Exploring Cultural and Heritage Attributes at Mount Yunqiu, China, Using Importance–Performance Analysis

Abstract

The preferences of tourists regarding their experiences play a crucial role in the management of tourism destinations; understanding tourist satisfaction enables managers to offer facilities and services that are aligned with tourists’ expectations. This study aimed to identify the destination attributes at Mt. Yunqiu and utilize the traditional and revised IPA approaches and compare their results. It applied three different approaches of IPA to assess the perceived importance and performance of the attributes and factors. The three approaches comprised the traditional and modified IPA approaches. The research site was Mt. Yunqiu in Shanxi, which is one of the earliest sites from which humans originated and the center of the Yellow River civilization. A total of 350 questionnaires were utilized, and a total of 41 attributes of cultural and heritage sites were identified to determine the key attributes that attract tourists to the destination. The results present tourists’ perceptions of the destination and their satisfaction regarding various cultural and heritage attractions at Mt. Yunqiu. They thus provide empirical evidence and can be used to suggest various approaches to understand travelers’ perceptions of the importance and performance of different attributes at cultural and heritage sites in the context of Mt. Yunqiu.

1. Introduction

Tourism attributes centered on natural and local resources include plants and animals and their habitats, natural landscapes, and cultural heritage [1]. Destination attributes attract tourists and influence their decision-making processes [2] because they are considered an influential motivation in selecting a destination [3]. Many scholars have conducted research on various aspects of natural and cultural heritage tourism in China [4,5,6,7]. Previous research has examined tourists’ preferences, values, and behaviors at destinations, including luxury attractions [8,9,10]. There is a call for research on national parks and destination management for the promotion of tourism growth and the management of resources in an innovative and sustainable way [11]. Given the importance of tourism in the global economy, especially in regions facing resource depletion and economic transformation [12], the topic of this study is not only of academic value but also of great significance for practical policy-making and regional economic development.

Historically dependent on coal for economic growth, Shanxi Province (hereafter, Shanxi) in China is currently encountering economic challenges due to declining coal prices, a trend primarily driven by China’s sustainable development policies. Consequently, coal is no longer enough to sustain Shanxi’s economic development. Renowned for its rich history and natural and cultural heritage resources, the province is striving to develop a tourism-based economy to counteract the economic stagnation resulting from the decline in coal. The revitalization of rural areas influenced by the decline in the coal industry is critical for economic diversification, the promotion of rural tourism, and sustainable development. Mt. Yunqiu, a developed scenic spot in Shanxi, has been designated a national scenic area. It is distinguished by its abundant natural resources and numerous cultural and heritage sites. Previous studies have examined various aspects relating to Mt. Yunqui, including rural settings, vegetation, cultural performance, ecological dimensions, and promotional activities [13]. Some studies have used the importance–performance analysis (IPA) method to analyze China’s tourist attractions, demonstrating the effectiveness of the IPA method [5,14,15,16,17,18,19,20,21]. However, there are only a small number of studies identifying the destination attributes of Mt. Yunqiu and applying the traditional and modified IPA approaches.

Therefore, this study aims to identify the destination attributes at Mt. Yunqiu and to utilize the traditional and revised IPA approaches and compare the results of the three approaches. This study can provide important insights for an understanding of destination decision-making among tourists and their subjective evaluation using the IPA approaches. The results can contribute to identifying strengths and weaknesses and provide guidance to overcome the challenges faced and improve the quality of the travel experience. Managing cultural and heritage destinations effectively and efficiently can increase a destination’s competitiveness and enhance its unique image through improvements in its attributes. The results of this study can provide a deeper understanding to improve tourists’ satisfaction and promote sustainable tourism development. Moreover, this study can help local governments and managers to formulate more effective policies, optimize resource allocation, and promote local economic diversification, which could lead to the economic revitalization of Shanxi Province.

2. Literature Review

2.1. Destination Attributes

Destination attributes are crucial in understanding what motivates tourists, shapes their images of destinations and their preferences, and influences their decision-making processes [8]. Destination attributes refer to the prominent features of a travel destination that travelers consider important ([22], p. 857). Um and Crompton [23] and Crompton and Ankomah [24] propose the destination choice set model and the three-stage funnel model, respectively. Tourists’ destination choice is affected by the push and pull factors of travel destinations and travel constraints [23,24].

Previous studies have proposed various destination attributes that are related to pull factors and demonstrated the multi-dimensional aspects of travel destinations [8,9,25]. Destination attributes influence tourists’ emotions, the perceived destination’s image, and their destination choice [26,27,28,29,30]. Many scholars have proposed that destination attributes can be contextual, emphasizing that the importance of destination attributes varies according to the cultural background, tourist segment, and each destination’s characteristics [8,25,31,32]. This has been reflected in research conducted in various contexts, such as ski resorts [33], cruise ships [34], medical tourism [35], the Muslim population [36], wine tourism [37], and religious heritage destinations [38].

Numerous studies have demonstrated that destination attributes are positively associated with memorable travel experiences, tourist satisfaction, and destination loyalty [25,36]. In this regard, the IPA approaches are essential approaches for the identification of tourists’ perceptions and the performance of destination sites [39]. Researchers have examined destination attributes by using different IPA approaches [39,40,41]. Recently, Wu and Yang [42] derived important destination attributes from customer views and comments and applied the IPA approach. Nguyen, Ryu, and Yim [39] investigated key destination attributes among Vietnamese tourists visiting Korea, employing three different IPA approaches. Their study demonstrated the usefulness of the IPA methods in comparing and evaluating destination attributes. Previous research on travel attributes around mountains included their history and culture, safety, facilities, activities, natural environments, and accommodation [43,44,45,46,47]. The present study utilizes the IPA approaches [39] to compare the destination attributes at cultural heritage sites and assess their importance and performance at these destinations.

