The Role of Value Co-Creation, Delight and Satisfaction on Tourism Loyalty: An Empirical Study in Hospitality

Value co-creation consists of the customers’ active participation and engagement, using their skills and knowledge, in the process of service (Prahalad & Ramaswamy, 2000), leading to more personalised and meaningful experiences (Solakis et al., 2022). To enhancevisitor satisfaction at tourism destinations, it is essential to provide activities that encourage customers’ active participation, as visitors now tend to be part-producers of their experiences rather than mere consumers (Tunde-Ajayi, 2021).

Several scales on value co-creation have been developed. The scale of value co-creation developed by Busser and Shulga (2018) is grounded in the principles of the theory of value (Hartman, 1967) and service-dominant logic (Vargo & Lusch, 2004a). It aims to evaluate the axiological aspects of value co-creation involving five dimensions: meaningfulness, collaboration, contribution, recognition, and emotional response. It is essential to note, however, that while the scale is used to assess value co-creation, it is primarily focused on customer value assessment, as highlighted in the study by Ribeiro et al. (2021). Another commonly used scale is the DART model (dialogue, access, risk, and transparency) (Albinsson et al., 2016), considered practical and interesting as a core framework of value co-creation, applied by academics and companies like Nike^® (Solakis et al., 2022). Prahalad and Ramaswamy (2004) introduced the DART model as a key set of requirements for understanding value co-creation. Scale development and validation of the DART model were conducted by Albinsson et al. (2016) and refined by Taghizadeh et al. (2016).

The active involvement of tourists in value co-creation appears to influence their behavioural intention and satisfaction with tourism resources and activities. Recent studies have examined the effects of value co-creation in the tourism and hospitality sector, such as those conducted by Solakis et al. (2017), Tunde-Ajayi (2021), Solakis et al. (2022), and Ribeiro et al. (2023). Inviting tourists to participate in co-creation and fostering an engaged business relationship leads to stronger relational outcomes between customers and companies (Busser & Shulga, 2019; Ribeiro et al., 2021). This emphasises the positive impact of value co-creation on customer–company interactions, possibly on delight and satisfaction in the tourism industry. According to Sweeney et al. (2015), co-creating value contributes to consumer satisfaction as well as other behavioural characteristics. Accordingly, hypotheses H1 and H2 are presented: –

Hypothesis 1: Value co-creation has a positive and significant impact on delight.

Foroughi et al. (2019) state that prior research has (implicitly) conceptualised delight as a nonlinear and positive response to satisfaction and neglected it as a distinct concept. Even though delight and satisfaction share common characteristics, however, satisfaction and delight are two separate constructs (Finn, 2005; 2012). The concept of consumer satisfaction is based on comparing expectations and the performance of a service, whereas the concept of delight is based on positive experiences and surprises (Berman, 2005). In addition, satisfaction is linked to utility, while delight has a hedonic and emotional orientation (Chitturi et al., 2008). In entertainment services, Oliver et al. (1997) developed a seminal work with a structural model of the antecedents and consequences of customer delight, acting in parallel with satisfaction. Thus, delight was originally conceptualised as a profoundly positive emotional state generally resulting from having one’s expectations exceeded to a surprising degree (Oliver et al., 1997). Simply stated, “delighted customers are those whose expectations have been exceeded by the service provider” (Elias-Almeida et al., 2016, p.13). Parasuraman et al. (2021) recognise that pleasant customer experiences can be a combination of multiple delight properties with more or less weight in different settings or even within the same service encounter. So, a broader and recent conceptualisation of how customer delight can be defined is proposed by Parasuraman et al. (2021, p.1) as follows: “Customer delight is associated with various combinations of six properties including the customer experiencing positive emotions, interacting with others, successful problem-solving, engaging customer’s senses, timing of the events and sense of control that characterise the customer’s encounter.”

Researchers analysed the drivers of customer delight in the hotel industry and concluded that while some universal service elements exist, tourists from different cultures can also be delighted by various services and amenities (Torres et al., 2014). Also, in tourism, some previous research has worked to explain the significance of customer delight as an antecedent of customer loyalty, for instance, in five-star hotel spas in Portugal (Elias-Almeida et al., 2016) or in hotels, restaurants, retail and theme parks (Torres et al., 2020). Empirical findings from Mangini et al. (2021) reveal the favourable impact of delight and satisfaction on tourists’ loyalty. The study also confirms the convergent and discriminant validity between the delight and satisfaction constructs, adding depth to the discussion surrounding these variables. Delight is suggested to be a greater predictor of loyalty than satisfaction (Ahrholdt et al., 2019; Mangini et al., 2021). Torres et al. (2020) also confirm the impact of customer delight on loyalty, suggesting that this variable can be used in addition to the current satisfaction measures to gain a deeper view of emotional and motivational relationships between customers and service organisations. Kim et al. (2015) propose customer delight as an emotion or affective response, and extend support for the conceptualisation of customer delight as one of the antecedents of loyalty, specifically showing that customer delight has significant relationships with cognitive, affective, and conative loyalties. Thus, the following hypothesis (H3) is proposed: –

Hypothesis 3: Customer delight has a positive and significant impact on loyalty.

