Journal of Travel & Tourism Marketing Satisfying ...

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Journal of Travel & Tourism Marketing

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Satisfying and Delighting the Rural Tourists

Sandra M. C. Loureiroa a Department of Economy, Management, and Industrial Engineering (DEGEI), University of Aveiro, Aveiro, Portugal Online publication date: 28 May 2010

To cite this Article Loureiro, Sandra M. C.(2010) 'Satisfying and Delighting the Rural Tourists', Journal of Travel &

Tourism Marketing, 27: 4, 396 — 408 To link to this Article: DOI: 10.1080/10548408.2010.481580 URL: http://dx.doi.org/10.1080/10548408.2010.481580

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Journal of Travel & Tourism Marketing, 27:396–408, 2010 Copyright © Taylor & Francis Group, LLC ISSN: 1054-8408 print / 1540-7306 online DOI: 10.1080/10548408.2010.481580

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SATISFYING AND DELIGHTING THE RURAL TOURISTS Sandra M. C. Loureiro

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Sandra M. C. Loureiro

ABSTRACT. This study applies the concept of customer delight and the model proposed by Oliver, Rust, and Varki (1997) and modified by Finn (2005) to the context of rural tourism. The model is applied to rural tourism lodgings in northern Portugal and validated using PLS technique. The results suggest that satisfaction is a more significant determinant of loyalty than delight and disconfirmation is an important predictor of both satisfaction and delight. This study further supports the conceptualization of customer delight and customer satisfaction as distinct constructs. Results may help managers of rural tourism accommodations to develop and implement more successful relationship marketing strategies. KEYWORDS. Customer delight, satisfaction, loyalty, rural tourism

INTRODUCTION Rural tourism is regarded as tourism in the countryside that embraces the rural environment and rural products (e.g., food, fairs) as essential to the tourist package offered. The Portuguese legislation emphasizes the role of rural tourist accommodation, defining rural tourism as “combination of paid activities and services provided in rural areas, in establishments with family character . . . aiming at offering a complete and diversified tourism product in rural areas . . . so as to preserve, restore and value the regions’ architectonic, historical, natural and landscape heritage” (Dec.-Lei nº 54/2002). The lodgings, located in rural areas, include houses with outstanding architectural and historical value, country houses, representing the typical regional architecture and farms, with owners mostly living in these establishments and providing a personalized hospitality experience.

Rural tourism is further identified as a potential opportunity for diversifying the product portfolio of declining mass tourism destinations (Sharpley, 2002), or for enriching the positioning of these destinations, or of countries associated with a corresponding image, like Portugal with “sun and beach” tourism in the Algarve. Rural tourism should correspondingly adapt to current market mechanisms, which are becoming extremely competitive and are dominated by powerful communication techniques in a context of strategically developed marketing action (Gannon, 1994; Moutinho, 1991). In this context, it should be most interesting to keep loyal visitors in order to guarantee long-run success and enhance the destination’s sustainability. This article reviews the main antecedents of customer delight and applies the model proposed by Oliver, Rust, and Varki (1997) and modified by Finn (2005) to the context of rural tourism. The model is validated with data collected from tourists staying in rural tourism

Sandra M. C. Loureiro, PhD, is Professor in the Department of Economy, Management, and Industrial Engineering (DEGEI) at the University of Aveiro, Campus of Santiago, 3810-193 Aveiro, Portugal (E-mail:[email protected] or [email protected]). 396



lodging in northern Portugal, using a PLS structural equation modeling approach. According to Oliver et al. (1997) and Finn (2005), the effect of delight on intention differed by the service context, as it was significant for the symphony ticket purchasers but insignificant for the theme park patrons, and so they recommend other empirical tests in future research. As far as we know, the model has never been applied to the context of rural tourism. Therefore, the intent of this study is to contribute to the body of the knowledge applying the model to this context and seeking to understand which latent variables have the relatively strongest impact on loyalty, with conclusions suggesting managerial implications for the rural lodging business. The rural tourism is a development tool of rural areas, frequently economically and socially depressed (Gannon, 1994; Greffe, 1994; Page & Getz, 1997; National Institute of Statistics Portugal [INE], 2007); thus, understanding the main factors that influence rural tourists’ loyalty is important to rural lodging managers and local governments of rural areas, not only in Portugal, but also in other countries with rural areas with the same main trends (such as: economically and social depressed).

