توسعه و اعتبارسنجی مقیاس ارزش مشارکت مشتری در صنعت گردشگری
الموضوعات : Journal of Business Analysis
1 - فارغ التحصیل دکترای مدیریت بازرگانی/ دانشکده مدیریت دانشگاه آزاد واحد تبریز/ تبریز. ایران
الکلمات المفتاحية: مشارکت مشتری, ارزش مشارکت, تجربه مشتری, صنعت گردشگری,
ملخص المقالة :
هدف : این مطالعه با هدف توسعه و اعتبارسنجی مقیاس ارزش مشارکت مشتری در صنعت گردشگری با استفاده از روش فرا ترکیب انجام شد.
روش‌شناسی : در مرحله اول، با استفاده از روش فراترکیب، مطالعات انجام‌شده طی سال‌های 2000 تا 2025 بررسی و ارزش‌های حاصل از تعامل گردشگران شناسایی شد. در مرحله دوم، پرسشنامه‌ای بر اساس مرور ادبیات مرتبط تهیه و در اختیار برخی از اساتید دانشگاه و محققان بازاریابی و گردشگری قرار گرفت. پس از اعمال نظرات و اصلاحات لازم، پرسشنامه‌ها بین 440 گردشگر که در 5 سال گذشته به مالزی، امارات متحده عربی، آذربایجان و ترکیه سفر کرده بودند، توزیع شد. سپس مدل با استفاده از نرم‌افزارهای AMOS24 و SPSS24 اعتبارسنجی شد.
یافته‌ها : نتایج تحلیل عاملی تأییدی، برازش مدل 5 عاملی را تأیید کرد. بر این اساس پرسشنامه تحقیق می‌تواند مقیاس مناسبی برای سنجش ارزش تعامل گردشگر باشد.
نتیجه‌گیری : پیامدهای تعامل گردشگر در قالب پنج ارزش شامل تصویر ذهنی، اقتصادی، دانش، طول عمر و نفوذ گردشگر شناسایی شد.
نوآوری : استفاده از روش ترکیبی برای شناسایی پیامدهای تعامل گردشگر و معرفی مقیاسی مناسب برای ارزش تعامل گردشگر از جمله نوآوری‌های این مطالعه است.
کلمات کلیدی : مشارکت مشتری، ارزش مشارکت، تجربه مشتری، صنعت گردشگری
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The JOURNAL OF BUSINESS ANALYSIS
Vol. 1, No.1, Spring 2025
DOI:
Developing and Validating a Customer Engagement Value Scale for measuring the outcomes of tourist engagement
Soghra taghipour
Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran
(Received: 2025/06/25; Accepted: 2025/10/21)
Online publication: 2025/11/04
Abstract
This study aimed to develop and validate a customer engagement value scale in the tourism industry using the mixed method. The first stage used the meta-synthesis (Sandelowski and Barroso’s method) to examine studies conducted during 2000-2025 and identify the values resulting from tourists’ engagement. In the second stage, a questionnaire was prepared based on the review of the relevant literature and administered to some university professors and marketing and tourism researchers. After applying the necessary comments and corrections, the questionnaires were distributed among 440 tourists (The samples were randomly selected from tourists who had traveled to 4 countries for recreation.), traveling to Malaysia, UAE, Azerbaijan, and Turkey in the last 5 years. The model was then validated using AMOS24 and SPSS24 software.The results of confirmatory factor analysis confirmed the 5-factor model fit. As shown, the research questionnaire can be an appropriate scale for the value of tourist engagement.
The outcomes of tourist engagement were identified in the form of five values, including mental image, economic, knowledge, lifetime(Refers to customer lifetime value), and tourist influence.
The use of the mixed method to identify the outcomes of tourist engagement and the introduction of a suitable scale for the value of tourist engagement are among the innovations of this study.
