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Current Issues in Tourism

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Risks and marketing in online transactions: a qualitative comparative analysis

Nikolaos Pappas

To cite this article: Nikolaos Pappas (2017) Risks and marketing in online transactions: a qualitative comparative analysis, Current Issues in Tourism, 20:8, 852-868, DOI: 10.1080/13683500.2016.1187586

To link to this article: https://doi.org/10.1080/13683500.2016.1187586

Published online: 02 Jun 2016.

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Risks and marketing in online transactions: a qualitative comparative analysis

Nikolaos Pappas*

Sunderland Business School, University of Sunderland, Sir Tom Cowie Campus, St. Peter’s Way, SR6 0DD Sunderland, UK

(Received 18 December 2015; accepted 4 May 2016)

The article focuses on the perspectives of holidaymakers who have used internet to book a part or the whole spectrum of their holidays’ accommodation. Using qualitative comparative analysis (QCA), the research examines the complex relations between product and web-vendor risks, and marketing activities on consumer trust, also employing predictive validity. It examines the perspectives of 735 holidaymakers returning to Manchester International Airport, through the use of structured questionnaires. The findings reveal three sufficient configurations dealing with the focus on the impact of price and quality relationships, the influence of product and web-vendor risks on consumer trust, and the importance of marketing for the minimization of perceived risks in online tourism shopping. Theoretically, the study contributes on the understanding of online decisions’ complexity, and explores the attributes that affect accommodation e-purchasing and associated linkages. Methodologically, it implements QCA, which is new in tourism and hospitality domain. It also progresses from fit to predictive validity, an analysis that only a handful of studies has implemented in the service industry.

Keywords: complexity theory; perceived risk theory; tourism and hospitality; marketing strategies; consumer trust

Introduction

There is a growing need for new knowledge, theories, and models of Internet consumer behavior due to the evolution of electronic commerce as it becomes a vital aspect of customer relations and marketing strategy (Close & Kukar-Kinney, 2010; Racherla, Hu, & Hyun, 2008). The online purchasing behavior needs to be further understood (Herrero & San Martin, 2012) hence, it attracts increasing research attention (Mosteller, Donthu, & Eroglu, 2014). As several studies have pinpointed, the key to long-term success for e-retailers is to build consumer trust (Pavlou & Fygenson, 2006; Vos et al., 2014), but the latter is nega- tively influenced by the perceived risks (Hong & Cha, 2013) associated with both products (Ward & Lee, 2000) and web-vendors (Jiang, Jones, & Javie, 2008). Thus, it is important to examine the risk factors affecting trust in Internet shopping, while the purchasing intentions of online consumers need to be further investigated.

In tourism and hospitality, the Internet has considerably altered consumers’ behavior (Mendez, Leiva, & Fernandez, 2015; Wen, 2009) since it gave them the opportunity to directly interact and engage with suppliers, tourist destinations, and hotel firms (Buhalis

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Current Issues in Tourism, 2017 Vol. 20, No. 8, 852–868, http://dx.doi.org/10.1080/13683500.2016.1187586

& Law, 2008). Consumers also share experiences directly with other consumers through electronic Word of Mouth (Akehurst, 2009) something that increases the operational com- plexity in modern business. Social media (e.g. Facebook, YouTube, Twitter) play a signifi- cant part on these attributes since they become a major opportunity and challenge for many tourism and hospitality companies, while most of those enterprises actively participate in social media information exchange (Sparks, Perkins, & Buckley, 2013). The Internet has ultimately altered the booking share from which tourist agencies and especially hotels receive their business (Law & Cheung, 2006). Online shopping has changed tourist behav- ior since for travel and hospitality suppliers it represented a new and potentially powerful communication means for product distribution (Law, Leung, & Wong, 2004), contributing to the minimization of the gap between consumers and suppliers (Buhalis, 1998), and ulti- mately increasing the sales for travel and hotel products (Inversini & Masiero, 2014). In addition, Information Technology (IT) gave the opportunity to tourism and hospitality com- panies to facilitate better knowledge for their consumers and their purchasing patterns (Cohen, Prayag, & Moital, 2014; Okumus, 2013). In 2011 the Internet generated world- wide revenue of more than 340 billion US dollars, establishing it as an important channel for distributing travel, tourism, and hospitality products (Amaro & Durate, 2015). Even if the popularity of IT has led to extensive research on IT and tourism (San Martin & Herrero, 2012), the literature is somehow silent in terms of consumers and their online purchasing intentions (Amaro & Durate, 2015; Buhalis & Michopoulou, 2011). Thus, further research examining consumer motivations to buy tourism and hospi- tality products online is necessary (O’Connor & Murphy, 2004; Pappas, 2016).

