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The global health crisis caused by COVID-19 has drastically changed human society in a relatively short time. However, this crisis has offered insights into the different roles that such a worldwide virus plays in the lives of people and how those have been affected, as well as eventually proposing new solutions. From the beginning of the pandemic, technology solutions have featured prominently in virus control and in the frame of reference for international travel, especially contact tracing and passenger locator applications.
The objective of this paper is to study specific areas of technology acceptance and adoption following a unified theory of acceptance and use of technology (UTAUT) research model.
We presented a research model based on UTAUT constructs to study the determinants for adoption of COVID-19–related apps using a questionnaire. We tested the model via confirmatory factor analysis (CFA) and structural equation modeling (SEM) using travelers’ data from an insular tourist region.
Our model explained 90.3% of the intention to use (N=9555) and showed an increased understanding of the vital role of safety, security, privacy, and trust in the usage intention of safety apps. Results also showed how the impact of COVID-19 is not a strong predictor of adoption, while age, education level, and social capital are essential moderators of behavioral intention.
In terms of scientific impact, the results described here provide important insights and contributions not only for researchers but also for policy and decision makers by explaining the reasons behind the adoption and usage of apps designed for COVID-19.
There have been many papers addressing COVID-19–related impacts; more than 23,000 papers have been published between January and May 2020, and hence, it has proved difficult to remain up to date with all the released studies [
The pandemic’s socioeconomic impacts [
The pandemic has challenged our progress and growth-based society and its capitalistic nature, and tourism, as a growth-based phenomenon, has suffered from these challenges [
The general aim of this paper is to examine users’ perceptions and attitudes toward a COVID-19–based app through a case study on a European island, which deployed a successful safety system to mitigate the impact of the pandemic, while preserving mobility after lockdown and isolation. More specifically, the research aims of this work are (1) to investigate the effects of the COVID-19 pandemic on technology adoption and especially safety, security, privacy, and trust; (2) to increase our understanding of differences in the determinants of safety in technology use; and (3) to improve the predictive accuracy and explanatory power of a parsimonious questionnaire based on a known unified theory of acceptance and use of technology (UTAUT) [
A vital component of this research’s successful execution was the evaluation’s contained and isolated nature (ie, small European island with an extensive tourism economy [
However, the urgency to study COVID-19 phenomena could increase errors in the research and then decrease both rigor and validity. To avoid making such mistakes, we designed and distributed a questionnaire based on the UTAUT model [
The rest of this paper is organized as follows: We start by providing an overview of the current literature pertinent to this study. Next, we describe the research questions, hypotheses, and methods adopted for this study and the results. The work outcomes are analyzed and discussed, and the limitations of the research are presented, also considering the particular context of the research, the COVID-19 pandemic. At the end of the paper, we present the conclusion and future works.
This section presents the background and literature review related to the main topics of this paper. The first subsection provides an overview on the transformations of the tourism sector and research caused by COVID-19; the second subsection deals with the technological measures and their ethical challenges involved with the COVID-19 pandemic; the third subsection touches on citizen engagement and social capital studies also in the context of COVID-19; and finally, the last subsection surveys the technology adoption scales and methodology that we used and extended in our study.