Keywords: Tourist Engagement, Engagement Value, Tourist Experience, Tourism Industry, Scale Development
Introduction
Creating, providing, and maintaining value is one of the most significant concerns in any business. Accordingly, many researchers have focused on understanding the concept of value and value creation in the last decade (Keng et al, 2013). Many researchers consider value creation as a multi-stage, dynamic and ongoing process in the context of time and believe that paying attention to customer value as a strategic tool is central to many businesses and can serve as a link between people within the organization and the external customers(Saarijärvi et al ,2013) . According to Vargo and Lusch, value creation results from the interactions between the organization and the individual (Vargo & Lusch, 2016). Value creation is a new form of business strategy in modern marketing sciences, leading to competitive advantages and ultimately increasing loyal customers. Value creation forms in the social context and through interactions. In this regard, tourism activities can contribute significantly to economic, social, cultural, and environmental changes in the host community and other people (Lee, 2013). The growing increase in competition and service diversity in the tourism industry has made stakeholders in the tourism industry shift their activities toward customer-based strategies (Keng et al., 2013) and seek ways to connect with their valuable loyal customers as their source of profitability (Bose & Bansel, 2002). Today, purchase decision-makers no longer focus merely on the functional and profitability dimensions of the products but rather take into account the emotional dimension and the pleasant experience of their purchase in addition to being rational (Kumar, 2010). Thus, the quality of experience is supposed to be one of the main elements in the success of marketing strategies. Researchers and tourism experts believe that tourists choose places offering them unique, interesting, and distinct experiences (Ahn & Back, 2018). Accordingly, tourism industry activists seek to achieve competitive advantages by providing memorable experiences and benefiting from value creation. Meanwhile, one of the prerequisites for value creation in the tourism industry is to attract active tourist engagement (Dekhili& Hallem ,2020). Experience-based management seeks to attract more tourist engagement in value creation by adopting a suitable platform. It is believed that co-creation behaviors must increase to achieve sustainable growth in the tourism industry (Grönroos, 2015). Co-creation is a managerial initiative achieved through collaboration and leading to mutual value creation (Liu et al., 2019). Co-creation enables tourists to improve their tourism services by sharing their opinions with others. Pansari and Kumar (2018) defined customer engagement as the direct and indirect cooperation in the value creation process. Kris Johnson and Maude (2014) argue that engagement of service providers with tourists is beneficial to both parties, resulting in social impact and loyalty to each other. Researchers are recently paying considerable attention to the value of customer engagement to compete dynamically and reduce differentiation in offers. Complexity and experience are among the main features of tourism services. Therefore, several studies are required to identify the factors affecting the behavior of tourists (Cao et al., 2018). A desirable experience contributes significantly to tourist satisfaction, playing a central role in tourism marketing. Such an experience is associated with understanding the experiential value, obtained after interacting with products and services. Hence, the quality of the tourist experience seems to be one of the key elements for a successful marketing strategy (Prebensen et al., 2018). Despite the importance of the value of customer engagement, created as a result of gaining experience, no scale has been provided so far by domestic and international researchers to measure this value in the tourism industry. Hence, given the movement of businesses towards value creation and its importance in business development, particularly the high-income tourism industry, the present study aimed to develop and validate a scale to measure the value of tourist engagement.
Research Theoretical Foundations
Value Creation from the Perspective of Service Providers:This perspective includes three main steps of selecting, providing, and sharing value propositions, by which the service providers seek to provide customers with value through an appropriate platform (Liu et al., 2018). The perspective of service providers considers an inside-out approach whereby service providers determine value propositions according to their resources, capabilities, and limitations (Ellegaard et al., 2014). Value propositions are valuable offers designed by service providers and provided to the customers. Such offers are one-way and designed to be accepted and welcomed by customers. Customers have no engagement in the design of this perspective, and service providers are responsible for identifying, creating, and providing value to customers (Tuominen, 2004).
Value Creation from the Customer-Oriented Perspective:Here, the emphasis is on understanding the customers’ perspectives and experiences during the consumption process, whereby service providers try to determine the features valued by the customers through interaction and communication with them (Alam Tabriz et al., 2017). Accordingly, service providers design value propositions based on customer experiences and provide them to the customers. Based on this perspective, value is not inherent in products but created by customers and based on their perceptions and experiences; thus, service providers seek to discover the dimensions of value from the customers’ perspective and incorporate it into their activities (Saarijärvi, et al ,2013).
Value Creation form the Co-Creation Perspective:This perspective considers the concept of value creation as a mutual process based on cooperation between customers and service providers, leading to the formation of co-creation value (Alves et al., 2016).
