Navigating the New Normal: The Role of Residents’ Involvement and Support in Sustainable Tourism Recovery

Abstract

This study seeks to provide a nuanced understanding of how residents’ views on tourism impacts and their awareness of the pandemic’s effects shape their attitudes and, consequently, influence their involvement and support for tourism by integrating emotional solidarity, the knowledge–attitude–practices theory, and social exchange theory. Furthermore, the study explores potential moderating factors such as gender and professional ties to tourism. Data were gathered from 545 residents of Manavgat, Turkey, following a cluster sampling scheme, confirming all ten hypotheses. The results reveal that residents’ knowledge and perceived positive impacts of tourism significantly forecast attitudes toward tourism and tourists, which explains their involvement and support. The results also demonstrate that residents with strong ties to tourism were more positive and involved than those with limited links to tourism. The complementary merger of theoretical frameworks enriches our understanding of residents’ support and involvement in tourism, shedding light on the intricacies of these relationships.

1. Introduction

In 2021, tourism directly or indirectly supported 289 million jobs (World Travel and Tourism Council (WTTC) [1]). Considering tourist arrival figures in 2022, the Middle East welcomed nearly the same number of visitors as in 2019—the highest percentage (83%) compared to other global regions [2]. The UNWTO theorized that tourists would prefer to visit destinations closer to home and look for the best value given the trying economic times. Turkey, commonly described as a bridge connecting the Middle East and Europe, welcomed 30 million international tourists in 2021. During that same year, tourism provided 693 million in earnings (11% of GDP) and created approximately 2.5 million jobs in the country. According to the WTTC, the majority of visitors (93%) in 2021 selected Turkey for leisure travel, with most (62%) arriving from other countries [1].

Antalya is often considered the hub of Turkish tourism, having received approximately 13 million visitors in 2022 [3]. Such statistics highlight how crucial tourism is for Turkey, specifically within Antalya. In fact, many Turks look to Antalya as a barometer in demonstrating a return to pre-pandemic levels.

As countries consider how to recover from COVID-19, there may never be a better opportunity to reflect on how crucial residents’ support for and involvement in tourism are in returning to a sense of normalcy [4,5,6]. It is improbable that tourism will be successful without the support and involvement of key stakeholders [3,7]. Similarly, the potential of sustainable tourism is contingent upon the quality of interactions between the stakeholders’ [8,9] and residents’ perceived impacts of tourism [10].

Various works [4,10,11,12,13,14,15,16] have indicated that residents’ perceptions of tourism and its accompanying impacts and emotional solidarity with tourists play a significant role in explaining residents’ support for sustainable tourism. While several scholars [17,18] have focused on residents’ support for tourism development (STD), little attention has been paid by scholars to determine residents’ involvement in tourism (IT) [4,5,11,15]. However, the above factors (i.e., STD and IT) are rarely [3,4,19] examined together, and researchers barely look into whether differences exist in residents’ attitudes, support for, and involvement in tourism across demographic (i.e., gender) and economic variables (i.e., professional ties to tourism) [20,21,22].

This study has multiple aims: (1) to determine how residents’ perceptions about the positive tourism impacts and their knowledge about the effect of the pandemic on tourism explain their attitudes and, ultimately, how those attitudes (i.e., towards tourism and tourists) influence their IT and STD. (2) To examine these relationships, we consider emotional solidarity along with the knowledge–attitude–practices theory and social exchange theory. (3) The final aim is to consider the role that gender and professional ties to tourism may have in moderating the proposed relationships.

The current research contributes to theory through the complementary merger of theoretical frameworks in explaining residents’ STD and IT. In addition to this, discovering residents’ knowledge of the effect of the pandemic on tourism, perceptions of tourism impacts, attitudes, and their behaviors will be beneficial to destinations and tourism authorities (e.g., tourism planners, DMOs, etc.) because they can base their decision to loosen precautionary or preventive measures and return to normalcy on residents’ insight. Briefly, the findings stated in this study will help DMOs identify which factors are most important for gaining residents’ support and fostering local involvement in tourism.

2. Literature Review

2.1. The Positive Impacts of Tourism and Social Exchange Theory (SET)

Social exchange theory (SET) has found widespread application in studying the link between residents’ perceptions of tourism impacts and their attitudes. This approach has been employed in various studies [18,19,23,24]. Researchers employing SET in the context of tourism have argued that residents who perceive positive impacts from tourism tend to be more supportive of and hold favorable attitudes toward tourism and tourists [14,25]. On the other hand, if they perceive tourism costs more than it benefits, the potential exists for residents to oppose tourism development, and by default, act hostilely toward tourists [24].

In a recent study, Nugroho and Numata [24] investigated the relationship between residents’ perceptions of tourism and their supportive attitudes, employing the SET framework. Their results revealed that residents who perceived tourism as beneficial were more likely to express support, consistent with earlier research findings [14,18]. Additionally, Nugroho and Numata [24] identified a negative association between residents’ supportive attitudes toward community-based tourism development in Indonesia and their perceived costs.

