization’s RC was a key factor the affecting leader’s responsible behaviors.

This study explores a thriving leadership style, gaining research attention across fields. To the authors' knowledge, no prior research has studied this leadership style in the security sector. The authors use COR to increase understanding of an important antecedent (company’s RC) and consequence (employees' POS) of RL, highlighting this emerging style’s mediating role.

Este artículo de enfoque cuantitativo favorece una mayor comprensión del liderazgo responsable (RL) en el sector de la seguridad privada. Analiza el papel mediador de este estilo de liderazgo en la relación entre un clima de regulación (RC) y la percepción de los trabajadores del apoyo organizacional (POS).

Los datos fueron recolectados a través de un cuestionario online que se administró a guardas de seguridad privados empleados a tiempo completo en la región de Ankara (Turquía). Se propone un modelo de mediación y se comprueban las hipótesis propuestas a través de ecuaciones estructurales con el software (SEM).

Los resultados obtenidos indican una relación positiva y significativa entre el RC y el liderazgo responsable de los superiores, así como una relación también positiva y significativa entre el RL y la POS de los empleados. Se encuentra que el RL media la relación entre el RC y el POS.

Bajo el marco teórico de la Teoría de Conservación de Recursos (COR) se considera que el RL es un recurso altamente efectivo para las organizaciones. El RC de la organización se perfila como un factor clave que afecta los comportamientos del líder responsable.

El diseño transversal del estudio (datos recogidos en un memento del tiempo) y el uso de una muestra de conveniencia son las principales limitaciones del trabajo.

Este estudio explora un estilo de liderazgo aún desconocido, pero con gran potencial, que está ganando fuerza en el ámbito de la investigación científica en los últimos años. Según nuestro conocimiento, se trata del primer estudio que explora este novedoso estilo de liderazgo en el sector de la seguridad privada. El empleo de la Teoría COR favorece la comprensión del RC como un antecedente de este estilo de liderazgo (RL), y también contribuye a comprender la POS de los empleados como clara consecuencia, destacando el papel mediador de este estilo de liderazgo emergente.

This chapter estimates causality properties between real money demand and a number of determinants, that is, real output, the lending rate and the real exchange rate, across 10 Asian economies through linear and nonlinear causality methodologies spanning the period 1990–2012. The results document both bidirectional and unidirectional causality between monetary aggregates (M1 and M2) and their determinants for different country groups. The empirical findings exemplify the role of the demand for money as a policy tool and can provide useful policy recommendations to the Asian monetary authorities in their vision of forming a future monetary union.

This study investigates the relationship between responsible leadership (RL) and employee sustainable performance (ESP), utilizing the CSR theory as a theoretical framework. Furthermore, this study aims to examine the role of ethical climate as a potential mediator in the relationship between RL and ESP.

A sample of 415 employees from the healthcare sector of India was collected through a questionnaire-based survey by using the convenience sampling technique. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) with the help of SmartPLS 4.

The study's findings demonstrated a significant, positive association between RL and ESP [employee well-being (EWB) and employee performance (EP)]. Additionally, the findings show that ethical climate partially mediates the link between RL and ESP (EWB and EP).

The generalizability of the study's data collection is limited because it is based on the responses of Indian healthcare sector employees to an online and offline survey. The authors propose that the healthcare industry implement an intensive leadership training program in light of the findings of this study, which will aid human resource (HR) managers in comprehending the significance of RL and fostering related behaviors, such as encouraging employees to maintain ethical behavior and positive attitudes.

To the authors' understanding, this study is among the earliest attempts to present an integrative model that examines the relationship between RL, ethical climate and ESP in the context of Indian healthcare employees, incorporating the theory of corporate social responsibility (CSR). Moreover, the novelty of this research study examines the relationship between RL and ESP, with an ethical climate serving as a mediator. The focus is specifically on employees working in the Indian healthcare sector.

This study aims to use stewardship theory to investigate the effect of responsible leadership (RL) on organisational citizenship behaviour for the environment (OCBE) through the mediatory role of environmental corporate social responsibility (ECSR).

Data was collected from a sample of 224 individuals employed in the manufacturing sector of Pakistan. The study used partial least squares structural equation modelling to estimate the relationships within the model.

The findings indicate that RL positively impacts OCBE, and ECSR is a partial mediator that mediates the relationship between RL and OCBE.

This research offers novel perspectives for organisational leaders, highlighting the significance of RL quality for instilling OCBE among the employees of manufacturing sectors. Organisational leaders must prioritise ECSR in their CSR initiatives.

This study’s novelty lies in confirming the mediating role of ECSR to drive the positive impact of RL on OCBE through stewardship theory.

This research attempts to explore how reverse logistics (RL) and sustainable supply chain initiatives (SSCI) affect sustainability performance (SP) in the manufacturing industry under the moderating effects of organisational learning capability (OLC). At the same time, this study is expected to allow manufacturers to advance towards a high level of model generation in the green economy.

The data for this study was obtained from 451 manufacturing companies in the Hebei Province, China. This study develops a model and tests all hypothesised relationships using partial least square–structural equation modelling (PLS-SEM) with two-step analytical procedures.

The results indicate that SSCI and RL have a positive relationship with SP, and SSCI is positively related to RL. Moreover, the OLC moderates the relationship between RL and SP as well as the relationship between SSCI and SP.

