This study investigates the motivational barriers influencing blockchain adoption across hospitality and tourism operations. Drawing on expectancy theory and extended with trust and perceived data protection, it explores how motivational asymmetries hinder the realisation of blockchain’s potential in Spain’s service ecosystem.
A mixed-method triangulation design was employed. Quantitative data from 173 stakeholders and service users were analysed using Chi-square and Mann–Whitney U tests, while qualitative data from open-ended responses were thematically coded with NVivo.
Results indicate that expectancy and instrumentality perceptions are high, but low valence, driven by limited trust and concerns over consumer data protection – impedes adoption.
The proposed Expectancy-Trust-Adoption Framework explains motivational imbalance across groups. The study focuses on Spain and uses cross-sectional data. The study provides actionable implications for tourism managers, policymakers and technology providers by highlighting the need for trust-building strategies, transparent data governance and targeted communication to enhance perceived value. Policymakers are encouraged to reinforce regulatory clarity to improve consumer confidence.
Tourism managers should better communicate blockchain’s consumer benefits, while policymakers must strengthen regulatory clarity (GDPR, MiCA). Joint industry initiatives and training can foster trust, interoperability, and inclusivity.
Blockchain adoption can enhance fairness by reducing fraud, safeguarding digital rights and building trust between destinations and visitors. However, digital divides highlight the need for literacy initiatives.
This study pioneers a motivational–trust approach to blockchain adoption, extending expectancy theory and offering actionable insights for advancing digital transformation in hospitality and tourism.
1. Introduction
Blockchain applications are transforming both hospitality and tourism operations, from hotel reservations and loyalty programs to tour bookings and supply chain traceability (Rashideh, 2020). With its decentralised, secure and fully transparent features, this technology holds the key to overcoming challenges in the tourism sector, notably issues of reliability, confidentiality, data protection and operational efficiency (Sigala, 2020). Spain’s tourism sector constitutes a key pillar of the national economy, generating an estimated 6.5% of GDP. Forecasts suggest growth of 3.6% in 2025, increasing its contribution to approximately 13.2% of GDP – two percentage points above 2024 levels. With regard to the evolution of the markets, domestic tourism is expected to grow slightly, from 0.8% in 2024 to 1.2% in 2025 (CaixaBank Research, 2025). International tourism will grow by 4.5%, consolidating Spain as one of the world’s leading tourist destinations (Instituto Nacional de Estadística INE, 2025).
According to the World Tourism Organisation (UNWTO), Spain is the second most visited country in the world and Costa del Sol in Andalusia is among the most attractive and economically competitive tourist destinations, welcoming upon millions of international tourists annually World Tourism Organization (UNWTO) (2022). Experts emphasise the crucial importance of sustainability, transparency in finance and digitalisation as pillars to maintain the tourism sector’s competitiveness in a changing global context marked by regulatory changes and the green transition driven by the European Union (Council of the European Union, 2022).
Spain’s hospitality and tourism ecosystem provides a suitable testbed due to its global leadership in digitalisation and service innovation. Blockchain is an emerging technology with the potential to overcome the main security concerns affecting tourism operations, such as “information systems fragmentation, lack of trust, poor reservations and inefficient payment processes, as well as data privacy issues” (Mountije et al., 2025). Blockchain’s transparent and decentralised infrastructure contributes to enhancing service reliability by enabling secure real-time transactions, smart contract-based bookings, digital identity verification and interoperable loyalty programs (Önder and Treiblmaier, 2018; Saberi et al., 2019).
Despite the growing body of research highlighting blockchain’s technical feasibility and operational benefits in hospitality and tourism, actual adoption across destinations and service ecosystems remains limited (Sigala, 2020; Treiblmaier, 2020). Existing studies predominantly focus on system-level attributes such as transparency, automation and efficiency. However, empirical evidence suggests that technological readiness alone does not translate into adoption behaviour (Casino et al., 2019; Tao et al., 2021). In data-intensive and trust-dependent tourism contexts, adoption decisions are shaped not only by perceived usefulness but also by motivational and psychological factors that influence whether stakeholders and service users perceive blockchain outcomes as valuable and desirable (Chapagain et al., 2022).
Prior adoption frameworks applied to tourism technologies, particularly the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), predominantly emphasise cognitive evaluations such as performance expectancy and ease of use. While these models explain intention formation under conditions of technological familiarity, they offer limited insight into adoption under uncertainty, especially for disruptive technologies like blockchain that directly affect data ownership, privacy, and governance structures. Research in hospitality technology adoption (e.g. Chen et al., 2020) shows that experiential evaluations and perceived risks shape tourists’ behavioural responses in service contexts, reinforcing the notion that adoption is influenced by subjective assessments of technology benefits and risks.
Expectancy theory (Vroom, 1964) offers a more suitable motivational lens for examining blockchain adoption in tourism because it conceptualises behaviour as a function of expectancy (belief that effort leads to performance), instrumentality (belief that performance leads to outcomes) and valence (the perceived desirability of those outcomes). However, prior tourism research applying expectancy theory has insufficiently examined why valence remains weak even when expectancy and instrumentality perceptions are high.
In data-sensitive service environments, this motivational imbalance is closely linked to concerns surrounding trust in digital systems and perceived protection of personal data. Accordingly, this study addresses a critical gap by extending expectancy theory through the integration of trust in blockchain systems and perceived consumer data protection as antecedents of valence. By doing so, it explains why positive expectations regarding blockchain’s technical performance do not necessarily result in adoption readiness within hospitality and tourism ecosystems and pursues the following key research questions:
How do tourism stakeholders and service users assess the challenges and opportunities of blockchain technology in the tourism sector?
What are the expectations of stakeholders and users, as framed by expectancy theory, affecting the willingness to embrace blockchain solutions in the tourism sector?
The study makes four distinct contributions to hospitality and tourism research. First, it advances theory by extending Vroom’s expectancy theory through the integration of trust and perceived data protection as motivational antecedents of valence, resulting in the proposed Expectancy-Trust-Adoption Framework. This extension addresses a critical limitation of dominant adoption models (e.g. TAM, UTAUT) by incorporating affective and trust-based mechanisms that are particularly salient in blockchain-enabled service environments. Second, the study contributes methodologically by employing a mixed-method triangulation design that integrates non-parametric quantitative analysis with NVivo-based thematic analysis of open-ended responses. This approach captures both measurable attitudinal differences and nuanced motivational concerns, offering a richer explanation of adoption barriers than single-method survey studies. Third, the research provides contextual contribution by empirically examining blockchain adoption within Spain, specifically the Costa del Sol, one of the world’s most mature and digitally innovative tourism destinations. By situating the analysis within a GDPR-regulated European context, the study responds to calls for geographically grounded adoption research. Finally, the study offers actionable managerial and policy implications by demonstrating that blockchain adoption in tourism depends not only on demonstrating operational efficiency but also on strengthening trust, transparency and data governance.
The remainder of the present article is structured as follows. Section 2 outlines the theoretical framework that underpins this work. Section 3 provides a descriptive overview of the research design, comprising data collection and analysis. Section 4 reports the findings of the quantitative and qualitative assessment. Section 5 gives the conclusion and discusses the findings in light of the body of literature and expectations theory and address practical implications, limitations and guidelines for future research.