The aim of this article is to examine the complexity of the attributes affecting online purchasing intentions in tourism and hospitality. More specifically, it evaluates the influence of product and web-vendor marketing activities and risks, and consumer trust on tourists who were asked as they returned from their vacations and purchased online the accommo- dation of their holidays. The study contributes to both the theoretical and methodological domains. In terms of the literature, it provides an understanding of the complexity formu- lation of online tourism and hospitality decisions. It further explores the attributes that affect online tourism and hospitality decisions and associated linkages. Methodologically, the study implements qualitative comparative analysis (QCA), which is new in tourism domain and just a handful of studies have generally employed it in the service sector. It also progresses from fit validity and provides predictive validity for the models suggested.

Complexity theory

The science of complexity studies, describes and explains the behavioral patterns of complex adaptive systems (Olmedo & Mateos, 2015). The complexity theory has been developed from chaos theory and focuses on research with complex characteristics. Complexity theory is based on ontological realism and supports the view that events occur independently of the researcher (Byrne, 1998). It ‘deals with systems that have many interacting agents and although hard to predict, these systems have structure and permit improvement’ (Zahra & Ryan, 2007, p. 855).Since ontology is characterized by nonlinearity there are no universal standards or necessary natural forms in society (Young, 1991). However, the system in not uncontrolled and even in chaotic situations there is some sort of order. Even if the system appears to work in a random and complex way with each element seeming to act indepen- dently, it finally operates within specific boundaries (Zahra & Ryan, 2007). As a result, com- plexity evolves over time (Byrne, 1998). According to Fitzgerald and Eijnatten (2002), complexity theory focuses on three aspects: (i) the simple behaviors emerging from

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complex systems (ii) the higher-level patterns produced by simple interactions, and (iii) the identification of recognizable patterns under a holistic examination of the complicated system. When the degree of complexity increases the behavioral patterns of the system are less amendable to predict (Fitzgerald & Eijnatten, 2002). The theory works with nonlinear system having a sensibility to initial conditions, and this unpredictable behavior is limited in a quasi-stable pattern (Olmedo & Mateos, 2015). In service industries, complexity theory and QCA are used in order to sufficiently explain the customer attributes, evaluations and decision-making processes by implementing alternative asymmetric combinations of indicators (Wu, Yeh, Huan, & Woodside, 2014).

Complexity in tourism and hospitality

Up till now, tourism research has not adequately focused on complexity since its approach was predominantly a reductionist one (McDonald, 2009). In tourism and hospitality, the be- havior of travelers depends on numerous factors creating a complexity on its formulation. As a result, the relationships produced have an inherent nonlinearity preventing the direct relation of causes and consequences (Olmedo & Mateos, 2015). As suggested by Boukas and Ziakas (2014), tourist behavior can be affected by endogenous and exogenous system shocks. Even so, all tourism related factors create some emergent features since they include some kind of order in their operations (Olmedo & Mateos, 2015). Still, tourism complexity makes Newtonian (linear) thinking inadequate and indicates a need for asymmetric analysis (Laws & Prideaux, 2005).

Concerning tourism and hospitality research the methodological challenge lies on the identification of a way to express the complexity and layered nature of these dynamic be- havioral patterns of consumers (Hollinshead, 2004; Todd, 2005). Sadly, research progress in tourism has lagged behind, as previous studies have only had a passing interest in com- plexity approaches, despite the significant contribution such examination could provide within the multidisciplinary environment tourism and hospitality operates (Farrell & Twining-Ward, 2004; Olmedo & Mateos, 2015). It is imperative that critical research should examine different research positions and methodologies, and provide a further understanding of the tourism and hospitality complexity (Ankor, 2012; Reisinger & Steiner, 2006). Thus, the application of complexity theory can provide substantial infor- mation concerning tourist behavior (Russell & Faulkner, 2004), helping to better under- stand the dynamics of change (Faulkner & Russell, 2000).

Literature review

In recent years, the importance of online experience in the tourism and hospitality industry has rapidly increased, since it has emerged as a crucial issue in developing favourable be- havioral responses and outcomes in the online tourism environment (Huang, Backman, & Backman, 2010; Nusair & Parsa, 2011). The rapid development of online retailing world- wide, has given consumers more choices than before on where and what they shop (Gao & Bai, 2014), still the complexity of consumer decision-making is under-researched. Within this context, the study examines the perceptions of leisure travelers that use online booking for their accommodation and the complexity entailed in their decisions.

