Blockchain Technology Adoption Behavior and Sustainability of the Business in Tourism and Hospitality SMEs: An Empirical Study

: This paper investigates the factors inﬂuencing the intention to adopt cryptocurrency payments among small to medium-sized enterprises (SMEs) in tourism and hospitality through the lens of the technology acceptance model (TAM). This paper uses an original sample of 101 SMEs employing a total of 15,831 people in Taiwan. Structural equation modeling is used to examine the joint e ﬀ ect of both internal and external factors inﬂuencing the intention to use cryptocurrency payments. Results show that (1) strategic orientation, owner / managers personal characteristics (self-e ﬃ cacy and innovativeness) and social inﬂuence have a strong e ﬀ ect on the intention to adopt new technology; (2) perceived usefulness mediates the e ﬀ ects of strategic orientation and social inﬂuence; and (3) perceived ease of use mediates the e ﬀ ect of self-e ﬃ cacy on the intention to adopt cryptocurrency payments. The present study is one of few empirical inquiries about cryptocurrency payment adoption among SMEs. This study extends the theoretical foundations of the TAM into the speciﬁc characteristics of SMEs. Limitations of the study are sample size and a single survey design. However, ﬁndings of this research on the cryptocurrency payment adoption o ﬀ er practical implications for tourism stakeholders towards supporting SMEs competitiveness. The originality of this study is based on the fact that cryptocurrency payment is a new technology, and on the potential of cryptocurrency payments to disrupt the traditional way of operating tourism and hospitality SMEs. Hence the importance to consider major factors inﬂuencing SMEs’ intentions to adopt this technology.


Introduction
Information technology (IT) is a strategic asset for organizational performance and competitiveness in the tourism and hospitality industry [1] and for small and medium enterprises (SMEs) [2,3]. IT adoption in this study is defined as a process of accepting and implementing IT to deliver services such as booking, reservation and payment. SMEs comprise the majority of businesses in many regions of the world [4,5] and are crucially important for many economies [6][7][8]. There is a gap in our understanding of the behavior of tourism and hospitality SMEs in IT adoption in general [9][10][11][12] and in blockchain technology adoption in particular [13].
Tourism and hospitality SMEs operate in a challenging business environment and face intense competition from traditional (e.g., established hotel chains, such as the Hilton, Marriot and Sheraton) and

Predicting Technology Adoption
Well-established theoretical frameworks about IT adoption behavior include the theory of reason action [53], theory of planned behavior [54], technology acceptance model (TAM) [34], TAM 2 [55], TAM 3 [56] and the unified theory of acceptance and use of technology (UTAUT) [57]. Rad's 2018 comprehensive review examining the trend of technology adoption research stated that the TAM is the most dominant of the theories [30]. TAM's influence is explained by its simplicity and validity in terms of theoretical attributes, empirical foundation and general applicability to technology adoption issues in diverse domains. TAM allows estimating IT adoption based on usage measures, objectively and subjectively [58]. TAM also allows focusing on social and individual factors [59], which are considered in this study.
The theoretical assumption in the TAM framework is that the behavioral intention (BI) to adopt new IT is determined by two main factors: perceived usefulness (PU) and perceived ease of use (PEOU). PU is the extent to which a person believes that using an IT will enhance performance, and PEOU is the degree to which a person believes that using an IT will be free of effort [34,55,57]. PE and PEOU have direct impact on IT adoption [30,60,61]. Additionally, it is theorized and tested empirically that PEOU operates indirectly through PU [34,[62][63][64]. The present study examines the relationships between PU and PEOU and their influence on the intention to adopt cryptocurrency payment in order to replicate the existing theory in the novel context of blockchain technology. The following hypotheses are proposed: Hypothesis 1. PU of cryptocurrency payment positively affects SMEs' BI.

