The Relationships between Tourism Destination Competitiveness, Empowerment, and Supportive Actions for Tourism

Abstract

This study examines the relationships between tourism destination competitiveness (TDC), empowerment, and support for tourism among residents. It also examines the role of empowerment as a mediator in the relationship between TDC and residents’ support for tourism. A total of 711 respondents were used. This study uses descriptive analysis, two-stage factor analysis, and structural equation modeling (SEM). The results show that seven antecedents of TDC, namely natural, cultural, contextual competitiveness, complementarity, accommodation, infrastructure, and technology, are identified. Psychological empowerment has most positive effects on five facets of destination competitiveness. The results reveal a mediation effect of empowerment between the destination competitiveness and resident supportive behaviors for tourism. The outcome of this proposed model is support for tourism, which means active supportive actions for tourism among residents. The psychological and economic empowerment dimensions are shown to have positive effects on resident support for tourism. Understanding residents’ empowerment and support can be useful for those who develop political policies and action plans for sustainable tourism development. The results can suggest a model for a sustainable destination management, increasing the global reputation of travel destination, and advocacy for sustainable development goals.

1. Introduction

Borderlands reflect the characteristics of borderlines that divide neighboring countries [1]. The Demilitarized Zone (DMZ) between North and South Korea is an alienated borderland, with the lowest level of contact or interaction between the two countries. Many areas in this zone are located beyond the Civilian Control Line (CCL) and are not accessible to civilians or tourists. As a result, the zone has been protected from human disturbance for approximately six decades and unintentionally protects the habitats of wild animals and wild plants such as the red-crowned or Manchurian crane. Wetlands and freshwater and coastal ecosystems have also been well-preserved [2].

Previous research regarding DMZ areas and tourism has been explored in various perspectives such as the motivation of dark tourism [3], the economic values of well-preserved natural environments [4,5], border images and visual representations [6], and the role of tourism as a peaceful connection between North and South Korea [7]. Recently, relevant stakeholders’ behaviors in DMZ areas have been studied [8,9,10,11].

The South Korean government has attempted to establish a new symbolic representation of the DMZ as a destination in an effort to move away from negative images such as strict border-crossing restrictions and continuous conflicts with North Korea by using promotional messages and destination images such as security, defense, peace, and ecology in the DMZ areas [2]. Border areas combine unique natural resources and geopolitical characteristics of nations [12]. The attractive components for tourism around border territory such as historical and cultural battle sites, well-preserved wildlife habitats, and economic values of natural resources have been identified [3,4,5,13].

In this regard, tourism destination competitiveness (TDC) can be a useful concept to identify attractiveness and scarcity of tourism resources [14,15,16]. TDC has been studied for decades and is crucial to understanding regional resources and attractions at a tourism destination [16,17,18,19,20,21,22,23]. Empowerment is one of the most important fundamental elements in sustainable destination management and in relevant stakeholders’ decision-making processes for tourism development [24,25]. DMZ areas are composed of different communities across the Korean peninsula.

Research has been carried out regarding the demand for tourism in the DMZ areas; however, few studies have considered the perspective of residents living in provinces near the DMZ areas [8,11]. Moreover, scholars have called for further research to be carried out regarding host communities in the DMZ areas and residents’ support for tourism and engagement in decision-making processes which are crucial for sustainable tourism [8,9,11]. A need arises for research regarding residents’ behavior in support of destination management and tourism development. Therefore, the results of this study can fill these literature gaps; here, we empirically tested social exchange theory (SET) [26]. Social exchange theory (SET) has been widely utilized to investigate resident support for tourism [27]. Social exchange theory (SET) has its root in sociology theory and illustrates the resource exchange between actors such as individuals or groups in the interactive context [26]. Previous research has demonstrated that a mutually reciprocal relationship between residents and the tourism industry helps facilitate positive social exchanges [27]. Local residents perceived positive outcomes of tourism have also been demonstrated, and they are more willing to be empowered to support tourism for their communities [28].

In this study, we aim to investigate the TDC components of the DMZ areas. It also examines the relationships between TDC, empowerment, and support for tourism among residents. Furthermore, it examines the role of empowerment as a mediator in the relationship between TDC and residents’ support for tourism. The results of this study can enrich the previous literature, as we present empirical evidence. These results enable researchers and practitioners to examine welcoming or hostile reactions and bottom-up support for tourism among residents and visitors [24]. We also attempt to identify TDC at a regional level and its effect on support for tourism among residents living in the provinces close to DMZ areas, and we further examine whether such an association is indirectly related through empowerment.