Furthermore, previous studies [19,25,26] used SET to explain how impacts perceived by residents alter their degree of attitudinal support for tourism. For instance, Martin et al. [25] and Plaza-Mejía et al. [14] revealed that residents tend to hold more positive attitudes toward both tourism and tourists if they perceive that they receive more positive impacts of tourism (PIT). As a result, this study suggests the following hypotheses:

H₁. 

Residents’ perceived PIT will be a significant predictor of their attitudes toward tourists (AT).

H₂. 

Residents’ perceived PIT will be a significant predictor of their attitudes toward tourism development (ATD).

2.2. Knowledge Regarding the Effect of the Pandemic on Tourism and the KAP Theory

The UNWTO reported that a 70% decline in foreign visitors to Turkey resulted in a loss of USD 93 billion in tourism income and a 9% decrease in the GDP [2]. However, in 2019, Turkey welcomed 51 million foreign tourists, generating approximately USD 30 billion in income [1]. In 2021, Turkey received around 25 million foreign visitors. Antalya was the second most visited city in the country, hosting 35% of these visitors (approximately 9 million) [2]. These findings underscore the critical importance of tourism both globally and in Antalya.

The knowledge–attitudes–practices (KAP) theory was first introduced by Mayo (1960) and applied to examine the interconnectedness of these three constructs [27]. The theory is a widely used framework to determine health-seeking travel behavior in the field of medicine [28,29]. However, few studies [27,30] have applied the KAP theory in a tourism context to examine residents’ attitudes and behaviors. The theory consists of three stages and seeks to explain how knowledge influences attitudes, ultimately determining behavior.

The first stage is knowledge, which refers to a person’s information about a particular destination or activity. In the current study, knowledge includes information about the risks associated with a particular activity or destination, such as health or safety concerns (i.e., residents’ knowledge about the impacts of the pandemic on tourism). An individual’s opinions and beliefs about a particular destination or activity are assessed later in the second stage—attitudes. In our research, we determine residents’ attitudes toward tourists (as an emotional response) and attitudes toward tourism (their perception of tourism development) in this second stage. Finally, practices refer to a person’s actual behavior concerning the destination or activity and include residents’ STD and the decisions they make concerning the degree to which they become involved in tourism [27,30].

Briefly, the KAP theory suggests that residents’ behaviors (i.e., tourism support and involvement) are influenced by their attitudes (towards tourism and tourists), which in turn are shaped by knowledge of the COVID-19 pandemic. Research shows that tourism was highly vulnerable to the coronavirus pandemic [31], which had a devastating effect on tourism activities, tourism-related jobs, tourism workers’ health, and operating costs [32,33,34], affecting residents of tourism destinations heavily. Shen and Yang [30] applied the KAP theory and found that residents’ knowledge of the pandemic significantly predicted their risk perception and their attitudes toward tourists and tourism. Furthermore, the results of their study indicated that residents’ knowledge and attitudes, in tandem with risk perception and social media usage, were predictors of support for tourism in China. Understanding the factors of KAP and using it as a framework in tourism studies can be critical for tourism businesses and policymakers to create effective marketing strategies, design sustainable tourism practices, and promote positive destination images. Therefore, the following hypotheses were proposed:

H₃. 

Residents’ subjective knowledge (SK) about the impacts of the pandemic on tourism will significantly influence their AT.

H₄. 

Residents’ SK will significantly influence their ATD.

2.3. Residents’ Attitudes about Tourism and Emotional Solidarity (ES) Theory

Spanning nearly two decades, the emotional solidarity (ES) construct and its accompanying theoretical framework have been employed with regularity in the tourism literature [17,35,36,37,38]. According to Woosnam [16], the theory examines the affective bond between locals and visitors, and the formers’ interactions, shared values, and behaviors influence how well they get along with the latter. Despite the ES scale possessing three distinct dimensions (i.e., sympathetic understanding, emotional closeness, and welcoming nature), several studies [3,38] have concentrated on the welcoming nature dimension as either an independent or dependent variable to investigate the intimate relationship between residents and tourists. For example, residents’ welcoming nature has been significantly influenced by their attitudes concerning both positive and negative tourism impacts [38].

Four items speaking to pride, community benefits of tourism, and treating visitors fairly are used to measure welcoming nature. Residents’ welcoming nature toward tourists (hereafter referred to as attitudes toward tourists) emphasizes how the former embraces or welcomes the latter and appreciates their visits [3]. In general, residents’ attitudes toward tourists (i.e., welcoming nature) have frequently been employed to predict their STD. This approach has been observed in various studies, including those by Hashemi et al. [18] and Joo et al. [13]. Rarely has the construct been examined to determine residents’ tourism involvement, including the following three current studies by Erul et al. [3], Aleshinloye et al. [11], and Rao and Lai [15].

For example, Hashemi et al. [18] examined residents’ support for snow ski tourism in Iran. The authors found that residents’ perceived economic benefits and their attitudes toward tourists were significant predictors of ski tourism support, while community attachment did not determine the outcome variable. Furthermore, Aleshinloye et al. [11] found that residents’ attitudes toward tourists significantly predicted their tourism involvement in tandem with two distinct forms of empowerment (i.e., social and political) in Central Florida. However, the question remains about how residents’ attitudes toward tourists (AT) predict their support and involvement in tourism. Based on those findings, the following hypotheses were created:

H₅. 

