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In the context of cittaslow (slow city), this research attempts to examine the effects of memorable tourism experience (MTE) on tourist satisfaction and revisiting intention. The research used a structured questionnaire and collected 605 valid responses from tourists that visited Sanyi Cittaslow. Analyses of the measurement model and structural model by AMOS 22.0 were used to find the associations between each MTE dimension and satisfaction, and revisit intention. Test results revealed that all MTE dimensions are predictors of tourist satisfaction, among which knowledge and involvement are the strongest predictors; satisfaction is the strongest predictor for revisiting and is the mediator for the associations of most MTE dimensions and revisiting. Since the cittaslow movement was closely related to sustainable tourism, research finding from this research provide a good reference with evidence for the promotion of cittaslow through MTE creation.

Partial Least Squares Structural Equation Modeling has gained considerable attention across diverse academic fields, including business, social sciences, marketing, and management. A key challenge in utilizing PLS-SEM is balancing explanatory and predictive power when selecting the most suitable model from competing alternatives. This paper explores the effectiveness of various quality criteria for evaluating causal-predictive models, with a focus on resistance to change in Vietnamese SMEs. The study emphasizes the importance of both in-sample and out-of-sample predictions, using metrics such as R², BIC, AIC, Q², RMSE, MAE, and CVPAT. The findings reveal that traditional criteria like R² may not be sufficient for identifying the best model, while PLSpredict, CVPAT, BIC, and AIC offer superior performance in determining the optimal balance between explanatory and predictive capabilities. These insights provide practical implications for researchers and practitioners, highlighting the need to tailor model selection to specific objectives, such as theory development or real-world forecasting. For practitioners, the study underscores the benefits of leveraging simpler, more generalizable models for robust decision-making in dynamic or resource-constrained environments.

The Cultural and Creative Industries (CCI) thrive on innovation and creativity, necessitating robust cross-domain collaboration among diverse stakeholders. However, the current state of CCI co-creation mechanisms is marked by inadequacies. The primary objective is to provide practical and effective strategic guidance for co-creation endeavors within the dynamic CCI landscape. This study employs the Root Cause Analysis (RCA) method to identify and analyze critical elements for industrial co-creation, offering a comprehensive framework for understanding the intricacies of co-creation processes. The Cultural and Creative Industries Digital Business Ecosystem (CCI-DBE) model is then developed to encompass four foundational pillars: participation of multiple stakeholders, mechanism of co-creation, uniqueness of consumer experience, and output of co-created value. Participation of multiple stakeholders recognizes the importance of integrating perspectives from content creators, other companies, suppliers, and customers. The mechanism of co-creation delves into the necessity of a robust platform, supported by technological infrastructure, to facilitate seamless resource and knowledge exchange. The uniqueness of consumer experience emphasizes the need for tailoring offerings to meet consumers' unique experiential and meaningful needs. Output of co-created value focuses on enhancing product novelty, resource sharing, communication efficiency, information interaction, and cross-domain integration. This CCI-DBE model not only contributes to academic discourse but also provides actionable insights for practitioners seeking to navigate and enhance the co-creation process within the evolving CCI landscape. It stands as a valuable guide for fostering innovation, collaboration, and strategic alignment in the CCI sector.

Partial Least Squares Structural Equation Modeling has gained considerable attention across diverse academic fields, including business, social sciences, marketing, and management. A key challenge in utilizing PLS-SEM is balancing explanatory and predictive power when selecting the most suitable model from competing alternatives. This paper explores the effectiveness of various quality criteria for evaluating causal-predictive models, with a focus on resistance to change in Vietnamese SMEs. The study emphasizes the importance of both in-sample and out-of-sample predictions, using metrics such as R², BIC, AIC, Q², RMSE, MAE, and CVPAT. The findings reveal that traditional criteria like R² may not be sufficient for identifying the best model, while PLSpredict, CVPAT, BIC, and AIC offer superior performance in determining the optimal balance between explanatory and predictive capabilities. These insights provide practical implications for researchers and practitioners, highlighting the need to tailor model selection to specific objectives, such as theory development or real-world forecasting. For practitioners, the study underscores the benefits of leveraging simpler, more generalizable models for robust decision-making in dynamic or resource-constrained environments.

The Cultural and Creative Industries (CCI) thrive on innovation and creativity, necessitating robust cross-domain collaboration among diverse stakeholders. However, the current state of CCI co-creation mechanisms is marked by inadequacies. The primary objective is to provide practical and effective strategic guidance for co-creation endeavors within the dynamic CCI landscape. This study employs the Root Cause Analysis (RCA) method to identify and analyze critical elements for industrial co-creation, offering a comprehensive framework for understanding the intricacies of co-creation processes. The Cultural and Creative Industries Digital Business Ecosystem (CCI-DBE) model is then developed to encompass four foundational pillars: participation of multiple stakeholders, mechanism of co-creation, uniqueness of consumer experience, and output of co-created value. Participation of multiple stakeholders recognizes the importance of integrating perspectives from content creators, other companies, suppliers, and customers. The mechanism of co-creation delves into the necessity of a robust platform, supported by technological infrastructure, to facilitate seamless resource and knowledge exchange. The uniqueness of consumer experience emphasizes the need for tailoring offerings to meet consumers' unique experiential and meaningful needs. Output of co-created value focuses on enhancing product novelty, resource sharing, communication efficiency, information interaction, and cross-domain integration. This CCI-DBE model not only contributes to academic discourse but also provides actionable insights for practitioners seeking to navigate and enhance the co-creation process within the evolving CCI landscape. It stands as a valuable guide for fostering innovation, collaboration, and strategic alignment in the CCI sector.

In the context of cittaslow (slow city), this research attempts to examine the effects of memorable tourism experience (MTE) on tourist satisfaction and revisiting intention. The research used a structured questionnaire and collected 605 valid responses from tourists that visited Sanyi Cittaslow. Analyses of the measurement model and structural model by AMOS 22.0 were used to find the associations between each MTE dimension and satisfaction, and revisit intention. Test results revealed that all MTE dimensions are predictors of tourist satisfaction, among which knowledge and involvement are the strongest predictors; satisfaction is the strongest predictor for revisiting and is the mediator for the associations of most MTE dimensions and revisiting. Since the cittaslow movement was closely related to sustainable tourism, research finding from this research provide a good reference with evidence for the promotion of cittaslow through MTE creation.