Satisfaction and performance expectations for the adoption of the metaverse in tourism SMEs

Ledesma-Chaves, Pablo; Gil-Cordero, Eloy; Navarro García, Antonio; MaldonadoLópez, Belén Article Satisfaction and performance expectations for the adoption of the metaverse in tourism SMEs Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Ledesma-Chaves, Pablo; Gil-Cordero, Eloy; Navarro García, Antonio; MaldonadoLópez, Belén (2024) : Satisfaction and performance expectations for the adoption of the metaverse in tourism SMEs, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 9, Iss. 3, pp. 1-14, https://doi.org/10.1016/j.jik.2024.100535 This Version is available at: https://hdl.handle.net/10419/327438 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Satisfaction and performance expectations for the adoption of the metaverse in tourism SMEs Pablo Ledesma-Chaves, Eloy Gil-Cordero, Antonio Navarro-García*, Bel en Maldonado-L opez Departamento de Administraci on de Empresas y Marketing, Universidad de Sevilla, Sevilla, Espa~ na ARTICLE INFO Article History: Received 26 February 2024 Accepted 31 July 2024 Available online 7 August 2024 ABSTRACT The last decade has seen major transformations in the tourism sector, influenced by changes in consumer behaviour and the integration of technology. The Metaverse has emerged as a transformative force but with certain limitations. In this sense, the literature highlights the need to address research gaps in terms of the levels of acceptance of this new technology and with regard to the challenges faced by small and mediumsized enterprises (SMEs) in the tourism sector. Thus, the main objective of this paper is to assess the adoption intention of high technology in tourism by small and medium-sized enterprises, focusing on the key role of the Metaverse as a new paradigm. For data collection, a total of 172 small and medium-sized enterprises have been surveyed using a double methodology, PLS-SEM and QCA, for the analysis. The results show that small and medium-sized tourism enterprises intend to adopt technologies associated with the Metaverse for the concept of Business Satisfaction rather than for the possible performance it may represent. These results represent an important advance in the understanding of the integration of Metaverse technologies in the tourism sector in this type of companies, where it can help to adopt diverse strategies in the business environment without having an excessive Resistance to Change, helping to be able to face it in conjunction with other capabilities. © 2024 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Keywords: Small and medium-sized companies Tourism Metaverse Intention to use JEL Classification: O00 Economic development Innovation Technological Change and Growth O32 Management of technological innovation and R&D O39 Technological change Other Introduction In recent years, substantial transformations have been implemented in traditional sectors such as tourism (Karaca & Baran, 2024), these developments are not only due to the change in consumer behavior when it comes to choosing destinations (Khan et al., 2024), and generational changes (Morrone et al., 2024), but the latter have been a complement so that the technology can be established and sustainable over time in this sector (Gonz ales-Santiago et al., 2024). In this sense, and over time, we have witnessed an exponential growth in the adoption of high technology (de Lurdes Calisto & Sarkar, 2024), where the concept of the Metaverse has been key to establish a new paradigm whereby users have the opportunity to immerse themselves in an alternative virtual reality, taking advantage of a virtual environment without having to leave their own homes (Ghali et al., 2024). The Metaverse is emerging as a transformative agent in the tourism industry, altering the way in which customers make decisions regarding the choice of accommodations and destinations to visit (Karaca & Baran, 2024). It is anticipated that this technology will have a revolutionary impact on the management and marketing of tourism companies, influencing all phases of the trip, from pre-planning, through the experience itself, to the end of the trip (Buhalis et al., 2023). However, all this technology comes at a cost of implementation, promotion and structural changes (Rohit et al., 2024). It is undeniable that large companies are investing in this concept (Hadi et al., 2024) and, therefore, the scientific community tries to explain their evolution when they incorporate the Metaverse into their operations (Bao et al., 2024;Kang & Ki, 2024), but there is no denying that the Metaverse has certain limitations (Mkedder & Das, 2024). Among the limitations posed by the adoption of the Metaverse is the issue of how these innovations will be adopted by small and medium-sized enterprises. There is a notable increase in public investment for the adoption and integration of companies into digital technologies within the EU framework, especially in the regulation of Metaverse systems. However, there is a gap in research on how to develop policies related to the operability of SMEs in the adoption of immersive * Corresponding author. E-mail address: [email protected] (A. Navarro-García). https://doi.org/10.1016/j.jik.2024.100535 2444-569X/© 2024 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Journal of Innovation & Knowledge 9 (2024) 100535 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge technologies. This research aims to fill the gap concerning the reality of tourism SMEs and their considerations regarding their resources and capabilities. Thus, there are several publications of a conceptual nature, highlighting the importance of the Metaverse in the tourism industry (Buhalis et al., 2023;Gursoy et al., 2022) that invite to cover, among other aspects, the research gap in relation to the level of acceptance and the challenges in the adoption of the Metaverse in the tourism industry. This is why, in the literature, we find works such as that of Gil-Cordero