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Cryptocurrency adoption in travel and tourism – An exploratory study
of Asia Pacific travelers
Horst Treiblmaier
Department of International Management, Modul University Vienna
Vienna, Austria
horst.treiblmaier@modul.ac.at
Daniel Leung
School of Hotel and Tourism Management, The Hong Kong Polytechnic University
Hong Kong
daniel.yc.leung@polyu.edu.hk
Andrei Kwok
Department of Management, Sunway University Business School
Petaling Jaya, Malaysia
andreik@sunway.edu.my
Aaron Tham
USC Business School, University of the Sunshine Coast
Sippy Downs, Queensland, Australia
mtham@usc.edu.au
Citation: Treiblmaier, H., Leung, D., Kwok, A. O. J. and Tham, A. (2020)
"Cryptocurrency Adoption in Travel and Tourism – an Exploratory Study of Asia
Pacific Travellers", Current Issues in Tourism, Vol. 24, No. 22, pp. 3165-3181
DOI: https://doi.org/10.1080/13683500.2020.1863928
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Cryptocurrency adoption in travel and tourism –
An exploratory study of Asia Pacific travelers
Abstract: Blockchain technologies are predicted to substantially transform the tourism
industry. At present, cryptocurrencies are the most advanced application of public
blockchains that promise benefits such as a universal means of payment and minimal fees
through the removal of intermediaries. In the tourism industry, though many tourism
vendors have been accepting cryptocurrencies and the potential of using cryptocurrencies
in travel-related consumption has been intensively documented, existing knowledge
about travelers’ intention to use cryptocurrencies for payment purposes is limited.
Traditional models do not account for the idiosyncrasies of cryptocurrencies and are
therefore less appropriate to foster the understanding of travelers’ adoption of travel-
related payments. To fill this knowledge gap, an exploratory study was conducted with
161 travelers from the Asia-Pacific region who have previously consumed travel-related
services with cryptocurrencies. Their previous usage experiences are analyzed and
reported. Through harnessing the correspondence analysis, several technological
contingency factors were identified, as well as positive and negative perceptual
antecedents. Additionally, their levels of satisfaction and intention to re-use the
technology in future trips were investigated. Based on these findings, several propositions
are suggested for guiding future research on travelers’ cryptocurrency adoption in the
travel and tourism contexts.
Keywords: cryptocurrencies, blockchain, Bitcoin, technology adoption, contingency
theory, correspondence analysis
Introduction
The globalization and digitization of travel and tourism are driving the demand for easy
to use and cheap international transaction processes and payment systems. Tourists are
beginning to discover that cryptocurrencies offer such opportunities - by simplifying
cross-border transactions and overcoming the associated foreign currency exchange
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costs. However, there is still a dearth of knowledge regarding the factors and mechanisms
affecting tourists’ inclination to adopt cryptocurrencies for travel-related consumption.
In the year 2008, an individual or a group of software developers operating under the
pseudonym ‘Satoshi Nakamoto’ found a way of avoiding the multiple spending of digital
assets (i.e., double spending). This laid the foundation for Bitcoin, a digital currency that
enables payment over the Internet without the intervention of intermediaries such as
banks or credit card companies (Nakamoto, 2008). Bitcoin’s open source client was
released in January 2009 and initially attracted only limited attention outside dedicated
circles of computer scientists and cryptographers. It was not until the mid-2010s that the
economic potential of blockchain was fully recognized, which subsequently lead to far-
reaching speculation about what can potentially be achieved with blockchain (Tapscott
& Tapscott, 2016). Defined as ‘a digital, decentralized, and distributed ledger in which
transactions are logged and added in chronological order with the goal of creating
permanent and tamper-proof records’ (Treiblmaier, 2018, p. 547), blockchain is often
used synonymously with distributed ledger technology, which is a broader concept that
also includes technologies that do not follow a chain-like structure. In this paper, the
common convention is followed and the term blockchain is used throughout, independent
of the characteristics of the underlying technology. Most of the popular cryptocurrencies,
such as Bitcoin, Ethereum, Litecoin, Dash, and Monero, make use of permissionless
public blockchains that are open to anyone to participate in the creation and validation of
transactions. As of November 2020, the cryptocurrency tracking website
CoinMarketCap.com listed more than 3,700 cryptocurrencies. Bitcoin is by far the
leading cryptocurrency in terms of total market capitalization (USD 250 billion). Another
six, including Ethereum, Tether, XRP, Bitcoin Cash, Chainlink and Binance Coin, exceed
a market capitalization of USD 4 billion respectively (CoinMarketCap, 2020).
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At the end of 2017 the blockchain hype peaked with the rise of countless
cryptocurrencies and the emergence of numerous Initial Coin Offerings, many of which
were fraudulent and caused substantial loss among unsuspecting investors. This bubble
negatively impacted the overall image of the blockchain, leading to speculation about
whether the technology can make a concrete contribution to future business value
generation (Kietzmann & Archer-Brown, 2019). However, the downfall of
cryptocurrencies and blockchain have given way to a more differentiated perspective that
calls for the identification and scrutiny of those use cases that can actually benefit from
the application of a distributed ledger. In recent years, more blockchain-related studies
were conducted and published in tourism and hospitality journals. For instance, some
studies discuss the potential of blockchain, and especially cryptocurrencies, to foster
innovative market structures and processes (Kwok & Koh, 2019). Some recent studies
also posit that blockchain can work in conjunction with hospitality operations and even
smart tourism frameworks (Filimonau & Naumova, 2020; Nam et al., 2019). Although
much scholarly effort has been made, several researchers still argue that the tourism
industry lags behind in the actual implementation of blockchain solutions (Kizildag et al.,
2020).