2. Theoretical foundation
2.1 Blockchain technology overview
Blockchain technology, initially pioneered by Nakamoto (2008) to underpin Bitcoin, has evolved into a potentially transformative infrastructure offering applications that extend beyond crypto-currencies. This technology is a decentralised digital ledger and records transactions among several nodes in a secure, transparent method (Yli-Huumo et al., 2016). The chain stores each block with data, a chronological date stamp and a “cryptographic hash” of the preceding block, which guarantees that the information, once recorded, cannot be changed subsequently without the full agreement of the whole group. The transition of paradigms from Web 2.0 to Web 3.0 has amplified the significance of blockchain by providing peer-to-peer digital transactions and automated processes through the use of smart contracts (Zheng et al., 2018).
This technology has potential in a wide range of fields, including healthcare (Angraal et al., 2017), supply chain (Saberi et al., 2019), education (Chen et al., 2018) and, crucially, tourism (Tao et al., 2021). Mobile and cloud computing are key technologies for the tourism sector, and while the hospitality industry has traditionally been relatively lagging in the acceptance of disruptive technologies owing to its highly service-intensive feature and segmented infrastructure, blockchain delivers vital solutions to pressing challenges – including data breaches, information asymmetry and operational inefficiencies (Treiblmaier and Önder, 2019).
In 2020, Spain, with the support of the Spanish Association for Standardization (UNE) and the Alastria consortium, was the first country in the world to approve the “blockchain identity standard” (Blázquez, 2020b). In this spectrum, around 80 industry experts were involved (Blázquez, 2020a). Ursula Von der Leyen, the President of the European Commission, announced that the Commission “will shortly propose a secure European electronic identity” for “any public or private transaction anywhere in Europe” (Blázquez, 2020c). This development highlights blockchain’s potential as a robust solution for secure online data processing. In addition, in 2021, the European Standardization Organizations have adopted the Spanish standard with a view to making it European and this “is positioned to become the world standard”. The policymakers perceive the significance of this technology and the potential it brings “to lead the global industry” (Blázquez, 2021).
2.2 Blockchain relevance for tourism
Tourism is one of the world’s major actors in international trade and its turnover exceeds exports of oil, groceries as well as automobiles. In 2024, 93.8 million tourists visited Spain, representing an increase of 10.1% compared to the previous year, reaching a new historical record (INE, 2025). Tourism operations tend to be hindered by a number of stakeholders (e.g. travel agents, booking platforms and banks), each adding delays, costs and trust risks. With blockchain, direct, peer-to-peer financial transfers are checked and stored in an unbreakable ledger. It reduces the risk of duplicate bookings and fraud-issues that traditionally have affected online travel agencies (OTAs). Smart contracts, on the other hand, are self-enforcing deals coded to perform on predetermined conditions. Their application in tourism has shown promise for routinely automated reservations, cancellations and refunds – enhancing efficiency and reducing bureaucratic overview costs (Melkić and Čavlek, 2020).
Blockchain has the capability to supply secure, user-controlled digital identity systems that ensure confidentiality while simultaneously aiding rapid and auditable access across all customer service points (Duan et al., 2020). The Known Traveller Digital Identity (KTDI) initiative – a partnership between the World Economic Forum and a number of governments – seeks to utilise blockchain to both streamline border checks and boost data security (World Economic Forum, 2019). Using blockchain enhances transparency and traceability along this chain, verifying origins, sustainability and conformity (Kouhizadeh and Sarkis, 2018). With blockchain, it is possible to aggregate loyalty rewards into convenient tokenised assets that can be leveraged across different suppliers and online platforms, thereby leading to greater engagement and better customer retention (Jeong and Kim, 2019).
While customer feedbacks play a crucial role in tourism policy-making, they are often subject to misuse or corruption. Blockchain technology has the potential to maintain the integrity of feedback by connecting it to trusted digital identities and saving it in an unmodified ledger (Treiblmaier and Önder, 2019). Considering the delicate personal and financial details shared by passengers, hospitality organisations are confronted with growing cybersecurity challenges. In particular, blockchain provides decentralised data storage and cryptography, thereby minimising exposure to piracy and illegal access (Casino et al., 2019; Treiblmaier, 2020). Destination Management Organisations (DMOs) can benefit from the use of blockchain to collect real-time data and make better decisions. Through decentralised applications, rule-based blockchain allows real-time tracking of tourist flows, usage of infrastructure and patterns of consumption. Such data can be crucial for controlling tourist saturation, assigning resources and tailoring promotional activities (Rodríguez Bolívar et al., 2024a, b).
2.3 Barriers of blockchain adoption in tourism
Initial investigations into the blockchain in tourism sector were dedicated to its basic functionalities. For instance, Tyan et al. (2020) discussed the possible ways in which blockchain technology can improve the experience of “smart tourism destinations” by strengthening data security, transparency and operational efficiency. They highlighted the technology’s contribution to supporting the transparent flow of information across diverse stakeholders. Based on this conceptual foundation, Prados-Castillo et al. (2023) systematically reviewed the literature to evaluate the sustainability perspective on blockchain application in tourism sector. Their review outlined the blockchain’s ability to foster sustainable business practices through transparent supply chain and by boosting mutual trust among stakeholders. However, the review highlighted some challenges, including technological ramifications and legal issues that could hinder broad implementation of this technology.
In the Spanish scenario, Prados-Castillo et al. (2023) explored more closely the function of blockchain technology with regard to tourism platforms on a virtual peer-to-peer system. One of the highlights of this research is the way in which blockchain promises to deliver improved sustainability and customer satisfaction by creating secure online transactions and authentic feedback. The findings underlined the critical role of consumer trust and the necessity of regulatory structures to underpin the adoption of this technology in tourism services. Rodríguez Bolívar et al. (2024a, b) studied the public perceptions and barriers related to the acceptance of blockchain in the tourism sector, with a particular interest in confidentiality and security issues.
This investigation took another level with 135 industry stakeholders involved in their quantitative study across Europe; the findings showed that attitudes were divided. Although respondents acknowledged the promise of blockchain to deliver a better customer service experience, data privacy and security concerns were largely prevailed. A study by Nur Muharam et al. (2023) has developed a conceptual model to support the user acceptance of blockchain-based payment platforms.
By employing a grounded theory approach, the investigation determined the drivers impacting user acceptance, encompassing both perceived advantages, such as lower transaction fees and enhanced data reliability. While most prior studies examine blockchain’s technical feasibility in hospitality (Ivanov et al., 2020; Kizildag et al., 2020) or consumer perception in tourism (Sigala, 2020), limited attention has been given to motivational factors that jointly affect adoption across both sectors.
Despite these opportunities, actual blockchain adoption in hospitality and tourism remains limited. Prior research has focused largely on technical and functional feasibility rather than on motivational and psychological determinants influencing adoption behaviour. While frameworks such as the TAM and the Unified Theory of Acceptance and Use of Technology (UTAUT) explain cognitive appraisals like usefulness and ease of use, they neglect the affective and motivational mechanisms that determine whether stakeholders truly value and trust new technologies.
2.4 Research gap and theoretical contribution
The expectancy theory, first introduced by Vroom (1964), argues that people are driven to engage in activities by their expectation that the performance will result from effort, that the performance will produce the intended output, and that the output is valid. Such a theory provides an agent-centred perspective for analysing decision-making in new or uncertain domains, such as the implementation of transformative technologies. Lu and Su (2009) employed the elements of expectancy theory to evaluate the introduction of mobile services in the Taiwanese tourism market, and concluded that consumers’ behavioural intention was strongly linked to their level of effort expectancy.