One of the sectors most scarred by the COVID-19 pandemic crisis is tourism [
As described by Sigala [
The redesigning in the tourism sector could also benefit from the use of COVID-19 technologies. An invitation for a change in the domain of e-tourism research has been made [
COVID-19 has also changed our relationship with technology [
Several tools have been developed and proposed to mitigate the risks associated with the COVID-19 and the spread of the disease and to perform diagnosis. Kumar et al [
Many surveys have been performed to classify and discuss contact-tracing apps [
Due to the ethical issues involving COVID-19 digital tools, many authors have examined this dimension [
The users’ perception and acceptance of COVID-19 contact-tracing approaches were investigated by Lu et al [
Despite the plethora of digital tools proposed for COVID-19 (see, eg, [
Public and citizen engagement is based on communication and building relationships between authorities and citizens, for instance, through dialogue and participation [
Digital technology for citizen engagement can also facilitate the development of
Models are widely used to study people’s intentions to adopt technology. The Technology Acceptance Model (TAM) [
As reviewed by Venkatesh et al [
The literature shows that users perceive as risky several of the so-called new products, so perceived risk has been often included in UTAUT [
The UTAUT model, including its extensions, was also used in 2020 in the context of COVID-19 technologies. For instance, Békés and Aafjes-van Doorn [
This work was motivated by a unique set of circumstances to deploy safety measures at scale in a European island with a significant tourism industry in order to better understand the factors affecting the adoption and use of dedicated COVID-19 apps. We were particularly interested in investigating the role of safety, security, privacy, and trust in the context of the adoption of a voluntary COVID-19 app that supports air and sea access to an insular region. We also wanted to understand the effect of moderator variables (gender, age, education, and social capital) in the adoption of COVID-19 safety systems.
The
During their vigilance period, travelers received reminders for submitting their health inquiries via the Short Message Service (SMS). Those using the Madeira Safe to Discover app could receive their test results and submit their daily health inquiry electronically. In addition, they could decide to share their location while using the app voluntarily, but the system could not implement any automated contact-tracing mechanism. In summary, the Madeira Safe to Discover app is an optional digital tool that would improve COVID-19 safety measures for health authorities, while providing some practical benefits for travelers at their data expense.
The researchers involved in this study were asked to assist with the system’s design and advise on data protection and privacy issues, while producing an independent adoption and usage report. This set the stage to investigate at scale the effects of safety, privacy, and trust in the adoption of mobile apps and safety-monitoring systems.
More specifically, the research purposes of this work were (1) to investigate the effects of the COVID-19 pandemic on technology adoption, especially safety, security, privacy, and trust; (2) to increase our understanding of differences in the determinants of safety in technology use; and (3) to increase the analytical potential and predictive precision of a parsimonious questionnaire based on a known UTAUT model for broader application in HCI research.
This study proposes a questionary adapted from a UTAUT model that incorporates variables such as safety, trust, perceived security, perceived usefulness (performance expectancy), and ease of use (effort expectancy).
For testing the hypothesis, the questionnaire comprised 27 questions (items) for responses on a Likert-type scale: 1 for strongly disagree, 2 for disagree', 3 for undecided, 4 for agree, and 5 for strongly agree. Concerning the questionnaire’s validity, the questions (items) were both adapted from the existent literature and reformulated considering the COVID-19 Madeira Safe to Discover app, which can generalized for safety-monitoring systems.
Proposed research model. H: hypothesis.
For the purpose of this research, several hypotheses were developed on the basis of the original UTAUT constructs; we will lay them out in detail here.
Hypothesis 1a (H1a): The facilitating conditions (eg, owning a smartphone) for using the COVID-19 Madeira Safe to Discover app positively influences users’ intentions to use it.
H1b: The facilitating conditions (eg, knowledge to use the app) for using the COVID-19 Madeira Safe to Discover app positively impacts effort expectancy.
H2a: The social influence (eg, recommendation from significant others) for using the COVID-19 Madeira Safe to Discover app positively predicts effort expectancy.
H2b: The social influence (eg, recommendation from significant others) for using the COVID-19 Madeira Safe to Discover app directly and positively influences perceived security.
H2c: The social influence (eg, recommendation from health authorities) for using the COVID-19 Madeira Safe to Discover app directly and positively influences performance expectancy.
H3: Performance expectancy (ie, usefulness) positively affects behavioral intention to use the COVID-19 Madeira Safe to Discover app.
As the Madeira Safe to Discover app provides a new way to secure travel, we expect that the perceived ease to use such an app will influence the behavioral intention of the users. Following the previous analyses and UTAUT’s hypotheses, we formulated that:
H4: Effort expectancy (ie, ease of use) positively affects performance expectancy (ie, usefulness) to use the COVID-19 Madeira Safe to Discover app.