Residents’ AT will significantly explain their IT.

H₆. 

Residents’ AT will significantly explain their STD.

2.4. Linking Residents’ Attitudes about Tourism, Tourism Support, and Tourism Involvement

Tourism impacts have directly and sometimes adversely influenced residents [12,39]. Hence, considering residents’ support for tourism, examining their involvement, and determining the predictors of this support and involvement is crucial for sustainable tourism development, especially during and after global health pandemics [40]. A substantial body of literature has focused on residents’ tourism support [17,18,23,25,35,36,41,42]. On the other hand, some studies have examined tourism involvement [11,15,24,26] or analyzed both residents’ tourism support and involvement [3,4,19].

Furthermore, residents’ attitudes about tourism development were found to be one of the most vital predictors of support by tourism scholars [12,19,25,35,36]. For example, some researchers [12,43] have applied the theory of planned behavior to determine residents’ tourism support and found that residents’ attitudes about tourism, in tandem with other two TPB factors, significantly predict their tourism support. However, a few studies [24,26] tested the path between residents’ attitudes about tourism and involvement. For example, Nugroho and Numata [24] found that as residents’ involvement in tourism increased, they tended to be more supportive of tourism. Contrary to previous studies, this study has different claims that such a significant relationship can also be opposite (i.e., tourism involvement can be a dependent variable and attitudes about tourism can be an independent variable). By looking at the above studies, following two hypotheses were proposed:

H₇. 

Residents’ ATD will significantly explain their IT.

H₈. 

Residents’ ATD will significantly explain their STD.

2.5. Professional Ties to Tourism as a Moderator

Research has shown that employment within the tourism industry can shape individuals’ attitudes toward and support for tourism [20,25]. For instance, a study by Szromek et al. [22] discovered that local business owners engaged in tourism-related activities in Krakow, Poland, displayed a more positive attitude toward tourists than residents not involved in tourism businesses. Similarly, Andriotis [20] discovered that in Crete, Greece, residents who rely on tourism-related employment exhibited a more favorable attitude towards tourism and were more inclined to endorse tourism expansion than residents who worked in non-tourism-related industries.

Despite substantial research on employment and its impact on residents’ attitudes and STD, few studies have examined this construct as a moderating variable. Moreover, there has been a lack of in-depth examination of how employment, as a moderating variable, influences residents’ involvement in tourism. To fill this gap, the present study explores the moderating effect of professional ties to tourism (as measured through employment in the industry) on residents’ involvement in tourism and proposes the following hypotheses:

H_9a. 

The association between perceptions of positive tourism impacts and attitudes toward tourists is moderated by employment, with a greater effect for residents employed in tourism.

H_9b. 

The association between perceptions of positive tourism impacts and attitudes about tourism is moderated by employment, with a greater effect for residents employed in tourism.

H_9c. 

The association between subjective knowledge of the pandemic and attitudes toward tourists is moderated by employment, with a greater effect for residents employed in tourism.

H_9d. 

The association between subjective knowledge of the pandemic and attitudes about tourism is moderated by employment, with a greater effect for residents employed in tourism.

H_9e. 

The association between attitudes toward tourists and tourism involvement is moderated by employment, with a greater effect for residents employed in tourism.

H_9f. 

The association between attitudes toward tourists and tourism support is moderated by employment, with a greater effect for residents employed in tourism.

H_9g. 

The association between attitudes about tourism and tourism involvement is moderated by employment, with a greater effect for residents employed in tourism.

H_9h. 

The association between attitudes about tourism and tourism support is moderated by employment, with a greater effect for residents employed in tourism.

2.6. The Moderating Role of Gender

Previous research has suggested that gender differences may shape residents’ attitudes about tourists and tourism and participation in tourism [21,41,43]. For example, a study by Boğan et al. [44] demonstrated that gender significantly moderates the relationship between the perception of hotels’ social responsibility practices and overall attitudes about tourism. In addition, Jani [21] discovered that male residents living close to Mount Kilimanjaro are more likely to support tourism than their female counterparts. Similarly, Jaafar et al. [43] identified that gender significantly moderates the connection between residents’ favorable perceptions and their engagement in tourism-related activities on Langkawi island. Based on this evidence, gender may mediate the proposed relationships between model constructs. For this reason, the following hypotheses are proposed (Figure 1):

H_10a. 

The association between perceptions of positive tourism impacts and attitudes toward tourists is moderated by gender, with a more significant effect for men.

H_10b. 

The association between perceptions of positive tourism impacts and attitudes about tourism is moderated by gender, with a more significant effect for men.

H_10c. 

The association between subjective knowledge of the pandemic and attitudes toward tourists is moderated by gender, with a more significant effect for men.

H_10d. 

The association between subjective knowledge of the pandemic and attitudes about tourism is moderated by gender, with a more significant effect for men.

H_10e. 

The association between attitudes toward tourists and tourism involvement is moderated by gender, with a more significant effect for men.

H_10f. 