Hypothesis 3.
There is a positive relationship between the PEOU of cryptocurrency payment and PU of its adoption.
Previous studies have predominantly analyzed internal factors and external factors separately. The following constructs influence technology acceptance: individual differences [55,[65][66][67][68][69], new technology characteristics [34,70,71], social influences [55,56,[71][72][73][74] and organizational characteristics [70]. Identification of both internal and external factors is relatively scarce in IT innovation literature [46]. Wang and Qualls [12] adapted TAM and proposed an integrated model of IT adoption behavior in tourism and hospitality organizations with consideration of both internal and external factors. Their model focuses on such internal antecedents of adoption as strategic orientation, organization technology climate, technology characteristics, information processing characteristics and external factors-supplier marketing strategies [12]. They also mentioned that SMEs' owner/manager individual characteristics have to be taken into consideration, as the owner/manager's technology expertise makes a positive impact on adoption [75].
This study draws from the models of TAM [12] and integrates two additional important antecedents of IT adoption in SMEs: owner/manager individual characteristics [76] and social influence [77,78]. Figure 1 illustrates the research model. This study empirically examines the joint effect of such internal factors as strategic orientation and owner/manager individual characteristics, and external factors-social influence and technology characteristics influencing IT adoption behavior among SMEs in tourism and hospitality. The details of these constructs are discussed below.

Strategic Orientation
Strategic orientation is defined as strategic directions taken by the organization in order to create a supportive infrastructure and behavior for business competitiveness [79]. It is also the mechanism for identifying and responding to a competitive environment [12]. Previous research noted that SMEs' strategic orientation is an important factor for IT adoption behavior [39][40][41]80]. There are three parts of strategic orientation important for tourism and hospitality: customer, competitor and technology [12,14,18].
Customer orientation is defined as ''the set of beliefs that put the customer interest first [81] and a firm's understanding of its target customers that allows creating superior customer value" [79]. Wang and Qualls [12] hypothesize that if a customer-oriented hospitality organization decides that using technology can improve customer service, it will be more likely to adopt the technology.
Competitor orientation is defined as the ability to identify, analyze and respond to competitors' behavior in the marketplace [79]. Competitor-oriented tourism and hospitality organizations are hypothesized to not hesitate to adopt new IT if their competitors are utilizing those IT for a competitive advantage [12].
Technological orientation is defined as the ability of a firm to obtain the necessary IT background and use IT in the development of new products and services [12]. Technological-oriented hospitality organizations are described as research-oriented and proactive in acquiring new IT [82,83].
From the above it follows that SMEs' strategic orientation positively influences PU. SMEs operating in highly competitive and innovative industries, like tourism and hospitality, are more likely to be driven toward IT adoption than those in other less intensive types of industries, such as agriculture, construction etc. [76,84,85]. Hence the present study hypothesizes:

Strategic Orientation
Strategic orientation is defined as strategic directions taken by the organization in order to create a supportive infrastructure and behavior for business competitiveness [79]. It is also the mechanism for identifying and responding to a competitive environment [12]. Previous research noted that SMEs' strategic orientation is an important factor for IT adoption behavior [39][40][41]80]. There are three parts of strategic orientation important for tourism and hospitality: customer, competitor and technology [12,14,18].
Customer orientation is defined as "the set of beliefs that put the customer interest first [81] and a firm's understanding of its target customers that allows creating superior customer value" [79]. Wang and Qualls [12] hypothesize that if a customer-oriented hospitality organization decides that using technology can improve customer service, it will be more likely to adopt the technology.
Competitor orientation is defined as the ability to identify, analyze and respond to competitors' behavior in the marketplace [79]. Competitor-oriented tourism and hospitality organizations are hypothesized to not hesitate to adopt new IT if their competitors are utilizing those IT for a competitive advantage [12].
Technological orientation is defined as the ability of a firm to obtain the necessary IT background and use IT in the development of new products and services [12]. Technological-oriented hospitality organizations are described as research-oriented and proactive in acquiring new IT [82,83].
From the above it follows that SMEs' strategic orientation positively influences PU. SMEs operating in highly competitive and innovative industries, like tourism and hospitality, are more likely to be driven toward IT adoption than those in other less intensive types of industries, such as agriculture, construction etc. [76,84,85]. Hence the present study hypothesizes: Hypothesis 4. There is a positive relationship between SMEs' strategic orientation and their PU of cryptocurrency payment.

Social Influence
Social influence is the extent to which individuals perceive that important others (e.g., family and friends) believe they should use a particular technology [77]. It reflects the extent to which an individual places importance on the views of others versus their own view of IT use [86]. It is well established that social influence shapes IT adoption behavior and in turn affects IT use [86][87][88][89]. Social influence has a stronger impact on the behavioral intention to use new payment technology than convenience, usefulness and fun [90]. Peer-to-peer communication and social norms are important antecedents of IT adoption [78]. Consequently, a SME's owner/manager's social circle might influence their perceptions about the usefulness of IT and its adoption. Social influence has an indirect effect via PU on BI [91,92] and is moderated by gender, age, experience and voluntariness [57,93]. Following the above, a positive relationship between social influence and SMEs IT adoption behavior is expected.