Security, trust, and risk have become critical additional constructs in studies on technology adoption [
H5a: The perceived security of the COVID-19 Madeira Safe to Discover app positively and directly predicts perceived trust.
H5b: The perceived security of the COVID-19 Madeira Safe to Discover app positively and directly predicts the behavioral intention to use the COVID-19 Madeira Safe to Discover app.
H6a: The privacy risk of using the COVID-19 Madeira Safe to Discover app directly and negatively impacts perceived security.
H6b: The privacy risk of using the COVID-19 Madeira Safe to Discover app directly and negatively impacts perceived trust.
H6c: The privacy risk of using the COVID-19 Madeira Safe to Discover app directly and negatively impacts performance expectancy.
H7a: Trust positively impacts the performance expectancy (ie, usefulness) to use the COVID-19 Madeira Safe to Discover app.
H7b: Trust positively affects the effort expectancy (ie, ease of use) to use the COVID-19 Madeira Safe to Discover app.
The COVID-19 pandemic had a significant social, economic, and personal behavioral impact on citizens worldwide. Most countries in Europe were on complete lockdown for several weeks and months, and many closed airports and borders to prevent the spread of the pandemic. After COVID-19 lockdown, measures were enforced in public spaces (eg, use of masks, temperature screening, hand hygiene) to mitigate the risk of contagion. As introduced in the Literature Review section, technology adoption models are inspired by the TRA; according to this, subjective norms and the attitude toward an action impact the behavioral intention to use, so these 2 influence how individuals perform an action [
H8a: The extent to which someone is impacted by COVID-19 positively affects the intention to follow safety measures.
H8b: The willingness to follow COVID-19 safety measures positively affects the intention to use the COVID-19 Madeira Safe to Discover app.
Furthermore, it is noteworthy that the attitude of a person concerning a particular behavior is dependent upon their beliefs as well as evaluations, and different works have stressed the relationship between security, safety, and behavioral intentions [
H9a: The willingness to follow COVID-19 safety measures positively and directly influences perceived security.
H9b: The extent to which someone is impacted by COVID-19 positively and directly predicts perceived trust.
This study followed the recommendation for a 2-stage analytical procedure [
The questionnaire was sent via email to 58,954 participants who were registered in the system and who gave prior permission to be contacted via email. The questionnaire was sent at the end of August 2020 to travelers who had already finalized their trips or had stayed after the 14-day monitoring period (July and August 2020). The email was sent in all the 5 different languages supported by the app and contained a general explanation of the study, the details of the privacy policy and data treatment, and a link to a Google Forms survey. The questionnaire was translated into 5 languages corresponding to the supported idioms of the app according to the following breakdown: 36,930 (62.6%) in Portuguese (PT), 10,178 (17.3%) in English (EN), 6575 (11.2%) in German (DE), 3735 (6.3%) in French (FR), and 1536 (2.6%) in Spanish (ES). In total, we collected data from 9555 participants; corresponding to the overall participation of 16.2%, the participation was higher in DE (18.6%) and PT (17.7%) and lower in FR (12.2%), EN (11.6%), and ES (11.4%).
In terms of the general demographics (N=9555, summary in
The study took place within the scope of the Science4Covid Research project funded by the Portuguese National Science Foundation in collaboration with regional and national health authorities.
Characteristics of respondents (N=9555).