2. Literature Review

2.1. The DMZ and Tourism

The DMZ is 250 km (i.e., 155 miles) long and has a total width of 4 km (i.e., 2.5 miles) on the military demarcation line (MDL). The DMZ extends from three administrative provinces (i.e., Incheon, Gyeonggi-do, and Gangwon-do) and includes nine cities and counties (i.e., Ganghaw, Gimpo, Paju, Yeoncheon, Cheolwon, Hawcheon, Yanggu, Inje, and Goseong [2,29]. Tourists visiting the DMZ and Panmumjom are required to present ID cards at the closest access point to North Korea [30]. There is little information on North Korea, and when tourists visit Mt. Gumgang, they are not permitted to take photographs or make contact with residents [31,32].

Understanding borderlands remains a research interest of many scholars; a handful of studies concern the DMZ and tourism [3,4,5,6,7]. Hunter [6] analyzed pictures of the DMZ and Dokdo posted online. The results showed that visual representations can reflect the effect of territorial boundaries and produce soft power resources to build bilateral balance and transform the identity of the border by sharing festivals, events, and various international organizations’ engagement on social media. In previous studies, the push–pull motivation theory was utilized as a theoretical framework, and the motivations of dark tourism were explored [33]. Bigley and colleagues [3] examined the motivations behind traveling to war-related destinations such as the DMZ among Japanese tourists. They revealed five motivations (e.g., a nature-based motivation, a war- and battle-field-related motivation, a political motivation, a learning motivation, and an exploration motivation). Furthermore, Lee and colleagues [34] demonstrated the relationship between emotional, functional, and economic values; satisfaction; and behavioral intentions. In a few studies regarding the DMZ, the ecological values of major attractions and resources, such as endangered species, Panmunjom, and cultural heritage sites (i.e., DMZ), were assessed, and the economic values of these aspects were estimated [4,5]. Tensions have eased between North and South Korea as a result of communication through summits and their joint participation in the 2018 Winter Olympics opening ceremony in Pyeongchang; the two Korean governments have also agreed to develop new economic cooperation in terms of tourism development [35].

2.2. Destination Competitiveness

Destination competitiveness refers to the ability of tourism destinations to offer a competitive experience and a high standard of environment [15]. An increasing capacity for destination competitiveness is crucial, because it can increase the number of tourists and the amount of travel expenditure and improve the physical environments for both residents and tourists [16]. A travel destination with a high level of destination competitiveness offers unique experiences and overall superior attractiveness to visitors than that of competing or substitutable destinations [15]. Moreover, destination competitiveness has well-known benefits, e.g., it boosts positive economic impacts [36], maintains market shares and creates advantageous market positions [14,17,37], and enables sustainable tourism development and destination management [38]. Scholars proposed have multiple dimensions of TDC [15,16,18,19,21,39,40], suggested different approaches in different contexts [22,41,42,43,44,45,46], and important pillars of the Travel and Tourism Competitiveness Index (TTCI) have been reported by WEF [47].

In this study, after examining multiple facets of TDC, we included seven factors which fit the regional context. By utilizing the tripartite model [48], we created multiple components of TDC based on the previous literature [15,16,19]. The nucleus that indicates core attractions at the travel destinations includes natural, cultural, and contextual competitiveness. Natural competitiveness refers to natural attractiveness, wild animals, fauna and flora, and well-preserved habitats within the travel destination. Cultural competitiveness refers to cultural heritage and traditional customs. Contextual competitiveness refers to unique destination attributes. The tourist belt, which is related to facilities and services for tourists, includes three dimensions: infrastructure, technology, and accommodation. Infrastructure refers to basic facilities such as transportation, health care centers, and safety and security. Technology refers to technologies that enhance travel experiences such as travel information applications, AR/VR applications, and reservation applications through tourists’ mobile phones. Accommodation, which is included in the superstructure, refers to traditional and alternative accommodation options at the destination. Finally, complementarity is related to the entertainment aspect. Complementarity includes special events and activities and entertainment. The effect of these multiple components of TDC on social impacts such as empowerment and support for tourism among residents can expand our knowledge of competitiveness at destinations.