2.2. Importance–Performance Analysis

IPA is one of the most widely used methodological tools within the tourism literature [40,48,49,50,51,52,53,54,55,56]. IPA was first proposed by Martilla and James [53] to determine the product or service attributes that enterprises should pay attention to in order to improve their customer satisfaction [57]. In the traditional IPA of Martilla and James [53], importance is commonly determined along the X-axis, while performance is represented on the Y-axis in two-dimensional plots. These plots are divided into four quadrants based on the average values of importance and performance. By visualizing results through an IPA matrix, it is possible to understand the importance of certain attributes; this can be used to indicate which specific attributes are important for improvement, and whether the current state of another property should be continued. The four quadrants are Quadrant I (keep up the good work), Quadrant II (possible overkill), Quadrant III (low priority), and Quadrant IV (concentrate here) [58].

Previous research on IPA has emphasized the importance of tourists’ expectations before visiting a destination and their satisfaction with the travel experience, to gain insights for the development of the destination [59,60,61]. IPA is employed to identify differences between stakeholders’ perceptions of the importance of attributes and how they are managed [62]. Albayrak et al. [63] used the IPA approach to understand the competitiveness of destinations. Moreover, Wang et al. [19] evaluated advantages and disadvantages using the IPA method. Recently, Suárez-Rojas et al. [64] evaluated the performance of whale-watching operators using the traditional IPA method and suggested strategies to overcome challenges, achieve the optimal operational conditions, and ensure the sustainable management of this activity.

The IPA approaches has been a topic of ongoing discussion, highlighting and examining various challenges and limitations [58,65,66]. One of the issues regarding the traditional IPA is that importance and satisfaction are not independent of each other, and the relationship between them is asymmetrical [58,65]. To address the problems of Martilla and James [53]’s traditional IPA, the three-factor theory was proposed by Kano [65]. Lai and Hitchcock [62] studied the importance of specific characteristics of leisure resorts by combining IPA with three-factor theory. Abenoza et al. [67] examined the determinants of traveler satisfaction. According to three-factor theory [65,68], service attributes are divided into three factors, and the different effects of attributes on satisfaction are identified. Yin et al. [69] evaluated the satisfaction with and importance of living environments by applying IPA and three-factor theory. The priorities of community self-improvement and competition among different communities were determined. Matzler et al. [70] used IPA and Kano’s customer satisfaction model as their research method in an empirical study on the customer satisfaction of a supplier in the automobile industry.

Vavra [66] proposed a modified method for the manipulation of Kano’s theory for measurements. Unlike conventional IPA, instead of using an IPA matrix, as shown in Figure 1, the direct evaluation of tourists is set as the explicit importance, and the Y-axis uses overall satisfaction as a variable to obtain a regression coefficient to form the implicit importance [70]. Lee [71] used the IPA of Martilla and James and that of Vavra to study and analyze the audience’s views on the broadcasting of Korean baseball organizations. Jou and Day [72] integrated three-factor theory and Vavra’s [66] IPA into a three-dimensional IPA method to identify key service quality attributes for online hotel bookings. Deng and Li [73] explored the derived importance of tourist destination attributes using the revised IPA. Preziosi et al. [74] explored how consumers evaluate the green attributes implemented by hotels that have received ecolabels, to assess whether they can be considered stimulating factors among hotel service quality attributes. Hu et al. [75] introduced a research model that combines impact asymmetry analysis (IAA) with IPA [66]. Asymmetric impact–sentiment–performance analysis (AISPA) examines how different service features affect customer satisfaction by measuring their importance and performance.

However, the regression coefficient may reflect the multicollinearity between the independent variables during the analysis [66], so the use of partial correlation analysis is recommended instead [58]. In one study, each attribute was transformed using the natural logarithm, and a partial correlation coefficient (PCC) with overall satisfaction was calculated through partial correlation analysis and used as the importance value of each attribute [57,58]. As a result of the direct evaluation of consumers, satisfaction is transformed into a natural logarithm (LN) on the X-axis, and the overall satisfaction and PCC are transferred to the Y-axis, i.e., relative importance. Furthermore, the respective attributes can be set. This better illustrates the change in consumers’ actual perceptions compared to the absolute value of materiality and prevents the attributes in the traditional IPA from being concentrated in Quadrants I and III [58], thereby reducing the errors that can result from invalid IPA results [58,73].

The IPA method proposed by Deng [58] is used by many scholars in various fields. For example, Coghlan [76] used Deng’s IPA to determine the extent of the impact of various attributes on visitor satisfaction and their asymmetric contributions. Huang [77] adopted Deng’s [58] IPA to assess domestic and foreign tourists’ perceptions of Chinese tour guides’ performance. Ku and Mak [78] adopted the method to evaluate the difference between the perceived implicit importance and the performance of attributes among residents and tourists in Hualien, Taiwan. Liu [79] used Deng’s [58] IPA to study the UK’s cultural tourism image. Caber et al. [41] used the same analytical approach to compare distinct destination attributes. Ku and Hsieh [80] compared the traditional and revised IPA to explore whether fitness programs align with the competencies required of fitness professionals and further explored the similarities and differences between them. Schroeder et al. [81] applied the traditional and revised IPA [58], importance grid analysis (IGA), and penalty–reward contrast analysis (PRCA) to investigate how identified activities influence turkey hunters’ satisfaction. Each of the three methods has its advantages and disadvantages, and the conclusions drawn through comparisons are more accurate and convincing than those drawn individually. The IPA method has been applied to other fields, such as cultural ecosystem services [44], marine tourism [82], the halal tourism market [83], gastronomic tourism [84], culture heritage sites [85], and sports [86]. In addition, scholars have used a method combining IPA with structural equation modeling (SEM) to study urban sustainable development [87]. Information about the four quadrants of the three IPA approaches is presented in Figure 1.

3. Methodology

3.1. Research Site

There are many cultural and heritage attractions and beautiful scenic areas that attract tourists in China [88]. Shanxi is one of the earliest sites from which humans originated and the center of the Yellow River civilization. It not only has a beautiful natural landscape of mountains and water, but it also integrates nature and humanity, which characterizes its ecotourism industry. However, the development of tourism in Shanxi faces three challenges: (1) the nexus between economic growth and ecological preservation; (2) tourists’ problematic behavior, impacting the natural and cultural environment and overall experience; and (3) a decline in satisfaction due to low-quality travel experiences [89].