The association between attitudes toward tourists and tourism support is moderated by gender, with a more significant effect for men.

H_10g. 

The association between attitudes about tourism and tourism involvement is moderated by gender, with a more significant effect for men.

H_10h. 

The association between attitudes about tourism and tourism support is moderated by gender, with a more significant effect for men.

Figure 1. The structural model.

Figure 1. The structural model.

3. Methods

Manavgat (Turkey), located 75 km east of Antalya, covers an area of 2283 km². The latest data indicate that 252,941 residents live in Manavgat, making it the second most populous district following Alanya [45]. To put things in perspective, while the number of tourists coming to Antalya was recently roughly 13.5 million, 4.5 million chose Manavgat for their holidays [45] Historically, Manavgat has been known as a prominent Turkish tourist destination for its sea–sand–sun tourism, but recently, it is drawing more attention based on the region’s natural resources (e.g., waterfalls, caves, streams, mountainous areas, and flora/fauna species) [46]. This focus on nature-based tourism lines up with shifting economies from traditional agrarian lifestyles to those focused on providing tourism opportunities based on development investments beginning in the 1980s. Such growth in tourism offerings provides employment for many living in Manavgat’s city center.

3.1. Data Collection

Two researchers collected data between June and October of 2021 following a convenience sampling scheme. This non-probability form of sampling was chosen due to factors such as the large size of the population, the difficulty of creating a sampling framework due to its breadth and cost, and ensuring easy access to participants [47]. The researchers distributed 680 questionnaires, but only 545 were used in this study due to incomplete and incomprehensible responses.

Addressing common method bias and non-response errors is critical to ensuring our research findings’ validity and reliability. To mitigate common method bias, we have taken several preventive measures, including using different response formats, ensuring the anonymity of responses, and employing procedural remedies such as randomizing the order of questionnaire items. Additionally, we have conducted a thorough literature review to identify potential sources of bias and have incorporated appropriate controls into our analytical approach. Regarding non-response errors, we have implemented strategies to maximize response rates, such as employing multiple contact attempts; providing incentives for participation, pre-notifications, and reminders; introducing the paper’s purpose; and communicating the importance of respondents’ input.

3.2. Characterization of the Questionnaire and Data Analysis

The back-translated questionnaire consisted of two sections. In the first section, residents were asked to respond to 26 items (across the six model constructs) using 5-point Likert scales of agreement. The other section included demographic questions (i.e., age, gender, marital status, educational status, employment, frequency of interaction with tourists, and monthly household income). Data were analyzed using IBM SPSS and AMOS, both v25. The six model constructs were all unidimensional and based on existing scales. Positive tourism impacts (seven items) and attitudes toward tourists (four items) were each adapted from Woosnam [16]. Subjective knowledge about the pandemic (three items) and tourism involvement (three items) were adopted from Aleshinloye et al. [11]. Five items pertaining to residents’ support for tourism were also adopted from existing research [13]. Finally, attitudes about tourism (four items) were adapted from Erul et al. [35].

4. Results

4.1. Sample Characteristics

According to sample demographics (

Table 1. Descriptive summary of the demographic profile of Manavgat residents.

Demographics	n	%
Gender (n = 545)
Male	290	53.2
Female	255	46.8
Age (n = 545)
≤20	50	9.2
20–29	204	37.4
30–39	152	27.9
40–49	97	17.8
50–59	36	6.6
≥60	6	1.1
Marital Status (n = 542)
Married	248	45.4
Single	247	45.3
Divorced or separated	36	6.6
Widowed	13	2.4
Education (n = 542)
High school diploma or less	205	37.6
Associate degree	54	9.9
Bachelor’s degree	241	44.2
Master’s degree or higher	42	7.7
Employment (n = 544)
Not tourism related	218	40.2
Tourism related	326	59.8
Frequency of interacting with tourists (n = 544)
None	150	27.5
1 day/week	120	22.0
A few days/week	105	19.3
Almost everyday	169	31
Monthly household income (n = 542)
Under TRY 4000	164	30.1
TRY 4000–5999	178	32.7
TRY 6000–7999	90	16.5
TRY 8000–9999	55	10.1
TRY 10,000 or more	55	10.1

Note: Turkish Lira (TRY; ₺) is the currency of Turkey.

), nearly half (46.8%) of the respondents were women. In terms of age, slightly more than half (53.4%) were over the age of 30. Most respondents were highly educated (51.9%), having completed either an undergraduate or graduate degree. Six of ten individuals claimed to have professional connections to the tourism industry. Roughly 50% of the sample indicated they interacted with tourists at least a few days per week. Finally, a minority (37.2%) of households reported earning over TRY 6000 (TRY 19 at that time were the equivalent of 1 U.S. dollar).

4.2. CFA and SEM Results

To determine if the data followed a normal distribution, we examined skewness and kurtosis values, following the methodology outlined by Kline [48]. AMOS results indicated that each item had a skewness and kurtosis value below 2 and 3, respectively, which is generally considered acceptable [38]. Additionally, the data were examined for potential common method bias (CMB). To test for CMB, Harman’s one-factor test was used as recommended by Podsakoff et al. [49]. The results showed that the total variance extracted by the single factor for the sample was 41.04%, minimizing any concern for CMB in this study. Lastly, the Common Latent Factor (CLF) was tested and compared with the standardized regression weights of all items for measurement models with and without CLF. The differences in these regression weights were determined to be very small (<0.200), which confirmed that CMB is not a major issue in this study [50].

