Factors Influencing Tourists’ Intention to Use COVID-19 Contact Tracing App

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Factors Influencing Tourists’ Intention to Use COVID-19 Contact Tracing App © The Author(s) 2021 Published version Ukpabi, Dandison; Olaleye, Sunday; Karjaluoto, Heikki Ukpabi, D., Olaleye, S., & Karjaluoto, H. (2021). Factors Influencing Tourists’ Intention to Use COVID-19 Contact Tracing App. In W. Wörndl, C. Koo, & J. L. Stienmetz (Eds.), Information and Communication Technologies in Tourism 2021 : Proceedings of the ENTER 2021 eTourism Conference (pp. 504-516). Springer. https://doi.org/10.1007/978-3-030-65785-7_48 2021 Factors Influencing Tourists’Intention to Use COVID-19 Contact Tracing App Dandison Ukpabi 1(&) , Sunday Olaleye 2 , and Heikki Karjaluoto 1 1 University of Jyväskylä, Jyväskylä, Finland {dandison.c.ukpabi,heikki.karjaluoto}@jyu.fi 2 University of Oulu, Oulu, Finland [email protected] Abstract. The purpose of this study was to develop and test a model that explores the antecedents of tourists’acceptance of COVID-19 contact tracing app (CTA). Data was obtained from a crowdsourcing platform (Pollfish), in which 400 respondents answered the questionnaire. We used SmartPLS to analyse the data. Results reveal that trust and structural assurance have the strongest relationship. Furthermore, the relationship between trust and destination safety was positive. Finally, self-efficacy moderated the relationship between trust and intention, implying that trust was stronger for tourists who have higher levels of self-efficacy. Recommendations are offered. Keywords: COVID-19 COVID-19 contact tracing app Destination safety  Trust Structural assurance Self-efficacy Intention 1 Introduction According to the United Nations World Tourism Organisation [1], travel restrictions as a result of COVID-19 has had a devastating effect on the tourism industry, so much so that international tourist arrivals have fallen to 97%. Accordingly, the net effect of this is that about 1.2 trillion US Dollars have been lost, resulting in 120 million tourismrelated job cuts [2]. While the devastating effect of COVID-19 remains and some countries witnessing a new surge in the number of infections, many countries have started to lift travel restrictions. To this end, the UNWTO has called on operators in tourism-related businesses to ensure safety, responsibility and security, as the world adjusts to the new normal. In the wake of the outbreak of COVID-19, majority of countries adopted containment measures. This meant that cities and even countries were totally locked down. Since containment measures have not completely eradicated the virus, in addition to the World Health Organisation warning that the virus will live with us for a long time [3], health experts are advocating for community-wide monitoring [4], as management measures. To this end, many countries and cities are adopting digital contact tracing applications [5], as they emerged from the lock down. While there are many variants in the implementation of the CTAs across different countries, some countries have witnessed stiff resistance to their use due to many unresolved questions bothering on trust, users’privacy and their actual benefits [6]. ©The Author(s) 2021 W. Wörndl et al. (Eds.): Information and Communication Technologies in Tourism 2021, pp. 504–515, 2021. https://doi.org/10.1007/978-3-030-65785-7_48 Interestingly, some destinations have made their use compulsory for both locals and tourists [7]. From a destination safety point of view, managers would ensure that critical measures are put in place for the safety of the destination, including the use of CTA to track possible surge [8]. However, it is unclear if and how destination image, particularly tourists’perception of a destination safety influences their trust and adoption of COVID-19 CTA in that destination. Additionally, in the midst of the stiff resistance to CTA adoption, users’concerns that have resonated across countries and destinations is the assurance of the protection of their privacy [32]. To the best of our knowledge, there is no study that has yet examined the role of structural assurance on users’trust and their intention to use the CTA, even as previous studies [5,31] have highlighted the critical place of an empirical evidence to guide relevant authorities. In the domain of location-based system, literature establishes that the need for safety is positively related to trust, however, the mechanisms underlying the moderating role of self-efficacy on the relationship between trust and intention is still lacking [9]. This is particularly important because the debate generated by the implementation of CTA will