In sum, tourism resources need to be protected; there is a need to bolster the development of the market and improve the facilities. There are many mountains in Shanxi; however, many of them have not yet been developed. There are also mountain-related tourist attractions that have been developed, such as Wutai Mountain. Compared to other tourist attractions, Mt. Yunqiu remains relatively unknown and is in the early stages of development. These less developed tourist attractions are considered more valuable for research than fully established ones. With a diverse ecosystem and abundant natural resources, such destinations offer substantial opportunities for tourism [52]. Mt. Yunqiu is a distinctive destination that seamlessly combines tourism, entertainment, wellness, and cultural experiences and exchanges.

The Mt. Yunqiu Scenic Area has received 710,000 tourists, with an income of USD 14.21 million. While the number of tourists in Mt. Yunqiu is lower than it was in 2019, it holds a significant share of the tourism industry (55%) in the city that it is situated in [13,90]. The preferences of tourists regarding their experiences play a crucial role in the management of tourism destinations; understanding tourist satisfaction enables managers to offer facilities and services that are aligned with tourists’ expectations [52]. To date, there has been limited exploration of tourists’ perspectives regarding Mt. Yunqiu.

3.2. Research Procedure

The research procedure is illustrated in Figure 2. A pool of cultural and heritage attributes around Mt. Yunqiu in Shanxi Province, China, was identified. These cultural and heritage attributes encompassed various aspects of travel attractions and tangible and intangible cultural heritage components based on previous research, such as facilities, services, outdoor activities, natural and cultural environments, and price [8,22,35,39]. Furthermore, an exploratory factor analysis (EFA) was conducted to uncover underlying factors or dimensions within the identified cultural and heritage attributes. Following this identification, this study applied three different approaches of IPA to assess the perceived importance and performance of the attributes and factors. The three approaches comprised the traditional and modified IPA approaches of (1) Martilla and James [53], (2) Vavra [66], and (3) Deng [58]. Martilla and James’s [53] method is suitable for the intuitive understanding of the relationship between importance and satisfaction. Vavra [66] helped to optimize and improve low-performing attributes. Deng [58] extended the traditional IPA by considering the overall impact of the attributes on a destination’s competitiveness and attractiveness, reflecting a broader discussion on destination management and development strategies. In summary, these three IPA methods each have their own advantages in analyzing destination attributes and are commonly used by researchers, and they are therefore selected here. Finally, this study presents the results of t-tests conducted on the IPA to examine the differences between the mean importance and performance scores of the attributes. IPA plots were created for all three approaches. Moreover, comparisons of the IPA plots provide important information and identify similarities and differences.

3.3. Data Collection and Analysis

The questionnaire provided a brief explanation of this study on the front page and included two sections. The first was related to measuring the importance and performance of the cultural and heritage attributes around Mt. Yunqiu in Shanxi Province, including satisfaction. The second measured demographic characteristics, which were used to describe the respondents’ characteristics. The questionnaire was created on online websites and distributed to the panel of a research company in China. This study focused on data collection from travelers who were aged 18 years and above and had visited Mt. Yunqiu in Shanxi Province within the last year. This study used screening questions to obtain data from individuals who had traveled to Mt. Yunqiu in Shanxi Province, China. A total of 401 questionnaires were distributed from February to March 2023. After removing the respondents who were not fully engaged in the questionnaire and did not respond to all of the questions, a total of 350 questionnaires were utilized.

SPSS 25.0 was used for data analysis. First, a descriptive analysis was conducted to illustrate the demographic characteristics of the respondents. Second, an exploratory factor analysis (EFA) was conducted to identify the underlying factors of the cultural and heritage attributes and to test the convergent and divergent validity and internal reliability of the measurement items [91]. Third, three IPA approaches, those of Martilla and James [53], Vavra [66], and Deng [58], were used, and the results were visualized in the four quadrants. Finally, a t-test analysis was conducted to compare the results of the three IPA approaches.

4. Results

4.1. Demographic Information

As shown in

Table 1. Demographic information.

Variable	Category	Frequency	Percentage (%)
Gender	Male	182	52.0
	Female	168	48.0
Age	Less than 20	56	16.0
	20–29	72	20.6
	30–39	56	16.0
	40–49	61	17.4
	50–59	51	14.6
	60 or more	54	15.4
Job	Expert	30	8.6
	Staff	75	21.4
	Student	65	18.6
	Civil servant	49	14.0
	Self-employed	38	10.9
	Teacher	45	12.8
	Unemployed	28	8.0
	Soldier	20	5.7
Marital status	Unmarried	139	39.7
	Married	211	60.3
Monthly income	Less than CNY 2000–5000	69	19.7
	CNY 2000–5000	80	22.9
	CNY 5000–8000	75	21.4
	CNY 8000–10,000	74	21.1
	CNY 10,000 or more	38	10.9
	Other	14	4.0
Place of Residence	Eastern China	97	27.7
	Southern China	52	14.9
	Central China	37	10.6
	Northern China	76	21.7
	Northeast China	27	7.7
	Northwest China	18	5.1
	Southwest China	40	11.4
	Hong Kong, Macau, Taiwan	3	0.90
Education	High school or less	108	30.9
	Attended college	47	13.4
	Bachelor’s degree	150	42.9
	Postgraduate degree	45	12.8

Note: USD 1 = CNY 7.26.

, a descriptive analysis was used to determine the demographic information of the 350 participants. Among them, 182 were males (52%) and 168 were females (48%). The majority of the respondents fell within the 20 to 29 age range, accounting for 72 respondents (20.8%), followed by the 40–49 age category (17.4%). Regarding occupation, the participants consisted of 75 employees (21.4%), 65 students (18.6%), 49 civil servants (14.0%), 45 teachers (12.9%), and 38 self-employed respondents (10.9%). Approximately 60.3% of the respondents reported that they were married. The household income levels and the residence areas of the respondents varied. The RMB 2000–5000 range represented the highest portion (n = 80, 22.9%). In terms of the place of residence, Eastern China was the most frequently reported (n = 97, 27.7%). In terms of education, approximately 42.9% of the respondents reported having a bachelor’s degree.

