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. 2022 Nov 22;71:103207. doi: 10.1016/j.jretconser.2022.103207

A cross-cultural analysis of ridesharing intentions and compliance with COVID-19 health guidelines: The roles of social trust, fear of COVID-19, and trust-in-God

Md Nazmus Sakib a, Fuad Hasan b, Md Al-Emran a, Reto Felix c,∗
PMCID: PMC9678840

Abstract

Ridesharing services such as Uber and Lyft have been substantially affected by the ongoing COVID-19 pandemic. Drawing on social capital theory, the current research investigates how social trust relates to three types of trust in compliance with COVID-19 guidelines and consumers' ridesharing intentions. Analyzing data from two economically and culturally distinct countries, the results suggest that social trust positively affects trust in platform companies' compliance with COVID-19 guidelines (TPC), but not (or to a lesser extent) trust in drivers' (TDC) and other riders (TRC) compliance with COVID-19 guidelines in both the United States and Bangladesh. Importantly, TPC, TDC, and TRC are positively related with consumers' ridesharing intentions in the United States but not in Bangladesh. Furthermore, the analysis reveals two counterintuitive moderating effects of fear of COVID-19 and trust in God. The results provide important insights on factors affecting the ridesharing industry in the context of the COVID-19 pandemic, and they emphasize the importance of considering cultural context in understanding consumers’ intentions to engage in the sharing economy.

Keywords: Sharing economy, Social trust, Fear of COVID-19, Trust in God, Ridesharing intention, Compliance with COVID-19 guidelines

1. Introduction

Although sharing is one of the oldest features of mankind (Sahlins, 1972), the sharing economy has emerged as a recent phenomenon facilitated by advances in Internet connectivity and mobile technology (Hawlitschek et al., 2016a). The quest for gaining value from under-exploited resources has prompted a global transformation in several service industries by introducing convenient consumption alternatives without the cost and burden of maintaining ownership (Eckhardt and Bardhi, 2015; Eckhardt et al., 2019; Mikołajewska-Zając, 2019). One particularly important manifestation of the sharing economy is ridesharing, which constitutes the context of the current study. The popularity of ridesharing is spreading quickly from developed countries to emerging economies, thereby making it a global phenomenon (Adam, 2018). For example, Statista (2022) reports that as of 2019, Uber had launched its operations in 69 countries and carried out over seven billion trips worldwide. On a global basis, the sharing economy as an industry is expected to be worth $335 billion by 2025 (PwC, 2015). Prior research has identified important antecedents that increase consumers' willingness to participate in ridesharing. For example, cost savings, convenience, and environmental concern are important aspects motivating consumers to engage in ridesharing (Davidson et al., 2018; Hartl et al., 2018; Kumar et al., 2018). Furthermore, given that sharing is assumed to be directly related to trust (Belk, 2010), it is not surprising that previous research finds trust to be an important determinant of ridesharing intentions (Cha and Lee, 2022; Hartl et al., 2018; Hawlitschek et al., 2016b). In fact, acknowledging the close relationship between trust and sharing, Botsman and Rogers (2010) referred to trust as the currency of the sharing economy. However, the outbreak of the COVID-19 pandemic aggravated consumer trust issues and resulted in an added sense of vulnerability (Kursan Milaković, 2021). Furthermore, people’s sense of safety may vary by the degree of COVID-19 compliance with hygiene attributes (Siddiqi et al., 2022).

Despite these important insights, important knowledge gaps regarding a better understanding of ridesharing persist. For example, additional research is needed to understand the role of specific trust perceptions towards the three main actors in ridesharing (i.e., drivers, riders and service enablers), and how fundamental value and belief systems affect the influence of specific actor-directed trust on consumers willingness to participate in ridesharing. Furthermore, the specific role of trust towards compliance with COVID-19 guidelines remains unclear, and additional research is needed on the boundary conditions of these effect.

The current research addresses these questions by investigating how general social trust and trust into compliance with COVID-19 guidelines affect consumers' willingness to use ridesharing services. Our study examines this relationship in a cross-cultural setting since trust seems to have a positive (albeit weak) connection with consumer participation in sharing in some countries, while other countries show conflicting results (Cha and Lee, 2022). For example, Asian countries are among those that make most use of shared assets due to their high population density (Ramizo, 2019), but they are predominantly known as low-trust countries (Cho, 2018). In addition to ridesharing entities' compliance with COVID-19 health guidelines, this study also acknowledges the possible effect of the fear of COVID-19 contagion on ridesharing intentions. Fear of COVID-19 has disrupted markets worldwide (Kabadayi et al., 2020; Mehrolia et al., 2021). It is supposed to leave a long-lasting impact on virtually every industry (Kim et al., 2022), and the ridesharing industry is no exception since potential risks have been found to deter users from participating in such collaborative consumption (Aziz and Long, 2022). On the other hand, prior research suggests that trust in God can boost users' collaborative consumption even within this pandemic (Agag et al., 2022). Hence, this study investigates the role of fear of COVID-19 and trust in God as two moderating variables for consumers’ intention to use ridesharing services.

The contribution of the current study is threefold. First, by drawing on social capital theory (Coleman, 1988), we situate the notion of general social trust (Delhey and Newton, 2005) into the context of the ridesharing industry and investigate to which extent social trust influences perceived trust in COVID-19 guidelines relating to the platform, driver, and other riders. We investigate these effects for a highly individualistic (United States) and a highly collectivist (Bangladesh) country. Thus, these findings contribute to the extant literatures on social trust and perceptions of COVID-19 protocols. Second, our research reveals substantial differences between these two countries in how consumers' beliefs that ridesharing platforms, drivers, and other riders follow established COVID-19 guidelines affect intentions to use ridesharing. These findings contribute to the emerging stream of research on the sharing economy and shed additional light on how cultural differences influence consumer decision making in a ridesharing context. Finally, our tests of moderating effects for fear of COVID-19 and trust in God provide initial insights on potential boundary conditions for the effect of social trust in the sharing economy, and also contribute to the extant literatures on COVID-19 fear and to what extent consumers’ religious beliefs influence decision making in the sharing economy.