2.3. Destination Competitiveness and Empowerment

Previous research regarding empowerment in tourism focuses on various concepts of empowerment [24,28]. In this study, we focused on three types of empowerments, namely psychological, political, and economic empowerment [25,28]. Psychological empowerment refers to self-esteem and pride among residents because visitors value unique natural and cultural and heritage resources at destinations [25]. It is identified as one of the aspects that are most positively related to the intangible benefits of tourism. Previous research indicated that psychological empowerment is one of the most influential factors which have effects on place attachment [49]. Political empowerment refers to a sense of control and power. It is also related to the motivation to engage in decision-making processes and the intention to make suggestions for collective actions [25]. Political empowerment is rarely exhibited among residents because of a lack of knowledge and low awareness regarding tourism development and tourism marketing [50]. Political empowerment tends to have different effects because of the democratic culture and the autonomous and bottom-up tourism development approaches [49]. Economic empowerment refers to economic benefits generated from tourism and improved environments (i.e., infrastructure) around destinations as tourism develops [25]. Tourism activities foster residents’ economic empowerment [51]. The economic benefits derived from tourism are considered to be multiple empowerment dimensions which lead to resident support [28]. Residents can increase their household incomes through partaking in tourism business activities such as selling handcrafted goods, providing guest houses, running restaurants, and serving as local travel agencies and guides [51]. The recognition of resources, facilities, information, and available activities can enhance empowerment [50]. Competitive attributes in a destination which are positively associated with a volume of tourists increase the economic benefits. As a result, residents show a higher level of empowerment as they can utilize their resources [52]. Recently, technology has been identified as an important element of destination competitiveness and a facilitator for the experience of tourism at destinations [38,53]. For example, Joo and colleagues [54] found that knowledge about tourism and attractions at destinations influence the formation of empowerment among residents. Strzelecka, Boley, and Woosnam [49] revealed that residents who have more in-depth knowledge and understand natural and cultural resources show a higher level of place attachment. Moreover, those with higher levels of place attachment show more positive associations with empowerment. However, a lack of knowledge regarding TDC and a lack of sense of control in decision-making processes regarding tourism can disempower residents [50]. Su and colleagues [55] found that female residents in rural areas attained empowerments through rich tangible and intangible cultural and heritage resources that attracted tourists to communities and provided them meaningful experiences. Empowered women’s engagement in tourism industry helped overcome poverty, desire to learn, express positive emotions, and show active participation in decision-making process. Park and Kim [56] identified that slow city tourism movement facilitated residents’ empowerment and increase residents’ involvement managing tourism resources such as infrastructure, facilities, local food, and landscape for sustainability. Therefore, in this study, we proposed the following hypothesis:

Hypothesis 1(a,b,c): 

Natural competitiveness is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 2(a,b,c): 

Cultural competitiveness is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 3(a,b,c): 

Contextual competitiveness is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 4(a,b,c): 

Infrastructure positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 5(a,b,c): 

Technology is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 6(a,b,c): 

Accommodation is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

Hypothesis 7(a,b,c): 

Complementarity is positively associated with three dimensions of empowerment (psychological(a)/political(b)/economic(c) empowerment).

2.4. Resident Support for Tourism

Residents represent one of the core stakeholders, and supportive behavior from residents, such as positive and friendly attitudes toward tourists, can generate positive images of a destination and promote tourism [57]. Support for tourism refers to supportive behaviors associated with loyalty toward tourism development [58]. Residents play a crucial role in developing sustainable tourism, and in particular, empowerment in host communities is positively related to supportive behaviors for tourism and active engagement in tourism development [49].

A mutually reciprocal relationship between residents and the tourism industry helps to facilitate positive social exchanges [27]. In the previous literature, it was identified that when residents perceive benefits from tourism, they are more willing to be empowered to support tourism for their communities [28]. In previous studies, the different effects of multiple empowerments were shown, and empirical studies regarding empowerment and residents’ action were called for [8,28]. Specifically, Nunkoo and So [58] examined several antecedents of residents’ support. Positive benefits from tourism, a sense of control and power in tourism, and positive economic impacts had positive effects on support among residents. Boley and the colleagues [28] examined the effects of three empowerments on residents support. Economic benefits, and psychological empowerment showed positive effects on resident support. However, political empowerments showed statistically negatively significant effect on support. Movono and Dahles [59] identified that empowerment of women entrepreneurs in tourism showed a positive effect on supportive actions for tourism in their communities. Recently, Joo and colleagues [54] found that social and political empowerment had positive associations with participation in political action among residents. In this study, we proposed the following hypothesis (Figure 1):

Hypothesis 8: 

Psychological empowerment has a positive effect on support.

Hypothesis 9: 

Political empowerment has a positive effect on support.

Hypothesis 10: 

Economic empowerment has a positive effect on support.

3. Method

3.1. Measures

The survey questionnaires included three sections. First, screening questions and general resident information were provided. The second section of the questionnaire included measurement items of the proposed model with regard to TDC, empowerment, and resident support. The last section included questions about demographic characteristics. There were seven TDC dimensions, namely natural, cultural, and contextual competitiveness components, infrastructure, technology, accommodation, and complementarity [15,16,19]. A total of 36 items of TDC were tested. After repeatedly analyzing the latent dimensions, 10 items were excluded in the final data. Specific information regarding the measurement items of TDC is presented in the CFA results. A total of 13 items from three empowerment dimensions [25,28,54,60] were used, and after testing the overall model fit indices, social empowerment was not included in this model. Previous research regarding supportive behavior for tourism was used to assess the level of supportive behavior for tourism [57,58]. Six items of support for tourism were measured, and two items were excluded (e.g., ‘Tourism helps my community growth in the right direction’). Four items of supportive behavior for tourism were included [58]. The measurement items were assessed by using a five-point Likert scale anchored from 1) strongly disagree to 5) strongly agree. When designing the pool of questionnaire items, we enlisted the help of tourism practitioners who were DMZ tour program administrators and experts and graduate students who were majoring in hospitality and tourism to proofread our items and provide us with comments on them.