CUSTOMER DELIGHT AND LOYALTY Customer Delight In their seminal work about service quality, Parasuraman, Zeithaml, and Berry (1991) introduce the concept of “zone of tolerance,” suggesting that consumers will allow certain exceptions to the fulfillment of expectations about the service (disconfirmation) and whenever such exceptions are within the “zone of tolerance,” the experience remains satisfactory. However, when the perceived outcome of the service goes beyond the upper thresholds of this zone, the customer will feel delighted or disgusted (McNeilly & Barr, 2006). If the opposite occurs, the customer will feel disgusted. That is, only if the zone of tolerance is positively overcome, the service experience will generate a durable effect that is able to create a sensation of delight (Keinningham, Goddard, Vavra, & Laci, 1999). In this

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point of view, customer delight is an extension of providing basic satisfaction. The opportunities to delight the consumer depend on the organization’s capacity to continuously improve the way the service is provided. Long-term relationships between firms and their customers, based on a personalized service, may exceed expectations, thereby achieving the customers’ delight (Zeithaml & Bitner, 2003). Briefly, customer delight involves going beyond satisfaction by delivering a pleasurable experience. Although the term consumer delight arose in the 1990s, its foundation in the literature is associated with the discussion of affective (Westbrook, 1987) and experimental (Holbrook & Hirschman, 1982) responses to consumption. Plutchik (1980) proposes a circular pattern of eight basic emotions, which combine to generate more numerous second level emotions. In Plutchik’s theory, delight is a blend of joy and surprise, conceptually similar to Westbrook and Oliver’s (1991) consumer behavior dimension “pleasant surprise.” Later, Mano and Oliver (1993) found a strong direct effect of arousal on positive affect, with arousal as a direct antecedent of affect and an indirect determinant of satisfaction. Thus, arousal is interpreted as playing an activation role in the emotional process leading directly to delight and indirectly to satisfaction. Empirical evidence reported by Kumar and Olshavsky (1996) shows that consumer satisfaction is associated with meeting expectations (through the perception of a “fair service” and a feeling of contentment), while consumer delight is associated with overcoming expectations leading to feelings of happiness, euphoria, victory, intense satisfaction, or optimism. Similarly, Patterson (1997) argues that consumer delight implies going beyond satisfaction and providing the consumer with what can be described as a pleasant experience, and so consumer delight is being regarded as the best tool to increase the ratio of customer retention. Rust and Oliver (2000) suggest that delight is more desirable in the following situations: (a) when satisfaction has a strong influence on behavior intentions; (b) when customer satisfaction with competition has a strong impact on the capacity

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of customer retention; (c) when significant attention is paid to future benefits; and (d) when the organization is able to capitalize on the unsatisfied customers of competitors by switching them into their own customers.


Cognitive focus of Customer Delight Oliver’s (1980, 1981, 1999) model of expectation-disconfirmation is the basis for the concept of delight. According to this model, if the perception of a service provided is worse than expected, the resulting judgment is labeled negative disconfirmation; positive disconfirmation occurs if service performance perception is better than expected; and simple confirmation if as expected. Woodruff, Cadotte, and Jenkins (1983) made their contribution presenting a range of experience-based norms, which represent the range of likely performance outcomes experienced over time or learned via the experience of others. Thus, performance deviations within this range are not considered to be disconfirming, as would be those outside. In later studies (Oliver, 1989; Oliver & Winer, 1987), the existence of three categories of (dis)confirming events has been suggested: (a) performance in a confirmation region where performance deviations are considered normal; (b) a range of disconfirming performance that is plausible but infrequent so that the performance deviation is noted as unusual; and (c) more discrepant performance levels which are highly unlikely, unexpected, or surprising. This last level has been denominated “surprising disconfirmation” (Oliver, 1997; Oliver et al., 1997), and can be considered as the base of the concept of customer delight. So, the arousal potential of a discrepancy between performance and expectation is a function of the consumer’s acceptance range and the level of surprise experienced (Oliver et al., 1997).

