Navigating the Efficiency Landscape: A Data Envelopment Analysis of Tourist Resorts in Jiangsu Province for Optimized Socio-Economic Benefits

Abstract

Tourist resorts stand out as a focal point in the academic discourse on tourism, garnering significant attention within the tourism academic community. Assessing the efficiency of these resorts serves as a crucial tool for steering their management strategies, optimizing resource allocation, and contributing to regional economic development. This study centers on tourist resorts in Jiangsu Province, employing the data envelopment analysis method to gauge their tourism efficiency. The research delves into the impact of decomposing the efficiency of tourist resorts and investigates the spatiotemporal dynamic patterns of various efficiencies. Key findings indicate that: (1) The overall tourism efficiency of tourist resorts in Jiangsu Province registers as low, with an average of only 0.119, signaling ample room for improvement towards optimal levels. Among different efficiencies, scale efficiency exhibits the highest average value, followed by pure technical efficiency, with comprehensive efficiency ranking the lowest. (2) The comprehensive efficiency of tourist resorts in Jiangsu Province is influenced by the combined effects of various decomposition efficiencies. Notably, pure technical efficiency plays a more substantial role in overall efficiency compared to scale efficiency. (3) Spatial differentiation in efficiency values is evident among tourist resorts in Jiangsu Province. High-efficiency areas, particularly the southern Jiangsu region, display concentrated clusters, emphasizing a pronounced agglomeration of scale efficiency. In contrast, the central and northern regions of Jiangsu witness a rising number of tourist resorts demonstrating pure technical efficiency and high overall efficiency. (4) Over the research period, the focus of various efficiency factors in tourist resorts shifted towards the north, albeit without significant deviation. Simultaneously, the standard deviation ellipse area of various efficiencies exhibits a general trend of expansion. Drawing from these research outcomes, the article recommends practical measures such as enhancing the diversity of vacation resort services, establishing interactive mechanisms, and attracting management talent. These suggestions aim to provide actionable guidance for the development of tourist resorts, contributing to their sustained growth and success.

1. Introduction

The tourism industry’s pivotal role in stimulating consumption and driving economic growth persists, displaying characteristics of diverse demand entities and profound product demand [1]. To enhance competitiveness in an increasingly diverse market, researchers globally are focusing on improving tourism efficiency [2,3,4,5].

Tourism efficiency serves as a crucial indicator characterizing the utilization and effectiveness of tourism resources [6]. It aims to maximize output per unit factor input within a specific timeframe during tourism industry development, allowing stakeholders to maximize total value [7]. Currently, research on tourism efficiency primarily encompasses hotels [8,9,10], scenic areas [11,12], and tourism transportation [13,14], and extends to city and provincial levels as attention shifts towards sustainable tourism development [15,16]. With increasing focus on the ecological impact of tourist destinations, the concept of tourism ecological efficiency has gained prominence in academic exploration [17,18,19].

A tourist resort is defined as a relatively self-sufficient destination offering a diverse array of tourism facilities and services to cater to the entertainment and relaxation needs of visitors [20]. These destinations are designed to provide a comprehensive luxury or experiential experience for short-term tourists [21]. In the early stages of research, especially before the 21st century, conceptual exploration of tourism resorts garnered widespread attention from academia and the business community [22]. The life cycle, development, and management of tourist resorts were prominent subjects of research during this period. For example, in the 1980s, Butler introduced a seminal life-cycle model for tourist resorts, positing that a resort undergoes stages such as exploration, involvement, development, consolidation, stagnation, decline, or rejuvenation [23]. Subsequent studies have revised and supplemented Butler’s model, offering varied perspectives on the vacation life cycle [24,25,26]. For instance, Agarwal conducted a case study on a seaside resort in the UK, demonstrating the applicability and effectiveness of a life-cycle model [27]. Prideaux has proposed a development map for tourist resorts grounded in market operations [28]. In the 21st century, there has been a shift in research focus toward the operation, management, marketing, and other aspects of tourist resorts, accompanied by a gradual increase in empirical research [29,30,31]. Recent studies have increasingly delved into the experience and perception of resort customers [32,33,34]. The research content transitions from the resort itself to the behavior and preferences of holidaymakers.

For the tourism industry, the efficiency of input–output is crucial [35]. Pursuing high efficiency is the goal of tourism enterprises such as hotels and travel agencies. Therefore, it has attracted the attention of many scholars to the topic of efficiency [36,37,38]. The existing research on tourism efficiency and tourist resorts indicates that there are some shortcomings in exploring the efficiency of tourist resorts [5,39]. Especially, there is insufficient disclosure of the spatiotemporal dynamic characteristics and influencing factors of the efficiency of tourist resorts. Although some studies have initiated a focus on resort efficiency, they often concentrate on case studies. For instance, Deng compared the efficiency of the gambling tourism industry between Macau and Las Vegas resorts, analyzing factors influencing their efficiency [39]. Similarly, Corne explored the operational efficiency of ski resorts in France [40]. Currently, there is insufficient exploration of the efficiency of tourist resorts, and several factors contribute to this gap. Firstly, the data related to tourist resorts are challenging to obtain. Compared to the transparency of social statistics, data on scenic spots, information from listed tourism enterprises, and details about tourist resorts are seldom disclosed to the public. Secondly, as an industry with a lengthy investment cycle, efficiency measurement results for comprehensive resorts may not be immediately attention-grabbing. However, it is essential to recognize that tourist resorts constitute the core of vacation tourism development. Optimizing the allocation and efficient utilization of their resource elements is crucial for the healthy growth of the entire tourist resort industry. Simultaneously, as crucial destinations for relaxation and vacations, enhancing the operational efficiency of tourist resorts is a key goal for the high-quality development of the tourism industry. In light of these considerations, the current academic community needs to redirect attention towards tourist resorts, particularly when examining the rationality of spatial distribution and the sustainability of tourism efficiency from a regional scale perspective. Jiangsu Province, being one of the most developed provinces in China [41], is situated in the Yangtze River Delta region, boasting a solid economic foundation and expansive prospects for the vacation tourism market. The provincial government has prioritized the development of vacation areas, as evidenced by the issuance of the “Jiangsu Provincial Tourism Resort Management Measures” in 2022. As of 2022, Jiangsu Province boasts 7 national-level tourist resorts and 55 provincial-level tourist resorts, securing its leading position nationally. This article will focus on tourist resorts in Jiangsu Province as a representative research object. Utilizing the data envelopment analysis (DEA) model, the study aims to measure the efficiency of tourist resorts in Jiangsu Province and reveal their spatiotemporal dynamic patterns. Drawing from the research results, the article will propose targeted management suggestions based on actual circumstances, with the overarching goal of promoting the sustainable development of tourist resorts in Jiangsu Province. The research objectives of this article include the following: (1) measuring the efficiency of tourist resorts in Jiangsu Province. This objective involves quantifying and evaluating the overall efficiency of tourist resorts within the specified region, utilizing appropriate metrics and methodologies, such as the data envelopment analysis (DEA) model; (2) Analyzing the relationship between tourism efficiency and decomposition efficiency. This objective aims to delve into the intricate connections between tourism efficiency and decomposition efficiency. By examining how various components contribute to the overall efficiency, the research seeks to provide insights into the nuanced factors influencing the performance of tourist resorts; (3) Exploring the spatiotemporal distribution characteristics of efficiency in tourist resorts in Jiangsu Province. This objective involves a comprehensive exploration of the spatial and temporal patterns in the efficiency of tourist resorts across Jiangsu Province. Through geographic and time-based analyses, the study aims to uncover trends, concentrations, and deviations in efficiency values, contributing to a deeper understanding of the regional dynamics. By addressing these research objectives, the article aims to contribute valuable insights to the field of tourism research, offering practical knowledge that can inform the strategic development and management of tourist resorts in Jiangsu Province and potentially serve as a reference for similar regions.