The importance of travel performance expectations in online travel decision-making was also supported by Amaro and Duarte (2015), who found that travellers engage with digital booking platforms more readily when they consider such tools to increase both practicality and reliability. Vărzaru et al. (2021) investigated the expectancy theory constructs in the wider tourism business model, stressing that expectancy of performance is central to the ways in which tourism practitioners embrace digital integration. Rodríguez Bolívar et al. (2024a, b) employed a generative motivational perspective grounded in expectancy theory to study stakeholder readiness for blockchain integration in the tourism sector. They identified that service suppliers were likelier to embrace blockchain integration when they anticipated operational efficiency efficiencies, privacy of customer records and improved transparency of services. Della Corte et al. (2024) explored the determinants of blockchain adoption in the tourism sector, demonstrating the importance of both performance and effort expectancy in the development of end-user intentions.
The findings imply that users are more likely to engage with blockchain-based services when they view them as easy to use and advantageous, aligning with the theory of expectancy. Prior tourism research has also examined structural and contextual barriers to innovation adoption, including uncertainty-driven and systemic constraints. Studies analysing tourism development under uncertainty and barrier-based decision frameworks highlight how perceived risks, institutional readiness and stakeholder alignment influence technology-related decisions (Fathi et al., 2022, 2024; Torabi et al., 2022). While such approaches focus primarily on macro-level or system-level barriers, the present study complements this stream by examining motivational barriers at the individual and stakeholder level, thereby offering a behavioural explanation for adoption hesitation in hospitality and tourism contexts. Existing research largely applies TAM or UTAUT, emphasising perceived usefulness and ease of use (Rodríguez Bolívar et al., 2024a, b; Sigala, 2020).
The previous models overlook the psychological drivers of expectancy, trust and perceived data protection that underpin users’ motivational readiness. Relatively few investigations consider geographically or culturally defined backgrounds, in spite of strong evidence that adoption behaviours differ considerably from region to region. Looking at the Spanish scenario, Costa del Sol in particular, an area where tourism is a central economic factor, there is a remarkable gap in data-driven surveys that explore the attitudes of blockchain among tourism stakeholder’s and service and end-users. The majority of research studies are either quantitative or qualitative. A hybrid approach, comprising structured surveys and analysis of open-ended questions, can yield more comprehensive and triangulated results.
By extending the expectancy theory with trust and privacy considerations, the present study investigates how perceived expectancy (belief that effort will lead to performance), instrumentality (belief that performance leads to outcomes) and valence (value attached to outcomes) shape attitudes towards blockchain adoption in hospitality and tourism. Recognising that adoption also hinges on confidence in data privacy and system integrity, the framework integrates trust and perceived data protection as critical antecedents of valence. This integration yields the “Expectancy-Trust-Adoption Framework”, which bridges motivational psychology and digital-trust scholarship to explain why high technical optimism does not necessarily translate into adoption.
2.5 Study aim
Blockchain technology is increasingly recognised as a disruptive force reshaping the hospitality and tourism industries. Its capacity to enhance transparency, security and transactional efficiency has positioned it as a strategic enabler of digital transformation in areas such as hotel reservations, loyalty programs, guest data management and tourism supply chains (Kizildag et al., 2020; Sigala, 2020). Spain, one of the world’s leading tourism destinations and a digital innovation hub, provides a fertile context to explore how hospitality and tourism organisations engage with blockchain applications to rebuild trust and resilience in a post-pandemic environment.
The study aims to identify and compare stakeholders’ and users’ motivational drivers of blockchain adoption in Spain’s hospitality and tourism industries, and develop and empirically validate the Expectancy–Trust–Adoption Framework that extends expectancy theory with trust-based constructs. The research contributes theoretically by advancing motivation-based adoption theory beyond TAM/UTAUT, methodologically by demonstrating a mixed-method triangulation design, and practically by providing actionable insights for managers and policymakers seeking to enhance blockchain’s trustworthiness and value proposition in service contexts.
3. Method
3.1 Extending expectancy theory with trust
While expectancy theory conceptualises valence as the subjective value attached to anticipated outcomes, it does not specify the factors shaping this valuation in digital adoption contexts (Vroom, 1964). In blockchain-enabled tourism services, outcome valuation is inherently contingent on confidence in system integrity and assurance that personal and transactional data are adequately protected. Trust and perceived data protection therefore function as decision-enabling conditions rather than as generic attitudinal variables. Unlike perceived risk, which reflects anticipated losses, trust in blockchain systems reflects confidence in technological reliability, transparency and immutability, while perceived data protection captures beliefs regarding privacy preservation and regulatory compliance. These constructs directly influence whether anticipated outcomes such as efficiency gains or fraud reduction are perceived as desirable. Accordingly, trust and perceived data protection are conceptualised as antecedents of valence, enabling a more precise explanation of motivational barriers to blockchain adoption in hospitality and tourism. Figure 1 shows how Expectancy and Instrumentality influence Trust and Perceived Data Protection, which shape Valence, leading to adoption.
The conceptual path diagram is arranged from top to bottom with oval nodes connected by solid arrows. At the top, two oval nodes labeled “Expectancy (Technical Capability)” and “Instrumentality (Operational Outcomes)” are positioned side by side. From “Expectancy (Technical Capability)”, one solid arrow points downward to “Trust in Blockchain (System Reliability, Transparency)” and another solid diagonal arrow points to “Perceived Data Protection (Consumer Privacy, Security)”. From “Instrumentality (Operational Outcomes)”, one solid arrow points downward to “Perceived Data Protection (Consumer Privacy, Security)” and another solid diagonal arrow points to “Trust in Blockchain (System Reliability, Transparency)”. In the middle, the two oval nodes “Trust in Blockchain (System Reliability, Transparency)” and “Perceived Data Protection (Consumer Privacy, Security)” each have a solid arrow pointing downward to the oval labeled “Valence (Perceived Consumer Benefit)”. From “Valence (Perceived Consumer Benefit)”, a solid arrow points downward to the bottom oval labeled “Adoption Intention (Blockchain Use in Tourism)”.Expectancy-Trust-Adoption Framework for Blockchain in Tourism. Source: Developed by authors
The conceptual path diagram is arranged from top to bottom with oval nodes connected by solid arrows. At the top, two oval nodes labeled “Expectancy (Technical Capability)” and “Instrumentality (Operational Outcomes)” are positioned side by side. From “Expectancy (Technical Capability)”, one solid arrow points downward to “Trust in Blockchain (System Reliability, Transparency)” and another solid diagonal arrow points to “Perceived Data Protection (Consumer Privacy, Security)”. From “Instrumentality (Operational Outcomes)”, one solid arrow points downward to “Perceived Data Protection (Consumer Privacy, Security)” and another solid diagonal arrow points to “Trust in Blockchain (System Reliability, Transparency)”. In the middle, the two oval nodes “Trust in Blockchain (System Reliability, Transparency)” and “Perceived Data Protection (Consumer Privacy, Security)” each have a solid arrow pointing downward to the oval labeled “Valence (Perceived Consumer Benefit)”. From “Valence (Perceived Consumer Benefit)”, a solid arrow points downward to the bottom oval labeled “Adoption Intention (Blockchain Use in Tourism)”.Expectancy-Trust-Adoption Framework for Blockchain in Tourism. Source: Developed by authors
Spain represents a theoretically and empirically relevant context for examining blockchain adoption in hospitality and tourism. As one of the world’s leading tourism destinations, the Spanish tourism sector is characterised by high transaction volumes, fragmented service provision and extensive reliance on digital platforms. The Costa del Sol, in particular, hosts a dense concentration of hospitality firms, destination management organisations and international visitors, making it a suitable microcosm for studying technology adoption dynamics. From a regulatory perspective, Spain operates within the European Union’s advanced digital governance framework, including the General Data Protection Regulation (GDPR) and emerging Markets in Crypto-Assets (MiCA) regulation. These frameworks heighten awareness of data protection while simultaneously shaping trust perceptions towards blockchain-based services. Culturally, European consumers exhibit strong sensitivity to privacy and institutional trust, which directly influences technology valuation in service contexts.