Given that most blockchain-based applications only affect companies’ backend
processes and are not directly noticeable to final consumers, cryptocurrencies are
presumably the single blockchain-based technology that is most visible and
comprehensible for end users. Their application potential does not stop at mere payment
functions but also includes the creation of dedicated coins for specific purposes. However,
there is still a dearth of research and frameworks that help to better understand travelers’
underlying rationale on whether or not to use cryptocurrencies for travel-related
payments. Existing technology adoption models are mostly generic and do not account
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for the idiosyncrasies of cryptocurrencies. More specifically, neither the positive and
negative consequences of blockchain nor the rapid technological change in this area are
explicitly considered in these models. To close this gap and to acquire an enriched
understanding of why cryptocurrencies are used by travelers, through this study we
develop a comprehensive framework and identify answers to the following research
questions:
• In what ways are cryptocurrencies being used in the travel industry?
• How do travelers perceive cryptocurrencies?
• What factors impact the adoption of cryptocurrency payments among travelers?
An explorative survey was conducted with consumers in the Asia-Pacific region
who have previously used cryptocurrencies to pay for travel-related products and
services. Specifically, these travelers were asked to report the type/s of products/services
that they used cryptocurrencies to pay for, their usage experiences and intention to re-use
during future trips. The findings were integrated into a framework that extends previous
adoption research by accounting for the idiosyncrasies of cryptocurrencies and other
contingency factors. This study therefore contributes to current research by developing a
theory-based model that illustrates what questions need to be further explored to better
understand travelers’ intention to use cryptocurrencies for travel-related consumption.
The remainder of this paper is structured as follows: In the following literature
section, the term blockchain and its various use cases are explained. Then, previous
research related to blockchain technologies in tourism is scrutinized. Subsequently, the
methodology adopted in our study is elaborated, followed by a presentation of our results.
This section follows the structure of the final model and is separated into cryptocurrency
use, technological contingency factors and users’ perceptual antecedents, and, finally, a
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discussion of various moderating variables, users’ satisfaction, and their intention to pay
with cryptocurrencies in the future. Finally, findings are discussed in light of previous
research, various theoretical and practical implications are derived, and the paper is
concluded with an outlook on future research.
Literature Review
Blockchain and Cryptocurrencies in Tourism
In view of the hype that appeared in 2017, blockchain started to attract attention in
academic communities and particularly the tourism academic community in recent years.
Early articles were mainly conceptual papers, pointing out various research opportunities
in C2C markets (Önder & Treiblmaier, 2018), benefits of blockchain-based use cases for
small island economies (Kwok & Koh, 2019), and others. Leung and Dickinger (2017)
researched and reported that Bitcoin was rarely used for payments by European travelers,
but they shared a very positive sentiment toward using it during future trips. Besides
Leung and Dickinger, several use cases of blockchain in tourism have been proposed,
including inventory management, maintenance and tracking, loyalty programs, baggage
tracking, smart tourism applications, and the enabling of coopetition (i.e., simultaneous
cooperation and competition) among business partners (Nam et al., 2019; Treiblmaier,
2020). Filimonau & Naumova (2020) present a comprehensive framework that illustrates
the multitude of potential blockchain applications in hospitality operations connecting
suppliers, brand/franchise owners, policy makers / destination management
organizations, and consumers. In this regard, blockchain also offers some potential to
alleviate the negative implications of pandemics such as COVID-19 (Önder & Gunter,
2020).
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Although the tourism industry has already started to work on blockchain
implementations (Korže, 2019), rigorous theory-based academic research in that area is
still relatively scarce. Thees et al. (2020) proposed a value chain approach that differs
between primary (i.e., ‘customer journey’) and secondary activities as well as identifying
potential blockchain-based applications ranging from itinerary planning to payment along
the value chain. Rashideh (2020) based his research on the theory of disruptive innovation
and applied expert interviews to investigate the impact of blockchain on tourism
intermediaries in Saudi Arabia. Tham and Sigala (2020) pointed out that the benefits of
cryptocurrencies go beyond mere payment purposes, and that they can contribute to
sustainable tourism development by democratizing participation in economic systems and
redistributing economic power. Using a social network analysis among Twitter users,
Bolici et al. (2020) reveal that the exchange of blockchain-related information in tourism
is characterized by a high turnover of the participants and only a few contributors that
determine the topics of interest as well as the general sentiment of the discussions.
In their conceptual paper, Nam et al. (2019) underscored the incentives provided by
cryptocurrencies will be the major determinant leading to the facilitation of a higher level
of adoption by travelers. In the same study, the researchers emphasize that the future
adoption of blockchain is not only a technological issue, but also a behavioral one that
depends on consumers’ attitudes and behaviors. In general, several researchers point out
existing shortcomings of current blockchain research in tourism, such as missing
descriptions of the underlying architecture and mechanisms (Valeri & Baggio, 2020).
Table 1 summarizes the recent literature on the impact of blockchain and cryptocurrencies
in tourism.
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Table 1. Literature on blockchain and cryptocurrencies in tourism.
Authors
Methodology
Results/Findings
Leung & Dickinger
(2017)
Survey
Bitcoin is infrequently used for online purchases among
European travelers.
Önder & Treiblmaier
(2018)
Conceptual
Suggestion of three research propositions pertaining to
the emergence of trustworthy rating systems, adoption of
cryptocurrencies and increasing disintermediation.