3.2. Data Collection and Data Analysis

The brief purpose of this research project is provided at the beginning of this article. The content validity and face validity of all of the measurement items were determined, and their criterion validity was examined based on the previous literature. After receiving feedback and comments, we revised and improved the research questionnaire. All survey items are written in Korean because the target respondents are domestic residents living in cities and county-level municipalities in two provinces along border areas. The weblink created on an online survey platform was distributed by visiting several research sites and contacting residential communities. Respondents who completed the survey received small incentives. A quota sampling approach was used. The data are collected from residents living in cities and towns in two provinces (i.e., Gyeonggi-do and Gangwon-do) near the DMZ areas. Geonggi-do has two cities and one county-level municipality (i.e., Goyang, Kimpo, Paju, Yeoncheon) and Gangwon-do has four county-level municipalities (i.e., Inje, Cherwon, Hawcheon, Goseong). The data are collected from the subgroups based on residence areas. The respondents are from three cities and regions such as Gimpo/Goyang (n = 184, 25.9%), Paju (n = 71, 10%), and Yeoncheon (n = 54, 7.6%) in Geonggi-do (province) and five three cities and regions such as Cherwon (n = 51, 7.2%), Inje (n = 44, 6.2%), Hwacheon (n = 122, 17.2%), Yanggu (n = 157, 22.1%) and Goseong (n = 28, 3.9%) in Gwangwon-do (province).

Through the survey weblink, the respondents were provided with instructions and a brief explanation of this study and were asked to answer all of the questions and submit their survey. Approximately, 1144 potential respondents were identified—through screening questions—to fit to the research criteria and started the online questionnaire in 2021. Considering that the data were collected during the COVID-19 pandemic, online survey was selected. This study attempts to minimize common method bias (CMB) by following research procedures and providing no complex and ambiguous measurement items for respondents. Moreover, several indices of reliabilities and validities are checked. Respondents who dropped out and who did not fully engage in the survey and did not answer all questions were excluded. A total of 711 respondents’ surveys were used. Several analysis approaches, such as descriptive analysis, two-stage factor analysis, and structural equation modeling (SEM), were conducted with STATA 17.0 [61].

4. Results

4.1. Demographic Information

Respondents’ demographic information is provided in

Table 1. Demographic characteristics.

Variable	Category	n	%
Gender	Male Female	212 499	29.82 70.18
Age (M = 37)	18–24	24	3.38
	25–29	41	5.77
	30–34	207	29.11
	35–39	208	29.25
	40–44	136	19.13
	45–49	41	5.77
	50 and over	54	7.59
Marital status	Single	225	31.65
	Married	470	66.10
	Other	16	2.25
Education level status	High school Associate Bachelor’s degree Post-graduate	2 107 576 26	0.28 15.05 81.01 3.66
Occupation	Full-time	54	7.59
	Part-time	427	60.06
	Self-employment	69	9.70
	Unemployed	125	17.58
	Other	36	5.06
Monthly household income (KRW)	Under KRW 2,000,000 (Under USD 1700) KRW 2,000,000—less than KRW 4,000,000 (USD 1700—less than USD 3500) KRW 4,000,000—less than KRW 6,000,000 (USD 3500—less than USD 5200) KRW 6,000,000—less than KRW 8,000,000 (USD 5200—less than USD 7000)	63 286 214 122	8.77 39.83 29.81 16.99
	KRW 8,000,000—less than KRW 10,000,000 (USD 7000—less than USD 8700)	25	3.48
	KRW 10,000,000 and over (USD 8700 and over)	8	1.11

Note: USD1 = 1150 Korean Won (KRW).

. In terms of age groups, approximately half were in their 30s, and the mean age was 37. Regarding gender, there were more female participants (n = 499, 70.18%) than male participants (n = 212, 29.82%). Approximately 66.10% were married (n = 470), and 31.65% were single. In terms of employment status, about 60.06% were full-time workers (n = 427), 17.58% were operating their own business or self-employed (n = 125), and 9.70% were part-time workers (n = 69). The majority (81.01%) indicated that they had obtained a bachelor’s degree (n = 576), and 15.05% of the respondents had an associate degree (n = 107). Regarding monthly household income, household income levels varied; approximately 39.66% of the respondents fell into the KRW 2,000,000–less than KRW 4,000,000 bracket (n = 282), and 29.96% reported that they earned between KRW 4,000,000 and less than KRW 6,000,000 (n = 213).

