Segmenting tourism companies with relational and technological bases

Segmenting tourism companies with relational and technological bases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Segmenting tourism companies with relational and technological bases Maria Fuentes-Blasco, Beatriz Moliner-Velázquez, Irene Gil-Saura, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3427750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The objective of this study is to achieve a travel agencies segmentation based on both relational (trust, commitment, satisfaction, and loyalty) and technological (advancement and use of Information and Communication Technologies) criteria that improve the understanding of their strategic behaviours. The segmentation methodology uses a tandem approach: correspondence and hierarchical cluster analysis. From a sample of 256 travel agencies, four segments have been identified. Relational criteria have made it possible to segment only the retail agency market, while technological criteria have been shown to be more capable of segmenting the wholesale agency market. This work contributes to the advancement of the literature on business-to-business segmentation in tourism by providing a more complete vision of the segmentation of companies. From a practical approach, it allows a better knowledge of the agency segments, so it could be used for the selection not only of providers (customer perspective) but also of target segments (service provider perspective). Segmentation Business-to-Business Satisfaction Loyalty Information and Communication Technologies Tourism Figures Figure 1 1. Introduction Relational marketing literature highlights the importance of designing strategies aimed at creating and maintaining long-term relationships both in the B2C (hereafter B2C) and B2B market (hereafter B2B) (Das et al., 2022 ). The dichotomy between both markets has been the subject of academic debate (Nath et al., 2019 ). B2B market is especially complex due to the variety of parties involved in the exchange relationships and the simultaneity of roles that companies can have (Sales-Vivó et al., 2020). The study of relationships between companies has aroused research interest in the last decade (Heirati et al., 2016 ; Brotspies & Weinstein, 2019 ; Berenguer-Contrí et al., 2020). However, the literature still needs to advance in the knowledge of the variables and conditions that contribute to the improvement of relations and benefits for both parties. One of the key tools for good relationship management is segmentation. The literature is extensive, but there are still certain conceptual and methodological disagreements on the application of the most appropriate criteria and methods, and on their adaptation to different contexts (Casabayó et al., 2015 ; Brotspies & Weinstein, 2019 ). Segmentation in interorganizational context has received less attention than in the B2C market (Thomas, 2016 ; Silva & Dias, 2020 ) and presents more difficulties than the consumer market (Rezaei & Ortt, 2013 ). Descriptive variables of the companies have traditionally been used to identify segments. This type of segmentation has sometimes been criticised for being more of a sectorisation than a segmentation (Rezaei & Ortt, 2013 ). For this reason, in recent years there has been a tendency to use relational-type criteria, that is, linked to the behaviours and assessments that companies make of their relationship with their providers (Heirati et al., 2016 ; Thomas, 2016 ; Brotspies & Weinstein, 2019 ; Shi et al., 2022 ), as well as criteria associated with the use of technologies (Weinstein, 2014 ; Fuentes et al., 2017 ). In this context, tourism is one of the most dynamic, turbulent, and competitive sectors (World Tourism Organization, 2023 ) and one of the most affected by the development of technologies (Gössling, 2021 ). The literature on segmentation in the tourism B2B market has certain shortcomings. Since the year 2000 academic research is more limited compared to the consumer market. Regarding the criteria used, there is little empirical evidence on the formation of tourism business segments based on relational variables (Weinstein, 2014 ; Thomas, 2016 ; Fuentes et al., 2017 ). Added to this is the limitation on the simultaneous study of several criteria, since the contributions focus on the observation of one or two segmentation bases. Similarly, there are still few studies that address segmentation based on technological variables (Guo et al., 2017 ; Fuentes et al., 2017 ). Therefore, the tourism B2B market presents significant challenges in terms of segment identification based on variables of a different nature, whether descriptive, behavioural, attitudinal, and even related to technology. Given these gaps, we believe that the variables associated with the relationship between company and provider and the use of technology can improve the identification of segments. The purpose of this research is to contribute to the lack of empirical evidence that explores new segmentation criteria in the B2B tourism context. We pursue a dual objective: 1) Analyse the usefulness of two groups of criteria, relational and associated with Information and Communication Technologies (hereafter ICT), as segmentation bases to identify heterogeneous groups of travel agencies; 2) Characterise the segments obtained from descriptive variables in order to analyse the strategies used at the segment level and direct improvement towards greater adaptation to the needs of this type of tourism business. The novelty of this work lies in the joint study of these two groups of base segmentation criteria, relational and technological, in the tourism B2B context. Although there is some evidence in tourism on segmentation with relational (Thomas, 2016 ) and technological bases (Fuentes et al., 2017 ), no recent research has been found that simultaneously addresses these variables to form business segments. Therefore, this work contributes to the advancement of the literature on B2B segmentation in tourism by providing a more complete vision of the capacity of the variables linked to the relationship and the technologies in segment discrimination. 2. Segmentation bases in B2B context The complexity of this industry’s market makes the selection of criteria or segmentation bases a significantly challenging area of study. To contribute to this line of research, we propose two blocks of bases: relational and technological. 2.1. Relational bases To properly manage relationships between companies and maintain them in the long term, it is necessary for the parties to be satisfied (Geyskens & Steenkamp, ​​2000). Satisfaction is the key requirement for continued relationships and customer loyalty (Eggert & Ulaga, 2002 ). Satisfaction and loyalty are, therefore, fundamental variables in relational marketing. In the B2B context, satisfaction is a positive affective state that forms when a company evaluates its relationship with a provider (De Wulf et al., 2001 ). Several authors highlight the general and accumulated nature of satisfaction, pointing out that it is the result of the evaluation of the various aspects or stages of the relationship between the parties (Kundu & Datta, 2015 ). Two types of satisfaction are differentiated: economic and social (Chung et al., 2011 ; Ferro et al., 2016 ; Guan et al., 2022 ). Economic satisfaction refers to the assessment that a member of the channel makes of the economic rewards that occur in the relationship (e.g. efficiency), however social satisfaction is based on the psychosocial aspects of the relationship (e.g. gratification) (Geyskens & Steenkamp, ​​2000). Satisfaction is a clear antecedent of loyalty and this is demonstrated by the abundance of empirical evidence in the literature. The multidimensional character of loyalty is shared since loyalty can be manifested through attitudes or intentions and behaviours (Dick & Basu, 1994 ). Some studies have questioned the relationship between satisfaction and loyalty, qualifying that this link depends on the sector, the type of customers, the measures used, and the mediating or moderating variables (Kumar et al., 2013 ). In the B2B context, the contributions confirm that satisfaction exerts a certain influence, directly or indirectly, on loyalty (Elsäßer & Wirtz, 2017 ; Saragih et al., 2022 ). Therefore, companies that are satisfied with their provider are more likely to develop behaviours and/or attitudes related to the intention of maintaining the relationship. Trust and commitment are also key elements that must coexist for the success and continuity of relationships between companies. Recent studies confirm that trust in the B2B context is an antecedent of satisfaction (Sales-Vivó et al., 2020; Hogevold et al., 2021). It has been defined as the conviction of one party to the relationship that the other party will manage the business in pursuit of beneficial results for both parties. This trust influences the desire to maintain the relationship, generating a long-term bonding belief that is conceptualised in the literature as commitment (Kuhn & Mostert, 2016 ). Commitment has also been linked to satisfaction in B2B relationships (Hogevold et al., 2021) and refers to a partner's willingness to create and maintain a long-term relationship based on emotional or rational ties (Sung & Choi, 2010 ). The literature recognises two types of commitments. Just as affective commitment is related to loyalty and psychological or emotional attachment, calculating commitment is formed from the assessment of objective aspects such as switching costs or the scarcity of alternatives. Switching costs are therefore a particularly important variable in creating commitment (Ojeme et al., 2018 ). Literature has traditionally highlighted that switching costs favours the duration of the relationship (Patterson & Smith, 2001 ). They represent the perception of costs that a company may have when it changes providers (Pick & Eisend, 2014 ). They are barriers that protect against breaks in the relationship and loyalty (Heirati et al., 2016 ). When a company perceives that switching costs are high, its commitment to the provider increases and its decision to continue the relationship is reinforced. Empirical evidence on the effects of switching costs is scarce. Blut et al. ( 2016 ) confirm that relational costs are the most important to ensure B2B relationships and Ha ( 2017 ) reveals that the costs of losing personal relationships reduce the intentions to change companies and are the most closely linked to performance. In short, we understand that satisfaction, loyalty, trust, commitment, and switching costs are relational variables that can make an important contribution to the formation of tourism business segments. 