Affective Focus of Customer Delight The theories of affect and emotion also contribute, in a remarkable way, to the concept of customer delight. Although, traditionally the studies of satisfaction/dissatisfaction assume a cognitive focus (Oliver, 1980), nowadays researchers consider it a cognitive phenomenon with affective

elements (Han & Back, 2007; Martínez-Tur, Peiró, & Ramos, 2001; Wirtz & Bateson, 1999; Wirtz, Mattila, & Tan, 2000). The emotions’ theories from social psychology describe delight as an emotion of second order, characterized by a combination of first-order emotions. According to Plutchik (1980), who suggests a circumplex model of emotions, there are eight basic emotions that may be comprised into four dimensions with opposite poles (sadness-joy, surprise-anticipation, acceptanceboredom, and anger-fear). By combining these basic emotions, more complex emotions would arise. According to this theory, a secondary dyad is defined as a combination of two basic emotions which generate a more complex emotion. Thereby, delight would be defined as a secondary dyad comprised of joy and surprise. In a similar way, other emotional theories define delight as a combination of arousal and pleasure (Russell, 1980) or as a positive affect implying a high level of activation (Watson & Tellegen, 1985). On the other hand, Richins (1997), identifying the most outstanding emotions in marketing studies on consumption, includes delight inside the category of “joy.” Apart from joy and surprise, Oliver and Westbrook (1993) point out a third emotion associated with the concept of delight: interest. This concept of delight coming from the emotions literature has been assumed by the main researchers in consumer behavior. Accordingly, Westbrook and Oliver’s (1991) and Oliver and Westbrook’s (1993) studies show differences in the profile of consumers as a function of their state of simple satisfaction or manifest surprise or delight with the consumption experience. On the other hand, Oliver (1993) demonstrated the existence of significant relationships between positive affect (interest and joy) and satisfaction/dissatisfaction responses, distinguishing diverse dimensions of positive-negative affect—such as moderate arousal positive affect (pleasure), high arousal positive affect (delight), and high non-specific arousal (surprise). In short, the concept of delight is presented in these studies as a combination of pleasure and arousal/activation, or of joy and surprise (Oliver et al., 1997). However, the managerial



practice does not completely agree with this conceptualization. The requirement of surprising customers pleasantly to delight them becomes particularly complicated for organizations that have frequent transactions with the same customers. The cost of surprising customers at every transaction is impractical and prohibitive both technically and economically (Kumar, Olshavsky, & King, 2001). Still, customers may be delighted in a continuous way, without being continuously surprised. Indeed, Schactel (1959) differentiates two kinds of joy: “magic joy” and “real joy.” Magic joy is based on the experience of a person that feels that the unexpected fulfillment of a wish or need can change his/her situation. The other type of joy (real joy) can result from any ongoing activity which brings an individual into contact, physically and/or mentally, with some aspect of the world around him/her. From this point of view, surprise is one of the ways of causing delight, but other ways may exist to generate this emotion. Correspondingly, Kumar et al. (2001) encourage firms interested in delighting customers to not only focus on surprising customers at every transaction but to identify and focus on activities that are at the core of an ongoing pleasurable relationship between the customer and the firm.

Antecedents and Consequences of Delight Oliver et al. (1997) point out the surprising consumption, causing arousal and positive affect, as the main antecedents of delight. Arousal is presented as a function of surprising levels of consumption. They also hypothesize that delight creates a desire for further pleasurable service in the future: future intentions. The satisfaction sequence in the same model starts with the traditional construct “disconfirmation of expectations” as an exogenous initiator of satisfaction. Disconfirmation is shown as influencing both satisfaction and positive affect. So, disconfirmation and positive affect are direct antecedents of satisfaction and intention is shown as a consequence of satisfaction. Finally, they also consider that disconfirmation and surprising consumption are correlated exogenous variables.

