Human resource management practices and total quality management in insurance companies: Evidence from Jordan

This study evaluated the relationships among variables in electronic customer relationship management (e-CRM) success. The purpose of this paper is to examine the effect of technological readiness, privacy, COVID-19, customer pressure, trust, level of service quality, and customer satisfaction. Quantitative research methods were applied in examining the causal associations among the primary variables of the study. We used a sample approach called purposive sampling. Data was obtained from 390 completed questionnaires collected from employees, who took part in CRM technology activities, working in Jordanian firms. Data was analyzed via AMOS software version 22, and hypotheses were examined by applying structural equation modelling (SEM). Results revealed that technological readiness, COVID-19, customer pressure, and customer satisfactions had a positive effect on e-CRM success system. Moreover, the study showed that technological readiness, privacy, and level of service quality positively affected customer satisfaction. Additionally, the mediation of customer satisfaction on the association between trust and success of e-CRM system, and also between level of service quality and e-CRM system success, was demonstrated. Practical implications: In practice, this study can help managers and practitioners in implementing e-CRM systems. Moreover, it can drive further empirical studies on the relations among technological readiness, privacy, COVID-19, customer pressure, trust, and level of service quality, besides customer satisfaction. Only few studies had looked into the variables in the success of e-CRM system, in emerging industries of developing countries, specifically, among technological readiness, privacy, COVID-19, customer pressure, trust, level of service quality, and customer satisfaction. These gaps were addressed in this study. The current model is also relatively constrained, so it may be expanded in further studies. For instance, the construct of culture might be incorporated to the model to make it more thorough in its examination of the topic at issue.

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“…Success of E-CRM System [132][133][134][135][136][137] E-CRM1 "Complaints (suggestive) encourages sharing of problems by customers".…”

Section: Cs3mentioning

confidence: 99%

Evaluating Electronic Customer Relationship Management System Success: The Mediating Role of Customer Satisfaction

et al. 2022

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“… It can better serve social development. To meet the requirements of the continuous progress of China's democratization, it is necessary to establish a democratic decision-making support system based on an extensive analysis of public opinion [ 12 , 13 ]. …”

Section: Economic Management Information System and Data Resource All...mentioning

confidence: 99%

Application of Data Resource Allocation in Economic Management Information System

Guan

Chen

2022

Computational Intelligence and Neuroscience

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With the development of the digital economy and the sharp increase in economic information, a large amount of data needs to be processed. Therefore, the demand for an economic management information system is also increasing, but how to realize the allocation of data resources in the system has become an urgent problem to be solved. This study conducts research on the above problems, first analyzes the research results of domestic and foreign scholars, finds out the content suitable for this research, and then introduces the structure of the economic management information system and the method of data resource allocation in detail. Finally, by comparing and testing the algorithm of data resource allocation and other allocation algorithms, it is concluded that the allocation algorithm used in this study is more effective than the research algorithm of previous scholars, thus proving the feasibility of this research. The test results show that the number of protocol function modules allocated by the GAA algorithm is 20% to 30% less than that of the RAA algorithm. So, it can be concluded that compared with the RAA algorithm, the GAA algorithm saves more network resources.

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“…In the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study such as chemical engineering [29][30][31][32][33][34][35][36][37], electrical engineering [38][39][40][41][42][43][44][45][46], biomedical engineering [47][48][49][50][51][52][53][54], civil engineering [55][56][57][58], social sciences [59][60][61][62][63][64][65][66][67][68][69], mechanical engineering [70][71][72][73][74][75][76]…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

et al. 2021

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Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

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Human resource management practices and total quality management in insurance companies: Evidence from Jordan

Cited by 5 publications

References 46 publications

Evaluating Electronic Customer Relationship Management System Success: The Mediating Role of Customer Satisfaction

Evaluating Electronic Customer Relationship Management System Success: The Mediating Role of Customer Satisfaction

Application of Data Resource Allocation in Economic Management Information System

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

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