et al. (2023) where the intention to use the Metaverse by small and medium-sized companies is examined. But it would be interesting and significant to extend this study to find out whether small and medium-sized companies in the tourism sector are willing to use this new technology, despite the structural changes and economic challenges that it entails for these types of organizations with limited resources (Urbano et al., 2024). Therefore, the main objective of this manuscript is to evaluate the inclination of small and medium-sized enterprises toward embracing advanced technology within the realm of the tourism industry, focusing on the key role of the Metaverse as a new paradigm. In order to establish the framework of the research, small and medium-sized companies working and operating in the tourism sector in the territory of one of the main tourist powers - Spainhave been asked questions (M as-Ferrando et al., 2024;Rodríguez-V azquez et al., 2023), taking into account key constructs such as Business Satisfaction (BS), Performance Expectancy (PE) and Behavioral Intention (BI). In the tourism sector, the adoption of virtual reality technologies can ensure successful tourism offerings (Yung & Khoo-Lattimore, 2019) and the recent literature has linked Business Satisfaction with the adoption of technology, such as Metaverse systems, in the field of tourism (Flavi an et al., 2021;Marasco et al., 2018;Wei, 2019).In this sense, the analysis of behavior linked to technology adoption highlights the crucial importance of Business Satisfaction (Almaiah et al., 2022; Kosiba et al., 2022). This factor is considered one of the most influential in understanding the willingness of companies to use a specific technology (Atiyah et al., 2023;V arzaru et al., 2021). Perceived Compatibility plays a significant role in Performance Expectancy, underscoring the relevance for companies to assess the fit of Metaverse technology with their SME structures, given the limited resources available to these types of companies (Gil-Cordero et al., 2023). In this vein, and taking into account the adaptive capabilities of SMEs and the compatibility they may have with a small structure to adopt the Metaverse, the Performance Expectancy is a construct that should be taken into account, since it positively affects the intention to adopt Metaverse platforms where this fact has been verified in large companies (Lee et al., 2023). Finally, considering the difficulty of gauging tangible actions in the realm of advancing technologies, it becomes imperative to rely on a construct linked to Behavioral Intention as a signifier and indicator of conduct in virtual settings (Aburbeian et al., 2022). To sum up, previous research emphasizes the close connection between the success of a technology and the willingness to adopt and employ it, as highlighted by Faqih (2022). Consequently, all the aforementioned factors will be considered in evaluating the inclination of small and medium-sized enterprises in the tourism sector to engage with the Metaverse. The results of this research show that tourism companies intend to adopt technologies associated with the Metaverse through the concept of Business Satisfaction rather than for the possible performance it may represent. This variable influences both directly and through the expectation of performance to create the intention in tourism companies, explaining a high percentage of their behavior, and demonstrating a strong combinative capacity with other variables. Perceived Usefulness appears in all the proposed solutions, as well as Resistance to Change, although the latter is not a direct influence. The introduction of a new technology in the tourism sector could lead one to think that the Performance Expectancy could be significant. Yet this is not the case, so companies in the sector seek to feel comfortable and integrate processes that may be aligned with their practices and therefore allow a correct development. For the development of the study we will follow the following structure: after this introduction we will present the related hypotheses and the proposed model, in the third section we will explain how the data collection was carried out, in the fourth section we will analyze the data and results and verify the hypotheses previously presented, and in the fifth section we will set out the discussion, ending with the conclusions drawn from the research. Literature review The Metaverse is defined as the new era of the Internet, which utilizes virtual reality devices, interactive avatars, augmented reality technology and blockchain technology within a three-dimensional virtual environment (Dwivedi et al., 2022). The influence of immersive technology has become increasingly evident in various social spheres, at the same time that the tourism industry has turned its attention to Metaverse systems (de Lurdes Calisto & Sarkar, 2024; Gegung, 2023;Lim et al., 2024). However, although these platforms have been available since the early to mid-2000s with a large number of users worldwide, in the context of the Metaverse they are limited in their independence and functionality (Comprehensive Technological Research, 2022). The constant presence of technological innovations in the tourism industry is essential, as they are required to adjust to the changing demands of users and provide them with high levels of satisfaction (S anchez Calero et al.,2021), in addition to offering the possibility of modifying the business models of tourism companies (Tajeddini et al., 2017). Therefore, despite the challenges presented by the technological adoption of the Metaverse, businesses continue to explore the opportunities it presents to create tourism experiences that overcome physical constraints (G€ ossling & Schweiggart, 2022). Within the travel industry, the Metaverse opens doors to improve travel planning and advance interaction with tourism experiences. Virtual experiences in this sector are gaining popularity as a growing trend (Gursoy et al., 2022). According