4.2. Latent Dimension Identification and Factor Analysis

The sequential stages of factor analyses were used to explore the dimensions of the measurement items. Two factor analyses, i.e., exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), were conducted [62]. From the EFA results, seven factors of destination attractiveness, three empowerment dimensions, and one dimension of support for tourism were found. All of the results met the recommended values (the Kaiser Meyer Olkin value was above 0.8, and Bartlett’s tests were all significant (p < 0.000)). First, a total of 25 items of TDC were classified into seven dimensions. The factor loadings ranged from 0.533 to 0.844. Second, three dimensions of empowerment were found from 13 items. The factor loadings were between 0.658 and 0.808. Third, the EFA results of supportive behaviors for tourism showed good fit and included four items which presented factor loadings ranging from 0.783 to 0.811. The EFA results indicated that the Cronbach’s Alpha ranged from 0.706 to 0.853 and reached the recommended value [63].

Several approaches were adopted to determine the reliability and validity of the results. The results affirmed the discriminant validity, convergent validity, and composite reliability [64,65]. The standardized regression coefficients estimated via the CFA and the recommended model fit indices are presented in

Table 2. The measurement items of TDC, descriptive analysis, and CFA results.

	Item	Factor Loading	Items (n)	M (SD)
(1) Natural	National parks, nature reserves	0.675	4	3.762 (0.744)
	Wild animals, flora, and fauna	0.730
	Natural scenery	0.723
	Well-preserved habitats for extinct animals	0.696
(2) Cultural	Traditional play, dance, and performance	0.699	3	3.467 (0.789)
	Agricultural village culture	0.673
	Traditional cultural heritage	0.635
(3) Contextual	Front-line visit	0.642	4	3.819 (0.806)
	DMZ iron fence around border	0.754
	DMZ caves	0.787
	DMZ observatory	0.746
(4) Infrastructure	Safety, security, accident prevention, and problem-solving actions	0.724	4	3.203 (0.880)
	Communication, internet	0.674
	Transportation	0.757
	Health care/clinic/hospital	0.744
(5) Technology	DMZ Ecosystem information App	0.737	4	3.524 (0.789)
	DMZ AR/VR App	0.674
	DMZ local tourism internet information	0.745
	DMZ visit reservation App	0.744
(6) Accommodation	Motel, inn	0.722	3	3.192 (0.864)
	Hotel	0.730
	Alternative accommodation (e.g., homestay, Airbnb)	0.725
(7) Complementarity	Sports events, sports competition	0.676	3	3.444 (0.789)
	Music concerts and festivals	0.711
	Cultural events (movies, literature, art)	0.710
(8) Psy. Em	Tourism in the DMZ
	PsyEm 1	0.740	5	3.395 (0.749)
	PsyEm 2	0.694
	PsyEm 3	0.716
	PsyEm 4	0.701
	PsyEm 5	0.735
(9) Political Em	PoEM1	0.757	4	3.052 (0.848)
	PoEM2	0.799
	PoEM3	0.730
	PoEM4	0.693
(10) Eco. EM	EcoEM 1	0.771	4	3.085 (0.950)
	EcoEM 2	0.795
	EcoEM 3	0.742
	EcoM 4	0.751
(11) Support for tourism (ST)	ST1	0.722	4	3.708 (0.752)
	ST 2	0.731
	ST 3	0.676
	ST 4	0.626

and

Table 3. The CFA results of the dimensions.

	AVE	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) Natural	0.500	0.818 a	0.296 b	0.300	0.219	0.274	0.212	0.233	0.265	0.119	0.104	0.303
(2) Cultural	0.448	0.088 c	0.712	0.267	0.382	0.317	0.383	0.348	0.309	0.302	0.273	0.266
(3) Contextual	0.539	0.090	0.071	0.825	0.207	0.254	0.142	0.193	0.254	0.124	0.048	0.247
(4) Infra	0.526	0.048	0.146	0.043	0.788	0.308	0.360	0.323	0.303	0.308	0.185	0.224
(5) Technology	0.527	0.075	0.100	0.065	0.095	0.822	0.318	0.282	0.317	0.243	0.244	0.294
(6) Accom	0.527	0.045	0.147	0.020	0.130	0.101	0.754	0.299	0.251	0.300	0.303	0.209
(7) Complementarity	0.490	0.054	0.121	0.037	0.104	0.080	0.089	0.753	0.268	0.172	0.162	0.264
(8) Psy. EM	0.515	0.070	0.095	0.065	0.092	0.100	0.063	0.072	0.853	0.358	0.265	0.347
(9) Political EM	0.556	0.014	0.091	0.015	0.095	0.059	0.090	0.030	0.128	0.823	0.471	0.220
(10) Eco. EM	0.585	0.011	0.075	0.002	0.034	0.060	0.092	0.026	0.070	0.222	0.812	0.214
(11) Support	0.476	0.092	0.071	0.061	0.050	0.086	0.044	0.070	0.120	0.048	0.046	0.814

Goodness-of-fit of the model: Note: χ² (764) = 1818.835; p < 0.001; χ²/df = 2.381. Indices: GFI = 0.875; CFI = 0.923; TLI = 0.913; RMSEA = 0.044; SRMR = 0.042. a = the composite reliability. b = the correlation coefficients. c = the squared correlations.