Demographic and group | Frequency, n (%) | ||
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Woman | 5019 (52.5) | |
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Man | 4493 (47.0) | |
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Other | 43 (0.5) | |
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<18 | 142 (1.5) | |
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18-35 | 3122 (32.7) | |
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36-49 | 3203 (33.5) | |
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50-65 | 2581 (27.0) | |
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>65 | 484 (5.1) | |
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N/Aa | 23 (0.2) | |
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Portuguese | 5847 (61.2) | |
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German | 1310 (13.7) | |
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United Kingdom | 532 (5.6) | |
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France | 516 (5.4) | |
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Spain | 328 (3.4) | |
|
Italian | 125 (1.3) | |
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Other EUb | 603 (6.3) | |
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Other non-EU | 125 (1.3) | |
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Other (non-European) | 169 (1.8) | |
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Basic | 277 (2.9) | |
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Secondary | 2307 (24.1) | |
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Graduation | 3686 (38.6) | |
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Postgraduation | 3035 (31.8) | |
|
N/A | 250 (2.6) |
aN/A: not applicable.
bEU: European Union.
Given that the study did not involve sensitive or health-related information, did not involve risks or benefits, and was completely voluntary, it was not necessary to obtain an ethics board review. Nevertheless, the study complied with the provisions of the General Data Protection Regulation—Regulation (EU) 2016/279 of the European Parliament and of the Council of April 27, 2016—and follows the recommendations of the Declaration of Helsinki for research.
Two questions addressed the main motivations and sources of influence for travel. Among the motivations for the trip, 1940 (20.3%) participants reported the sun, 1891 (19.8%) rest, 1749 (18.3%) nature, and 1491 (15.6%) family, followed by 1414 (14.8%) for COVID-19. Culture, work, and wellness were ranked much lower in terms of preference (n=612, 6.4%; n=325, 3.4%; n=134, 1.4%, respectively). In terms of nationality breakdown, family ranked higher for Portuguese nationals, while COVID-19 was higher for German and Spanish nationals. In terms of travel frequency, COVID-19 was almost equally higher for local residents and first-time visitors, which suggests that some people choose to travel to a destination because of COVID-19. This was confirmed by analysis of the sources of influence where safety had 3019 (31.6%) responses ranked first, followed by personal (n=2933, 30.7%) and family (n=2169, 22.7%) responses and a much lower influence on media, tour/agencies, and social media (n=812, 8.5%; n=401, 4.2%; and n=201, 2.1%, respectively). In terms of age, motivations were not significantly different, although COVID-19 consistently rose from 1041 (10.9%) for lower-age groups (<18 years) to 1815 (19.0%) for higher-age groups (>65). The same trend was not observed for safety in the sources of influence.
Inspired by the methodology described by Khalilzadeh et al [
Specifically, the GFI was 0.959, the AGFI was 0.928, the comparative fit index (CFI) was 0.959, the normative fit index (NFI) was 0.958, and the Tucker-Lewis index (TLI) was 0.950. Similarly, there was no misfit evidence, with satisfactory levels of 0.053 for the root-mean-square error of approximation (RMSEA) and 0.063 for the standardized root-mean-square residual (SRMR), which compared favorably to the benchmarks reported by Wilkowska and Ziefle [
In this model, we analyzed the moderating effect of the model factors and their effect on variables. In this sense, we can expect that the model will show unexpected moderating relationships [
The measurement model.
Construct and item |
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SE | |||
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Impact_1 | .75 | N/Aa | N/A | N/A |
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Impact_2 | .76 | 0.015 | 58.294 | .001 |
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Impact_3 | .55 | 0.016 | 46.660 | .001 |
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FacCon_1 | .90 | N/A | N/A | N/A |
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FacCon_2 | .94 | 0.009 | 117.527 | .001 |
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Privacy_1 | .92 | N/A | N/A | N/A |
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Privacy_2 | .90 | 0.014 | 69.501 | .001 |
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SocInfl_1 | .65 | N/A | N/A | N/A |
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SocInfl_2 | .61 | 0.024 | 44.3131 | .001 |
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Safety_1 | .74 | N/A | N/A | N/A |
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Safety_2 | .62 | 0.021 | 50.066 | .001 |
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Safety_3 | .72 | 0.021 | 55.529 | .001 |
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EffExp_1 | .90 | N/A | N/A | N/A |
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EffExp_2 | .93 | 0.007 | 136.111 | .001 |
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PerfExp_1 | .89 | N/A | N/A | N/A |
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PerfExp_2 | .85 | 0.010 | 105.849 | .001 |
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Security_1 | .84 | N/A | N/A | N/A |
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Security_2 | .84 | 0.010 | 108.731 | .001 |
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Security_3 | .92 | 0.008 | 126.270 | .001 |
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Trust_1 | .79 | N/A | N/A | N/A |
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Trust_2 | .94 | 0.014 | 79.631 | .001 |
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IntUse_1 | .65 | N/A | N/A | N/A |
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IntUse_2 | .82 | 0.015 | 66.639 | .001 |
aN/A: not applicable.