to Buhalis et al. (2023) the Metaverse has the potential to radically transform the tourism industry and it is anticipated that, rather than supplanting it, a virtual exploration of tourist destinations will motivate people to take real trips (Gursoy et al., 2022;Rauschnabel, 2022) and even encourage entirely new travel experiences (Go & Kang, 2022). It is therefore necessary to carefully examine the willingness of SMEs tourism enterprises to adopt Metaverse systems so that companies can adjust their service offerings effectively (Yoo et al., 2023). In the tourism sector, the Metaverse is a technology that can offer highvalue tourism proposals (Yung & Khoo-Lattimore, 2019). Previous researchers have highlighted the importance of examining the uptake of immersive technology in the tourism sector (Flavi an et al., 2021;Marasco et al., 2018;Wei, 2019), as although the integration of technologies such as the Metaverse into the tourism industry can offer significant advantages (Maythu et al., 2024), companies face difficulties in making decisions on the adoption of innovations or new technologies. This is because of the need to consider various factors that may influence the decision (Yang & Wang, 2023). From the approach to the adoption of the technology necessary for the implementation of Metaverse systems in tourism companies, Business Satisfaction (BS) (Mazzarolo et al., 2021;Tawafak, et al., 2021;Wu et al., 2014) and Performance Expectancy (PE) (Collado & Evans, 2019) are key constructs for analyzing the Behavioral Intention of companies (BI) (Soliman et al., 2019;V arzaru et al., 2021). In addition, the Ease of Use of technology (EU) (Yilmaz, 2016) and Perceived Usefulness (PU) (Abdullah et al., 2016) are crucial aspects that indicate the simplicity with which a company can incorporate new technology and the extent to which it is anticipated to improve its performance (Grover et al., 2019). In line with the ideas of Yilmaz (2016), and in the context of the focus on technology adoption crucial P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 2 to Metaverse systems, it is highlighted that small and medium-sized tourism businesses are influenced in their behavior by the ease with which they can use Metaverse-related technology. Another important factor for technology adoption is Resistance to Change (RC) in that it has been inversely related to Business Satisfaction in studies such as that of Lyu et al., (2023). The notion of Resistance to Change can obstruct the overall process of change within companies, delaying the start of the change process and impeding its implementation or raising its cost in relation to technology adoption as will be developed below. Last but not least, factors such as Perceived Compatiblity (PC) (AlSharafiet al., 2023), Social Influence (SI) (Gao et al., 2015;Pelegrin-Borondo et al., 2017) and Effort Expectancy (EE) (Venkatesh et al., 2003)are constructs identified as significant in the literature for the adoption of a new technology within small and medium-sized companies in the tourism sector in terms of their Performance Expectancy (Al-Sharafiet al., 2023;Bretos et al., 2023;Cheng et al., 2022). Performance Expectancy refers to the perception that a certain behavior will lead to the achievement of specificoutcomes(Ojiaku et al., 2024), which will influence the likelihood that a company will allocate its resources and strive to reach that particular goal (Diana et al., 2024). Both, Performance Expectancy and Business Satisfaction are important variables for understanding the Behavioral Intentions of tourism SMEs in relation to technology adoption. To recapitulate, given the importance of the analysis of Metaverse technologies and the small and medium-sized tourism enterprises (Buhalis et al., 2022,2023) the objective of this research is to enhance the comprehension of the factors implicated in the process of adoption of the Metaverse by small and medium enterprises in the tourism sector, for which we will develop the theoretical model as follows. Business satisfaction In a setting where the adoption of new technologies is embraced, the pivotal concept influencing Business Satisfaction is the Perceived Usefulness and Ease of Use (Tawafak et al., 2018). In this scenario, Wu et al. (2014) propose a definition for Business Satisfaction, describing it as a comprehensive assessment undertaken by a company during the implementation of a new technological system. The primary focus of Business Satisfaction is to address the question of how content companies are with the procedure of integrating a new Metaverse technology (Almaiah et al., 2022;Kosiba et al., 2022), as it is considered to be one of the most influential factors explaining the willingness of companies to use a given technology (Atiyah et al., 2023;V arzaru et al., 2021). In this regard, the Technology Acceptance Model (TAM) developed by Davis, Bagozzi and Warshaw in 1989 (Davis et al., 1989) is a theory of information systems that explores how companies adopt and use new technologies. According to TAM, the acceptance and use of a technology is determined by several factors. Among these factors is Perceived Usefulness. This factor refers to the degree to which the company believes that the use of a particular technology will improve business performance (Abdullah et al., 2016). That is, it is the company’s perception of the benefits it will derive from using the technology. Perceived Usefulness is a key antecedent of Business Satisfaction, since if a company perceives a technology to be useful, it is more likely to generate Business Satisfaction (Rachmi et al., 2023). As to the adoption of the Metaverse, authors such as Al-Adwan et al., (2023) have linked Perceived Usefulness and Satisfaction, and, specifically in the tourism sector, recent researchers have measured Perceived Usefulness in the adoption of Metaverse systems (Corne et al., 2023;Jo, 2023;Wu & Yu, 2023) On the other hand, also among these factors is Perceived Ease of Use. This factor refers to the