Next Article in Journal
Recent Trends in Copper Metallization
Previous Article in Journal
Simulation and Optimization of Piezoelectric Micromachined Ultrasonic Transducer Unit Based on AlN
Previous Article in Special Issue
The Impact of the COVID-19 Pandemic on the Global Web and Video Conferencing SaaS Market
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Electronics
Volume 11
Issue 18
10.3390/electronics11182916
electronics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Building Trust toward Sharing Economy Platforms beyond the COVID-19 Pandemic

by
Dan-Cristian Dabija
1,*,
Luiela Magdalena Csorba
2,
Florin-Lucian Isac
2 and
Sergiu Rusu
2
1
Department of Marketing, “Babeș-Bolyai” University Cluj-Napoca, 400591 Cluj-Napoca, Romania
2
Faculty of Economic Sciences, “Aurel Vlaicu” University of Arad, 77 Revoluției Str., 310130 Arad, Romania
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(18), 2916; https://doi.org/10.3390/electronics11182916
Submission received: 9 August 2022 / Revised: 7 September 2022 / Accepted: 12 September 2022 / Published: 14 September 2022
(This article belongs to the Special Issue Impact of COVID-19 on Multimedia Transformation)
Browse Figures
Versions Notes

Abstract

:
The sharing economy has seen a worldwide boom in recent years. In tourism, such platforms are being increasingly used; however, analysis of consumers’ behavior toward such platforms in the context of technological innovation since the COVID-19 pandemic is almost non-existent. The aim of the present research is to fill this gap by studying, with the aid of structural equations, factors that influence the attitudes and behavior of consumers of tourist services offered through sharing economy platforms. Under the impact of the adoption of new technologies, the perceived value generated by these platforms is considered in the context of the COVID-19 pandemic. Trust in tourism sharing economy platforms and their perceived value is determined by consumers’ experience of using them, the authenticity of platforms, propensity toward technological innovation, and users’ demand for novelty. Data collected from 548 respondents were modeled using structural equations in SmartPLS. Results show that experience, the authenticity of platform, openness to new things, and technological innovation are the determining factors that influence consumers’ behavior on sharing economy tourism platforms. The originality of the research consists of the implementation, in a sharing economy context, of a new construct for this sector, which can also be used in other fields, namely that of pandemic fear. Furthermore, the way in which other constructs are included in the proposed conceptual model and the links between them presents additional novelty. Given the trend of increasing demand for such platforms and tourists’ appetite for technological innovation, it will become a managerial challenge for businesses in this field to keep up with the constant evolution of cutting-edge technologies.

1. Introduction

Worldwide, sharing economy activities represent a new and revolutionary approach to business models in many sectors, generating, in recent years, an increasing trend in profit levels obtained in this field [1]. Statistics estimate that the annual value of sharing economy activities globally will increase by more than 20 times by 2025 compared to 2014, reaching USD 335 billion [2]. The accuracy of this forecast is directly influenced by the medical challenges generated by the recent COVID-19 pandemic and by the contemporary socioeconomic challenges facing humanity, e.g., the Russian–Ukrainian war and the global raw material crisis.
The COVID-19 pandemic has had a major impact not only on sharing economy platforms but also on service providers, customers, and governments, which face the impossibility of collecting taxes generated by these activities [3]. The pandemic has also influenced a reset of activities in the hospitality industry, generating numerous changes and/or adaptations to the new daily realities caused by canceled flights, closed borders, health and/or travel restrictions, interrupted supply chains, lost jobs, closed businesses, etc. [4,5]. Due to social and travel restrictions imposed by countries across the world and the health and safety measures introduced globally to limit the spread of the COVID-19 pandemic [6], many clients of sharing economy platforms have been forced to cancel their accommodation reservations through Airbnb and to stop using Uber services [7]. For sharing economy platforms, among the most considerable challenges generated by the COVID-19 pandemic has been the difficulty in continuing their activity in compliance with health and safety measures, in addition to the reimbursement of payments provided by customers for reservations [8,9]. As the number of guests accommodated via sharing economy platforms, such as Airbnb, significantly decreased in 2020 and 2021, customers making reservations have showed increased concern with respect to the maintenance of sanitary rules to reduce the risk of infection with the SARS-CoV-2 virus [7]. Hence, the COVID-19 pandemic has transformed the sharing of goods and services, representing a source of fear and uncertainty, with social contact being regarded as perilous [10].
Sharing mobility affects many aspects of contemporary society: socially, environmentally, economically, financially, and not least, technologically [3]. The sharing economy has developed along with the Internet as part of digitization, and information and communication technology (ICT) has penetrated our everyday lives [11]. The sharing economy has also led to changes in the tourism sector; thanks to the progress of ICT [12], digitization has become a driver of the collaborative economy [13].
Factors that determine consumers’ acceptance of technology can be analyzed with the aid of the diffusion of innovation theory (DIT) [14]. Innovation diffusion is defined as the innovation transmission process through specific channels to members of a social system over a given period [15]. The innovation diffusion theory explains the concrete way in which individuals adopt an innovation due to the exercise of a certain behavioral model, which will lead them to understand its advantages [15,16].
Although the sharing economy phenomenon is increasingly analyzed in specialized literature, the approach to this perspective in tourism is still deficient; few authors have investigated consumers’ behavior, specifically the way in which they relate to sharing economy platforms, such as Airbnb, CouchSurfing, House Trip, Wiindu, 9flats, and Uber [17,18,19,20,21,22,23]. Moreover, the few existing studies in this area refer rather to the evolution of sharing economy platforms prior to the COVID-19 pandemic rather than after it [24,25,26]. From this perspective, the aim of the present research aims is to fill this gap, studying, with the help of structural equations, the factors that influence the attitudes and behavior of consumers of services offered through sharing economy platforms [27,28,29,30] under the impact of the adoption of new technologies in the context of the COVID-19 pandemic. To measure and test this relationship, the following constructs were used: consumers’ experience, technology seeking, novelty seeking, perceived value, trust in sharing economy platforms, and pandemic fear.
The remained of this paper is structured as follows. The first section consists of a literature review and hypothesis development. In the second section, we analyze the research methodology and design. The third section includes the research results and discussion, and in the last section, we analyze the theoretical and managerial implications of the obtained results, along with the limitations of the current study and perspectives on research in the field.

2. Literature Review: Hypothesis and Theoretical Model Development

The “sharing economy” phenomenon has benefited from increased interest in recent specialized literature [3,4,19,21,22,23]. Sharing is a form of economic human behavior, which is distinguished by specific characteristics and is based on the reciprocity of the parties involved [31]. The rapid expansion of the sharing economy began in 2008 during an economic crisis, with both economic and social effects [32]. Although the sharing process (goods and services) is not a new practice, the sharing economy is a recent phenomenon, which arose due to the influence of the online environment and digital platforms [33,34,35]. The extension of the act of sharing from family ties to individuals who do not know each other is a radical discovery, determined and facilitated by Web 2.0, which connects users from around the globe, regardless of their origin [31].
The sharing economy is a summative, multidimensional concept based on other concepts, such as collaborative consumption, the access economy, the collaborative economy, the platform economy, and the community-based economy [33,36]. The “sharing economy” phenomenon has offered increased opportunities for the extension of small businesses, such as through Airbnb, CouchSurfing, House Trip, Wiindu, etc., owing to the preferable price–quality ratio compared to traditional services, as well as the possibilities for flexible and fast reservations and/or cancellations [35,37,38]. The sharing economy constitutes an umbrella construct for a wide range of non-ownership consumption activities [39,40], as it relies on three foundational cores: the access economy, the platform economy, and the community-based economy [41]. Promoters of the sharing economy concept tend to formulate broad definitions, including “peer-to-peer” initiatives, market mechanisms, and centralized “peer-to-peer” systems available to users mainly online [42]. Opponents of this approach propose a more restrictive definition of the term, emphasizing that it refers to temporary access to certain goods and services that individuals make available to each other to obtain various monetary benefits from these transactions [43].
The concept of the sharing economy is often used to describe transactions that take place on the market different individuals [33], namely the added value resulting from the transformation of an unused asset into one accessible online to a community interested in it [44]. Furthermore, the “sharing economy” is understood to encompass the digital platforms that allow consumers to interact and use, via mobile applications and/or web pages, various products and/or services [32] and assets [45] without a transfer of ownership [46].
The sharing economy is the result of a complex of economic, social, technological, and environmental changes that have taken place worldwide [47]. The subject is relevant for ongoing research, as the literature highlights its complex meanings and implications [36,39,41]. From a sociological perspective, this type of economy highlights an evolution in the relationship between society and the market [48]. The sharing economy potentially has a lot to offer in terms of social development, i.e., an increase in social cohesion [33].
The sharing economy can be defined as the sharing of goods, products, and services among people who do not know each other, with sharing occurring in a peer-to-peer format with a digital platform acting as intermediary in transactions [49]. This process has generated social changes [50] and social innovations, also triggering societal changes and adaptations [51]. Sharing platforms are the object of controversy and uncertainty despite their social, economic, technological, and environmental advantages because they generate an increase in social inequality [45] due to their externalities. For instance, some neighbors may feel uncomfortable in the presence of strangers, and there may be an impact on the traditional market concerning tourists’ expectations, income distribution, etc. [43]. The sharing economy may also trigger risks; therefore, regulatory frameworks must be considered, building and deploying future social capital [52].
Usually, sharing platforms provide space for product and service sharing. Before the COVID-19 pandemic, the business of sharing economy platforms, such as Airbnb, was booming [53]. Although the recent COVID-19 crisis has become an obstacle for most economic sectors [54], affecting millions of vulnerable people, trust-based commercial sharing platforms have quickly adapted to the situation, finding innovative solutions to keep businesses running [55]. The COVID-19 pandemic represents a considerable risk for sharing economy platforms, as it has affected not only platforms providers but also customers. People worldwide have lost their jobs, triggering an increased number of cancellations; this has affected the activities of platform service providers, many of them being forced to reduce their activities [7].
The diffusion of innovation theory and the technology acceptance model can provide rich explanations for the adoption of sharing economy platforms [15,56], providing considerable empirical support in describing users’ acceptance of technology in many socio-economic fields, both in the products and services markets [57]. Both models were also used to analyze mobile tourism shopping in a developing country [14]. To improve efficiency, businesses in this sector must constantly adapt, not only by implementing the latest technology but also by creating their own innovations, aiming to make their business more profitable [58].