In the absence of measurement misfit, we applied SEM to perform multiple regression analysis of the data. This kind of technique is adopted to evaluate the fitting of the data upon the theoretical measurement model [
Results of the research model. H: hypothesis.
To investigate demographic moderator effects, we followed the work of Shin [
We compared different groups to test the moderating effects of these variables after testing for measurement invariance using X2 difference tests and the fit indexes (provided in
Results of moderator effects.
Hypothesis | Gender | Age (years) | Education | Social capital | |||||||
|
Woman | Man | <36 | ≥36 | Basic/secondary | Higher education | Low | Medium | High | ||
H1a | 0.25a | 0.23a | 0.26a | 0.25a | 0.27a | 0.25a | 0.24a | 0.24a | 0.40a,b | ||
H1b | 0.43a | 0.49a | 0.45a | 0.47a | 0.51a,c | 0.45a | 0.49a | 0.43a | 0.52a,c | ||
H2a | 0.42a | 0.37a | 0.38a | 0.41a | 0.36a | 0.40a | 0.36a | 0.41a | 0.40a | ||
H2b | 0.60a | 0.50a,d | 0.51a | 0.60a | 0.62a,c | 0.53a | 0.52a | 0.57a | 0.57a | ||
H2c | 0.44a | 0.48a | 0.41a | 0.47a | 0.43a | 0.45a | 0.39a,d | 0.43a | 0.60a,b | ||
H3 | 0.61a | 0.62a | 0.61a | 0.61a | 0.59a | 0.61a | 0.63a | 0.63a | 0.42a,d | ||
H4 | 0.43a | 0.43a | 0.46a | 0.41a | 0.48a | 0.42a | 0.48a | 0.45a | 0.24 a,d | ||
H5a | 0.68a | 0.74a | 0.71a | 0.70a | 0.71a | 0.70a | 0.67a | 0.72a | 0.71a | ||
H5b | 0.16a | 0.19a,c | 0.18a | 0.16a | 0.18a | 0.17a | 0.20a,c | 0.13a,d | 0.24a,b | ||
H6a | –0.29a | –0.32a | –0.34a,c | –0.27a,d | –0.22a,d | –0.33a | –0.29a | –0.32a | –0.26a,d | ||
H6b | 0.02 (nse) | 0.01a,d,e | 0d,e | 0.03b,e | 0.01d,e | 0.02e | –0.02d,e | 0.04b,f | 0.02b,e | ||
H6c | –0.11a,c | –0.07a,d | –0.09a | –0.11a,c | –0.08a,d | –0.10a | –0.10a | –0.10a | –0.08a,g | ||
H7a | 0.09a,c | 0.05d,g | 0.07a | 0.08a | 0.05d,g | 0.09a,c | 0.09a,c | 0.07a,d | 0.07f | ||
H7b | 0.10a | 0.10a | 0.12a,c | 0.08a,d | 0.09a,d | 0.10a | 0.10a | 0.13a,b | 0.01d,e | ||
H8a | 0.66a | 0.67a | 0.63a | 0.70a | 0.65a | 0.68a | 0.63a | 0.65a | 0.78a,c | ||
H8b | 0.06a,b | 0.02d,e | 0.03d,e | 0.06a,b | 0.05f | 0.05a | 0.04d,f | 0.05a,c | 0.04d,e | ||
H9a | 0.14a | 0.15a | 0.18a,c | 0.11a,d | 0.14a | 0.15a | 0.18a,c | 0.12a,d | 0.17a,c | ||
H9b | 0.15a | 0.18a,c | 0.14a,d | 0.17a | 0.16a | 0.17a | 0.17a | 0.16a | 0.08d,g |
aSignificant at
bHighly significant increase in the
cSignificant increase in the
dSignificant decrease in the
ens: not significant.