2.2. Technological bases The tourism industry is closely linked to the development of technologies (Gössling, 2021 ). The rapid evolution of ICTs has brought about a continuous process of digitalisation and globalisation in the tourism market, offering new and better value creation tools (Berné et al., 2015 ). The use of ICT as a distribution channel management tool has aroused significant academic and practical interest in recent years (Breidbach & Maglio, 2016; De Leon & Chatterjee, 2017 ). In the interorganisational context, there is empirical evidence on the effect that the development and use of ICT has on some relational variables. For example, Huo et al. ( 2015 ) confirm that companies that perceive that their partner is investing in technology feel more optimistic about the future of the relationship, are more committed, and show greater loyalty to their provider. According to Kauffman and Pointer (2022), technologies streamline relationships and improve commitment, integration, trust, and value creation. Boccia et al. ( 2022 ) confirm the relationship between digitalisation and internationalisation. Research in tourism is also scarce and not very recent. For example, according to Bastakis et al. ( 2004 ), the use of ICT improves relations between hotels, tour operators, and travel agencies. Bigne et al. (2008) conclude that the intensity of the relationship between agencies and their providers favours the adoption of ICTs, and Berné et al. ( 2015 ) reveal that ICTs intensify relations between tourism companies, thereby improving financial results and market share. Based on these contributions, we consider that the variables related to the development and use of ICT among tourism companies will demonstrate a certain capacity to identify heterogeneous segments. 3. Methodology 3.1. Measurement scales and fieldwork A quantitative investigation has been carried out, using a structured questionnaire. Six items were used to measure economic satisfaction and social satisfaction adapted from Chung et al. ( 2011 ), Geyskens and Steenkamp ( 2000 ) and Anderson and Narus (1990). Three items were adapted from Zeithaml et al. ( 1996 ) to measure loyalty. Trust was adapted from Ferro et al ( 2016 ) and measured through three items. Commitment was measured on a four-item scale derived from Morgan and Hunt ( 1994 ). Six items were used to measure switching costs, three of them adapted from Patterson and Smith ( 2001 ). ICT advancement and use scales were derived from Wu et al. ( 2006 ), Buhalis and Law ( 2008 ), and Neuhofer et al. ( 2014 ) and respectively measured using three and four items. The items have been measured using a 7-point Likert scale. Travel agencies from Spain were considered. The database of companies was obtained from secondary information available in the form of own listings, updated through the ALIMARKET and DUNS 100 databases. A list was drawn up of 900 travel agencies in the autonomous communities of Catalonia, the Valencian Community, and the Community of Madrid. A total of 256 effective interviews were definitively obtained (77 from Barcelona, 102 from Valencia, and 77 from Madrid), achieving a response rate of 30.73%. The key informant was the travel agency manager or supervisor (Table 1 ). Table 1 Sample profile Type of agency Geographic scope Tourist operation Tour operator 1.56% International 47.57% Outbound agency 78.13% Wholesaler 7.03% National 32.58% Inbound agency 17.19% Retailer 62.89% Local 19.85% Domestic agency 4.69% Mixed 28.52% Size Main supplier Average number of employees 14.20 (± 9.10) Integrated in a hotel chain 25.39% Average age (years) 21.61 (± 11.91) Franchise 6.64% Relationship characteristics Hotel bank (Bedbank) 13.67% Average length of patronage with the main supplier (years) 11.75 (± 6.76) Wholesaler 17.58% Average % of activity with the main supplier 44.56 (± 21.05) Reservation center 36.72% Average spending on ICT (10 3 euros) 11.69 (± 32.78) Main client Families/Individuals 45.7% Travel agencies 8.2% Event organization 3.1% Companies 29.3% Groups 10.9% Other 2.7% 3.2. Dimensionality, reliability and validity of scales The reliability and validity of the scales was evaluated by estimating a first-order measurement model using Partial Least Squares (PLS) (Ringle et al., 2015 ). This methodology allows for less restrictive assumptions than the covariance-based approach as it involves nonparametric procedures. Bootstrapping with 5000 subsamples of identical size (n = 256) was used to determine the significance of the estimates, generating the standard errors and the t-value statistics (Henseler et al., 2009 ). All constructs were considered reflective. First, the internal consistency of the measurement scales was evaluated using Cronbach’s Alpha (α) and composite reliability (CR) coefficients, whose minimum thresholds are 0.7 (Nunally, 1978 ; Anderson & Gerbing, 1988 ), and through the variance extracted from each of the scales (AVE), whose value must exceed 0.5 (Fornell & Larcker, 1981 ) (Table 2 ). Secondly, the validity of the scales was contrasted: (1) content validity, since the scales are formed according to the bibliographic review; (2) convergent validity, when verifying that the factor loadings were significant at 99% (t-Student statistic > 2.58) (Anderson & Gerbing, 1988 ); and (3) discriminant validity, since the linear correlation between each pair of scales is less than the square root of the AVE of the scales involved (Table 2 ). It was also analysed using the heterotrait-monotrait (HTMT) ratio (Henseler et al., 2015 ), showing that the highest ratio between correlations reached 0.832 between continuity-commitment, lower than the permitted maximum threshold of 0.9. Table 2 Descriptive statistics, reliability indexes and measurement scales correlations Mean SD α CR AVE 1. 2. 3. 4. 5. 6. 7. 8. 1. Trust 5.85 0.84 0.842 0.905 0.762 0.873 2. Commitment 5.48 1.03 0.894 0.925 0.756 0.598 0.870 3. Switching costs 5.15 0.97 0.848 0.908 0.767 0.421 0.517 0.876 4. Economic satisfaction 5.16 1.24 0.789 0.880 0.713 0.513 0.493 0.467 0.844 5. Social satisfaction 5.95 0.88 0.832 0.899 0.749 0.542 0.562 0.503 0.682 0.865 6. Loyalty 5.25 0.98 0.838 0.892 0.676 0.568 0.620 0.548 0.325 0.754 0.822 7. ICT advancement 4.17 1.37 0.872 0.922 0.799 0.131 0.263 0.400 0.315 0.257 0.325 0.894 8. ICT use 4.79 1.40 0.813 0.872 0.572 0.256 0.217 0.064 0.268 0.229 0.263 0.164 0.756 α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted Elements on the main diagonal in italics are the square root of AVE 4. Results We propose a segmentation analysis following a tandem approach (Schaffer & Green, 1998 ). We chose multiple correspondence analysis as the segmentation method due to the fact that it allowed us to jointly study the types of agencies in the sample based on their common characteristics, as well as the interrelation between these characteristics, as a factorial method, through a simple graphical representation. The multivariate technique was executed with R 4.1.2. In the multiple correspondence analysis, we used as active segmentation variables the relational and technological dimensions together with other characterising variables of the relationship (Table 3 ). Due to the nominal nature of the variables, we recoded all these dimensions based on the median value of the variables that make up each factor: low value for those agencies that present levels below the sample median, and high value in the case of presenting higher values. The choice of two categories for each variable is due to the fact that the more modalities the variables have, the lower the percentage of inertia in each summary factor (Grande & Abascal, 1999 ). Lastly, we include various descriptive variables of the agencies as supplementary variables (Table 3 ). Table 3 Categories for MCA (active and supplementary variables) Act/Suppl. Variable Categories Label (Fig. 1 ) Active (red colour in Fig. 1 ) Trust Low (≤6) High (> 6) Ltru Htru Commitment Low (≤5.5) High (> 5) Lcom Hcom Switching costs Low (≤4.67) High (> 4.67) Lsc Hsc Economic satisfaction Low (≤5.33) High (> 5.33) Lesat Hesat Social satisfaction Low (≤6) High (> 6) Lssat Hssat Loyalty Low (≤5.25) High (> 5.25) Lloy Hloy ICT advancement Low (≤4) High (> 4) Ladv Hadv ICT use Low (≤5) High (> 5) Luse Huse Main supplier Integrated in a hotel chain C Franchise F Hotel bank (Bedbank) B Wholesaler W Reservation center R Length of patronage Up to 11.4 years 11.4 Over 11.4 years > 11.4 % of activity Up to 40% 40% Over 40% > 40% Supplementary (green colour in Fig. 1 ) Type of agency Tour operator Tour Wholesaler Who Retailer Ret Mixted Mix Geographic scope International Int National Nat Local Loc Tourist operation Outbound tourist agency Out Inbound tourist agency In Domestic tourist agency Dom Number of employees Up to 25 employees 25 Over 25 employees > 25 Main client Families/Individuals F/I Travel agencies TA Event organization Eve Companies Com Groups Gro Other Oth The results of the multiple correspondence analysis gather together 14 factors or axes, explaining between the first two 32.62% of the variance (they are the only ones that explain more than 10% of the variability). We will limit ourselves to the interpretation of these first two axes, since, although it may seem like a weak amount of explained information, it is sufficient in the presence of multiple factors (Grande & Abascal, 1999 ). It should be added that with this analysis we intend to define the groups based on the positioning (Fig. 1 ). The first