2. Materials and Methods

2.1. DEA Model

The data envelopment analysis (DEA) model stands as a well-established method for measuring efficiency in the current academic community [42,43]. It was introduced by Charnes and Cooper in 1978 [44]. Compared to traditional methods, the DEA model excels in determining whether a decision-making unit lies on the effective frontier and possesses absolute advantages in multi-input and multi-output scenarios [45]. In the context of this study, the DEA model will be employed to measure the tourism efficiency of tourist resorts in Jiangsu Province. Two fundamental models, namely the CCR model and the BCC model, are commonly utilized in DEA methods to gauge the efficiency of similar production entities [46]. The combination of these models enables the measurement of total efficiency, technical efficiency, and scale efficiency in tourism investment in resorts [15]. The calculation formula for this combined measurement is as follows:

• CCR efficiency model

$$\begin{array}{l}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}Min{\theta }_k\\ s.t.\{\begin{array}{l}{\displaystyle \sum _{j=1}^n{\lambda }_j{x}_{ij}\le x_{ik},i=1,2\cdot \cdot \cdot m}\hfill \\ {\displaystyle \sum _{j=1}^n{\lambda }_j{y}_{rj}\ge {\theta }_k{y}_{rk},r=1,2\cdot \cdot \cdot s}\hfill \\ {\displaystyle \sum _{j=1}^n{\lambda }_j\ge 0,j=1,2\cdot \cdot \cdot n}\hfill \end{array}\end{array}$$

• BCC efficiency model

$$\begin{array}{l}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}Min{\theta }_k\\ s.t.\{\begin{array}{l}{\displaystyle \sum _{j=1}^n{\lambda }_j{x}_{ij}\le x_{ik},i=1,2\cdot \cdot \cdot m}\hfill \\ {\displaystyle \sum _{j=1}^n{\lambda }_j{y}_{rj}\ge {\theta }_k{y}_{rk},r=1,2\cdot \cdot \cdot s}\hfill \\ {\displaystyle \sum _{j=1}^n{\lambda }_j=1,j=1,2\cdot \cdot \cdot n}\hfill \end{array}\end{array}$$

Among them, $x_{ij}$ represents the input value $i$ of the tourism resort $j$; $y_{rj}$ represents the output value $i$ of the tourism resort $j$; ${\lambda }_j$ is the weight vector, ${\theta }_k$ is the relative phase ratio of the tourist resort $k$. For $\theta (0<\theta <1)$, the closer the value is to 1, the higher the overall efficiency. If $\theta =1$ then that the region is at the forefront of production and has achieved optimal overall efficiency [47]. In addition, the CCR model measures the overall efficiency of tourist resorts, while the BBC model measures the technical efficiency of tourist resorts, which can be obtained by dividing the two.

2.2. Coordinate of Gravity Center

The coordinates of the center of gravity originate from geometric analysis in mathematics and serve as a measurement method used to describe the spatial characteristics of objects, typically applied to objects with geometric coordinates. The center of gravity method is commonly utilized in geographical distribution research to analyze and address spatial changes in regional attributes. Geographical centers of gravity are employed to represent the spatiotemporal distribution characteristics of geographical elements [48]. This includes the examination of regional economic gravity centers, measurement of regional spatial structure equilibrium, and more. It characterizes the clustering features and deviation trajectories of spatial attributes [49]. The movement of the center of gravity is indicative of the high-density areas of spatial phenomena, with its direction pointing toward these regions. The distance of the center of gravity movement reflects the spatial differences in the amplitude of changes in the elements. In recent years, this method has witnessed widespread application and expansion. The mathematical expression for this methodology is as follows:

$$p_i\left(x_j,y_j\right)=\left[\frac{{\displaystyle \sum _{i=1}^n{w}_i{x}_i}}{{\displaystyle \sum _{i=1}^n{w}_i}},\frac{{\displaystyle \sum _{i=1}^n{w}_i{y}_i}}{{\displaystyle \sum _{i=1}^n{w}_i}}\right]$$

Among them, $p_i(x_j,y_i)$ represents the gravity center coordinate of the region $i$, and $w_i$ is the attribute value of the region $i$; $p_i(x_j,y_i)$ denotes the coordinates of the gravity center for the $j$th year of the large region.

2.3. Standard Deviation Ellipse

The standard deviation ellipse in spatial analysis was initially introduced by Lefever in 1926 [50]. This geographical research method serves to elucidate the overarching characteristics of geographical features in spatial distribution. It is employed to measure the degree of dispersion, directional distribution, and distribution morphology of discrete points [51,52].