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Empirical evidence generally supports the model for two entertainment services studied: symphony ticket purchase and theme park patronage. However, the direct effect of delight on intention to consume was significant for symphony ticket purchase but not for theme park patronage. One possible explanation could be that delight captures a nonlinear, but inconsistent effect of satisfaction on intention (Fullerton & Taylor, 2002; Mittal & Kamakura, 2001). Finn (2005) applies the model developed by Oliver et al. (1997) to online retail services, adding to the model a direct relationship between disconfirmation and arousal, and trying to solve the above mentioned problem incorporating nonlinear effects, quadratic, and interaction effects. However, neither the quadratic nor the interaction effect yields a conventional level of significance. Rust and Oliver (2000) point out that customer delight will generate positive outcomes only if satisfaction strongly affects repurchase intention, the level of satisfaction with competing brands or companies is important, the firm values long-term benefits, and the firm is able to attract unsatisfied customers of competitors. On the other hand, generating delight among customers can result in higher future expectations, so that it will be more difficult for the firm to generate delight repeatedly (Arnold, Reynolds, Ponder, & Lueg, 2005). Torres and Kline (2006) argue that, if delight is considered as a function of customer’s expectations, then satisfaction is the logical antecedent of delight. Based on a needs model such as the one proposed by Schneider and Bowen (1999), one might associate satisfaction with the fulfillment of certain needs like security or justice, whereas delight would be associated with the fulfillment of higher order needs like self-esteem. Some researchers have suggested moderator variables in the relationship between delight and psychological antecedent constructs, such as customer involvement and service variability (Oliver et al., 1997). Other researchers suggest variables such as consumer self-regulation (Babin & Darden, 1995; Bagozzi, Baumgartner, & Yi, 1992; Kuhl, 1986) or industry competitiveness (Anderson, Fornell, & Lehmann, 1994; Fornell,

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1992) to act as moderators in the relationship between delight and behavioral outcome.


MODEL OF RURAL TOURIST DELIGHT This study applies the model proposed by Oliver et al. (1997), which was modified by Finn (2005), to the context of rural tourists (see Figure 1). According to the theories of affect and emotion (e.g., Plutchik, 1980; Russell, 1980), as well as Oliver et al. (1997) and Finn (2005), delight is a function of surprising consumption, arousal, and positive affect. Arousal is a function of surprising levels of consumption.

H5: Arousal has a positive effect on tourists’ positive affect. To close the affective and emotional sequence, Oliver et al. (1997) claim that delight can create a desire for further pleasurable encounter in the future. H6: Delight has a positive effect on tourists’ loyalty. The disconfirmation paradigm (e.g., Oliver, 1989) is the starting point to the satisfaction sequence. The disconfirmation of expectations is shown as influencing both satisfaction and positive affect (Oliver et al., 1997; Finn, 2005). Satisfaction is being shown as a dual function of cognitive and effect (e.g., Oliver, 1997) and so positive affect can influence satisfaction.

H1: Surprising consumption has a positive effect on tourists’ delight. H2: Arousal has a positive effect on tourists’ delight. H3: Surprising consumption has a positive effect on tourists’ arousal. H4: Positive affect has a positive effect on tourists’ delight.

H7: Disconfirmation has a positive effect on tourists’ positive affect. H8: Disconfirmation has a positive effect on tourists’ satisfaction. H9: Positive affect has a positive effect on tourists’ satisfaction.

Mano and Oliver (1993) found that generalized arousal products heightened levels of positive affect.

The loyalty is showed to be significantly predicted by satisfaction (e.g., Anderson & Sullivan, 1993; Andreassen & Lindestad, 1998; Heung & Qu, 2000; Loureiro & Miranda González, 2008).

FIGURE 1. Research Model of Rural Tourist Delight


H10: Satisfaction has a positive effect on tourists’ loyalty. Finally, Finn (2005) proposed a direct link between disconfirmation and arousal and suggested that it should be investigated further in new data. Later, Finn (2006) found support for the mentioned link in the context of retail websites and highlighted the importance to test the same causal order in other types of service providers. H11: Disconfirmation has a positive effect on arousal.


Variables and Measurement This research uses a seven-item scale adapted from previous researchers (Bigné, Sanchez, & Sanchez, 2001; Brady, Cronin, & Brand, 2002; Loureiro & Miranda González, 2008; Setó Pamies, 2003), once satisfaction with a service provider is perceived as being both an evaluative and emotion-based response to a service encounter (Oliver, 1997). Disconfirmation and delight are measured with two items each. Surprising consumption, arousal, and positive affect with three items each. All items of these five variables were based on the work of Oliver et al. (1997) and Finn (2005). Finally, loyalty is measured with four items adapted from scales previously developed (Bigné et al., 2001; Loureiro & Miranda González, 2008; Prayag, 2009; Zeithaml, Berry, & Parasuraman, 1996). Each item of all variables mentioned above was recorded on a 5-point scale.