2.1. Experience Using Collaborative Platforms

Experience is a way of understanding the interactions that take place between individuals and different places and/or situations, as an internal experience of people, understood and interpreted by each one differently, depending on motivations, traits, lifestyle, personality, etc. [59,60,61,62,63]. Today’s consumers are looking for increasingly authentic opportunities and are oriented toward new experiences that involve meaningful interactions with peers and especially with locals who are considered to be the keepers of ancestral traditions [64].
Travelers expect meaningful social interactions with local inhabitants of visited destinations, as well as unique experiences in authentic settings. Such occurrences have the power to persuade individuals to travel more often, stay longer, and engage in more exotic activities [64]. The three themes important to Airbnb users are the uniqueness of accommodation interiors and atmosphere, interaction with hosts, and experience of local culture [65].
Authenticity is an essential characteristic of the sharing economy experience. Tourists are often seeking authenticity during their trips, preferring unique, attractive, and, above all, authentic experiences [65]. Authenticity is sought by tourists through stays in rural and/or agritourism guesthouses [66], as well as through cultural or historical tourism [67,68]. Authenticity generates the formation of behavioral intentions in relation to tourist destinations [37,38,69]. Therefore, we infer that:
Hypothesis 1 (H1).
Experience has a positive influence on trust in sharing economy platforms.

2.2. Technology Seeking and Personal Innovativeness in Information Technology toward Sharing Economy Platforms

The COVID-19 pandemic triggered massive structural changes in the world’s economy, enhancing digitization and the widespread use of online technologies. Dissemination of digital technologies has enabled the rapid growth of sharing economy platforms [70], with this process accelerating the transition from traditional economies to a collaborative, Internet-based economy and digitization [13], in addition to innovation. As information and communication technology advances, the sharing of goods and services becomes simplified [71]. The use of ICT in the sharing economy is a growing trend, with the Internet playing the role of mediator to reduce transaction costs between providers offering goods and/or services and those wishing to obtain them [72].
Technological communication platforms have been important in facilitating interpersonal communication during the pandemic, with the whole industry enjoying a boom [73]. Therefore, collaborative platforms have represented an efficient communication method in tourism during the COVID-19 pandemic. Innovation during the global pandemic has brought the ICT process to a higher level, with the crisis offering an opportunity to improve creativity and innovation [74]. Digital platforms have therefore become an important communication tool [73].
Individuals react differently to a new idea or practice due to the unique way in which innovation is perceived, accepted, and used [15,31]. The degree of acceptance of innovation by individuals is a concept derived from the theory of innovation diffusion [75,76,77,78,79] and is proposed to measure the willingness of individuals to try new technologies. This degree of acceptance determines the relationship between information about a new technology and its perception [80]. Users with a high level of innovation acceptance are more likely to develop favorable attitudes toward new technologies [15].
In the field of tourism and hospitality, the degree of innovation acceptance is often used to assess consumers’ repurchasing intentions [37,81]; tourists with a higher level of acceptance are more likely to spend money online than those with a low level of acceptance [82,83]. The degree of innovation acceptance can represent an antecedent of trust on sharing economy platforms, such as Airbnb, with innovative people being more inclined to show increased trust [81]. According to these assumptions, we hypothesize that:
Hypothesis 2 (H2).
Technology seeking exerts a positive impact on trust toward sharing economy platforms.
Hypothesis 3 (H3).
Technology seeking exerts a positive influence on the perceived value of sharing economy platforms.

2.3. Novelty Seeking and Sharing Economy Platforms

A novelty-oriented individual is one who desires to obtain new, unique information and/or experiences regarding various products and/or services [83]. Individuals’ novelty-seeking ability is closely related to their orientation toward innovation, specifically toward the acceptance of innovations. The literature considers that through novelty-seeking behavior, a tourist will want to experience new, unfamiliar stimuli [84]. Conceptually speaking, novelty is part of the unique experience felt by an individual when consuming a product and/or service [81,85].
Numerous Airbnb customers have been motivated to use the service partly due to their desire to be up-to-date with the latest innovations, experiences, and situations; this motivation is a relevant criterion for segmentation of frequent users of sharing economy platforms (such as Airbnb) versus those who use them only occasionally [24]. Novelty-seeking behavior is higher in the case of people who seek accommodation via sharing economy platforms [64] and is often positively and significantly associated with the perceived value of a transaction [86]. Based on these arguments, we posit that:
Hypothesis 4 (H4).
Novelty seeking positively impacts the perceived value of sharing economy platforms.

2.4. Perceived Value and Sharing Economy Platforms

Trust in sharing platforms and the value perceived by tourists is determined by their experience of sharing economy platforms; by the relationships established with representatives from the targeted tourist destinations; and by individual tourists’ preferences, attitudes, behaviors, feelings, etc. [53,61,62,83]. The concept of perceived value has often been used to understand consumers’ purchasing decisions and is mostly based on the utility felt by consumers within a consumption situation [87]. Significant connections between perceived value and consumer attitudes are often observed [88], with perceived value exerting a positive effect on the intention to use sharing economy platforms. Thus, the higher the perceived value, the stronger the user’s intention to use such a platform [37,38,89]. The perceived value of the experience of using sharing economy platforms has a significant and direct impact on the intention to use them, as well as trust [85,90]. Therefore, we argue that:
Hypothesis 5 (H5).
Perceived value has a positive influence on trust in sharing economy platforms.
Hypothesis 6 (H6).
Perceived value has a positive impact on behavioral intention with respect to using sharing economy platforms.

2.5. Platform Trust and Sharing Economy Platforms

The mechanisms of trust development in the sharing economy are have not been extensively studied [91,92]. Strengthening trust in online platforms, including those of the sharing economy type, is one of the most considerable challenges for their developers, as trust is often associated with the potential risks perceived by users [93]. Evidently, building trust in such platforms depends on both users’ previous experience of such platforms and the extent to which a platform itself has met individual expectations. Individual use of a platform can affect the degree of other users’ trust [94]. Individuals can improve their social presence within a sharing economy platform, which, in turn, can improve the trust of others [95]. Trust is a significant factor in the adoption of individual decisions concerning the repeated use of sharing economy platforms [85,96]. Moreover, trust in Airbnb platforms is a key determinant of users’ continued intention to use them [37,38,97]. For example, the mediating role of trust and attitude toward sharing economy platforms was developed and validated in a sociotechnical framework to investigate Indian urban and semiurban participants’ intention toward a crowd-shipping platform for last-mile delivery [98]. Therefore, we infer that:
Hypothesis 7 (H7).
Trust in sharing economy platforms exerts a positive influence on behavioral intention with respect to using sharing economy platforms.

2.6. The COVID-19 Pandemic and Sharing Economy Platforms

Many people were afraid of the risk of being infected when traveling during the COVID-19 pandemic [99]. The sharing economy sector was heavily affected by the COVID-19 pandemic from the moment the World Health Organization declared a public health emergency of international interest [7]. The importance of the sharing economy increased during the pandemic period because sectors such as healthcare tourism and the restaurant industry were heavily dependent on it [7]. Renting a property through Airbnb, CouchSurfing, or other platforms was impossible during the pandemic due to restrictions; isolation; and customer-perceived risk of exposure to the virus, travel, physical distancing, and/or the various hygiene protocols that had to be strictly followed [4]. The travel comfort of the pre-pandemic period has become a dream over the last two years. The sharing economy accommodation sector suffered, with weekly bookings on Airbnb between January and March 2020 falling, e.g., by 96% in Beijing, by 46% in Seoul, and by 29% in Milan [100,101]. We argue that:
Hypothesis 8 (H8).
The COVID-19 pandemic had a negative influence on trust in sharing economy platforms.
Hypothesis 9 (H9).
The COVID-19 pandemic had a negative influence on the behavioral intention to use sharing economy platforms.
Based on the literature [81,85,86,90,98], we propose the research model presented in Figure 1, which shows how consumers’ experience, technology-seeking behavior, novelty-seeking behavior, perceived value, and trust in sharing economy platforms influences their behavioral intention to use tourism platforms during the COVID-19 pandemic.