The co-creation perspective considers an outside-in approach whereby service providers integrate their resources and cooperate with customers in value creation while seeking to incorporate knowledge and competencies into their resources and benefiting from it during their activities (Saarijärvi, et al ,2013). According to the theory of Prahalad and Ramaswamy (2004), co-creation is a managerial initiative created through collaboration between different partners and jointly leading to a mutual outcome (Liu et al., 2019).
Literature Review
Researchers have increasingly focused on the experiential value of the customers for the dynamic competition, reducing the range of differentiation in offers, changes in the customer demands, and providing valuable customer services (Ponsonby & Boyle, 2004). Woodall (2003) defined value as the consumers’ mental comparison of the product features with their perceived actual value. Keng et al. (2013) argue that customers perceive value through low prices, whatever they want in a product, the quality received for the price paid, and what they get for what they give. They believe that value comprises service excellence, aesthetic appeal, return on investment, and playfulness. According to Chen (2015), value is the customers' mental judgments, based on perceptions and formed through direct and indirect interactions during the consumption process. Cetin et al, (2014) considers the creation of a positive experience as a value for Istanbul hotel guests and focus on the establishment of lasting experiences through manpower training and the use of new technologies. Chang (2018) believes that service diversity in the tourism industry can make the experience unique and memorable for tourists, leading to value creation. According to Schmitt (2010), experiential values include affective, behavioral, intellectual, and cognitive dimensions.
Assiouras et al. (2019) have examined value co-creation and customer citizenship behavior, emphasizing the growing importance of the role of tourism and hospitality enterprises in value creation. In their opinion, value creation leads not only to repurchase but also to the formation of citizenship behaviors.
González-Mansilla et al. (2019) examined value creation behaviors and referred to them as an important process in maintaining competitive advantages.
Kumar et al. (2010) proposed four components of a customer’s engagement value (CEV) with a firm:
Customer referral value is related to the acquisition of new customers through initiated and incentivized formal referral programs of the firms (Kumar et al., 2010). In other words, customer referral behavior is a form of word-of-mouth advertising in which customers are motivated to introduce a new customer.
Customer influence value includes the customers' behavior to influence other customers through voluntary word of mouth and share of consumption experiences (Verhoef et al., 2010). Word of mouth is a behavior through which tourists exchange their experiences without any incentives. Recommendations are made to other tourists after the experience exchange and evaluation. Therefore, customer influence value can be examined through advertising.
Customer knowledge value represents the transfer of customer knowledge to the company by providing feedback on innovative ideas to improve firm performance (Kumar et al., 2010), examined through content production and co-creation. Co-creation is the creation of interactive value by the tourists and tourism service providers (Prahalad & Ramaswamy, 2004). The development of service-dominant logic in recent years emphasizes that value creation occurs in a dynamic network and service system (Vargo & Lusch, 2016). A service system is a configuration of value creation, including people, technology, other internal and external systems, and shared information such as language, processes, prices, policies, and rules (Spohrer et al., 2007). Examples of service systems are cities, destinations, and places that form a body of people, technology, and resources, leading to value creation as a whole (Viglia & Dolnicar, 2020).
Content production includes the comments of tourists, which provides other tourists with decision-making and facilitates the enrichment of their experiences. These comments appear as rankings and texts (Tang et al., 2014). Today, tourists share their comments on hotels, restaurants, destinations, and facilities on websites. These comments are available to other tourists on social networks and the Internet, whose evaluation helps them to make decisions about the tourist destination (Cox et al., 2009). Such information plays a critical role in the hospitality and tourism industry.
Customer lifetime value, also known as direct engagement, emphasizes the transactional aspect of the engagement and includes customer-related purchase behaviors, including repeated or more purchases each time (Kumar, 2010; Kumar & Reinartz, 2016), ultimately leading to loyalty.
Loyalty to a particular destination is a significant issue in tourism management because repeated tourist visits represent a critical business opportunity for tourism purposes (Meleddu et al., 2015). Numerous studies have shown that loyalty leads to a reduction in costs to attract tourists and tourists' sensitivity to financial issues. Hence, loyalty has been recognized as the main driving force and an important indicator of success in marketing and tourism resources (Son et al., 2013).