To establish a measurement model, Confirmatory Factor Analysis (CFA) was employed, including all six unidimensional constructs, following the procedure described by Byrne (2016) [51]. Most items (n = 21) had good factor loadings (i.e., larger than 0.70). In contrast, a few (i.e., five items) possessed factor loadings less than 0.70, yet greater than 0.50, which is considered acceptable according to Hair et al. [52]. Most items (n = 21) had good factor loadings (i.e., larger than 0.70). In contrast, a few (i.e., five items) possessed factor loadings less than 0.70, yet greater than 0.50, which, according to Hair et al. (2018) [52], is considered acceptable (see

Table 2. Measurement model results.

Constructs and Indicators	Standardize Factor	t-	CR	AVE	α
	Loadings	Values
Support for Tourism Development (STD)			0.93	0.73	0.935
STD1	0.86	N/A
STD2	0.84	28.59 ***
STD3	0.90	26.33 ***
STD4	0.86	24.25 ***
STD5	0.81	21.87 ***
Attitudes towards Tourist (AT)			0.84	0.57	0.840
AT1	0.83	N/A
AT2	0.80	20.69 ***
AT3	0.72	17.90 ***
AT4	0.66	16.23 ***
Attitudes towards Tourism Development (ATD)			0.94	0.79	0.942
ATD1	0.88	N/A
ATD2	0.92	35.14 ***
ATD3	0.88	24.37 ***
ATD4	0.88	25.09 ***
Subjective Knowledge (SK)			0.90	0.75	0.897
SK1	0.83	N/A
SK2	0.93	25.79 ***
SK3	0.84	23.10 ***
Positive Impacts of Tourism (PIT)			0.88	0.52	0.883
PIT1	0.58	N/A
PIT2	0.65	14.50 ***
PIT3	0.66	12.20 ***
PIT4	0.84	13.92 ***
PIT5	0.79	13.59 ***
PIT6	0.73	13.12 ***
PIT7	0.75	13.14 ***
Involvement in Tourism (IT)			0.80	0.58	0.767
IT1	0.55	N/A
IT2	0.94	12.84 ***
IT3	0.74	12.44 ***

Note: *** p < 0.001. N/A: not applicable.

).

Furthermore, the Average Variance Extracted (AVE) estimates for each of the six constructs were robust, ranging from 0.52 to 0.79, while Composite Reliabilities (CRs) ranged from 0.80 to 0.94. According to Byrne’s guidelines in 2016, CR values should exceed 0.70, and in line with Hair et al.’s recommendations in 2018, AVE scores should be higher than 0.50. As shown in

Table 3. Fit indices of models (measurement and structural).

Fit Indices	χ2	df	χ2/df	p	CFI	IFI	TLI	RMSEA
Measurement Model	859.718	275	3.126	0.000	0.944	0.944	0.934	0.063
Structural Model	1210.868	281	4.309	0.000	0.911	0.911	0.900	0.078

, the RMSEA value was less than 0.08, while CFI, TLI, and IFI were each greater than 0.90 for both measurement and structural models. These results indicated that CFA and SEM have good fit indices [51].

Two approaches were utilized to assess discriminant validity. The authors began by examining convergent validity as determined by AVE. The findings revealed that all six components had AVE estimates greater than 0.50 [52]. In addition, construct correlations should be less than the square root of the AVE for each construct, according to Fornell and Larcker [53]. As seen from

Table 4. Discriminant validity analysis.

Measures	CR	AVE	STD	SK	AT	ATD	IT	PIT
Support for Tourism Development (STD)	0.93	0.73	0.86
Subjective Knowledge (SK)	0.90	0.75	0.45	0.87
Attitudes towards Tourist (AT)	0.84	0.57	0.62	0.29	0.76
Attitudes towards Tourism Development (ATD)	0.94	0.79	0.63	0.32	0.69	0.89
Involvement in Tourism (IT)	0.80	0.58	0.54	0.38	0.53	0.47	0.76
Positive Impacts of Tourism (PIT)	0.88	0.52	0.56	0.25	0.58	0.48	0.51	0.72

Notes: All items were asked on a 5-pt scale, where 1 = strongly disagree and 5 = strongly agree. Bolded elements are the square root of AVE.

, the six model constructs demonstrated discriminant validity.

The proposed model relationships (as represented through hypotheses) are shown in

Table 5. Hypotheses tests’ results from the structural model.