The standard deviation ellipse mainly consists of three elements, namely the rotation angle $\theta$, the standard deviation along the Y-axis, and the standard deviation along the X-axis. The formula is as follows:

$$\begin{array}{l}\tan \theta =\frac{\left({\displaystyle \sum _{i=1}^n{\omega }_i^2{(x_i^{’})}^2}-{\displaystyle \sum _{i=1}^n{\omega }_i^2{(y_i^{’})}^2}\right)+\sqrt{{\left({\displaystyle \sum _{i=1}^n{\omega }_i^2{(x_i^{’})}^2}-{\displaystyle \sum _{i=1}^n{\omega }_i^2{(y_i^{’})}^2}\right)}^2+4{\left({\displaystyle \sum _{i=1}^n{\omega }_i^2{(x_i^{’})}^2{(y_i^{’})}^2}\right)}^2}}{2{\displaystyle \sum _{i=1}^n{\omega }_i^2{x}_i^{’}{y}_i^{’}}}\\ {\delta }_x=\sqrt{\frac{{\displaystyle \sum _{i=1}^n{({\omega }_i{x}_i^{’}\cos \theta -{\omega }_i{y}_i^{’}\sin \theta )}^2}}{{\displaystyle \sum _{i=1}^n{\omega }_i^2}}}\\ {\delta }_y=\sqrt{\frac{{\displaystyle \sum _{i=1}^n{({\omega }_i{x}_i^{’}\sin \theta -{\omega }_i{y}_i^{’}\cos \theta )}^2}}{{\displaystyle \sum _{i=1}^n{\omega }_i^2}}}\end{array}$$

In the formula, $x_i^{’}$ and $y_i^{’}$ are the relative coordinates of the distance between each scatter point and the gravity center of the region; According to $\tan \theta$, the rotation angle of the scatter distribution pattern can be obtained; ${\delta }_x$ and ${\delta }_y$ are the standard deviations along the X-axis and the standard deviations along the Y-axis, respectively.

2.4. Data Sourced and Indicator Selection

The measurement of tourism efficiency typically involves economic indicators related to both input and output, and the precision of efficiency calculations is directly influenced by the careful selection of variables. In economic theory, land, capital, and labor are commonly recognized as fundamental inputs for production activities, while economic, social, and ecological benefits are crucial aspects in gauging output [53,54,55]. Moreover, the DEA method, frequently used for calculating tourism efficiency, stipulates that the input–output indicators should not exhibit a strong linear relationship, and the sample size should be at least twice the number of input–output indicators. Within the existing literature on tourism efficiency research, scholars predominantly focus on the characteristics of their specific research subjects, such as hotels, travel agencies, tourism destinations, special tourism destinations, etc. They take into account the actual development situation and, based on data availability, select efficiency indicators from diverse perspectives [56]. Hence, it is evident that the choice of input–output indicators for measuring tourism efficiency varies among researchers and research objects, lacking a standardized approach. The tourism industry exhibits strong interdependence, and in practical research, most indicators cannot be entirely isolated. Consequently, researchers must opt for scientifically sound indicators to measure tourism efficiency. Furthermore, tourism efficiency is inherently a relative value, determined by comparing the results of different production units. In studies focusing on tourism destinations like scenic spots and forest parks, inputs typically involve land, labor, and capital. Output indicators, on the other hand, vary significantly due to the nature of the destinations. However, tourism income and tourist arrivals consistently emerge as primary factors [57,58,59].

As a crucial component of the modern tourism industry, tourist resorts play a significant role as destinations for vacation tourism. Distinguished by their comprehensive reception facilities and diverse product offerings, they deviate from traditional tourism products and assume the status of a full-fledged tourist destination. Moreover, owing to variations in management methods and policies for tourist resorts across different regions, there lacks a standardized statistical framework and scale for data related to these resorts. Given this diversity, the selection of indicators for measuring the tourism efficiency of tourist resorts necessitates careful consideration of their nature and functional characteristics. It is essential to establish a comprehensive system of evaluation indicators for the tourism efficiency of tourist resorts, taking into account both the practical context and data availability. The identified input indicators encompass tourism investment and the number of employees, while output indicators include the number of receptionists and revenue (

Table 1. Indicator system of tourism resort efficiency.

Indicator Type	Name	Unit	Indicator Description
Input indicators	Number of tourism practitioners	Ten thousand people	Number of practitioners in tourist resorts
	Capital investment	Billion CNY	Investment in the development and management of tourist resorts
Output indicators	Number of receptionists	Ten thousand people	Number of vacationers received by tourist resorts
	Revenue	Billion CNY	Operating revenue of tourist resorts

).

Concerning data, the development of tourist resorts in Jiangsu Province exhibits distinct phases, with the province being a unique special tourism unit in select regions. During the initial phases of development, the mass holiday market had not yet formed, and tourist resorts primarily catered to public and external service recipients. Consequently, examining the development of tourist resorts during this period from a global perspective might lack representativeness. In consideration of the representative nature of the developmental stages of tourist resorts, along with the accuracy and availability of data, this study focuses on the onset of rapid development in tourist resorts in Jiangsu Province. This period coincides with the official launch of relevant management and reporting systems for tourist resorts. The assessment cycles chosen for analysis are 2013, 2016, and 2019. The research scope encompasses 13 national-level and provincial-level tourist resorts in Jiangsu Province. Data indicators are drawn from the Jiangsu Province Tourism Resort Management System and are supplemented and refined with on-site research data. To address any gaps in data for certain years or tourist resorts, interpolation is employed to supplement calculations, ensuring the rationality and completeness of the dataset.

3. Results

3.1. Decomposition Efficiency Characteristics of Tourist Resorts

3.1.1. Comprehensive Efficiency

Using the Deap2.0 software, the comprehensive tourism efficiency of tourist resorts in Jiangsu Province was assessed for the years 2013, 2016, and 2019. The findings revealed an average comprehensive efficiency of 0.119 across the three years, signifying substantial room for improvement to reach optimal levels. Notably, according to the ranking of average comprehensive efficiency over the three years (

Table 2. Mean comprehensive efficiency of tourist resorts.

