Search results

The purpose of this study was to determine how environmentally specific transformational leadership (ETFL), green training (GT) and psychological green climate (PGC) affect environmental passion (EP) of employees which leads to pro-environmental behaviors (PEBs).

The context of the study was set in textile industry of Pakistan which holds a promising future in the international markets as an emerging export-based industry. Using purposive sampling technique, cross-sectional self-report data were collected from the employees of textile exporting firms (n = 239) of Pakistan. Hypotheses were tested using PLS-SEM.

The findings revealed that ETFL and PGC act as important predictors of EP, whereas GT plays no role in influencing EP. EP predicts green behavioral intention (GBI) of employees, in turn, GBI predicts employees' PEBs. Further, mediation analyses revealed that EP and GBI sequentially mediated the link between PGC and PEBs.

The present study is not free from its limitations. First, the study adopted cross-sectional design that prevents the causal inferences which the researcher can make from the population. Second, the present study adopted purposive sampling technique which is a non-probability sampling technique. Third, the constructs of this study were assessed with self-report measures which are associated with social desirability bias (Podsakoff and Organ, 1986) or common-method variance (Podsakoff et al., 2003). Hence, this research suggests on the future direction of research based on these limitations.

As per the results of this study, it is also suggested that managers may focus more on the overall organizational and psychological climate rather than on leadership styles alone. While conducting training, the leaders must pay close attention to training objectives to ensure that they do not lead to counterproductive behaviors.

This research adds to the literature in the area of PEBs at micro-level by focusing on how and why employees engage in PEBs.

This study aims to systematically review the existing literature and develop an understanding of work-life balance (WLB) and its relationship with other forms of work-related behavior and unearth research gaps to recommend future research possibilities and priorities.

The current study attempts to make a detailed survey of the research work done by the pioneers in the domain WLB and its related aspects. A total of 99 research work has been included in this systematic review. The research works have been classified based on the year of publication, geographical distribution, the methodology used and the sector. The various concepts and components that have made significant contributions, factors that influence WLB, importance and implications are discussed.

The paper points to the research gaps and scope for future research in the area of WLB.

The current study uncovered the research gaps regarding the systematic review and classifications based on demography, year of publication, the research method used and sector being studied.

Employees as citizens and companies as part of our society have to deal with the implications of grand challenges such as the global climate change or the COVID-19 pandemic in this turbulent twenty-first century. Internal versus external Corporate Social Responsibility (CSR) acquires an integrative element of sustainable business strategies. Human Resource Management (HRM) contributes by defining modern internal Workplace CSR concepts for supporting sustainable business strategies. The focus of the sustainable HRM strategy is the employees as the key asset of companies, applying especially to those who are particularly talented as future leadership successors and CSR ambassadors. On the basis of the current Green HRM and Workplace CSR review, theoretical and practical implications are concluded to foster Workplace CSR strategy as part of a modern working culture and an integrative HRM frame. The COVID-19 pandemic as an accelerator of Green and social transformation is also discussed in the context of this sustainable HRM framework.

This chapter aims to contribute to the literature on sustainability in the public sector by discussing how human resource and human resource management can help to integrate environmental management into organizations and improve environmental performance. Public sector scholars have neglected the study of Green Human Resource Management (GHRM) until now. Nevertheless, implementing such practices could lead to positive outcomes regarding awareness of environmental issues, organizational reputation and attractiveness, job satisfaction and organizational performance. The authors discuss the relevance and the necessity of developing a field of research on GHRM in public organizations. Starting from a conceptual review of the main literature on GHRM, this chapter provided some directions for future research.

This research offered a systematic and comprehensive literature review in analysing current studies on employee ecological behaviour (EEB) strategies and settings to determine various emphasised workplace ecological behaviour areas and contribute a precise mapping for future research.

This systematic literature review method involved 106 peer-reviewed articles published in reputable academic journals (between 2000 and the first quarter of 2021). This study was confined to a review of empirical papers derived from digital databases encompassing the terms ‘Employee green behaviour’, ‘Green behaviour at workplace’, ‘Employee ecological behaviour’, ‘Employee Pro-environmental behaviour’ and ‘Pro-environmental behaviour at workplace’ in the titles.

This study identified relevant journal articles (classified as EEB at work) from the current body of knowledge. Notably, much emphasis was identified on EEB over the past two decades. Overall, most studies employing quantitative approaches in both developed and emerging nations. Notably, ecological behaviour application garnered the most significant attention from scholars among the four focus areas in the literature review: (i) EEB concepts, models, or reviews, (ii) EEB application, (iii) EEB determinants and (iv) EEB outcomes.

Significant literature gaps indicate this field to be a relatively novel phenomenon. Thus, rigorous research on the topic proves necessary to develop a holistic understanding of the subject area.

This study expands the current body of knowledge by providing the first comprehensive systematic review on EEB themes, methods, applications, determinants, contextual focus, outcomes and recommending future research agenda.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.

This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

Despite the growing interest regarding companies' sustainability, its social dimension has mostly been neglected by academics and practitioners. Consequently, this study aims to address this issue by investigating if the adoption of human resource (HR) analytics can positively influence the impact of social sustainable operations practices (SSOP) on employees' motivation and engagement and the effect of these lasts on organizational retention.

Data were collected through online questionnaires addressed to 281 HR managers of heterogeneous companies from Europe and analyzed through a structural equation modeling (SEM) technique.

The findings confirmed the positive effect of SSOP on employees’ motivation and engagement, and of these last on employees’ retention. Furthermore, they confirmed that the usage of HR analytics positively moderates the relationship between SSOP and employees’ motivation and engagement.

This study contributes to both sustainable operations management and HR management literature streams. First, it adopts a multidisciplinary perspective which also considers evidence from HR management literature, allowing the authors to concentrate on the social dimension of sustainability. Second, it provided further insight regarding the adoption of a data-driven approach in relation to social sustainable operations management. Finally, it contributes to HR analytics-related literature by demonstrating its impact also on organizational aspects that are not directly controlled by the HR department.