degree to which the company believes that using a particular technology will be easy and effortless (Venkatesh & Davis, 1996). If the company’s users perceive a technology to be easy to use, they are more likely to adopt it and be satisfied with it (Warsono et al., 2023). Within the technology adoption framework, Perceived Ease of Use emerges as a crucial element reflecting the simplicity with which firms can employ new technology and the extent to which they anticipate that technology will improve their performance (Grover et al., 2019). Immersive technologies are seen as persuasive tools capable of influencing behavior and Business Satisfaction (Suh & Prophet, 2018). In this sense, several studies have focused on analyzing how the satisfaction of business users can be modified by the immersive sensation of immersive technology (Chua et al., 2007;Georgiou & Kyza, 2017;Kourouthanassis et al., 2015) It is important to note that the TAM model has undergone updates and expansions over time, such as TAM 2 and the Unified Theory of Acceptance and Use of Technology (UTAUT). These updates have allowed the model to be adapted to different contexts, including the technological adoption of the Metaverse (Alkhwaldi, 2023), where additional factors such as Trust, Perceived Risk in the use of a system and Resistance to Change have been considered (Kumar et al., 2023; Pillai et al., 2023). The concept of Resistance to Change can hinder the overall change process within companies (Jang et al., 2023), slowing down the initiation of the change process and hindering its implementation or increasing its cost in relation to technology adoption (Gani et al., 2023). Resistance to Change has been studied in the tourism sector (Kim et al., 2023)asitisareflection of the values and behaviors that can affect companies’technology adoption (Ronaghi & Ronaghi, 2022) and because of the particularities of the tourism sector in relation to technological change (Yoo et al., 2023). Resistance to Change has been inversely related to Business Satisfaction in studies such as that of Lyu et al. (2023) in relation to the technological adoption processes and specifically in the adoption of Chatbot in the tourism sector (Calvaresi et al., 2021). Following the above, we establish the following hypotheses: H1: Perceived Usefulness directly and significantly affects the Business Satisfaction of tourism SMEs in relation to the adoption of Metaverse technologies. H2: Ease of Use directly and significantly affects the Business Satisfaction of tourism SMEs in relation to the adoption of Metaverse technologies. H3: Resistance to Change inversely and significantly affects the Business Satisfaction of tourism SMEs in relation to the adoption of Metaverse technologies. Performance expectancy Perceived Compatibility is the perception of companies that the way they operate and the adoption of a new technology will coincide (Wu & Yu, 2023). Perceived Compatibility can have an interaction effect on performance expectancies (Wang et al., 2017), It is therefore important for companies to know if the Metaverse technology is compatible with their SME structures (Gil-Cordero et al., 2023). In the literature we find Compatibility as a significant construct for the adoption of a new technology within small and medium-sized enterprises (Al-Sharafiet al., 2023). At the same time, a significant absence is found in the literature in the study of the compatibility of Metaverse adoption in the tourism sector, taking into account Performance Expectancy, where Perceived Compatibility implies the company’s acceptance that the technology is compatible with its background (Akour et al., 2022). On the other hand, Effort Expectancy is defined as the degree of ease associated with the use of the system (Venkatesh et al., 2003). Several studies consistently show that Effort Expectancy is a predictor of intention to use the technology (Alwahaishi & Sn asel, 2013; P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 3 Kohnke et al., 2014). It is also a construct linked in the literature to the study of new technologies applied to tourism (Bretos et al., 2023). It has sometimes been associated with different models of acceptance, which have been discussed in the literature, where it is proposed that the study of the Metaverse must go beyond and disassociate from traditional models (Pal et al., 2023). For example, Kohnke et al. (2014) confirm that Effort Expectancy is an important predictor of the acceptance of emerging systems. On the other hand, and along the same lines Kaluarachchi (2023), shows that the Effort Expectancy and outcome have a significant positive relationship. With regard to Social Influence, this is defined in the literature by Venkatesh et al. as the influence that people who are important to a company have on the decision to use a new system (Venkatesh et al., 2003). Social Influence has been found to have a positive relationship with the intention to use a device (Gao et al., 2015;Pelegrin-Borondo et al., 2017). The literature has related the Performance Expectancy to the Social Influence on the adoption of new technologies (Cheng et al., 2022), but there is a lack of adoption of new technology in the tourism sector. Finally, Satisfaction and Performance Expectancy have been studied together in the literature. (Abd Aziz et al., 2023), with Performance Expectancy being a dominant variable in the adoption of new technology with satisfaction in mind, as noted in the TI literature (Amin et al., 2022). Therefore, we formulate the following hypotheses: H4: Perceived Compatibility directly and significantly affects the Performance Expectancy of tourism SMEs in relation to the adoption of Metaverse technologies. H5: Effort Expectancy directly and significantly affects the Performance Expectancy of tourism SMEs in relation to the adoption of Metaverse technologies. H6: Social Influence directly and significantly affects the Performance Expectancy of tourism SMEs in relation to the adoption of Metaverse technologies. H8: Performance Expectancy directly and significantly affects the Business Satisfaction of tourism SMEs in relation to the adoption of Metaverse technologies. Behavioral intention The need to develop an innovative research approach and paradigm for assessing the integration of the Metaverse into the business environment is gaining increasing importance in critical terms (GilCordero et al., 2023;Han et al., 2022;Suh & Prophet, 2018). The concept of Behavioral Intention pertains to a company’s readiness, demonstrated by its exertion and deeds, to implement a particular course of action. (Bajunaied et al., 2023;Mukherjee et al., 2023). Earlier studies have emphasized that the success of a technology is intricately connected to the inclination or willingness to utilize it, as underscored by Faqih (2022). Existing research suggests a robust correlation between the intention to act and the subsequent behavior (Gansser & Reich, 2021;Moriuchi, 2021). Moreover, it highlights the association of the Behavioral Intention with the effective growth and advancement of a novel technology within the business landscape (Gunawan & Gunawan, 2019). Research, such as that of S anchez Calero et al. (2021), argues that the continuous integration of technological innovations is fundamental in the tourism industry. These innovations are considered indispensable to adjust to the changing demands of users in the business and to guarantee them a high level of satisfaction during their experience (Buhalis et al., 2023). In the tourism sector Performance Expectancy has been related to Behavioral Intention, considering the value placed on virtual reality applications (Yoon et al., 2021), as well as for the adoption of new technologies by companies (Hermawati et al., 2023;Tajeddini et al., 2017). In addition, Business Satisfaction is a pivotal factor in the examination of behavior concerning technology adoption, as it stands out as one of the most influential factors clarifying the inclination to utilize a particular technology (Almaiah et al., 2022;Atiyah et al., 2023; Kosiba et al., 2022). It is postulated that the evaluation of success in the incorporation of new technologies by companies should be based on the synergy between the implementation of human, technological and strategic resources to speed up improvement processes and boost profitability margins. Earlier research has identified a correlation between satisfaction and the inclination to adopt new technologies, particularly when there is awareness of their existence (Mitroulis & Kitsios, 2019) or by virtue of prior encounters with technology. This subject of investigation has gained insights and familiarity (Cimbaljevi c et al., 2023;Pappas et al., 2021). Within the domain of virtual reality, the exploration and cultivation of skills for engaging with the environment manifest themselves through interactive functionalities, providing companies with the means to investigate and exert an influence on the constructed surroundings (Sutcliffe, 2016). Business Satisfaction therefore relates to the extent of alignment between anticipations and the ultimate perception of the benefits disclosed following the adoption of the Metaverse (Mazzarolo et al., 2021). Within the tourism sector, ongoing surveillance of Business Satisfaction emerges as a pivotal factor for success in a fiercely competitive market. This surveillance mirrors the capacity to fulfill the expectations, wishes, and objectives initially established by the company members concerning the original perception of the provided tourism service (El Archi & Benbba, 2023;Ezzaouia & Bulchand-Gidumal, 2023). Building upon the aforementioned points, the following hypotheses are posited: H7: Performance Expectancy directly and significantly affects the Behavioral Intention of tourism SMEs to adopt Metaverse technologies. H9: Business Satisfaction directly and significantly affects the Behavioral Intention of tourism SMEs to adopt Metaverse technologies. The model proposed for this research is presented in Fig. 1: Methodology Sample and data collection For the elaboration of the research, the present work developed a questionnaire and applied an empirical study using Spanish tourism companies according to their categorization by the National Institute of Statistics (INE), and through the National Classification of Economic Activities (CNAE), specifically the companies associated with group 79 (Tourism activities), carrying out a representative sample of these. The list of extracted companies was of 409 companies, which were previously contacted through their CEOs or development managers to determine their knowledge about the Metaverse, and the options that this technology can offer in the tourism field. For convenience, a type of sample selection is carried out that does not follow a probabilistic pattern. This way of choosing implies that each of the elements in the population does not have the same opportunity to be chosen and depends entirely on the researcher’s criteria. Its use is very common in disciplines such as social sciences and business, given that the diversity in the possible characteristics of the elements studied requires establishing guidelines for the research (Liao et al., 2019). In our case, the choice of this non-probabilistic modality is based on the need for companies to understand the Metaverse concept and their favorable disposition toward its use (Gil-Cordero et al., 2023), Therefore, the interviewees were contacted beforehand to learn about their knowledge of Metaverse technologies and their P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 4 willingness to participate in the study, prior to sending the questionnaire. The questionnaires were then sent via a specific platform for self-administration. The research was done between October and December 2023. Finally, the valid questionnaires received amounted to 172, rejecting those that were incomplete or did not have adequate knowledge of Metaverse technologies. This represents a response rate of 42.05 %. The composition of the sample of companies can be seen in Table 1. In order to analyze the perspective of Metaverse development in tourism companies and their conception of it, the research analyzes both the presence of a digital and a marketing department in the analyzed tourism companies. The data show that 56.1 % and 40.2 %, respectively, have a marketing and digital department. In addition, within the non-probabilistic methodology and to ensure territorial representativeness and the generalization of the results, the sample is composed of companies representative of the main Spanish tourist areas. Measurement of variables The application of the scales on companies means that in the present study we have selected those scales already tested on companies, and derived from previous studies on technological behavior and adoption. Thus, the Perceived Usefulness and Ease of Use scales are adapted from Davis (1989). Resistance to Change is analyzed by means of the scale of Alaiad et al. (2019). For the variables Effort Expectancy, Performance Expectancy and Social Influence, adaptations of the scales of Venkatesh et al. (2003) are used, and Perceived Compatibility is extracted from Moore and Benbasat (1996). Behavioral Intention is an adaptation of Taylor and Todd (1995). Business Satisfaction is a construct previously used in studies on the adoption of Metaverse technologies (Gil-Cordero et al., 2023), and is adapted from the work of DeLone and McLean (2016). A seven-point Likert scale was used for all the variables, with 1 being total disagreement and 7 total agreement. The questions asked in the different constructs can be seen in Table 2. Linear and non-linear approach Regarding the tools employed, this study merges both linear and nonlinear strategies for two fundamental reasons. For the evaluation of the hypothesized relationships, we used Smart-PLS 4 (Ringle et al., 2022). The explanatory power of PLS-SEM has been demonstrated in numerous studies in various disciplines (Henseler, 2018). In the first Fig. 1. Proposed model Table 1 Sample characteristics Departments Presence of Digital Department 40.2 % Presence of Marketing Department 56.1 % Territorial representation Rural and inland tourism 16.4 % Northern beaches 17.9 % Levante beaches 21.5 % Southern beaches 23.8 % Island companies 15.6 % Other tourism companies 4.8 % P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 5 instance, business decisions often exhibit variability and are highly conditioned by external and internal elements, especially in decisions related to unknown issues (Gil-Cordero et al., 2023). Second, in line with the above, this study addresses an issue with high causal complexity and prominent external social elements, which makes the combined use of both methodological strategies more likely to capture the reality of the business decision-making process (GilCordero et al., 2023). Therefore, a PLS-SEM analysis approach is initially employed (Hair et al., 2019). This method will allow us to evaluate the significance of the hypotheses formulated, and, therefore, the direct relationships between the variables. However, business behavior is determined by combinations of these variables, which may even vary according to internal and external circumstances, approaching a situation of causal complexity. Subsequently, the fsQCA methodology is applied (Fiss, 2011), that makes it possible to identify all the combinations of factors that can lead to the desired outcome (Beynon et al., 2016), in this case, Behavioral Intention. The combination of both methodologies is mainly oriented to respond to the theoretical approach of complexity, which suggests that in complex situations such as business behavior, there may be multiple non-obvious causes (Schneider, 2018). Therefore, the present research contemplates the use of both techniques and the support of their results since, being an emerging field of research, both the dominant and secondary constructs and the relationships between them are not yet defined. As a result the approaches adopted will allow us to carry out a more complete analysis of the results as well as to propose better relationships for future models. In addition, an initial set of solutions is obtained, supported both by the statistical power of the hypotheses and by their presence in the different sample elements, which delimits the conceptual framework to be developed. Results PLS Measurement model In relation to data processing, we have used the Smart-PLS 4 program (Ringle et al., 2022). Prior to this, a preliminary assessment of the reliability and validity of the measurement scales was carried out, followed by the analysis of the structural model (Chin & Dibbern, 2010). This method has received extensive validation in various academic works and has particular significance in the realm of social investigations involving limited sample sizes (Chin, 1998). On relation to the reliability of the variables, the study follows the approach proposed by Roldan and Cepeda (2017), examining the individual loadings and Cronbach’s Alpha coefficient. Specifically, individual loadings are required to be greater than 0.7 and the Alpha coefficient needs to exceed 0.8 (Ringle et al., 2015). Both standards are met as detailed in Table 2. However, due to the limitations of Cronbach’s Alpha coefficient, the composite validity indicators are also evaluated, which as well exceed the acceptable values of 0.8 established (Sarstedt et al., 2017). In addition, the average variance extracted (AVE) is presented to assess convergent validity, which surpasses the threshold of 0.5, considered appropriate (Ringle et al., 2015). Discriminant validity assessment examines how distinctive a specific construct is in comparison to other constructs. Discriminant validity has been examined using the Heterotrait-Monotrait Ratio Table 2 Scales and constructs Construct/Dimension/Indicator Factor Loading Cronbachs Alpha Composite Reliability Average variance extracted (AVE) Perceived Usefulness (PU) (Davis, 1989)0.954 0.967 0.879 Using the Metaverse will improve my business development performance 0.952 Using the Metaverse in my activities will improve my business productivity 0.949 Using the Metaverse will enhance the effectiveness of my business activity 0.929 I believe the Metaverse possesses utility for my company and its growth. 