. Moreover, Table S1 includes specific measurement items. The results demonstrated that indices with a good fit to the model were achieved, as shown in Table 2 and Table 3, and all of the values were acceptable and met the recommended model fit criteria [65,66].

The composite reliability (CR) showed values between 0.712 and 0.883 (Table 3). The AVE values were above 0.50, except for in three constructs: cultural attractiveness, complementarity, and support for tourism. These three constructs had values lower than or close to 0.50. However, all CRs and Cronbach’s alpha were above the recommended level and affirmed the reliability. Two sequential factor analysis approaches demonstrated convergent validity and discriminant validity [67].

4.3. Latent Dimension Identification and Factor Analysis

The sequential stages of factor analyses were used to explore the dimensions of the measurement items. Two factor analyses, i.e., exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), were conducted [62]. From the EFA results, seven factors of destination attractiveness, three empowerment dimensions, and one dimension of support for tourism were found. All of the results met the recommended values (the Kaiser Meyer Olkin value was above 0.8, and Bartlett’s tests were all significant (p < 0.000)). First, a total of 25 items of TDC were classified into seven dimensions. The factor loadings ranged from 0.533 to 0.844. Second, three dimensions of empowerment were found from 13 items. The factor loadings were between 0.658 and 0.808. Third, the EFA results of supportive behaviors for tourism showed good fit and included four items which presented factor loadings ranging from 0.783 to 0.811. The EFA results indicated that the Cronbach’s Alpha ranged from 0.706 to 0.853 and reached the recommended value [63].

Several approaches were adopted to determine the reliability and validity of the results. The results affirmed the discriminant validity, convergent validity, and composite reliability [64,65]. The standardized regression coefficients estimated via the CFA and the recommended model fit indices are presented in Table 2 and Table 3. The results demonstrated that indices with a good fit to the model were achieved, as shown in Table 2 and Table 3, and all of the values were acceptable and met the recommended model fit criteria [65,66].

The composite reliability (CR) showed values between 0.712 and 0.883 (see Table 3). The AVE values were above 0.50, except for in three constructs: cultural attractiveness, complementarity, and support for tourism. These three constructs had values lower than or close to 0.50. However, all CRs and Cronbach’s alpha were above the recommended level and affirmed the reliability. Two sequential factor analysis approaches demonstrated convergent validity and discriminant validity [67].

4.4. Structural Model

The model fit indices of the SEM presented satisfactory values (χ² = 1876.343, df = 771, χ²/df = 2.433, GFI = 0.871, CFI = 0.919, TLI = 0.910, RMSEA = 0.045, SRMR = 0.044 [64,65]. The results of the SEM revealed that natural competitiveness was significantly associated with psychological empowerment positively (β = 0.160, p < 0.05) and political empowerment negatively (β = −0.229, p < 0.05). However, natural attractiveness was not positively associated with economic empowerment (β = −0.022, p > 0.05). Cultural competitiveness was not statistically associated with psychological empowerment (β = 0.060, p > 0.05), but it was positively associated with political empowerment (β = 0.517, p < 0.001) and economic empowerment (β = 0.549, p < 0.001). Contextual competitiveness was not statistically associated with political empowerment (β = -0.011, p > 0.05), but it was positively associated with psychological empowerment (β = 0.199, p < 0.001) and was negatively related to economic empowerment (β = −0.333, p < 0.01). Infrastructure was not statistically associated with economic empowerment (β = −0.111, p > 0.05), but it was positively related to psychological empowerment (β = 0.126, p < 0.05) and political empowerment (β = 0.203, p < 0.01). Technology was positively related to all three types of empowerments: psychological empowerment (β = 0.233, p < 0.001), political empowerment (β = 0.203, p < 0.01), and economic empowerment (β = 0.320, p < 0.001). Accommodation showed no statistically significant association with the three types of empowerments. Complementarity was not statistically associated with economic empowerment (β = −0.178, p > 0.05), but it was positively associated with psychological empowerment (β = 0.165, p < 0.05) and was negatively related to political empowerment (β = −0.282, p < 0.01).