axis collects 21.94% of the variance, with the relational dimensions contributing the most to its formation. The high values of the active variables trust, commitment, switching costs, satisfaction, satisfaction, and loyalty are in the positive part, compared to the low values that are in the negative part, showing much higher contributions than the rest of the variables (all above 8.5). In addition, agencies with a prominent level in relational dimensions are associated with F suppliers, while those with values below the median are associated with online R providers. Regarding the second axis, it manages to explain 10.68% of the variance. It is the technological variables that contribute greatly to its formation. High categories related to ICTs are located on the vertical positive semi-axis, compared to the low categories that are located on the negative side. These groupings are also clearly associated with primary provider types and relationship characteristics. The high valuations on the technological variables are associated with the W providers W with a longer relationship and a high percentage of the agency's activity with that provider. The association on the low valuations of the technological variables is related to the type of W or B provider, showing a shorter relationship time and a lower percentage of activity. As a complementary analysis, a hierarchical cluster analysis was performed on the axis scores obtained for each attribute, which helped to identify the groups more accurately. From the dendrogram obtained (Appendix I) and the position coordinates (Fig. 1 ), 4 groups were identified 1 . The first group of attributes (right side of the map) includes the higher categories of the relational variables. This group presents mean values significantly higher than the rest for the dimensions of trust (6.09), commitment (5.79), switching costs (5.06), economic satisfaction (5.65), social satisfaction (6.25), and loyalty (5.58). This segment, labelled ‘RELATIONSHIP-ORIENTED’ (n = 63), comprises mixed travel agencies that develop strong relationships with their main provider based on trust and commitment, generating elevated levels of satisfaction and loyalty. The costs of changing providers are also high. Its main provider are F providers. The second group (left side of the map) comprises the most active categories of attributes, since the association of valuations below the median of the relational variables is related to the main providers of R and C. This group shows mean scores of trust (5.61), commitment (5.18), switching costs (4.35), economic satisfaction (4.82), social satisfaction (5.79) and loyalty (4.98) significantly lower than the rest. It brings together retail agencies whose end customers are individuals and families. This group, labelled as ‘NOT RELATIONSHIP-ORIENTED’ (n = 159), is the largest and most homogeneous. It is also the one that has the greatest difficulty in establishing relationships based on affective aspects, resulting in less satisfactory and loyal relationships. The third group (upper quadrant of the map) brings together agencies with mean values higher than those of the rest of the segments for the two technological variables (ICT advancement = 4.68; ICT use = 5.20). Their main provider is a W agency. The most of these agencies indicate having been in a long relationship with their main provider and conducting a high activity with it. This group is associated with agencies that are larger than average, with a national and international scope of activity, and whose main customers are companies. This segment is labelled as ‘ICT-ORIENTED’ (n = 45) since it is made up of companies more oriented towards the intensive use of technology. They are mainly large and international wholesale companies. It is the segment with the least number of providers, which enables a safe investment in technology to maintain the relationship. The fourth group (lower quadrant of the map) corresponds to the associations with low valuations of the technological variables. Their main provider is B and present lower average scores on ICT development (4.03) and ICT use in the relationship (4.74). These lowest valuations are related to a shorter relationship time and a lower percentage of activity. It is made up mainly of local tour operators, whose main clients are travel agencies. This segment, labelled as ‘NOT ICT-ORIENTED’ (n = 35), is the most difficult group to characterise based on the segmentation criteria that is the object of study. Although the low use of ICT represents its main unifying element, its average evaluations do not present significant differences compared to the other segments. [1] Companies have not been added to the map so as not to hinder interpretation. The classification was carried out based on the categories of Figure 1, filtering the sample by the various groups of attributes. The verification of the significant differences was carried out from the ANOVA analysis of one factor (all the p-values were, at most, < 0.05). 5. Conclusions and implications The tourism intermediation sector has undergone structural changes motivated by various phenomena (e.g. emergence of innovative technologies, economic crisis, and appearance of new intermediation figures). The travel agency sector has not been immune to these changes. These companies can be classified according to multiple criteria such as their organisational structure (independent vs. chain), size (large vs. small), type of customer (wholesalers, retailers, or mixed) or role in providing the services (issuing or receiving). These classifications make it possible to differentiate travel agencies from the point of view of the market they serve. However, they are not as useful when seeking to create a group, not as service provider agencies, but as customers in an interorganisational relationship. In this context, our work has focused on deepening the relationship between travel agencies and their main accommodation provider and a segmentation has been proposed based on both relational and technological bases. The choice of these two types of criteria has made it possible to identify four large segments that are mostly related to an accommodation provider profile and a travel agency type. In view of these results, it is concluded that these bases constitute segmentation criteria for the tourism B2B market capable of clearly differentiating travel agencies. Firstly, there is a clear grouping that discriminates between agencies according to the intensity of the relationship with their provider: segment 1 (relationship-oriented’) and segment 2 (‘not relationship-oriented’). The common feature is that they are mainly retail travel agencies. The relationship-focused segment values commitment, trust, and the pursuit of relationship satisfaction and loyalty above all else. They adopt a strategic approach focused on maintaining satisfactory long-term relationships. However, the segment that is not focused on the relationship values convenience and standardisation of services over personalisation or differentiation. It is the largest group and the one that presents the greatest difficulties in developing committed relationships and trust, resulting in less satisfaction and loyalty. Secondly, another clear grouping is observed that differentiates agencies based on technology: segment 3 (‘ICT-oriented’) and segment 4 (‘not ICT-oriented’). In this case, the link is its wholesale nature. ICT-oriented companies value ​​technology as a key tool that facilitates the management of interactions and the development of stable relationships. Meanwhile companies that are not focused on ICT do not value the investment and use of technology as a strategic factor in the development of relationships. This type of segmentation contributes to the advancement of research on segmentation in the tourism B2B market. These are bases that allow a better interpretation of the situation of travel agencies in terms of their relationships within the service supply channel. Just as retail agencies differ in terms of involvement in their provider relationship, wholesale agencies do so based on the technologies they use with their provider. Therefore, the relational criteria constitute useful segmentation bases to segment only the retail agency market, while the technological criteria are more capable of being used to segment the wholesale agency market. From an academic point of view, the review of the literature showed a clear need to delve into segmentation criteria beyond those of a purely operational nature. Faced with this challenge, our research has confirmed that the variables linked to the relationship and technologies significantly improve the identification of segments at an industry level. In particular, in the tourism context, these variables have proven to have sufficient capacity to discriminate statistically heterogeneous groups of travel agencies. This segmentation has practical implications for managing relationships in the industry channel. The description of the segments allows a better understanding of the customer company, the provider company, the operating characteristics and, fundamentally, the type of relationship between the two. From the provider’s perspective, the companies that provide tourism services that identify segments based on their relationship with the customer agency and based on the ICT used will be able to select their target segment more accurately and to improve their strategic orientation, achieving a greater adjustment to the specific needs of their customers. From the perspective of the customer agency, these segmentation criteria could be used as key elements in the selection of service providers. If providers use these types of variables to choose a customer segment and adapt their strategies, it is reasonable that customers consider the same variables to evaluate and choose their providers. Consequently, this type of segmentation should include a dual provider-customer approach and may be useful not only for the selection of customer segment(s) but also for the selection of providers. 