4.2. Validity and Reliability Measurement

To verify the feasibility of projects aiming to increase the attraction of the destination attributes in Mt. Yunqiu, an EFA was conducted to identify the latent factors within them. The analysis results are shown in

Table 2. Results of factor analysis and reliability and validity.

Factor	Variable	Factor Loading	Eigenvalue	Variance Explained	α
Factor 1 Facilities	(11) Food taste	0.806	7.337	17.895	0.945
	(10) Number of restaurants	0.794
	(13) Senior lounge	0.792
	(7) Entertainment	0.788
	(6) Convenience facilities	0.784
	(14) Rooms for mothers and infants	0.782
	(8) Shopping centers	0.779
	(9) Transportation facilities	0.774
	(15) Guidance system	0.773
	(12) Food variety	0.766
	(5) Accommodation facilities	0.762
Factor 2 Services	(25) Souvenirs and specialties	0.797	6.573	16.031	0.935
	(31) Varied sightseeing routes	0.781
	(23) Tour guide’s explanation	0.776
	(30) Public welfare	0.764
	(22) Tour guide’s attitude	0.763
	(28) Bus station and airport pick-up service	0.762
	(27) Parking lot	0.761
	(24) Tour guide’s knowledge level	0.761
	(26) Service center	0.751
	(29) Business services	0.746
Factor 3 Outdoor Activities	(34) Zipline over water	0.783	4.590	11.196	0.901
	(37) Trapeze	0.771
	(38) Bungee jumping	0.766
	(32) Mount Yun Ice Cave Group	0.764
	(35) Cliff swing	0.747
	(36) Gliding	0.734
	(33) Mandarin Duck Bridge	0.732
Factor 4 Price	(21) Price of souvenirs	0.825	4.292	10.469	0.913
	(18) Cost of entertainment	0.794
	(16) Accommodation costs	0.790
	(19) Tickets	0.778
	(17) Food expenses	0.778
	(20) Transportation expenses	0.762
Factor 5 Visibility	(39) Visibility and awareness	0.743	1.784	4.352	0.836
	(40) Media	0.717
	(41) Tourist destination impression	0.638
Factor 6 Clean Environment	(3) Cleanliness	0.855	1.620	3.951	0.757
	(4) Safety	0.809
Factor 7 Natural and cultural Resources	(1) Natural scenery	0.862	1.528	3.727	0.713
	(2) Cultural attractions	0.787

Note: α = Cronbach’s KMO = 0.945; Bartlett’s χ² = 9000.037 (p = 0.000); total variance explained = 67.621%.

. Finally, 41 attribute items were obtained, which were then divided into 7 factors. The eigenvalues of all factors were above 1.0, the explanatory power of variance was between 3.727% and 17.895%, and the explanatory power of the total variance was 67.721%. It can be seen from the above data that the obtained factors were feasible. The sphericity result of Bartlett’s factor analysis was statistically significant (p < 0.001), and the Kaiser–Mayer–Olkin (KMO) value was 0.945, which was suitable for factor analysis. The commonness of each item of the factor analysis was 0.591 to 0.813, and the factor-bearing capacity of each item was 0.862 to 0.638. Cronbach’s alpha was obtained in order to verify the reliability of the extracted factors through the EFA. The results of Cronbach’s alpha ranged from 935 to 713, as shown in Table 2; however, the values of all factors were above 0.6, verifying the internal consistency and reliability of all factors [91].

4.3. Average Rankings of Importance and Performance

To understand the importance and performance of the selected attributes of the Yunqiu Mountain scenic spot, the answers of tourists who had experienced the sights of the mountain were analyzed through technical statistics. The results are shown in

Table 3. Average rankings of importance and performance.

Rank	Importance	Mean	Rank	Performance	Mean
1	(11) Food taste	4.20	1	(33) Mandarin Duck Bridge	4.20
2	(21) Price of souvenirs	4.20	2	(35) Cliff swing	4.14
3	(15) Guidance system	4.16	3	(23) Tour guide’s explanation	4.12
4	(10) Number of restaurants	4.13	4	(25) Souvenirs and specialties	4.09
5	(18) Cost of entertainment	4.13	5	(36) Gliding	4.08
6	(7) Entertainment	4.09	6	(27) Parking lot	4.06
7	(14) Rooms for mothers and infants	4.06	7	(17) Food expenses	4.03
8	(20) Transportation expenses	4.06	8	(26) Service center	4.02
9	(37) Trapeze	4.03	9	(37) Trapeze	4.02
10	(13) Senior lounge	4.02	10	(19) Tickets	3.97
11	(12) Food variety	3.99	11	(31) Various sightseeing routes	3.97
12	(19) Tickets	3.99	12	(32) Mount Yun Ice Cave Group	3.96
13	(34) Zipline over water	3.99	13	(28) Bus station and airport pick-up service	3.93
14	(8) Shopping centers	3.95	14	(34) Zipline over water	3.90
15	(6) Convenience facilities	3.92	15	(20) Transportation expenses	3.89
16	(17) Food expenses	3.92	16	(22) Tour guide’s attitude	3.88
17	(38) Bungee jumping	3.92	17	(6) Convenience facilities	3.86
18	(9) Transportation facilities	3.88	18	(8) Shopping centers	3.84
19	(16) Accommodation costs	3.85	19	(24) Tour guide’s knowledge level	3.84
20	(28) Bus station and airport pick-up service	3.85	20	(38) Bungee jumping	3.84
21	(36) Gliding	3.85	21	(2) Cultural attractions	3.83
22	(5) Accommodation facilities	3.84	22	(1) Natural scenery	3.81
23	(31) Various sightseeing routes	3.81	23	(18) Cost of entertainment	3.81
24	(33) Mandarin Duck Bridge	3.79	24	(10) Number of restaurants	3.80
25	(24) Tour guide’s knowledge level	3.77	25	(29) Business services	3.79
26	(41) Tourist destination impression	3.75	26	(9) Transportation facilities	3.77
27	(27) Parking lot	3.73	27	(30) Public welfare	3.75
28	(35) Cliff swing	3.72	28	(40) Media	3.75
29	(30) Public welfare	3.69	29	(15) Guidance system	3.73
30	(32) Mount Yun Ice Cave Group	3.65	30	(16) Accommodation costs	3.73
31	(29) Business services	3.64	31	(11) Food taste	3.69
32	(40) Media	3.62	32	(21) Price of souvenirs	3.65
33	(25) Souvenirs and specialties	3.61	33	(7) Entertainment	3.64
34	(23) Tour guide’s explanation	3.56	34	(41) Tourist destination impression	3.62
35	(2) Cultural attractions	3.53	35	(5) Accommodation facilities	3.61
36	(26) Service center	3.52	36	(12) Food variety	3.57
37	(1) Natural scenery	3.50	37	(14) Rooms for mothers and infants	3.52
38	(22) Tour guide’s attitude	3.48	38	(13) Senior lounge	3.48
39	(39) Visibility and awareness	3.48	39	(39) Visibility and awareness	3.48
40	(3) Cleanliness	3.45	40	(3) Cleanliness	3.42
41	(4) Safety	3.45	41	(4) Safety	3.41

