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This study explored how organizational leaders at different hierarchical levels may communicatively enhance employees' health and well-being. Drawing on interdisciplinary research, it proposed a model that connects health-oriented leadership communication at supervisory and executive levels with remote workers' self-care and stress levels during the COVID-19 pandemic.

Data collected through a survey of 363 full-time United States (US) employees were analyzed to test the model.

Results showed health-oriented communication at the two leadership levels directly influenced employees' self-care, which in turn reduced their stress levels. Further, executive leaders' health-oriented leadership communication indirectly impacted remote workers' self-care through its positive association with supervisors' health-oriented leadership communication.

This study offers much-needed guidelines for executive leaders, supervisors and communication practitioners seeking to meet employees' growing expectations for a healthy work environment in today's post-pandemic era.

Although the literature has established organizational leadership as a vital determinant for a healthy workforce, few studies have explored leaders' health-specific communication to enhance employee health. This study is the first to conceptualize health-oriented leadership communication at dual hierarchical levels and uncover its influence on employees. The results suggested the importance of health-oriented leadership communication across hierarchical levels in building a healthy workplace.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

In this study, to empirically test the relationship between ethical organizational climate, inner life (IL) and life satisfaction (LS) of employees, a field study was conducted on white-collar personnel working in the service sector in the Istanbul region. The main purpose was to extract an approach that could be applied to simultaneously boost LS and customer orientation for effective service delivery by organizations.

A two-wave time-lagged survey design was used to collect the data over a period of three months. Two sets of self-administrated survey questionnaires were developed for both waves, containing the details of the study and items for measuring variables. The questionnaires were developed in such a manner that the anonymity of the respondents and ethical considerations remained intact. In the first wave, data were collected for two variables, i.e. organizational ethical climate and IL. The measurement scale for organizational ethical climate was adapted from the study of DeBode et al. (2013) and for IL from the study of Fry et al. (2017). In the second wave, data on the remaining two variables (LS and organizational customer orientation) were collected. Direct effects and indirect effects in the hypotheses were tested by structural equation modeling (SEM).

This study has found that the organizational ethical climate strengthens the inner lives of employees, which is vital for the organizations from two angles: one, strong IL of an employee enhances his/her own LS and two, stronger IL accentuates customer orientation.

The fact that the context of this study is limited to Turkey and that the participants are selected from among white-collar personnel working in the service sector reduces the representativeness of the research result. In this sense, in the next stages, the model of the research can be retested in different industries or cross-cultural studies can be designed by comparing the study results with samples from different geographies, so that the validity of these relations for different cultures can be seen.

The implications of this study revealed that employees will enjoy their lives more when authorities in organizations adopt organizational policies supporting the inner lives of employees, feel respect for their private areas and make the organizational climate more ethical. Hence, with practices such as workplace spirituality or spiritual leadership that support the inner lives of employees, the motivation and satisfaction of employees can be increased.

This study revealed that inner life strength makes people comparative more ethical in their dealings, which gives them a sense of achievement and enhances work meaningfulness, boosting LS and customer-orientation. The findings of this study are vital for leaders, as they can achieve a conjoint elevation of the LS of their employees and enhance customer orientation for higher organizational performance.

This study is original in emphasizing the positive effect of spiritually powerful inner-life customer-orientedness in employees with empirical proof.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

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.