Williams and Soutar (2009) referred to ethical value, epistemological value, or conditional value. Some studies have also mentioned functional, affective, and social values. Sánchez et al. (2006) introduced the dimensions of experiential value as cognitive, affective, social, and ethical. Varshneya and Das (2017) introduced cognitive, hedonic, ethical, and social values as dimensions of experiential value. Kirillova et al. (2014) pointed to hedonic value in the tourism industry and considered it as an interactive value, contributing significantly to the tourist decision-making process.
Ukpabi et al. (2020) developed and validated a multidimensional scale of customer engagement (affection, absorption, and interaction) specifically in online travel communities. Their findings confirm the growing relevance of virtual interactions in shaping experiential value in tourism platforms.
Similarly, Xu, Bai, and Li (2020) investigated the antecedents of brand engagement among tourists through the lens of value co-creation. They highlighted that perceived control, relational benefits, and interactivity are significant predictors of customer engagement in tourism services, reinforcing the co-creation logic proposed by Vargo and Lusch (2016).
Expanding on this, Rasul et al. (2024) conducted a comprehensive meta-analysis of 72 studies and presented an integrated framework comprising over 300 antecedents and consequences of tourist engagement. Their study is a major step in synthesizing and unifying engagement-related constructs, further validating engagement value as a multidimensional and central factor in tourism research.
In the domain of wellness tourism, Xie et al. (2022) revealed that perceived value from wellness offerings—including emotional, cognitive, and relational aspects—significantly impacts customer engagement. This supports the notion that customers' psychological evaluations during service consumption are essential to value creation and long-term loyalty.
Furthermore, a study published in Frontiers in Psychology (2022) proposed and validated a model of perceived co-creation value in tourism settings, identifying three dominant dimensions: economic, relational, and informational value. Their model aligns closely with the dimensions identified in the present study, particularly the knowledge and economic value derived from tourist engagement.
our methodology supports the goal of developing a valid scale, but it would benefit from a final summary sentence explaining how the two phases align with the overall research purpose.
Suggestion:
“This two-stage mixed-method approach ensured that the resulting scale was both theoretically grounded through meta-synthesis and empirically validated through robust statistical analysis.
Methodology
This study was conducted using a mixed-method approach in two stages. The first stage was qualitative and used the meta-synthesis method. The statistical population included all previous studies (articles) related to the research topic during 2000-2020, extracted from the international scientific databases of Scopus, Emerald, and Science Direct Of this population, 34 articles were finally selected as the research sample. This stage used purposive sampling, and the statistical sample size was based on the framework of the meta-synthesis method. The values resulting from the tourist experience were then identified through review of the texts, summarizing, and semantic differentiation.
In the second stage, a questionnaire was prepared on a 5-point Likert scale (completely disagree, disagree, neutral, agree, completely agree) and administered to some university professors and marketing and tourism researchers. After applying the necessary comments and corrections, the questionnaires were distributed to validate the model. The statistical population of this stage included Iranian tourists traveling to Malaysia, UAE, Azerbaijan, or Turkey in the last 5 years. Since the sample size for the infinite population is 384, this study selected 440 statistical samples for more detailed examinations.
Since the study used a mixed-method design, the qualitative part focused on content validity, achieved using the experts’ opinions. Cohen's Kappa coefficient and Cronbach's alpha coefficient were applied to measure reliability in the qualitative and quantitative parts, respectively.
Findings and Results
a. Qualitative Findings Using the Meta-Synthesis Method:The present paper has used the meta-synthesis qualitative method to extract the values resulting from experience. Figure 1 indicates the steps of the meta-synthesis method used in this paper:
Figure 1.
The process of meta-synthesis implementation (Sandelowski & Barroso, 2007)
Setting Up the Research Question
Table 1 presents the research questions and how to answer them:
Table 1.
Research questions – meta-synthesis analysis
Indicators | Questions | Answers |
What | What are the values resulting from customer engagement in the tourism industry? | Identifying the values resulting from customer experience in the tourism industry from literature |
Who | Who is the study population to achieve these factors? | This study has used reliable scientific databases |
When | What period of time has been considered to examine the influencing factors? | All studies published in databases during 2000-2020 |
How | What method has been used to extract the studies? | Qualitative data were analyzed using document analysis method. |
Review of Literature
The data analyzed in the present study were taken from research published before May
2021 concerning values resulting from the tourist experience. The most popular databases and search engines used in this study, along with the keywords, are as follows:
Table 2.