3. Research Methodology

3.1. Research Design

The aim of the present research is to investigate behavioral intention to use sharing economy platforms in tourism, such as Airbnb, CouchSurfing, or House Trip, based on the perceived value of such platforms during the COVID-19 pandemic. Trust in sharing economy platforms, together with the perceived value of such platforms, is generated by the experience of users in accessing them, the extent to which users are oriented toward using the technology, and especially by the degree to which they seek to use the latest innovations and to benefit from the newest experiences. We propose a theoretical analysis model of the mediating impact of trust toward using sharing economy platforms in tourism, as well as the perceived value of generating behavioral intention toward such platforms. Based on the literature [4,63,80,83,84,85,89,94], we propose the research model presented in Figure 1, which considers generating behavioral intentions toward relying on sharing economy platforms during the COVID-19 pandemic.

3.2. Research Context

The COVID-19 pandemic shattered the worldwide economy and profoundly impacted the tourism sector. The recovery of the tourism sector will take time, although some experts speculate that the pandemic could end the sharing economy [102]. The nights spent in short-stay accommodation offered via online platforms decreased by 46.9% in 2020, followed by a strong recovery of 33.9% in 2021 [103]. Only Airbnb has generated a $250 million fund to mitigate the financial losses incurred by hosts due to booking cancellations [104]. The huge decrease was caused by travel restrictions and people’s fear of exposing themselves to health risks.
The 2020 COVID-19 pandemic pushed Romania into recession, with the economy contracting by 3.9% [105]. Even if ICT helped to create an environment of trust between users and service providers, lockdowns increased the cost of sharing goods and services. Transportation and accommodation are two sharing sectors profoundly affected by lockdown restrictions, with millions of workers losing their jobs [6]. Because Romania has an extensive, high-quality digital infrastructure with broad use of the Internet, the government should be capable of implementing appropriate and rapid recovery strategies [105,106].
The most used sharing economy platform in Romanian tourism is Airbnb. In May 2020, Airbnb announced that it would fire 1900 employees due to a dramatic 80% reduction in activity as a result of the COVID-19 pandemic. In 2021, Airbnb Romania was still under the cloud of uncertainty regarding its future development [104,106]. Tourism activity has recently started to increase again, so it is hoped that online platforms will help to relaunch the industry.
This research was carried out during the COVID-19 pandemic. Respondents were invited to complete an online questionnaire about their experiences during the COVID-19 pandemic with respect to how it had affected their access to sharing economy platforms at a time when many tourists were still reluctant to travel. Whereas the literature reveals that the COVID-19 pandemic has only been used as a general research framework [3,6,102], in this research, we developed a new construct regarding the influence of the COVID-19 pandemic on sharing economy platforms. This framework was used to measure the conceptual model. From this perspective, the research is even more original, offering a considerable contribution to the advancement of the literature.

3.3. Sampling and Data Collection

This research was based on an empirical investigation implemented through a quantitatively based survey and online interviews with users of sharing economy platforms, such as Airbnb, CouchSurfing, House Trip, etc., between January and April 2022. As it was not possible to determine the exact number of respondents because Romanian national statistics do not provide information on distribution by gender, age group, or domicile of people who use tourist services through sharing economy platforms, we employed a convenience sampling technique. We attempted to obtain information from travel agencies and distribute questionnaires to their clients, but the support received from such providers was limited. Therefore, the questionnaire was distributed online through Google Docs and on various social media platforms, specifically social media groups formed by declared users of sharing economy platforms in tourism. Through this approach, the aim was to obtain a diversity of answers, as the experience of social media users in these groups was and is vast, both before and during the COVID-19 pandemic.
Of the 548 respondents, 382 were women (69.7%), and 166 were men (30.3%); 141 (25.7%) reported their domicile to be located in a rural area, with 407 (74.3%) residing in urban areas. A total of 71 respondents (13.0%) were 19 years old or younger, 289 respondents (52.7%) were aged between 20 and 29 years, 99 respondents (18.1%) were aged between 30 and 39 years, and 89 respondents (16.3%) were older than 40 years old. A total of 50 respondents (10.8%) reported an average length of stay purchased through sharing economy platforms of one to two days, 236 respondents (43.1%) reported an average stay of between three and five days, 224 respondents (40.8%) reported an average stay of between six and ten days, and 29 respondents (5.3%) reported an average stay of more than ten days. The overwhelming majority of respondents declared that they resorted to bookings through such sharing economy platforms for tourism purposes (495 people, 90.3%), with the rest preferring to use these platforms for medical or business purposes. Sharing economy platforms were used mostly in conjunction with family and relatives by 291 people (53.1%), with friends by 199 people (36.3%), with colleagues by 20 people (3.6%), and alone by 38 people (6.9%). A total of 119 respondents (21.7%) had an average net monthly income of between EUR 401 and 600, 96 respondents (17.5%) reported an average net monthly income of between EUR 601 and 800, and 125 respondents (22.8%) reported an average net monthly income of more than EUR 1200. A total of 121 respondents (22.1%) declared that they had been using sharing economy platforms in tourism for about a year, 134 respondents (24.5%) for almost two years, 207 respondents (37.8%) between three and five years, and 86 respondents (15.6%) for more than five years. The most frequently used sharing economy platform was Airbnb (289 people or 52.7% of the sample), followed by Hotels.com by 170 respondents (31.0%), and Booking.com by 65 respondents (11.9%). Other sharing economy platforms preferred by respondents were Priceline (15 respondents), CouchSurfing (6 respondents), and Travelminit (3 respondents).

4. Results and Discussion

4.1. Evaluation of the Measurement Models

The conceptual model presented in Figure 1 was calculated in SmartPLS 3.0 with structural equations modeling (SEM). All reflective constructs were checked for validity and internal consistency; the item loadings, average variance extracted (AVE), reliability indicators, and discriminant validity are shown in Table 1. All item loadings are above the minimum recommended value of 0.70, which means that the investigated phenomenon was correctly measured, i.e., all items exhibited convergence validity [107]. The values of these item loadings vary between 0.798 and 0.959, which is well above the minimum requirement. Reliability was tested using Cronbach’s alpha, with the minimum value of 0.7 exceeded for all the considered constructs [108]. This situation indicates that the conceptual model shown in Figure 1 presents internal consistency. As shown in Table 2, all the extracted average variance values are above the minimum requirement threshold of 0.5, which means that the measurement model is suitable [109] and that all constructs have convergent validity. The composite reliability (CR) index, with a minimum threshold value of 0.7 according to the literature [107], confirms that all constructs of the proposed model are reliable.
The Fornell–Larcker criterion (Table 2) was used to test the discriminant validity of each construct. According to the Fornell–Larcker criterion [110], for each latent variable, the AVE value is higher than the correlation coefficient between the component and all the distinct variables.
The collinearity level of the items in the measurement model was also analyzed; we found that the variance inflation factor (VIF) value of all indicators are below the maximum level of 5 recommended in the literature [110]. The highest value is 4.695 (item BI3) for this sample, which confirms that there are no multicollinearity problems in the sample. Next, we resorted to a bootstrap procedure to test the nine hypotheses and the relationships between the latent constructs. Among these, eight hypotheses could be accepted, showing a strong and positive influence based on T statistics.