2. Cultural context of the study

Prior research suggests that national culture not only influences general consumer trust-building (Doney et al., 1998), but also particular perceptions of trustworthiness regarding the platform company, service provider, and other riders in a ridesharing context (Cha and Lee, 2022). Our study focuses on two economically and culturally different countries, the United States and Bangladesh, in order to test the stability of our hypotheses in a ridesharing context. From an economic perspective, the United States and Bangladesh represent two extremes, with an adjusted net national income per capita in 2020 of USD 53,3030 for the United States and USD 2032 for Bangladesh (World Bank, 2022). Furthermore, the United States and Bangladesh also differ substantially on Hofstede's (2001) five cultural dimensions of individualism, power distance, uncertainty avoidance, masculinity, and long-term orientation. Out of these, individualism-collectivism and power distance appear to have a relatively stronger effect on inter-personal trust (Triandis, 2001). Whereas the United States is a highly individualistic country, Bangladesh (as most other Asian countries) is highly collectivist (Hofstede et al., 2010). Furthermore, Bangladesh scores high on power distance whereas the United States scores relatively low on this dimension (Hofstede et al., 2010).

In addition, the two countries also differ on Gelfand et al.’s (2006) “cultural tightness” construct. Whereas tight cultures formally and clearly define social norms, loose cultures devise expectations about social norms, but people have ample scope to interpret them in their own way (Gelfand et al., 2006). A recent study based on 68 countries finds Bangladesh to score 6.6 on a cultural tightness-looseness scale (indicating that Bangladesh is a highly tight culture with clearly defined social norms), whereas the United States received a score of 58 (thus indicating that the United States is a culturally loose country) (Uz, 2015). These scores have direct implications for how trust is perceived, since loose cultures like the United States tend to be high-trust cultures and tight cultures like Bangladesh tend to be low-trust cultures (Yamagishi et al., 1998). The findings also resonate with prior research, suggesting that western cultures like the United States presume others as trustworthy until they behave otherwise (Dirks et al., 2009), whereas eastern cultures like Bangladesh are more cautious when trusting others (Delhey and Newton, 2003). Hence, we posit that the United States and Bangladesh constitute two economically and culturally diverse cultures, thereby allowing us to examine the robustness of our conceptual model.

3. Literature review and conceptual framework

3.1. Ridesharing: an overview

Ridesharing networks are characterized by a triadic relationship of service providers (drivers), service enablers (e.g., Uber or Lyft), and customers (riders) (Kumar et al., 2018). Driven by digital technology able to identify consumer demand through data analytics, ridesharing companies such as Uber are able to match service providers and riders needs without ownership transfer (Chen and Wang, 2019). Hence, for many urban consumers, car ownership (which has traditionally been a symbol for independence and status) is being replaced by access-based ridesharing (Morewedge et al., 2021). Prior research suggests that consumers with strong pro-environmental attitudes have a higher willingness to participate in ridesharing, but that more individualistic consumers have a lower tendency to participate in such services (Prieto et al., 2022). In addition, the ongoing COVID-19 pandemic is indeed a constraint for the proliferation of ridesharing services. For example, Sajid and Zakkariya (2022) find that environmental concern is an important motivation for consumers to use ridesharing services, and that this effect is positively mediated by attitudes, subjective norms, and perceived behavioral control, and negatively moderated by perceived COVID-19 health risks. Congruent with these findings, Zhang and Liu (2022) show that perceived COVID-19 health threats negatively moderate the effect of environmental concern and tolerance for ambiguity on consumers’ intention to adopt ridesharing services. Furthermore, Hofmann et al. (2017) show that consumers expect service enablers to use coercive power to punish opportunistic behaviors, whereas trust is more important for the direct relationship between drivers and riders. Kumar et al. (2018) observe that in ridesharing networks, a capital asset (i.e., a car) is accompanied by a labor asset (i.e., the activity of driving), and that low perceived quality of the capital and/or labor asset will lower the trust in the service enabler. Summarizing, previous research has identified important antecedents and moderators for consumers intention to use ridesharing services, but opportunities exist to provide additional insight regarding specific types of trust toward service enablers, service providers, and other customers in the ridesharing network, as well as the role of more fundamental norms and value systems, such as social trust and religious beliefs.

3.2. Social capital theory

Social capital has been defined in Putnam’s (1993) seminal work as the “features of social organization, such as trust, norms, and networks, that can improve the efficiency of society by facilitating coordinated actions” (p. 167). It includes existing and prospective assets that reside in or are obtained through a relationship network (Nahapiet and Ghoshal, 1998). Congruent with this view, Coleman's social capital theory (1998) suggests that social capital is a resource for action that complements the neoclassical view of human behavior as a function of goal-oriented maximization of individual utility. Hence, social capital facilitates social cooperation and collective action (Zmerli and Newton, 2008), thereby having a positive effect on resource interchange (Tsai and Ghoshal, 1998) and collaborative consumption (Kim and Yoon, 2021). As pointed out by Delhey et al. (2011), trust in others is indeed the core of social capital, and hence our research draws on social capital theory when developing our hypotheses.