Note: Attribute number in parentheses.

. In terms of importance, the most important attribute for tourists is “(11) food taste” (M = 4.20). In contrast, “(4) safety” (M = 3.45) is considered the least important. In terms of satisfaction, tourists are most satisfied with “(33) Mandarin Duck Bridge” (M = 4.20). Furthermore, “(4) safety” (M = 3.41) is considered the most unsatisfying attribute.

4.4. T-Test of Importance and Performance

The differences in satisfaction and importance in the sample of Mt. Yunqiu’s attributes were analyzed using the t-test. Regression analysis is the process of deriving the intrinsic importance (β) from the explicit importance, as described by Vavra [66]. Moreover, regression analysis is a partial correlation analysis to derive the relative importance (PCC) from the absolute satisfaction, as described by Deng [58]. The results are shown in

Table 4. T-test results.

No.	Variable	Martilla and James [53]’s IPA				Vavra [66]’s IPA	Deng [58]’s IPA
		Mean		Difference		Intrinsic Importance	Relative Importance
		I	P	I-P	p	β	PCC
1	Natural scenery	3.50	3.81	−0.31	0.000 ***	0.020	0.025
2	Cultural attractions	3.53	3.83	−0.30	0.001 **	0.066	0.061
3	Cleanliness	3.45	3.42	0.03	0.713	0.014	0.027
4	Safety	3.45	3.41	0.04	0.649	0.082	0.068
5	Accommodation facilities	3.84	3.61	0.24	0.004 **	−0.111	−0.072
6	Convenience facilities	3.92	3.86	0.06	0.435	0.101	0.060
7	Entertainment	4.09	3.64	0.45	0.000 ***	0.059	0.010
8	Shopping centers	3.95	3.84	0.11	0.179	0.028	0.047
9	Transportation facilities	3.88	3.77	0.11	0.160	0.002	−0.018
10	Number of restaurants	4.13	3.80	0.33	0.000 ***	0.009	−0.018
11	Food taste	4.20	3.69	0.51	0.000 ***	−0.060	−0.040
12	Food variety	3.99	3.57	0.42	0.000 ***	0.063	0.074
13	Senior lounge	4.02	3.48	0.54	0.000 ***	0.190	0.125
14	Rooms for mothers and infants	4.06	3.52	0.5	0.000 ***	−0.138	−0.117
15	Guidance system	4.16	3.73	0.44	0.000 ***	0.015	0.024
16	Accommodation costs	3.85	3.73	0.12	0.148	−0.120	−0.101
17	Food expenses	3.92	4.03	−0.11	0.166	0.010	0.018
18	Cost of entertainment	4.13	3.81	0.31	0.000 ***	0.153	0.108
19	Tickets	3.99	3.97	0.02	0.779	0.039	0.040
20	Transportation expenses	4.06	3.89	0.17	0.036 *	−0.067	−0.068
21	Price of souvenirs	4.20	3.65	0.55	0.000 ***	0.136	0.108
22	Tour guide’s attitude	3.48	3.88	−0.40	0.000 ***	−0.111	−0.097
23	Tour guide’s explanation	3.56	4.12	−0.56	0.000 ***	0.127	0.090
24	Tour guide’s knowledge level	3.77	3.84	−0.07	0.381	0.137	0.129
25	Souvenirs and specialties	3.61	4.09	−0.49	0.000 ***	−0.012	−0.019
26	Service center	3.52	4.02	−0.50	0.000 ***	0.138	0.095
27	Parking lot	3.73	4.06	−0.33	0.000 ***	0.118	0.103
28	Bus station and airport pick-up service	3.85	3.93	−0.08	0.323	−0.053	−0.012
29	Business services	3.64	3.79	−0.15	0.068	−0.030	−0.050
30	Public welfare	3.69	3.75	−0.06	0.441	−0.074	−0.036
31	Various sightseeing routes	3.81	3.97	−0.16	0.041 *	−0.018	0.015
32	Mount Yun Ice Cave Group	3.65	3.96	−0.31	0.000 ***	−0.018	−0.005
33	Mandarin Duck Bridge	3.79	4.20	−0.41	0.000 ***	0.031	0.035
34	Zipline over water	3.99	3.90	0.09	0.266	−0.106	−0.097
35	Cliff swing	3.72	4.14	−0.42	0.000 ***	0.118	0.056
36	Gliding	3.85	4.08	−0.23	0.003 **	−0.027	−0.057
37	Trapeze	4.03	4.02	0.01	0.911	0.028	0.062
38	Bungee jumping	3.92	3.84	0.08	0.338	0.068	0.053
39	Visibility and awareness	3.48	3.48	0.00	1.000	0.085	0.017
40	Media	3.62	3.75	−0.13	0.147	0.085	0.097
41	Tourist destination impression	3.75	3.62	0.13	0.149	−0.014	−0.024

Note: * p < 0.05; ** p < 0.01; *** p < 0.001.