Keywords used to search in databases
English Keywords | Databases |
Customer experience Tourism experience Customer engagement Tourist engagement The value of tourist engagement The value of customer engagement Tourism industry | Scopus Science Direct Emerald
|
Searching and Selecting Appropriate Texts
Data screening was based on the relevance of the title, abstract, and the content of the articles to the research topic, and finally 34 articles were examined for the extraction of codes.
Figure 2. The process of searching and selecting articles to implement meta-synthesis
Data Extraction from the Texts
The identified values of customer experience in the tourism industry were extracted from the studied articles as described in Table 3.
Table 3.
The identified values of customer experience in the tourism industry based on references
Identified values | Definition | References |
Customer cognitive value | Comparison between subjective and objective dimensions of a phenomenon and the curiosity and desire of potential customers to experience something new | Sánchez et al. (2006); Zhang et al. (2018); Sarawill et al. (2019); Varshneya & Das (2017); Williams & Soutar (2009), Yang Caoa et al (2019), Abubakar, & Mavondo (2014), Ali et al. (2016), Lai (2015), Yuan & Wu (2008), Benkenstein et al. (2003), Hao (2020), Kumar et al. (2023) |
Customer social value |
The transfer of emotions, which strengthens social relations, leads to a social value | Martinsuo & Hoverfält (2018). Zhang et al. (2018); Varshneya & Das (2017); Yi,Gong (2013); Prebensen et al. (2018); Sánchez et al. (2006), Hung-Che & Ya-Yuan (2018), Yang Caoa et al (2019), Abubakar & Mavondo (2014), Ali et al. (2016), Komsuoğlu & Çevirgen (2022); Rasul et al. (2024) |
Customer affective value |
Expressing emotions in the form of interest, happiness, anger, humiliation, disgust, shame, guilt, sadness, fear, and surprise | Varshneya & Das (2017); Kumar & Kumar (2020); Yi,Gong (2013) ; Prebensen et al. (2018); Sánchez et al. (2006), Ryu et al. (2010), Yuan & Wu (2008), Benkenstein et al. (2003), Khairani & Fachira (2022); Rasul et al. (2024) |
Customer historical and cultural value | It aims to protect creative works such as paintings, books, sculptures, historical monuments, etc. | Sarawill et al. (2019), Li, Liu (2020) |
Customer economic value |
Customers’ desire and willingness to invest | Martinsuo & Hoverfält (2018). Yi,Gong (2013); Zhang et al. (2018); Ali et al. (2016), Anderson, et al. (1994), Bahri-Ammari et,al. (2016), Caber, & Albayrak. (2014), Clemes et al. (2011), Kumar et al. (2023); Ukpabi et al. (2020) |
Customer educational value | Customer engagement provides individuals with an opportunity to obtain the most useful information about a tourist destination, even with their limited knowledge. | Prebensen et al. (2018); Martinsuo & Hoverfält (2018). Choi, Mattila & Upneja (2018) |
Customer Behavioral value | Ethical value includes trust and privacy | Varshneya & Das (2017); Sánchez et al. (2006); Williams & Soutar (2009), Hung-Che & Ya-Yuan (2018), Yang Caoa et al (2019), Bahri-Ammari et,al. (2016), Clemes et al. (2011), Ryu et al. (2010), Yuan & Wu (2008), Albert Barreda, et al(2015), Meleddu, et al (2015) |
Customer knowledge value | The customers transfer their knowledge by presenting innovative ideas about improving the firm performance | Kumar et al. (2010); Ukpabi et al. (2020); Komsuoğlu & Çevirgen (2022); Frontiers in Psychology (2022) |
Customer influence value | Customers perform word of mouth advertising voluntarily by sharing their experiences without any incentives. | Kumar et al. (2010); Woodruff et al. (2006); Khairani & Fachira (2022); Rasul et al. (2024) |
Customer lifetime value
| Selecting the destination again and increasing the travel time | Kumar et al. (2010); Kumar & Reinartz (2016), Kumar et al. (2023); Rasul et al. (2024) |
Functional value (service excellence) | The quality of services provided by tourism destinations | Zhang et al. (2018); Chen & Chen (2010), Chen & Hu. (2010), Clemes et al. (2011), Lai (2015), Ryu et al. (2010), Yuan & Wu (2008) |