Appropriate advertising may decrease the perceptions of product risk (Kopalle & Lehmann, 2006) and change the attitudes of consumers toward a specific product (Petty, Cacioppo, & Schumann, 1983). Marketing can significantly influence consumer beliefs about product performance (Nerkar & Roberts, 2004), and finally determine their likelihood

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to buy (Leenders & Wierenga, 2008). In terms of online shopping, with the passage of time the variety of marketing channels is increasing, as is the complexity of consumers’ purchas- ing behavior (Coughlan, Anderson, Stern, & El-Ansary, 2001). As Woodside, Vincente, and Duque (2011) suggest online marketing and dissemination of information through Internet can increase destination and hospitality firms’ brand name maximizing sales potential. Thus, the Internet has changed the ways tourism and hospitality companies promote, dis- tribute and price their products (Gazzoli, Kim, & Palakurthi, 2008). The majority of tourism organizations such as hotels, travel agencies, and airlines have adopted IT as a vital component of their promotional activities and marketing campaigns (Liang, 2014). Customers tend to switch between e-channels when buying products mainly because of the considerably increased financial, security and performance risks the Internet presents in comparison with offline shopping (Lee, 2009). Thus, they tend to buy the products and use the web-vendors that offer high quality and low risk (Chiu, Hsieh, Roan, Tseng, & Hsieh, 2011). As a result, e-retailers adjust their marketing strategies and focus on the minimization of product and web-vendor risks (Chikweche & Fletcher, 2010; Chiu et al., 2011). Still, little is known concerning the complexity of the impacts toward marketing strategies (e.g. the extent complexity influences marketing activities, branding, and selec- tion patterns in terms of products and web-vendors) and perceived risks with respect to pro- ducts and online channels.

Risk is one of the key concepts in buying behavior (Faroughian, Kalafatis, Ledden, Samouel, & Tsogas, 2012; Jonas & Mansfeld, 2015) which is defined as an attribute of an alternative decision reflecting the variance of its possible outcomes (Gefen, Rao, & Trac- tinsky, 2002). As Hong and Yi (2012) suggest, it is an important indication that consumers perceive the existence of risk whenever they alternate, postpone, or cancel their purchase, let alone the online consumers who perceive more risks than those shopping in stores, (i) because they cannot examine the product before they receive it, (ii) they are concerned about after-sales service, and (iii) due to the jargon involved in the sale. According to the perceived risk theory (PRT), the potential risks associated with the purchasing process influence consumers’ decisions (Yu, Lee, & Damhorst, 2012). The consumers try to reduce uncertainty when information is limited and when they do not expect potentially favorable consequences during the shopping process, through the development or adoption of strategies for the reduction of risk (Bauer, 1960). In online environments, the consumers ‘seek and assess information regarding product performance through virtual product experi- ence in order to reduce risk and increase certainty that the consequence of product perform- ance will be favorable’ (Yu et al., n.d., p. 253). In PRT, the components of perceived risk are finance, product performance, physical, privacy, and time loss related (Kaplan, Szybillo, & Jacoby, 1974), but online transactions do not incur any physical risk, such as threat to human life (Lee, 2009). Thus in this study PRT has focused on the remaining four perceived risks, divided between product (financial) and web-vendor (privacy; time loss) risks, while the performance aspects have been examined for both products and e-channels.

Especially in products that are characterized by intangibility (such as in hospitality) the perceived risks increase considerably (Laroche, McDougall, Bergeron, & Yang, 2004), thus services are thought to be riskier to purchase than goods (Mitchell & Greatorex, 1993). The provided product information is important for the minimization of perceived purchasing risks, thus potential buyers tend to collect and consider more information about the sources’ trustworthiness when relatively high product risks are involved (Wang & Chang, 2013). Trust is based on the buyer’s expectations that the seller will not have an opportunistic attitude and take advantage of the situation, but will behave in a dependable, ethical and socially appropriate manner, fulfilling his commitments despite the buyer’s

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vulnerability and dependence (Gefen, Karahanna, & Straub, 2003). The concept of trust was introduced by psychologists in 1950s, but despite its importance it has only recently introduced in tourism and hospitality industry (Wang, Law, Hung, & Guillet, 2014). Trust is even more important for online than for offline retailers, since consumers perceive more risk in e-commerce due to their inability to visit a physical store and examine the product they are interested in buying (Li, Jiang, & Wu, 2014). Online hospitality retailers place considerable emphasis on consumer trust, since they are more reluctant to purchase the products in which they are interested (Del Chiappa, Romero, Del Carmen, & Del Amo, 2015; Park, Gunn, & Han, 2012). As a result, the critical role of trust in the determi- nation of consumers’ purchasing intentions is affected by satisfaction with both products and online stores (Wu, 2013). Surprisingly, in tourism and hospitality industry there are only few studies that examine the relationship between website quality and e-trust (Wang, Law, Guillet, Hung, & Fong, 2015).