. The difference between satisfaction and importance was more than 5% for 18 attributes out of a total of 41 items: “(3) cleanliness”, “(4) safety”, “(6) convenience facilities”, “(8) shopping centers”, “(9) transportation facilities”, “(16) accommodation costs”, “(17) food expenses”, “(19) tickets”, “(24) tour guide’s knowledge level”, “(28) bus station and airport pick-up service”, “(29) business services”, “(30) public welfare”, “(34) zipline over water”, “(37) trapeze”, “(38) bungee jumping”, “(39) visibility and awareness”, “(40) media”, and “(41) tourist destination impression”. It was found that this difference was not significant. Meanwhile, other attributes were found to be statistically significant at a significance level of 5%, and it could be assumed that there was a difference in the mean values of importance and satisfaction.

The t-test results indicate whether there is a significant difference between the mean importance and performance ratings for each destination attribute. The differences in the average values of performance and importance for the tourist destination attributes of Mt. Yunqiu were identified. It was found that the “(21) price of souvenirs” (I-P = 0.55) was the attribute with the largest difference. Next, “(39) visibility and awareness” (I-P = 0.00) was found to be an attribute that did not have a difference. In addition, the “(23) tour guide’s explanation” (I-P = −0.56) can be considered to have a higher average value of satisfaction than importance.

4.5. Comparison of IPAs

This study drew conclusions by comparing the three IPA methods. Figure 3 presents the results of Martilla and James [53]. Figure 4 provides the results of Vavra [66], and Figure 5 presents the results of Deng [58]. Because 7 factors and 41 attributes were compared, 48 factors were included in the analysis. Each of the three methods has its own advantages and disadvantages, and the conclusions drawn through a comparison are more accurate and convincing than those drawn individually. Moreover, the outcomes obtained through the three approaches are compared in

Table 5. Results of IPA matrix.

Quadrant	Martilla and James [53]’s IPA	Vavra [66]’s IPA	Deng [58]’s IPA
1	Keep up the good work (12)	Keep up the good work (11)	Keep up the good work (16)
	F3, F4, 6, 8, 17, 19, 20, 28, 34, 36, 37, 38	F4, 6, 7, 8, 12, 13, 18, 19, 21, 37, 38	F2, F4, F7, 2, 6, 8, 17, 19, 23, 24, 26, 27, 33, 35, 37, 38
2	Concentrate here (13)	Concentrate here (13)	Concentrate here (9)
	F2, F7, 2, 22, 23, 24, 25, 26, 27, 31, 32, 33, 35	F5, F6, F7, 2, 4, 23, 24, 26, 27, 33, 35, 39, 40	F3, 20, 22, 25, 28, 31, 32, 34, 36
3	Low priority (10)	Low priority (10)	Low priority (12)
	F5, F6, 1, 3, 4, 29, 30, 39, 40, 41	F2, 1, 3, 22, 25, 29, 30, 31, 32, 41	F1, 5, 7, 9, 10, 11, 14, 16, 29, 30, 39, 41
4	Possible overkill (13)	Possible overkill (14)	Possible overkill (11)
	F1, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 21	F1, F3, 5, 9, 10, 11, 14, 15, 16, 17, 20, 28, 34, 36	F5, F6, 1, 3, 4, 12, 13, 15, 18, 21, 40

Note: (1) natural scenery; (2) cultural attractions; (3) cleanliness; (4) safety; (5) accommodation facilities; (6) convenience facilities; (7) entertainment; (8) shopping center; (9) transportation facilities; (10) number of restaurants; (11) food taste; (12) food variety; (13) senior lounge; (14) rooms for mothers and infants; (15) guidance system; (16) accommodation costs; (17) food expenses; (18) cost of entertainment; (19) tickets; (20) transportation expenses; (21) price of souvenirs; (22) tour guide’s attitude; (23) tour guide’s explanation; (24) tour guide’s knowledge level; (25) souvenirs and specialties; (26) service center; (27) parking lot; (28) bus station and airport pick-up service; (29) business services; (30) public welfare; (31) various sightseeing routes; (32) Mount Yun Ice Cave Group; (33) Mandarin Duck Bridge; (34) zipline over water; (35) cliff swing; (36) gliding; (37) trapeze; (38) bungee jumping; (39) visibility and awareness; (40) media; (41) tourist destination impression. Factor 1: facilities; Factor 2: services; Factor 3: outdoor activities; Factor 4: price; Factor 5: visibility; Factor 6: clean environment; Factor 7: natural and cultural resources.

. The names of the attributes are also listed in Table 5.

Firstly, the quadrant of the matrix presented using the IPA of Martilla and James [53] shows that 12 properties (F3, F4, 6, 8, 17, 19, 20, 28, 34, 36, 37, 38) appeared in Quadrant I, and 13 attributes (F2, F7, 2, 22, 23, 24, 25, 26, 27, 31, 32, 33, 35) appeared in Quadrant II. Ten attributes (F5, F6, 1, 3, 4, 29, 30, 39, 40, 41) were distributed in Quadrant III. Thirteen attributes (F1, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 21) were distributed in Quadrant IV.

Next, the analysis based on Vavra’s method [66] located 11 properties (F4, 6, 7, 8, 12, 13, 18, 19, 21, 37, 38) in Quadrant I. Thirteen attributes (F5, F6, F7, 2, 4, 23, 24, 26, 27, 33, 35, 39, 40) appeared in Quadrant II. Ten attributes (F2, 1, 3, 22, 25, 29, 30, 31, 32, 41) were distributed in Quadrant III. There were 14 properties (F1, F3, 5, 9, 10, 11, 14, 15, 16, 17, 20, 28, 34, 36) distributed in Quadrant IV.

Finally, Deng’s method [58] placed 16 properties (F2, F4, F7, 2, 6, 8, 17, 19, 23, 24, 26, 27, 33, 35, 37, 38) in Quadrant I. Nine attributes (F3, 20, 22, 25, 28, 31, 32, 34, 36) appeared in Quadrant II. Twelve attributes (F1, 5, 7, 9, 10, 11, 14, 16, 29, 30, 39, 41) were distributed in Quadrant III. Finally, 11 attributes (F5, F6, 1, 3, 4, 12, 13, 15, 18, 21, 40) were distributed in Quadrant IV.