be reduced if users take responsibility for their own safety in the midst of the COVID-19 [10]. Thus, using tourist mobility as a theoretical standpoint, this study examines the role of trust on destination safety and the moderating role of self-efficacy on the relationship between trust and tourists’intention to use CTA. Specifically, this study develops a model and empirically tests tourist trust, destination safety, structural assurance and intentions to use CTA. In the model, we used self-efficacy as a moderating variable to test the relationship between tourist trust and intentions. We argue that in the midst of the confusion that has enveloped the global economy, a study of this nature that examines how CTAs impact tourists’choice of destinations and their travel patterns will be of interest to destination managers, health authorities and the scholarly community. 2 Tourist Mobility Tourist mobility is an established theoretical stream in the tourism literature. Tourist mobility represents the spatio-temporal “movements of people, objects, and information and their complex relational dynamics”[11, p. 1075]. [11] advocates that tourist mobility encompass three components: movement, representation and practice. Movement entails the physical movement from one place to another. Representation depicts the shared “meanings assigned to the act of movement”while practice “refers to the experience and embodied practice of movement.”[12] argue that there is a connection between tourist mobility and destination attractiveness and competitiveness. According to them, since tourism involves movements, attractive destinations usually experience high mobility. Different techniques have been used to understand tourists’ travel styles and patterns within a destination, including different technologies to track tourist movements [13]. Currently, COVID-19 has introduced an interesting perspective to tracking tourists’movements. As destination safety and security are cardinal evaluation criteria for tourists, majority of destinations will use CTAs in post-COVID19 era as part of destination safety and security strategies [14]. Factors Influencing Tourists’Intention 505 Literature has examined how different variables influence tourists’travel patterns. Thus, [13] identify six variables namely; visitor personal characteristics, user group type, knowledge of the destination, resources and constraints and infrastructure as predictors of travel patterns. Similarly, [15] examined the role of perceived quality factors on usage intention of location-based application. The current study will combine the destination, user and application factors as predictors of intention. 2.1 Trust and Destination Safety Safety is critical to the choice of a destination. Literature classifies destination safety incidents as natural disasters such as tornadoes, hurricanes, earthquakes, floods and tsunamis; man-made tragedies, for example, terrorism, crime, and war; or health hazards such as the current COVID-19 [16]. Interestingly, the effect of safety incidents are not specific to a destination as one occurrence in one destination can have a spiraling effect of fear on the entire country or region. However, safe destinations imbue a sense of peace, confidence and trust in the tourist. Destination safety will therefore be a critical determinant of destination choice in the post-COVID-19 era. Destination safety therefore becomes relevant in this study because destinations that are considered safe (in relation to COVID-19) will appeal more to tourists than others. [9] examined the relationship between need for safety and trust and found that trust positively and significantly impact need for safety. Similarly, [17] found that safety guarantees significantly impact travelers’trust. The introduction of CTAs for COVID19 will have many implications for tourism. Trusts for CTAs in destinations with a track record of safety will be higher than those who have experienced safety issues. In fact, based on scholarly evidence, tourists’destination loyalty depends on perceived safety [18], implying that the trust for a destination with a positive record of safety will translate to a positive perception of COVID-19 CTA and their intention to use it. In the light of the foregoing, we argue that: H1. Trust for COVID-19 CTA is positively related to destination safety. H2. Destination safety is positively related to intention to use COVID-19 CTA. 2.2 Trust and Intention to Use In this study, we follow [19]definition of trust, as exchange partners’belief in others’ trustworthiness, which is underlined by their benevolence, competence, and integrity. As a new technology, trust is important for tourists to be able