• Journal of Sport and Applied Science
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• 제4권2호
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• pp.45-51
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• 2020

Purpose: The purpose of this study is to investigate how sports fans' loyalty is built via CSR activities of professional sports teams. Furthermore, the study sought to suggest the model presenting the process of developing loyalty of sport fans by teams' CSR performance. Research design, data, and methodology: For this purpose, a survey was conducted on 450 professional sports fans through the convenience sampling method. A total of 357 of the data were used for the final analysis. Based on the collected data, frequency analysis, reliability analysis, confirmatory factor analysis, and structural equation model analysis were conducted. Results: The results showed that CSR activities contribute to building a positive image of team. Regarding fan identification, team image has also a positive effect on enhancing identification. The finding has supported the notion that attitudinal loyalty is enhanced by fan identification and further attitudinal loyalty significantly influences behavioural loyalty of fans. Conclusions: The results of this study explored the function of CSR of the teams on attitudinal and behavioural outcomes, loyalty. Moreover, the study suggested the constructual model presenting its role on enhancing fans' attitudes and behaviour affecting participation and consumption. Academic and practical implications were discussed for sport marketers and practitioners.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• 아태비즈니스연구
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• 제14권1호
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• pp.411-426
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• 2023

Purpose - The purpose of this study was to explore and diagnose the characteristics and behavioural patterns of rural tourists after COVID-19 using decision tree analysis to classify and identify key segmentation groups. Design/methodology/approach - The CHAID algorithm was used as the analysis technique for the decision tree. The explanatory variables used in the analysis of each decision tree model were demographic variables and rural tourism usage behaviour and perception variables, and the target variables were the preferences of rural tourists' activities after COVID-19. From the Rural Tourism 2020 survey data, 614 samples with rural tourism experience were extracted and used in the analysis. Findings - The variables that significantly explained the preference for each type of rural tourism activity after COVID-19 were rural tourism safety perception, repeated visits to the region, rural tourism priority activity, rural tourism accommodation experience, gender, age group, marital status, occupation, and education level. Among them, rural tourism safety perception was the most important explanatory variable in each analysis model. Research implications or Originality - Overall, to promote rural tourism, it is necessary to enhance the safety image of rural tourism, strengthen loyalty programs for repeat visitors, and develop customized products that reflect the preferred trends of rural tourism.