Kwok & Koh (2019)
Conceptual
Discussion of key blockchain applications to enhance
tourism in small island economies.
Nam et al. (2019)
Conceptual
Discussion of the key characteristics of blockchain
technology in conjunction with smart cities and tourism.
Derivation of four propositions on how the technology
can evolve and impact this industry.
Korže (2019)
Literature review
Examples of blockchain applications and smart contracts
in the tourism industry.
Thees et al. (2020)
Content analysis
Examination of blockchain use cases along the value
chain in the tourism industry.
Rashideh (2020)
Qualitative analysis
of expert interviews
Identification of various factors that lead to
disintermediation in the tourism industry.
Treiblmaier (2020)
Conceptual
Description of blockchain-based use cases in tourism and
a suggestion for future theory-based research.
Filimonau &
Naumova (2020)
Conceptual
An evaluation and framework development regarding the
potential of blockchain for future integration into
hospitality operations management.
Tham & Sigala (2020)
Literature review
Blockchains and cryptocurrencies increase trust,
democratize participation in economic systems and re-
distribute power.
Bolici et al. (2020)
Social network
analysis
Tourism information networks on Twitter dealing with
blockchain have a high participant turnover. Relatively
few contributors determine the topics and the overall
sentiments.
Valeri & Baggio
(2020)
Conceptual
Discussion of potential drivers and drawbacks of
blockchain adoption in tourism and suggestions for future
research.
Önder & Gunter
(2020)
Conceptual
Exploration and identification of use cases for blockchain
in the tourism and hospitality industry.
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Technology Adoption
Being one of the most researched topics in the academic community, studies on
technology adoption start with the seminal work from Rogers (1962) on the diffusion of
innovation which, over the years, led to the creation and refinement of numerous theories
and models, like the Technology Acceptance Model (TAM), the Task-Technology Fit
Model (TTF), and the Unified Theory of Acceptance of Use of Technology (UTAUT)
(Venkatesh et al., 2016). All of these theories and models have gained widespread
acceptance in tourism research and have been used with many modifications in a
multitude of contexts. Ayeh et al. (2013), for example, applied the conventional TAM to
investigate travelers’ antecedents to use consumer-generated media for travel planning.
Kim and Hall (2020) used UTAUT as a theoretical lens to examine the impact of digital
storytelling on consumers’ crowdfunding behavior. Lacka (2020) assessed the impact of
location-based augmented reality games on tourism destination visits and Kamboj and
Gupta (2020) modified and extended TAM to investigate the use of smartphone apps in
co-creative hotel service innovation.
Combining blockchain technology and adoption research in a tourism context,
Kizildag et al. (2020) proposed that the diffusion of innovation theory is an appropriate
theoretical lens to understand how and why blockchain-based technologies are adopted.
However, Nam et al. (2019), as well as Rashideh (2020), suggested that the theory of
disruptive innovation could serve as a valid foundation to explicate the process of
adopting technology. Recently, tom Dieck and Jung (2018) employed an inductive
approach to propose an augmented reality acceptance model for heritage tourism sites.
Through analyzing the qualitative data solicited from site visitors, they demonstrated how
qualitative data could be used to extend the applicability of past theories (in their case
TAM) to a new context. Considering the prominence of tom Dieck and Jung’s (2018)
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study, a similar approach is pursued in this study using a mix of quantitative and
qualitative data to create a comprehensive model for cryptocurrency adoption.
Motivations and Characteristics of Asia Pacific Travelers
According to the World Tourism Organization (2019), the Asia Pacific region is an
important corridor of growth for the global tourism economy because it features a rising
number of outbound tourism markets from countries such as China and India.
Specifically, they report that Asian outbound tourism grew at an average rate of 7% p.a.
between 2010 and 2018, with intraregional (i.e., within the Asia Pacific region) mobility
accounting for 76% of all Asian outbound tourism. The rise in numbers of Asia Pacific
travelers over the last decade can be explained by a combination of socio-economic
factors across the continent: The increase in the middle-income population, accompanied
by the ease of international mobility offered by visa-free entry and low cost carriers, has
triggered waves of outbound tourism for travelers seeking time and space away from their
usual place of residence (World Economic Forum, 2019).
Research has shown that Asia Pacific travelers have in the past been characterized
by no-fuss travel planning and generally a high risk-avoidance mindset when travel
planning, with short haul destinations likely to feature prominently in terms of short
vacation breaks, or group tour typologies (Ooi, 2019). However, as tourists’ tastes
become increasingly sophisticated, the change in Asia Pacific traveler characteristics is
revealing a greater sense of novelty, risk-taking, and engagement with a variety of global
destinations (Mohsin et al., 2017). Additionally, there is increasing evidence to suggest
that Asia Pacific travelers are likely to reflect a high degree of digital-savviness that
incorporates the use of mobile devices, apps, social media and other technologies to
further enhance their tourism experiences (Hsu et al., 2016). With digital innovation and
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immersion becoming a feature of several countries in the Asia Pacific region, it is evident
that outbound tourists from this continent are increasingly expecting that destinations
fulfil their needs and interests through a better integration of technology with their desired
tourism experiences (Chon et al., 2020).
Method
An explorative questionnaire survey was conducted with cryptocurrency users in the
Asia-Pacific region with the goal to better understand the current perception of
cryptocurrencies and the underlying rationale for using these assets. The survey was
designed and conducted using Qualtrics and contained a mix of closed- and open-ended
questions to measure respondents’ views quantitatively as well as to gain additional
insights that were used for model development.