All three types of empowerments were statistically significantly related to supportive behavior for tourism. Moreover, psychological empowerment (β = 0.947, p < 0.001) and economic empowerment (β = 0.247, p < 0.001) were positively associated with supportive behavior for tourism, but political empowerment (β = −0.424, p < 0.001) showed a negative effect on supportive behavior for tourism. The SEM results in the proposed model are presented in Figure 2. Regarding the R squares, the R square for psychological empowerment was 0.592. The R square for political empowerment was 0.400. The R square for economic empowerment was 0.340. Finally, the R square for support for tourism was 0.658 (

Table 4. The SEM results.

				Coef.	z
H1a	Natural	→	Psych. EM	0.160 *	2.24
H1b	Natural	→	Political EM	−0.229 *	−2.55
H1c	Natural	→	Economic EM	−0.022	−0.23
H2a	Cultural	→	Psych. EM	0.060	0.56
H2b	Cultural	→	Political EM	0.517 ***	3.68
H2c	Cultural	→	Economic EM	0.549 ***	3.77
H3a	Contextual	→	Psych. EM	0.199 **	2.64
H3b	Contextual	→	Political EM	−0.011	−0.11
H3c	Contextual	→	Economic EM	−0.333 **	−3.32
H4a	Infra	→	Psych. EM	0.126 *	2.44
H4b	Infra	→	Political EM	0.203 **	3.09
H4c	Infra	→	Economic EM	−0.111	−1.63
H5a	Technology	→	Psych. EM	0.233 ***	4.18
H5b	Technology	→	Political EM	0.203 **	3.09
H5c	Technology	→	Economic EM	0.320 ***	4.39
H6a	Accommodation	→	Psych. EM	0.008	0.11
H6b	Accommodation	→	Political EM	0.175	1.88
H6c	Accommodation	→	Economic EM	0.181	1.88
H7a	Complementarity	→	Psych. EM	0.165 *	2.34
H7b	Complementarity	→	Political EM	−0.282 **	−3.09
H7c	Complementarity	→	Economic EM	−0.178	−1.90
H8	Psych. EM	→	Support	0.947 ***	18.59
H9	Political EM	→	Support	−0.424 ***	−5.01
H9	Economic EM	→	Support	0.247 ***	3.87

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

).

4.5. Indirect Effects

The results of indirect effects are presented in

Table 5. Indirect effects.

Indirect Paths			Coef.	z
Natural	→	Support	0.243 ***	3.54
Cultural	→	Support	−0.027	−0.28
Contextual	→	Support	0.111	1.63
Infra	→	Support	0.006	0.13
Technology	→	Support	0.214 ***	4.18
Accommodation	→	Support	−0.022	−0.35
Complementarity	→	Support	0.232 **	3.45

Note: ** p ≤ 0.01; *** p ≤ 0.001.

. Three dimensions of destination competitiveness presented statistically significant indirect effects on support, such as natural competitiveness (β = 0.243, p < 0.001), technology (β = 0.214, p < 0.001), and complementarity (β = 0.232, p < 0.01). However, four dimensions, i.e., cultural, and contextual competitiveness, infrastructure, and accommodation, did not show statistically significant indirect effects on residents’ supportive action for tourism.

5. Discussion and Conclusions

5.1. Theoretical Implications

In this study, we examine the relationship between TDC, three dimensions of empowerment, and residents’ supportive action for tourism. Furthermore, we examine the mediating role of three empowerment components between TDC and residents’ supportive action for tourism. The majority of DMZ areas have previously been restricted in terms of regional development and decisions regarding tourism development have been made by the government sectors. Therefore, the proposed model and the results can contribute to expand the knowledge and enrich previous research in hospitality and tourism. Understanding three empowerment components and residents’ supportive actions toward tourism development is crucial to maximize the benefits for host communities, develop unique regional features, and optimize sustainable destination management.

In this study, we provide important theoretical implications. First, the results show that seven antecedents of TDC, namely natural, cultural, contextual competitiveness, complementarity, accommodation, infrastructure, and technology, are identified. It is shown that contextual competitiveness had the highest mean scores among destination competitiveness factors, followed by natural competitiveness, technology, cultural competitiveness, and complementarity (see Table 2). However, it is shown that accommodation and infrastructure had the lowest mean scores. TDC is estimated by the volume of tourists and travel expenditure which lead to economic growth by focusing on the country level [17,36,47]. These results contribute to providing empirical evidence that residents perceive multiple facets of destination competitiveness as being strongly related to unique local environments and resources available at destinations.

Second, psychological empowerment has most positive effects on five facets of destination competitiveness—natural, contextual, infrastructure, technology, and complementarity—which is consistent with previous research [28,49]. Political empowerment is shown to have negative effects on two dimensions of destination competitiveness: natural and complementary competitiveness. Moreover, it presents positive associations with cultural competitiveness, infrastructure, and technology. Economic empowerment is shown to positively influence cultural competitiveness and technology, but it negatively influences contextual competitiveness. These results indicate that residents living in the provinces near DMZ areas are positively or negatively empowered in this research context.