Customers’ perceptions of actual service encounters are compared to their expectations (Oliver, 1999) to determine customer satisfaction. Fornell (1992) elucidated that satisfaction stems from the comparison between a consumer’s expectations and the perceived performance of a product or service. When customers experience high satisfaction, it enhances the likelihood of repurchasing, ultimately contributing to the formation of loyalty (Santos & Fernandes, 2008). This highlights the crucial role satisfaction plays in building lasting relationships with customers and fostering loyalty to a brand or service provider, with consumer satisfaction having future consequences for profitability (Gupta & Zeithaml, 2006). In the hotel industry, satisfaction is derived from the quality of service experienced by the consumer by directly influencing their expectations and perceptions (Shah et al., 2018). Tourism service providers must continuously enhance their service quality, focusing on the perceived quality of performance based on evaluating services and facilities to boost customer satisfaction and thereby increase loyalty (Huddin et al., 2024). Ali et al. (2018) proposed that various dimensions of customer experience, such as the physical environment, interaction with staff, and interaction with other customers, served as antecedents of customer experience, while satisfaction and loyalty were their consequences. Therefore, the following hypothesis (H4) is proposed: –

Hypothesis 4: Customer satisfaction has a positive and significant impact on loyalty.

Achieving customer loyalty remains one of the main challenges for organisations. According to Ahrholdt et al. (2019), conceptualising the parallel roles of satisfaction and delight as antecedents of loyalty draws on Oliver et al. (1997). Loyalty is “a deeply held commitment to re-buy and re-patronise a preferred product or service constantly in the future” (Oliver, 1999, p. 34). Loyalty in the context of consumer behaviour involves repurchase behaviour and brand commitment (Oliver et al., 1997). It is not solely determined by the repurchase process but is also influenced by cognitive and affective factors (Larán & Espinoza, 2004). A strong relationship between the customer and the company leads to increased customer loyalty (Sashi, 2012), which is vital for business growth and success in the market. Delighted customers are more likely to be retained, and retained customers are poised to transition into loyal customers, necessitating marketing programs to enhance their experiences and foster customer loyalty (Aityassine, 2022). Terrah et al. (2022) demonstrate a robust connection between experiential context (service quality, physical environment, and authenticity), positive emotions, and surprise as precursors to delight, with behavioural intentions, satisfaction, and loyalty as outcomes of delight. According to Ali et al. (2018), in a study focusing on theme parks, customer delight and satisfaction have a significant influence on customer loyalty. Therefore, the capability to guarantee customer delight and satisfaction through the development and provision of appropriate customer experiences can serve as a competitive advantage, potentially leading to customer loyalty.

In this research, a quantitative study sought to develop and test relationships among the key variables relying on the literature review. Figure 1 displays the conceptual model illustrating the relationship between exogenous and endogenous latent constructs. Accordingly, delight is influenced by value co-creation, and value co-creation also influences satisfaction. Moreover, both delight and customer satisfaction have a positive impact on loyalty. The study hypotheses are as follows:

H1: Value co-creation has a positive and significant impact on delight.

H2: Value co-creation has a positive and significant impact on customer satisfaction.

H3: Customer delight has a positive and significant impact on loyalty.

H4: Customer satisfaction has a positive and significant impact on loyalty.

Figure 1:

Conceptual model

Source: Research data

The survey instrument’s measurement items were derived from validated research instruments and adjusted to fit the specific context of this study. In all instances, latent variables were assessed using a 7-point Likert scale, where 1 indicated ‘strongly disagree’ and 7 indicated ‘strongly agree’. To investigate the impact of value co-creation processes on the tourist experience, this research employs the DART model proposed by González-Mansilla et al. (2019), which utilises the buildup method based on the four DART dimensions: dialogue, access, risk assessment, and transparency, with three indicators each. The scales also covered delight (Ali et al., 2018; Finn, 2005; Kim et al., 2015), customer satisfaction (Ali et al., 2018; Westbrook & Oliver, 1991), and customer loyalty (Ali et al., 2018; Kao et al., 2008).

A pre-test of the questionnaire was carried out with six customers, and some modifications were implemented. Additionally, academic specialists in tourism planning and management reviewed the modification of items to ensure content validity. The pre-test, the analysis conducted by the specialists and the literature review, resulted in the measurements presented in the Appendix.