6. Future lines Research on segmentation in the B2B market presents interesting challenges and opportunities. At a theoretical level, the incorporation of other relational bases of segmentation could help to deepen the discrimination of heterogeneous segments. Regarding the variables linked to the relationship, relational value and relational benefits are variables that are particularly prominent in the literature, but with little empirical evidence in the field of segmentation (e.g. Ruiz et al., 2015 ; Fuentes et al., 2017 ). Adding these variables as segmentation bases could improve the process of identifying tourism business segments. Regarding the variables related to the technologies, the capacity that each one of the technologies (for internal use vs. for external use) has could be addressed in regard to segment formation. At the methodological level, another alternative method of segmentation could be used, such as the latent segmentation methodology (Casabayó et al., 2015 ), which allows the size and structure of the segments to be estimated simultaneously. To improve the representativeness of the results, it is proposed to use larger and random samples. Finally, ICTs have not permitted the formation of retail travel agency segments, so the study of segmentation based on technological criteria is an interesting line of future research in the market for this type of agency. This work could also be extended to other tourism B2B contexts where relationships between companies are key in the service supply channel, such as the restaurant or cultural tourism sector. Declarations Acknowledgments and Funding: This research has been developed within the framework of the project Grant PID2020-112660RB-I00 funded by MCIN/AEI/10.13039/501100011033 and the consolidated research group AICO/2021/144/GVA funded by the Conselleria d’Innovacio, Universitats, Ciencia i Societat Digital of the Generalitat Valenciana (Reference no.: UV-INV-AE-1553911). Author contributions All authors have contributed equally Disclosure statement The authors declare no conflict of interest References Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin , 103 (3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 . Blut, M., Evanschitzky, H., Backhaus, C., Rudd, J., & Marck, M. (2016). Securing business-to-business relationships: The impact of switching costs. Industrial Marketing Management , 52 , 82–90. https://doi.org/10.1016/j.indmarman.2015.05.010 . 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Industrial Marketing Management , 42 (4), 507–517. https://doi.org/10.1016/j.indmarman.2013.03.003 . Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS . SmartPLS GmbH. Ruiz, M. E., Gil, I., & Moliner, B. (2015). Relational benefits, value, and satisfaction in the relationships between service companies. Journal of Relationship Marketing , 14 (1), 1–15. https://doi.org/10.1080/15332667.2015.1006011 . Saragih, R., Liu, R., Putri, C. A., Fakhri, M., & Pradana, M. (2022). The role of loyalty and satisfaction in forming word-of-mouth influence in a B2B environment: Evidence from the knitting industry of Indonesia. Journal of Eastern European and Central Asian Research (JEECAR) , 9 (3), 543–553. http://dx.doi.org/10.15549/jeecar.v9i3.889 . Schaffer, C. M., & Green, P. E. (1998). Cluster based market segmentation: some further comparasions of alternative approaches. Journal of The Market Research Society , 40 , 155–163. https://doi.org/10.1177/147078539804000203 . Shi, F., Ji, S., Weaver, D., & Huang, M. F. (2022). From curious to connoisseur: a longitudinal segmentation of attendees at a Chinese wine festival. International Journal of Contemporary Hospitality Management , 34 (3), 885–907. https://doi.org/10.1108/IJCHM-03-2021-0331 . Silva, C. M. S., & Dias, O. C. (2020). Markets segmentation and differentiation of reverse logistics offers. Revista Brasileira de Marketing – REMark , 19 (4), 862–887. http://dx.doi.org/10.5585/remark.v19i4.16392 . Sung, Y., & Choi, S. M. (2010). I won’t leave you although you disappoint me’: The interplay between satisfaction, investment, and alternatives in determining consumer-brand relationship commitment. Psychology & Marketing , 27 (11), 1050–1074. https://doi.org/10.1002/mar.20373 . Thomas, R. J. (2016). Multistage market segmentation: an exploration of B2B segment alignment. Journal of Business & Industrial Marketing , 31 (7), 821–834. https://doi.org/10.1108/JBIM-12-2015-0245 . World Tourism Organization (2023). International Tourism Highlights. The Impact of COVID-19 on Tourism (2020–2022), 2023 Edition , UNWTO, Madrid. https://www.e-unwto.org/doi/epdf/10.18111/9789284424504 . Weinstein, A. (2014). Segmenting B2B technology markets via psychographics: an exploratory study. Journal of Strategic Marketing , 22 (3), 257–267. http://dx.doi.org/10.1080/0965254X.2013.876072 . Wu, F., Yeniyurt, S., Kim, D., & Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. Industrial Marketing Management , 35 , 493–504. https://doi.org/10.1016/j.indmarman.2005.05.003 . Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing , 60 (2), 31–46. https://doi.org/10.1177/002224299606000203 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3427750","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245353950,"identity":"17bd400d-a1cd-4dc8-b369-64c4bebf37ea","order_by":0,"name":"Maria Fuentes-Blasco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDAC5gMMzAwFDHIkaGFLAGoxYDAmXUtiA9E6dNt4DB8XGNilb7jdfIDhwx8itJgd4zE2nmGQnLvhzrEExpltxGi532MmzWPAnLvhRo4BMy8xzgPaYv6bx6A+3QCk5Q+RDjNj5jE4nADWwsBGlBa2YukZBscNZwL9crCXKL8cY974uaCiWp7vdvPBBz+IcRgCSDAwHCBJA1jLKBgFo2AUjAKsAABU+jS0FuHg/wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7082-7068","institution":"Pablo de Olavide University Faculty of Business Sciences: Universidad Pablo de Olavide Facultad de Ciencias Empresariales","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"","lastName":"Fuentes-Blasco","suffix":""},{"id":245353951,"identity":"6f076712-b46d-4511-8e2d-23c376f4f39a","order_by":1,"name":"Beatriz Moliner-Velázquez","email":"","orcid":"","institution":"University of Valencia Faculty of Economics: Universitat de Valencia Facultat d'Economia","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Moliner-Velázquez","suffix":""},{"id":245353952,"identity":"4f5dcb1f-e32b-42d0-aeaa-2abd89ea9786","order_by":2,"name":"Irene Gil-Saura","email":"","orcid":"","institution":"University of Valencia Faculty of Economics: Universitat de Valencia Facultat d'Economia","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"","lastName":"Gil-Saura","suffix":""},{"id":245353953,"identity":"2b585943-9f91-4533-8749-9ee10afa1869","order_by":3,"name":"Gloria Berenguer-Contrí","email":"","orcid":"","institution":"University of Valencia Faculty of Economics: Universitat de Valencia Facultat d'Economia","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Berenguer-Contrí","suffix":""}],"badges":[],"createdAt":"2023-10-10 14:06:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3427750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3427750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46028793,"identity":"a9576111-5cfb-4463-9425-1036e9d1c76e","added_by":"auto","created_at":"2023-11-07 17:46:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310841,"visible":true,"origin":"","legend":"\u003cp\u003eMCA. Positioning map for axes 1 and 2\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3427750/v1/13ce0911c6a7d04d5b725850.jpeg"},{"id":51311227,"identity":"f84f5a27-c330-4b95-868e-e01540d6f579","added_by":"auto","created_at":"2024-02-19 11:05:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":362047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3427750/v1/40b47007-70df-4827-962e-0cb053c3439e.pdf"}],"financialInterests":"","formattedTitle":"Segmenting tourism companies with relational and technological bases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRelational marketing literature highlights the importance of designing strategies aimed at creating and maintaining long-term relationships both in the B2C (hereafter B2C) and B2B market (hereafter B2B) (Das et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dichotomy between both markets has been the subject of academic debate (Nath et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). B2B market is especially complex due to the variety of parties involved in the exchange relationships and the simultaneity of roles that companies can have (Sales-Viv\u0026oacute; et al., 2020). The study of relationships between companies has aroused research interest in the last decade (Heirati et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Brotspies \u0026amp; Weinstein, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Berenguer-Contr\u0026iacute; et al., 2020). However, the literature still needs to advance in the knowledge of the variables and conditions that contribute to the improvement of relations and benefits for both parties.