Data collection was conducted over a two-week period from early- to mid-August
2019, following a pilot testing period. The Qualtrics’ panel management team recruited
and forwarded the online questionnaire to eligible participants. Purposive sampling was
adopted in this study and all eligible participants had to meet three selection criteria: (1)
having a place of residence in the Asia-Pacific region, (2) traveled in the 12 months prior
to completing the survey, and (3) used cryptocurrencies during their travel. In the online
questionnaire, respondents’ past usage experience was firstly checked by asking them to
report all type/s of services they paid for using cryptocurrencies (e.g., accommodation,
car rental). Afterwards, respondents were asked to indicate their motivation to use
cryptocurrencies (e.g., “I am interested in using cryptocurrencies because a
cryptocurrency account is not connected to an owner’s identity information”), their
overall satisfaction with the usage experience (e.g., “How would you rate the overall
satisfaction with your previous cryptocurrency usage experience?”), and their intention
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to re-use cryptocurrencies in the future (e.g., “How likely is it that you will use
cryptocurrencies during future trips?”). The questions were adapted from validated
statements in prior studies (e.g., Leung & Dickinger, 2017). The questionnaire ends by
asking respondents to report their demographic and socioeconomic information (e.g.,
gender, age group, household income, and education qualifications). A total of 163
responses were received and two invalid cases were excluded from the analysis.
In line with the explorative nature of this study, respondents were asked to answer
open-ended questions and to narrate their previous cryptocurrency usage experience,
using terms that best summarize their positive experience, negative experience, and areas
for improvement. A total of 2,082 terms were obtained and these responses were
translated from native Asian languages (e.g., Korean, Chinese) into English. Figure 1
shows how the initial pool of terms was clustered by synonyms via first coding the
responses into 744 independent terms, which were subsequently refined into 55 highest
frequency and unique keywords that were used as a basis for a subsequent correspondence
analysis, which will be explained in the following sections.
Figure 1. Data preparation.
Results
Respondents’ Profile
Table 2 shows the demographic profiles of the survey respondents. They represent several
countries from the Asia-Pacific region, with most cryptocurrency users coming from
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India, Indonesia, Malaysia, the Philippines, Singapore, China, and Hong Kong (SAR).
When it comes to gender, the sample population is fairly balanced with 58.4% being male
and 41.6% being female. Nearly half of the respondents (47.8%) belong to the age group
of 26-35, but responses from cryptocurrency users between 18-25 (17.4%) and 36+
(34.8%), were also received. The latter group was made up of 48 users between 36 and
45 years old (29.19%) and only 8 respondents were 46 years and older. Those were
combined into a single group named 36 and above. The yearly gross household income
is fairly evenly distributed across the five categories that were used in the survey. When
it comes to education, the majority of respondents possessed a Bachelor’s (55.9%) or a
Master’s degree or above (27.3%), indicating a sample that exhibits a slightly above-
average education, which can be explained by the fact that the use of cryptocurrencies
mostly appeals to an educated audience and early technology adopters.
Table 2. Respondents’ demographic profile (N = 161).
Variable
Category
Frequency
Percentage
Gender
Male
94
58.4
Female
67
41.6
Age
18-25
28
17.4
26-35
77
47.8
36 and above*
56
34.8
Gross annual
household
income
Less than USD 20,000
18
11.2
USD 20,001 - USD 40,000
43
26.7
USD 40,001 - USD 60,000
36
22.4
USD 60,001 - USD 80,000
30
18.6
More than USD 80,001
34
21.1
Education
Secondary school
14
8.7
Diploma/Higher diploma
13
8.1
Bachelor’s degree
90
55.9
Master’s degree or above
44
27.3
Nationality
India
37
23
Indonesia
26
16.1
Malaysia
23
14.3
Philippines
22
13.7
14
Singapore
19
11.8
China
14
8.7
Hong Kong (S.A.R.)
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7.5
South Korea
5
3.1
Japan
3
1.9
Cryptocurrency Usage Experience and Underlying Motivations
The first step investigated the different ways that cryptocurrencies were used across
travel-related payments and what the underlying motivation is. To be able to do so,
possible payment options were first identified in the literature and on companies’
websites. The option ‘other’ in the questionnaire was not chosen by the respondents,
indicating that all possibilities of cryptocurrency payments that are currently available in
the travel industry were captured. Figure 2 shows the results. Half the respondents had
used cryptocurrencies to pay for accommodation and air tickets. A little more than one
third paid for tour packages. Interestingly, all of the other options (e.g., admission tickets,
souvenirs, train tickets, car rental, restaurants and cafes, public transport fares, ride
sharing) were previously used by at least 10% of the respondents. Over 40% (41.6%) of
respondents had previously used cryptocurrencies to pay for buying three or more types
of tourism products/services. Another 26.1% claimed that they had used cryptocurrencies
for two types of products/services.
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Figure 2. Types of travel product/service respondents paid for using
cryptocurrencies (N = 161).
Table 3 shows the respondents’ underlying motivations to use cryptocurrencies. There
was general consent regarding the benefits of cryptocurrencies. The highest rating was
given to cryptocurrencies’ universal usability that renders local currencies unnecessary.
It is also the technology itself that is especially intriguing to early adopters of a new
technology. More specific benefits include cost savings (which often goes hand in hand
with disintermediation), enhanced privacy, and the easy verification of transactions,
which means that a personal handwritten signature is not needed for each individual
transaction.
Table 3. Respondents’ motivations to use cryptocurrencies.