Hypothesis 5a.
There is a positive relationship between social influence and the PU of cryptocurrency payment adoption.

Hypothesis 5b.
There is a positive relationship between social influence and the SMEs' BI to adopt cryptocurrency payment.

SMEs Owner/Manager's Individual Characteristics
Individual differences are important factors in the IT adoption process [56,65,67]. Individual differences influence perceptions of PU and PEOU and are reflected in personality or demographic variables [56].
Innovativeness is an individual characteristic that predisposes individuals to try out new IT [62,101] and plays a positive role on IT adoption behavior [62,102]. Innovativeness is a reliable predictor of individuals' attitude, PU, PEOU [103] and IT acceptance [67]. We expected that the innovativeness of SMEs' owner/manager plays a major role in the behavioral intention to adopt cryptocurrency payment technology. It is expected that innovativeness has a positive effect on PEOU and as a result influences the intention to adopt cryptocurrency payments. Hence this study hypothesizes the following: Hypothesis 6. The SMEs owner/manager innovativeness has a positive effect on the PEOU of cryptocurrency payment.
Self-efficacy is defined as an individual's beliefs regarding his/her ability to use IT [56] or as "judgment of one's ability to use a technology to accomplish a particular job or task" [104,105]. Self-efficacy is positively correlated with IT acceptance and adoption [106][107][108][109] including among SMEs [110]. Self-efficacy has a strong influence on PEOU [55] and IT adoption [34,106,111]. Hence this study hypothesizes the following: Hypothesis 7. The SMEs' owner/manager's self-efficacy has a positive impact on PEOU and behavioral intention to adopt cryptocurrency payments.

IT Characteristics and Cryptocurrency Payments
IT characteristics are the features that help an individual or organization to develop perceptions regarding PU or PEOU [12,56]. Technology characteristics directly affect both PEOU and PU of IT [34]. The literature on blockchain and cryptocurrency payments states that important characteristics are security (e.g., viability, security risk, third party service failure risk, risk of user error, risk of privacy loss, risk of counterparty fraud, risk of illicit association) and convenience (e.g., free participation, instant transfers, simple interface, control of one's own money, disintermediation, high speed of transfer, low cost of transfer, high transaction security, limited supply, international scope, lowers merchants cost, increase customer trust requirements, higher price volatility) [13,25,31,33,112,113].
In this study we define security as the degree to which users believe in the security of IT [59]. On the one hand, cryptocurrency payments have been negatively associated with illicit dark web commerce [114,115], and on the other hand, every cryptocurrency transaction is recorded on the publicly viewable blockchain, which is designed to increase security [31,116,117]. Convenience is related to time and place utility for users [62,118] as IT is intended to make peoples' life easier and simplify everyday tasks [119]. Convenience is one of the most important factors in the success of such IT as mobile commerce [120]. Convenience is also one of characteristics of cryptocurrency payments.
In the context of SMEs in tourism and hospitality, researchers argue that IT characteristics tend to exert a moderating effect on the relationship between the perception of technology and the ultimate adoption behavior [12,121]. Following the above we take into consideration the moderating effect of security and convenience of cryptocurrency payments on the effect of PU and PEOU on BI. Additionally, this study considers SMEs' owners/managers' age and gender, which are a moderating construct in the TAM [55,57].

Sample and Data
The original data were collected through a survey carried out among tourism and hospitality SMEs owners/managers in the Hualien area in Taiwan. SMEs owners/managers were contacted through the Hualien County homestay association, which has a database that includes information about more than 200 firms. A total of 150 firms from the association met the requirements of our sample. Firms were selected in line with the adopted definition of SMEs with fewer than 250 employees [95,122]. The survey was conducted during the annual association gathering among the owners/managers of hotels, motels, resorts, bed and breakfast and restaurants in the Hualien area in Taiwan in May-June 2018. The questionnaire explored a range of factors related to SMEs' IT adoption behavior. During data collection, each SME owner/manager was approached face to face and was verbally asked whether he/she has knowledge or experience regarding cryptocurrency payments. Respondents who were unaware of cryptocurrency payments were not invited to take part in this survey. The questionnaire also contained two questions on knowledge/usage of cryptocurrency payments ("how would you qualify your level of your knowledge of CC payment?" (1-very low and 5-very high) and "how many times did you make CC payments?" (1-never and 5-several times or more). Following this process the authors received replies from 106 owners/managers, and after reviewing each of the responses, 5 responses were removed, as some questions had not been completed. Therefore, a total of 101 valid questionnaires was collected (response rate of 67.3% percent) from family owned and non-family owned SMEs. The sample size recommended by the previous research ranges from 30 to 460 cases [123]. Hence the sample size of this study is meaningful for analyzing the constructs and research model. Table 1 shows the demographic profile of respondents and the average age of the SMEs.

