Data Envelopment Analyses
Mohammad Alimoradi; Seyed Mohammad Ali Khatami Firouzabadi,; Maghsoud Amiri; Iman Raeesi Vanani
Abstract
Evaluation of performance and productivity and the process of its changes over time is considered essential in the process of improvement in organizations, production, industrial and service units. Regarding the estimation of productivity and productivity changes over time, different models, methods, ...
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Evaluation of performance and productivity and the process of its changes over time is considered essential in the process of improvement in organizations, production, industrial and service units. Regarding the estimation of productivity and productivity changes over time, different models, methods, approaches and indicators have been extracted and presented in various studies, one of the most important, common and widely used of them is the Malmquist Productivity Index. On the other hand, during production processes, undesirable products and outputs are produced simultaneously with desirable products. Therefore, with the development and expansion of the Malmquist Productivity Index, an index called the Malmquist Luenberger was introduced, which took into account the undesirable outputs while seeking to reduce them, and at the same time aimed at expanding the desirable outputs. Therefore, the current research and review paper, based on the PRISMA methodology, with the aim of a structured and systematic analysis of articles and researches on this subject. This research is of a review type that is conducted through searching in domestic and foreign databases such as Academic Jihad Scientific Information, Scopus, Science Direct, Elsevier and Google Scholar in the period from 1980 to 2022 with the approach of selecting the most important, most valid and prominent papers and Internal and external scientific and applied research has been carried out. The results of the current research show the existing scientific gaps and future suggestions regarding productivity evaluation using the Malmquist/Malmquist Luenberger Productivity Index.
Decision based on Neural Networks/ Deep Learning
Mohamad Ali Khatami Firoz Abadi; Mona Jahangir Zade; Amir Mazyaki; Seyed Soheil Fazeli
Abstract
Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have ...
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Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have more market share, and more money may return on investment. This article's main aim is to identify the factors affecting customer loyalty in insurance companies.Methodology: This research was quantitative, analytical-descriptive. In gathering information, Data was collected through the survey, and the findings are practical. In this way, two methods, Confirmatory Factor Analysis (CFA) and Artificial Neural Networks (ANN) were used. For localizing the factors extracted from other similar prior literature, first, the elements were examined by CFA with SMART PLS application due to some conflicts in the literature to evaluate whether each factor affects customer loyalty or not. Then, the elements were introduced to the ANN for training by this program.Findings: In this article, by using the MORGAN table, the sample size detected 384 people in 0.05 error. Questionnaires were distributed randomly between four Iranian insurance companies, ASIA insurance company, ALBORZ insurance company, and PARSIAN insurance company. Based on Confirmatory Factor Analysis, elements of commitment, perceived quality, trust, perceived value, empathy, brand image, the attraction of other alternatives, and customer satisfaction impact the customer loyalty of insurers in these companies. The cost of change, nevertheless, did not have a significant effect on customer loyalty. Then, the factors used as inputs for the multi-layer perceptron training also customer loyalty are indicated as output. The model was designed with eight inputs, 110 nodes in the hidden layer, and one output the error was E= 0.00992 and the regression = 0.98684.Originality/Value: the finding of this research is, expanding a model for predicting customer loyalty in Iranian insurance companies.