0.920 Ease of Use (EU) (Davis, 1989)0.951 0.964 0.871 Learning how to use the Metaverse will be easy for my company 0.928 In general, I believe that the Metaverse will be easy to use 0.931 Interaction with the Metaverse will be flexible for my company 0.922 Interacting with the Metaverse will be easy for my company 0.953 Behavioral Intention (BI) (Taylor & Todd, 1995)0.949 0.975 0.951 I plan to leverage the Metaverse for the advancement of my business operations 0.975 My aim is to increasingly conduct a substantial portion of my business activities within the Metaverse. 0.976 Perceived Compatibiity (PC) (Moore & Benbasat, 1996)0.951 0.968 0.910 Using the Metaverse in my company would be compatible with my business model 0.939 Using the Metaverse to promote or advertise my company would be consistent with my company’s values 0.967 Using the Metaverse in my company would fit with the values of my company 0.955 Resistance to Change (RC) (Alaiad et al., 2019)0.952 0.942 0.952 I wouldn’t want the Metaverse to change the way my business is conducted 0.937 I do not want the Metaverse to change my company’s interaction with the consumer 0.969 By assuming changes in the business methodology of my business that the Metaverse entails, I would find it easy to adapt to it. 0.901 Effort Expectancy (EE) (Venkatesh et al., 2003) 0.811 0.886 0.721 The application of the Metaverse would be transparent and easily comprehensible for my business 0.906 I anticipate no challenges in implementing the Metaverse within my company 0.783 I perceive that the Metaverse could bring valuable benefits to my business. 0.855 Performance Expectancy (PE) (Venkatesh et al., 2003) 0.736 0.850 0.663 Leveraging the Metaverse would enable me to expedite inquiries or business transactions 0.901 Integrating the Metaverse would enhance the quality of the products or services provided by my company 0.913 My company would have the capability to access the Metaverse from any device. 0.985 Social Influence (SI) (Venkatesh et al., 2003) 0.949 0.975 0.951 It seems that if my company does not manage the Metaverse, it is outdated 0.855 My consumers think I should use the Metaverse 0.911 Business Satisfaction (BS) (DeLone & McLean, 2016)0.936 0.959 0.888 I hold the view that incorporating the Metaverse into the business environment is valuable 0.894 I am convinced that integrating the Metaverse could yield numerous benefits for my company 0.970 I believe it would be worthwhile to incorporate the Metaverse into my business activities. 0.961 P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 6 (HTMT) (Sarstedt et al., 2017). It has been verified that in all instances, the values are below the recommended threshold of 0.9, and even below 0.85, using a more cautious approach (Ringle et al., 2022) (see Table 3). This discriminant validity analysis indicates that constructs that theoretically should not be related do not, in fact, show a significant connection (Roldan & Cepeda, 2017). Since the reflective measurement model has demonstrated both reliability and validity, we proceed with the evaluation of the structural model. Structural model The model is evaluated through the path coefficients of the endogenous variables, as well as through the explained variance (R 2 ). For the first step, the use of bootstrapping (5,000 resamples) generates standard errors and t-statistics to assess the statistical significance of the paths and their significance (Henseler, 2018). The results and significance of the direct effects examined are presented in Table 4. Of the nine expected connections, six are supported while three are not confirmed. The observation in Table 4 regarding the hypothesis contrasts offers us some interesting results. Companies’satisfaction in using the Metaverse is similarly determined by Perceived Usefulness (tvalue 4.265) as well as Ease of Use (t-value 4.009). However, when faced with a novel technology, Resistance to Change does not appear as a determinant. With respect to Performance Expectancy, another determinant in the business world, Effort Expectancy, is the most important determinant (t-value 6.093), while Perceived Compatibility, although important, has less impact (t-value 3.224). Social Influence is not significant. We can observe these results in Table 4 and Fig. 2. Satisfaction does determine the intention to use Metaverse technologies by tourism companies (t-value 6.614), something that does not occur with Performance Expectancy. However, Performance Expectancy has a significant impact on the satisfaction of tourism businesses (t-value 2.733). Nevertheless, it is important to note that Satisfaction also plays an important role in boosting Performance Expectancy, as indirect effects indicate the significance of the relationship (t-value 2.919). With respect to the values of the explained variance (R 2 ), these can be seen in Table 5, where we can observe an explanatory level above 50 % for the final variable (53.4 %), which is high for a parsimonious model with few antecedent variables (Sarstedt et al., 2020). The explanatory results for the Performance Expectancy and Satisfaction variables also show high levels, despite having only three antecedents each. fsQCA The application of the fsQCA approach followed the three fundamental stages outlined by Ragin (2008): data calibration, truth table construction and logical minimization. Calibration assigns membership values to the phenomenon, shifting from a quantitative to a qualitative approach, supported by previous studies (Greckhamer et al., 2018). Ensuring precise calibration is pivotal in fsQCA to prevent inaccuracies that could compromise the validity and reliability of the outcomes. Fuzzy