METHOD Sample and Data Collection The questionnaire was administered to tourists that stayed in rural lodging, and only the guests that were going to leave the accommodation and had already stayed during at least one night were invited to complete the questionnaire, which was collected immediately. The survey was conducted during the months of March and April of 2008 in northern Portugal. This region has the highest concentration of the country’s rural lodging units (INE, 2007). Data collection aimed at an approximately representative sample

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of respondents from these units in spring of 2008. Fifty-five rural lodgings were randomly selected from 448 existing in the northern region and the guests were approached by the research team. Almost 200 (198) guests answered the questionnaire, 32 did not want to answer, 5 did not respond completely, and 161 were useable for validating the model, corresponding to a response rate of 81%. The questionnaire was developed in two languages, Portuguese and English, with the help of language professors, to address both the domestic and international tourists. Back translation was used to ensure that both questionnaires communicated similar information to all respondents (Brislin, 1970). A pilot sample was used to ensure that the wording of the questionnaire was clear. Twenty rural (Portuguese and foreign) tourists were invited to take part in this test. After this concern, the language professors reviewed the questionnaire. Very few adjustments were made in statements and the questionnaire was prepared for data collection. Table1 shows that the respondents were split almost equally in terms of gender. More than 60% of the sample fell in the age group 21–40 years. The majority of respondents were Portuguese as expected, since INE (2007) reveals a preponderance of domestic visitors compared with foreign tourists. Oppermann (1996) also points out that rural tourism demand is found to be mostly domestic.

Data Analysis The Partial Least Squares (PLS) approach was employed to estimate structural paths coefficients, R2, Q2, and Bootstrap technique. PLS is based on an iterative combination of principal components analysis and regression, and it aims TABLE 1. Survey Customer Profile Gender Male: 54% Female: 46%

Age

Country of origin

< 21: 3.7% 21–30: 27.3% 31–40: 33.5% 41–50: 18.0% 51–60: 12.4% > 60: 5.0%

Portugal: 87.0% Spain: 8.7% United kingdom: .6% Brazil: 1.2% Germany: 1.2% Belgium: .6% Colombia: .6%


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to explain the variance of the constructs in the model (Chin, 1998). In terms of advantages, PLS simultaneously estimates all path coefficients and individual item loadings in the context of a specified model and, as a result, it enables researchers to avoid biased and inconsistent parameter estimates. Based on recent developments (Chin, Marcolin, & Newsted, 2003), PLS has been found to be an effective analytical tool to test interactions by reducing type II error. By creating a latent construct which represents the interaction term, a PLS approach significantly reduces this problem by accounting for the error related to the measures. PLS technique is based on prediction-oriented measures and works to maximize variance explained in constructs and/or variables, depending on model specification. There are several fit indices on PLS technique, such as communality and redundancy measures and Stone-Geisser’s Q2 measure, which can be used to evaluate the predictive power of the model (Barclay, Higgins, & Thompson, 1995). This technique doesn’t need large samples to be applied. Tenenhaus, Vinzi, Chatelin, and Lauro (2005) propose the geometric mean of the average communality (outer mode) and the average R2 (inner model) that is limited between values of 0 and 1 as overall goodness-of-fit (GoF) measures PLS (Cross-validated PLS GoF):

GoF = communality . R 2 .