Rank	Resort Name	Average Value	Rank	Resort Name	Average Value
1	Wuxi Jiangnan Ancient Canal Tourist Resort	0.515	48	Jiangyin Xuxiake Leisure Tourism Resort	0.052
2	Danyang Crystal Mountain Tourist Resort	0.400	49	Lianyungang Hot Spring Tourist Resort	0.052
3	Nanjing Pearl Spring Tourist Resort	0.258	50	Suzhou Yangcheng Lake Peninsula Tourist Resort	0.052
4	Nantong Langshan Tourist Resort	0.256	51	Taicang Yangtze River Estuary Tourist Resort	0.050
5	Suzhou Taihu Lake National Tourist Resort	0.239	52	Yangzhou Guazhou Tourist Resort	0.047
6	Lianyungang Dayishan Tourist Resort	0.233	53	Suzhou Western Ecological Tourism Resort	0.046
7	Dafeng Elk Ecological Tourism Resort	0.196	54	Wuxi Hongshan Tourist Resort	0.045
8	Yangzhou Fenghuang Island Ecological Tourism Resort	0.194	55	Xuyi Tianquan Lake Tourist Resort	0.044
9	Jianhu Jiulongkou Tourist Resort	0.183	56	Changshu Yushan Cultural Tourism Resort	0.042
10	Jurong Maoshan Lake Tourist Resort	0.173	57	Nantong Kaisha Island Tourist Resort	0.007

), 10 tourist resorts, including Wuxi Jiangnan Ancient Canal Tourist Resort, Danyang Crystal Mountain Tourist Resort, and Nanjing Pearl Spring Tourist Resort, emerged as top performers in the province. However, certain tourist resorts, such as Nantong Kaisha Island Tourist Resort, Changshu Yushan Cultural Tourist Resort, and Xuyi Tianquan Lake Tourist Resort, exhibited comparatively lower average comprehensive efficiency. The substantial gap between the highest and lowest performers suggests a need for a transformative and upgraded development model for these specific tourist resorts. Urgent attention is required to bridge this gap and enhance the efficiency of these resorts.

Building upon the results of the comprehensive efficiency measurement, further statistical analysis was conducted on the comprehensive efficiency values of tourist resorts in Jiangsu Province. The outcomes, as depicted in

Table 3. Statistical characteristics of comprehensive efficiency of tourist resorts.

Year	Statistical Index	Comprehensive Efficiency
2013	Number	34
	Average value	0.117
	Standard deviation	0.117
2016	Number	50
	Average value	0.099
	Standard deviation	0.056
2019	Number	57
	Average value	0.140
	Standard deviation	0.178

and Figure 1a, revealed an overall fluctuating upward trend in the comprehensive efficiency of tourist resorts. The average value demonstrated an overall performance, initially decreasing from 0.117 in 2013 to 0.99 in 2016, followed by an increase to 0.14 in 2019. These results indicate that, during this period, the tourism efficiency of tourist resorts in Jiangsu Province remained in a relatively low state. The fluctuating trend suggests the presence of inefficiencies in the production units of tourist resorts, leading to resource redundancy and suboptimal development. Addressing these inefficiencies is crucial for optimizing the overall performance of tourist resorts in the Jiangsu province.

3.1.2. Pure Technical Efficiency

The average pure technical efficiency of tourism resorts over the 3 years was determined to be 0.280, highlighting substantial room for improvement before reaching the optimal level. A closer look at the ranking of the average pure technical efficiency over the past three years reveals notable differences (

Table 4. Mean pure technical efficiency of tourist resorts.

Rank	Resort Name	Average Value	Rank	Resort Name	Average Value
1	Lianyungang Dayishan Tourist Resort	1.000	48	Xuyi Tianquan Lake Tourist Resort	0.118
2	Jiangyin Xuxiake Leisure Tourism Resort	0.975	49	Liyang Tianmu Lake Tourist Resort	0.116
3	Hongze Laozishan Hot Spring Tourist Resort	0.970	50	Kunshan Tourist Resort	0.108
4	Lianyungang Coastal Tourism Resort	0.848	51	Yangzhou Grand Canal Cultural Tourism Resort	0.093
5	Yangzhou Fenghuang Island Ecological Tourism Resort	0.608	52	Suzhou Yangcheng Lake Peninsula Tourist Resort	0.092
6	Jianhu Jiulongkou Tourist Resort	0.585	53	Tangshan Hot Spring Tourist Resort in Nanjing	0.091
7	Siyang Chengzi Lake Tourist Resort	0.553	54	Wuxi Hongshan Tourist Resort	0.078
8	Xuzhou Lvliangshan Tourist Resort	0.549	55	Nantong Kaisha Island Tourist Resort	0.066
9	Wuxi Jiangnan Ancient Canal Tourist Resort	0.523	56	Suzhou Western Ecological Tourism Resort	0.059
10	Funing Jinsha Lake Tourist Resort	0.481	57	Changshu Yushan Cultural Tourism Resort	0.058

). The top 10 tourist resorts in terms of pure technical efficiency, such as Lianyungang Dayishan Tourist Resort, Jiangyin Xuxiake Leisure Tourist Resort, and Hongze Laozishan Hot Spring Tourist Resort, demonstrated an impressive average efficiency of 0.709. This stands in stark contrast to the average of 0.088 observed in the bottom 10 resorts. The lower-ranked tourist resorts, particularly in terms of technology adoption, management practices, and the overall quality of employees, need to focus on improvement. Enhancing the utilization and allocation efficiency of resource elements in tourist resorts is crucial for boosting overall pure technical efficiency. Addressing these aspects can contribute to a more effective and efficient operation of tourist resorts.

On the basis of the pure technical efficiency measurement results, further statistical analysis was conducted on the pure technical efficiency values of tourist resorts in the province. The results showed that (

Table 5. Statistical characteristics of pure technical efficiency of tourist resorts.

Year	Statistical Index	Pure Technical Efficiency
2013	Number	34
	Average value	0.224
	Standard deviation	0.178
2016	Number	50
	Average value	0.255
	Standard deviation	0.239
2019	Number	57
	Average value	0.362
	Standard deviation	0.296

, Figure 1b), the overall pure technical efficiency showed a significant upward trend, with the average value increasing from 0.224 in 2013 to 0.255 in 2016, and further to 0.362 at the end of 2019. While the high-value area of southern Jiangsu still predominates, there is observable volatility in the pure technical efficiency of tourist resorts in this region. As the tourism industry continues to develop, other regions have also witnessed improvements in pure technical efficiency. This upward trend signals positive advancements in the efficiency and effectiveness of resource utilization in tourist resorts, contributing to the overall enhancement of the tourism sector in the province.

3.1.3. Scale Efficiency

The findings indicate that the average scale efficiency of tourist resorts in the 3 years was 0.518, reflecting a commendable overall level. Notably, the investment factors of tourist resorts in the province continued to maintain a relatively high standard. Examining the average ranking of scale efficiency over the three years (

Table 6. Mean scale efficiency of tourist resorts.