3.2 Sample and data collection
This study was conducted in full accordance with institutional, national and international ethical standards for research involving human participants. Ethical approval for the study was granted by the Doctoral Research Ethics Committee of the University of Málaga and complied with the principles outlined in the Declaration of Helsinki and the European General Data Protection Regulation (GDPR). All participants were informed about the purpose of the research, the voluntary nature of their participation, and the confidentiality and anonymity of their responses. Prior to completing the survey, participants were presented with an information sheet detailing that (a) the study aimed to examine attitudes and motivational factors influencing blockchain adoption in the hospitality and tourism sectors; (b) no personally identifying information would be collected; (c) all responses would remain anonymous and be stored securely on password-protected institutional servers accessible only to the research team and (d) there were no foreseeable physical, psychological or social risks associated with participation. By proceeding with the questionnaire, participants provided informed consent for the use of their anonymised data for academic research and publication purposes.
Based on the extended framework, the study conducted a questionnaire survey among participants who were selected using purposive non-probability sampling to ensure they had operational experience with tourism services or active involvement in tourism policy or business practices. Before handing the questionnaires to the participants, the authors explained the key functions and role of blockchain technology with the support of a video. The authors do not have any mutual interests with the organisation that generated the video. The study collected a total of 173 valid responses; 94 valid responses from tourism service users (including domestic and international travellers) and 79 valid responses from tourism professionals (including hotel and travel agency managers, consultants, tourism technology professionals, academics, DMOs, policymakers, etc.) located in Malaga region, Spain.
The survey covered a diverse sample of participants of various ages, genders, professional backgrounds and skill levels in digital tech and blockchain. This diversity enabled, meaningful comparisons across stakeholders’ and service users’ roles and demographic categories were possible. The data were collected between June 2024 and March 2025 through two structured, self-administered online questionnaires, the first one dedicated to tourism service users and the second to tourism stakeholders. The questionnaires were developed in both Spanish and English to maximise accessibility and regional representation. The questionnaires were designed using Online Microsoft Forms and dispatched via email invitations, WhatsApp groups, networking groups in the tourism and hospitality, and the university. Participants were briefed on the study’s purpose, confidentiality protocols and the voluntariness of their participation via a consent form at the onset of the survey.
3.3 Measures
The questionnaire covered demographic items as well as core items to evaluate attitudes in the tourism sector towards blockchain technology adoption (Caddeo and Pinna, 2021; Maythu et al., 2024; Papamatthaiou, 2025; Rodríguez Bolívar et al., 2024a, b). The questionnaires included 10 core items, summarised as follows:
“Prior knowledge and familiarity with blockchain and its potential application in the tourism sector”;
“Many tourism functions are still paper-based and offline, which leads to long processing times, Blockchain supports end-to-end traceability and improves the security of customer personal data sharing”;
“Unlike traditional tools of tourism data management, Blockchain technology enables tourism stakeholders to handle an increased amount of data while keeping track of more transactions by creating an encrypted digital record”;
“Blockchain creates transparent, secure, and immutable booking systems that reduce fraud and ensure accurate booking records”;
“Blockchain guarantees secure and decentralised identity verification, enhancing security and reducing the risk of identity theft in travel transactions”;
“Blockchain tracks the provenance of goods and services in the tourism supply chain, ensuring authenticity and quality”;
“Blockchain-powered smart contracts automate and secure transactions between tourism providers, reducing costs and improving efficiency”;
“Blockchain allows transparent and interoperable loyalty programs that enhance customer engagement and retention”;
“Confidence in Blockchain for data protection in the tourism sector”.
“Blockchain technology is the key to a successful collaboration between the organisation and the tourism stakeholders (hotels, airlines, tour operators, etc.)”.
The measurement of attitudes towards blockchain technology was based on items adapted from validated instruments widely applied in the literature, notably in the works of Venkatesh et al. (2003) and Hau et al. (2019). Respondents evaluated each item using a seven-point Likert-type scale (1 = very negative, 7 = very positive). Higher scale values denoted increasingly favourable attitudes towards blockchain technology, whereas lower values corresponded to less favourable attitudes. The constructs measuring stakeholders’ and service users’ familiarity with blockchain technology, alongside their confidence in its application within the tourism sector, were operationalised using items adapted from previously validated scales in studies by Calvaresi et al. (2019) and Caddeo and Pinna (2021).
Familiarity was assessed using a 5-point Likert-type scale ranging from 1 (no familiarity) to 5 (very familiar), while confidence was evaluated via a 4-point Likert-type scale ranging from 1 (no confidence) to 4 (very confident). These scales were adapted from prior tourism technology research (Rodríguez Bolívar et al., 2024a, b; Sigala, 2020). The survey incorporated open-ended items to generate qualitative data on advantages, challenges and motivations related to blockchain implementation and it was aligned with expectancy “effort”, instrumentality “performance” and valence “outcome” components of Expectancy Theory.
Content validity was ensured through alignment with expectancy theory constructs and prior empirical studies, followed by refinement to reflect the hospitality and tourism context. The questionnaire was reviewed by professors’ board of tourism faculty of the University of Malaga and piloted by domain experts to ensure clarity and conceptual consistency. The purposive sampling strategy was employed to capture informed perceptions from both tourism professionals and service users, consistent with exploratory adoption research in emerging technology domains.
3.4 Data analysis
The collected data were assessed in two consecutive phases. In the first phase, quantitative data were analysed using Pythons (Version 3.11). The descriptive statistics were undertaken to summarise respondents’ blockchain familiarity, confidence, and adoption readiness in the tourism sector. Mann–Whitney U and Chi-Square tests were applied to compare demographic characteristics and attitudes of stakeholders’ and service users, analyse variant group differences and evaluate associations between categorical variables. Post hoc pairwise comparisons were conducted to identify significant group differences.
The criterion for statistical significance was established at (p < 0.05). In the second phase, the collected qualitative data from open-ended questions were assessed employing NVivo (Version 14), following the analytical approach highlighted by Braun and Clarke (2006). A mixed inductive-deductive methodology was used: initial open coding prepared the ground for the identification of evolving items (security concerns, legal barriers, training requirements), which were aligned with the framework, explicitly investigating the linkage among effort and reward expectations.
3.5 Methodological contribution
The study makes an important methodological contribution by employing a mixed-method triangulation design to investigate blockchain adoption in tourism. Quantitative analysis was conducted using Chi-square and Mann–Whitney U tests, which enabled robust non-parametric comparisons between stakeholders and service users and allowed us to detect statistically significant differences across demographic and attitudinal profiles. These tests are particularly suitable given the ordinal nature of the data and the asymmetry of group sizes.
Complementing this, qualitative analysis of open-ended responses was carried out using NVivo thematic coding, generating word frequencies, code clusters and thematic patterns that contextualised and deepened the statistical results. By combining statistical rigor with qualitative interpretation, this study avoids the limitations of quantitative adoption models and provides a more holistic and context-sensitive understanding of motivational dynamics. This design represents a methodological innovation in the tourism technology literature, which has often relied on single-method survey approaches.