Hypotheses Tests	B	Beta (β)	t-Values	Supported?
H1: Positive Impacts of Tourism ➔ Attitudes towards Tourist (PIT and AT)	0.67	0.61	10.16 ***	Yes
H2: Positive Impacts of Tourism ➔ Attitudes towards Tourism Development (PIT and ATD)	0.55	0.48	8.94 ***	Yes
H3: Subjective Knowledge ➔ Attitudes towards Tourist (SK and AT)	0.20	0.20	4.89 ***	Yes
H4: Subjective Knowledge ➔ Attitudes towards Tourism Development (SK and ATD)	0.23	0.22	5.33 **	Yes
H5: Attitudes towards Tourist ➔ Involvement in Tourism (AT and IT)	0.30	0.45	5.83 ***	Yes
H6: Attitudes towards Tourist ➔ Support for Tourism Development (AT and STD)	0.48	0.44	6.83 ***	Yes
H7: Attitudes towards Tourism Development ➔ Involvement in Tourism (ATD and IT)	0.11	0.17	9.54 **	Yes
H8: Attitudes towards Tourism Development ➔ Support for Tourism Development (ATD and STD)	0.38	0.36	6.27 ***	Yes

Notes: ** p < 0.01 and *** p < 0.001, R² _SMC: ATD: 0.34, AT: 0.48, STD: 0.45, and IT: 0.30.

. The first two hypotheses (H₁ and H₂) examined the specific paths between PIT, AT, and ATD. The results demonstrated that these perceived positive impacts of tourism (PIT) were significant predictors of both attitudes toward tourists (AT) (H₁: β = 0.61, t = 10.16, p < 0.001) and attitudes about tourism (ATD) (H₂: β = 0.48, t = 8.94, p < 0.001). Similar to this, subjective knowledge about the pandemic significantly predicted AT (H3: β = 0.20, t = 4.89, p < 0.001) and ATD (H₄: β = 0.22, t = 5.33, p < 0.01). Therefore, H_1–4 were supported.

Residents’ AT was a significant determinant of their tourism involvement (H₅: β = 0.45, t = 5.83, p < 0.001) and support (H₆: β = 0.44, t = 6.83, p < 0.001). Similarly, the relationship between ATD and IT was significant (H₇: β = 0.17, t = 9.54, p < 0.01). Finally, ATD explained the last variable (i.e., STD) (H₈: β = 0.36, t = 6.27, p < 0.05); hence, H_5–8 were supported.

The SEM model revealed that residents’ perceived subjective knowledge of the pandemic and positive tourism impacts explained 34% of the variance in attitudes about tourism and 48% of the variance in attitudes toward tourists. Furthermore, attitudes (toward tourists and tourism), in turn, explained a moderate degree of variance in tourism involvement (R²_SMC = 0.30). Finally, R²_SMC for STD was 0.45. All eight model hypotheses were confirmed, as shown in Table 5.

The current study combined demographic information to eliminate bias and generate balanced groups, as per Henseler and Fassott’s [54] advice when considering moderation testing. The demographic data were converted into dichotomous grouping variables (e.g., male = 1 and female = 2) after being cross-validated. AMOS v.25 was used to conduct a multi-group analysis between the subgroups of gender and employment within the tourism industry (

Table 6. Moderating effects.

	Professional Ties to Tourism		Gender Perspective Differences
Hypothesized Relationship	H9 Not Tourism Related (n = 218)	Tourism Related (n = 326)	H10 Male Residents (n = 290)	Female Residents (n = 255)
a: PIT ➔ AT	6.23 ***	7.32 ***	8.02 ***	6.29 ***
b: PIT ➔ ATD	5.71 ***	6.44 ***	7.54 ***	5.03 ***
c: SK ➔ AT	1.48 ns	4.70 ***	5.64 ***	0.89 ns
d: SK ➔ ATD	1.74 ns	5.93 ***	6.17 ***	1.40 ns
e: AT ➔ IT	2.54 *	4.91 ***	4.02 ***	3.94 ***
f: AT ➔ STD	3.07 **	5.62 ***	4.98 ***	4.26 ***
g: ATD ➔ IT	1.96 ns	2.56 *	3.21 **	0.95 ns
h: ATD ➔ STD	3.85 ***	5.95 ***	6.22 ***	2.41 *
Fit Statistics
CMIN(χ2)	3093.93	3093.93	3093.93	3093.93
χ2/df (df = 1124)	2.753	2.753	2.753	2.753
RMSEA	0.040	0.040	0.040	0.040
IFI	0.91	0.91	0.91	0.91
TLI	0.90	0.90	0.90	0.90
CFI	0.91	0.91	0.91	0.91
R2 SMC
ATD	0.32	0.35	0.47	0.21
AT	0.48	0.43	0.56	0.39
STD	0.34	0.51	0.59	0.29
IT	0.18	0.34	0.37	0.23

Notes: *** p < 0.001, ** p < 0.01, * p < 0.05; ns: not significant.

).

A majority of the relationships in the model were significant based on employment and gender. More specifically, the paths between knowledge about the impacts of tourism and attitudes (toward tourism and tourists) were similar regardless of professional ties to tourism. Therefore, hypotheses H_9c and H_9d were not supported. Likewise, attitudes toward tourism did not influence involvement among those with professional ties to tourism. Hence, H_9g was not supported. In addition to these, the paths between perceived PIT and attitudes (H_10a and H1_0b), attitudes toward tourists and tourism involvement (H_10e), AT and STD (H_10f), and, finally, ATD and STD (H_10h) differed across gender.

