Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.

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The rapid urbanization and industrial development have resulted in water contamination and water quality deterioration at an alarming rate, deeming its quick, inexpensive and accurate detection imperative. Conventional methods to measure water quality are lengthy, expensive and inefficient, including the manual analysis process carried out in a laboratory. The research work in this paper focuses on the problem from various perspectives, including the traditional methods of determining water quality to gain insight into the problem and the analysis of state-of-the-art technologies, including Internet of Things (IoT) and machine learning techniques to address water quality. After analyzing the currently available solutions, this paper proposes an IoT-based low-cost system employing machine learning techniques to monitor water quality in real time, analyze water quality trends and detect anomalous events such as intentional contamination of water.

Purposecurrent study intends to examine key human resources practices (human capital, training and rewards) that influence employee commitment and service recovery performance (SRP) of Takaful industry agents in Southeast Asian region. The Takaful industry is facing stiff competition with conventional insurance industry in Malaysia and Indonesia as the Southeast Asian region has a large Muslim population. SRP is crucial in insurance industry specifically in the Islamic Insurance (Takaful) industry and plays a vital and key role in sustainable competitive advantage for value addition for firms in future to acquire market.Design/methodology/approachThe data was collected from 350 front line agents of the Takaful industry operating in Malaysia and Indonesia on convenience sampling technique. Data was analyzed by using PLS-SEM to examine the relationship between constructs.FindingsThe results show that human capital, training and reward significantly influence commitment of employee which further influenced SRP to be improved. Mediation effect was also found to be influential and statistically positive and significant by employee commitment between key HR practices (human capital, training, rewards) and SRP.Originality/valuecurrent study contributed to the body of knowledge in explaining relationship of human capital to employee commitment and SRP, further, inconclusive findings between training and rewards was also explained in the Takaful industry of the Southeast Asian region.

The purpose of this article is to examine the relationship between developmental human resource (HR) practices and work engagement by focusing on the moderating role of service climate. Specifically, employee training opportunities, career developmental opportunities, and developmental performance appraisal were cast as the key dimensions of developmental HR practices. We used cross-sectional data with survey from 277 employees in six large banks in Pakistan. The results suggest that each of the dimensions of developmental HR practices was positively related to work engagement. Also, service climate was found to moderate the relationship between training opportunities and work engagement. Similarly, results showed that service climate moderated relationship between career developmental opportunities and work engagement. Regarding the practical implications, results suggest that policymakers should consider enriching HR factors by providing supportive environment, feedback and service climate to enhance employee engagement. In terms of originality, we contended that, to date, there is paucity of empirical study linking developmental HR practices to employees’ work engagement. Hence, the present study addressed this gap by examining the relationship between developmental HR practices and work engagement, as well as the boundary condition on these relationships.

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Purpose The purpose of this paper is to investigate the perceptions of faculty members about the influence of family motivation on their self-efficacy and organizational citizenship behavior-individual (OCBI). Design/methodology/approach The proposed model was tested on a sample of 353 faculty members from different public and private universities of Pakistan. Partial least squares structural equation modeling was used to analyze data. Findings Surprisingly, results reveal that family motivation was not positively related to faculty members’ OCBI; instead, this relationship is fully mediated by self-efficacy. The findings suggest that it is employees’ self-efficacy belief through which their family motivation translates to their increased OCBI. This study also finds that supporting the family is a powerful source of motivation to work, offering meaningful practical and theoretical implications for policy-makers, leaders, managers and researchers on the new dynamics of work and family engagements. Originality/value The study contributes to human resource management (HRM) and organizational behavior (OB) literatures by providing some useful practical implications for managers and HRM and OB consultants who are interested in understanding the underlying psychological mechanisms (i.e. self-efficacy) through which employees’ family motivation results in the increased OCBI.

Abstract:The ground aim of this study was