\u003c/p\u003e \u003cp\u003eOne of the key tools for good relationship management is segmentation. The literature is extensive, but there are still certain conceptual and methodological disagreements on the application of the most appropriate criteria and methods, and on their adaptation to different contexts (Casabay\u0026oacute; et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brotspies \u0026amp; Weinstein, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Segmentation in interorganizational context has received less attention than in the B2C market (Thomas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva \u0026amp; Dias, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and presents more difficulties than the consumer market (Rezaei \u0026amp; Ortt, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Descriptive variables of the companies have traditionally been used to identify segments. This type of segmentation has sometimes been criticised for being more of a sectorisation than a segmentation (Rezaei \u0026amp; Ortt, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For this reason, in recent years there has been a tendency to use relational-type criteria, that is, linked to the behaviours and assessments that companies make of their relationship with their providers (Heirati et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Brotspies \u0026amp; Weinstein, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as criteria associated with the use of technologies (Weinstein, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, tourism is one of the most dynamic, turbulent, and competitive sectors (World Tourism Organization, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and one of the most affected by the development of technologies (G\u0026ouml;ssling, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The literature on segmentation in the tourism B2B market has certain shortcomings. Since the year 2000 academic research is more limited compared to the consumer market. Regarding the criteria used, there is little empirical evidence on the formation of tourism business segments based on relational variables (Weinstein, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Added to this is the limitation on the simultaneous study of several criteria, since the contributions focus on the observation of one or two segmentation bases. Similarly, there are still few studies that address segmentation based on technological variables (Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the tourism B2B market presents significant challenges in terms of segment identification based on variables of a different nature, whether descriptive, behavioural, attitudinal, and even related to technology. Given these gaps, we believe that the variables associated with the relationship between company and provider and the use of technology can improve the identification of segments. The purpose of this research is to contribute to the lack of empirical evidence that explores new segmentation criteria in the B2B tourism context. We pursue a dual objective: 1) Analyse the usefulness of two groups of criteria, relational and associated with Information and Communication Technologies (hereafter ICT), as segmentation bases to identify heterogeneous groups of travel agencies; 2) Characterise the segments obtained from descriptive variables in order to analyse the strategies used at the segment level and direct improvement towards greater adaptation to the needs of this type of tourism business.\u003c/p\u003e \u003cp\u003eThe novelty of this work lies in the joint study of these two groups of base segmentation criteria, relational and technological, in the tourism B2B context. Although there is some evidence in tourism on segmentation with relational (Thomas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and technological bases (Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), no recent research has been found that simultaneously addresses these variables to form business segments. Therefore, this work contributes to the advancement of the literature on B2B segmentation in tourism by providing a more complete vision of the capacity of the variables linked to the relationship and the technologies in segment discrimination.\u003c/p\u003e"},{"header":"2. Segmentation bases in B2B context","content":"\u003cp\u003eThe complexity of this industry\u0026rsquo;s market makes the selection of criteria or segmentation bases a significantly challenging area of study. To contribute to this line of research, we propose two blocks of bases: relational and technological.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Relational bases\u003c/h2\u003e \u003cp\u003eTo properly manage relationships between companies and maintain them in the long term, it is necessary for the parties to be satisfied (Geyskens \u0026amp; Steenkamp, ​​2000). Satisfaction is the key requirement for continued relationships and customer loyalty (Eggert \u0026amp; Ulaga, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Satisfaction and loyalty are, therefore, fundamental variables in relational marketing.\u003c/p\u003e \u003cp\u003eIn the B2B context, satisfaction is a positive affective state that forms when a company evaluates its relationship with a provider (De Wulf et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Several authors highlight the general and accumulated nature of satisfaction, pointing out that it is the result of the evaluation of the various aspects or stages of the relationship between the parties (Kundu \u0026amp; Datta, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Two types of satisfaction are differentiated: economic and social (Chung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ferro et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Guan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Economic satisfaction refers to the assessment that a member of the channel makes of the economic rewards that occur in the relationship (e.g. efficiency), however social satisfaction is based on the psychosocial aspects of the relationship (e.g. gratification) (Geyskens \u0026amp; Steenkamp, ​​2000).\u003c/p\u003e \u003cp\u003eSatisfaction is a clear antecedent of loyalty and this is demonstrated by the abundance of empirical evidence in the literature. The multidimensional character of loyalty is shared since loyalty can be manifested through attitudes or intentions and behaviours (Dick \u0026amp; Basu, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Some studies have questioned the relationship between satisfaction and loyalty, qualifying that this link depends on the sector, the type of customers, the measures used, and the mediating or moderating variables (Kumar et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the B2B context, the contributions confirm that satisfaction exerts a certain influence, directly or indirectly, on loyalty (Els\u0026auml;\u0026szlig;er \u0026amp; Wirtz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saragih et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, companies that are satisfied with their provider are more likely to develop behaviours and/or attitudes related to the intention of maintaining the relationship.\u003c/p\u003e \u003cp\u003eTrust and commitment are also key elements that must coexist for the success and continuity of relationships between companies. Recent studies confirm that trust in the B2B context is an antecedent of satisfaction (Sales-Viv\u0026oacute; et al., 2020; Hogevold et al., 2021). It has been defined as the conviction of one party to the relationship that the other party will manage the business in pursuit of beneficial results for both parties. This trust influences the desire to maintain the relationship, generating a long-term bonding belief that is conceptualised in the literature as commitment (Kuhn \u0026amp; Mostert, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Commitment has also been linked to satisfaction in B2B relationships (Hogevold et al., 2021) and refers to a partner's willingness to create and maintain a long-term relationship based on emotional or rational ties (Sung \u0026amp; Choi, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The literature recognises two types of commitments. Just as affective commitment is related to loyalty and psychological or emotional attachment, calculating commitment is formed from the assessment of objective aspects such as switching costs or the scarcity of alternatives.\u003c/p\u003e \u003cp\u003eSwitching costs are therefore a particularly important variable in creating commitment (Ojeme et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Literature has traditionally highlighted that switching costs favours the duration of the relationship (Patterson \u0026amp; Smith, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). They represent the perception of costs that a company may have when it changes providers (Pick \u0026amp; Eisend, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They are barriers that protect against breaks in the relationship and loyalty (Heirati et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When a company perceives that switching costs are high, its commitment to the provider increases and its decision to continue the relationship is reinforced. Empirical evidence on the effects of switching costs is scarce. Blut et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) confirm that relational costs are the most important to ensure B2B relationships and Ha (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reveals that the costs of losing personal relationships reduce the intentions to change companies and are the most closely linked to performance.\u003c/p\u003e \u003cp\u003eIn short, we understand that satisfaction, loyalty, trust, commitment, and switching costs are relational variables that can make an important contribution to the formation of tourism business segments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Technological bases\u003c/h2\u003e \u003cp\u003eThe tourism industry is closely linked to the development of technologies (G\u0026ouml;ssling, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rapid evolution of ICTs has brought about a continuous process of digitalisation and globalisation in the tourism market, offering new and better value creation tools (Bern\u0026eacute; et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The use of ICT as a distribution channel management tool has aroused significant academic and practical interest in recent years (Breidbach \u0026amp; Maglio, 2016; De Leon \u0026amp; Chatterjee, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the interorganisational context, there is empirical evidence on the effect that the development and use of ICT has on some relational variables. For example, Huo et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) confirm that companies that perceive that their partner is investing in technology feel more optimistic about the future of the relationship, are more committed, and show greater loyalty to their provider. According to Kauffman and Pointer (2022), technologies streamline relationships and improve commitment, integration, trust, and value creation. Boccia et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) confirm the relationship between digitalisation and internationalisation. Research in tourism is also scarce and not very recent. For example, according to Bastakis et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), the use of ICT improves relations between hotels, tour operators, and travel agencies. Bigne et al. (2008) conclude that the intensity of the relationship between agencies and their providers favours the adoption of ICTs, and Bern\u0026eacute; et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reveal that ICTs intensify relations between tourism companies, thereby improving financial results and market share.