Study tenets

In service research contexts, ‘tenet’ is the term in-use for expressing testable precepts of complexity theory, since the adequacy testing for complex configurations in predicting outcome scores does not usually include consistency metrics and does not test statistical hypothesis (Wu et al., 2014). The study set out to investigate important attributes that affect tourism decisions, as identified from the relevant literature (Ahn et al., 2007; Chik- weche & Fletcher, 2010; Gefen et al., 2003; Hong & Yi, 2012; Sanchez, Callarisa, Rodri- guez, & Moliner, 2006; Sparks & Browning, 2011). Thus, all combinations of binary states (meaning their presence and absence) for the following five attributes were evaluated: product marketing activities, web-vendor marketing activities, product risks, web-vendor risks, and consumer trust.

Passing from linear to asymmetric analysis the configuration theory suggests that the same set of factors is possible to lead to different outcomes depending on the way these factors are arranged (Ordanini, Parasuraman, & Rubera, 2014). As Greckhamer, Misangyi, Elms, and Lacey (2008) suggest, the outcomes rarely result from one and only causal factor, since the same factor may produce different or even opposing effects in relation to the overall context. Considering the above, the study has formulated the following tenet:

T1: The same attribute can determine different tourism decisions depending on its con- figuration with the other attributes.

On the other hand, the same outcome is likely to be achieved through different configur- ations of causal factors, which is actually the concept of ‘equifinality’ (Ragin, 2000). This means that configuration complexity can affect the produced outcome leading different complex configurations to result the same outcome. Since the study focuses on the aspects that affect travelers’ online decision-making, the following tenet has been created:

T2: Complex configurations affect traveler evaluations for online tourism decisions. As Wu et al. (2014) indicate ‘a simple antecedent condition is a positive indicator in

some configurations and a negative indictor in other configurations on high scores in an outcome condition’ (p. 1651). For example, online marketing activities may have a positive influence in consumption patterns, substantially increasing the product sales (Pappas, 2016). Conversely, due to the massive quantities of information that Internet shopping pro- vides (Marom & Seidmann, 2011), online consumers can be easily confused by marketing activities leading to the reduction of sales (Tarnanidis, Owusu-Frimpong, Nwankwo, & Omar, 2015). These observations lead to the following tenet:

856 N. Pappas

T3: Within different configuration combinations simple conditions may positively or negatively affect online tourism decisions.

Method

Participants

The research focused on holidaymakers returning to Manchester international airport who had used the Internet in order to book their holidays’ accommodation. The research was conducted during June and July 2014. This study used the distribution of structured ques- tionnaires as the most appropriate method of obtaining the primary data, since it offers high respondents’ anonymity and the response rate, while a substantial amount of popu- lation can be examined in a short period of time (Sekaran & Bougie, 2013). This made feasible to ask a substantial amount of respondents to participate on research. Following Zhen, Zoebisch, Chen, and Feng’s (2006) sampling method, the respondents were selected through purposive sampling (holidaymakers using Internet for accommodation booking) combined with random sampling (random selection at Manchester Airport’s bus and train station). The recruitment of participants in communal areas is a usual prac- tice for researchers in order to reduce the survey bias (Hamilton & Alexander, 2013). The participants’ selection is based on an exclusion question prior distributing the question- naire, which asked whether they had used online purchasing of accommodation for their current vacations. The average time for questionnaire completion was five minutes. Although the proportion of missing data is low, listwise deletion (the entire record is excluded from the analysis) is used because this is the least problematic method of handling missing data (Allison, 2001).

Sample determination and collection

Appropriate representation is a fundamental criterion in determining the sample size. According to Akis, Peristianis, and Warner (1996), when there are unknown population proportions, the researcher should choose a conservative response format of 50/50 (meaning the assumption that 50% of the respondents have negative perceptions, and 50% have not) to determine the sample size. A confidence level of at least 95% and a 5% sampling error were selected. As Akis et al. (1996) suggest, the sample size is:

N = (t − table) 2(hypothesis)

S2 ⇒ N = (1.96)

2(0.5)(0.5)

(0.5)2 ⇒ N = 384.16 Rounded to 400.

The calculation of the sampling size is independent of the total population size, hence the sampling size determines the error (Aaker & Day, 1990). Participants were approached in the airport’s train station (400 people), bus station (400 people), and car parking facilities (400 people). Of the 1200 holidaymakers asked, 735 completed the questionnaire (response rate: 61.25%). The overall statistical error for the sample population is 3.6%.

Measures

The questionnaire is based on prior research, and consisted of 38 Likert Scale (1 strongly agree/7 strongly disagree) statements, plus one exclusion question concerning online

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Table 1. Descriptive statistics.

Statement Means S. D.