Value co-creation | Alignment of the interests of tourists and tourism destinations | Vargo & Lusch (2016); Zhang et al. (2018); Hur, Lim, Lyu (2020), Dekhili, Hallem (2020), González-Mansilla, et al (2019), Frontiers in Psychology (2022); Rasul et al. (2024); Hao (2020) |
Digital Engagement Value | Customer involvement via digital content and online platforms | Khairani & Fachira (2022); Ukpabi et al. (2020); Komsuoğlu & Çevirgen (2022) |
Psychological Ownership Value | Emotional attachment and identification with the tourist experience | Kumar et al. (2023); Rasul et al. (2024) |
Extracting the Model of Tourist Engagement Value
The topics were identified in this section using two basic principles: semantic differentiation and completion of the research question. Accordingly, the codes were extracted and integrated when possible, leading to five categories of values resulting from customer engagement.
1. The Value of Toursits’ Mental Image
2. Tourist Economic Value
3. Tourist Knowledge Value
4. Tourist Influence Value
5. Tourist Lifetime Value
Quality Control
Cohen's kappa coefficient was used for the final test and quality examination. Hence, two experts (supervisors) received the coding results of the extracted concepts and topics, and the Cohen's kappa coefficient was calculated based on their agreement or disagreement concerning the extracted concepts. Cohen's kappa coefficient was 0.856 for this study at a significance level of 0.000, confirming its significance. A questionnaire was prepared and distributed among the statistical sample mentioned in the research method after extracting the values and related propositions.
Quantitative Findings
Standardized coefficients of factor loadings and t-value significance were calculated as shown in Table 4 to examine the correlation between the relevant latent variables and their corresponding indices.
Table 4.
Standardized factor loadings and t-statistic
Components |
Questions | Standardized factor loadinga | t-value | Sig | Coefficient of determination | Cronbach's alpha | Result |
Value of Mental Image | 1. I share my feelings about the tourist destination with others. | 0.789 | - | - | 0.627 | 0.919 | Optimal |
2. I discuss the cultures dominating the tourist destination with others. | 0.819 | 18.880 | *** | 0.673 | Optimal | ||
3. I guide others to know the tourist destination better. | 0.830 | 19.180 | *** | 0.690 | Optimal | ||
4. I talk to others about the reliability of the tourist destination. | 0.794 | 18.121 | *** | 0.625 | Optimal | ||
5. I try to deliver a clear image of my findings to others. | 0.813 | 18.6900 | *** | 0.660 | Optimal | ||
6. I talk to others about the compatibility of the tourist destination with my needs. | 0.810 | 18.590 | *** | 0.651 | Optimal | ||
Economic Value | 7. I am interested in investment (e.g., buying a property) in my preferred tourist destination. | 0.794 | - | - | 0.644 | 0.847 | Optimal |
8. Sometimes, I think of staying in my preferred tourist destination. | 0.860 | 17.582 | *** | 0.700 | Optimal | ||
9. I easily spend money during my trip. | 0.784 | 16.570 | *** | 0.604 | Optimal | ||
Knowledge Value | 10. I deliver my knowledge gained during the trip to others. | 0.863 | - | - | 0.744 | 0.916 | Optimal |
11. I take every opportunity to come up with innovative ideas to improve tourism-related activities while addressing shortcomings. | 0.849 | 22.656 | *** | 0.721 | Optimal | ||
12. I share the most useful and important information I have experienced about the tourist destination with others. | 0.864 | 23.327 | *** | 0.746 | Optimal | ||
13. I offer suggestions and feedback on new services of tourism destinations. | 0.847 | 22.566 | *** | 0.718 | Optimal | ||
Influence Vlaue | 14. I advertise this tourist destination through word of mouth voluntarily and without any incentives. | 0.888 | - | - | 0.789 | 0.943 | Optimal |
15. I engage in virtual advertising of this tourist destination out of interest and without any incentives. | 0.910 | 28.815 | *** | 0.828 | Optimal | ||
16. I talk to others about the benefits of traveling to a tourist destination. | 0.859 | 25.400 | *** | 0.738 | Optimal | ||