\u003c/p\u003e \u003cp\u003eBased on these contributions, we consider that the variables related to the development and use of ICT among tourism companies will demonstrate a certain capacity to identify heterogeneous segments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Measurement scales and fieldwork\u003c/h2\u003e\n \u003cp\u003eA quantitative investigation has been carried out, using a structured questionnaire. Six items were used to measure economic satisfaction and social satisfaction adapted from Chung et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), Geyskens and Steenkamp (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Anderson and Narus (1990). Three items were adapted from Zeithaml et al. (\u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) to measure loyalty. Trust was adapted from Ferro et al (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and measured through three items. Commitment was measured on a four-item scale derived from Morgan and Hunt (\u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). Six items were used to measure switching costs, three of them adapted from Patterson and Smith (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). ICT advancement and use scales were derived from Wu et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), Buhalis and Law (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), and Neuhofer et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and respectively measured using three and four items. The items have been measured using a 7-point Likert scale.\u003c/p\u003e\n \u003cp\u003eTravel agencies from Spain were considered. The database of companies was obtained from secondary information available in the form of own listings, updated through the ALIMARKET and DUNS 100 databases. A list was drawn up of 900 travel agencies in the autonomous communities of Catalonia, the Valencian Community, and the Community of Madrid. A total of 256 effective interviews were definitively obtained (77 from Barcelona, 102 from Valencia, and 77 from Madrid), achieving a response rate of 30.73%. The key informant was the travel agency manager or supervisor (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample profile\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eType of agency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGeographic scope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTourist operation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTour operator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutbound agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesaler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInbound agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetailer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDomestic agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain supplier\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAverage number of employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.20 (\u0026plusmn;\u0026thinsp;9.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrated in a hotel chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAverage age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.61 (\u0026plusmn;\u0026thinsp;11.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFranchise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHotel bank (Bedbank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAverage length of patronage with the main supplier (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.75 (\u0026plusmn;\u0026thinsp;6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesaler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAverage % of activity with the main supplier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.56 (\u0026plusmn;\u0026thinsp;21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReservation center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAverage spending on ICT (10\u003csup\u003e3\u003c/sup\u003e euros)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.69 (\u0026plusmn;\u0026thinsp;32.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain client\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamilies/Individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTravel agencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvent organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompanies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Dimensionality, reliability and validity of scales\u003c/h2\u003e\n \u003cp\u003eThe reliability and validity of the scales was evaluated by estimating a first-order measurement model using Partial Least Squares (PLS) (Ringle et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). This methodology allows for less restrictive assumptions than the covariance-based approach as it involves nonparametric procedures. Bootstrapping with 5000 subsamples of identical size (n\u0026thinsp;=\u0026thinsp;256) was used to determine the significance of the estimates, generating the standard errors and the t-value statistics (Henseler et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAll constructs were considered reflective. First, the internal consistency of the measurement scales was evaluated using Cronbach\u0026rsquo;s Alpha (\u0026alpha;) and composite reliability (CR) coefficients, whose minimum thresholds are 0.7 (Nunally, \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e; Anderson \u0026amp; Gerbing, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e), and through the variance extracted from each of the scales (AVE), whose value must exceed 0.5 (Fornell \u0026amp; Larcker, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSecondly, the validity of the scales was contrasted: (1) content validity, since the scales are formed according to the bibliographic review; (2) convergent validity, when verifying that the factor loadings were significant at 99% (t-Student statistic\u0026thinsp;\u0026gt;\u0026thinsp;2.58) (Anderson \u0026amp; Gerbing, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e); and (3) discriminant validity, since the linear correlation between each pair of scales is less than the square root of the AVE of the scales involved (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). It was also analysed using the heterotrait-monotrait (HTMT) ratio (Henseler et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), showing that the highest ratio between correlations reached 0.832 between continuity-commitment, lower than the permitted maximum threshold of 0.9.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics, reliability indexes and measurement scales correlations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.873\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.870\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Switching costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.876\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Economic satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.844\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Social satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.865\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Loyalty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.822\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. ICT advancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.894\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. ICT use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.756\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"14\"\u003e\n \u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha; CR\u0026thinsp;=\u0026thinsp;composite reliability; AVE\u0026thinsp;=\u0026thinsp;average variance extracted\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eElements on the main diagonal in italics are the square root of AVE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eWe propose a segmentation analysis following a tandem approach (Schaffer \u0026amp; Green, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). We chose multiple correspondence analysis as the segmentation method due to the fact that it allowed us to jointly study the types of agencies in the sample based on their common characteristics, as well as the interrelation between these characteristics, as a factorial method, through a simple graphical representation. The multivariate technique was executed with R 4.1.2.\u003c/p\u003e\n\u003cp\u003eIn the multiple correspondence analysis, we used as active segmentation variables the relational and technological dimensions together with other characterising variables of the relationship (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Due to the nominal nature of the variables, we recoded all these dimensions based on the median value of the variables that make up each factor: low value for those agencies that present levels below the sample median, and high value in the case of presenting higher values. The choice of two categories for each variable is due to the fact that the more modalities the variables have, the lower the percentage of inertia in each summary factor (Grande \u0026amp; Abascal, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Lastly, we include various descriptive variables of the agencies as supplementary variables (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCategories for MCA (active and supplementary variables)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAct/Suppl.