The findings of the study yield valuable managerial insights on how the effective implementation of green practices, coupled with the utilisation of learning capabilities, can contribute to improving the sustainability of manufacturing firms. The study has certain limitations that suggest potential avenues for future research, the most significant of which is our reliance on data from a single country, which may impede the generalisability of the findings.

This study contributes to the existing literature on SP by considering RL and SSCI and offers a unique theoretical argument that describes the relationships by considering the moderating effect of OLC, which has not been empirically explored.

Despite its potential advantages in a wide range of environmental subjects, green intellectual capital has received scant attention in the field of reverse logistics (RL). This research focuses on exploring the relationship between significant green intellectual capital assets (namely green human capital, green relational capital, and green structural capital) and RL competency. The moderating role of regulatory measures was also hypothesized and tested.

A survey-based research instrument was employed to collect data and partial least square structural equation modeling (PLS-SEM) based approach was utilized to test hypotheses.

The results indicate that green relational capital and green structural capital are positively associated with RL competency. Interestingly, regulatory measures are also found only to moderate the relationship between green human capital and RL competency.

This study extends the present literature by enhancing the knowledge of RL competency, which is a critical ingredient of the circular economy, by revealing the relation with green intellectual capital. Additionally, this study offers insights into the manufacturing industry, especially in emerging economies for academics and practitioners.

The study explores the current research trends within the responsible leadership (RL) domain and proposes a future research agenda by conducting an extensive review of past research. The study aims to understand recent developments in theories, constructs and contexts in RL literature.

Scopus database is used for the data collection on RL and patterns from 1998–2022. In total, 138 articles were covered for a systematic literature review (SLR) of RL behaviors. Further, the search was extended, and 109 more articles were included for bibliometric analysis of RL using R software. In total, 247 papers were reviewed.

The results present the consequences and antecedents of RL behaviors with external and internal stakeholders. Literature also indicates that researchers are more attentive to empirical studies with internal stakeholders, such as responsible leaders' impact on employee outcomes. Among theories, stakeholder theory's normative integrative and instrumental perspectives are used with RL.

The first limitation of the study is that this study collected data only from the Scopus database and the choice of language was English. Future studies may use other databases, languages and keywords. Instrumental and integrative RL behavioral styles would help balance organizations' financial and social goals.

This research enhances the literature on RL by combining content and bibliometric analysis to develop a more systematic and comprehensive understanding of integrative and instrumental leadership behaviors.

This study presents a resource framework for reverse logistics operations based on widespread literature and industry experience. A common and comprehensive definition of resources that affect the implementation of reverse logistics is still patchy in the extant literature. Hence, a complete scheme, such as this, for the categorisation of resources that affect the implementation of reverse logistics is essential. This provides a useful information for business managers and extends reverse logistics research.

This study systematically identified, and validated with the help of reverse logistics experts, the resources that affect the implementation of reverse logistics.

The result is a classification scheme, termed as “reverse logistics resource matrix” (RLRM). This matrix offers a holistic overview of the resources that affect the implementation of reverse logistics for both scholars and practitioners.

To best of the researchers' knowledge, the RLRM is the first attempt to comprehensively structure the resources that affect the implementation of reverse logistics in an integrated framework.

User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User Satisfaction is a function of the actual relevance. This paper extends the authors' previous work by building a reinforcement learning (RL) model to predict the relevance. The Attractiveness, Examination and User Satisfaction are estimated using a limited number of the features of the utilized benchmark data set and then they are incorporated in the construction of an RL agent. The proposed RL model learns to predict the relevance label of documents with respect to a given query more effectively than the baseline RL models for those data sets.

In this paper, User Satisfaction is used as an indication of the relevance level of a query to a document. User Satisfaction itself is estimated through Attractiveness and Examination, and in turn, Attractiveness and Examination are calculated by the random forest algorithm. In this process, only a small subset of top information retrieval (IR) features are used, which are selected based on their mean average precision and normalized discounted cumulative gain values. Based on the authors' observations, the multiplication of the Attractiveness and Examination values of a given query–document pair closely approximates the User Satisfaction and hence the relevance level. Besides, an RL model is designed in such a way that the current state of the RL agent is determined by discretization of the estimated Attractiveness and Examination values. In this way, each query–document pair would be mapped into a specific state based on its Attractiveness and Examination values. Then, based on the reward function, the RL agent would try to choose an action (relevance label) which maximizes the received reward in its current state. Using temporal difference (TD) learning algorithms, such as Q-learning and SARSA, the learning agent gradually learns to identify an appropriate relevance label in each state. The reward that is used in the RL agent is proportional to the difference between the User Satisfaction and the selected action.

Experimental results on MSLR-WEB10K and WCL2R benchmark data sets demonstrate that the proposed algorithm, named as SeaRank, outperforms baseline algorithms. Improvement is more noticeable in top-ranked results, which usually receive more attention from users.

This research provides a mapping from IR features to the CM features and thereafter utilizes these newly generated features to build an RL model. This RL model is proposed with the definition of the states, actions and reward function. By applying TD learning algorithms, such as the Q-learning and SARSA, within several learning episodes, the RL agent would be able to learn how to choose the most appropriate relevance label for a given pair of query–document.