Product marketing activities PMA1 Direct marketing activities (i.e. direct mail and e-mails) influence my

online purchasing decisions 2.29 .563

PMA2 The ‘above the line’ promotional activities (i.e. TV and radio advertisements) influence my online purchasing decisions

3.02 .573

PMA3 The tourism product’s branding influences my online purchasing decisions 2.18 .437 PMA4 The online promotions influence my decision to select the tourist product/

package I intend to buy 2.35 .254

PMA5 The offline promotions influence my decision to select the tourist product/ package I intend to buy

2.97 .659

Web-Vendor Marketing Activities WMA1 Direct marketing activities (i.e. direct mail and e-mails) by web-vendors

influence the e-channel I select when buying tourism products 2.05 .580

WMA2 The ‘above the line’ promotional activities (i.e. TV and radio advertisements) by web-vendors influence the e-channel I select when buying tourism products

2.72 .681

WMA3 The branding of web-vendors influences the e-channel I select when buying tourism products

1.78 .366

WMA4 The online promotions influence my decision to select a particular e-channel when buying a tourist product/package

2.15 .482

WMA5 The offline promotions influence my decision to select a particular e-channel when buying a tourist product/package

2.63 .395

Product risks PR1 I think about the risk of not having made a good purchase bearing in mind

the price I pay 1.75 .705

PR2 The tourist product/package I purchase should be reasonably priced 1.42 .823 PR3 The price is the main criterion for my purchasing decision 2.43 .634 PR4 When buying a tourist product/package I consider the potential risks in the

way the product/package is organized 1.70 .492

PR5 When buying a tourist product/package I consider the potential risk that I will not receive what I expected

1.55 .420

PR6 When buying a tourist product/package I consider its quality compared with other relevant tourist products/packages

1.51 .389

Web-vendor risks WR1 It is important that the Website vendor provides detailed information 1.82 .537 WR2 It is important that the Website vendor provides accurate information 1.97 .599 WR3 It is important that the Website vendor can be depended upon to provide

whatever is promised 1.70 .846

WR4 It is important that the Website vendor creates a feeling of confidence in users through the reduction of uncertainty (i.e. joint problem-solving)

1.52 .735

WR5 It is important that the Website vendor understands and adapts to the user’s specific needs

1.69 .623

WR6 It is important that the website vendor deals with high quality products 2.46 .410 WR7 It is important that the Website vendor deals with various tourism products 2.88 .455 WR8 Purchasing online would involve a trivial payment procedure when

compared with more traditional ways of shopping 2.21 .361

WR9 Purchasing online would involve taking more time to seek out information when compared with more traditional ways of shopping

5.28 .450

WR10 Purchasing online involves the risk of credit loss when compared with more traditional ways of shopping

2.55 .782

(Continued)

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purchasing of tourist products. The full statements along with descriptive statistics are pre- sented in Table 1. The reliability and validity of this selection rationale is supported by studies such as Kyle, Graefe, Manning, and Bacon (2003) and Gross and Brown (2008). The statements were selected from six different studies. These studies were those of: Chik- weche and Fletcher (2010) for the statements evaluating the product and web-vendor mar- keting strategies, Sanchez et al. (2006) for the statements dealing with product risks, Ahn et al. (2007) and Hong and Yi (2012) for the statements focusing on web-vendor risks, and finally Gefen et al. (2003) and Sparks and Browning (2011) for the statements focusing on consumer trust.

The study investigates the configurations through the use of fuzzy-set qualitative comparative analysis. This is a theoretical method for the examination of relationships which are believed to have a bearing upon the outcome of interest and any potential binary set combinations generated from its predictors (Longest & Vaisey, 2008). QCA is considered to be a mixed-method technique, since it combines in the same analysis quantitative empirical testing (Longest & Vaisey, 2008) and qualitative inductive reason- ing through case analysis (Ragin, 2000). QCA handles logical complexity by allowing for the fact that different combinations of characteristics may produce different results when combined with other events or conditions (Kent & Argouslidis, 2005). The study also had to estimate negated sets (presence or absence of a given condition; Wood- side & Zhang, 2013). In a negated set, the membership calculation is made by taking one minus the score of membership of the examined case in the original fuzzy set (Skarmeas, Leonidou, & Saridakis, 2014). As illustrated in Table 2, the absence of an attribute is indicated by the symbol ‘∼’.

According to Ordanini et al. (2014), in set theory a sub relation with fuzzy measures is consistent when in a given attributional causal set the membership scores are equal or consistently less than the membership scores in the outcome set. Accordingly, the coverage includes the assessment of sufficient configurations’ empirical importance

Table 1. Continued.

Statement Means S. D.