to use the CTA. This is particularly important because the application may have access to critical information relating to the user. Trust has featured prominently on studies relating to information sensitivity and users’safety. For instance, in the context of location systems, [9] and [15] found a positive and significant impact of trust on intention to use. As digital contact tracing application works in similar context, that is, users location and mobility, we argue that the tourists’trust on the application will have positive and significant impact on their intention to use it. Thus, we hypothesize that: H3. Trust for COVID-19 CTA is positively related to intention to use it 506 D. Ukpabi et al. 2.3 Trust and Structural Assurance Structural assurance refers to safeguards such as regulations, legal resources and guarantees provided to increase consumers’confidence in a new innovation [20]. Empirical evidence suggests that consumers’trust and attitude towards a new technology is positively influenced by structural assurance [20,21]. As novel as the COVID-19 CTA appears to be, much of the controversy surrounding its acceptance is because users lack the safeguards and guarantees from relevant authorities on the protection of their privacy information [7]. As such, this study argues that provision of necessary safeguards and legal resources will increase users’trust and their intention to use the application. Thus: H4. Trust for COVID-19 CTA is positively related to structural assurance. H5. Structural assurance is positively related to intention to use COVID-19 CTA. The role of self-efficacy on users’adoption of technology has also been examined in previous studies [22]. Self-efficacy implies people’s belief in their capabilities to execute certain actions to attain some performances [23]. In the context of COVID-19 CTA, if tourists’belief in their ability to use the application, they will more likely have higher willingness in using it. Majority of studies relating to technology adoption used self-efficacy as a direct effect [23]. However, in an organisational context, [24] drew more insight when they tested the relationship between self-efficacy and job performance using trust as a moderator variable. They found that self-efficacy had stronger relationship with job satisfaction with employees who have higher level of trust. In this study, we use self-efficacy as a moderator variable between trust and intention. We thus argue that the strength of the relationship between trust and intention will be determined by the tourist’s self-efficacy. H6: The relationship between trust and intention to use COVID-19 contact tracing app is moderated by self-efficacy (Fig. 1) Factors Influencing Tourists’Intention 507 3 Research Methodology This study adopts quantitative methodology and used Structural Equation Modelling to test the proposed model. Data were collected in August, 2020, via Pollfish. Pollfish algorithm provides for the opportunity for a researcher to select target audience (e.g. tourists, students and so on). A researcher can also select a target group within a specific region or country. Earlier study [25] has validated the Pollfish algorithm’s robustness with potential to disqualify ineligible respondents in real-time. In total, the datasets comprised of 400 responses. Respondents were predominantly male with 50.5% and age bracket 25–34 records 31.5%. Also, under marital status, the single dominated the study with 40.8% while university students constitutes 39.5% respectively. This study draws from existing validated questions with modifications to suit COVID-19 tracing mobile app context. Destination safety items were drawn from [25], perceived trust [15], structural assurance [21], self-efficacy [22], and intention to use [26]. Seven-point Likert scales strongly disagree (1) as the lowest scale and strongly agree (7) as the highest were employed. Table 1indicates the measurement items for this study. Self efficacy Destination safety Perceived trust Structural assurance Intention to use H1 H2 H4 H3 H5 H6 Fig. 1. Conceptual framework of our study 508 D. Ukpabi et al. 4 Measurement and Structural Model Analysis This study utilised SmartPLS version 3.3.2 software to analyse dataset with Partial Least Squares Structural Equation Modelling (PLS-SEM). This data analysis technique has been proved useful in the social sciences as a means of not imposing distributional assumptions on the data while working with indicator variables and structural paths aside from the benefits of complex models estimation [27]. PLS-SEM is growing along with CB-SEM. The study conducts an algorithms data analysis with SmartPLS to ascertain the quality criteria of the proposed