Third, the results reveal a mediation effect of empowerment between destination competitiveness, i.e., natural competitiveness, technology, and complementarity and supportive behaviors for tourism. Natural resources in DMZ areas, festivals, various types of events and forums, and tourism-related technology appear to empower residents and facilitate residents’ supportive action for tourism.

Finally, the outcome of this proposed model is support for tourism, which means active supportive actions for tourism among residents. The psychological and economic empowerment dimensions are shown to have positive effects on residents’ support for tourism. However, political empowerment is shown to have a negative effect on supportive behavior for tourism. As pointed out in previous research [24], positive supportive action for tourism is not the only reaction among empowered residents. Empowered residents can express negative opinions toward tourism development. DMZ areas have been developed in the top-down manner and are still under strict regulations and development restrictions by law. Moreover, North Korea’s political actions can increase the negative impacts among host communities living near the borderlines of North and South Korea.

5.2. Practical Implications

These results can provide important practical implications for sustainable tourism management in DMZ areas. First, residents perceive a higher level of certain aspects of TDC, such as contextual, natural, and cultural competitiveness. Moreover, technology such as mobile applications, AR/VR, and online tourism information is identified as important aspects of destination competitiveness. Practitioners and governments need to maximize the utilization of competitive resources, develop travel products and activities, and build strategies for sustainable destination management.

Unique regional features such as contextual and natural competitiveness are considered as an important tourism resource among residents. As the number of the local population gradually decreases and the size of the military camps and units also reduces, the regions around DMZ areas need to determine solutions for economic revitalization. Practitioners and government officials need to develop effective strategies for utilizing tangible and intangible competitive components along the DMZ areas. Insignificant effects of accommodation on three empowerment dimensions were found. These results may indicate that residents perceive a lack of hotels and alternative accommodation for tourists within DMZ areas. Instead, these accommodation facilities are located in more popular mass touristic destinations away from DMZ areas. Moreover, tourists visiting DMZ areas tend to plan for a day trip or stay in other destinations after traveling to the DMZ. Interactions between tourists and host communities in superstructure such as traditional and alternative accommodations, restaurants, and local markets can facilitate memorable travel experiences at destinations [68]. Practitioners and governments develop core superstructure and create experiential programs that enable to facilitate interactions between tourists and residents and learn history and cultural heritage around the border areas. They also need to build short- and long-term strategic plans for destination management and development and provide capacity-building programs for relevant stakeholders at destinations.

Second, in this study, we focus on residents’ perception of TDC. The multiple dimensions of TDC can be useful for accumulating knowledge regarding comparatively competitive elements perceived by residents. The results can highlight the social impacts of tourism [69]. The results reveal that residents are both empowered and disempowered. Regarding DMZ areas, residents may perceive unique and advantageous resources around the destination. On the other hand, they may feel frustrated with strict regulations and legal restrictions on economic activities. Practitioners and governments need to focus on the improvement of the environment in terms of infrastructure, health-related facilities, and safety.

Third, in this study, we propose a model and allow researchers and practitioners to understand important antecedents that enhance empowerment among one of the important stakeholders, residents. Moreover, we obtain varying opinions of residents living in the provinces along DMZ areas. There are many cases of top-down tourism development in South Korea. Given that the defense is one of the most important priorities around border areas, opportunities have been scarcely available to provide residents’ opinions and engage in the decision-making process. In this study, we highlight residents’ perceptions of destination competitiveness, empowerment, and resident supportive action for tourism. Understanding residents’ empowerment and support can be useful for those who develop political policies and action plans for sustainable tourism development. The results can be used to suggest a model for a sustainable destination management, increasing the global reputation of travel destination, and advocacy for sustainable development goals (SDGs).

5.3. Limitation and Further Research Suggestions

The results of this study provide empirical evidence and have important implications; however, the limitations of this study should be noted. First, in this study, we focus on residents’ perceptions within a specific travel destination. The results do not provide generalized outcomes, and future research should replicate the proposed model in different contexts and among relevant stakeholders at destinations. Second, we utilize several destination competitiveness dimensions based on the tripartite model [48]. Multiple dimensions in this model may be suitable in this research context. Continuous research for scale development is necessary for the measurement of TDC at the local level. Future research is needed to identify contextual competitiveness dimensions and further critical dimensions that estimate the components of destination competitiveness. Third, we use one outcome factor: support for tourism among residents. There can be important outcome variables such as subjective well-being led by TDC and empowerment. Future researchers can have opportunities to identify the double-edged outcomes of tourism destination management and development and suggest important implications for sustainability.