A Two-Tuple Linguistic Model for the Smart Scenic Spots Evaluation

Introduction

Smart scenic spots play a crucial role in serving tourists and promoting sustainable development in scenic areas; evaluating these spots is essential for the successful planning and development of smart tourism. Although the term smart scenic spot is less common outside China, it has a significant historical background that has captured considerable academic attention regarding the technical advancements and applications of smart technology in scenic areas (Dimitrios Buhalis, 2008; Owaied et al., 2011; Borràs et al., 2014; Taehyee & Namho, 2019). Studies in China have primarily concentrated on the intrinsic concept of smart scenic spots, the development of smart tourism systems, the tourists’ spatial behaviors, and investigations into the willingness of using the smart tourism systems (Dang et al., 2011; Ruan, 2017, Li et al., 2019; Xu & Huang, 2018). However, there is a noticeable lack of research on evaluating the smart level of these sites. Such evaluations aim to identify the factors contributing to the development of smart scenic spots, establish a weighted index system, and calculate corresponding grades. For instance, Tang (2014) developed an index system encompassing management, service, marketing, and support, and employed the analytic hierarchy process (AHP) to assign weights to the indices. Through a multi-factor comprehensive evaluation method, Tang conducted an empirical study on Nanjing Zhongshan Mausoleum (Tang, 2014). Similarly, Li and Shi (2017) constructed an index system for Lanzhou smart scenic spot, considering dimensions like environmental monitoring, intelligent security, energy management, traffic management, scenic spots public release platform, and intelligent management service. They used the analytic hierarchy process to weigh the indices, established an evaluation model, and proposed policy suggestions for the Lanzhou smart scenic spot (Li & Shi., 2017). Pan (2018) and Chen et al. (2019) developed a concise evaluation system that includes infrastructure, service smartness, marketing smartness, and management smartness, the CRITIC and AHP methods are employed to determine index weights and extended the application to evaluate the smart level of scenic spots above 4A in Jiangsu province. Moreover, Guo et al. (2022) utilized the entropy method to assess navigation, guide, and shopping in China’s first and second batches of smart scenic cities above 3A, providing a crucial evaluation of their smart level.

Many researchers have treated the evaluation of smart level and level of smart scenic spots as interchangeable problems to solve. In this context, the comprehensive evaluation value of the smart scenic spots is obtained, and the associated smart level is determined by comparing the value against a predefined threshold. However, it is important to recognize that, conceptually and methodologically, the ranking evaluation of smart scenic spots differs from the ranking evaluation of the degree of smart. To address this issue and enhance the quality and credibility of evaluation on the smart level of scenic spots, this paper clearly defines the smart level evaluation on scenic spots. Additionally, a general model for conducting an evaluation on the smart level is developed with a rational, mathematical methodology. By differentiating the two types of evaluations, this approach aims to refine the assessment process and ensure accurate results for evaluating the smart level of scenic spots.