A self-administered survey was utilised to gather data from individuals who had visited a tourism destination within the past 12 months, either during a weekend or an extended vacation, and stayed in collective tourism accommodations. Given the limitations in time and resources, a convenience sampling method was used. The questionnaire was distributed through online social media platforms, and it was available in an electronic format using the LimeSurvey platform.

A total of 178 valid responses were obtained from May 17th to July 17th, 2023, after removing incomplete answers. The data obtained were analysed using the Jamovi statistical program (version 2.2.5) with univariate analysis. Additionally, for this study, the researchers tested the hypotheses outlined above using structural equation modelling, employing the partial least squares (PLS) method with Smart PLS M3 Version 4.0. The sociodemographics are in Table 1.

The gender distribution of respondents indicates that 28.1% are male, while 71.9% are female. The participants’ average age is 41.7 years, with a median of 44, and the age data ranges from 20 to 69, with a standard deviation of 10.5. Regarding education, 2.2% completed ninth grade, 13.5% finished secondary school, 44.9% hold a bachelor’s degree/undergraduate degree, and 39.3% pursued postgraduate studies/MBA/master’s degree/doctorate. Marital status reveals that 64.6% are married/common-law, 6.2% are divorced, 28.7% are single, and 0.6% are widowed. The survey encompasses individuals with various occupations, including 7.9% students, 0.6% retired individuals, 77.5% employees, and 14.0% self-employed. As for monthly net income (individual), 4.4% earn less than 760 euros, 20.3% earn 760-999 euros, 24.1% earn 1000-1249 euros, 22.2% earn 1250–1499 euros, 8.2% earn 1500-1749 euros, and 20.9% earn 1750 or more euros. The respondents are from diverse districts, with Vila Real having the highest percentage at 27.5%, followed by Bragança at 23.6%, and Braga and Lisbon both at 11.2%. Other districts such as Coimbra, Guarda, Leiria, R. A. Açores, Santarém, Évora, Castelo Branco, Viana do Castelo, Faro, R. A. Madeira, Viseu, Setúbal, and Aveiro show lower percentages, ranging from 0.6% to 2.8%. Out of the 178 respondents, a significant number enjoyed the tourist experience with their families (34.8%) and spouses/partners (48.3%). Additionally, a smaller portion chose to share the experience with friends (11.8%), while a few preferred to explore alone (3.4%). There were also a few instances where the respondents were accompanied by others (1.7%).

In the tourism experience that the respondent had during a vacation destination, concerning the type of accommodation (Table 2), respondents chose to stay in a hotel, aparthotel, or inn, which was the most popular choice, representing 59.6% of the stays. Rural tourism options, such as country houses, agrotourism, and rural hotels, were chosen by 9.0% of the tourists. Meanwhile, 8.4% of people preferred local accommodations, and 7.9% opted for home tourism. Other alternatives, like tourist apartments, tourist complexes, resorts, camping, and caravan parks, had a smaller share of choices, ranging from 1.7% to 6.7%.

Regarding the hotel classification, from a 5 to 1-star rating system, the 106 respondents were divided into 2 stars - one respondent, 3 stars - 21 respondents, 4 stars - 65 respondents, and 5 stars - 15 respondents (four respondents couldn’t remember).

Measurement model evaluation revealed that adjustments need to be made. As part of the value co-creation construct, item R3 was removed due to a factor loading below 0.7 (Hair et al., 2017). As a second-order variable, value co-creation is composed of dialogue, access, risk, and transparency. After these adjustments, it is possible to affirm that there is internal consistency in the scales, with Cronbach’s alpha and composite reliability greater than 0.7, the minimum reference (Ringle et al., 2014). These indicators are shown in Table 3, as well as those referring to convergent validity (Fornell & Larcker, 1981). In Table 3, the AVE values support the convergence of the Conceptual Model constructs.

Table 3 also presents the discriminant validity that indicates the distinction of one construct concerning another (Hair et al., 2009); that is, each construct is unique without being represented in another construct of a model (Hair et al., 2017). Two criteria were employed: the Fornell Larcker index and the heterotrace–monotrace matrix. Table 3 shows the square root of the AVE (Fornell-Larcker criterion) and the confidence interval of the HTMT matrix (values less than 1). Therefore, it is possible to affirm that there is discriminant validity in the adjusted model.