I am interested in using cryptocurrencies because…
Mean (SD)
Universal usability:
Cryptocurrency works anywhere and anytime
4.06 (1.09)
Intriguing technology:
The underlying technology of cryptocurrency is intriguing
4.01 (1.09)
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Cost saving:
Lower transaction cost is involved in cryptocurrency-based payment as compared to
other payment systems
3.98 (1.05)
Disintermediation:
Establishment of cryptocurrency account does not require credit card/bank account
3.88 (1.14)
Privacy:
A cryptocurrency account is not connected to owner’s identity information
3.75 (1.23)
Easy verification:
Cryptocurrency-based payment does not require pin or signature for verification
3.71 (1.31)
Technological Contingency Factors and Users’ Perceptual Antecedents
To create categories for technological contingency factors and users’ perceptual
antecedents, we needed to thoroughly understand the context of usage experience
expressed and determine how the different contexts associate with one another. To
achieve this, we used the qualitative data analysis software NVivo 12 to cluster the
respondents’ qualitative comments into categories of similar meaning to avoid
redundancies. For instance, similar terms such as ‘exciting’, ‘excited’, and ‘excitement’
were grouped as one common word – ‘exciting’. This condensed the 2,082 raw terms into
a total of 744 independent terms by synonyms. Among those independent terms, 182
related to positive experiences, 242 to negative experiences, and 320 to areas of
improvement. Next, the 25 most frequently mentioned terms from each of those three
categories were identified. Subsequently, each keyword was analyzed by the researchers
for eligibility. Finally, all three categories yielded 55 final keywords that were unique and
non-overlapping across categories. Next, a correspondence analysis was performed to
examine how those 55 keywords corresponded with one another within and across the
three categories. The analysis shows the two extracted dimensions that explain the
relationship between keywords (rows) and usage experience (columns), explaining 100%
of the total inertia. Dimension 1 (represented by the horizontal axis [x-axis]) and
Dimension 2 (represented by the vertical axis [y-axis]) account for 65.9% and 34.1% of
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the total inertia or total Chi-square values, respectively. The principal inertia explains the
degree of keyword variation in each axis, hence, Dimension 1 with an inertia of 0.54 had
a higher keyword variation than Dimension 2 with an inertia of 0.28. The singular values
(0.73 for Dimension 1 and 0.53 for Dimension 2; eigen-values) above 0.2 (Greenacre,
2017) denote the feasibility of each dimension. Both dimensions fulfill this criterion, thus
indicating that there is significant dependency between keywords (rows) and usage
experience (columns). Due to space constraints, we offer a sample of the keywords in
Table 4. The large mass (0.106) for the keyword ‘easy’ indicates high row relative
frequency, while also indicating a significant contribution (0.084) to the inertia of
Dimension 1 as compared to Dimension 2 (0.001).
Table 4. Sample descriptive statistics of correspondence map coordinates.
overall
dimension_1
dimension_2
Categories
mass
%inert
coord
sqcorr
contrib
coord
sqcorr
contrib
Keywords
acceptance
0.073
0.046
0.637
0.796
0.055
0.323
0.204
0.027
boring
0.007
0.036
1.626
0.674
0.037
-1.132
0.326
0.034
convenience
0.043
0.042
-0.833
0.876
0.056
-0.313
0.124
0.015
easy
0.106
0.056
-0.650
0.994
0.084
-0.051
0.006
0.001
fun
0.007
0.003
-0.392
0.483
0.002
-0.406
0.517
0.004
hassle-free
0.007
0.007
-0.839
0.872
0.010
-0.322
0.128
0.003
regulation
0.005
0.009
0.228
0.034
0.000
1.210
0.966
0.026
security
0.058
0.020
0.278
0.277
0.008
0.449
0.723
0.042
slow
0.020
0.096
1.626
0.674
0.098
-1.132
0.326
0.092
volatile
0.007
0.013
1.160
0.916
0.019
-0.351
0.084
0.003
Experience
POSITIVE
0.400
0.345
-0.770
0.844
0.443
-0.331
0.156
0.158
NEGATIVE
0.203
0.443
1.190
0.800
0.537
-0.596
0.200
0.260
IMPROVEMENT
0.397
0.212
0.167
0.064
0.021
0.637
0.936
0.582
Figure 3 shows the biplot that visualizes the locations of the keywords on a two-
dimensional plane. The distance between keywords shows the strength of their
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association with one another. Physical proximity indicates similarity in quality (e.g.,
‘amazing’ and ‘cool’ are indicative for the positive category), whereas remoteness
signaled dissimilarity (e.g., ‘fun’ and ‘fluctuation’ belong to the opposing positive and
negative categories). Keywords close to the zero-coordinates at the center of the map
indicated higher average similarity (e.g., ‘payment’, ‘cost’, and ‘transaction’ share
similarity in characteristics for all three categories), while being farther away from the
center indicated higher average dissimilarity (e.g., ‘happy’, ‘boring’, and ‘regulation’ are
unique to each respective category). Terms were further combined in the respective
clusters to identify those constructs that deserved attention in future research projects.
More specifically, the most important technological improvements were summarized into
categories of technological contingency factors and the positive and negative sentiments
into two different kinds of perceptual antecedents. We will elaborate on these three
clusters in the following sections.
Figure 3. Respondents’ previous cryptocurrency usage experience.
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Cluster: Positive antecedents
Blockchain technology, which by itself consists of a multitude of protocols, platforms,
consensus mechanisms, and cryptographic primitives, is far from being fully developed.