4.2. Evaluation of the Structural Models

We also assessed the collinearity of the constructs of the structural model was. In this regard, the highest variance inflation factor of the inner model is 2.248 (PT → BI). As this value is under the recommended threshold value 5 [110], it indicates that there is not any multicollinearity between constructs. The goodness of fit of the saturated model is more than acceptable, as the square root mean residual (SRMR) highlights a value of SRMR = 0.047, which is under the recommended threshold of <0.08 [110]. COVID-19 pandemic, platform trust, and perceived value explain 77.0% of the variance in behavioral intention (R2 = 0.770), whereas experience, technology seeking and perceived value explain 61.8% of the variance in platform trust (R2 = 0.618). Furthermore, 45.4% of the variance in perceived value (R2 = 0.454) is explained by technology seeking and novelty seeking, indicating a strong predictive power of the structural model (see Figure 2).
Table 3 indicates a positive significant influence between tourists’ experience in using sharing economy platforms and their trust in such platforms (β = 0.201; T-value = 4.264 and p < 0.001), which also confirms previous results [61,63,64]. Experience differs among consumer generations, depending on motivations, traits, lifestyle, personality, etc. [59,60]. Therefore, H1 is supported.
H2 assumes that the technology-seeking behavior of tourists exerts a positive influence on their trust toward sharing economy platforms. The results (β = 0.197; T-value = 4.542 and p < 0.001) highlight a meaningful relationship between these two constructs, a tendency which was also pinpointed by previous research [80]. The degree of acceptance of innovations can represent an antecedent of trust in sharing economy platforms [81]. Therefore, H2 is supported.
H3 presumes that respondents’ technology-seeking behavior has a positive impact on tourists’ perceived value with respect to the use of sharing economy platforms. The results (β = 0.407; T-value = 8.520 and p < 0.001) prove that this relationship is significant and positive, in line with literature reports [81]. The level of acceptance determines the relationship between information about a new technology and its perception [80], so H3 is accepted. H4 infers that the novelty-seeking behavior of respondents has a positive influence on tourists’ perceived value regarding the use of such sharing economy platforms. The obtained results (β = 0.389; T-value = 9.808 and p < 0.001) highlight the positive and significant influence of respondents’ novelty-seeking behavior on the perceived value of using such sharing economy platforms. Similar results were obtained by other researchers [84], with numerous Airbnb customers having also been motivated to use the service due to their desire to be up-to-date with the latest innovations [24]. Therefore, H4 is supported by the empirical data.
H5 asserts that tourists’ perceived value of sharing economy platforms exerts a positive influence on the trust in such platforms. The results (β = 0.498; T-value = 9.827 and p < 0.000) show a very strong and positive influence of the perceived value of sharing economy platforms on developing trust. Similar results have been previously highlighted [90]. The perceived value of the experience of using sharing economy platforms has a significant and direct impact on the intention to use such platforms, as well as on trust in them. Hence, H5 is accepted.
H6 assumes that the perceived value of online platforms positively influences the behavioral intention to use sharing economy platforms. H6 was also confirmed, as the results (β = 0.192; T-value = 4.931 and p < 0.000) show a strong and positive relationship between these constructs. The higher the perceived value, the more customers demand from online platforms [89].
H7 presumes that trust in sharing economy platforms exerts a positive influence on the behavioral intention to use such platforms. The results (β = 0.725; T-value = 18.435 and p < 0.001) prove that there is a very strong positive and significant relation between these constructs, allowing us to accept H7 and validating this hypothesis, similar to previous literature reports [94]. Trust plays a significant role in the adoption of individual decisions regarding the repeated use of sharing economy platforms [85,96].
H8 infers that the COVID-19 pandemic has had a negative influence on trust in sharing economy platforms, as pinpointed by the literature [4], as the sharing economy accommodation sector suffered during the pandemic. Renting a property through Airbnb or other platforms was almost impossible during the pandemic due to restrictions. The obtained results (β = −0.062; T-value = 2.008 and p = 0.045) show a weak negative and insignificant relationship between these constructs; thus, H8 is supported by the empirical data.
H9 asserts that the COVID-19 pandemic has had a negative influence on behavioral intention to use sharing economy platforms. Although at a lower intensity, the results (β = −0.016; T-value = 0.648 and p = 0.511) show that there is no relationship between the constructs; thus, H9 is rejected. The sharing economy became a precarious environment during the COVID-19 pandemic, which triggered numerous risks [90], representing an immense shock for governments, societies, and businesses. The pandemic has dramatically changed how work is carried out in organizations, as such entities had to follow regulations and health instructions and adapt to the challenges associated with the new situation [111]. Collaborative tourism suffered considerably due to job losses, salary cuts, booking cancellations, social distancing, restrictions, and the closure of sharing services due to fear of infection.
The research scope of the present study was to explore how the seven analyzed dimensions (experience, technology, novelty seeking, perceived value, intention, pandemic, and trust in platforms) impacted tourists’ attitudes and behaviors toward the services offered through sharing economy platforms by relying on new technologies during the COVID-19 pandemic in Romania. Of the nine hypotheses, eight were accepted with a positive and significant influence. The obtained results confirm, to a considerably extent, previous findings reported in the international literature from studies carried out in other emerging markets [21,36,82], such as Romania, as well as in more developed markets [85,97].
An important factor leading to the increase in consumer confidence in sharing economy platforms [112,113,114,115,116] is individuals’ inclination toward technological innovations. The direct link between these factors previously highlighted [80], with researchers emphasizing the importance of the acceptance of technical innovations by society in any field, as the rate of innovation adoption is deeply influenced by the social system in which an innovation diffuses. We propose that governments should intervene in ways that exert an impact on the social environment, pinpointing the role of the public in supporting the process of technological development, which eases the acceptance of innovation technology by society. Therefore, there is a direct relationship between consumer confidence and technological innovations [115,117,118,119], which was further confirmed by the current research. Furthermore, technology seeking positively influences both perceived value and trust, which is why managers must be concerned with designing simple and attractive interfaces through which relevant details regarding the latest aspects of the respective platform can be immediately communicated.
A comparison of earlier, later, and non-adopters of Airbnb according to a variety of patterns, behaviors, and characteristics highlighted that in the past decade, Airbnb has attracted millions of new customers [84]. Diffusion theory suggests that over time, different types of customers adopt innovations and become interested in novelty seeking. Adopting the innovation diffusion theory (IDT) Wang and Jeong [81] examined Airbnb customers’ psychological behavior toward the Airbnb experience via a self-administered online survey. The results indicate that personal innovativeness represents a significant antecedent of perceived ease of use, usefulness, and trust. Their results are in line with the current findings, indicating that novelty-seeking behavior and the perceived value of using sharing economy platforms have a significant and positive relationship [84,120].
We claim that tourists’ perceived value of sharing economy platforms exerts a positive influence on the trust in such platforms, also positively impacting behavioral intention to use sharing economy platforms. This conclusion is concordant with the literature [89,90], which indicates that there is a positive relationship between tourists’ perceived value and trust in platforms, as well as the behavioral intention to use sharing economy platforms.

5. Conclusions

From a theoretical perspective, in the present study, we analyzed the factors that persuade Romanian consumers to adopt technological innovations and sharing economy platforms in contemporary society in the context of the COVID-19 pandemic. The original contribution of the paper consists of extending studies focusing on innovation diffusion theory and the technology acceptance model, highlighting the extent to which, in an emerging market, tourists’ experience, search for authenticity and novelty, level of trust in sharing economy platforms, perceived value, and pandemic fear compete in generating trust toward these platforms and, implicitly, in using and relying on them. In contrast to previous research, this study also pinpoints the impact of pandemic fear on generating trust in sharing economy platforms, assuring a new theoretical added value in the literature.
The pandemic impacted all areas of the global economy, including the tourism industry. Romanian sharing economy platforms experienced a dramatic decline in 2020, amid rapid digitization and increase in Internet connectivity. Repeated lockdowns and long-lasting restrictions have extensively influenced sharing economy platforms. Airbnb, Hotels.com, Booking.com, Priceline, CouchSurfing, Travelminit, etc., had to realign their businesses to remain in the market, reconsolidating their position and redefining their market development strategies.
One of the oldest social theories is the diffusion of innovation theory, which suggests that a person (in our case, a potential tourist or a potential sharing platform customer) purchases or uses a new product (or service) in a novel way, which generates not only the adoption of the new idea, behavior, or innovation but also acceptance of a technology. Understanding of the diffusion theory helps people to adapt rapidly to programs, platforms, and new technologies, which may change not only the consumers’ attitudes but also the behavior of an entire social system. This is only one of the reasons why we chose to implement this theoretical model in the field of platform tourism in Romania.
Another reason is that collaborative platforms, compared to classic tourist reservations, ensure that Romanian customers have access to a more varied and rapid offering, just a click away. Therefore, sharing platforms must theoretically contribute to the rapid dissemination of online information regarding tourist services, especially because Romania is a country with widespread access to digital resources. Moreover, these platforms must generate not only trust among customers but also the desire to adapt to new innovative ways of contracting tourist services. The public will successfully adopt innovation and accept new technologies when it understands the advantages of sharing platforms, as well as the ease of accessing them. Once innovations are adopted and new technologies are accessed, people will form a permanent attitude and intentions with respect to the adoption of new technologies. In this way, it will be possible to extend the preference of Romanian customers to other tourist platforms, not only to Airbnb. The diffusion of innovation theory (which, in our case, proposes rapid adoption of an innovation applicable to the sharing economy) is reflected in the results of our research, which show that Romanian sharing platform consumers are open to the direction of innovation and technology and are capable of quickly adapting to the latest technology, even during health crises with economic and travel restrictions.
The testing of hypotheses derived from the specialized literature confirmed the relationships between the constructs previously highlighted in the literature, with pandemic fear exerting only a moderate influence on the generation of trust in sharing economy platforms. Therefore, Romanian tourists were not heavily influenced by COVID-19 pandemic risks and still sought accommodation through sharing economy platforms, seeking out unique, authentic experiences after restrictions were lifted. The experience of sharing economy platforms influences trust in them. People’s inclinations toward new, unique technologies and their adoption thereof have a favorable impact on trust in such platforms. Widespread adoption of technological innovations by consumers and the perceived value of online platforms favorably influence consumption behavior associated with these services.
From a managerial perspective, this paper highlights the determining role of some essential factors that can positively influence consumers’ behavior. The authenticity of a platform and destinations and individuals’ desire seek novelty and to experiment with new technologies are essential factors associated with generating trust in these platforms. Thus, the managers of these sharing economy platforms must consider their construction so that they are able to generate a unique, interesting, and authentic experience based on the latest tools and technologies. Owners of sharing economy platforms can benefit from promotional and market-testing opportunities, making consumers willing to reuse them based on their individual characteristics, thus contributing to the increase in consumers’ social welfare. The sharing economy has also generated changes in the tourism sector thanks to the progress of ICT. Thus, managers must constantly adapt to novelties that appear in the field of innovation technology.
The ways in which sharing companies seek to adapt to the changes caused by the pandemic differ depending on the organization. Therefore, it is up to collaborative platform managers to streamline this area in company strategies, with measures to improve tourists’ trust in collaborative platforms. This change is made possible by offering consumers online space to present their experience of a platform, raising consumer awareness regarding the use of collaborative platforms, and facilitating users’ access to such platforms. The COVID-19 restrictions have the power to force sharing economy platforms to improve their services, not only by recovering lost profits but also by becoming more customer-oriented. In this way, many companies will have the opportunity to rapidly recover after lockdowns.
From a policy maker’s perspective, governments should work together with managers of sharing economy platforms to relaunch this sector after the pandemic. A lack of regulations and excessive bureaucracy can be highlighted. If governments and platform providers cooperate, they may foster the safety of users and sharing businesses by regulating future development. Furthermore, governments should invest more in the sector, using rapid digitization to create new jobs. In this vein, governments will be capable of sustaining and enhancing sharing economy platforms, which may, in the long run, represent a new opportunity triggered by the pandemic.
Among the limitations of this research is the fact that it focuses only on social media users. Future studies could, for example, be more geographically representative, considering developmental regions, age groups, or generations of consumers, as well as respondents’ backgrounds, e.g., urban versus rural or large, medium, and small cities. With this research, we did not aim to delimit the respondents according to the purpose of their trip, i.e., business or tourism/pleasure. Future research should consider such comparisons, as sharing economy platforms can often be used for both personal and business purposes. To more clearly highlight the trends regarding the use of sharing economy platforms, such research should be carried out periodically. Furthermore, future research could make the connection between consumers’ travel motivations, the use of sharing economy platforms, and tourists’ preference for social media and online versus offline information. Airbnb represents a growing trend, increasingly becoming a viable, interesting, and popular alternative to classic accommodation in hotels and guesthouses.