RESULTS The adequacy of the measures is assessed by evaluating the reliability of the individual item and the discriminant validity of the constructs (Hulland, 1999). Item reliability is assessed by examining the loading of the measures on their corresponding construct. All the loadings (see Table 2) of scales measuring reflective constructs approach or exceed .707, which indicates that more than 50% of the variance in the observed variable is explained by the construct (Carmines & Zeller, 1979). Composite reliability was used to analyze the reliability of the constructs since this has been

considered a more exacting measurement than Cronbach’s alpha (Fornell & Larcker, 1981). Table 2 indicates that all constructs are reliable since the composite reliability values exceed the threshold of .7 and even the strictest one of .8 (Nunnally, 1978; Hair et al., 1998). The measures also demonstrated convergent validity as the average variance of manifest variables extracted by constructs was at least .5, indicative that more variance was explained than unexplained in the variables associated with a given construct. Finally, to assess discriminant validity of the constructs, Fornell and Larcker (1981) suggest the use of the average variance extracted (AVE). This measure should be greater than the variance shared between the construct and other constructs in the model (i.e., the squared correlation between two constructs). Table 3. shows the correlation matrix for the constructs and the square root of the AVE. This measure is greater than the correlation with other constructs present below in the same column for all constructs, so the criterion of discriminant validity is verified. All constructs are reliable since the composite reliability values exceed of .7 and all the measures demonstrated convergent validity. Discriminant validity was also found to be evident and so the structural model can be assessed. The full structural results are presented in Figure 2. Path coefficients, interpreted like standardized betas, indicate the strength of direct relationship between constructs. This study used a nonparametric approach, named Bootstrap, for estimating the precision of the PLS estimates and supported the causal order between constructs. So, 500 samples sets (usually considered as large enough to test the causal order between constructs) were created in order to obtain 500 estimates for each parameter in the PLS model. Each sample was obtained by sampling with replacement to the original data set (Fornell & Larcker, 1981; Chin, 1998). Five path coefficients were found to be significant at the .001 level, three are significant at the .01 level, another three are significant at the level .05, and all the signs were in the expected direction. The Q2 statistic can be used to evaluate the predictive relevance of the model. All values of


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TABLE 2. Measurement Results


Construct

Mean (Standard Deviation)

Disconfirmation D1: How well did this lodging service meet my requirements? (1= Below my Requirements, 5= Above my Requirements) D2: Overall, when compared to my expectations, I found the experience to be (1= Much Worse, 5= Much Better) Surprising Consumption** E1: Astonishment E2: Surprise E3: Dazzlement Delight** E4: Enchantment E5: Delight Arousal** E6: Enthusiasm E7: Incitement E8: Encouragement Positive Affect** E9: Joy E10: Contentment E11: Pleasure Satisfaction** S1: The stay here has been very satisfactory. S2: The rural lodging satisfies my necessities. S3: The rural lodging facilities are worthy of highlighting. S4: I find the lodging personal pleasant. S5: The rural lodging delivers the service that I expected to receive. S6: The lodging rural delivers an excellent service S7: In general, my experience here is positive. Loyalty** L1: I will speak well about this lodging service to other people. L2: I will recommend the lodging if someone ask for my advise L3: I will encourage my friends and relatives to visit this rural lodging. L4: In next vacations I intend to return to this lodging.

Index values

Item loading

4.4 4.4 (0.64)

0.901

4.4 (0.62)

0.919 3.4

3.3(0.96) 3.5 (0.84) 3.6 (0.89)

Composite reliability (rc)

AVE*

0.91

0.83

0.90

0.76

0.90

0.83

0.90

0.75

0.95

0.87

0.95

0.72

0.90

0.83

0.826 0.904 0.875 3.8

3.9 (0.84) 3.7 (0.78)

0.916 0.902 3.8

3.9 (0.90) 3.6 (0.85) 3.9 (0.87)

0.863 0.877 0.850 4.4

4.3 (0.77) 4.4 (0.75) 4.4 (0.78)

0.917 0.957 0.918 4.7

4.8 (0.44) 4.7 (0.53) 4.6 (0.55) 4.7 (0.52) 4.6 (0.58)

0.867 0.840 0.823 0.829 0.848

4.6 (0.60) 4.7 (0.51)

0.881 0.847 4.69

4.8 (0.40) 4.8 (0.40) 4.7 (0.56)

0.917 0.891 0.796

4.5 (0.69)

0.780

*AVE = average variance extracted. **The items of these constructs were evaluated with 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).