Measurement of Constructs
The research model is composed of 9 constructs including strategic orientation, social influence, innovativeness, self-efficacy, complexity, security, PU, PEOU and BI. Table A1 in the Appendix A shows each construct measurement using multiple items. All the measurement items were adapted from previously validated and reliable measurement scales to preserve content validity. Each item is measured on a seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree).

Strategic Orientation (SO)
Referring to previous research, we use 10 items to assess SMEs' strategic orientation. Customer orientation is measured with 3 items [12,79,81]; competitor orientation is measured with 3 items [12,79,82]; and technology orientation is measured with 4 items [12,82,83]. For example: "we are likely to take advantage of the benefits new technology can bring to our business" [12]. After deleting 4 low-loading items, 6 items were used to run the check of the SO construct in the confirmatory factor analysis (CFA). The composite reliability (CR) of the construct was 0.95.

Social Influence (SI)
Social influence was measured using a total of 6 items adapted from previous studies [55][56][57]71,77]. For example "People who are important to me are likely to recommend using cryptocurrency payment technology"". The composite reliability (CR) of the construct is 0.92.

SMEs Owner/Manager Characteristic Innovativeness and Self-Efficacy
SMEs' owner/manager's innovativeness was assessed using a personal innovativeness scale from [62] consisting of 3 items (e.g., I know more about new products before other people do). The composite reliability (CR) of the construct is 0.90. SMEs' owner/manager's self-efficacy was measured using 3 items adapted from [124] (also used in [125,126]. For example "I would feel comfortable using the cryptocurrency payment on my own; "if I wanted to, I could easily operate any of the steps in the cryptocurrency payment technology on my own"; "I would be able to use the equipment in the cryptocurrency payment even if there was no one around to show me how to use it". The composite reliability (CR) of the construct was 0.92.

Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)
The PU was measured using 6 items from [77] and [124]. The PEOU was measured using 3 items adapted from [77]. Every statement measuring PU and PEOU constructs was adapted to the topic of cryptocurrency payment technology. The composite reliability (CR) of the PU construct of total social capital was 0.91 and the PEOU construct of total social capital was 0.96.

Behavioral Intention (BI) to Adopt IT
Behavioral intention to adopt cryptocurrency payment technology was assessed using 4 items adapted from the works of [77,127]. The composite reliability (CR) of the construct of total social capital was 0.95.

Technology Characteristics, Security and Convenience
Security is conceptualized as perceived risk in the context of digital/online payments, consistent with the previous literature [91,128]. Security is measured with four items adapted from [129]. For example, "there are high chances of losing money if my business uses cryptocurrency payments". The composite reliability (CR) of the construct of total social capital was 0.96. Convenience is measured using four items adopted from [62]. For example, "cryptocurrency payment is convenient because I can use it anytime". The composite reliability (CR) of the construct of total social capital was 0.95.

Analysis and Results
This study used several stages of statistical analysis in testing the above hypotheses illustrated in the model (Figure 1). First, data screens, checks and dimension reduction are necessary procedures before determining the content of the construct. Exploratory factor analysis (EFA) was employed to determine the high-loading items of each dimension. Secondly, a confirmatory factor analysis (CFA) [130] was applied to test the measurement model and to check the relationships among constructs. Third, a structural equation modeling (SEM) analysis was used to estimate the causal effect among constructs and to test the research model. Compared to linear regression methods, SEM gives a clearer explanation of potential variables needed for the final model [131,132]. The method helps to verify the problems of common method variance (CMV), nonresponse bias and question response bias. It also examines the mediated or moderated relationships to indicate the strength of the relationships and if they are direct or indirect [131][132][133].

Exploratory Factor Analysis
Initially, principal axis factoring was used to find the common variance (correlation) of a set of variables and build the construct dimensions. We excluded low loading items from building the construct dimensions. Oblique rotation and Kaiser eigenvalues were utilized as a criterion (eigenvalues ≥1.0) to form the nine dimensions. We deleted a few low-loading reflective items after implementing exploratory factor analysis. We are confident that the selected items present adequate measurements for each of the dimensions.