sets present benefits by utilizing calibration instead of measurement, assigning membership values within the range of 0 to 1 to distinct data categories (conditions and results). The precision is influenced by the selection of the calibration function and the threshold cutoffs that establish the degree of membership (Beynon et al., 2016). The algorithm provides the main statistical variables of the antecedents, as shown in Table 6. Data calibration involves modifying the original 7-point Likert scale to create a suitable data set. This process consists of two steps: 1) calculate the mean for each construct using the responses and, 2) modify the obtained data according to the percentile of the average score for each construct. (Ragin & Strand, 2008). The outcomes of calibration are contingent upon the chosen calibration type and the selected cut-off points. In this case, we opted for the 90th, 50th and 10th percentiles, following the methodology proposed by Kraus et al. (2018).Table 6 displays the statistical characteristics of the outcomes and the preceding conditions, while Table 7 details the necessary conditions. Following the criteria of Fiss (2011), we can determine those conditions that are necessary if the consistency exceeds the threshold of 0.9. Looking at Table 7, Satisfaction (0.9435) and Perceived Compatibility (0.9343) are considered as such and will be borne in mind later in the construction of the truth table (Pappas & Woodside, 2021). This is the next step and comprises the set of solutions that can be obtained under the possible combinations of antecedents. Through a logical minimization of combination numbers, the outcomes become mutually independent. (Fiss, 2011). The obtained truth table (Table 8) contains the main parameters that the algorithm requires for its validity, offering three solutions for the presence of Intention to Use. Perceived Usefulness and Perceived Compatibility are present in all three solutions, thus indicating their importance in the analysis. The consistency levels of the solutions are above 0.8. Furthermore, in a more restrictive criterion, those Table 3 Heterotrait-monotrait radio (HTMT) Constructs PC EE PE EU SI BI RC BI PI PC EE 0.797 PE 0.817 0.749 EU 0.662 0.797 0.683 SI 0.547 0.646 0.749 0.340 BI 0.815 0.678 0.672 0.626 0.563 RC 0.407 0.800 0.654 0.677 0.325 0.275 BI 0.817 0.814 0.832 0.554 0.516 0.773 0.352 PU 0.836 0.762 0.817 0.597 0.603 0.719 0.357 0.812 Table 4 Hypothesis testing He puesto puntos decimales:- Effects of endogenous variables Path coefficient Confidence intervals (95 %) t-value Significance of the effect (P-value) 5 % CIhi 95 % Cihi PU −>BS (H1) 0.481 0.275 0.720 4.265 Sí (0.000***) EU −>BS (H2) 0.393 0.177 0.404 4.009 Sí (0.000***) RC −>BS (H3) -0.079 -0.265 0.185 0.679 No (0.497) PC −>PE (H4) 0.468 0.236 0.560 3.224 sí (0.001**) EE −>PE (H5) 0.631 0.440 0.843 6.093 Sí (0.000***) SI −>PE (H6) 0.149 -0.021 0.307 1.803 No (0.071) PE −>BI (H7) 0.121 -0.064 0.343 1.173 No (0.241) PE −>BS (H8) 0.372 0.071 0.612 2.733 Sí (0.006**) BS −>BI (H9) 0.646 0.433 0.816 6.614 Sí (0.000***) *** p<0.001, ** p<0.01, * p<0.05, ns: not significant (based on t(4999), two-tailed test). P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 7 solutions whose unique coverage was above 1 % were selected (Pappas & Woodside, 2021). Overall solution coverage is at the recommended 80 % (Greckhamer et al., 2013), and the consistency is found to be 0.9622, well above the minimum of 0.75 indicated in the literature (Rihoux & Ragin, 2008), thus providing good parameters. In the present investigation, absence refers to a condition that is irrelevant to the proposed solution. As indicated by Pappas and Woodside (2021), absence and negation have been used interchangeably in the research, so that a negation also refers to the absence of the condition. Discussion The results obtained allow us to achieve the objectives of the study with a high level of success. However, we will now analyze the different constructs and the relationships obtained. Regarding the relationship between Perceived Usefulness and Satisfaction (H1), the results show it to be significant. This result coincides with the research of Andarwati et al. (2019) regarding SMEs’adoption of mobile technology, indicating that the results were due to the increased effectiveness of transactions that the new technology brought. Nirwanto and Andarwati (2019) also speak of the perception that tasks are completed quickly, effectively and with a high degree of ease, the relationship being also significant, as is the case in their study on the implementation of information systems in organizations. Selamat and Windasari (2021) as well highlight the positive orientation of the relationship, in a context of gradual adoption of virtual technology, such as chatbox. The presence of Perceived Usefulness in the three solutions proposed in the applied methodology speaks of the importance of the construct in the process of the identification by the companies of the technological functionality of the Metaverse, a view shared by previous studies such as that of Gil-Cordero et al. (2023). Perceived Usefulness is one of the antecedents, together with Resistance to Change, that appears as present in all solutions. In other words, Perceived Usefulness provides an enormous combinative capacity for the adoption of Metaverse technologies in tourism companies. Table 5 Variance explained R 2 Ajusted Performance Expectancy 0.714 Business Satisfacci on 0.644 Behavioral Intention 0.534 Table 6 Statistics Variable Mean Std. Dev. PU 0.4500000 0.2929580 EU 0.4498780 0.2823140 PC 0.4843902 0.2960310 RC 0.4709756 0.2482751 EE 0.4618293 0.2537145 PE 0.4647561 0.2670079 SI 0.3737805 0.2544753 BS 0.5026829 0.2895204 BI 0.4084146 0.2963332 Fig. 2. Proposed model with loadings and hypothesis testing *** p<0.001, ** p<0.01, * p<0.05, ns: not significant (based on t(4999), two-tailed test). P. Ledesma-Chaves, E. Gil-Cordero, A. Navarro-García et al. Journal of Innovation & Knowledge 9 (2024) 100535 8