From the above results, we can see that the same attribute will be distributed in different quadrants based on the different IPA research methods. The reason is that the three IPAs use different methods. Specifically, Martilla and James mainly rely on simple weighted average and two-dimensional matrix icon visualization analysis. It was also confirmed that the results of the three IPA techniques were distributed in the same quadrant as each other. The compared results of the three IPA approaches are presented in Table 5. The six attributes of (F4) Factor 4—expenses; (6) convenience facilities; (8) shopping center; (19) tickets; (37) trapeze; and (38) bungee jumping—were all distributed in Quadrant I. The importance and satisfaction levels are all high, so this referred to as a “Keep up the good work” area. This is an area where tourists visiting Mt. Yunqiu indicate a high level of importance and satisfaction, so it is necessary to maintain the attributes in this area. The three attributes “(29) business services”, “(30) public welfare”, and “(41) tourist destination impression” were all distributed in Quadrant III. Although the tourists were dissatisfied with the attributes located in this area, they themselves did not value them. The need to increase the investment in these areas is low, and it should not be considered at present. Finally, one attribute, “(15) guidance system”, was distributed in Quadrant IV. This is a case where satisfaction was low but consumers still found it important, even though they were not satisfied with the product or service. Therefore, this is an area that needs to be significantly improved by investing in the service or product in order to increase its satisfaction.

5. Discussion

It is important to explore the attributes of areas that are in the process of developing into popular tourist destinations and to create effective and efficient strategies to enhance their attraction in the growth stage. This study examines and compares the outcomes using the IPA approaches of Martilla and James [53], Vavra [66], and Deng [58] to understand tourists’ perceptions of and satisfaction with the destination attributes of Mt. Yunqiu. The results provide empirical evidence and suggest various approaches to understand travelers’ perceptions of the importance and performance of the cultural and heritage sites in the area surrounding Mt. Yunqiu.

5.1. Theoretical Implications

This study has important theoretical implications. A total of 41 attributes of cultural and heritage sites were identified to determine the key attributes that attract tourists to the destination. In addition, it is important to explore the attributes of areas that are in the process of developing into popular tourist destinations and to create effective and efficient strategies to enhance their attraction in the growth stage. A total of seven factors were identified: (F1) facilities, (F2) services, (F3) outdoor activities, (F4) price, (F5) visibility, (F6) clean environment, and (F7) natural and cultural resources. These destination attributes are closely related to tourist satisfaction and future intended behaviors. To the best of the authors’ knowledge, no previous studies have focused on identifying these in the specific context of Mt. Yunqiu. Therefore, this study has contributed significantly to expanding our knowledge about destination attributes.

After calculating the mean scores of the destination attributes, the corresponding sample t-tests were conducted to identify the differences between the importance and satisfaction of the selected attributes of Mt. Yunqiu. The difference between satisfaction and importance was not significant for 18 attributes out of a total of 41: “(3) cleanliness”, “(4) safety”, “(6) convenience facilities”, “(8) shopping centers”, “(9) transportation facilities”, “(16) accommodation costs”, “(17) food expenses”, “(19) tickets”, “(24) tour guide’s knowledge level”, “(28) bus station and airport pick-up service”, “(29) business services”, “(30) public welfare”, “(34) zipline over water”, “(37) trapeze”, “(38) bungee jumping”, “(39) visibility and awareness”, “(40) media”, and “(41) tourist destination impression”. In addition, other metrics were found to be statistically significant at a significance level of 5%, and it can be assumed that there is a difference in the mean values of importance and satisfaction.

Previous studies have identified tourist destination attributes. For instance, Suhartanto and Triyuni [92] applied the destination loyalty model and confirmed that the two attributes of expenses and convenience affect tourists’ satisfaction and thus their loyalty [92]. Soliman [93] applied the theory of planned behavior (TPB) and confirmed that tourists’ impressions of a destination have a certain influence on their intention to visit it again. In comparison, this study offers more comprehensive perspectives by considering multiple attributes and contributing to providing evidence on specific tourist destination attributes.

More specifically, this study compared the results of the traditional and modified IPA approaches. The destination attributes were plotted in four quadrants after following the three IPA approaches, and the results were compared. Specifically, six attributes were placed in Quadrant I: expenses; convenience facilities; shopping centers; tickets; trapeze; and bungee jumping. These results indicate that the Mt. Yunqiu tourist attraction needs to be continuously maintained by management. Three categories, namely business services, public welfare, and tourist destination impression, were all placed in the “low priority” section of Quadrant III. Tourists were dissatisfied with these items, but they are also of low importance, so they do not require additional investments in resources. Finally, one attribute, the guidance system, was in the area of “concentrate here” in Quadrant IV. The plotted results provide a deeper understanding of these attributes and demonstrate the usefulness of the IPA approaches. However, each method has certain limitations. For example, Martilla and James’s (1977) method has the disadvantages of asymmetry and non-independence; Deng [58] proposed a new IPA method to solve the multicollinearity problem that may appear in the regression analysis used by Vavra [66]. Therefore, the results that were consistent across the three methods highlight the areas that need continuous investment (maintaining the status quo) and the areas that should be improved first, thereby improving the accuracy of the overall evaluation. By comparing the three IPA approaches, the disadvantages of the traditional IPA can be minimized.

Many previous studies on IPA typically use either one IPA method [44,94] or two IPA methods [57,71,80], with only a few investigations employing all three IPA methods simultaneously. Although the results derived from analyzing the three IPA methods are different, they are still useful in identifying similarities and differences. From an academic point of view, this study used three IPA methods, and the results provide various approaches to evaluate destination attributes.