Author Contributions

Conceptualization, D.-C.D., F.-L.I. and S.R.; methodology, D.-C.D. and F.-L.I.; validation, D.-C.D.; formal analysis, D.-C.D.; investigation, L.M.C., F.-L.I. and S.R.; data curation, D.-C.D., L.M.C., F.-L.I. and S.R.; writing—original draft preparation, D.-C.D., L.M.C. and F.-L.I.; writing—review and editing, D.-C.D. and L.M.C.; visualization, D.-C.D. and L.M.C.; supervision, D.-C.D.; funding acquisition, D.-C.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Babeș-Bolyai University (grant number GS-UBB-FSEGA), DabijaDanCristian, and the Romanian Ministry of Education and Research (CNCS-UEFISCDI, project number PN-III-P1-1.1-TE-2021-0795, within PNCDI III).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kauffman, R.J.; Naldi, M. Research Directions for Sharing Economy Issues. Electron. Commer. Res. Appl. 2020, 43, 100973. [Google Scholar] [CrossRef] [PubMed]
  2. Mazareanu, E. Value of the Global Sharing Economy, 2014–2025. Statista, 9 August 2019. Available online: https://www.statista.com/statistics/830986/value-of-the-globalsharing-economy (accessed on 3 February 2022).
  3. Daglis, T. Sharing Economy. Encyclopedia 2022, 2, 1322–1332. [Google Scholar] [CrossRef]
  4. Gerwe, O. The COVID-19 pandemic and the accommodation sharing sector: Effects and prospects for recovery. Technol. Forecast. Soc. Chang. 2021, 167, 120733. [Google Scholar] [CrossRef]
  5. Ding, W.; Wang, Q.-G.; Zhang, J.-X. Analysis and prediction of COVID-19 epidemic in South Africa. ISA Trans. 2021, 124, 182–190. [Google Scholar] [CrossRef]
  6. Batool, M.; Ghulam, H.; Azmat Hayat, M.; Zahid Naeem, M.; Ejaz, A.; Ali Imran, Z.; Spulbar, C.; Birau, R.; Gorun, T.H. How COVID-19 has shaken the sharing economy? An analysis using Google trends data. Econ. Res.-Ekon. 2021, 34, 2374–2386. [Google Scholar] [CrossRef]
  7. Hossain, M. The effect of the COVID-19 on sharing economy activities. J. Clean. Prod. 2021, 280, 124782. [Google Scholar] [CrossRef]
  8. Popescu, C.K.; Olah, J. Use of the Bland-Altman plot for graphical demonstration of results in the sharing economy. Ekon.-Manaz. Spektrum 2020, 14, 90–99. [Google Scholar] [CrossRef]
  9. Atsız, O.; Cifci, I. Can we imagine the meal-sharing economy without service providers? The impact of COVID-19. J. Hosp. Tour. Manag. 2021, 49, 172–177. [Google Scholar] [CrossRef]
  10. Schor, J.B.; Vallas, S.P. The sharing economy: Rhetoric and reality. Annu. Rev. Sociol. 2021, 47, 369–389. [Google Scholar] [CrossRef]
  11. Matzler, K.; Veider, V.; Kathan, W. Adapting to the sharing economy. MIT Sloan Manag. Rev. 2015, 56, 71–77. [Google Scholar]
  12. Akbar, Y.H.; Tracogna, A. The sharing economy and the future of the hotel industry: Transaction cost theory and platforms economics. Int. J. Hosp. Manag. 2018, 71, 91–101. [Google Scholar] [CrossRef]
  13. Fodranova, I.; Antalova, M. How can digital sharing economy reduce unemployment? J. Technol. Manag. Innov. 2021, 16, 51–57. [Google Scholar] [CrossRef]
  14. Gu, D.; Khan, S.; Khan, I.U.; Khan, S.U. Understanding Mobile Tourism Shopping in Pakistan: An Integrating Framework of Innovation Diffusion Theory and Technology Acceptance Model. Mob. Inf. Syst. 2019, 2019, 1490617. [Google Scholar] [CrossRef]
  15. Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003. [Google Scholar]
  16. Haider, M.; Kreps, G. Forty Years of Diffusion of Innovations: Utility and Value in Public Health. J. Health Commun. 2004, 9 (Suppl. 1), 3–11. [Google Scholar] [CrossRef]
  17. Gurran, N. Global Home-Sharing, Local Communities and the Airbnb Debate: A Planning Research Agenda. Plan. Theory Pract. 2018, 19, 298–304. [Google Scholar] [CrossRef]
  18. Leoni, G.; Parker, L.D. Governance and control of sharing economy platforms: Hosting on Airbnb. Br. Account. Rev. 2019, 51, 100814. [Google Scholar] [CrossRef]
  19. Altinay, L.; Taheri, B. Emerging themes and theories in the sharing economy: A critical note for hospitality and tourism. Int. J. Contemp. Hosp. Manag. 2019, 31, 180–193. [Google Scholar] [CrossRef]
  20. Lima, V. Towards an understanding of the regional impact of Airbnb in Ireland. Reg. Stud. Reg. Sci. 2019, 6, 78–91. [Google Scholar] [CrossRef]
  21. Akarsu, T.N.; Foroudi, P.; Melewar, T.C. What makes Airbnb likeable? Exploring the nexus between service attractiveness, country image, perceived authenticity and experience from a social exchange theory perspective within an emerging economy context. Int. J. Hosp. Manag. 2020, 91, 102635. [Google Scholar] [CrossRef]
  22. Demir, E.; Emekli, G. Is Airbnb no longer a sharing economy platform? Evidence from Europe’s top 10 Airbnb destinations. Anatolia 2021, 32, 470–488. [Google Scholar] [CrossRef]
  23. Kabadayi, T.E.; Aksoy, N.K.; Yazici, N.; Alan, A.K. Airbnb as a sharing economy-enabled digital service platform: The power of motivational factors and the moderating role of experience. Tour. Econ. 2022, 28, 748–771. [Google Scholar] [CrossRef]
  24. Guttentag, D.; Smith, S.; Potwarka, L.; Havitz, M. Why tourists choose Airbnb: A motivation-based segmentation study. J. Travel Res. 2018, 57, 342–359. [Google Scholar] [CrossRef]
  25. Adamiak, C. Current state and development of Airbnb accommodation offer in 167 countries. Curr. Issues Tour. 2019, 1696758. [Google Scholar] [CrossRef]
  26. Gil, J.; Sequera, J. The professionalization of Airbnb in Madrid: Far from a collaborative economy. Curr. Issues Tour. 2020, 1757628. [Google Scholar] [CrossRef]
  27. Hopkins, E. Machine Learning Tools, Algorithms, and Techniques in Retail Business Operations: Consumer Perceptions, Expectations, and Habits. J. Self-Gov. Manag. Econ. 2022, 10, 43–55. [Google Scholar] [CrossRef]
  28. Kliestik, T.; Zvarikova, K.; Lăzăroiu, G. Data-driven Machine Learning and Neural Network Algorithms in the Retailing Environment: Consumer Engagement, Experience, and Purchase Behaviors. Econ. Manag. Financ. Mark. 2022, 17, 57–69. [Google Scholar] [CrossRef]
  29. Nica, E.; Sabie, O.-M.; Mascu, S.; Luțan (Petre), A.G. Artificial Intelligence Decision-Making in Shopping Patterns: Consumer Values, Cognition, and Attitudes. Econ. Manag. Financ. Mark. 2022, 17, 31–43. [Google Scholar] [CrossRef]
  30. Rowland, M. Consumer Sentiment, Cognitive Attitudes, and Behavior Patterns toward Delivery Apps. Econ. Manag. Financ. Mark. 2022, 17, 44–56. [Google Scholar] [CrossRef]
  31. Laurenti, R.; Singh, J.; Cotrim, J.M.; Toni, M.; Sinha, R. Characterizing the Sharing Economy State of the Research: A Systematic Map. Sustainability 2019, 11, 5729. [Google Scholar] [CrossRef]
  32. Cockayne, D.G. Sharing and neoliberal discourse: The economic function of sharing in the digital on-demand economy. Geoforum 2016, 77, 73–82. [Google Scholar] [CrossRef]
  33. Belk, R. You are what you can access: Sharing and collaborative consumption online. J. Bus. Res. 2014, 67, 1595–1600. [Google Scholar] [CrossRef]
  34. Sutherland, W.; Jarrahi, M.H. The sharing economy and digital platforms: A review and research agenda. Int. J. Inf. Manag. 2018, 43, 328–341. [Google Scholar] [CrossRef]
  35. Hossain, M. Sharing economy: A comprehensive literature review. Int. J. Hosp. Manag. 2020, 87, 102470. [Google Scholar] [CrossRef]
  36. Majetić, F. Conceptual Framework for Explorations of the Collaborative Economy. Društvena Istraživanja 2021, 30, 615–634. [Google Scholar] [CrossRef]
  37. Pop, R.; Săplăcan, Z.; Dabija, D.C.; Alt, A.M. The Impact of Social Media Influencers on Travel Decisions: The Role of Trust in Consumer Decision Journey. Curr. Issues Tour. 2022, 25, 823–843. [Google Scholar] [CrossRef]