3.6 Ethical approval
The study received ethical approval from the relevant institutional ethics committee prior to data collection. All procedures complied with national and international ethical standards for research involving human participants. Prior to participation, respondents were informed about the study’s objectives, their right to withdraw at any time without penalty and the voluntary nature of participation. Anonymity and confidentiality were strictly maintained. No personally identifiable information was collected, and all data were securely stored and used exclusively for academic research purposes.
4. Results
4.1 Descriptive statistics
The study seeks to explore the potential opportunities and barriers presented by the introduction of blockchain in tourism sector. It is essential to fully comprehend the stakeholders’ attitudes, as they can significantly impact the adoption and effectiveness of innovative technologies in this sector. To accomplish this aim, the demographic characteristics and behavioural measures of stakeholders and tourists were compared using the Mann–Whitney U test and the chi-square test. A total of two stakeholder groups were surveyed: tourism sector professionals and tourism service users (including national and international tourists). Their demographic characteristics are summarised in Tables 1 and 2 as follows:
Summary of respondents’ characteristics
| Variable | Users (n = 94) | Stakeholders (n = 79) | Total (N = 173) |
|---|---|---|---|
| Gender | Male: 45 (47.9%) | Male: 43 (54.4%) | 88 Male (50.9%) |
| Female: 49 (52.1%) | Female: 36 (45.6%) | 85 Female (49.1%) | |
| Age Groups | 18–24: 12 (12.8%) | 25–44: 39 (49.4%) | Majority: 25–44 years (55.5%) |
| 25–44: 57 (60.6%) | 45–64: 34 (43.0%) | ||
| 45–64: 25 (26.6%) | 65+: 6 (7.6%) | ||
| Occupation/Role | N/A (service users) | Hotels: 29 (36.7%) | – |
| Travel agencies: 23 (29.1%) | |||
| Transport/logistics: 14 (17.7%) | |||
| DMOs/Policy: 13 (16.5%) | |||
| Experience in Tourism | Not applicable | <5 years: 25 (31.6%) | – |
| 5–10 years: 28 (35.4%) | |||
| >10 years: 26 (32.9%) |
Distribution of Stakeholders across the Tourism Services that they Represented (N = 79)
| Department/Service | Frequency | Percentage (%) |
|---|---|---|
| Hotel Manager | 17 | 21.52% |
| Academic | 15 | 18.99% |
| Tourism Technical Expert | 11 | 13.92% |
| Administrative Staff | 5 | 6.33% |
| Marketing and PR Specialist | 4 | 5.06% |
| Travel Consultant | 2 | 2.53% |
| Other (Various unique roles such as Director, HR, etc.) | 25 (each 1) | 1.27% each |
The Chi-square test of independence revealed no statistically significant association in the demographic characteristics of the group membership, χ2(5, N = 173) = 4.19, p = 0.522. Similarly, the test revealed no statistically significant association between group membership and gender distribution, χ2(2, N = 173) = 1.85, p = 0.397. The majority of respondents reported over 5 years of industry experience, demonstrating a knowledgeable and representative sample.
The results of the survey were obtained through the application Mann–Whitney U tests to compare stakeholder and tourist attitudes towards the use of blockchain technology in tourism. Mean scores for all attitude items exceeded the scale midpoint (>3.5), indicating generally positive perceptions. The results are summarised in Table 3 as follows:
Significance of the Difference in Attitudes towards Blockchain Adoption
| Attitude items | Professionals, (mean, SD) | Service users, (mean, SD) | U statistic | p-valuea |
|---|---|---|---|---|
| (1) Prior knowledge and familiarity with Blockchain in tourism | 2.38 (1.36) | 2.01 (1.21) | 4295.0 | 0.0617 |
| (2) Paper-based tourism processes cause inefficiencies | 3.86 (2.06) | 4.09 (1.85) | 3464.5 | 0.4444 |
| (3) Blockchain enables efficient data handling | 5.51 (1.48) | 5.23 (1.39) | 4232.5 | 0.1031 |
| (4) Blockchain enables secure booking and reduces fraud | 5.47 (1.49) | 5.24 (1.46) | 4135.5 | 0.1855 |
| (5) Blockchain enables decentralised identity verification | 5.46 (1.55) | 5.17 (1.46) | 4240.5 | 0.1001 |
| (6) Blockchain ensures supply chain traceability | 5.37 (1.44) | 4.91 (1.45) | 4432.5 | 0.0249 |
| (7) Blockchain smart contracts improve efficiency | 5.48 (1.25) | 4.95 (1.50) | 4494.0 | 0.0137 |
| (8) Blockchain enhances loyalty programs | 5.51 (1.47) | 4.85 (1.47) | 4699.5 | 0.0021 |
| (9) Confidence in Blockchain for data protection | 2.85 (0.87) | 2.76 (0.84) | 3923.0 | 0.3965 |
| (10) Blockchain fosters collaboration among tourism businesses | 5.48 (1.25) | 4.95 (1.50) | 4494 | 0.0137 |
“Values are significant at the 0.05 level of significance”
Respondents from both groups showed moderately positive attitudes overall, with average mean values above the midpoint of the measure for most items (3.7–5.0; Table 3). Statistically significant differences were revealed in the following items: (i.e. item 6) “blockchain’s potential to ensure supply chain traceability”, (i.e. item 7) drive efficiency using smart contracts and (i.e. item 8) enhance loyalty programmes and (i.e. item 10) enhance collaboration among businesses. Tourism professionals were significantly more favourable than service users in their perceptions of these areas, suggesting that industry professionals are more sensitive to the operational and strategic implications of blockchain in customer engagement and business processes.
Conversely, no significant differences were apparent in the following items: (i.e. item 4) booking security, (i.e. item 5) identity verification or efficient data processing, reflecting either a mutual perspective or a similar degree of uncertainty regarding these implementations. However, both groups indicated relatively low familiarity with blockchain (i.e. item 1) and a limited trust in its ability to safeguard sensitive data (i.e. item 9), underlining a gap in overall knowledge and concerns about personal data security. Table 4 represents the mean and standard deviation of prior knowledge and familiarity with blockchain technology among tourism stakeholders and service users.
Mean and Standard Deviation
| Group | Mean | SD |
|---|---|---|
| Professionals (Stakeholders) | 2.38 | 1.36 |
| Tourism Service Users | 2.01 | 1.21 |
Figure 2 visualises the mean scores of the ten blockchain adoption attitude items for tourism stakeholders and service users. The figure highlights statistically significant differences in perceptions related to supply chain traceability, smart contracts, loyalty programs and inter-organisational collaboration, where stakeholders exhibit stronger positive evaluations. Conversely, both groups show comparatively low scores for familiarity with blockchain and confidence in data protection, underscoring a shared trust-related barrier to adoption.