Existing applications are permanently being refined and new solutions as well as
cryptocurrencies are regularly introduced to the market. Constant change is not only a
feature of the technology itself, but also of its surrounding regulatory environment that is
needed to provide the legal certainty that allows merchants to offer cryptocurrency
payments and gives consumers the confidence that they are not operating within a legal
vacuum. This is especially important in an area that is strongly associated with illegal
activities, such as money laundering, extortion, and terrorism financing (Foley et al.,
2019).
Based on the qualitative analysis of respondents’ inputs, terms were clustered into
five groups of technological contingency factors that describe the legal and economic
ecosystem within which cryptocurrencies operate. These factors are (1) legislation and
regulation, (2) widespread acceptance, (3) security, (4) usability, and (5) costs. The first
one considers the rules and regulations that define the regulatory framework of
cryptocurrencies. Obviously, this framework varies considerably between countries. The
current acceptance of cryptocurrencies, which is shaped by the opinions of social
connections but also by general media coverage, lays the foundation of how a technology
is perceived. Security, usability, and costs are determinants that are strongly technology-
driven and might differ from one cryptocurrency to the other.
These five factors determine the context within which a particular study is being
conducted and therefore need to be integrated into adoption models either as drivers or as
contextual variables. Additionally, researchers have to consider that it is not the actual
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state of the art of a specific technology, but rather its perception that finally determines
consumers’ intention of whether or not to use it. While this is true for most technologies,
it is especially the realm of cryptocurrencies that is undergoing fundamental and constant
change and it is advisable that researchers consider current developments and consumers’
awareness thereof in their models. More specifically, the following five propositions are
suggested as independent or contingency variables that impact positive and negative
antecedents of consumers’ perceptions:
P1: Legislation and regulation impact cryptocurrency development
P2: Cryptocurrency acceptance impacts cryptocurrency development
P3: Security impacts cryptocurrency development
P4: Usability impacts cryptocurrency development
P5: Costs impact cryptocurrency development
Cluster: Positive antecedents
Similarly, the positive sentiments that the travelers expressed were clustered into five
categories that were labeled as novelty, ease of use, safety and reliability, hedonic aspects,
and trust in cryptocurrency payment systems. The first refers to the aspect that novel
systems are appealing to early adopters and technology-savvy people who enjoy trying
out new technologies. Ease of use is a common construct in technology adoption research,
but it gains paramount importance in the sphere of cryptocurrencies in which the
complexity of the underlying technology and practices are unfamiliar to users, such as
the inability to recover a private key. Interestingly, the security and reliability of the
21
technology were highlighted by several respondents as positive as well as negative
aspects of the technology (see also the section below). In other words, blockchain was
simultaneously perceived as a safe technology that uses distributed ledgers to avoid or
mitigate potential attacks, but it may also open new attack vectors. Finally, several
respondents pointed out that the technology fulfills their hedonic needs and also has a
playful character.
Trust is fundamental in the use of cryptocurrencies. Although there are no
intermediaries to be trusted, users need to trust the payment system as a whole. As
Shahzad et al. (2018) have found, perceived trustworthiness determines the use of
cryptocurrencies. In our study, based on the qualitative feedback from the respondents,
we find that the issue of trust has been mentioned in all three categories, though it was
skewed toward a more positive experience. This means that respondents need to take the
leap of faith in order to first use a cryptocurrency. Furthermore, we measured the
respondents’ level of trust toward their cryptocurrency wallet and also their level of trust
in using cryptocurrency as a payment mode with 7-point Likert scales (1: very dissatisfied
… 7: very satisfied; 1: very unlikely … 7: very likely). The respondents demonstrated a
high trust level in using cryptocurrencies (m = 5.46, sd = 1.22) leading to intentions for
future use (m = 5.59, sd = 1.39). We therefore surmise that a positive experience enhances
users’ perceived trust, thus increasing their intention to use cryptocurrencies. However,
negative experience will undermine their intention to use. Summarizing, we propose that
perceived trust resulting from prior experience with cryptocurrency use will have a
significant impact on behavioral intention. It is therefore proposed that:
P6: Novelty of cryptocurrencies positively impacts users’ level of satisfaction
P7: Ease of use of cryptocurrencies positively impacts users’ level of satisfaction
22
P8: Safety and reliability of cryptocurrencies positively impact users’ level of
satisfaction
P9: Hedonic aspects of cryptocurrencies positively impact users’ level of
satisfaction
P10: Trust in cryptocurrency payment systems positively impacts users’ level of
satisfaction
Cluster: Negative antecedents
Interestingly, the negative categories of cryptocurrencies, as perceived by the
respondents, overlapped with the positive categories. For example, several users
experienced poor usability of cryptocurrency payment systems, which underscores the
current early state of the technology. Additionally, several keywords were identified that
indicated travelers’ unhappiness with the performance of current systems. In the case of
Bitcoin, by far the most widely used cryptocurrency, slow payment confirmation is a
feature rather than a bug, which is caused by a laborious proof-of-work system that
ensures that roughly every 10 minutes a new block is added to the blockchain. Depending
on the current throughput, the actual waiting time before a transaction is finally confirmed
can be much longer. In a similar vein, the lack of intermediaries yields cost savings but
also leads to a decreased service level, which can deter users who are used to having
contact persons and service centers at their disposal in case problems occur. Finally, the
current price volatility and concerns regarding the security of blockchain are subsumed
into a single category. The former is caused by speculative investments that are beyond
an ordinary user’s control and the latter represent a new attack vector that was created by
23
blockchain technology and includes, amongst others, so-called 51% attacks (in which a
majority of participants take over the network), sybil attacks, wallet attacks, or attacks
regarding the underlying cryptography. Summarizing, the following propositions are
suggested:
P11: Poor usability of cryptocurrencies negatively impacts users’ level of
satisfaction
P12: Low performance of cryptocurrencies negatively impacts users’ level of
satisfaction
P13: Missing service of cryptocurrencies negatively impacts users’ level of
satisfaction
P14: Volatility and insecurity negatively impact users’ level of satisfaction
Moderating Variables, Satisfaction and Intention to use
To gain a better understanding of which moderating variables might be of importance in
future cryptocurrency studies, four frequently used control variables, namely gender, age,
education, and income, were investigated to yield different levels of satisfaction among
separate user groups. Demographic and socioeconomic variables are frequently
incorporated in adoption models to account for the differences between customer groups’
adoption of a new technology (Chen & Huang, 2016). Conflicting research results exist
as to whether these controls exert a significant influence. For example, when it comes to
the impact of age on software adoption, Morris and Venkatesh (2000) showed that
younger workers’ usage decisions were strongly influenced by their attitudes, while
24
subjective norm and perceived behavioral control were more important for older workers.