Reliability and Validity
The following measures and criteria were used to check components' reliability and construct validity. The value of composite reliability (CR) verifies reliability if the value is greater than 0.7. The value of average variance extracted (AVE) [134] verifies convergent validity when it is greater than 0.5; and the square root of AVE indicates discriminant validity when it is greater than inter-construct correlations [132,135,136]. As suggested by Fornell and Larcker (1981) regarding CR and AVE, we estimate the reliability and convergent validity by standardized regression weights (loadings) of items. We found that all CR values were above 0.7, and the AVEs were higher than the 0.5 threshold, which indicates that the items reflect the concepts consistently (see Table 2). Thus, convergent validity was established and the measurement scale considered is reliable [132]. Comparison of the square root of AVE (on the diagonal) with the inter-construct correlations (see Table 2) indicate that the validity of the measurement scale and construct discriminant are adequate.

CFA/SEM Model Analysis
Before testing the hypotheses of the model, it is necessary to examine a common method bias (CMB) that causes high correlation among constructs. We used a common latent factor (CLF) to capture the common variance among all observed variables in the model. We added a marker variable (security) and identified a significant method bias in our model indicated by a significant chi-square difference between a zero-constrained and unconstrained model. Therefore, we controlled for the security factor in our structural analyses [137,138].
Next, we checked the overall measurement model using the maximum likelihood method to estimate the level of model fit. The results of the model fit showed that χ2/df was 1.825 (χ2 = 540.337, df = 296, p < 0.000), Confirmatory factor analysis = 0.925 and Standardized Root Mean Square Residual = 0.059, suggesting an adequate model fit [130]. Based on our assumptions, there are four covariance matrices composed of independent variables: strategy orientation, social influence, innovativeness and self-efficacy. Behavioral intention (BI) to adopt IT is a dependent variable; perceived usefulness (PU) and perceived ease of use (PEOU) are two mediator variables. In addition, we assume security, convenience, age and gender have a moderating effect and consider them as potentially confounding variables in the model.
The results of hypothesis testing are presented in Table 3. Eight regression coefficients are statistically significant (Figure 2). Firstly, four independent variables all have a significant effect on two mediator variables. Strategy orientation (SO) and social influence (SI) have a statistically significant effect on perceived usefulness (PU), both significant at a 1% level. Innovativeness and self-efficacy have a statistically significant effect on perceived ease of use (PEOU), significant at 10% and 1%, respectively. The standardized regression weights are as follows: (1) from SO to PU, r = 0.186, p ≤ 0.001; (2) from SI to PU, r = 0.330, p ≤ 0.001; (3) from innovativeness to PEOU, r = 0.163, p ≤ 0.1 and (4) from self-efficacy to PEOU, r = 0.674, p ≤ 0.001. Secondly, one independent variable, SI, has a significant effect on dependent variable BI. The standardized regression weight from SI to BI are r = 0.546, p ≤ 0.001. Third, the relationship between mediators is significant. PEOU has a significant effect on PU with a standardized regression weight of r = 0.614, p ≤ 0.001. Finally, two mediators have a significant effect on the dependent variable. The standardized regression weight from PU to BI is r = 675 p ≤ 0.001 and from PEOU to BI is r = 859 p ≤ 0.001.  For the sake of parsimony, we did not present the effects of control variables (i.e. Age, Gender, Income, and Education level) here; † p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion and Conclusions
This study seeks to extend our understanding of cryptocurrency payments adoption by extending TAM to the context of SMEs in tourism and hospitality. The research model in this study enhances the theoretical foundations of TAM through the integration of both internal and external forces shaping the adoption behavior with consideration of characteristics of SMEs in tourism and hospitality. This study theorizes the effect of such internal factors as SMEs' strategic orientation, owner/manager's individual characteristics and external factors-social influence and technology characteristics on cryptocurrency payments adoption. We found that the effect of social influence, strategic orientation and SMEs' owner/manager's individual characteristics have a significant effect on the behavioral intention to adopt cryptocurrency payments. However, the moderating effects of technology characteristics, gender and age on BI were not significant. The significant effect of social influence on PU and BI could be explained by a strong collective culture in Taiwan, which is dominant in shaping behavior [139,140]. The absence of a significant moderating effect of gender and age could also be due to collectivist values of Taiwanese culture, where individuals are more likely to comply with the opinions of salient others regardless of age and gender [93,141].
This paper makes a few important contributions to the research. First, this is one of the first studies that empirically examine the adoption of new blockchain technology with the example of cryptocurrency payments. Generally, there are very few empirical studies on IT adoption in the tourism and hospitality literature [13,30]. Second, several authors have suggested that the TAM model and its extensions are applicable to the adoption of cryptocurrency payments [31,33]. This study confirmed that PU and PEOU are salient factors in the behavioral intention to adopt cryptocurrency payments, consistent with prior TAM theories [62,77]. Additionally, the present work is one of the few empirical investigations of the joint effect of internal and external factors influencing SMEs' IT adoption behavior considering specific industry characteristics. Due to the model including four independent variables and two mediator variables, we conducted the direct and indirect effect analyses separately. The results show the following: PU mediates the relationship from SO to BI and from SI to BI; and PEOU mediates the relationship from self-efficacy to BI. However, PEOU was not found to mediate the relationship from innovativeness to BI (See Table 3).