measurement model (Table 2) and the result shows that the Cronbach Alpha, composite reliability (CR) and rho_A of the model reached and above the set criterion of 0.7. The average variance extracted (AVE) results were above the recommended boundary of 0.50 which indicates the 50% of the items variance. The AVE results also indicate convergent validity of the model [28]. Besides, the study also established discriminant validity as the shared variance for all the model constructs are larger than their corresponding AVE [28]. Table 1. COVID-19 tracing mobile app measurement Items (arranged by authors) Variables Items Trust (PT) PT1. COVID-19 contact tracing app is trustworthy PT2. COVID-19 contact tracing app keeps its promise PT3. COVID-19 contact tracing app keeps users’interests in mind Structural assurance (SA) SA1. COVID-19 contact tracing app has appropriate legal safeguards SA2. am assured that COVID-19 contact tracing app has features that adequately protect me from hacking SA3. believe COVID-19 contact tracing app is safe because it provides adequate protection Destination safety (DS) DS1. Additional security measures at airport make traveling safer DS2. Safety is the most important attribute a destination can offer DS3. Safety is a serious consideration when I am choosing a destination Self-efficacy (SE) SE1. I am skilled at avoiding dangers while using COVID-19 contact tracing app SE2. I am active in securing my environment when using COVID-19 contact tracing app SE3. I am confident that I can remove any hazards while using COVID19 contact tracing app SE4. I have the ability to protect myself from dangers while using COVID-19 contact tracing app Intention (IU) IU1. I think more and more will use COVID-19 contact tracing app in the future IU1. I think I will use COVID-19 contact tracing app when organising and taking trips IU1. In the future, I will encourage my friends to use COVID-19 contact tracing app Factors Influencing Tourists’Intention 509 In the second stage of the data analysis, the study utilised SmartPLS bootstrapping technique to assess the structural model [27–29]. The goal of using bootstrapping is to test the proposed hypotheses. The study found all the five formulated hypotheses significant. The perceived trust as a direct predictor of destination safety (H1) reveals strong path coefficients (b= 0.31, p = 0.000), destination safety predicts intention to use (b= 0.08, p = 0.031), perceived trust predicts intention to use (b= 0.27, p = 0.000), perceived trust predicts structural assurance (b= 0.79, p = 0.000) while structural assurance predicts intention to use (b= 0.51, p = 0.000). Structural assurance records the highest R 2 with 62.6%, seconded by intention to use COVID-19 tracing app with 59.4% and destination safety insignificant R 2 with 9.3%. Table 2. Quality criterion results of COVID-19 tracing mobile app study Variables and items DS IU PT SA CA rho_A CR AVE R square Destination safety 0.854 0.855 0.911 0.774 0.093 DS1 0.866 DS2 0.903 DS3 0.869 Intention to use 0.794 0.795 0.907 0.829 0.594 IU1 0.909 IU3 0.913 Perceived trust 0.884 0.885 0.928 0.812 PT1 0.913 PT2 0.920 PT3 0.869 Structural assurance 0.871 0.873 0.921 0.795 0.626 0.879 0.895 0.901 Destination safety 0.880 Intention to use 0.317 0.911 Perceived trust 0.309 0.699 0.901 Structural assurance 0.302 0.749 0.792 0.892 510 D. Ukpabi et al. To get insight that the PL-SEM could not reveal, the study embarked on moderation analysis and used a contingent variable [29,30]. In Table 3and Fig. 3, perceived trust as a key variable in the proposed model was used as an independent variable, selfefficacy as a moderator and intention to use COVID-19 tracing app as the dependent variable (b=−0.085, p = 0.000) (Fig. 2). The result established interaction with small size effects (0.028) and shows how self-efficacy changes the relationship between trust and intention to use tracing app and Table 3. Tested hypotheses results Hypotheses Path coefficient Beta Std. Dev. t-values Confirmation H1 Perceived Trust -> Destination Safety 0.309 0.055 5.585*** Sig. H2 Destination Safety -> Intention to use 0.079 0.037 2.166* Sig. H3 Perceived Trust -> Intention to use 0.269 0.059 4.541*** Sig. H4 Perceived Trust -> Structural Assurance 0.792 0.025 31.060*** Sig. H5 Structural Assurance -> Intention to use 0.512 0.055 9.275*** Sig. H6 Self-Efficacy*Trust-> Intention to use -0.085 0.020 4.183*** Sig Notes. Significant levels *p < 0.05; ***p < 0.001 Destination Safety Self efficacy 0.08* -0.085*** 0.31* Perceived trust Intention to use 0.79* 0.51* Structural assurance 0.27* Fig. 2. COVID-19 model result (Notes. Significant levels *p < 0.05; ***p < 0.001) Factors Influencing Tourists’Intention 511