3.3. Social trust

Social trust has been defined as the “belief that others will not deliberately or knowingly do us harm, if they can avoid it, and will look after our interests, if this is possible” (Delhey and Newton, 2005, p. 311). It comprises people’s willingness to accept vulnerability based on positive expectations from others (Rousseau et al., 1998). It is social trust through which individuals feel that society is reliable (Cha and Lee, 2022). In economic exchange, this generalized sense of trust leads to people’s initial trust formation with the members of a society (Glanville et al., 2013; Leibrecht and Pitlik, 2020). It is such a general social trust in societies lead people toward a common goal and people perceive strong responsibility to contribute to that goal (Schiefer and Van der Noll, 2017). We suggest that the COVID-19 pandemic has motivated at least some parts of society to think in terms of common goals to mitigate or eliminate the risks of this pandemic, and achieving this goal requires large scale and long-term cooperation among people (Schiefer and Van der Noll, 2017). Moreover, social capital theory posits that in high trust societies, peoples' compliance to rules tends to be strong and any deviations are sanctioned promptly (Coleman, 1988; Delhey et al., 2011). We expect that when social trust is high, consumers' compliance with COVID-19 guidelines will act as a catalyst for attaining the common goal of minimizing or eliminating the adverse effects of COVID-19. In addition, we extend previous research showing that consumer trust in providers (e.g., hosts or drivers) and consumer trust in the platform company (e.g., Airbnb, Uber) are different constructs (Lee and Cha, 2021; Mao et al., 2020), and provide a more fine-grained picture of trust in the ridesharing industry by explicitly differentiating between trust in the platform company, trust in drivers, and trust in other hosts when it comes to compliance with COVID-19 guidelines. Thus,

H1a

Consumers' social trust perception is positively related with trust in platforms' compliance with COVID-19 guidelines (TPC).

H1b

Consumers' social trust perception is positively related with trust in drivers' compliance with COVID-19 guidelines (TDC).

H1c

Consumers' social trust perception is positively related with trust in other riders' compliance with COVID-19 guidelines (TRC).

3.4. TPC, TDC, TRC, and ridesharing intentions

Prior research on peer-to-peer sharing services has provided evidence that consumers’ trust in the provider positively affects their repurchase intentions (Ert and Fleischer, 2019; Liang et al., 2018). Thus, we posit that consumers in ridesharing who trust that drivers are in compliant with COVID-19 guidelines (i.e., TDC) will also positively affect their ridesharing intention. We further suggest that trust in other riders in peer-to-peer services depends on their citizenship behavior or misbehavior and the quality of their reviews (Assiouras et al., 2019; Xu, 2020). Given the context of the COVID-19 pandemic, we expect that consumers who trust that other riders comply with basic COVID-19 guidelines (e.g., covering cough or sneeze, rolling down windows to improve ventilation) will show higher ridesharing intentions. Lastly, we argue that it is the ridesharing platform company that is in-charge to ensure that their drivers and riders behave in compliance with COVID-19 guidelines while providing or enjoying their services to mitigate the spread of this infectious disease. In fact, ridesharing platforms typically use a review-rating system through which they attest the identity and performance of each individual involved in the service (i.e., drivers and riders) (Kong et al., 2020) and thus can influence their behavior by constantly promoting/rewarding citizenship behavior and punishing behaviors which are detrimental to the quality of service. Thus, if consumers trust that a ridesharing company complies with COVID-19 guidelines while offering their services, we expect that companies can enforce to some extent mechanisms to ensure that drivers and riders are in compliance with the COVID-19 guidelines. Thus,

H2a

TPC is positively related with consumer ridesharing intention.

H2b

TDC is positively related with consumer ridesharing intention.

H2c

TRC is positively related with consumer ridesharing intention.

3.5. The moderating effect of fear of COVID-19

The rapid spread of the COVID-19 pandemic has led people to experience mental health problems such as stress, anxiety, depressive symptoms, insomnia, denial, fear, and anger across the world (Galea et al., 2020; Torales et al., 2020). Fear has been one of the most common psychological reactions by people during the COVID-19 pandemic (Wang et al., 2020). Fear is defined as an unpleasant emotional state that is triggered by the perception of threatening stimuli such as danger, pain, or harm (De Hoog et al., 2008). Experiencing fear can increase people’s risk perceptions and promote protective behaviors (Boyraz et al., 2020; Kim et al., 2022). Galoni et al. (2020) show that contagious disease cues (such as the one triggered by COVID-19) do not only generate disgust, but also fear in consumers who are exposed to such cues. Furthermore, Harper et al. (2021) find that individuals engage in more preventive behaviors when they perceive the threat as severe, and this perceived threat can act as a motivational factor for individuals to engage in COVID-19 prevention behaviors (e.g., washing hands and maintaining social distance).

Thus, although ridesharing companies can pledge safety to their customers from the perspective of contacting COVID-19, consumers can still have a psychological fear of COVID-19, and this fear can reduce their trust in ridesharing platform entities' compliance with COVID-19 guidelines. Moreover, psychology and service researchers have highlighted that consumers' fear of COVID-19 transmission is likely to stimulate suspicion as well as psychological distress in consumers' mind regarding shared facilities and places, and result in avoidance behavior (Duong, 2021; Laato et al., 2020; Siddiqi et al., 2022). Thus, we contend that consumers who have more fear of COVID-19 will have less trust in the ridesharing platform entities’ compliance with COVID-19 guidelines, as compared to consumers who have less fear of COVID-19. Hence,

H3a

Fear of COVID-19 attenuates the positive effect of social trust perception on TPC.

H3b

Fear of COVID-19 attenuates the positive effect of social trust perception on TDC.

H3c

Fear of COVID-19 attenuates the positive effect of social trust perception on TRC.

3.6. The moderating effect of trust in God

The COVID-19 pandemic has placed consumers across the world into a unique vulnerable situation which forced them to deal with experiences such as unexpected loss of human lives, fears about their own and loved ones’ safety, and involuntary loss of personal possessions and collective landmarks (Yazdanparast and Alhenawi, 2022). In such vulnerable situations, people actively look for ways in pursuit of getting their lives back to normal (Baker, 2006). Hence, people vary by their need for certainty or continuity in social settings, and reduction of uncertainty contributes to their psychological stability (Ketelaar et al., 2015). Multiple studies on the COVID-19 pandemic have found that one way people psychologically cope with the uncertainties caused by this pandemic is through holding upon their trust in God (i.e., religiosity) (Nath et al., 2022; Pirutinsky et al., 2020). Trust in God generally comes with conformity to religious rules, norms, and prohibitions, which provide continuity and reduce uncertainty (Saroglou et al., 2009).

Furthermore, Saroglou et al. (2009) argue that religious people engage in more compliant behavior. Similarly, people who seek sure psychological footing in a stable world may also choose to trust authorities (Adorno et al., 2019) and conform to their rules. In turn, conformity to a certain authority implies perceived trustworthiness of the information provided by that authority (Moorman et al., 1993). Adorno et al. (2019) argue that social institutions can also function as authorities in this respect, including marketing and advertising professionals. In fact, past research has found that religious people are more inclined to perceive the information provided by authorities, including social institutions, as honest and without the intention to manipulate (Wisneski et al., 2009) and have less critical attitude toward the marketing of products and services (Evrard and Boff, 1998). Therefore, we argue that consumers who have more trust in God will exhibit greater trust in ridesharing platform entities’ compliance with COVID-19 guidelines as compared to consumers who put less trust in God. Thus,

H4a

Trust in God strengthens the positive effect of social trust perception on TPC.

H4b

Trust in God strengthens the positive effect of social trust perception on TDC.

H4c

Trust in God strengthens the positive effect of social trust perception on TRC.

Fig. 1 shows the conceptual framework and the expected relationships in the model.

Fig. 1.

Fig. 1

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Conceptual framework.

4. Method

4.1. Sample and data collection

Data were collected via online surveys created with the Qualtrics data collection software. Five-hundred respondents from the United States were recruited with help of online market research firm Centiment (www.centiment.co) and 367 respondents in Bangladesh were recruited from a large public university. After participants read the informed consent notice and agreed to participate, they were exposed to a screening question which asked whether they had previously used any ridesharing service such as Uber, Lyft, or Pathao. Participants who did not have any ridesharing experience were thanked and the survey ended. Next, participants were exposed to questions about their ridesharing experience, social trust, trust in compliance with COVID-19 guidelines, fear about COVID-19, and trust in God.

The two versions of the questionnaire for the United States and Bangladesh were identical, except for an adjustment of the specific ridesharing companies listed in the questionnaire. Specifically, given that Uber and Lyft are the dominant ridesharing platforms in the United States (Hahn and Metcalfe, 2017), we used the term “Uber/Lyft” to indicate a ridesharing service to the U.S. population. On the other hand, Uber and Pathao are the most popular ridesharing services in Bangladesh (Afrin and Hassan, 2020), and hence we used the term “Uber/Pathao” for participants in the Bangladeshi sample.

Apart from the screening question mentioned above, the questionnaire also included two attention check items. After removing respondents who did not qualify based on the screening question or who did not pass the attention checks, the final U.S. sample consisted of 204 responses and the final Bangladeshi sample consisted of 196 responses. A sample size estimation using G*Power (Faul et al., 2009) based on the recommendations from Memon et al. (2020) showed that in order to detect effect sizes of 0.15 at α = 0.05 with power set at 0.95, a minimum sample size of n = 184 is needed. Our samples of n = 204 (U.S.) and n = 196 (Bangladesh) exceed these requirements.1 As shown in Table 1 , the demographics for the two samples are overall quite similar. However, as expected, there are also differences between the two samples. For example, the Bangladeshi sample is substantially younger than the United States sample, which reflects the differing age demographics of the two countries.

Table 1.

Sample characteristics.

USA (%) Bangladesh (%) USA (%) Bangladesh (%)
Gender Frequency of
Male 60.2 68.6 rideshare
Female 39.8 31.4 Once 2.7 10.2
2–3 times 32.6 27.1
Age 4–9 times 28.7 16.9
18–29 years 27.2 76.6 10 times or more 35.9 45.8
30–39 years 46.1 13.5
40–49 years 17.1 9.0 Purpose of
50 years and above 9.6 0.9 rideshare
Commute to work/school 26.9 36.4
Education Short outing 40.4 39.2
High school 32.3 29.6 Travel 30.1 13.1
Undergraduate 54.2 53.9 Other 1.9 11.2
Graduate 0 13.9
Other 13.5 2.6 Social class
Lower 13.5 27.1
Residence area Lower middle 35.9 27.1
City 91.0 91.1 Middle 42.2 22.0
Rural 9.0 8.9 Upper middle 8.1 14.4
Upper 0.3 9.3
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4.2. Measures

Social trust was measured with six items based on Yamagishi and Yamagishi (1994). To measure TPC, TRC, and TDC, we constructed scales based on the COVID-19 health guidelines for drivers and customers provided on the websites of Uber, Lyft, and Pathao. Following recommendation from Hardesty and Bearden (2004), face validity for these scales was assessed via an expert panel consisting of five marketing professors from different universities who were knowledgeable about ridesharing and familiar with scale development procedures. Fear of COVID-19 was measured with seven items adopted from Ahorsu et al. (2022). Trust in God was measured with 11 items adopted from Rosmarin et al. (2009). However, we removed two items (“God attends to my needs” and “God is in complete control”) due to low factor loadings and because subsequent informal conversations with consumers from Bangladesh revealed that these items may cause confusion for participants. Finally, we measured ridesharing intentions with four items based on Cheah et al. (2022). All items were measured on seven-point Likert scales (1 = strongly disagree to 7 = strongly agree). Table 2 shows the scale items for our constructs as well as important psychometric properties of the scale.

Table 2.

Construct measurement.

Construct Sample 1 (United States) Sample 2 (Bangladesh)
Social trust (Yamagishi and Yamagishi, 1994) α = 0.88, CR = 0.91, AVE = 0.68 α = 0.90, CR = 0.93, AVE = 0.72
 Most people are basically honest. 0.84*** 0.88***
 Most people are trustworthy. 0.81*** 0.89***
 Most people are good and kind. 0.83*** 0.79***
 Most people are reliable. 0.84*** 0.88***
 Most people would try to be fair rather than take advantage of you if they got the chance. 0.79*** 0.78***
TPC (self-constructed) α = 0.94, CR = .95, AVE = 0.81 α = 0.88, CR = 0.91, AVE = 0.68
 Ridesharing companies ensure that their drivers are vaccinated. 0.87*** 0.79***
 Ridesharing companies ensure that a driver stays away from providing services if he/she is tested COVID 19 positive or experience such symptoms. 0.91*** 0.83***
 Ridesharing companies continuously update their policies to ensure that their services are grounded in current COVID-19 health guidelines. 0.90*** 0.90***
 Ridesharing companies provide free masks and sanitizing supplies to their drivers. 0.89*** 0.76***
 Ridesharing companies allow a driver, or a rider cancel a ride due to experiencing COVID-19 related sickness without penalty. 0.91*** 0.84***
TDC (self-constructed) α = 0.96, CR = 0.97, AVE = 0.85 α = 0.90, CR = 0.92, AVE = 0.66
 Uber/Lyft/Pathao drivers are vaccinated. 0.92*** 0.79***
 Uber/Lyft/Pathao drivers frequently sanitize their vehicle seats, handles, and other areas where customers can touch. 0.92*** 0.90***
 Uber/Lyft/Pathao drivers wash their hands or use hand sanitizers frequently. 0.92*** 0.88***
 Uber/Lyft/Pathao drivers cover their cough or sneeze. 0.93*** 0.77***
 Uber/Lyft/Pathao drivers offer passengers sufficient space in their vehicle to maintain social distance. 0.92*** 0.81***
 Uber/Lyft/Pathao drivers allow customers to roll down vehicle windows to improve ventilation. 0.92*** 0.74***
TRC (self-constructed) α = 0.91, CR = 0.94, AVE = 0.75 α = 0.90, CR = 0.93, AVE = 0.71
 Other riders of Uber/Lyft/Pathao frequently wash their hands or use hand sanitizers. 0.90*** 0.88***
 Other riders of Uber/Lyft/Pathao cover their cough or sneeze. 0.88*** 0.85***
 Other riders of Uber/Lyft/Pathao refrain themselves from using ridesharing if they experience COVID 19 related sickness or tested COVID-19 positive. 0.90*** 0.68***
 Other riders of Uber/Lyft/Pathao maintain social distance with the driver or with fellow riders in a shared vehicle. 0.89*** 0.90***
Ridesharing intention (Cheah et al., 2022) α = 0.82, CR = 0.88, AVE = 0.64 α = 0.90, CR = 0.93, AVE = 0.77
 I would love to use a ridesharing service 0.78*** 0.90***
 I would consider using a ridesharing service. 0.73*** 0.91***
 I would expect to use a ridesharing service. 0.86*** 0.89***
 I would plan to use a ridesharing service. 0.84*** 0.80***
Fear of COVID-19 (Ahorsu et al., 2022) α = 0.92, CR = 0.94, AVE = 0.68 α = 0.85, CR = 0.88, AVE = 0.51
 I am most afraid of COVID-19. 0.80*** 0.69***
 It makes me uncomfortable to think about COVID-19. 0.79*** 0.70***
 My hands become sweaty when I think about COVID-19. 0.84*** 0.69***
 I am afraid of losing my life because of COVID-19. 0.79*** 0.66***
 When watching news and stories about COVID-19 on television/online, I become anxious. 0.85*** 0.66***
 I cannot sleep because I’m worrying about getting COVID-19. 0.83*** 0.81***
 My heart races or palpitates when I think about getting COVID-19. 0.86*** 0.77***
Trust in God (Rosmarin et al., 2009) α = 0.95, CR = 0.96, AVE = 0.72 α = 0.93, CR = 0.94, AVE = 0.62
 God watches over me. 0.87*** 0.75***
 God knows what my needs are. 0.87*** 0.82***
 God knows what is harmful for me. 0.87*** 0.74***
 Nothing can happen without God’s permission. 0.79*** 0.69***
 I can’t be successful without God’s help. 0.76*** 0.76***
 God loves me immensely. 0.89*** 0.79***
 God cares about my deepest concerns. 0.85*** 0.82***
 No matter how bad things may seem, God’s kindness to me never ceases. 0.88*** 0.84***
 God is generous to me even when I don’t deserve it. 0.85*** 0.83***
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TPC = Trust in platforms' compliance with COVID-19 guidelines; TDC = Trust in drivers' compliance with COVID-19 guidelines; TRC = Trust in other riders' compliance with COVID-19 guidelines.

***p < 0.001; **p < 0.01; *p < 0.05; column entries are standardized factor loadings.

5. Analysis and results

To test our hypotheses, we used structural equation modeling (SEM) based on partial least squares (PLS) for the following reasons: First, PLS-SEM is a non-parametric estimation technique and thus robust against non-normally distributed data (Chin and Newsted, 1999; Hair et al., 2017). For example, McIntosh et al. (2014) observe that bootstrapped confidence intervals in PLS-SEM “appear reasonably robust to violations of normality and divergence between analytical and bootstrap sampling distributions” (p. 229). Second, prior research suggests that the use of PLS-SEM reduces measurement error by simultaneously considering the entire model structure when estimating parameters (Hair et al., 2019). To conduct the analyses, we used SmartPLS version 3.2.5 (Ringle et al., 2015), which is the PLS-SEM software most widely used in the behavioral sciences (Sarstedt and Cheah, 2019). Following the procedures described in Hair et al. (2022), we employed a two-step approach for analyzing the data, in which we first assessed the validity and reliability of the measurement model, followed by the analysis of the structural model where the relationships between the latent variables were examined.

5.1. Measurement model analysis

To assess the measurement model, we examined scale reliability, convergent validity, and discriminant validity. Reliability was assessed by analyzing Cronbach’s alpha and composite reliability scores (CR) of the measurement scales (Table 2). The Cronbach alpha and CR values of all constructs in both samples were above the recommended threshold of 0.7 (Bagozzi and Yi, 1988; Hair et al., 2022). Convergent validity was determined by testing two criteria based on Fornell and Larcker (1981). First, all the item loadings exceeded the recommended threshold of 0.6 and were significant (p < 0.001). Second, all average variance extracted (AVE) values exceeded 0.5. To test for discriminant validity, the square root values of all AVEs were compared to all the corresponding interfactor correlations (Fornell and Larcker, 1981). The results suggest that discriminant validity was achieved (Table 3 ).

Table 3.

Discriminant validity assessment (Fornell and Larcker, 1981): United States/Bangladesh.

Mean (US/Bangladesh) SD (US/Bangladesh) 1 2 3 4 5 6 7
1. Fear of COVID-19 4.49/3.64 1.43/1.24 0.82/0.71
2. Ridesharing intention 5.36/5.39 1.00/1.10 0.30/0.17 0.80/0.88
3. Social Trust 5.25/4.17 1.06/1.40 0.41/0.07 0.55/0.20 0.82/0.85
4. TDC 5.10/3.94 1.21/1.44 0.32/0.21 0.53/0.16 0.38/0.33 0.92/0.82
5. TPC 5.10/4.15 1.15/1.27 0.33/0.38 0.57/0.06 0.49/0.35 0.53/0.59 0.9/0.83
6. TRC 5.00/3.94 1.16/1.29 0.34/0.15 0.46/0.12 0.39/0.34 0.40/0.55 0.38/0.58 0.86/0.85
7. Trust in God 3.18/4.66 1.10/0.54 0.51/-0.06 0.34/0.07 0.43/-0.02 0.33/0.01 0.39/-0.04 0.40/0.08 0.85/0.78
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Note: TPC = Trust in platforms' compliance with COVID-19 guidelines; TDC = Trust in drivers' compliance with COVID-19 guidelines; TRC = Trust in other riders' compliance with COVID-19 guidelines. Bold numbers on the diagonal represent the square root of AVE. Numbers on the off-diagonal represent the correlations between constructs.

In addition, we used the Heterotrait-Monotrait ratio (HTMT) criterion as suggested by Henseler et al. (2015) to further assess discriminant validity among the constructs. HTMT refers to the ratio of correlations within the construct to correlations between the construct (Henseler et al., 2015). As shown in Table 4 , the HTMT scores for all constructs were below the most stringent HTMT threshold of 0.85, thereby providing additional support for discriminant validity in both samples.

Table 4.

Discriminant validity assessment (HTMT criterion): United States/Bangladesh.

1 2 3 4 5 6 7
1. Fear of COVID-19
2. Ridesharing intention 0.34/0.26
3. Social Trust 0.46/0.14 0.65/0.24
4. TDCCG 0.33/0.20 0.59/0.16 0.42/0.34
5. TPCCG 0.35/0.36 0.64/0.09 0.54/0.38 0.56/0.65
6. TRCCG 0.36/0.17 0.52/0.17 0.43/0.37 0.43/0.61 0.40/0.65
7. Trust in God 0.55/0.13 0.38/0.11 0.47/0.06 0.34/0.09 0.41/0.09 0.42/0.10
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Note: HTMT should be lower than 0.85 (Henseler et al., 2015).

5.2. Common method bias

Several steps were followed to decrease the chances of common method bias (CMB) (Podsakoff et al., 2003). First, except for the newly constructed scale items of TPC, TRC, and TDC, all scale items were adopted or adapted from previously validated scales. Second, to avoid potential order effects in participants' responses, the order in which questions of different constructs appeared in the survey was altered. Third, participants were assured of the anonymity and confidentiality of their responses. Fourth, we ran Harman’s single-factor test to empirically test for CMB. Even though the limitations of this test have been pointed out in the extant literature (MacKenzie and Podsakoff, 2012), recent research suggests that it is a viable and meaningful test for detecting CMB (Fuller et al., 2016). According to this test, the percent of total variance explained by the common factor needs to be less than 50% based on the unrotated solution of an exploratory factor analysis. In the current study, the percent of total variance explained by the common factor in the samples from the United States and Bangladesh were 36.63% and 22.97%, respectively. Hence, even though we acknowledge that common method bias was present in our samples, we posit that it was not a major concern in our study.

5.3. Structural model analysis

We estimated the relationships in our model by running 5000 bootstrapping subsamples separately for each of the two samples in SmartPLS 3.5.5 (Hair et al., 2022). As shown in Fig. 2 , for the U.S. sample, social trust positively influenced TPC (β = 0.38, t = 4.86, p < 0.001), TDC (β = 0.27, t = 3.06, p < 0.01, and TRC (β = 0.25, t = 2.80, p < 0.01). Thus, H1a, H1b, and H1c were supported. Furthermore, ridesharing intention in the U.S. sample was positively related to consumers' trust in all three platform entities’ compliance with COVID-19 guidelines – TPC (β = 0.35, t = 4.24, p < 0.001), TDC (β = 0.25, t = 3.14, p < 0.01), and TRC (β = 0.22, t = 3.57, p < 0.001). Therefore, H2a, H2b, and H2c were supported for the U.S. sample.

Fig. 2.

Fig. 2

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Results from structural equation modeling.

US=United States, B=Bangladesh. Numbers in parentheses indicate t-values. Numbers in brackets below constructs indicate adjusted R2. *p < 0.05, **p < 0.01, ***p < 0.001.

We found no moderating influence of fear of COVID-19 among the respondents from the U.S. on the relationships between social trust and TPC (β = −0.10, t = 1.11, p = 0.268), social trust and TDC (β = −0.10, t = 1.76, p = 0.079), and social trust and TRC (β = −0.02, t = 0.53, p = 0.597). Thus, H3a, H3b, and H3c were not supported. We also examined the moderating role of trust in God in strengthening the relationships among U.S. respondents’ social trust perception and TPC, TDC, and TRC. Contrary to our predictions, we found no significant moderating influence of trust in God on the relationships between social trust and TPC (β = 0.09, t = 0.80, p = 0.328), social trust and TDC (β = 0.16, t = 1.85, p = 0.065), and social trust and TRC (β = 0.05, t = 1.14, p = 0.253). Hence, H4a, H4b, and H4c were not supported.

For the sample from Bangladesh, social trust perception positively influenced TPC (β = 0.31, t = 4.96, p < 0.001), TDC (β = 0.30, t = 4.48, p < 0.001), and TRC (β = 0.33, t = 5.15, p < 0.001). Thus, H1a, H1b, and H1c were supported. Furthermore, and contrary to the findings from the U.S. sample, none of the three trust factors TPC, TDC, and TRC predicted Bangladeshi consumers’ ridesharing intentions (TPC: β = −0.13, t = 0.96, p = 0.336); TDC: β = 0.20, t = 1.84, p = 0.066; TRC: β = 0.09, t = 0.69, p = 0.494). Hence, H2a, H2b, and H2c were not supported for the sample from Bangladesh.

Interestingly, fear of COVID-19 strengthened rather than weakened the relationships between social trust and TPC (β = 0.11, t = 2.17, p < 0.05) and between social trust and TDC (β = 0.21, t = 3.93, p < 0.001). Fear of COVID-19 did not attenuate the relationship between social trust and TRC (β = 0.13, t = 1.91, p = 0.056). Hence, even though two of the three moderating effects were statistically significant, we did not find support for H3a, H3b, and H3c since the statistically significant effects were in the opposite direction as predicted. Finally, trust in God did not strengthen the relationships between social trust and TPC (β = 0.10, t = 1.52 p = 0.129), TDC (β = 0.01, t = 0.21, p = 0.883), and TRC (β = −0.10, t = 1.41, p = 0.158) for the sample from Bangladesh. Therefore, H4a, H4b, and H4c were not supported.

Table 5 provides an overview of the results from the hypotheses tests. In addition, we also inspected effect sizes f2 based on the output from SmartPLS. Values of 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively (Hair et al., 2022). As shown in Table 5, most effect sizes were small – however, some effect sizes were medium (social trust → TPC and TPC → ridesharing intention for the U.S. sample) or close to the 0.15 value for medium effect sizes (social trust → TPC and social trust → TRC for the sample from Bangladesh).

Table 5.

Overview of hypotheses testing.

Hypothesis Effect U.S. sample
 \Bangladesh sample
Effect Effect size f2 Support Effect Effect size f2 Support
Main effects
H1a Social trust → TPC 0.38*** 0.15 ✓ 0.31*** 0.13 ✓
H1b Social trust → TDC 0.27** 0.07 ✓ 0.30*** 0.11 ✓
H1c Social trust → TRC 0.25** 0.06 ✓ 0.33*** 0.13 ✓
H2a TPC → ridesharing intentions 0.35*** 0.15 ✓ −0.13 0.01 –
H2b TDC → ridesharing intentions 0.25** 0.07 ✓ 0.20 0.02 –
H2c TRC → ridesharing intentions 0.22*** 0.07 ✓ 0.09 0.004 –
Moderating effects
H3a Fear on social trust → TPC −0.10 0.01 – 0.11* 0.02 R
H3b Fear on social trust → TDC −0.10 0.02 – 0.21*** 0.07 R
H3c Fear on social trust → TRC −0.02 0.002 – 0.13 0.03 –
H4a Trust in God on social trust → TPC 0.09 0.01 – 0.10 0.01 –
H4b Trust in God on social trust → TDC 0.16 0.03 – 0.01 0.00 –
H4c Trust in God on social trust → TRC 0.07 0.01 – −0.10 0.01 –
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TPC = Trust in platforms' compliance with COVID-19 guidelines; TDC = Trust in drivers' compliance with COVID-19 guidelines; TRC = Trust in other riders' compliance with COVID-19 guidelines.

R = Reverse (opposite direction as hypothesized).

*p < 0.05, **p < 0.01, ***p < 0.001.

6. Discussion and implications

The current study provides a timely discussion of how the COVID-19 pandemic relates to consumers' ridesharing behavior across cultures. Drawing on social capital theory, we examine to what extent consumers' social trust perception relates with consumers’ beliefs that platforms, drivers, and other riders comply with COVID-19 ridesharing guidelines. We also test the moderating effect of two personality and individual difference factors, fear of COVID-19 and trust in God, on the relationships between social trust and TPC, TDC, and TRC. Finally, the relationship between TPC, TDC, and TRC and the focal outcome variable of this study – ridesharing intention – is examined. Overall, the results shed light on the complex nature of consumer ridesharing intentions across cultures in the light of the still looming COVID-19 pandemic and provide important implications for existing ridesharing as well as general transportation companies, drivers, and marketers.

First, our results indicate that consumers with high levels of social trust believe that platforms (TPC), drivers (TDC), and other riders (TRC) will comply with specific COVID-19 ridesharing guidelines in both the U.S. and Bangladesh. Hence, consumers’ beliefs that other people generally have good intentions and are not interested in causing harm (Delhey and Newton, 2005) seem to relate to their expectation that platforms, drivers, and other riders will comply with COVID-19 ridesharing guidelines. Extending prior work on trust in institutions and networks (Cook et al., 2009), these findings suggest that social trust conveys to trust in institutions (i.e., sharing platforms) as well as individuals (such as drivers and other riders).

Second, TPC, TDC, and TRC were positively related with consumers’ ridesharing intentions in the U.S, but not in Bangladesh. This finding may reflect country-specific idiosyncrasies since western cultures usually seem to assume that others can be trusted, whereas consumers in eastern cultures are more cautious when trusting others (Delhey and Newton, 2003; Dirks et al., 2009). Specifically, consumers in Bangladesh may feel that trust in compliance with COVID-19 guidelines is not relevant for their decision to use ridesharing, whereas such a trust in guidelines is important for consumers from the U.S. As an alternative explanation, it may also be argued that other factors may shift the focus for consumers in Bangladesh away from trust in complying with COVID-19 guidelines, such as the economic need for transport at reasonable prices.

Our results regarding the moderating effect of fear of COVID-19 on the relationship between social trust and TPC, TDC, and TRC were in part unexpected and may look counterintuitive at first sight. Specifically, we did not find a moderating effect of fear of COVID-19 for consumers in the U.S., but we found a statistically significant and positive effect of fear of COVID-19 for TPC/TDC in Bangladesh. Additional scrutiny of literatures related to fear in the marketplace (compare, e.g., Laros and Steenkamp, 2004) led us to deduce that fear may actually amplify (rather than attenuate) mechanisms of trust by elevating the role of social trust to a more dominant position, which in turn could explain that the association between social trust and TPC/TDC increases for those consumers who express higher fear of COVID-19. However, it remains unclear why such an effect seems to occur for consumers in Bangladesh and not for consumers in the United States. One possible explanation for the non-significant moderating effect of fear of COVID-19 in the U.S. might be that in loose cultures such as the U.S., individuals rely more on interpersonal trust than on institutional trust (i.e., trust in platform company) (Yamagishi et al., 1998). Given that TPC, TDC, and TRC all represent institutionalized forms of compliance with COVID-19 guidelines, consumers’ fear of COVID-19 may not affect the relationship between social trust on the one hand and TPC, TDC, and TRC on the other hand in the U.S.

Finally, our analysis revealed that trust in God did not moderate the relationship between social trust and TPC/TDC/TRC in the U.S. and Bangladesh. For the relationship between social trust and TDC in the U.S., the moderating effect of trust in God almost reached significance (β = 0.16, t = 1.85, p = 0.065). Hence, future research is needed to provide additional insights regarding the role of trust in God for the development of social trust and more specific manifestations of trust towards compliance with COVID-19 guidelines. For example, Bangladesh represents an overall low-trust culture in which people tend to trust others less than in many western cultures (Cho, 2018). The interplay between high trust in God and high social trust may then influence consumers' beliefs that other riders comply with COVID-19 guidelines when using ridesharing services. It is also possible that a distrust towards people who do not belong to the same religious community (compare, e.g., Ketelaar et al., 2015) may influence in how consumers perceive other riders’ willingness to comply COVID-19 guidelines.

Our findings also have important implications for transportation service providers and other relevant stakeholders in the ridesharing economy. First, our findings show that ridesharing intentions are positively related with TPC, TDC, and TRC in the United States but not in Bangladesh. These differences may reflect variations in consumers' purchasing power and infrastructure development in the two countries. For instance, due to the low purchasing power of Bangladeshi consumers, owning a personal vehicle is not common in Bangladesh, and at the same time, the quality of public transport in Bangladesh is problematic (Andaleeb et al., 2007). Hence, ridesharing services can be considered more of a necessity in emerging economies like Bangladesh, as compared to the United States. This conclusion is also supported by the ridesharing usage patterns of consumers in these two countries from our survey (compare Table 1), where Bangladeshi consumers use ridesharing more for daily commuting to work/school (US: 26.9% vs. B: 36.4%) and less for travelling purposes (US: 30.1% vs. B: 13.1%). Hence, especially in the United States, ridesharing companies should try to comply with COVID-19 guidelines because such a compliance directly relates to consumers' ridesharing intentions. Furthermore, the non-significant relationship between TPC, TDC, and TRC and ridesharing intention in Bangladesh does not necessarily imply that complying with COVID-19 guidelines is not important for Bangladeshi consumers. Rather, it is possible that TPC, TDC, and TRC are not sufficient conditions to increase ridesharing intentions, but they can nevertheless be necessary conditions for consumers to consider ridesharing. Future research could investigate to what extent TPC, TDC, and TRC may be necessary (but not sufficient) conditions for ridesharing intentions through a necessary condition analysis (NCA; compare, e.g., Dul et al., 2020). Finally, our research also provides insights to public policy makers by showing that social trust (a construct that represents social adhesion and consumers' feelings of embeddedness in society) is positively related to consumers’ perceptions that ridesharing platforms comply with COVID-19 guidelines, and that fear of COVID-19 moderates to some extent the relationship between social trust and TPC/TDC (compare Fig. 2).

7. Limitations and future research

Although the current work generates important insights for the emerging stream of research on the sharing economy and how COVID-19 affects consumer decision making, our findings should be assessed in light of several limitations which in turn provide opportunities for future research. First, although representative for a larger number of similar cultures, the results from our study are limited to two specific countries, the United States and Bangladesh. Future research is encouraged to explore other countries that may be fundamentally different from the two cultures included in this study, such as, e.g., countries from Eastern Europe or Africa. Second, measuring consumer ridesharing intention in the still lingering COVID-environment period is a complex issue, especially because the virus keeps continuously developing new variants (Burki, 2022) which may change consumers' risk perceptions and trust towards other people in relatively short periods of time. Future research may benefit from adopting a longitudinal approach and measure consumers' fear of COVID-19 as well as actual ridesharing behavior at different points in time to track the differences as humankind continues to recover from this pandemic. Finally, contrary to the U.S., TPC, TDC, and TRC were not related to consumers’ ridesharing intentions in Bangladesh. Hence, other factors seem to influence ridesharing intentions in this country, which provides important opportunities for future research.

Footnotes

1

We selected a conservative approach to determine the number of predictors in our sample size estimation. Specifically, we considered one independent variable, three mediators, two direct effects for the moderators, and six interaction effects for the moderators that (directly or indirectly) influence our dependent variable, for a total of 12 predictors. However, no construct in our model directly receives more than three predictors. Running the sample size estimation with three rather than 12 predictors yields a substantially smaller required sample size of n = 119.

Data availability

Data will be made available on request.

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