Rank	Resort Name	Average Value	Rank	Resort Name	Average Value
1	Yangzhou Grand Canal Cultural Tourism Resort	0.861	48	Taicang Yangtze River Estuary Tourist Resort	0.265
2	Suzhou Taihu Lake National Tourist Resort	0.831	49	Zhangjiagang Shuangshan Xiangshan Tourist Resort	0.261
3	Wuxi Taihu Lake Landscape City Tourist Resort	0.827	50	Lianyungang Dayishan Tourist Resort	0.233
4	Liyang Tianmu Lake Tourist Resort	0.822	51	Siyang Chengzi Lake Tourist Resort	0.211
5	Wuxi Jiangnan Ancient Canal Tourist Resort	0.817	52	Yangzhou Guazhou Tourist Resort	0.156
6	Suzhou Western Ecological Tourism Resort	0.788	53	Lianyungang Coastal Tourism Resort	0.136
7	Tangshan Hot Spring Tourist Resort in Nanjing	0.782	54	Liyang Caoshan Tourist Resort	0.127
8	Nantong Langshan Tourist Resort	0.768	55	Nantong Kaisha Island Tourist Resort	0.108
9	Changshu Yushan Cultural Tourism Resort	0.727	56	Hongze Laozishan Hot Spring Tourist Resort	0.099
10	Gaochun International Slow City Tourism Resort	0.703	57	Jiangyin Xuxiake Leisure Tourism Resort	0.053

), it is evident that the mean scale efficiency of the top 10 tourist resorts, including Yangzhou Grand Canal Cultural Tourism Resort, Suzhou Taihu Lake National Tourism Resort, and Wuxi Taihu Lake Mountain City Tourism Resort, stands at 0.793. In contrast, the mean scale efficiency of the bottom 10 tourist resorts is only 0.165. This reveals a significant disparity in the input of resource elements across tourist resorts in the province. Addressing this gap is crucial for ensuring more equitable and efficient resource utilization across all tourist resorts.

Building upon the measurement results of scale efficiency, further statistical analysis was conducted on the scale efficiency values of tourist resorts in the province. The outcomes, as presented in

Table 7. Statistical characteristics of scale efficiency of tourist resorts.

Year	Statistical Index	Scale Efficiency
2013	Number	34
	Average value	0.549
	Standard deviation	0.224
2016	Number	50
	Average value	0.541
	Standard deviation	0.235
2019	Number	57
	Average value	0.459
	Standard deviation	0.262

and Figure 1c, revealed an overall trend of decreasing scale efficiency. However, in specific regions, particularly in northern Jiangsu, scale efficiency demonstrated sustained growth. These findings underscore significant regional differences in the development of tourist resorts in Jiangsu Province. The southern region of Jiangsu, being one of the most active regions in national economic activities, has consistently led in terms of tourism investment and product iteration. This region boasts a solid foundation in the tourism industry, resulting in relatively mature development among tourist resorts. Some resorts in this area have progressed to the stage of transformation and upgrading, indicating a more advanced and evolved phase of development.

3.2. The Relationship between Tourism Efficiency and Decomposition Efficiency in Tourist Resorts

To further analyze the impact of decomposition efficiency on comprehensive efficiency, the relationships between comprehensive efficiency and decomposition efficiency of tourist resorts in 2013, 2016, and 2019 were examined. From the physical interpretation of decomposition, the proximity of scatter points to the 45° diagonal indicates the strength of the explanatory power of decomposition efficiency on comprehensive efficiency—closer points signify greater explanatory power, while more distant points signify weaker explanatory power. As depicted in Figure 2, the quantity and density of scatter points for pure technical efficiency–comprehensive efficiency exhibit a significant advantage. The trend of pure technical efficiency and scale efficiency diverging from the diagonal is increasing, suggesting a longitudinal diffusion along the diagonal direction. Simultaneously, the scatter points also show a trend of evolving from relatively concentrated to dispersed. Examining the changes in the scatter plot, it is apparent that the distribution of scatter points for pure technical efficiency–comprehensive efficiency surpasses that of scale efficiency–comprehensive efficiency, especially in terms of proximity to the diagonal. This evolution is noticeable, transitioning from a significant difference in 2013 (Figure 2a vs. Figure 2b) to a relatively balanced state in 2019 (Figure 2e vs. Figure 2f). However, even at this stage, the distribution of scatter points for pure technical efficiency–comprehensive efficiency along the diagonal remains notably dense. This implies that the impact of pure technological efficiency on comprehensive efficiency in tourist resorts is gradually increasing. The trajectory indicates a shift in tourism development, moving away from resource-scale investment-led development toward high-quality development driven by technology.

From the standpoint of tourism efficiency effectiveness, the phenomenon of DEA being more effective was observed in 2019, as reflected in the scatter plot. The closer the scatter is to DEA being more effective, the farther away it is from the diagonal and closer to the top of the diagonal. In 2013 and 2016, both pure technical efficiency–comprehensive efficiency and scale efficiency–comprehensive efficiency displayed a certain degree of clustering, with the clustering characteristics of the former being more pronounced. This further indicates that pure technical efficiency contributed more to comprehensive efficiency than scale efficiency. Moreover, the recurring clustering phenomenon over the years suggests a trend of overall tourism efficiency for tourist resorts moving towards a relatively close state. This recurring pattern offers insights into the inherent characteristics of tourist resorts’ development and provides favorable conditions for the formulation of relevant policies as well as investment and product development strategies in tourist resorts. Analyzing these patterns over time aids in understanding the evolving dynamics of tourism efficiency in the context of tourist resorts.

3.3. Spatial Distribution Characteristics of Tourism Efficiency in Tourist Resorts

Using Deap2.0 software, the tourism efficiency of tourist resorts in Jiangsu Province was measured for the years 2013, 2016, and 2019 (Figure 3). The results indicate a trend of initially decreasing and then increasing comprehensive efficiency from 2013 to 2019, reflecting an overall fluctuation and upward trajectory. In terms of spatial characteristics, the distribution of high-value areas in the comprehensive efficiency of tourist resorts in Jiangsu Province is relatively balanced. The southern region of Jiangsu appears relatively dense, while the central and northern regions show a rising trend. Southern Jiangsu hosts numerous densely distributed tourist resorts, leading to intense competition and a saturated vacation market. Consequently, most tourist resorts in this region underwent transformation and upgrading during this period. There is a pressing need to create distinctive vacation products, establish a brand identity, and continuously update and improve the product system to secure a larger market share. This dynamic situation is reflected in the overall efficiency, resulting in fluctuations in growth rates. For the central and northern regions of Jiangsu, the relatively latecomer stage allows various tourist resorts to have a higher starting point in their creation and subsequent development, while also drawing more advanced experience in their development, which keeps the overall efficiency of these areas steadily improving.

