Keywords

1 Introduction

In today’s rapidly evolving digital landscape, the tourism industry is undergoing a paradigm shift, driven by the acceptance of blockchain technology. This transformative technology has the potential to revolutionize the way transactions are conducted, information is shared, and trust is established within the tourism ecosystem. Blockchain, which is essentially a decentralized and transparent ledger system, offers immense benefits. This study focuses on the acceptance of blockchain technology in the tourism industry. This highlights the need for enterprises and organizations in tourism to leverage technology to meet consumer expectations and address competitive challenges.

2 Literature Review

Blockchain technology, characterized by its coherence in a distributed system with consensus algorithms and reliance on encryption technology (public and private keys) for transactions [1], offers a combination of complex technologies that establish a shared truth among system members [2]. With its data protection capabilities, blockchain is considered a secure and reliable technology for data storage [3], recording each transaction as a hash code with transparency and imputability [4].

The potential of blockchain to disrupt traditional business models [5] and its relevance in small and medium enterprises (SMEs) for competitive advantage and cost reduction [6] have attracted attention. However, limited adoption rates in the tourism industry are attributed to insufficient understanding of the influencing factors [7].

In tourism, blockchain technology promises a transformative future [5, 8]. The competitive and innovative tourism landscape can benefit from blockchain solutions, including market identification, transparency in transactions, payment facilitation, reviews’ reliability, supply chain control, and more [5, 8, 9].

Public acceptance of blockchain in smart tourism has been positive, highlighting the importance of security, trust, and transparency [10]. Factors influencing technology acceptance encompass user education, system characteristics, and implementation process [11]. Studies emphasize utility, ease of use, performance expectancy, and facilitation conditions as significant influencers [12,13,14,15].

Intention to use (IN) is pivotal, defined as an individual’s plan for future engagement [16]. Blockchain’s implementation aligns with trust and security, ensuring secure transactions with user agreement and verification [17].

3 Developing the Research Hypotheses

Researchers predicted that intention to use would catalytically mediate the relationship between trust in technology and acceptance. An important result was also presented: the association between intention and acceptance. The ensuing research discussion describes how various factors, including intention, relate to acceptance [16]. Therefore, it is worthwhile to investigate the rate of intention to use blockchain technology in tourism-related businesses and organizations on its acceptance by users and use. For this purpose, the following hypothesis is proposed:

H1: The intention to use blockchain technology influences its acceptance by users in the tourism industry.

Trust can lead to a change in customer decisions regarding technology or service [18]. Therefore, if trust comes from a blockchain-based consensus protocol, it encourages this “chain” of trust [19]. The findings of these studies highlight that trust is the main factor influencing consumer behavior and decisions, and ultimately, acceptance [18]. Therefore, the following research hypothesis on user trust in blockchain technology can be formulated:

H2: Trust towards blockchain technology influences its acceptance by users in the tourism industry.

4 Methodology and Data

This study aims to highlight the factors that contribute to the acceptance of blockchain technology in tourism. Specifically, the aim is to investigate the intention and trust tourists or travelers feel about blockchain technology by utilizing it in their tourism experiences. This survey was conducted using a combination of census surveys and snowball sampling. Quantitative data analysis was conducted and the questionnaires developed to conduct this research were structured with 26 closed-ended questions. The measurements used in the research are an adaptation of Taylor and Todd [20] for intention and an adaption of Gefen et al. [21] for trust. The collected sample was 160 answered questionnaires. I erased the sentence that it was a good sample please elaborate here.

After the completion of the data collection, the consolidation of the two questionnaires, Greek and English, was followed by the coding of the answered questionnaires, statistical processing, and analysis to highlight the results of the research and link them to the research hypotheses to determine whether they proved to be valid. The IBM SPSS Statistics program was used to perform statistical analysis. Table 1 presents the variables used in the study.

Table 1 Regression Formulas
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To calculate the effect of the dependent variables on the intent to use blockchain technology in the tourism industry, we used the following OLS regression formulas:

$$\begin{aligned} {\varvec{Intention}}_{{\varvec{i}}} & = {\varvec{a}} + { }{\varvec{\beta}}_{1} {\varvec{NATIONALITY}}_{{\varvec{i}}} + { }{\varvec{\beta}}_{2} {\varvec{GENDER}}{ } \\ & \quad + {\varvec{\beta}}_{3} {\varvec{AGEGROUP}}_{{\varvec{i}}} + {\varvec{\beta}}_{4} {\varvec{EDUBACK}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{5} {\varvec{YEARSKNOWBC}}_{{\varvec{i}}} + {\varvec{\beta}}_{6} {\varvec{YEARSUSEBC}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{7} {\varvec{YEARSUSECC}} + {\varvec{\beta}}_{8} {\varvec{BCINTOURISM}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{9} {\varvec{BUSTRAVEL}}_{{\varvec{i}}} + {\varvec{\beta}}_{10} {\varvec{ENTERTRAVEL}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{11} {\varvec{TRAVELS}} + { }{\varvec{u}}_{{\varvec{i}}} \\ \end{aligned}$$
(1)
$$\begin{aligned} {\varvec{Trust}}_{{\varvec{i}}} & = {\varvec{a}} + { }{\varvec{\beta}}_{1} {\varvec{NATIONALITY}}_{{\varvec{i}}} + { }{\varvec{\beta}}_{2} {\varvec{GENDER}}{ } \\ & \quad + {\varvec{\beta}}_{3} {\varvec{AGEGROUP}}_{{\varvec{i}}} + {\varvec{\beta}}_{4} {\varvec{EDUBACK}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{5} {\varvec{YEARSKNOWBC}}_{{\varvec{i}}} + {\varvec{\beta}}_{6} {\varvec{YEARSUSEBC}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{7} {\varvec{YEARSUSECC}} + {\varvec{\beta}}_{8} {\varvec{BCINTOURISM}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{9} {\varvec{BUSTRAVEL}}_{{\varvec{i}}} + {\varvec{\beta}}_{10} {\varvec{ENTERTRAVEL}}_{{\varvec{i}}} \\ & \quad + {\varvec{\beta}}_{11} {\varvec{TRAVELS}} + { }{\varvec{u}}_{{\varvec{i}}} \\ \end{aligned}$$
(2)