The vertical bar chart is drawn on a coordinate plane. The horizontal axis lists two categories from left to right as “Stakeholders” and “Users”. The vertical axis is labeled “Mean Familiarity with Blockchain (1 to 5 scale)” and ranges from 0 to 4 in increments of 1 unit. There are 2 bars in the chart. The data values are presented as follows: Stakeholders: 2.38; Users: 2.01. Each bar includes a vertical error bar. The error bar for “Stakeholders” extends from 1.0 to 3.8, and the error bar for “Users” extends from 0.8 to 3.2. Note: All numerical data values are approximated.Comparative Mean Scores of Blockchain Adoption Attitudes among Stakeholders and Service Users. Source: Developed by authors
The vertical bar chart is drawn on a coordinate plane. The horizontal axis lists two categories from left to right as “Stakeholders” and “Users”. The vertical axis is labeled “Mean Familiarity with Blockchain (1 to 5 scale)” and ranges from 0 to 4 in increments of 1 unit. There are 2 bars in the chart. The data values are presented as follows: Stakeholders: 2.38; Users: 2.01. Each bar includes a vertical error bar. The error bar for “Stakeholders” extends from 1.0 to 3.8, and the error bar for “Users” extends from 0.8 to 3.2. Note: All numerical data values are approximated.Comparative Mean Scores of Blockchain Adoption Attitudes among Stakeholders and Service Users. Source: Developed by authors
Figure 3 synthesises individual attitude items into the core dimensions of the Expectancy-Trust-Adoption Framework. While expectancy and instrumentality dimensions demonstrate relatively high values, particularly among stakeholders, the valence dimension remains weak across both groups due to concerns related to trust and consumer data protection. This visual summary reinforces the existence of a motivational imbalance that constrains blockchain adoption in hospitality and tourism.
The horizontal box plot is drawn on a coordinate plane. The horizontal axis is labeled “Standardized Score (z-score)” and ranges from negative 2 to 6 in increments of 2 units. The vertical axis is labeled “Attitude Item” and lists categories from top to bottom as “Blockchain enables decentralized identity verification”, “Blockchain enables secure booking and reduces fraud”, “Blockchain enhances loyalty programs”, “Blockchain ensures supply chain traceability”, “Blockchain fosters collaboration among tourism businesses”, “Blockchain smart contracts improve efficiency”, “Confidence in Blockchain for data protection”, “Familiarity with Blockchain in tourism”, “Paper-based tourism processes cause inefficiencies”, and “Blockchain enables efficient data handling”. Each category contains two horizontal box plots corresponding to the legend groups “Professionals” and “Users”. For “Blockchain enables decentralized identity verification” both groups show high positive scores, with medians around 5 to 6 and whiskers extending from 2 to 7. For “Blockchain enables secure booking and reduces fraud” both groups again show high positive scores near 5 to 6, with a few lower outliers around 2 to 3. For “Blockchain enhances loyalty programs” both groups have strong positive distributions with medians near 5 and ranges from about 1 to 7. For “Blockchain ensures supply chain traceability” both groups show positive scores centered around 4 to 5. For “Blockchain fosters collaboration among tourism businesses” both groups have moderate positive scores around 4 to 5 with some variability and outliers. For “Blockchain smart contracts improve efficiency” both groups show moderate positive values around 4 to 5 with several lower outliers near 2 to 3. For “Confidence in Blockchain for data protection” both groups show lower positive scores centered around 2 to 3. For “Familiarity with Blockchain in tourism” both groups show moderate scores, with Professionals centered around 2 to 3 and Users slightly lower around 1 to 3. For “Paper-based tourism processes cause inefficiencies” both groups show high positive scores, with medians around 5 to 6. For “Blockchain enables efficient data handling” both groups show values around 0 to 1, with some negative whiskers extending to negative 2. The legend identifies the two groups as “Professionals” and “Users”. Outliers are shown as individual points beyond the whiskers. Note: All numerical data values are approximated.Visual Synthesis of Expectancy, Instrumentality and Valence Differences between Stakeholders and Service Users. Source: Developed by authors
The horizontal box plot is drawn on a coordinate plane. The horizontal axis is labeled “Standardized Score (z-score)” and ranges from negative 2 to 6 in increments of 2 units. The vertical axis is labeled “Attitude Item” and lists categories from top to bottom as “Blockchain enables decentralized identity verification”, “Blockchain enables secure booking and reduces fraud”, “Blockchain enhances loyalty programs”, “Blockchain ensures supply chain traceability”, “Blockchain fosters collaboration among tourism businesses”, “Blockchain smart contracts improve efficiency”, “Confidence in Blockchain for data protection”, “Familiarity with Blockchain in tourism”, “Paper-based tourism processes cause inefficiencies”, and “Blockchain enables efficient data handling”. Each category contains two horizontal box plots corresponding to the legend groups “Professionals” and “Users”. For “Blockchain enables decentralized identity verification” both groups show high positive scores, with medians around 5 to 6 and whiskers extending from 2 to 7. For “Blockchain enables secure booking and reduces fraud” both groups again show high positive scores near 5 to 6, with a few lower outliers around 2 to 3. For “Blockchain enhances loyalty programs” both groups have strong positive distributions with medians near 5 and ranges from about 1 to 7. For “Blockchain ensures supply chain traceability” both groups show positive scores centered around 4 to 5. For “Blockchain fosters collaboration among tourism businesses” both groups have moderate positive scores around 4 to 5 with some variability and outliers. For “Blockchain smart contracts improve efficiency” both groups show moderate positive values around 4 to 5 with several lower outliers near 2 to 3. For “Confidence in Blockchain for data protection” both groups show lower positive scores centered around 2 to 3. For “Familiarity with Blockchain in tourism” both groups show moderate scores, with Professionals centered around 2 to 3 and Users slightly lower around 1 to 3. For “Paper-based tourism processes cause inefficiencies” both groups show high positive scores, with medians around 5 to 6. For “Blockchain enables efficient data handling” both groups show values around 0 to 1, with some negative whiskers extending to negative 2. The legend identifies the two groups as “Professionals” and “Users”. Outliers are shown as individual points beyond the whiskers. Note: All numerical data values are approximated.Visual Synthesis of Expectancy, Instrumentality and Valence Differences between Stakeholders and Service Users. Source: Developed by authors
4.2 Qualitative analysis
The qualitative analysis is undertaken to investigate the open-ended responses received from tourism professionals and service users. The purpose of this stage of the analysis is to deeply explore the complex attitudes and behaviours towards blockchain technology that were difficult to measure through quantitative methods. Following Braun and Clarke’s (2006) conceptual framework for conducting qualitative research, a hybrid inductive-deductive coding strategy was used. Inductive coding supported the identification of unforeseen issues directly from participants’ narratives (e.g. “concerns about legal frameworks or training needs”), while deductive coding enabled engagement with the ten key items and the extended theoretical framework of expectancy theory. The qualitative evaluation of open-ended responses is analysed in 6 phases as follows:
Phase 1: Respondents’ open-ended statements were imported into NVivo (Version 14) for systematic analysis. To facilitate a structured comparative study among tourism professionals and service users, all respondents were categorised into individual cases. A deductive coding strategy was employed, mapping qualitative data to the ten pre-established attitude from the quantitative phase.
Phase 2: Ten nodes were framed in NVivo with regard to the 10 pre-established items employed in the survey and represent a solid ground for structuring the qualitative analysis.
Phase 3: Respondents’ answers were systematically analysed and coded into relevant attitude nodes. A hybrid approach of manual coding and NVivo-assisted data analysis was used to deliver both methodological rigour and in-depth interpretation. Participants’ statements were ranked according to the orientations expressed, such as support, concern, risk aversion or general lack of understanding, towards specific blockchain functions. For instance, statements highlighting potential efficiency gains (e.g. “Blockchain could eliminate manual verification processes”) were coded under “Identity Verification,” while expressions of uncertainty (e.g. “I don’t really know how blockchain works”) were coded under “Prior knowledge and Familiarity with Blockchain”.