High-value areas with pure technological efficiency have always maintained a high level in southern Jiangsu, especially in the Suzhou–Wuxi–Changzhou region, forming clusters. During the period from 2013 to 2019, the growth rate of pure technological efficiency in northern Jiangsu was relatively fast, and the growth rate and trend were significantly stronger than those in other regions. This growth has been facilitated by the active adoption of new technologies, assimilation of advanced management experiences, emphasis on talent cultivation and introduction, a more relaxed development environment, and more precise policy support. These factors collectively have enabled tourist resorts in northern Jiangsu to maintain a high rate of development in recent years. The distinct regional patterns in pure technological efficiency underscore the dynamic nature of tourism development in response to economic, technological, and policy factors in different regions of Jiangsu Province.

The high-value areas of scale efficiency have consistently been stably distributed in southern Jiangsu, a trend determined by the inherent north–south differences in Jiangsu Province. Throughout the research period, there has been a downward trend in the scale of resource investment in tourist resorts in southern Jiangsu. This trend can be attributed to factors such as the specific development stage and policy changes. While the vacation tourism market in southern Jiangsu is thriving, there is never a shortage of investment in resource elements. However, with increasing competition and growing market demand, there is a need for more precise and effective investment in resource elements. The previous strategy of extensive resource stacking is being phased out, prompting a preference for scarcity rather than excess in the selection of development directions and project update investments for southern tourism resorts. In contrast, the northern Jiangsu region, characterized by more available land space, looser fiscal and tax policies, and a larger potential market, has become a favored resource. In recent years, the development trend of tourist resorts in the northern Jiangsu region has been positive, with the scale of resource investment increasing year by year. Factors such as land availability, financial incentives, tax policies, and the opening of new markets contribute to the growing investment attention directed towards tourist resorts in northern Jiangsu. These regional variations highlight the complex interplay of economic, policy, and market dynamics shaping the resource investment landscape in different parts of Jiangsu Province.

3.4. Dynamic Pattern of Decomposing Efficiency in Tourist Resorts

Utilizing the standard deviation ellipse in spatial statistics module of ArcGIS10.6, we generated standardized ellipses for various efficiency indicators of tourism resorts in Jiangsu Province from 2013 to 2019. This analysis aimed to explore the directional distribution and evolution characteristics of efficiency indicators across the province.

Regarding comprehensive efficiency, the θ angle of the standard deviation ellipse decreased from 144.96° in 2013 to 139.68° in 2016, then rose to 144.47° in 2019 (

Table 8. Elliptic parameter of standard deviation for tourism efficiency of tourist resorts.

Tourism Efficiency	Year	X-Axis Standard Deviation (km)	Y-Axis Standard Deviation (km)	Corner θ
Comprehensive efficiency	2013	106.95	192.53	144.96°
	2016	105.10	217.59	139.68°
	2019	109.21	206.89	144.47°
Pure technical efficiency	2013	121.18	191.13	149.95°
	2016	94.25	223.06	136.39°
	2019	110.98	233.35	148.06°
Scale efficiency	2013	95.38	222.70	143.18°
	2016	97.58	197.23	143.03°
	2019	105.53	208.38	141.35°

). The Y-axis standard deviation increased from 192.53 km in 2013 to 206.89 km in 2019, while the X-axis standard deviation increased from 106.95 km in 2013 to 109.21 km in 2019, with a slight decrease to 105.10 km in 2016. In summary, the comprehensive efficiency center of tourist resorts in Jiangsu Province primarily ranges between 119°40′ E and 119°47′ E, and between 32°23′ N and 32°5′ N, forming an overall inverted L-shaped distribution (Figure 4a). The approximate location is in the eastern part of Zhenjiang and the southeastern part of Yangzhou. Analyzing the gravity center’s movement, we observe a northward shift with a migration distance of 47.57 km and 13.1 km (Table S1) between the years, showcasing a gradual slowdown in migration speed.

In terms of pure technical efficiency, the corner θ of the pure technical efficiency standard deviation ellipse decreased from 149.95° in 2013 to 136.39° in 2016 and then increased to 148.06° in 2019 (Table 8). The standard deviation of the Y-axis increased from 191.13 km in 2013 to 233.35 km in 2019, while the X-axis decreased from 121.18 km in 2013 to 110.98 km in 2019. Furthermore, the pure technical efficiency of tourist resorts in Jiangsu Province predominantly fluctuates between 119°35′ E–119°49′ E and 32°17′ N–32°37′ N, exhibiting an overall inverted L-shaped distribution (Figure 4b). Observing the movement trajectory of the center of gravity, it becomes apparent that the pure technical efficiency center of gravity is generally shifting northwestward, with movement distances of 44.3 km and 14.65 km (Table S2), respectively, and a gradual slowdown in migration speed.

In terms of scale efficiency, it can be seen that the corner θ of the standard deviation ellipse of scale efficiency decreased from 143.18° in 2013 to 141.35° in 2019. The Y-axis direction of the standard deviation ellipse of scale efficiency, the standard deviation of the main axis decreased from 222.7 km in 2013 to 208.38 km in 2019. Conversely, the direction of the X-axis of the scale efficiency standard deviation ellipse increased from 95.38 km in 2013 to 105.53 km in 2019. The scale efficiency of tourist resorts in Jiangsu Province primarily varies between 119°44′ E and 119°50′ E and 32°16′ N and 32°60′ N, forming an overall reverse L-shaped distribution (Figure 4c). The approximate location is in the eastern part of Zhenjiang. Analyzing the movement trajectory of the gravity center, it is evident that the scale efficiency center of gravity is generally shifting northeastward, with movement distances of 11.15 km and 23.86 km, respectively (Table S3), and the migration speed is gradually accelerating.