5 Empirical Results and Discussion

Initially, steps were followed to perform factor analysis of the questions, which examined the survey variables, and then to check their reliability. The reliability of the scales used to assess the internal consistency of the constructs was assessed using Cronbach’s alpha, and the value was 0.891 > 0.8 for intention was considered a good and acceptable value, and 0.901 > 0.9 for trust, which was considered an excellent value.

Principal component analysis (PCA) was used, and the data were rotated using the orthogonal method (rotation method: varimax). From the orthogonal rotation (varimax rotation), we observed that the variables recorded a positive load of > 0.5. Therefore, based on the factor analysis results, these measurement scales are valid and can be used to measure these variables. The total number of questions was 26, corresponding to the independent and dependent variables of perceived usefulness, perceived ease of use, intention, and confidence. We identified two dependent variables, intention and trust. Table 2 presents the descriptive statistics.

Table 2 Variables definition
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The results of the empirical analysis aimed to explore the relationships between various independent variables and the dependent variable of intention. Table 3 displays the unstandardized coefficients (B), t-values, and significance levels (Sig.) for each variable. Regression analysis revealed several significant variables that influenced intention. Specifically, at the 1% significance level, the variable “ENTER. TRAVEL”, which means travel for entertainment, demonstrates a negative relationship with intention, indicating that individuals engaged in this type of travel are less likely to have the intention to accept blockchain technology in the tourism industry. Furthermore, at the 5% significance level, the variable “NATIONALITY” shows a negative relationship with intention, indicating that different nationalities have varying levels of intention toward blockchain technology [12, 21]. The nationality of the respondents is divided into those who answered the Greek questionnaire (120/160) and those who answered the English (40/160), without specifying the exact nationality of each. Additionally, the variables “YEARS KNOW BC” and “BUS. TRAVEL” shows a negative relationship with intention at the 10% significance level, implying that individuals with more experience in using blockchain technology are more likely to have an intention to accept it in the tourism industry [6]. These findings highlight the importance of understanding individual intentions and the impact of specific factors on the acceptance of blockchain technology.

Table 3 Descriptive statistics
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The analysis also identified significant variables influencing trust in accepting Blockchain Technology as tourists or travelers to relevant enterprises and organizations. Notably, at the 1% significance level, the variable “YEARS USE BC” exhibits a positive relationship with trust, suggesting that individuals with more experience using blockchain technology are more likely to trust it in the context of the tourism industry [15]. These findings emphasize the importance of trust-building efforts and the need to understand how various factors shape individuals’ trust in blockchain technology for successful accept in the tourism sector (Table 4).

Table 4 OLS estimates for intention and trust
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Overall, these findings provide insights into the factors that significantly impact the intention and trust to accept Blockchain Technology in the tourism industry. The variables that showed significant effects can serve as important indicators for understanding individuals’ intentions and levels of trust in accepting blockchain technology as tourists or travelers to relevant enterprises and organizations [14].

6 Conclusions

This study was conducted to highlight the factors affecting the acceptance of Blockchain Technology among intention and trust in the tourism industry. In terms of intention, cultural and contextual factors associated with different nationalities significantly influence individuals’ willingness to adopt blockchain technology. Initially, people who travel for entertainment are not willing to accept blockchain solutions in this industry, which is a worthy element to be considered by tourism enterprises and organizations.

Surprisingly, individuals with more knowledge of blockchain technology exhibit lower levels of intention, possibly because of concerns or perceptions regarding its implementation. Additionally, engagement in business travel negatively affects intention, suggesting that existing systems or processes in the business travel sector may hinder the acceptance of blockchain technology. If we must make a conclusion about trust, it is obviously mainly influenced by years of knowledge of the technology. Years of knowledge of blockchain technology affect trust, indicating reservations or concerns among industry professionals regarding its implementation or potential impact. Other unobserved variables may also contribute to individuals’ acceptance of blockchain technology in the tourism industry, highlighting the need for a comprehensive understanding of the various factors that influence intention and trust. These findings underline the significance of considering cultural factors, types of travel, and people who already know about this technology when designing strategies to promote the acceptance and adoption of blockchain technology in the tourism industry. By addressing concerns, fostering trust, and tailoring approaches to specific contexts, successful implementation of blockchain technology can be facilitated. Based on the identified factors, stakeholders in the tourism industry can develop intervention strategies to address concerns, enhance trust, and promote the benefits of blockchain technology.

This study had several limitations that should be considered. First, the generalizability of the findings is limited because the sample used may not fully represent a broader population or diverse geographic regions. Furthermore, the temporal limitations of the data restrict their ability to capture the current landscape accurately.

In conclusion, we propose suggestions for future research, such as supplementing quantitative research with qualitative approaches, to offer a deeper understanding of individuals’ perspectives and decision-making processes.