fSignificant at
gSignificant at
Results from the study demonstrated that our research model explains 90.3% of the intention to use the Madeira Safe to Discover app compared to previous research [
Contrary to other empirical studies on mobile payments [
In terms of privacy and trust, our results differed significantly from previous studies [
In addition to the COVID-19 impact, which is a new construct introduced here, security and privacy had a reduced impact on trust as well. Our results suggested that the impact of COVID-19 potentially affects privacy more than it does trust (1 of the unexpected results). Therefore, working on users’ privacy concerns is crucial for other similar COVID-19 systems since privacy influences perceived security and affects users’ trust toward these apps. Privacy also emerged as a more interrelated construct influencing performance expectancy and security but also showing significant relationships with the COVID-19 impact, facilitating conditions, and social influence. This clearly indicates that privacy needs to be addressed carefully while designing these apps and that its impact is not mitigated by the COVID-19 impact or the users’ willingness to follow safety measures.
Overall, the results indicated that performance expectancy (usefulness) is the biggest predictor of behavior intention to use (H3), which suggests that usability and ease of use are still crucial in designing COVID-19 systems. Effort expectancy was followed by facilitating conditions, COVID-19 safety measures, and, finally, security. Our results suggest that the willingness to follow COVID-19 safety measures (H9b) is a stronger predictor of usage behavior than security (H5b). This influence of H9a (
Finally, from all the moderator effects analyzed, clearly our indirect measure of social capital was the one showing more differences across the hypotheses. The predictors of the intention to use were significantly stronger for this group than any other group (
The COVID-19 pandemic should be a stimulus to re-examine how we approach existing challenges (eg, social inequalities, sustainable tourism) and study some aspects of human behavior, such as our relationship with technology and its role during emergencies, for instance, in tourist destinations.
Against the backdrop of the COVID-19 pandemic, this paper provided the first detailed research on adopting mobile safety apps designed to mitigate the pandemic’s consequences. Although we expect that some of our findings will not be generalized beyond the context of the COVID-19 Madeira Safe to Discover app, others can provide early insight into the increasingly important role of safety, security, privacy, and trust in mobile app adoption and usage.
This research aimed at improving the predictive and explanatory power of technology use and adoption research models in the COVID-19 context. In addition, we investigated the variations in the determinants of COVID-19 systems’ acceptance in a reasonably diverse European demographic context.
The results from this work make apparent how privacy is a fundamental aspect when dealing with users’ perceptions of COVID-19–related systems. Indeed, privacy influences essential aspects, such as security and performance expectancy. Moreover, privacy concerns still stand, even when the impact of COVID-19 on the personal context of the user increases, showing the importance of privacy even in an emergency context. More generally, the impact of COVID-19 on people positively influences the adoption of safety measures (eg, use of masks, temperature screening, hand hygiene). Moreover, users who are more willing to follow COVID-19 safety measures are also more prone to using the COVID-19 Madeira Safe to Discover app. Several steps can be taken to further improve the usefulness of the app and ensure user trust and security, as was achieved with COVID-19 contact-tracing apps [
Finally, this work’s fundamental contribution is an increased understanding of the essential role of privacy, security, and trust in the intention to use safety apps. Although security has a strong, direct and indirect effect on the model’s fundamental construct, it emerges to be as equally important as safety concerns. Furthermore, our research shows an increased role of social influence in security, of security in trust, and of trust in performance expectancy compared to previous research that inspired our model. Conversely, we observed a reduced negative impact of privacy on security and a rejection of the hypothesis of the positive role of privacy on trust compared to previous research. Together with a more complex influence of privacy on the overall model, these are significant results for future research implications.