4. Discussion

4.1. The Overall Efficiency of Tourist Resorts in Jiangsu Province

The efficiency of tourism, as a key aspect and significant indicator of the quality of tourism development, holds crucial practical importance in expanding the scope of tourism development, enhancing the overall competitiveness of tourist resorts, and achieving sustainable growth [60,61]. While the comprehensive efficiency of tourist resorts in Jiangsu Province appears relatively low, this finding is consistent with the outcomes of efficiency evaluation studies conducted on various tourist attractions across China [62,63]. It highlights a widespread challenge of extensive development and resource wastage in the management of tourist resorts, accompanied by issues in the development model and path. The lack of a clear understanding of tourist resorts among development managers at all levels has resulted in a deviation in comprehending the development trajectory. As a consequence, there has been a limited market response to project construction outcomes, and the promotional impact on the development of tourism resorts has been constrained. However, it is noteworthy that there is a fluctuating upward trend in comprehensive efficiency during the research period, suggesting a gradual improvement in the efficiency of tourist resorts in Jiangsu Province. The efficiency of tourist resorts demonstrates spatiotemporal differentiation characteristics. In the southern region of Jiangsu, high-efficiency areas are relatively densely clustered, particularly in terms of scale efficiency. Concurrently, the pure technological efficiency in southern Jiangsu is relatively high, primarily attributed to the favorable economic environment providing ample financial and human support for local tourist resorts. While the comprehensive efficiency in the northern region of Jiangsu is relatively low, the concerted efforts in developing the local social economy and government attention to tourist resorts have significantly improved both pure technical efficiency and comprehensive efficiency. Notably, the northern Jiangsu region consistently maintains a high level of pure technical efficiency (Figure 1b). In comparison to the southern region, the economic level of northern Jiangsu is relatively lower, and the investment scale is modest, resulting in consistently low scale efficiency. However, improvements in the quality and management level of practitioners have led to a significant overall efficiency enhancement during the research period.

4.2. Decomposition Efficiency Characteristics and Their Impact on Comprehensive Efficiency

The tourist resorts in Jiangsu Province demonstrate relatively high scale efficiency, indicating optimal operational efficiency concerning inputs and outputs. However, their performance in pure technical efficiency reveals weaknesses, suggesting that despite a relatively high level of investment, there exists substantial room for improvement in employee quality, capital investment quality, and overall management proficiency. An analysis of the contribution of these two efficiencies to overall efficiency reveals that pure technical efficiency plays a more critical role than scale efficiency. This underscores the significance of investing in technology to enhance the overall efficiency of resorts in Jiangsu Province. Despite the overall weaker average pure technical efficiency, the top-ranked tourist resorts exhibit notably high values in this aspect. The tourism resort with the highest pure technical efficiency value reached a perfect score of 1.000 (Table 4), while the highest scale efficiency value was recorded at 0.861 (Table 6). These findings shed light on an internal challenge within these resorts, where individual products and projects may succeed, but there is a lack of specialization in management and technology, resulting in lower overall efficiency. Collaboration between local governments at various levels and well-known domestic and foreign cultural and tourism enterprises, brands, or large capital teams during the investment process poses challenges from construction to operation. Although initially envisioned as strong alliances with mutually beneficial cooperation, these collaborations often encounter difficulties due to the pursuit of large-scale projects, pressure on land use indicators, and funding chain challenges, making sustained project viability challenging. The high level and inherent flaws in the project system create difficulties in attracting subsequent investments. A common issue identified is the blind pursuit of project trends, leading to repetitive, low-level development. Facility construction often lacks market demand demonstration, resulting in the homogenization of leisure facilities and services with urban leisure facilities, ultimately leading to significant resource waste. Addressing these challenges requires a strategic focus on improving the quality of investments, enhancing management and technological capabilities, and avoiding blind adherence to trends for more sustainable and efficient tourist resort development.

4.3. The Theoretical and Practical Implications

The tourism resort industry in China is currently experiencing rapid development, emphasizing the critical importance of conducting comprehensive research on the operational efficiency of these resorts. Such research is essential for accurately evaluating the developmental patterns within this industry. Current theoretical research on tourist resorts primarily focuses on two aspects: conceptual exploration involving the concept, types, characteristics, site selection, and construction condition evaluation of tourist resorts; and empirical research delving into the planning and design, land use, management systems, community participation, and marketing of specific tourist resorts. Field surveys and in-depth interviews, along with a qualitative research approach, are the predominant methods employed to gather data. The existing literature tends to concentrate on problem analysis with fewer discussions on countermeasures, and there is a noticeable imbalance between qualitative and quantitative analyses. Moreover, provincial-level research on the operational efficiency of tourism resorts is particularly lacking, with limited studies measuring and evaluating the current situation and future development from an efficiency perspective. This study addresses these gaps by focusing on all tourist resorts in Jiangsu Province, measuring their tourism efficiency, and investigating the impact of decomposition efficiency on comprehensive efficiency. The analysis includes exploring spatiotemporal dynamic patterns to reveal influencing factors and differentiation characteristics of the comprehensive efficiency of tourist resorts. This contributes significantly to enriching the content of theoretical research on tourist resorts.

In terms of practical significance, the study offers targeted policy recommendations based on the calculated efficiency results of tourist resorts. The findings suggest that the overall efficiency of tourist resorts in Jiangsu Province is relatively low, primarily attributed to low pure technical efficiency, closely linked to the quality and management level of employees in the resorts. The research recommends enhancing the overall efficiency of tourist resorts by improving service types, establishing sound mechanisms, and introducing professional management talents. These practical insights provide a foundation for informed policy decisions and strategic development in the tourist resort industry.

4.4. Policy Recommendations

4.4.1. Reduce Facility Dependence and Improve Service Categories of Resorts

The construction and operation costs of tourist resorts tend to be relatively high, exacerbated by investors’ misconceptions about the development trajectory of vacation products. There is often an overemphasis on pursuing large-scale construction and high-end features, leading to a scenario of “high and low.” This approach ultimately results in elevated construction and operation expenses, significantly diminishing the economic efficiency of tourist resorts. Additionally, intense competition prompts some resorts in specific regions to engage in price wars with consumers, overlooking the enhancement of product and service quality. This emphasis on low-price competition diminishes the overall profitability of the entire tourist resort sector. For tourist resorts, a robust ecological environment and a diverse vacation product system are foundational elements for achieving high-quality development. Following an environmentally friendly path aligns with a trend for future development. Embracing this concept necessitates a gradual shift away from excessive emphasis and dependence on physical facilities. Resorts should exercise control over the scale of project construction within their boundaries, actively integrating and utilizing existing spatial resources to cater to diverse and personalized vacation needs. This approach provides more natural ecological space and flexibility, fostering the sustainable development of the resort. Furthermore, as a core pillar of the vacation product system, services should evolve from traditional, undifferentiated models to more refined and personalized approaches. There should be a proactive integration of modern information technology to create intelligent vacation areas, facilitating efficient and convenient information services. This transformation aims to enhance the overall experience for visitors and contributes to the overall competitiveness and success of tourist resorts in the modern tourism landscape.