  38. Pop, R.A.; Dabija, D.C.; Pelau, C.; Dinu, V. Usage Intentions, Attitudes, and Behaviours towards Energy-Efficient Applications during the COVID-19 Pandemic. J. Bus. Econ. Manag. 2022, 23, 668–689. [Google Scholar] [CrossRef]
  39. Habibi, M.R.; Davidson, A.; Laroche, M. What managers should know about the sharing economy. Bus. Horiz. 2017, 60, 113–121. [Google Scholar] [CrossRef]
  40. Ahmad, M.; Ahmed, Z.; Bai, Y.; Qiao, G.; Popp, J.; Oláh, J. Financial inclusion, technological innovations, and environmental quality: Analyzing the role of green openness. Front. Environ. Sci. 2022, 10, 851263. [Google Scholar] [CrossRef]
  41. Acquier, A.; Daudigeos, T.; Pinkse, J. Promises and paradoxes of the sharing economy: A organizing framework. Technol. Forecast. Soc. Chang. 2017, 125, 1–10. [Google Scholar] [CrossRef]
  42. Sundarajan, A. The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism; MIT Press: Cambridge, MA, USA, 2016. [Google Scholar]
  43. Frenken, K.; Schor, J. Putting the Sharing Economy into perspective. Environ. Innov. Soc. Transit. 2017, 23, 3–10. [Google Scholar] [CrossRef]
  44. Stephany, A. The Business of Sharing-Making It in the New Sharing Economy; Plagrave: MacMillan, NY, USA, 2015. [Google Scholar]
  45. Schor, J. Does the sharing economy increase inequality within the eighty percent? Findings from a qualitative study of platform providers. Camb. J. Reg. Econ. Soc. 2017, 10, 263–279. [Google Scholar] [CrossRef] [Green Version]
  46. Klarin, A.; Suseno, Y. A state-of-the-art review of the sharing economy: Scientometric mapping of the scholarship. J. Bus. Res. 2021, 126, 250–262. [Google Scholar] [CrossRef]
  47. Lyaskovskaya, E.; Khudyakova, T. Sharing Economy: For or against Sustainable Development. Sustainability 2021, 13, 11056. [Google Scholar] [CrossRef]
  48. Arcidiacono, D.; Gandini, A.; Pais, I. Sharing what? The ‘sharing economy’ in the sociological debate. Sociol. Rev. 2018, 66, 275–288. [Google Scholar] [CrossRef]
  49. Benoit, S.; Baker, T.L.; Bolton, R.N.; Gruber, T.; Kandampully, J. A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors. J. Bus. Res. 2017, 79, 219–227. [Google Scholar] [CrossRef]
  50. Wittmayer, J.M.; Backhaus, J.; Avelino, F.; Pel, B.; Strasser, T.; Kunze, I.; Zuijderwijk, L. Narratives of change: How social innovation initiatives construct societal transformation. Futures 2019, 112, 102433. [Google Scholar] [CrossRef]
  51. Lazzari, A.D.R.; Petrini, M.; Souza, A.C. Sharing economy and the social-economic context: Mercenarism or common good? Rev. De Adm. Mackenzie 2021, 22, 1–28. [Google Scholar] [CrossRef]
  52. Ferrari, M. Beyond Uncertainties in the Sharing Economy: Opportunities for Social Capital. Eur. J. Risk Regul. 2016, 7, 664–674. [Google Scholar] [CrossRef]
  53. Trenz, M.; Frey, A.; Veit, D. Disentangling the facets of sharing: A categorization of what we know and don’t know about the sharing economy. Internet Res. 2018, 28, 888–925. [Google Scholar] [CrossRef]
  54. Sun, Y.; Yao, S.N. Sustainability trade-offs in media coverage of poverty alleviation: A content-based spatiotemporal analysis in Chinas’ provinces. Sustainability 2022, 14, 10058. [Google Scholar] [CrossRef]
  55. Köbis, N.C.; Soraperra, I.; Shalvi, S. The Consequences of Participating in the Sharing Economy: A Transparency-Based Sharing Framework. J. Manag. 2021, 47, 317–343. [Google Scholar] [CrossRef]
  56. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef]
  57. Agag, G.; El-Masry, A.A. Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Comput. Hum. Behav. 2016, 60, 97–111. [Google Scholar] [CrossRef]
  58. Tohănean, D.; Buzatu, A.I.; Baba, C.A.; Georgescu, B. Business Model innovation through the use of digital technologies: Managing risks and creating sustainability. Amfiteatru Econ. 2020, 22, 758–774. [Google Scholar] [CrossRef]
  59. Morgan, M.; Elbe, J.; Curiel, J.E. Has the experience economy arrived? The views of destination managers in three visitor-dependent areas. Int. J. Tour. Res. 2009, 11, 201–216. [Google Scholar] [CrossRef]
  60. Jonas, E.; Mühlberger, C. Social Cognition, Motivation, and Interaction: How Do People Respond to Threats in Social Interactions? Front Psychol. 2017, 27, 1577. [Google Scholar] [CrossRef] [PubMed]
  61. Von Terzi, P.; Tretter, S.; Uhde, A.; Hassenzahl, M.; Diefenbach, S. Technology-Mediated Experiences and Social Context: Relevant Needs in Private Vs. Public Interaction and the Importance of Others for Positive Affect. Front. Psychol. 2021, 12, 718315. [Google Scholar] [CrossRef]
  62. Zhang, X.; Huang, W. Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties. Fractal Fract. 2020, 4, 50. [Google Scholar] [CrossRef]
  63. Zhang, X.; Liu, J.; Li, X.; Ling, H.; Shao, J.; Peng, H. Does Social Interaction Improve Emotional Experience Across Different Relationships? A Study Using Experience Sampling in Older Chinese Adults. J. Soc. Pers. Relatsh. 2022, 39, 640–651. [Google Scholar] [CrossRef]
  64. Tussyadiah, I.P.; Pesonen, J. Impacts of Peer-to-Peer Accommodation Use on Travel Patterns. J. Travel Res. 2016, 55, 1022–1040. [Google Scholar] [CrossRef]
  65. Paulauskaite, D.; Powell, R.; Coca-Stefaniak, J.A.; Morrison, A.M. Living like a local: Authentic tourism experiences and the sharing economy. Int. J. Tour. Res. 2017, 19, 619–628. [Google Scholar] [CrossRef]
  66. Daugstad, K.; Kirchengast, C. Authenticity and the pseudo-backstage of agri-tourism. Ann. Tour. Res. 2013, 43, 170–191. [Google Scholar] [CrossRef]
  67. Buchmann, A.; Moore, K.; Fisher, D. Experiencing film tourism: Authenticity & Fellowship. Ann. Tour. Res. 2010, 37, 229–248. [Google Scholar] [CrossRef]
  68. Lu, L.; Chi, C.G.; Liu, Y. Authenticity, involvement, and image: Evaluating tourist experiences at historic districts. Tour. Manag. 2015, 50, 85–96. [Google Scholar] [CrossRef]
  69. Kim, H.; Bonn, M.A. Authenticity: Do tourist perceptions of winery experiences affect behavioral intentions? Int. J. Contemp. Hosp. Manag. 2016, 28, 839–859. [Google Scholar] [CrossRef]
  70. Ustyuzhanina, E.V.; Komarova, I.P.; Novikova, E.S.; Sigarev, A.V. The phenomenon of the sharing economy: Development problems and prospects in the context of digital revolution. Rev. Espac. 2018, 39, 59368842. [Google Scholar]
  71. Hamari, J.; Sjöklint, M.; Ukkonen, A. The sharing economy: Why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 2016, 67, 2047–2059. [Google Scholar] [CrossRef]
  72. Pouri, M.J.; Hilti, L.M. Conceptualizing the digital sharing economy in the context of sustainability. Sustainability 2018, 10, 4453. [Google Scholar] [CrossRef]
  73. Dilette, A.; Ponting, S.S. Diffusing innovation in times of disasters: Considerations for event management professionals. J. Conv. Event Tour. 2021, 22, 197–220. [Google Scholar] [CrossRef]
  74. Devine, A.; Devine, F. The challenge and opportunities for an event organiser during an economic recession. Int. J. Event Festiv. Manag. 2012, 3, 122–136. [Google Scholar] [CrossRef]
  75. Johnson, E.; Nica, E. Connected Vehicle Technologies, Autonomous Driving Perception Algorithms, and Smart Sustainable Urban Mobility Behaviors in Networked Transport Systems. Contemp. Read. Law Soc. Justice 2021, 13, 37–50. [Google Scholar] [CrossRef]
  76. Frajtova Michalikova, K.; Blazek, R.; Rydell, L. Delivery Apps Use during the COVID-19 Pandemic: Consumer Satisfaction Judgments, Behavioral Intentions, and Purchase Decisions. Econ. Manag. Financ. Mark. 2022, 17, 70–82. [Google Scholar] [CrossRef]