Despite the contributions described previously, this research had some limitations, which also provide useful avenues for additional research discussed in the next section. Here, we reported on 1 of the first empirical studies to examine the technology acceptance of the COVID-19 Madeira Safe to Discover app by applying multidisciplinary constructs to the best of our knowledge. Still, several limitations affected the range of our results. Although we had a significant sample of several European nationalities and cultures, there was still a bias toward a specific nationality. To understand this bias’s effect, we analyzed the moderator effects of nationality in our model, which showed the same evidence of invariance measurement compared to other moderators (gender, age, etc). However, we did not record cultural and nationality differences in our sample. Previous work shows a significant impact of cultural diversity on social influence, usefulness, and behavior intention [
Another significant limitation of our study is that it involved people who traveled during the pandemic period. Given the mobility restrictions in place, the drastic reductions in travel, and the pandemic’s economic consequences, our sample could be biased. The sample accessed in this study could express different perceptions toward the COVID-19 Madeira Safe to Discover app compared to the general public. This potential bias effect limits the generalizability of this research, although the design method reduces the impact of the common method bias (CMB), which we encountered in this research, particularly for the new COVID-19 constructs. In addition, objectively measuring outcome variables separately (eg, frequency of use) will lead to results less likely to produces biases related to the measurement and methods used.
Despite the aforementioned limitations, we believe that this study advances the understanding of the intention to use mobile apps and those associated with safety concerns, such as COVID-19, and will provide a useful set of design guidelines and recommendations for the provision of mobile services with safety, security, and trust concerns to different user groups.
In this research, recognizing the moderating role of demographics is especially significant. The intention to use the COVID-19 Madeira Safe to Discover app differs among demographic groups. Notably, the impact of social influence varies with gender, age, education, and social capital. We also observed a significant change in the role of the COVID-19 impact over demographics. Finally, high indicators of users’ social capital have a tremendous effect on the intention to use COVID-19 safety systems, which suggests that localized versions of these apps are likely to be more successful than general ones.
Anticipating user behavior is notoriously tricky, especially under unprecedented circumstances. An obvious direction for future work would be to apply our measurement model to a longitudinal approach on a more comprehensive technology, such as digital contact tracing. Such a study will sample a more extensive and more culturally diverse user base. This could be accomplished using quota sampling or stratified sampling to guarantee a specific demographic distribution. Longitudinal research could observe changes in the importance of constructs over time. However, a more thorough validation of the generalized application of our research model would imply a widespread data collection process. Nevertheless, this would enable examining the significant effects of safety, privacy, and trust on behavioral intention over time. Future research could also consider supplementing other precursors of behavioral intention. The results of this study could open new avenues for future research. For instance, this research model could be applied to other contexts where safety plays an important role, such as health care, and where privacy is a major concern, such as surveillance and social networking. In addition, understanding how to study the UTAUT model through more parsimonious items can reduce the overload of the questionnaires.
Goodness-of-fit indices.
Discriminant validity through the heterotrait-monotrait ratio.
Validity measures.
Summary of hypothesis tests.
Fit indices for invariance checks of moderator effects.
artificial intelligence
adjusted goodness-of-fit index
average variance extracted
confirmatory factor analysis
European Union
human-computer interaction
heterotrait-monotrait ratio of correlations
structural equation modelling
squared multiple correlation
standardized root-mean-square residual
models of technology adoption
theory of reasoned action
unified theory of acceptance and use of technology
We would like to thank the Madeira Islands health authorities for collaborating with the development and promotion of the Madeira Safe to Discover app for COVID-19 and for facilitating access to statistical data crucial for the research. This work was supported by the project LARSyS UID/50009/2020.
None declared.