Discussion and Conclusions
This study seeks to extend our understanding of cryptocurrency payments adoption by extending TAM to the context of SMEs in tourism and hospitality. The research model in this study enhances the theoretical foundations of TAM through the integration of both internal and external forces shaping the adoption behavior with consideration of characteristics of SMEs in tourism and hospitality. This study theorizes the effect of such internal factors as SMEs' strategic orientation, owner/manager's individual characteristics and external factors-social influence and technology characteristics on cryptocurrency payments adoption. We found that the effect of social influence, strategic orientation and SMEs' owner/manager's individual characteristics have a significant effect on the behavioral intention to adopt cryptocurrency payments. However, the moderating effects of technology characteristics, gender and age on BI were not significant. The significant effect of social influence on PU and BI could be explained by a strong collective culture in Taiwan, which is dominant in shaping behavior [139,140]. The absence of a significant moderating effect of gender and age could also be due to collectivist values of Taiwanese culture, where individuals are more likely to comply with the opinions of salient others regardless of age and gender [93,141].
This paper makes a few important contributions to the research. First, this is one of the first studies that empirically examine the adoption of new blockchain technology with the example of cryptocurrency payments. Generally, there are very few empirical studies on IT adoption in the tourism and hospitality literature [13,30]. Second, several authors have suggested that the TAM model and its extensions are applicable to the adoption of cryptocurrency payments [31,33]. This study confirmed that PU and PEOU are salient factors in the behavioral intention to adopt cryptocurrency payments, consistent with prior TAM theories [62,77]. Additionally, the present work is one of the few empirical investigations of the joint effect of internal and external factors influencing SMEs' IT adoption behavior considering specific industry characteristics.
Considering limited research that has examined the topic of blockchain adoption among SMEs, we propose specific areas for future research to better understand the relationship between cryptocurrency payments and SMEs' performance. Although we have acknowledged the roles of the SMEs' legislative and technological environments affecting cryptocurrency payment adoption, we did not explicitly measure this. Future studies could explore wider implications of blockchain technology among SMEs as a way to attract and respond to customer demands and sustain pressure from large established and new players (e.g., Airbnb) in tourism and hospitality. Future studies could also employ other analytical methods, such as spatial analysis [142], in order to analyze the diffusion of blockchain innovation.
SMEs face challenges from the innovations embraced by the younger "digitally native" generation [92]. Alternative payment technology, such as cryptocurrency payments, may disrupt traditional financial payments, and its adoption by SMEs becomes vital for their successful competitiveness and development. However, research in SMEs and tourism and hospitality is falling behind in addressing this issue. This study highlights this key issue and makes an important step towards informing relevant stakeholders, government representatives, industry professionals and policy makers. Relevant stakeholders could develop strategies, provide early training programs and foster development of an environment that supports SMEs' IT adoption and as a result facilitate their sustainable development and competitiveness.
Author Contributions: All of the authors contributed equally in the development of the present paper. All of the phases of the paper development proper have been discussed and worked on by the authors. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.

Acknowledgments:
The authors are very grateful to all the participants from the Hualien County tourism and hospitality firms association who filled in the questionnaire. We would like to thank the anonymous reviewers for their helpful comments and suggestions, which improved the quality of this paper greatly.

Conflicts of Interest:
The authors declare no conflict of interest.