5. Discussion

Considering the KAP theory, SET, and ES, all ten of our proposed model hypotheses were supported. Residents perceived positive tourism impacts as significant determinants of their attitudes toward tourists (H₁) and about tourism (H₂). These results are similar to previous findings [14,19,23,25,26] and supported through SET. For example, Martin et al. [25] found that both residents’ attitudes concerning tourists and tourism overall were positively influenced by perceived positive tourism impacts. In effect, the more residents perceive positive tourism impacts, the more positive they will feel about tourists and tourism.

Subjective knowledge about the pandemic significantly predicted attitudes toward tourists (H₃) and about tourism (H₄). Previous scholars [23,30] tested the relationship between tourism knowledge and attitudes about tourism. Similar to our findings, Shen and Yang [31] applied the KAP theory and found that residents’ knowledge of the COVID-19 pandemic significantly predicted their perceptions of tourists and the industry overall. In contrast, Chang et al. [23] found that residents’ knowledge about tourism did not predict attitudes about sports tourism in Malaysia. Furthermore, the current study was the first to examine how knowledge of the pandemic’s impacts determines attitudes towards tourists and tourism. As such, our study filled the gap by testing these two hypotheses (H₃ and H₄).

Similar to the current study, earlier research [3,11,12,13,15,18,25,30,36] examined how attitudes toward tourists was a strong predictor of residents’ tourism involvement (H₅) and/or tourism support (H₆) that may be explained by the ES framework. For instance, Manosuthi et al. [36] examined the relationship between residents’ attitudes toward international Muslim travelers and support intentions toward said tourists in South Korea. Like our findings and those of Martin et al. [25], attitudes toward tourists were significant predictors of tourism support. In addition, Aleshinloye et al. [11] found that attitudes toward tourists are significant predictors of residents’ involvement in tourism. Hence, the current study claims that residents with more positive (i.e., favorable, welcoming) attitudes toward tourists will likely be more supportive of and more involved in tourism development.

Furthermore, residents’ ATD was a significant predictor of tourism involvement (H₇) and support for tourism (H₈). Similar to our findings, Shen and Yang [30] found that attitudes toward tourism in tandem with attitudes to tourists significantly predicted their STD. Ultimately, a model based on the complementary nature of emotional solidarity, social exchange theory, and knowledge–attitudes–practice theory constructs was supported based on results for H_1–8. Finally, based on the moderating role of employment and gender, results indicated that male residents perceived the positive impacts of tourism more, had more favorable attitudes, and were more supportive of tourism, which is similar to Hu et al.’s [41] finding. Therefore, our study offers valuable insights to assist tourism scholars and authorities (e.g., DMOs, tourism planners, etc.) in understanding the factors contributing to residents’ perceptions of IT and STD.

6. Implications and Limitations

6.1. Theoretical Implications

The theoretical contributions of the study conducted in Manavgat, Turkey, lie in its integration of social exchange theory (SET), emotional solidarity (ES), and the knowledge–attitude–practice (KAP) framework to provide a nuanced understanding of residents’ perceptions, attitudes, and behaviors towards tourism impacts. The study sheds light on the dynamics underlying residents’ attitudes and behaviors by examining the reciprocal relationships between perceived benefits, knowledge, and emotional factors. Notably, the findings corroborate previous research regarding the influence of positive tourism impacts on residents’ attitudes toward tourists and tourism. Furthermore, the study extends existing knowledge by demonstrating the significance of subjective knowledge about the pandemic in predicting attitudes toward tourists and tourism, filling a critical gap in the literature. Additionally, the study contributes to understanding the relationship between attitudes toward tourists and residents’ tourism involvement and support, aligning with the emotional solidarity framework. Overall, the theoretical integration presented in the study offers valuable insights for policymakers and practitioners aiming to promote sustainable tourism development while addressing residents’ concerns, thereby advancing theoretical understanding in the field of tourism.

6.2. Practical Implications

Community residents’ involvement and support for the tourism industry are crucial for the competitiveness and survival of the destination in today’s global market [11]. The knowledge or understanding of tourism entities and their impacts on the local community will most likely serve as a precursor to residents’ involvement and support. It is incontestable that tourism destinations without supportive and hospitable residents will struggle to be competitive. Residents’ involvement and participation in tourism activities are unquestionable for the growth and sustenance of the community because they live with and experience firsthand the positive and negative impacts of the industry.

Moreover, the devastating impact of the pandemic and the gradual recovery of the tourism industry make it imperative for residents to be active stakeholders. Findings from the study indicated that knowledge was a significant predictor of residents’ attitudes towards tourists and tourism development. Also, the residents’ perceived positive tourism impacts influence their attitudes surrounding tourists and tourism, which in turn contribute to residents’ involvement in and support for tourism. This study answers the call by Aleshinloye et al. [11] regarding the need to determine what drives residents’ involvement in tourism planning and development. Also, this study reinforces the utility of considering both the economic and non-economic measures of assessing the relationship between residents and tourists in community tourism development as depicted through the knowledge–attitude–practices theory, social exchange theory, and emotional solidarity [12,25,30].