17. I talk to others about my travel experience. | 0.850 | 24.873 | *** | 0.723 | Optimal | ||
18. I talk to others about my belonging to a tourist destination | 0.873 | 26.275 | *** | 0.762 | Optimal | ||
Lifetime Value | 19. I plan to travel to this destination again in the future. | 0.813 | - | - | 0.656 | 0.921 | Optimal |
20. I am pleased to choose this tourist destination (under study). | 0.849 | 20.855 | *** | 0.712 | Optimal | ||
21. I am satisfied with choosing this tourist destination (under study). | 0.849 | 20.885 | *** | 0.717 | Optimal | ||
22. I recommend others to travel to this destination (under study). | 0.833 | 20.291 | *** | 0.685 | Optimal | ||
23. I try to increase my travel time to this destination (under study). | 0.857 | 21.144 | *** | 0.731 | Optimal |
The dashes in T values indicate that the parameter is fixed in the model, and *** represents the significance level (0.000).
As shown in the table above, the factor loading of all items and t-values were >0.4 and t>1.96, respectively, confirming the validity of the constructs. Also, Cronbach's alpha of all components of engagement value resulting from the customer experience was >0.7. Therefore, there was no need to remove any item from the model. Concerning the adequacy of the sample size, KMO and Bartlett tests were used, the results of which are presented in Table 5:
Table 5.
KMO and Bartlett tests
Indices |
| |||
KMO statistic | 0.924 | |||
Bartlett’s test of sphericity | Chi-square | 7636.005 | ||
Df. | 253 | |||
Sig. | 0.000 | |||
As shown in the above table, the KMO statistic is 0.924, which is >0.7, and the Bartletts test is also significant, confirming the adequacy of the sample size.
The model fit indices have to be acceptable to confirm the model of factor analysis and document the results. Table 6 shows the indices used along with their values:
Table 6.
Indices of fit of the first-order factor analysis model
Indices studied | Latin symbol | Standard value | Estimated value | |
Chi-square/df. | (x2/df) | <3 | Carmines & Mclver (1981) | 1.858 |
Root Mean Square Error of Approximation | (RMSEA) | <0.08 | Hair et al. (1998) | 0.044 |
Comparative Fit Index | (CFI) | >0.9 | Bentler & Bonett (1980) | 0.97 |
Incremental Fit Index | (IFI) | >0.9 | 0.97 | |
Goodness-of-Fit Index | (GFI) | >0.8 | Etezadi & Farhoomand (1996) | 0.91 |
As shown in Table 6, the chi-square is 1.858 in the factor analysis model, which is <3. Also, RMSEA has a value of 0.044, which is <0.08. finally, CFI, IFI, and GFI have all acceptable estimated values, supporting the goodness of fit of the model.The figures below show the first- and second-order factor analysis model for engagement value resulting from tourist experience.
Figure 3. Standardized factor loadings of the first-order factor analysis model for engagement value resulting from tourist experience
Figure 4. Second-order confirmatory factor analysis model for engagement value resulting from tourist experience
|
Table 7 shows t-values and standardized path coefficients between experience management and its components.
Table 7.
Standardized factor loadings and t-statistic between variables
Variable | Components | Standardized factor loading | t-statistic | Sig. | Coefficient of determination | Cronbach's alpha | Result |
Engagement Resulting from Customer Experinece | Mental Image Value | 0.605 | - | - | 0.353 |
0.927 | Optimal |
EconomicValue | 0.592 | 7.665 | *** | 0.326 | Optimal | ||
Knowledge Value | 0.674 | 8.525 | *** | 0.463 | Optimal | ||
Influence Value | 0.610 | 8.221 | *** | 0.375 | Optimal | ||
Lifetime Value | 0.719 | 9.619 | *** | 0.500 | Optimal |
The dashes in T values indicate that the parameter is fixed in the model
As shown in the table above, the t-statistics of engagement resulting from customer experience wtih its components were significant and >1.96. Therefore, the engagement resultig from the tourist experience can be divided into five sub-categories or sub-components. Cronbach's alpha coefficient for engagement resulting from the tourist experience was 0.927, which is > 0.7 and shows the desired level of the constructs’ reliability. Table 8 shows the model fit indices:
Table 8.
