The growing attention paid to industrial tourism can be seen as one of the major trends in cultural tourism and marketing and has given currency to the proposition that customer experience of industrial tourism acts as a direct personal source of information about their perceptions of companies visited and is essential for customer relationship management of companies. This study applies the service theater theory and proposes a model to explore the structural relationships among theatrical elements of industrial tourism (including setting, performance, and actor), the dimensions of customer experience (enjoyment, learning, and escape), and customers’ behavior intentions. A survey of 500 industrial tourists in a transparent factory in the health food industry was conducted in Zhuhai, Guangdong, China. The results of structural equation modeling indicate that two theatrical factors (setting and performance) relate positively to all dimensions of customer experiences. In contrast, the theatrical factor “actor” only relates positively to the learning experience. Furthermore, all dimensions of customer experience, in turn, positively affect customers’ behavioral intentions. This study will be helpful for corporate managers and tourism organizers who aim to develop and implement marketing strategies based on the service theatre theory to improve their services.

This study aims at predicting the interrelationship between among Chat GPT with its six dimensions, tourist’s satisfaction and Chat GPT usage intention as perceived by tourist, and as well as to examine the moderating effect of traditional tour operator services on the relationships between all the variables. Data were collected from 624 tourists. The study hypotheses were tested and the direct and indirect effects between variables were examined using the PLS-SEM. The SEM results showed that Chat GPT’s six dimensions have a positive and significant direct impact on tourist’s satisfaction, and emphasis the moderating role of Traditional Tour Operator Services “TTOS” on the relationship between GPT’s six dimensions and “TS”, and on the relationship between ‘TS” and Chat GPT usage intention. These findings yield valuable insights for everyone interested in the use of IT in the tourism industry, and provide effective strategies for optimizing the use of technological applications by traditional tour operators.

This study explores benefits, barriers and willingness to pay for bike-sharing service in tourism context. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, trends in the barriers and benefits related to bike-sharing service from tourists’ point-of-view were explored. The results show that bike-sharing is appreciated for many reasons, notably for its fun/relaxing, cost saving, ease of city exploration, and promotion of better physical and mental health. However, bike-sharing services are considerably less likely to be viewed as options for faster transportation to a destination or reducing traffic hazards. Notably, eighty-six percent of non-riders indicated contentment with their existing transportation options and a lack of interest in bike-sharing services, a proportion significantly higher than any other group. Predictably, barriers related to the availability of bike-sharing and infrastructure, such as lack of sufficient number of shared bikes, far destination, and poor road conditions were notably more likely to be selected by one-time riders. The results are also evident that a significant portion of tourists is willing to pay to enhance their tourist experience with a bike-sharing service. On average, tourists were willing to pay $0.92 per hour (with a standard deviation of $0.24). This amount reflects the tourists’ recognition of the value added to their mode experience. These findings suggest that bike-sharing service play a significant role in fulfilling an essential transportation niche and have the potential to contribute to enhance tourists’ experience. Efforts aimed at addressing barriers associated with bike-sharing usage could further enhance their contribution to improve tourist satisfaction and boost attraction demand.

Amidst China’s escalating aging population challenge, the efficacy and quality of private elderly care services are garnering increasing scrutiny. This research focuses on evaluating how service quality and customer perceived value influence the loyalty of elderly clients, with customer satisfaction acting as a mediating factor. Grounded in established service quality frameworks and loyalty theories, the study utilizes a quantitative methodology, administering surveys across eight private elderly care institutions in H city, China. A total of 600 surveys were collected, providing a comprehensive data set that encompasses five dimensions of service quality—tangibility, assurance, responsiveness, reliability, and empathy—as well as customer perceived value, satisfaction, and loyalty. Structural Equation Modeling (SEM) was employed to validate the hypothesized relationships. Findings reveal that service quality significantly boosts customer perceived value and satisfaction, which in turn markedly enhance customer loyalty. Notably, customer satisfaction emerged as a crucial mediator between service quality and loyalty, as well as between perceived value and loyalty. This study not only advances theoretical understanding of service quality impacts but also offers actionable insights for enhancing service delivery and customer loyalty in the context of private elderly care.

Consumer satisfaction can be defined as the user’s response to a service or experience compared to the user’s expectations and perceived practical benefits. After reviewing consumer satisfaction models, it can be argued that there is no single model of consumer satisfaction assessment that is suitable for every service and every region of the world, as the causes and outcomes of satisfaction often vary. The research is original in its methodology: at the beginning, a theoretical research model is presented, then hypotheses are formulated, and correlation, factorial, regression analyses were made, which results confirmed hypotheses. The crop insurance system consists of relations between the state institution regulates insurance activities, farmers, insurers and insurance intermediaries. The aim of this article is to identify the factors that determine consumer satisfaction with crop insurance and to assess their impact. The empirical study found that consumer satisfaction is determined by the factors of recognizable value, functional (process) and technical (result) quality, consumer expectations, and image. The most important factors that determine consumer satisfaction of crop insurance are recognizable value, functional quality, and consumer expectations. Consumer satisfaction can be assessed by the cost paid and the quality received, the quality expected, and the consumers’ evaluation of the services. It was found that the socio-demographic elements of consumers do not have a decisive influence on the factors that determine service satisfaction and consumer satisfaction. It is also established that socio-demographic elements of consumers (farmer experience and insurance experience) have direct statistically significant but weak links with consumer satisfaction.

Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.