In practical terms, this research offers valuable insights for destination management organizations (DMOs), tourism planners, and authorities. By uncovering residents’ knowledge, perceptions, and attitudes, the study provides a basis for informed decision-making. This knowledge can guide the adjustment of preventive measures and aid in the return to normalcy, fostering a symbiotic relationship between residents and the tourism industry. Ultimately, this research aims to empower DMOs to identify key factors influencing residents’ support and involvement in tourism, contributing to the sustainable development of tourist destinations.

Tourism planners should create a forum composed of opinion and political leaders, trade organizations representatives, tourism educationists, urban and city planners, small- and large-business entrepreneur representatives, tourism organizations and investors, and other important groups. Such a forum will allow for regular consultation with stakeholders on tourism matters. Also, a public complaint commission should be established to provide an avenue for residents to air their concerns, grievances, and contributions toward issues of community concern. As residents grow in their knowledge about tourism activities, their involvement and support will follow. Therefore, tourism authorities should consider providing residents with educational materials on the benefits of tourism and training opportunities focused on interacting with and welcoming visitors.

Furthermore, the current study found that residents’ welcoming attitude towards tourists was crucial in determining support for tourism development and involvement. In other words, the study’s outcomes suggest that as residents’ level of welcoming disposition grows, their involvement in tourism development and inclination to support tourism also increase. This conclusion aligns with the stages of knowledge–attitude–behavior theory. Tourism authorities, government bodies, policymakers, and planners must recognize this connection and concentrate on nurturing the close relationship between tourists and residents. To enhance residents’ welcoming attitudes (and consequently bolster support for tourism), tourism managers and planners should actively involve residents in every phase of the tourism planning process, including creating or augmenting tourism activities, events, and organizations.

Similarly, to increase resident involvement in tourism, tourism authorities should recognize the importance of resident support. This involves respecting their role and influence, listening to residents’ needs, valuing their perspectives, treating them as equal partners, involving them in decision-making, and participating in tourism planning. Finally, the DMOs should create more community-based tourism development projects, collaborative decision-making processes involving residents, and capacity-building programs to enhance residents’ involvement, increase their support and empower communities, and ensure the benefits of tourism development are equitably shared among all stakeholders, particularly residents.

6.3. Limitations and Future Research

Despite the contributions this work makes to the literature, some limitations of the research exist. Only one district of Antalya was included. While our focus was intentional in only considering Manavgat, given the high percentage of its visitors and diverse tourist offerings, subsequent research may consider including additional districts with Antalya or even other comparable destinations similar to Manavgat. Further, we used only positive scales (i.e., positive impacts of tourism, positive attitudes, and supportive behavior), so future studies should also include negative perceptions (i.e., negative impacts of tourism, negative attitudes, and oppositional behavior). The results showed that most residents are male, highly educated, young, and have a professional tie in tourism; hence, the diversity of those demographic variables can increase support and involvement.

Another limitation of this current study is that the involvement construct was measured as self-reported experiences, just as Aleshinloye et al. [11] did in their Orlando resident study. More precisely, involvement should be counted as actual behavior. For example, questions based on frequency (e.g., number of times attending meetings or other activities within a timeframe) or extent (e.g., “never” through “always”) may have shown greater accuracy in capturing the respondents’ perceptions. Further studies should consider using other non-economic theories, such as social identity, social representation, and intimacy theories, to assess residents’ attitudes to tourism and tourism development. Such studies will give us a better and broader understanding of the concept. Replicating this study in other emerging tourism destinations, especially in non-Western developing countries within Africa and Asia, will further validate our findings.

Furthermore, this study employed a simple model to explain residents’ perceptions, attitudes, and behaviors and did not apply a more complex model. Incorporating additional variables within the context of a single leading theory could strengthen the research’s theoretical foundation and provide deeper insights into the factors influencing residents’ perceptions, attitudes, and behaviors. Briefly, additional variables may increase the R square. Hence, future studies should test the model by incorporating additional theoretically driven variables to compare their findings with ours. Moreover, the results showed that the sample of this current study comprised younger individuals which may have influenced the R-square; hence, future studies should apply stratified sampling or increase the sample diversity.

7. Conclusions

This research aimed to unravel the complex interplay between residents’ attitudes, support for tourism development, and involvement in tourism. By integrating emotional solidarity, the knowledge–attitude–practices theory, and social exchange theory, the study sought to provide a nuanced understanding of how residents’ views on tourism impacts and their awareness of the pandemic’s effects shape their attitudes and, consequently, influence their involvement and support for tourism. Furthermore, the study explored potential moderating factors such as gender and professional ties to tourism, contributing novel insights into demographic and economic variables. This research utilized a quantitative approach, collecting data from a self-administered survey distributed to 545 Manavgat residents between June and October 2021, following convenience sampling. Among the eight hypotheses tested, all of them were supported. It is evident that residents’ perceptions (indirectly) and attitudes (directly) are vital in their involvement in tourism and support for tourism development. The complementary merger of theoretical frameworks enriches our understanding of residents’ support and involvement in tourism, shedding light on the intricacies of these relationships.