Second-order model fit indices for the value of tourist engagement
x2/ df<3 | RMSEA<0.08 | CFI>0.9 | IFI>0.9 | GFI>0.8 |
1.866 | 0.044 | 0.97 | 0.97 | 0.91 |
According to Table 8, the model fit indices are generally optimal and at an acceptable level. All Cronbach's alpha criteria and the significance of the factor loadings between the questions and the latent variables confirm the goodness of fit of the measurement model.
Finally, the values of customer engagement were provided in the form of five concepts using a meta-synthesis method and the results of quantitative studies, followed by confirming the model based on the validation results. Given the validation of the questions, the questionnaire can be used as a suitable scale to assess the value of tourist engagement.
Conclusion and Recommendations
The tourism industry is rapidly becoming one of the most profitable industries across the world and thus needs better management to achieve a more positive global goal. This tourism capability enables the industry to attract the attention of policymakers and leaders worldwide. One of the strategies that can lead to competitive advantages in modern marketing is value creation through engagement. The active engagement of tourists with service providers leads to value creation in the tourism industry. Service providers must adopt appropriate management practices to engage tourists in the co-creation process, use their experiences to meet their needs and expectations, and achieve competitive advantages. Tourists share their travel experiences on social media. Meanwhile, many tourists seek to take advantage of the experiences of others and search for their required information on the Internet and online social networks. Although tourists share their experiences voluntarily, they are willing to express and share their memorable experiences with others. Therefore, it is necessary to take the required measures to increase the satisfaction of tourists. Although satisfaction is a mental and subjective concept, tourists seek a pleasant experience from a tourist destination. Tourist satisfaction is an important factor in selecting a tourist destination again, recommending it to others, and ultimately achieving sustainable competition in the tourism industry. The results of this study examined the value of tourist engagement in five dimensions as follows:
Value of Mental Image: Tourists create an image of the tourist destination for the audience by sharing their feelings and talking about the cultures that govern the lives of the host people. They can also create a good mental image for the audience about the reliability of the tourist destination and its ability to meet the needs of tourists in an ideal way, which can affect the selection of the tourist destination.
Economic Value: According to the results, although tourism leads to economic prosperity in the destination country, people are sometimes so satisfied with their travel that they decide to invest in or even migrate to the new destination. The results of the study also showed that people spend money more easily in tourist destinations and do not take financial issues seriously.
Knowledge Value: Tourists share their most useful information and experiences of the tourist destination through engagement while providing innovative suggestions, ideas, and feedback to deal with the shortcomings and improve the performance of service providers.
Influence Value: Tourists share travel experiences and benefits and talk to others about tourism destinations through word-of-mouth advertising in physical space and content production in cyberspace on a larger scale.
Tourist Lifetime Value: In this case, tourists express their satisfaction with the tourist destination, select the tourist destination again, and increase the duration of their trip to that destination while recommending their desired destination to others.
Loyalty is one of the consequences of tourist engagement. Loyalty to a particular destination contributes significantly to tourism management because repeated tourist visits represent a significant business opportunity for tourism purposes (Meleddu et al., 2015). Numerous studies have shown that loyalty leads to a reduction in costs to attract tourists and tourists' sensitivity to financial issues. Hence, loyalty has been recognized as the main driving force and an important indicator of success in marketing and tourism resources (Son et al., 2013).
Service providers can use the results of this study and provide a platform for tourist engagement and benefit from their loyalty.
Suggestions for Future Research: The present study used the qualitative method of meta-synthesis. Future studies can use other qualitative methods such as content analysis, in-depth interviews, focus groups, ethnographic research, and case studies, along with quantitative analyses to develop qualitative studies and their generalizability and so Cross-cultural comparative studies to test the generalizability of the five-dimension model, Longitudinal studies to explore how engagement value evolves over multiple visits, The integration of social media analytics to measure influence and knowledge value in real-time
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