Table 4 shows the VIF (variance inflation factor) of each latent variable, which indicates the degree of multicollinearity between variables (Hair et al., 2009). The highest VIF is 2.486 (relationship between delight and loyalty and between satisfaction and loyalty). The value for this index should be below the 3.0 threshold (Hair et al., 2019), which suggests that the model presents low collinearity between the constructs of the model. Pearson’s coefficient of determination (R²) expresses the predictive accuracy of a model (Hair et al., 2017). In social and behavioural sciences, an R² of up to 2% is considered a small effect, R² around 13% as a medium effect, and R² above 26% as a significant effect (Ringle et al., 2014). The coefficients of determination of the Conceptual Model, reported in Table 4, show high explanatory power of the regressions because the values of adjusted R² are between 46.8% and 78.3%. Cohen’s d (f²), for effect size, serves to assess the usefulness of each construct for the adjustment of a model, 0.02, 0.15, and 0.35 indicate, respectively, weak, medium, or substantial influence of an exogenous latent variable on an endogenous latent variable. For the Conceptual Model, in Table 4, most of the f² are high. The only low effect is between Delight and Loyalty (f² = 0.057). These values were possible due to the use of the PLS algorithm in the SmartPLS software.

Figure 2 shows the adjusted model, with the path coefficient between latent variables, the R² of each endogenous variable, and the factor loadings of each item measured in the respective latent variable.

Figure 2:

Adjusted model

Source: Research data

Bootstrapping was used in SEM, a nonparametric procedure that extracts several subsamples and estimates models for each of them, and, finally, estimates parameters from the set of models (Hair et al., 2018). The relationship between the constructs is measured with Student’s t-test, whose coefficients assess the relationship between constructs at an adopted significance level. The values generated by bootstrapping, with 10,000 resamples for the overall sample, are shown in Table 5 and Figure 3. All hypotheses are supported.

Figure 3:

Validated model

Source: Research data

According to goods-dominant logic, value creation is the main outcome (Blocker & Barrios, 2015), and the process of delivering value encompasses selection, supply, and communication (Zeithaml, 1988). Nevertheless, the customer is a highly passive actor in the exchange of goods, and the purpose of the exchange of value is only to deliver benefits to the customer (Vargo & Lusch, 2004a; 2008). A service-dominant logic, on the other hand, plays a fundamental role in the process of generating value, where both customers and service providers are involved (Vargo & Lusch, 2004b). In fact, services, especially hospitality, have a high degree of interactivity between customers and service providers. According to Vargo and Lusch (2004a), such interaction is the basis for the value co-creation process that leads to satisfaction, delight, and loyalty. Ribeiro et al. (2023) suggest that interaction, engagement, and service innovation are the most studied antecedents of value co-creation, while satisfaction, perceived value, and loyalty are the main consequences. The results presented in this article show the importance of value co-creation, measured by the DART model (Grönroos & Voima, 2013; Albinsson et al., 2016; Gbandi & Oware, 2023), in which there is a positive and significant relationship with delight (H1) with β = 0.737, t(178) = 17.757 and p < 0.001. Delight formation was found to be positively influenced by consumer experience in this study, which is similar to the findings of Hao and Chon (2022). It is important to note that Finn (2005) has already highlighted the importance of surprise with a positive effect on the formation of delight, and with this in mind, the study’s results confirm the importance of transparency and dialogue in the value co-creation process, as illustrated by the DART model. The concept of communication was previously explored by Lee (2019) as a factor that generates delight in health care, as well as by Pera (2017) when evaluating the practice of storytelling in tourism. It is important to emphasise that dialogue, access, and transparent communication are the basis of the value co-creation model used in this research.

According to Table 3, the value co-creation process positively correlates with satisfaction levels. Consequently, when the customer participates in the service provision as an active agent, the tendency is to generate customer satisfaction, as previously demonstrated by Sweeney et al. (2015). This can be explained by the fact that the consumer becomes an active co-creator (Grönroos & Voima, 2013), and this directly affects his/her perception of service performance and how it compares to their expectations (Oliver, 1980). As a result of presenting the four dimensions of value co-creation, the DART model produces a series of benefits that contribute to the development of satisfaction (Vega-Vazquez et al., 2013).

The study shows both the classic relationship between satisfaction and loyalty generation (H4) as well as the relationship between delight and loyalty generation (H3). Both satisfaction and delight are based on the consumption experience (Sarstedt et al., 2014). In this study, the relationship between delight and loyalty was positive and significant (β = 0.174, t(178) = 2.743 and p < 0.01) and corroborated the studies of Ahrholdt et al. (2019), Lee and Park (2019), and Torres et al. (2020), because similar to satisfaction, delight is linked to the positive experience perceived by the consumer that exceeded his/her expectations (Silva & Júnior, 2016). As Kim et al. (2015) proposed, customer satisfaction is more strongly related to loyalty than delight. The stronger relationship between satisfaction and loyalty in this study than between delight and loyalty contrasts with the study of Mangini et al. (2021), where delight appears to have a stronger influence on customer loyalty when compared to satisfaction. Hypothesis 4 presents a β = 0.745, proving the positive relationship with a t-test (178) of 11.517 and a p value of less than 0.1%, which shows a high relationship between constructs, as reported by several researchers such as Oliver (1999), Chen and Wang (2016), and Bernardes et al. (2018), among others.