4.4.2. Establishing a Benign Interactive Mechanism and Exploring Competitive and Cooperative Development

To achieve the common goal of sustainable and stable development for tourist resorts across the province, it is imperative to ensure coordinated development among various resorts, promoting mutual support and avoiding actions that sacrifice or inhibit one resort for the benefit of another. The government’s industry management and leadership play a pivotal role in this endeavor. Establishing a competitive mechanism that includes cooperation, mutual assistance, support, and interest compensation is crucial to regulating the relationships between tourist resorts in the province. Among them, the cooperation mechanism refers to the formulation and improvement of a complete set of cooperation rules or mechanisms for tourist resorts under binding contractual protection. The mutual aid mechanism is aimed at promoting the exchange and mutual assistance of talents, funds, technology, and information between tourist resorts. The support mechanism refers to arranging relatively mature tourist resorts to assist and support underdeveloped areas, including but not limited to resource grafting, talent exchange, and cooperative development. The benefit compensation mechanism refers to providing various compensations to tourist resorts that make concessions and sacrifices during the coordinated development of the whole province and the maximization of regional interests, in order to balance and balance the interests of all parties. This is also the core foundation and guarantee of regional tourism cooperation. Competitive development, within the framework of coordinated development, recognizes the need for reasonable competition in the industry. To adapt to the increasingly personalized market and avoid path-dependent development models, competitive development can transform the rivalry between similar products in different regions into larger coexistence spaces. This approach promotes staggered development between tourist resorts, leveraging their respective strengths, avoiding redundancy in product systems, and shaping characteristic products based on individual advantages. A well-structured competitive development pattern is beneficial for shaping the unique strengths of each tourist resort. It contributes to building the overall brand of tourist resorts in Jiangsu Province, fostering a cooperative and competitive environment that ensures sustainable growth and success across the province’s tourism sector.

4.4.3. Cultivate through Multiple Channels and Introduce Professional Talents

The high-quality development of tourist resorts is contingent on robust human resource support. However, the prevalent issues of insufficient high-quality management and operation talents, coupled with inadequacies in employee quality, pose challenges across all tourist resorts. Common problems include a low proportion of mid to senior-level talents and a homogeneous knowledge structure among staff. Addressing these challenges requires strategic measures focused on talent cultivation and absorption. Several key considerations in this regard include: (1) strengthen collaboration with higher education institutions to establish closer ties between academia and the industry. Facilitate direct dialogue between academic majors and corresponding positions within tourist resorts. (2) Enhance job training programs for staff within tourist resorts, emphasizing professionalism and practical effectiveness in the training modules. This ensures that employees acquire the necessary skills and knowledge for their roles. (3) Establish a system for communication and on-the-job learning by selecting middle and senior-level personnel to visit mature tourism resorts or upper management departments. This allows for reference, on-the-job learning, information exchange, and collaboration opportunities to be established over the long term. (4) Implement policies to attract various high-quality talents to settle in the region. Introduce measures that encourage individuals to return to their hometowns or take up temporary positions. Optimize the overall talent environment in the area to make it more conducive to attracting and retaining skilled professionals. By implementing these measures, tourist resorts can proactively address human resource challenges, fostering an environment that not only attracts top-tier talent but also cultivates a diverse and skilled workforce. This, in turn, contributes significantly to the high-quality development of tourist resorts.

5. Conclusions

Tourism efficiency is an important measure of the quality of tourism industry development, and improving efficiency is the fundamental direction and ultimate goal of improving the quality and efficiency of current tourism resort development. This study is based on the DEA method and uses analysis tools such as ArcGIS and Deap to comprehensively explore the temporal changes in tourism efficiency from the mean, distribution pattern, decomposition efficiency, and comprehensive efficiency of each efficiency. The spatial evolution characteristics of tourism efficiency in tourist resorts are explored from the perspective of directional distribution characteristics and center of gravity transfer characteristics. Based on the objective situation of on-site research, the spatiotemporal evolution characteristics of various efficiencies are summarized, and the main conclusions are as follows:

(1)

The overall tourism efficiency of tourist resorts in Jiangsu Province is relatively low, with an average of only 0.119, and there is still a lot of room to reach the optimal level. From 2013 to 2019, the local comprehensive efficiency and pure technical efficiency showed a fluctuating upward trend in terms of temporal changes. The average pure technical efficiency is 0.28, showing a significant upward trend overall. The average scale efficiency is 0.518, indicating a slow downward trend. Among various efficiencies, the average value of scale efficiency is the highest, followed by pure technical efficiency, and the average comprehensive efficiency is the lowest.

(2)

Through the analysis of the scatter plot, it can be seen that the comprehensive efficiency of tourist resorts in Jiangsu Province is simultaneously affected by the combined effect of various decomposition efficiencies. However, pure technical efficiency contributes more to overall efficiency than scale efficiency.

(3)

The efficiency value of tourist resorts in Jiangsu Province shows obvious spatial differentiation characteristics. The comprehensive efficiency distribution of tourist resorts is relatively balanced, and the high-efficiency areas of tourist resorts in southern Jiangsu are relatively densely clustered, especially the agglomeration of scale efficiency is more obvious. The pure technical efficiency of southern Jiangsu is relatively high, but the pure technical efficiency of tourist resorts in northern Jiangsu has significantly improved.

(4)

During the research period, the focus on the efficiency of tourism resorts in Jiangsu Province shifted towards the north. Through the analysis of the standard deviation ellipse of various efficiencies in tourist resorts in Jiangsu Province, it can be seen that the overall distribution of efficiency directions shows a significant spatial distribution pattern in the northwest–southeast direction. In addition, the standard deviation ellipse area of various efficiencies shows an overall trend of expansion.

Limitations

While the study not only scientifically measures tourist resorts in Jiangsu Province but also demonstrates the spatiotemporal dynamic characteristics of their efficiency, there are several limitations that should be considered. Firstly, the input–output index chosen in the article serves as the outcome indicator, and other factors influencing the development of tourist resorts have not been included in the research framework. Consequently, the impact of environmental and error factors on efficiency values cannot be isolated. Secondly, the article provides a comprehensive analysis of regional tourism resorts but does not specifically conduct a dedicated empirical study on their efficiency. Future research endeavors could integrate environmental, regional, and other pertinent factors to analyze the impact mechanisms on tourism resort efficiency. Additionally, future empirical research could focus on high-efficiency or inefficient tourist resorts to gain deeper insights into the actual mechanisms influencing efficiency.