Ayeh et al. (2013) found that age has a significant impact on consumers’ intention to use
consumer-generated media for their travel planning, but not education and gender, while
Chung et al. (2010) found no moderating effect of age in their technology acceptance
study on online community participation. In an early TAM-based study about the use of
e-mail, Gefen and Straub (1997) found that men and women differ in their perceptions,
but not in the use of this technology. These few examples suffice to illustrate that
moderating or control variables are highly context-dependent.
Consequently, and in line with the basic tenets of contingency theory, it is
postulated that the context in which the respective constructs are applied is crucial in
determining whether a variable has an important impact or not. Given the focus on the
use of cryptocurrencies for payments and the exploratory nature of this study, a conscious
decision was to therefore refrain from making theory-based postulations regarding the
impact of moderating variables and compare the different groups of cryptocurrency users
from our sample according to their demographic and socioeconomic variables. No
significant effects were located for gender, t(159) = 1.95, p = .052) when it was tested for
users’ perception of cryptocurrencies. Similarly, a one-way ANOVA revealed that there
were no significant effects for age groups (F(4, 155) = .74, p. = .56), education groups
(F(3, 157) = 1.02, p. = .38) and income groups (F(4, 156) = 2.42, p. = .051). Although
our sample is not representative for the general population, especially when it comes to
the distribution of age, the findings can serve as a first indication that demographic and
sociographic variables do not moderate the impact of perceptual antecedents on
satisfaction as well as the impact of satisfaction on travelers’ intention to use
cryptocurrencies. Furthermore, it is proposed that gender, age, education, and income do
not have a significant moderating effect on the perception of cryptocurrencies. However,
25
this should not prevent future researchers from expanding the size and diversity of their
samples in their future studies, which will be helpful in validating whether travelers’
sociodemographic profiles exert a moderating impact on their level of satisfaction with
cryptocurrencies.
The sample was assessed for their perceptions towards cryptocurrency payment
processes, which can be seen as a strong indicator of whether cryptocurrencies in tourism
will succeed in the future. We measured their level of satisfaction as well their future
intention of using cryptocurrencies with 7-point Likert scales (1: very dissatisfied … 7:
very satisfied; 1: very unlikely … 7: very likely). Overall, the respondents were fairly
satisfied with their previous experience (m = 5.45, sd = 1.27) and mostly intended to
continue their use in the future (m = 5.59, sd = 1.39). Finally, a regression was conducted
on future intentions on satisfaction (B = .89, S.E. = .05) and the result was highly
significant F(1, 158) = 300.47, p < .001 with an R2 of .66, corroborating numerous
previous research studies that highlighted a strong relation between satisfaction and
intention to use (Jang et al., 2006). It is therefore proposed that:
P15: Satisfaction with the use of cryptocurrencies positively impacts the intention
to use them further in the future
Figure 4 summarizes the propositions in a comprehensive model that combines
several core elements of adoption theories (e.g., TAM, UTAUT) that were adjusted to fit
the context of cryptocurrencies in the dotted area and introduces blockchain-specific
contingency factors on the left that are of relevance for the future development of
cryptocurrencies.
26
Figure 4. Cryptocurrency adoption model.
Discussion
Over the past decades, technology has triggered numerous changes in the tourism
industry. The Internet has led to the development of e-tourism, which is characterized by
a digitization of processes and value chains in tourism (Buhalis, 2002). Subsequently, the
gradual replacement of websites by sensors and smartphones, the emergence of big data,
and the rise of public-private-consumer collaborations have led to the emergence of smart
tourism (Gretzel et al., 2015) and the creation of models that underscore the important
role of information and communication technologies (ICTs) in tourism management
(Ivars-Baidal et al., 2019). Blockchain and, more specifically, cryptocurrencies, are very
recent developments in the digitization of the tourism industry that hinge on the
pervasiveness of ICT. As such, our exploratory findings that reveal the perceptual
antecedents of cryptocurrency use in tourism offer potentially substantial implications for
researchers and practitioners alike.
27
Theoretical Implications
Technology adoption is a very popular research topic across academic communities that
helps create a better understanding regarding the underlying factors of why consumers
decide on whether or not to use a specific technology. The adoption of an application by
consumers ultimately decides upon its success or failure. Specifically, in tourism, the
extent of technology efficacy for travel has significant impact on tourist experience and
thus influences their adoption behavior (Neuhofer et al., 2015). In their search for
parsimony and operationalizable theory-based theories and models, academics have
substantially altered models and reduced measurement scales. Frequently, these models
are used without sufficient adaption to the characteristics of the research problem at hand.