5.2. Managerial Implications

The results present tourists’ perceptions of performance and their satisfaction regarding various cultural and heritage attractions at Mt. Yunqiu. This study identified several practical implications of its results. The six attributes in Quadrant I that attracted tourists to Mt. Yunqiu were expenses, convenience facilities, shopping centers, tickets, trapeze, and bungee jumping. To improve the “expenses” attribute, managers should strictly regulate consumption within the tourist area, including but not limited to restaurants and accommodation, to ensure more reasonable prices and prohibit vendors from overcharging. To enhance the “convenience facilities”, for instance, the managers should consider adding more rest areas and restrooms. Ensuring that these facilities are clean and regularly maintained will enhance the visitor experience. Managers should also take action to strengthen the “shopping center” attribute by including more local specialty shops and souvenir stores to diversify the offerings, thereby avoiding homogenization with other tourist destinations. Regarding tickets, the managers should simplify the ticket-purchasing process to avoid long queues. For trapeze and bungee jumping, the managers should regularly inspect and maintain the equipment to ensure its safety. In addition, marketers could promote the safety of these activities through marketing campaigns to attract adventure enthusiasts. Three categories, namely business services, public welfare, and tourist destination impression, have been placed in the “low priority” section of Quadrant III. Tourists are dissatisfied with these aspects. Despite their lower importance, it is crucial to continue monitoring them and ensure their basic maintenance. This approach can uphold the minimal standards, contributing to an overall improvement in tourists’ satisfaction without requiring significant additional investment.

Based on the results, several managerial implications can be suggested. First, tourists primarily use guidance systems and a range of services at mountains, such as trail information, maps and brochures, and digital travel information. Launching a guidance app and providing free Wi-Fi throughout the tourist area, along with a user-friendly interface for ease of use, could be a good choice. Additionally, the website currently offers language options consisting of Chinese, English, Korean, and Japanese. Managers should consider expanding this to more languages to accommodate visitors from other regions.

Second, the results suggest that the convenience of facilities such as transportation and parking are positively related to tourists’ satisfaction and intention to return. Improving the convenience and accessibility of transportation can make it easier for tourists to reach and travel around various attractions. By empirically demonstrating the importance of convenience facilities and transportation in influencing tourists’ satisfaction, the results provide important insights into the destination attributes driving tourist satisfaction.

Third, the results indicated that while safety was identified as the most unsatisfactory attribute, it was not perceived as the most important by the tourists. Nevertheless, improvements in safety can indirectly enhance the overall visitor experience. This includes maintaining well-marked and -maintained trails, providing clear safety instructions, and ensuring that emergency services are easily accessible. Additional safety features such as guardrails at viewpoints, regular patrols, and clear signage indicating potential hazards can contribute to a safer environment without overwhelming tourists with safety concerns. In this way, the overall satisfaction of tourists at Mt. Yunqiu can be increased.

Fourth, it is recommended that scholars adopt the focus and methods of this study to conduct comparative studies on other tourist attractions in China or internationally. The IPA method can be applied to a variety of tourist attractions around the world, such as national parks [95], the arid west, or the far north [96]. However, different regions and environments may require different variables and weights to be considered to accommodate specific geographic and seasonal variations. In addition, there may be potential biases in visitor feedback due to specific events, which need to be considered in future research.

Finally, the results suggest that the use of the traditional and modified IPA approaches is essential in understanding the destination attributes that tourists perceive as important and their evaluation of their performance. Empirical evaluation is important in improving the service quality of destination attributes and enhancing tourists’ satisfaction. Scholars and practitioners need to learn about and use the IPA approaches to allocate limited resources and improve destinations. The results of the IPA approaches can help them to make decisions based on data obtained from tourists. The identified attributes for improvement should be managed through step-by step strategies. Moreover, the results of the IPA approaches can be updated according to changing tourist preferences by integrating the obtained information into management practices to enhance tourist satisfaction and optimize the tourist experience. Workshops and research training programs for practitioners can ensure that these IPA approaches are implemented and integrated into destination management practices. Moreover, collaboration with scholars in research on tourist satisfaction and destination attribute evaluation can continue to enhance the accuracy and applicability of the findings.

5.3. Limitations and Suggestions for Future Studies

First, our results may not be generalizable, because this study concentrated on Mt. Yunqiu, China. While this is a well-known tourist destination in Mainland China, its destination attributes may be contextual and different from those of other tourist destinations. Therefore, it is recommended that scholars adopt the focus and approach of the current study and apply them to various tourist destinations for examination. The IPA approaches can be applied to compare travel destinations within China or across different destinations.

Second, this study used an online survey and included young tourists. However, Mt. Yunqiu is also popular among older adults, who may not have been able to access the online survey. Future research could include more older adults to identify similarities and differences based on generational cohorts. Moreover, the results of the IPA approaches depend on subjective evaluations among tourists. Tourists’ expectations or experiences may vary, and seasonality and external factors could influence the results of the IPA. Given these limitations of the IPA approaches, future research should consider determinants that can influence the variability in tourists’ perceptions, seasonality, weather, and external factors.

Finally, this study focused on the IPA approaches. Other methodological approaches can also be used. Using quantitative research, researchers can quantify and verify tourists’ perceptions of scenic spots and explore the influential factors regarding tourists’ satisfaction with and loyalty to scenic spots. Moreover, by using qualitative methods such as in-depth interviews, future research could obtain comprehensive insights into tourists’ perceptions of the scenic areas around Mt. Yunqiu.

6. Conclusions

Our results provide insights into the current status of selected attributes of Mt. Yunqiu in Shanxi Province, highlighting areas for maintenance and improvement and suggesting others that should be developed. This study provides a total of 41 destination attributes. It also provides the results of three IPA approaches. Finally, the results are compared. The results present overperforming or underperforming attributes relative to their perceived importance among tourists. The destination attributes are visualized and plotted in four quadrants. Moreover, the t-test results provide the relative importance and performance of the destination attributes and suggest how different areas can be prioritized for improvement. The destination attributes at Mt. Yunqiu can offer improved products and services to maintain a competitive edge in the tourism market. Natural and cultural heritage attributes are core destination attractions at Mt. Yunqiu. The t-test results, IPA plots, and comparisons of the attributes within the quadrants offer a deeper understanding of tourists’ perceptions and priorities regarding cultural and heritage attributes. It is important to develop strategies for sustainable development during the growth period of tourist destinations. It should be ensured that negative environmental impacts are minimized. Moreover, tourists should be encouraged to engage with natural and cultural heritage resources, use eco-friendly products and facilities, and follow responsible tourism practices.