Q2 (see Figure 2) are positive so the relations in the model have predictive relevance. The model also demonstrated a good level of predictive power (R2) as the modeled constructs explained 51% of the variance in loyalty, 61% of the variance in delight, 48% of the variance in satisfaction, 47% of the variance in arousal, and 47% of the variance in positive affect. Figure 2 shows a good value of the overall goodness of fit (GoF) proposed by Tenenhaus et al. (2005), since this index can range between 0 and 1. The good value of GoF

and the good level of predictive power (R2) reveal a good overall fit of the structural model. The multiplication between Pearson correlation value and path coefficient value of each two constructs (see below the path coefficient in Figure 2) reveals that 40.2% of variability of loyalty is explained by satisfaction and only 11.1% by delight. So, the strength of direct relationship between satisfaction and loyalty is higher than between delight and loyalty. Among surprise consumption, positive affect,

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TABLE 3. Discriminant Validity Analysis Construct

AVE½ Surprising Consumption Disconfirmation Arousal Positive Affect Delight Satisfaction Loyalty

Surprising consumption

Disconfirmation

Arousal

Positive affect

Delight

Satisfaction

Loyalty

.87 1.00

.91 .29

.86 .64

.93 .43

.91 .62

.85 .39

.85 .41

.29 .64 .43 .62 .39 .41

1.00 .42 .42 .42 .64 .51

.42 1.00 .67 .74 .49 .49

.42 .67 1.00 .60 .51 .50

.42 .74 .60 1.00 .44 .49

.64 .49 .51 .44 1.00 .69

.51 .49 .50 .49 .69 1.00


FIGURE 2. Structural Results .250* 15.6% Surprising Consumption

.571*** 36.7%

Arousal R2 = .471 Q2=.329

.594*** 39.6% .248** 10.4%

Delight R2 = .608 Q2 = .179

.450*** 33.3%

.229** 11.1%

.196* 11.9%

Loyalty R2 = .513 Q2 = .100

Positive Affect R2 = .470 Q2 = .092 .297 ** 15.2%

.176* 7.4%

Disconfirmation

*p < 0.05; **p < 0.01; ***p < 0.001

.515*** 33.0%

.586*** 40.2%

Satisfaction R2 = .482 Q2 = .267

GoF = 0.575

and arousal, this last one exercises a more intense strength relationship with delight. Surprising consumption is more important to arousal than disconfirmation. Arousal is more important to positive affect than disconfirmation, but this construct is more important to satisfaction than positive affect. Table 4 shows the path coefficients of the direct and indirect effect. The strength of the total effect of satisfaction on loyalty and even disconfirmation on loyalty are higher than the strength of the total effect of delight on loyalty. The total effect of arousal on loyalty and positive affect on loyalty shouldn’t be neglected.

DISCUSSION, LIMITATIONS, AND FURTHER RESEARCH The results emphasize the importance of satisfaction and delight over rural tourists’ loyalty. Disconfirmation exercises a direct effect on satisfaction, arousal, and positive affect. However, the effect on positive affect is less significant. Disconfirmation exercises an indirect significant effect on loyalty through satisfaction and even through delight. So, if the tourists consider that the lodging service meets their requirements or is above their requirements and if the tourists found the experience better or even much better when compared to their expectations, then the


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TABLE 4. Total Effect—Path Coefficient Casual order positive affect → delight positive affect → loyalty positive affect → satisfaction arousal → positive affect arousal → delight arousal → loyalty arousal → satisfaction delight → loyalty disconfirmation → positive affect disconfirmation → arousal

Path coefficient

Casual order

Path coefficient

.196* .219*** .297** .594*** .566*** .233*** .176* .229** .323*** .248**

disconfirmation → delight disconfirmation → loyalty disconfirmation → satisfaction satisfaction → loyalty Surprise consumption → positive affect Surprise consumption → arousal Surprise consumption → delight Surprise consumption → loyalty Surprise consumption → satisfaction

.175** .398*** .611*** .586*** .339*** .571*** .573*** .190** .101*


*p < .05; **p < .01; ***p < .001.