WR11 Purchasing online involves the risk of loss of private information when compared with more traditional ways of shopping

4.06 .698

WR12 Purchasing online involves after-sales service warrantee risks when compared with more traditional ways of shopping

3.87 .438

WR13 In general, providing credit card information through online shopping is riskier than providing it over the phone to an offline vendor

4.85 .482

WR14 Purchasing online involves the risk of fraudulent behaviour on the part of the website owner(s)

1.88 .711

Consumer trust CT1 The tourist product/package I purchased is trustworthy 1.85 .573 CT2 The tourist product/package I purchased is reliable 1.69 .824 CT3 The tourist product/package I purchased fills me with confidence 1.65 .466 CT4 The tourist product/package I purchased gives me the impression that it is

of good quality 1.57 .553

CT5 Shopping online is a trustworthy method of shopping 2.95 .688 CT6 The Website vendor I use gives the impression that they are honest 2.87 .548 CT7 The Website vendor I use gives the impression that they care for their users 2.41 .492 CT8 The Website vendor I use gives the impression that they have the ability to

fulfil my needs 2.56 .776

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(Ordanini et al., 2014). Thus, consistency and coverage should be calculated as follows:

Consistency(Xi ≤ Yi) = ∑

i

[min (Xi; Yi)]/ ∑

i

(Xi),

Coverage(Xi ≤ Yi) = ∑

i

[min (Xi; Yi)]/ ∑

i

(Yi),

where, for holidaymaker i, Xi is the score for membership in the X configuration and Yi is the score for membership in the outcome condition.

In QCA when the consistency index is above .80 and the coverage index is above .45 then membership scores in the outcome condition are considered high for almost all high scores in the antecedent statement, and a considerable number of cases fitting an asym- metric sufficiency distribution (Wu et al., 2014).

Fit and predictive validity

The vast majority of studies evaluating specific models focus on the examination of the model fit (Gigerenzer & Brighton, 2009) in order to ensure that the data support the

Table 2. Binary set configurations: distribution of best-fit cases.

Configurations Cases Percentage

1 PMA*WMA*∼PR*∼WR*∼CT 74 10.06 2 ∼PMA*WMA*∼PR*∼WR*CT 71 9.66 3 ∼PMA*WMA*PR*WR*∼CT 66 8.98 4 PMA*WMA*PR*∼WR*CT 62 8.43 5 PMA*∼WMA*∼PR*WR*CT 58 7.89 6 PMA*∼WMA*PR*WR*CT 49 6.67 7 PMA*∼WMA*PR*∼WR*∼CT 42 5.71 8 ∼PMA*WMA*PR*WR*CT 35 4.76 9 PMA*∼WMA*∼PR*∼WR*CT 32 4.35 10 ∼PMA*∼WMA*PR*∼WR*CT 31 4.22 11 PMA*WMA*PR*∼WR*∼CT 30 5.85 12 PMA*WMA*∼PR*WR*∼CT 27 3.67 13 PMA*WMA*PR*WR*CT 24 3.26 14 PMA*∼WMA*PR*WR*∼CT 21 2.86 15 ∼PMA*∼WMA*∼PR*WR*CT 17 2.31 16 PMA*∼WMA*PR*∼WR*CT 17 2.31 17 ∼PMA*∼WMA*PR*WR*∼CT 15 2.04 18 PMA*WMA*PR*WR*∼CT 13 1.77 19 ∼PMA*WMA*∼PR*WR*CT 12 1.63 20 ∼PMA*WMA*∼PR*∼WR*∼CT 9 1.22 21 ∼PMA*∼WMA*PR*WR*CT 9 1.22 22 ∼PMA*∼WMA*∼PR*∼WR*CT 6 0.82 23 ∼PMA*WMA*PR*∼WR*∼CT 5 0.68 24 PMA*WMA*∼PR*WR*CT 3 0.41 25 PMA*WMA*∼PR*∼WR*CT 3 0.41 26 PMA*∼WMA*∼PR*∼WR*∼CT 2 0.27 27 PMA*∼WMA*∼PR*WR*∼CT 1 0.14 28 ∼PMA*∼WMA*PR*∼WR*∼CT 1 0.14

Total 735 100

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relationships among the observed variables and their respective factors (Pappas, 2015). Still, only a few studies focus on predictive validity (Wu et al., 2014), since a good fit to observations does not necessarily indicate the existence of a good model (Gigerenzer & Brighton, 2009). This study also focuses on the estimation of the predictive validity. For testing predictive validity, the process described by Wu et al. (2014) is followed: The research sample is divided in a holdout and a modeling subsample, and since the patterns of decision-making are perceived as consistent indicators for the production of high scores, using half of the overall sample. The overall consistency exceeds .8 (C1 = .839) and the coverage is higher than .5 (C2 = .574). The results indicate that the model has good predic- tive validity.

Results

Table 2 illustrates the distribution of holidaymakers’ configuration best-fit cases, and pre- sents the configurations addressed in at least one case. From the 32 possible combi- nations (25 = 32), 28 of them had at least one case, since the study lacks empirical instances for four configurations. According to QCA guidelines (Fiss, 2011), the latter configurations had to be excluded from the analysis, since their number is relatively small (4 out of 32). Table 3 presents the results of fuzzy-test scores including all the vari- ables considered in the analysis. Table 4 provides a QCA summary and presents the suf- ficient configurations of attributes for tourism decisions with coverage and consistency measures for each configuration, and for the final solution. The combinations that have consistency scores higher than .80 are included in the table. High consistency (sol- ution consistency = .826) appears in the final solution, while its coverage is also high (total coverage = .762).

Sufficient configurations affecting online purchasing intentions in tourism

According to the results, three configurations can stimulate tourism decisions in online pur- chasing (Table 4). The first configuration indicates that product marketing activities, product risks, and consumer trust with the absence of veb-vendor marketing activities and risks, can have a considerable influence of accommodation decision-making. This pathway provides a fair consistency (.828) even if it is the lowest one compared with the other two. The second configuration indicates that product and web-vendor risks, and con- sumer trust with the absence of product and web-vendor marketing activities substantially influence online purchasing intentions, having a consistency of .850. The last sufficient

Table 3. Fuzzy-set scores: pairwise correlations.

Means S. D.

Product marketing activities

Web-vendor marketing activities

Product risks

Web- vendor risks

Customer trust

1 .64 .487 1 2 .43 .509 .404** 1 3 .55 .428 .497** .139** 1 4 .51 .527 .385* .074 .122 1 5 .46 .411 .187* .185* .049* .204* 1

*The significance is at .05 level (p < .05).** The significance is at .01 level (p < .01).

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configuration has the highest consistency (.884) and includes marketing activities and risks with the absence of consumer trust.

Discussion

According to the research findings the first sufficient configuration (PMA*∼ WMA*PR*∼WR*CT) focuses on product issues in terms of marketing activities, risks, and the consumer trust. This product oriented configuration confirms the study of Sanchez et al. (2006) suggesting that product elements such as price and quality crucially determine the consumers’ purchasing decisions, while marketing can strengthen the perceived product quality and performance and finally determine the likelihood to buy (Leenders & Wierenga, 2008). The second configuration (∼PMA*∼WMA*PR*WR*CT) highlights the importance of PRT, focusing on the influence of product and web-vendor risks on consumer trust, and the determination of the final purchasing decision. The results are in agreement with the findings of several previous studies such as Faroughian et al. (2012), Gefen et al. (2002), and Hong and Yi (2012). The third solution (PMA* WMA*PR*WR*∼CT) emphasizes on the importance of marketing for the minimization of perceived risks in online tourism and hospitality shopping. This aspect also pinpointed from the studies of Chikweche and Fletcher (2010) and Chiu et al. (2011) gives evidence for the importance of marketing activities and adds up to our knowledge for the complexity of the impacts toward marketing strategies and perceived risks with respect to tourism pro- ducts and online channels.

Confirmation of tenets

As the results suggest, the provided explanation of the three sufficient configurations pre- sented in Table 4 is high (total coverage = .762). In addition, product risks appear in all three solutions, while the other four do not appear in all sufficient configurations. This finding further underlines the importance of product risks in online tourism and hospitality decisions. With the inclusion of web-vendor risks appearing in the second and third con- figuration, this evidence emphasizes the importance of risks in online decision-making, strengthening the importance of PRT in tourism and hospitality. Product and web-vendor marketing activities appear on the first and third sufficient configuration, while consumer trust appears on the first and second solution.

Overall these findings support the first tenet (T1): The same attribute can determine different tourism decisions depending on its configuration with the other attributes.

It is necessary to highlight that QCA in not based on variables but on cases, thus the provided solutions deal with: (i) a combination of outcome related variables and (ii) the association of variable groups with that combination (Ordanini et al., 2014). As previously

Table 4. Sufficient configurations for purchasing online.

Models Raw coverage Unique coverage Consistency

PMA*∼WMA*PR*∼WR*CT 0.145920 0.053871 0.828475 ∼PMA*∼WMA*PR*WR*CT 0.177569 0.094723 0.850387 PMA*WMA*PR*WR*∼CT 0.214756 0.139679 0.883752

PMA: product marketing activities ; WMA: web-vendor marketing activities; PR: product risks; WR: web-vendor risks; CT: consumer trust. Total converge: 0.762. Solution consistency: 0.826.

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mentioned, the first sufficient configuration is product oriented and indicate the importance of product issues on consumer trust. The second is associated with risks and consumer trust, also highlighting the importance of PRT in online decision-making. The final sufficient sol- ution in connected with the potential contribution of marketing activities for the reduction of perceived risks.

These findings give substantial grounds for the confirmation of the second tenet (T2): complex configurations affect traveler evaluations for online tourism decisions.

The study analysis provides contrarian cases since the outcome of the provided sol- utions depends on the attributes included or excluded. For example in the third configur- ation marketing activities are present, while in the second one they are excluded, giving to PRT the dominant role for online decision-making. Moreover, consumer trust is impor- tant for product oriented decisions (first configuration) and its formulation through risk factors (second configuration), but it is excluded when marketing activities impact the for- mulation of perceived risks.

Thus, the findings support the third tenet (T3): within different configuration combi- nations simple conditions may positively or negatively affect online tourism decisions.

Study implications

In the theoretical domain, this study broadens our understanding of online tourism shopping and the formulation of consumers’ purchasing intentions. Using QCA the identification of three pathways through the combination of five different factors (product marketing activi- ties, web-vendor marketing activities, product risks, web-vendor risks, and consumer trust), helps to better comprehend the process of decision-making and its influence on online tourism decisions, and assists on the optimization of online retailing in tourism and hospi- tality. Moreover, it reveals that the inclusion of different factors substantially impacts on the e-consumers’ decision-making, while it highlights the importance of product and web- vendor marketing activities and perceived risks as significant factors for online shopping.

In terms of methodology, this research uses QCA for the identification of pathways, and the involvement of different combinations of factors in order to provide a specific outcome (Skarmeas et al., 2014). The implementation of QCA in the tourism domain is new (to the best of the author’s knowledge, the only other study is that of Ordanini et al. (2014), focusing on hotel service innovation), and very few studies generally employ it in the service sector (see Woodside & Zhang, 2013; Wu et al., 2014). The study also demonstrates its predictive validity, something that only a handful of service oriented studies have done (Wu et al., 2014), highlighting the sufficiency of the provided models.

Concerning managerial aspects, this research produces several implications. Taking under consideration the three sufficient configurations, maybe the most important manage- rial implication is associated with the product and web-vendor marketing activities of tourism e-retailers. As also emphasized by Nerkar and Roberts (2004) marketing crucially influence and transform the perspectives of consumers concerning products and services. The enterprises that activate online should emphasize on the promotion of tourism web- vendor benefits, and strengthen their branding through direct online marketing. This may include the distribution of information via personal e-mails to potential or previous custo- mers in terms of new products and services, optimisation of web-vendor usability, and easi- ness of e-use. This promotional activity can also include aspects of risk reduction combined with the beneficial impacts of online tourism shopping. Another suggestion could be the provision of e-vendor comparison of information and characteristics with other similar e-vendors existing in the market.

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As also suggested by the research findings, the cornerstone for e-purchasing remains the trust of consumers in products and e-vendors. It is imperative for the e-retailers to provide specific services that reduce the online consumers’ uncertainties. Due to the intangibility of tourist products these could include ad-hoc information about destinations and products, post-purchase services, quality guarantees, etc. Under this prism, e-retailers will be able to better accommodate their customers, instil them with confidence and trust, and develop the perspective that they honestly care for them.

Conclusions, limitations and future research

This study uses QCA in an effort to examine the complexity of the attributes affecting online tourism and hospitality decision-making, investigating the influence of product and web-vendor marketing activities and risks and consumers’ trust for holidaymakers returning from their vacations. Still, the limitations of the study need to be highlighted. The first limitation derives from the study’s contribution itself, due to the lack of QCA studies in the tourism sector. In order to examine the full potential of QCA in tourism, more QCA research involving complexity theory in additional tourism contexts needs to be implemented, even if QCA’s binary function (ability to use presence/absence data only), is a limitation that needs to be taken under consideration. Second, the examination of some other attributes such as time of shopping, amount of money spent in tourism and hospitality products, and comparison of online versus offline tourism and hospitality spending, can produce different outcomes. Thus, if this study is repeated to examine some other factors influencing tourism decisions the research implementation should be made with caution. Third, further research into different kinds of holidaymakers (packaged vs. individual tourists) in origin countries (e.g. France, Germany, Sweden) may produce different outcomes. Thus, the interpretation of findings should be made carefully. Finally the inclusion of respondents’ personal characteristics such as socio-demographic character- istics (e.g. level of education and income), disposable income available for tourism activi- ties, and frequency of participation in tourism activities, could further contribute to the understanding of tourism and hospitality decision-making and perception variations. Such examination could provide useful findings for the formulation of decision-making perspectives and the appreciation of purchasing behavior.

Methodologically, the ability of QCA to identify and demonstrate sufficient configur- ations in a specific aspect can also be of complementary use with other techniques like cor- relation and conjoint analysis. Moreover, QCA can further examine the effect of the behavioral complexity of consumers in tourism and hospitality decisions from exogenous (e.g. political and financial instability) and endogenous (e.g. career stage, expression of self- esteem) factors. All the above provide fruitful grounds for establishing QCA in the tourism and hospitality domain.

Disclosure statement No potential conflict of interest was reported by the author.

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