Phase 4: Under each parent node, both the emotive sub-themes and sentiment pathways were systematically mapped out through cluster analysis (Braun and Clarke, 2006). For instance, under the node “Trust in blockchain for data protection,” participants frequently stressed their concerns about trust, regulatory supervision and perceived insecurities regarding data protection. Node “Familiarity with blockchain” demonstrated repeated patterns of poor knowledge, common amalgamation with blockchain and explicit appeals for dedicated educational and training initiatives to address learning gaps. Equally, the theme “Smart contracts improve efficiency” also reflected significant levels of eagerness regarding automatisation, transparency in contracts and legal clarity, notably across industry professionals. Using NVivo’s advanced coding and query functions (QSR International, 2023) facilitated the categorisation of respondents’ sentiments into positive, negative and neutral, enabling a differentiation with nuance among constructive optimism and cautious scepticism regarding the technology adoption.
Phase 5: A cross-sectional comparative analysis was conducted to explore the divergences and convergences among tourism stakeholders. This was aided by following the guidelines of Miles et al. (2014), the query functions of the NVivo coding matrix (QSR International, 2023), as they allowed for the recognition of coding patterns particular to both groups.
Phase 6: To improve the reporting of qualitative results, visual materials such as word clouds generated by NVivo and coding frequency graphs were generated. Pivotal illustrative quotations were extracted to further illustrate representative insights within each item as portrayed in Figures 4 and 5. Such qualitative findings were then embedded into the quantitative outputs, thereby providing a holistic and multidimensional portrayal of professionals’ and service users’ attitudes towards blockchain adoption.
The network diagram shows three top-level circular nodes arranged from left to right, labeled “Perceived Benefits”, “Barriers and Concerns”, and “Future Outlook”, each connected by straight lines to related factors below. Under “Perceived Benefits”, lines connect to circular nodes labeled “Security”, “Efficiency (smart contracts)”, “Trust”, “Faster transactions”, and “Transparency”. Under “Barriers and Concerns”, lines connect to circular nodes labeled “Cost”, “Regulation or Compliance”, “Technical complexity”, “Interoperability”, and “Training needs”. Under “Future Outlook”, lines connect to circular nodes labeled “Optimism (5 to 10 year horizon)”, “Skepticism (hype vs. reality)”, and “Conditional adoption (resources)”.Coding Frequency Graphs. Source: Developed by authors
The network diagram shows three top-level circular nodes arranged from left to right, labeled “Perceived Benefits”, “Barriers and Concerns”, and “Future Outlook”, each connected by straight lines to related factors below. Under “Perceived Benefits”, lines connect to circular nodes labeled “Security”, “Efficiency (smart contracts)”, “Trust”, “Faster transactions”, and “Transparency”. Under “Barriers and Concerns”, lines connect to circular nodes labeled “Cost”, “Regulation or Compliance”, “Technical complexity”, “Interoperability”, and “Training needs”. Under “Future Outlook”, lines connect to circular nodes labeled “Optimism (5 to 10 year horizon)”, “Skepticism (hype vs. reality)”, and “Conditional adoption (resources)”.Coding Frequency Graphs. Source: Developed by authors
The thematic mapping diagram is arranged from bottom to top with ten vertical labeled items connected upward by arrows to corresponding columns of stakeholder statements. At the bottom, ten vertical boxes labeled from left to right “ITEM 1” through “ITEM 10” are shown. Each item has a descriptive label above it. “ITEM 1” is labeled “Prior knowledge and familiarity with Blockchain in tourism”. “ITEM 2” is labeled “Paper-based tourism processes cause inefficiencies”. “ITEM 3” is labeled “Blockchain enables efficient data handling”. “ITEM 4” is labeled “Blockchain enables secure booking and reduces fraud”. “ITEM 5” is labeled “Blockchain enables decentralized identity verification”. “ITEM 6” is labeled “Blockchain ensures supply chain traceability”. “ITEM 7” is labeled “Blockchain smart contracts improve efficiency”. “ITEM 8” is labeled “Blockchain enhances loyalty programs”. “ITEM 9” is labeled “Confidence in Blockchain for data protection”. “ITEM 10” is labeled “Blockchain fosters collaboration among businesses”. Above each item, multiple upward arrows connect to a large rectangular area divided into ten vertical columns of stakeholder statements aligned with each item. For “ITEM 1”, the column includes: “Stakeholders report moderate awareness through industry discussions”, “Many users admit little to no prior exposure”, and “Calls for workshops and basic training were frequently mentioned”. For “ITEM 2”, the column includes: “Blockchain viewed as a potential solution, especially by stakeholders”, “Universal agreement upon as a pain point by both groups”, and “Eliminating manual forms and delayed confirmations”. For “ITEM 3”, the column includes: “Stakeholders highlight real-time updates and data immutability”, “Users remain unsure how blockchain managed data differently from apps they already use”, and “A gap in digital literacy is evident”. For “ITEM 4”, the column includes: “Stakeholders highlight fraud prevention and secure transactions”, “Users are cautious, with some confusing blockchain with cryptocurrency-related scams”, and “Suggestions are made to link blockchain with verified review systems”. For “ITEM 5”, the column includes: “Stakeholders emphasize reduced dependency on central authorities”, “Users express limited understanding of how identity could be verified with blockchain”, and “Skepticism emerged around data misuse and digital identity safety”. For “ITEM 6”, the column includes: “Highly supported by stakeholders for its ability to trace supplier authenticity”, “Cases include hotel suppliers, eco-certification, and origin of products”, and “Users have limited comments, reflecting unfamiliarity with the concept”. For “ITEM 7”, the column includes: “Strong positive sentiment from professionals regarding automation and legal clarity”, “Examples include instant payment settlements and automated service agreements”, and “Users often find the term ‘smart contract’ confusing or too technical”. For “ITEM 8”, the column includes: “Professionals see opportunities for token-based rewards and automated loyalty tracking”, “Users find the concept novel but requested clearer examples”, and “Concerns about complexity and app usability were common among users”. For “ITEM 9”, the column includes: “Mixed opinions from stakeholders; some trusted the technology, others feared data breaches”, “Users commonly cited uncertainty about data safety”, and “General lack of understanding of blockchain’s security mechanisms”. For “ITEM 10”, the column includes: “Mentioned in relation to shared databases and inter-agency trust”, “Stakeholders link it with reducing duplication of effort and information silos”, and “Users are unaware of how collaboration would be facilitated”.Illustrative Findings for Each Attitude Item. Source: Developed by authors
The thematic mapping diagram is arranged from bottom to top with ten vertical labeled items connected upward by arrows to corresponding columns of stakeholder statements. At the bottom, ten vertical boxes labeled from left to right “ITEM 1” through “ITEM 10” are shown. Each item has a descriptive label above it. “ITEM 1” is labeled “Prior knowledge and familiarity with Blockchain in tourism”. “ITEM 2” is labeled “Paper-based tourism processes cause inefficiencies”. “ITEM 3” is labeled “Blockchain enables efficient data handling”. “ITEM 4” is labeled “Blockchain enables secure booking and reduces fraud”. “ITEM 5” is labeled “Blockchain enables decentralized identity verification”. “ITEM 6” is labeled “Blockchain ensures supply chain traceability”. “ITEM 7” is labeled “Blockchain smart contracts improve efficiency”. “ITEM 8” is labeled “Blockchain enhances loyalty programs”. “ITEM 9” is labeled “Confidence in Blockchain for data protection”. “ITEM 10” is labeled “Blockchain fosters collaboration among businesses”. Above each item, multiple upward arrows connect to a large rectangular area divided into ten vertical columns of stakeholder statements aligned with each item. For “ITEM 1”, the column includes: “Stakeholders report moderate awareness through industry discussions”, “Many users admit little to no prior exposure”, and “Calls for workshops and basic training were frequently mentioned”. For “ITEM 2”, the column includes: “Blockchain viewed as a potential solution, especially by stakeholders”, “Universal agreement upon as a pain point by both groups”, and “Eliminating manual forms and delayed confirmations”. For “ITEM 3”, the column includes: “Stakeholders highlight real-time updates and data immutability”, “Users remain unsure how blockchain managed data differently from apps they already use”, and “A gap in digital literacy is evident”. For “ITEM 4”, the column includes: “Stakeholders highlight fraud prevention and secure transactions”, “Users are cautious, with some confusing blockchain with cryptocurrency-related scams”, and “Suggestions are made to link blockchain with verified review systems”. For “ITEM 5”, the column includes: “Stakeholders emphasize reduced dependency on central authorities”, “Users express limited understanding of how identity could be verified with blockchain”, and “Skepticism emerged around data misuse and digital identity safety”. For “ITEM 6”, the column includes: “Highly supported by stakeholders for its ability to trace supplier authenticity”, “Cases include hotel suppliers, eco-certification, and origin of products”, and “Users have limited comments, reflecting unfamiliarity with the concept”. For “ITEM 7”, the column includes: “Strong positive sentiment from professionals regarding automation and legal clarity”, “Examples include instant payment settlements and automated service agreements”, and “Users often find the term ‘smart contract’ confusing or too technical”. For “ITEM 8”, the column includes: “Professionals see opportunities for token-based rewards and automated loyalty tracking”, “Users find the concept novel but requested clearer examples”, and “Concerns about complexity and app usability were common among users”. For “ITEM 9”, the column includes: “Mixed opinions from stakeholders; some trusted the technology, others feared data breaches”, “Users commonly cited uncertainty about data safety”, and “General lack of understanding of blockchain’s security mechanisms”. For “ITEM 10”, the column includes: “Mentioned in relation to shared databases and inter-agency trust”, “Stakeholders link it with reducing duplication of effort and information silos”, and “Users are unaware of how collaboration would be facilitated”.Illustrative Findings for Each Attitude Item. Source: Developed by authors
Stakeholders’ attitudes were analysed on the basis of extended expectancy theory, resulting in distinctive patterns across the expectation, instrumentality and valence perspectives as represented in Table 5. The two groups reported that they had high expectations of the functional capabilities of blockchain, notably in regard to booking security, security verification, and efficiency. Yet, stakeholders reported considerably greater expectations with respect to smart contracts, supply chain traceability, loyalty programmes and collaboration among actors (p < 0.05).
Extending Expectancy–Trust Framework Results Summary
| Extended expectancy theory dimensions | Professionals | Tourism service users |
|---|---|---|
| Performance Expectancy (PE) | “Highest confidence in blockchain for data security and review authenticity.” | “Emphasise secure bookings and identity verification.” |
| Effort Expectancy (EE) | “Emphasised integration costs and organisational readiness.” | “Raised more concerns regarding technical complexity and need for training.” |
| Valence (V) | “Consider trust enhancement and customer satisfaction improvements. Cost and regulatory uncertainty tempered enthusiasm.” | |
Regarding instrumentality, stakeholders had greater confidence in blockchain’s value-added outcomes, implying a perception that efficiencies will lead to tangible business advantages. As for valence, however, low trust was stated by both groups in blockchain’s ability to protect privacy and data, underscoring a low valuation of this outcome. Although measured on different scales, professionals reported stronger adoption expectations (M = 6.71) compared to users (M = 5.90). These findings reinforce the feasibility of the proposed extended expectancy theory in explaining divergent attitudes towards adoption and suggest that, although stakeholders have greater expectancy and instrumentality insights, both groups exhibit limited valence in the area of trust and personal data protection.
4.3 Triangulation
Overall, the results reveal a consistent motivational pattern across both groups. While expectancy and instrumentality perceptions are generally positive, indicating confidence in blockchain’s technical capabilities and operational outcomes, valence remains comparatively weak due to limited trust and concerns regarding data protection. This motivational imbalance explains why favourable technical perceptions do not translate into strong adoption readiness, thereby empirically supporting the proposed Expectancy-Trust-Adoption Framework.
Table 6 represents an inclusion of comparative visual summaries that further clarify how stakeholder and service user perceptions diverge across motivational dimensions. By visually contrasting expectancy, instrumentality and valence, the findings become more accessible and interpretable, particularly for practitioners and policymakers. These visualisations reinforce the study’s central contribution by demonstrating that adoption barriers stem less from technological scepticism and more from trust-related valuation deficits.
Summary of Stakeholder vs. Service User Differences across Expectancy Dimensions
| Extended expectancy theory dimensions | Professionals | Tourism service users | Key elements |
|---|---|---|---|
| Expectancy (Technical Capability) | High | Moderate–High | Both groups acknowledge blockchain’s technical potential |
| Instrumentality (Operational Outcomes) | High | Moderate | Stakeholders perceive clearer business and efficiency gains |
| Valence (Outcome Desirability) | Moderate–Low | Low | Trust and data protection concerns reduce perceived value |
| Familiarity | Low–Moderate | Low | Limited knowledge constrains confidence across groups |
| Trust/Data Protection | Low | Low | Shared motivational barrier to adoption |
5. Discussion and conclusions
5.1 Conclusions
This study demonstrates that blockchain adoption barriers in hospitality and tourism are primarily motivational rather than technological. Although stakeholders and service users generally recognise blockchain’s functional capabilities, persistent concerns related to trust and data protection substantially reduce outcome valence, thereby weakening adoption readiness. By empirically validating the Expectancy-Trust-Adoption Framework, the study explains why favourable technical perceptions do not necessarily translate into behavioural acceptance, particularly in data-sensitive service environments.
5.2 Theoretical implications
The study makes a strong theoretical contribution by extending expectancy theory beyond its traditional application through the integration of trust and perceived data protection as antecedents of valence. In doing so, it moves beyond dominant adoption models such as TAM and UTAUT, which primarily emphasise cognitive evaluations of usefulness and ease of use. The proposed Expectancy-Trust-Adoption framework demonstrates how motivational imbalances explain persistent adoption hesitation, particularly in data-intensive tourism contexts. This contribution enriches technology adoption literature by introducing a motivation-centred and context-sensitive explanatory lens.
5.3 Practical implications
The findings offer several practical implications. Tourism managers should prioritise trust-building initiatives by improving transparency around blockchain applications and clearly communicating consumer benefits. Technology providers should design blockchain solutions that visibly enhance data protection and user control. Policymakers and destination authorities should reinforce regulatory clarity and public awareness regarding data governance frameworks such as GDPR and MiCA. These actions can strengthen perceived value and improve motivational readiness for adoption. Blockchain adoption can also enhance fairness by reducing fraud, safeguarding digital rights, and building trust between destinations and visitors. However, digital divides highlight the need for literacy initiatives.
5.4 Limitations and future research
The study is subject to limitations. The use of purposive sampling and a single-country context limits generalisability. Future research should apply the proposed framework across different cultural and regulatory settings to examine cross-cultural variations in motivational barriers. Longitudinal studies could further assess how trust and valence evolve over time as blockchain adoption matures within tourism ecosystems.