However, researchers have already stressed the need to adapt existing models by
integrating context-specific factors to account for the idiosyncrasies of a specific adoption
situation (tom Dieck & Jung, 2018). In a similar vein, results from this exploratory study
that combined qualitative and quantitative data to empirically derive several research
propositions, serve to conceptualize a cryptocurrency adoption model that creates a more
nuanced understanding within the academic and non-academic tourism communities.
This model also integrates previous research on cryptocurrency adoption from a
consumer perspective, which is still scarce in academic literature. Notable examples
include Arli et al. (2020) who investigated how knowledge of cryptocurrencies, speed of
transactions, and trust in government impact trust in cryptocurrencies and subsequently
loyalty to banks, and Ajouz et al. (2020) who examined individuals’ intention to adopt
precious metal-backed cryptocurrencies.
Technology adoption research is combined with contingency theory to derive a
model that at its core contains frequently used constructs such as satisfaction and intention
to use, but also includes those aspects that specifically pertain to blockchain-based
28
technologies and especially cryptocurrencies in the tourism industry. When it comes to
the measurement of these constructs, researchers can partly rely on existing scales, which
most likely need to be modified to match the research context, but rigorous research is
also needed to operationalize and validate measurement scales for several cryptocurrency
features that are novel.
Managerial Implications
At present, only a relatively small number of individual tourism outlets including travel
agencies, hotels, transportation, souvenir shops, and restaurants, offer cryptocurrency
payments. This number varies widely by country and can partly be explained by different
jurisdictions, fluctuating consumer demand, and availability of technological knowledge
needed for the implementation and operation of cryptocurrency-enabled point of sale
platforms. Additionally, offering payment with cryptocurrencies demands an initial
investment from the side of the vendor. It is therefore crucial for merchants to better
understand why (or why not) consumers choose this particular form of payment and
which outside factors (such as legislation) potentially influence their usage intention. This
study offers a first glimpse on which factors come into question.
The study has focused on the Asia-Pacific region, in which several geographical
locations can be found that are fairly advanced in their use of cryptocurrencies for
payment purposes and already offer a wide range of products and services that can be
purchased with cryptocurrencies. The study found that the benefits of cryptocurrencies
might be quite considerable for consumers, but it also became clear that a substantial
amount of skepticism exists. A better understanding of technological contingency factors
as well as the positive and negative perceptual antecedents that shape users’ perceptions
29
will help merchants in the tourism industry to better understand travelers’ needs and to
customize their offerings accordingly.
Conclusion, Limitations and Future Research
In conclusion, from a traveler’s perspective, cryptocurrencies are the most tangible
application of blockchain technology. Their application potentials in the tourism industry
are manifold, as are the possible disadvantages. The underlying technology is complex
and often poorly understood by final consumers and the media plays a big part in shaping
the attitudes of the general public toward such forms of transactions.
In this paper, a comprehensive model for cryptocurrency adoption in the tourism
industry was developed, which builds on an existing theoretical foundation but extends it
by incorporating positive and negative perceptual antecedents that are specific to
cryptocurrencies. Furthermore, various technological contingency factors are included,
which make the model dynamic and also need to be fully understood in order to grasp
travelers’ level of satisfaction or their intention to use. These modifications are needed to
fine-tune existing adoption models that are mostly agnostic of a particular technology and
do not account for the idiosyncrasies of blockchain and cryptocurrencies. Using a sample
of 161 cryptocurrency users from the Asia-Pacific region, the extraction of insights from
quantitative and qualitative data revealed a wide array of positive and negative aspects of
cryptocurrencies that were integrated into a comprehensive model, together with several
contingency factors. Furthermore, 15 research propositions are suggested, which emerged
from the empirical findings.
This study has several limitations. First, the sampling process was intended to
obtain a relatively homogenous sample from the Asia-Pacific region. Although
participants from numerous countries are involved, their sentiments might not be
30
representative of other regions of the world. Since an exploratory rather than a
confirmatory approach was pursued, a geographical bias might not matter that much but
other scholars are still strongly encouraged to apply the proposed model in different
geographical regions. Second, the sample was purposefully restricted to cryptocurrency
users, since the behavior and perceptions of this particular group and their experience
with blockchain-based payments was of utmost interest in the study. However, it is highly
probable that in basically every country non-cryptocurrency users form the majority of
the population. While it is believed that the model is fairly generic and that most of the
positive and negative effects of cryptocurrencies are covered, the moderating variables
deserve further attention in future studies that compare users of cryptocurrencies with
non-users.
The growing popularity of blockchain and cryptocurrencies in the tourism
industry has induced an increasing body of research. In order to make a useful
contribution to the common body of knowledge in the tourism literature and to help
practitioners to better understand why (or why not) consumers choose to pay with
cryptocurrencies, further empirical research is needed that is tailored to the characteristics
of this technology. Given the perceptual antecedents offered in this study, the theoretical
implications of technology adoption warrant further exploration of the associated theories
in empirical research. We encourage researchers to take the proposed model, identify its
benefits, correct its shortcomings, and further refine it to be able to better understand
travelers’ intentions to use cryptocurrencies for payments as an asset of choice in the
future.
31
Disclosure Statement
No potential conflict of interest was reported by the authors.
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