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLabel (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"17\"\u003e\n \u003cp\u003eActive\u003c/p\u003e\n \u003cp\u003e(red colour in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;6)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLtru\u003c/p\u003e\n \u003cp\u003eHtru\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;5.5)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLcom\u003c/p\u003e\n \u003cp\u003eHcom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitching costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;4.67)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLsc\u003c/p\u003e\n \u003cp\u003eHsc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;5.33)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLesat\u003c/p\u003e\n \u003cp\u003eHesat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;6)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLssat\u003c/p\u003e\n \u003cp\u003eHssat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoyalty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;5.25)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLloy\u003c/p\u003e\n \u003cp\u003eHloy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICT advancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;4)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLadv\u003c/p\u003e\n \u003cp\u003eHadv\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICT use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;5)\u003c/p\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuse\u003c/p\u003e\n \u003cp\u003eHuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMain supplier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrated in a hotel chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFranchise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHotel bank (Bedbank)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesaler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReservation center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLength of patronage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp to 11.4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 11.4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e% of activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp to 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eSupplementary\u003c/p\u003e\n \u003cp\u003e(green colour in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eType of agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTour operator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTour\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesaler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetailer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGeographic scope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTourist operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutbound tourist agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOut\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInbound tourist agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDomestic tourist agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber of employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp to 25 employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 25 employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eMain client\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamilies/Individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTravel agencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvent organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompanies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGro\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the multiple correspondence analysis gather together 14 factors or axes, explaining between the first two 32.62% of the variance (they are the only ones that explain more than 10% of the variability). We will limit ourselves to the interpretation of these first two axes, since, although it may seem like a weak amount of explained information, it is sufficient in the presence of multiple factors (Grande \u0026amp; Abascal, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). It should be added that with this analysis we intend to define the groups based on the positioning (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe first axis collects 21.94% of the variance, with the relational dimensions contributing the most to its formation. The high values of the active variables trust, commitment, switching costs, satisfaction, satisfaction, and loyalty are in the positive part, compared to the low values that are in the negative part, showing much higher contributions than the rest of the variables (all above 8.5). In addition, agencies with a prominent level in relational dimensions are associated with F suppliers, while those with values below the median are associated with online R providers.\u003c/p\u003e\n\u003cp\u003eRegarding the second axis, it manages to explain 10.68% of the variance. It is the technological variables that contribute greatly to its formation. High categories related to ICTs are located on the vertical positive semi-axis, compared to the low categories that are located on the negative side. These groupings are also clearly associated with primary provider types and relationship characteristics. The high valuations on the technological variables are associated with the W providers W with a longer relationship and a high percentage of the agency\u0026apos;s activity with that provider. The association on the low valuations of the technological variables is related to the type of W or B provider, showing a shorter relationship time and a lower percentage of activity.\u003c/p\u003e\n\u003cp\u003eAs a complementary analysis, a hierarchical cluster analysis was performed on the axis scores obtained for each attribute, which helped to identify the groups more accurately. From the dendrogram obtained (Appendix I) and the position coordinates (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), 4 groups were identified\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e1\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe first group of attributes (right side of the map) includes the higher categories of the relational variables. This group presents mean values significantly higher than the rest for the dimensions of trust (6.09), commitment (5.79), switching costs (5.06), economic satisfaction (5.65), social satisfaction (6.25), and loyalty (5.58). This segment, labelled \u0026lsquo;RELATIONSHIP-ORIENTED\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;63), comprises mixed travel agencies that develop strong relationships with their main provider based on trust and commitment, generating elevated levels of satisfaction and loyalty. The costs of changing providers are also high. Its main provider are F providers.\u003c/p\u003e\n\u003cp\u003eThe second group (left side of the map) comprises the most active categories of attributes, since the association of valuations below the median of the relational variables is related to the main providers of R and C. This group shows mean scores of trust (5.61), commitment (5.18), switching costs (4.35), economic satisfaction (4.82), social satisfaction (5.79) and loyalty (4.98) significantly lower than the rest. It brings together retail agencies whose end customers are individuals and families. This group, labelled as \u0026lsquo;NOT RELATIONSHIP-ORIENTED\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;159), is the largest and most homogeneous. It is also the one that has the greatest difficulty in establishing relationships based on affective aspects, resulting in less satisfactory and loyal relationships.\u003c/p\u003e\n\u003cp\u003eThe third group (upper quadrant of the map) brings together agencies with mean values higher than those of the rest of the segments for the two technological variables (ICT advancement\u0026thinsp;=\u0026thinsp;4.68; ICT use\u0026thinsp;=\u0026thinsp;5.20). Their main provider is a W agency. The most of these agencies indicate having been in a long relationship with their main provider and conducting a high activity with it. This group is associated with agencies that are larger than average, with a national and international scope of activity, and whose main customers are companies. This segment is labelled as \u0026lsquo;ICT-ORIENTED\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;45) since it is made up of companies more oriented towards the intensive use of technology. They are mainly large and international wholesale companies. It is the segment with the least number of providers, which enables a safe investment in technology to maintain the relationship.\u003c/p\u003e\n\u003cp\u003eThe fourth group (lower quadrant of the map) corresponds to the associations with low valuations of the technological variables. Their main provider is B and present lower average scores on ICT development (4.03) and ICT use in the relationship (4.74). These lowest valuations are related to a shorter relationship time and a lower percentage of activity. It is made up mainly of local tour operators, whose main clients are travel agencies. This segment, labelled as \u0026lsquo;NOT ICT-ORIENTED\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;35), is the most difficult group to characterise based on the segmentation criteria that is the object of study. Although the low use of ICT represents its main unifying element, its average evaluations do not present significant differences compared to the other segments.\u003c/p\u003e\n\u003cp\u003e[1] Companies have not been added to the map so as not to hinder interpretation. The classification was carried out based on the categories of Figure 1, filtering the sample by the various groups of attributes. The verification of the significant differences was carried out from the ANOVA analysis of one factor (all the p-values were, at most, \u0026lt; 0.05).