  77. Kliestik, T.; Kovalova, E.; Lăzăroiu, G. Cognitive Decision-Making Algorithms in Data-driven Retail Intelligence: Consumer Sentiments, Choices, and Shopping Behaviors. J. Self-Gov. Manag. Econ. 2022, 10, 30–42. [Google Scholar] [CrossRef]
  78. Popescu, G.H.; Valaskova, K.; Horak, J. Augmented Reality Shopping Experiences, Retail Business Analytics, and Machine Vision Algorithms in the Virtual Economy of the Metaverse. J. Self-Gov. Manag. Econ. 2022, 10, 67–81. [Google Scholar] [CrossRef]
  79. Watson, R. Consumer Risk Perceptions, Behavioral Intentions, and Purchasing Habits toward Delivery Apps. J. Self-Gov. Manag. Econ. 2022, 10, 56–68. [Google Scholar] [CrossRef]
  80. Park, I.; Kim, D.; Moon, J.; Kim, S.; Kang, Y.; Bae, S. Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability. Sustainability 2022, 14, 579. [Google Scholar] [CrossRef]
  81. Wang, C.; Jeong, M. What makes you choose Airbnb again? An examination of users’ perceptions toward the website and their stay. Int. J. Hosp. Manag. 2018, 74, 162–170. [Google Scholar] [CrossRef]
  82. Lee, H.Y.; Qu, H.; Kim, Y.S. A study of the impact of personal innovativeness on online travel shopping behavior-a case study of Korean travellers. Tour. Manag. 2007, 28, 866–897. [Google Scholar] [CrossRef]
  83. Vinerean, S.; Budac, C.; Baltador, L.A.; Dabija, D.C. Assessing the Effect of COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach. Electronics 2022, 11, 1269. [Google Scholar] [CrossRef]
  84. Guttentag, D.; Smith, S.L.J. The diffusion of Airbnb: A comparative look at earlier adopters, later adopters, and non-adopters. Curr. Issues Tour. 2020, 1782855. [Google Scholar] [CrossRef]
  85. So, K.K.F.; Oh, H.; Min, S. Motivation and Constraints of Airbnb consumers: Findings from a mixed-methods approach. Tour. Manag. 2018, 67, 224–236. [Google Scholar] [CrossRef]
  86. Stollery, A.; Jun, S.H. The antecedents of perceived value in the Airbnb context. Asia Pac. J. Innov. Entrep. 2017, 11, 391–404. [Google Scholar] [CrossRef] [Green Version]
  87. Vătămănescu, E.M.; Dabija, D.C.; Gazzola, P.; Cegarra-Navarro, J.G.; Buzzi, T. Before and after the outbreak of COVID-19: Linking fashion companies’ corporate social responsibility approach to consumers’ demand for sustainable products. J. Clean. Prod. 2021, 321, 128945. [Google Scholar] [CrossRef]
  88. Mao, Z.; Liu, J. Why travelers use Airbnb again?: An integrative approach to understanding travelers’ repurchase intention. Int. J. Contemp. Hosp. Manag. 2017, 29, 2464–2482. [Google Scholar] [CrossRef]
  89. Liang, T.P.; Lin, Y.L.; Hou, H.C. What drives consumers to adopt a sharing platform: An integrated model of value-based and transaction cost theories. Inf. Manag. 2021, 58, 103471. [Google Scholar] [CrossRef]
  90. Chen, C.C.; Chang, Y.C. What drives purchase intention on Airbnb? Perspectives of consumer reviews, information quality, and media richness. Telemat. Inform. 2018, 35, 1512–1523. [Google Scholar] [CrossRef]
  91. Cohen, B.; Munoz, P. Sharing cities and sustainable consumption and production: Towards an integrated framework. J. Clean. Prod. 2016, 134, 87–97. [Google Scholar] [CrossRef]
  92. Nyamekye, M.B.; Kosiba, J.P.; Boateng, H.; Agbemabiese, G.C. Building trust in the sharing economy by signaling trustworthiness, and satisfaction. Res. Transp. Bus. Manag. 2022, 43, 100727. [Google Scholar] [CrossRef]
  93. Lub, X.D.; Rijnders, R.; Caceres, L.N.; Bosman, J. The future of hotels: The Lifestyle Hub. A design-thinking approach for developing future hospitality concepts. J. Vacat. Mark. 2016, 22, 249–264. [Google Scholar] [CrossRef]
  94. Räisänen, J.; Ojala, A.; Tuovinen, T. Building trust in the sharing economy: Current approaches and future considerations. J. Clean. Prod. 2021, 279, 123724. [Google Scholar] [CrossRef]
  95. Ye, S.; Ying, T.; Zhou, L.; Wang, T. Enhancing customer trust in peer-to-peer accomodation: A “soft” strategy via social presence. Int. J. Hosp. Manag. 2019, 79, 1–10. [Google Scholar] [CrossRef]
  96. Arteaga-Sánchez, R.; Belda-Ruiz, M.; Ros-Galvez, A.; Rosa-Garcia, A. Why continue sharing: Determinants of behavior in ridesharing services. Int. J. Mark. Res. 2020, 62, 725–742. [Google Scholar] [CrossRef]
  97. Li, C.-Y.; Tsai, M.-C. What makes guests trust Airbnb? Consumer trust formation and its impact on continuance intention in the sharing economy. J. Hosp. Tour. Manag. 2022, 50, 44–54. [Google Scholar] [CrossRef]
  98. Upadhyay, C.K.; Tewari, V.; Tiwari, V. Assessing the impact of sharing economy through adoption of ICT based crowdshipping platform for last-mile delivery in urban and semi-urban India. Inf. Technol. Dev. 2021, 27, 670–696. [Google Scholar] [CrossRef]
  99. Gajić, T.; Petrović, M.D.; Blešić, I.; Radovanović, M.M.; Syromiatnikova, J.A. The power of fears in the travel decision—COVID-19 against lack of money. J. Tour. Futures 2021, 1–22. [Google Scholar] [CrossRef]
  100. DuBois, D. Impact of the Coronavirus on Global Short-Term Rental Markets. Available online: https://www.airdna.co/blog/coronavirus-impact-on-global-short-term-rental-markets (accessed on 20 June 2022).
  101. Zheng, D.; Luo, Q.; Ritchie, B. Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic “travel fear”. Tour. Manag. 2021, 83, 104261. [Google Scholar] [CrossRef]
  102. Conger, K.; Griffith, E. The results are in for the sharing economy. They are ugly. New York Times, 13 May 2020. Available online: https://www.nytimes.com/2020/05/07/technology/the-results-are-in-for-the-sharing-economy-they-are-ugly.html (accessed on 6 August 2022).
  103. SEP. Sharing Economy Platforms Development. 2022. Available online: www.ec.europa.eu/eurostat/statistics (accessed on 8 August 2022).
  104. Airbnb. $250M to Support Hosts Impacted by Cancellations. 2020. Available online: https://www.airbnb.com/resources/hosting-homes/a/250m-to-support-hosts-impacted-by-cancellations-165 (accessed on 22 August 2022).
  105. ITU. The Economic Impact of Broadband and Digitization through the COVID-19 pandemic. Econom. Model. 2021. Available online: http://handle.itu.int/11.1002/pub/819126c2-en (accessed on 20 August 2022).
  106. Carp, A. Airbnb Arunca in Are Afacerile de la Noi. 2020. Available online: https://www.mediafax.ro/economic/airbnb-arunca-in-aer-afacerile-de-la-noi-compania-renunta-la-mii-de-angajati-in-urma-crizei-covid-19-19128573 (accessed on 22 August 2022).
  107. Hair, J.F.; Black, W.C.; Babin, B.J. Multivariate Data Analysis: A Global Perspective; Pearson Education: London, UK, 2010. [Google Scholar]
  108. Henseler, J.; Sarstedt, M. Goodness-of-fit indices for partial least squares path modeling. Comput. Stat. 2013, 28, 565–580. [Google Scholar] [CrossRef]
  109. Chin, W.W. The partial least squares approach for structural equation modeling. In Methodology for Business and Management. Modern Methods for Business Research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates Publishers: New York, NJ, USA, 1998; pp. 295–336. [Google Scholar]
  110. Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research; Homburg, C., Klarmann, M., Vomberg, A., Eds.; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  111. Nemțeanu, M.S.; Dabija, D.C. Best Practices of Nongovernmental Organisations in Combatting COVID-19. In Proceedings of the 6th BASIQ International Conference on New Trends in Sustainable Business and Consumption, Messina, Italy, 4–6 June 2020; Pamfilie, R., Dinu, V., Tăchiciu, L., Pleșea, D., Vasiliu, C., Eds.; ASE: Bucharest, Romania, 2020; pp. 626–633. [Google Scholar]
  112. Lăzăroiu, G.; Harrison, A. Internet of Things Sensing Infrastructures and Data-driven Planning Technologies in Smart Sustainable City Governance and Management. Geopolit. Hist. Int. Relat. 2021, 13, 23–36. [Google Scholar] [CrossRef]