tourists will feel satisfaction or even delight and these feelings increase the probability of recommending the lodging or returning to the lodging. Delight is a function of its predicted constituents (surprise consumption, arousal, disconfirmation, and positive affect) and predicts loyalty as in the second study (symphony ticket purchase) of Oliver et al. (1997). These antecedents of delight exercise an indirect significant effect on loyalty, especially the arousal and positive affect. However, this study introduced four items (about intentions to return, to recommend) to compose the construct loyalty and Oliver et al. (1997) used only one item in the construct return. The construct satisfaction was operationalized with seven items and Oliver et al. (1997) applied only one item. This research also introduces a different approach (PLS) to treat the data. PLS technique is more appropriate to prediction than techniques based on covariance. Just as in the studies of Oliver et al. (1997) and Finn (2005), this study finds support for the conceptualization of customer delight and customer satisfaction as distinct constructs. Moreover, the relationship between disconfirmation and arousal, proposed by Finn (2005) in an empirical way and supported in the context of retail websites (Finn, 2006), finds support in this study, too. Therefore, when the rural lodging service meets the tourists’ requirements and when the tourists find the experience better than they expect, then they can feel enthusiasm, excitement, and encouragement that lead to a delightful stay. Tourists may not be astoundingly

surprised by the experience provided in the lodging, but if the experience is better than they expected, they can feel enchantment and live a delightful experience. So, rural tourism managers cannot forget to pay serious attention on fulfilling expectations and preventing service failure, as Swanson and Hsu (2009) highlight, because this may be the first step toward achieving the delight of the rural tourist. Then, rural managers should go beyond the fulfillment of the expectations and provide an experience that is exciting, arousing, and enjoyable. A negative practice with some rural managers in the long-term can mediate the marketing communication and the revision of those statements. For example, if a tourist uses the websites to find information and compare them in order to make a decision about the rural lodging that he/she would like to select for staying, and if the websites are not updated, the tourist might select one closed during the period of his/her vacations, or might create an expectation for an activity that no longer takes place. Thus, the tourists may be negatively surprised when the experience of the service provided in the rural lodging is below their expectations. Local and county governments should help the lodging managers to keep the marketing communication updated. The support is crucial since the rural lodgings are very small family business without economic capacity for efficient management of communication. Another way to overcome this situation is being a member of an association of rural lodging managers. There are some of these organizations in Portugal but


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the cultural characteristics (distrust, isolation, suspicion) of the main rural lodging managers make their membership difficult. The Spanish rural lodging managers, for example, adhere more readily to those organizations. Nevertheless, in Portuguese rural lodgings, it is possible to find several practices to provide a delightful experience, such as: (a) organized excursions to rural activities, heritage, fair, and other cultural events around the lodging; (b) possibility of participation in rural activities (e.g., the process of making wine, picking fruit), wine tasting, honey tasting, and even olive oil tasting; (c) try local cheese, or stuffed pork; and (d) learn to make some handicrafts (clay, rattan, and other local raw materials). Sometimes, during the period of doing these planning and communicated activities, rural managers may create a pleasant surprise; for instance, make a short detour on the tour route to see something unexpected but beautiful (like a bird nest). Inside of the lodging, it is also possible to surprise the tourists, for instance, setting a beautiful breakfast table in a rustic way with tasting local fruit of the season, as well as n tradition cake made in the lodging or homemade in the region. The sample used in this study was collected in the northern region of Portugal, where this type of tourism began and where there is the largest number of rural lodgings (Ferreira, 2006; Turismo de Portugal, n.d.). The northwest region may represent the main aspects of rural tourism in Portugal with no major bias from reality. This study can also provide some insight to other rural regions where rural tourism has similar features. However, this study has several limitations. First of all, future research may explore other meaningful industry-specific indicators to measure antecedents of tourists’ loyalty. A second limitation is the restriction of the scope of the study to current tourists and, therefore, the noninclusion of potential tourists. No information was provided about the effect of demographics (e.g., gender, family income) and trip characteristics (e.g., destination chosen, period of the year) on satisfaction and loyalty. In spite of the homogeneity of the sample in the respondents’ profile, the inclusion of demographic and trip characteristics may add another level of complexity to the results. Finally, there might be additional factors

influencing and interacting with rural tourists’ loyalty. Future research should investigate additional antecedents of tourist loyalty.

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SUBMITTED: September 24, 2009 FINAL REVISION SUBMITTED: February 25, 2010 ACCEPTED: March 5, 2010 REFEREED ANONYMOUSLY