\u003c/p\u003e"},{"header":"5. Conclusions and implications","content":"\u003cp\u003eThe tourism intermediation sector has undergone structural changes motivated by various phenomena (e.g. emergence of innovative technologies, economic crisis, and appearance of new intermediation figures). The travel agency sector has not been immune to these changes. These companies can be classified according to multiple criteria such as their organisational structure (independent vs. chain), size (large vs. small), type of customer (wholesalers, retailers, or mixed) or role in providing the services (issuing or receiving).\u003c/p\u003e \u003cp\u003eThese classifications make it possible to differentiate travel agencies from the point of view of the market they serve. However, they are not as useful when seeking to create a group, not as service provider agencies, but as customers in an interorganisational relationship. In this context, our work has focused on deepening the relationship between travel agencies and their main accommodation provider and a segmentation has been proposed based on both relational and technological bases. The choice of these two types of criteria has made it possible to identify four large segments that are mostly related to an accommodation provider profile and a travel agency type. In view of these results, it is concluded that these bases constitute segmentation criteria for the tourism B2B market capable of clearly differentiating travel agencies.\u003c/p\u003e \u003cp\u003eFirstly, there is a clear grouping that discriminates between agencies according to the intensity of the relationship with their provider: segment 1 (relationship-oriented\u0026rsquo;) and segment 2 (\u0026lsquo;not relationship-oriented\u0026rsquo;). The common feature is that they are mainly retail travel agencies. The relationship-focused segment values commitment, trust, and the pursuit of relationship satisfaction and loyalty above all else. They adopt a strategic approach focused on maintaining satisfactory long-term relationships. However, the segment that is not focused on the relationship values convenience and standardisation of services over personalisation or differentiation. It is the largest group and the one that presents the greatest difficulties in developing committed relationships and trust, resulting in less satisfaction and loyalty.\u003c/p\u003e \u003cp\u003eSecondly, another clear grouping is observed that differentiates agencies based on technology: segment 3 (\u0026lsquo;ICT-oriented\u0026rsquo;) and segment 4 (\u0026lsquo;not ICT-oriented\u0026rsquo;). In this case, the link is its wholesale nature. ICT-oriented companies value ​​technology as a key tool that facilitates the management of interactions and the development of stable relationships. Meanwhile companies that are not focused on ICT do not value the investment and use of technology as a strategic factor in the development of relationships.\u003c/p\u003e \u003cp\u003eThis type of segmentation contributes to the advancement of research on segmentation in the tourism B2B market. These are bases that allow a better interpretation of the situation of travel agencies in terms of their relationships within the service supply channel. Just as retail agencies differ in terms of involvement in their provider relationship, wholesale agencies do so based on the technologies they use with their provider. Therefore, the relational criteria constitute useful segmentation bases to segment only the retail agency market, while the technological criteria are more capable of being used to segment the wholesale agency market.\u003c/p\u003e \u003cp\u003eFrom an academic point of view, the review of the literature showed a clear need to delve into segmentation criteria beyond those of a purely operational nature. Faced with this challenge, our research has confirmed that the variables linked to the relationship and technologies significantly improve the identification of segments at an industry level. In particular, in the tourism context, these variables have proven to have sufficient capacity to discriminate statistically heterogeneous groups of travel agencies.\u003c/p\u003e \u003cp\u003eThis segmentation has practical implications for managing relationships in the industry channel. The description of the segments allows a better understanding of the customer company, the provider company, the operating characteristics and, fundamentally, the type of relationship between the two. From the provider\u0026rsquo;s perspective, the companies that provide tourism services that identify segments based on their relationship with the customer agency and based on the ICT used will be able to select their target segment more accurately and to improve their strategic orientation, achieving a greater adjustment to the specific needs of their customers. From the perspective of the customer agency, these segmentation criteria could be used as key elements in the selection of service providers. If providers use these types of variables to choose a customer segment and adapt their strategies, it is reasonable that customers consider the same variables to evaluate and choose their providers. Consequently, this type of segmentation should include a dual provider-customer approach and may be useful not only for the selection of customer segment(s) but also for the selection of providers.\u003c/p\u003e"},{"header":"6. Future lines","content":"\u003cp\u003eResearch on segmentation in the B2B market presents interesting challenges and opportunities. At a theoretical level, the incorporation of other relational bases of segmentation could help to deepen the discrimination of heterogeneous segments. Regarding the variables linked to the relationship, relational value and relational benefits are variables that are particularly prominent in the literature, but with little empirical evidence in the field of segmentation (e.g. Ruiz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fuentes et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Adding these variables as segmentation bases could improve the process of identifying tourism business segments. Regarding the variables related to the technologies, the capacity that each one of the technologies (for internal use vs. for external use) has could be addressed in regard to segment formation.\u003c/p\u003e \u003cp\u003eAt the methodological level, another alternative method of segmentation could be used, such as the latent segmentation methodology (Casabay\u0026oacute; et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which allows the size and structure of the segments to be estimated simultaneously. To improve the representativeness of the results, it is proposed to use larger and random samples. Finally, ICTs have not permitted the formation of retail travel agency segments, so the study of segmentation based on technological criteria is an interesting line of future research in the market for this type of agency. This work could also be extended to other tourism B2B contexts where relationships between companies are key in the service supply channel, such as the restaurant or cultural tourism sector.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eAcknowledgments and Funding:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been developed within the framework of the project Grant PID2020-112660RB-I00 funded by MCIN/AEI/10.13039/501100011033 and the consolidated research group AICO/2021/144/GVA funded by the Conselleria d\u0026rsquo;Innovacio, Universitats, Ciencia i Societat Digital of the Generalitat Valenciana (Reference no.: UV-INV-AE-1553911).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed equally\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDisclosure statement\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson, J. 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The behavioral consequences of service quality. \u003cem\u003eJournal of Marketing\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(2), 31\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/002224299606000203\u003c/span\u003e\u003cspan address=\"10.1177/002224299606000203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Segmentation, Business-to-Business, Satisfaction, Loyalty, Information and Communication Technologies, Tourism","lastPublishedDoi":"10.21203/rs.3.rs-3427750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3427750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study is to achieve a travel agencies segmentation based on both relational (trust, commitment, satisfaction, and loyalty) and technological (advancement and use of Information and Communication Technologies) criteria that improve the understanding of their strategic behaviours. The segmentation methodology uses a tandem approach: correspondence and hierarchical cluster analysis. From a sample of 256 travel agencies, four segments have been identified. Relational criteria have made it possible to segment only the retail agency market, while technological criteria have been shown to be more capable of segmenting the wholesale agency market. This work contributes to the advancement of the literature on business-to-business segmentation in tourism by providing a more complete vision of the segmentation of companies. From a practical approach, it allows a better knowledge of the agency segments, so it could be used for the selection not only of providers (customer perspective) but also of target segments (service provider perspective).\u003c/p\u003e","manuscriptTitle":"Segmenting tourism companies with relational and technological bases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-07 17:45:59","doi":"10.21203/rs.3.rs-3427750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f18e87e1-4ce5-401c-bfee-a2610a378e58","owner":[],"postedDate":"November 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-19T10:57:15+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-07 17:45:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3427750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3427750","identity":"rs-3427750","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}