  113. Wallace, S.; Lăzăroiu, G. Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering. Contemp. Read. Law Soc. Justice 2021, 13, 79–92. [Google Scholar] [CrossRef]
  114. Kliestik, T.; Poliak, M.; Popescu, G.H. Digital Twin Simulation and Modeling Tools, Computer Vision Algorithms, and Urban Sensing Technologies in Immersive 3D Environments. Geopolit. Hist. Int. Relat. 2022, 14, 9–25. [Google Scholar] [CrossRef]
  115. Nica, E.; Kliestik, T.; Valaskova, K.; Sabie, O.-M. The Economics of the Metaverse: Immersive Virtual Technologies, Consumer Digital Engagement, and Augmented Reality Shopping Experience. Smart Gov. 2022, 1, 21–34. [Google Scholar] [CrossRef]
  116. Zvarikova, K.; Cug, J.; Hamilton, S. Virtual Human Resource Management in the Metaverse: Immersive Work Environments, Data Visualization Tools and Algorithms, and Behavioral Analytics. Psychosociol. Issues Hum. Resour. Manag. 2022, 10, 7–20. [Google Scholar] [CrossRef]
  117. Andronie, M.; Lăzăroiu, G.; Ștefănescu, R.; Ionescu, L.; Cocoșatu, M. Neuromanagement Decision-Making and Cognitive Algorithmic Processes inthe Technological Adoption of Mobile Commerce Apps. Oeconomia Copernic. 2021, 12, 863–888. [Google Scholar] [CrossRef]
  118. Blake, R. Metaverse Technologies in the Virtual Economy: Deep Learning Computer Vision Algorithms, Blockchain-based Digital Assets, and Immersive Shared Worlds. Smart Gov. 2022, 1, 35–48. [Google Scholar] [CrossRef]
  119. Kral, P.; Janoskova, K.; Dawson, A. Virtual Skill Acquisition, Remote Working Tools, and Employee Engagement and Retention on Blockchain-based Metaverse Platforms. Psychosociol. Issues Hum. Resour. Manag. 2022, 10, 92–105. [Google Scholar] [CrossRef]
  120. Tecău, A.S.; Constantin, C.P.; Lixăndroiu, R.C.; Chițu, I.B.; Brătucu, G. Impact of the COVID-19 Crisis on Heavy Work Investment in Romania. Amfiteatru Econ. 2020, 22, 1049–1067. [Google Scholar] [CrossRef]
Electronics 11 02916 g001 550
Figure 1. Behavioral intention to use/rely on sharing economy platforms during the COVID-19 pandemic. Source: authors’ own development.
Figure 1. Behavioral intention to use/rely on sharing economy platforms during the COVID-19 pandemic. Source: authors’ own development.
Electronics 11 02916 g001
Electronics 11 02916 g002 550
Figure 2. Structural model. Source: own calculations in SmartPLS.
Figure 2. Structural model. Source: own calculations in SmartPLS.
Electronics 11 02916 g002
Table
Table 1. Constructs and items.
Table 1. Constructs and items.
ItemMeasureLoadingCronbach’s AlphaAVECR
Experience (EX) (adapted from [61,62,63,89]).
EX1...offers an authentic local experience.0.8590.9370.7250.949
EX2...offers a unique experience.0.881
EX3...offers a different experience.0.877
EX4...offers a memorable experience.0.896
EX5...offers a pleasant accommodation experience.0.830
EX6...offers the possibility to accommodate in a dynamic, different, non-standardized environment.0.817
EX7...offers a good accommodation based on local cultural activities.0.798
Technology Seeking (TS) (adapted from [31,80,81]).
TS1I like using the technology-based devices (smart phone, tablet, laptop, smartwatch, etc.).0.8850.8520.7710.910
TS2I usually use products and/or services based on technology without other people’s help.0.890
TS3I am well-informed concerning the most recent technological evolutions in my fields of interest.0.860
Novelty Seeking (NS) (adapted from [81,84,86]).
NS1...it’s something new to me.0.8390.8340.7460.898
NS2...it offers an unusual memory/experience to me.0.877
NS3...it attracts me by offering completely new experiences as compared to classical accommodation (via tourist agency, reservation site, hotel page, etc.).0.876
Perceived Value (PV) (adapted from [85,89,90]).
PV1Accommodation through the collaborative platform.... provides added value for the money paid.0.8670.9230.8130.946
PV2...provides the expected quality.0.903
PV3...offers a good image to guests.0.912
PV4...offers a good overall accommodation experience.0.924
Platform Trust (PT) (adapted from [92,94,97]).
PT1The platform follows its promises and engagements.0.9270.9390.7710.910
PT2Interaction with the booking platform serves my interests.0.924
PT3The platform always keeps its promises and assumed commitments.0.925
PT4I find the platform to be user-friendly.0.902
Behavioral Intention (BI) (adapted from [85,97]).
BI1I will be using this platform more often in the future.0.9210.9320.8810.957
BI2I can always positively introduce the accommodation platform to my friends and/or acquaintances.0.946
BI3I would recommend the platform for making accommodation reservations to others.0.949
COVID-19 Pandemic (COV) (adapted from [99]).
COV1I feel nervous when thinking about planning a trip during the COVID-19 pandemic.0.9380.9220.8610.949
COV2I am afraid to travel during the COVID-19 pandemic.0.959
COV3I believe I could get infected with the pandemic virus if I traveled now.0.886
Note: own development based on the literature. Calculations made in SmartPLS.
Table
Table 2. Discriminant validity analyses (Fornell–Larcker).
Table 2. Discriminant validity analyses (Fornell–Larcker).
Fornell–Larcker
ConstructBICOVEXNSPVPTTS
BI0.939
COV−0.0600.928
EX0.633−0.0110.852
NS0.5440.0360.8200.864
PV0.7300.0100.6510.5640.902
PT0.868−0.0630.6330.5530.7420.920
TS0.597−0.0210.5410.4300.5750.5930.878
Note: Values in bold text represent the square root of the average variance explained. The diagonal values represent the partwise correlations between constructs. BI: behavioral intention; COV: COVID-19 pandemic; EX: experience; NS: novelty seeking; PV: perceived value; PT: perceived trust; TS: technology seeking. Source: own calculations in SmartPLS.
Table
Table 3. Path coefficients of the structural equations model.
Table 3. Path coefficients of the structural equations model.
PathPath CoefficientStandard
Deviation		T-ValueCI 1p-ValueHypothesis
EX → PT0.2010.0484.2640.109–0.2910.000 ***H1 (Supported)
TS → PT0.1970.0424.5420.121–0.4700.000 ***H2 (Supported)
TS → PV0.4070.0468.5200.320–0.4980.000 ***H3 (Supported)
NS → PV0.3890.0409.8080.304–0.4600.000 ***H4 (Supported)
PV → PT0.4980.0479.8270.396–0.5830.000 ***H5 (Supported)
PV → BI0.1920.0374.9310.119–0.2650.000 ***H6 (Supported)
PT → BI0.7250.03818.4350.652–0.7960.000 ***H7 (Supported)
COV → PT−0.0620.0332.008−0.122–0.0150.045 **H8 (Supported)
COV → BI−0.0160.0240.658−0.068–0.0380.511 n.s.H9 (Rejected)
Note: ** p < 0.01; *** p < 0.001; n.s.: not significant; BI: behavioral intention; COV: COVID-19 pandemic; EX: experience; NS: novelty seeking; PV: perceived value; PT: perceived trust; TS: technology seeking. 1 CI = confidence interval (2.5–97.5%). Source: own calculations in SmartPLS.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Dabija, D.-C.; Csorba, L.M.; Isac, F.-L.; Rusu, S. Building Trust toward Sharing Economy Platforms beyond the COVID-19 Pandemic. Electronics 2022, 11, 2916. https://doi.org/10.3390/electronics11182916

AMA Style

Dabija D-C, Csorba LM, Isac F-L, Rusu S. Building Trust toward Sharing Economy Platforms beyond the COVID-19 Pandemic. Electronics. 2022; 11(18):2916. https://doi.org/10.3390/electronics11182916

Chicago/Turabian Style

Dabija, Dan-Cristian, Luiela Magdalena Csorba, Florin-Lucian Isac, and Sergiu Rusu. 2022. "Building Trust toward Sharing Economy Platforms beyond the COVID-19 Pandemic" Electronics 11, no. 18: 2916. https://doi.org/10.3390/electronics11182916

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop