Sage Journals: Discover world-class research

Abstract

The purpose of this study was to confirm the causal relationship between COVID-19 social phobia, contamination concern, and purchase intention. Herein, contamination concern refers to consumers’ fear of exposure to COVID-19. This study sought to determine whether online communities’ trust in the seller acts as a moderator between contamination concern and purchase intention. In addition, the indirect effect of COVID-19 social phobia and contamination concerns on purchase intention was investigated. The causal relationships of the variables were examined by comparing differences in trust level (N_High = 139, N_Low = 144; M_age = 38.57; female = 51.9%, n = 147) in the used-goods market during the COVID-19 scenario. Using PROCESS macro model 14, the relationship between COVID-19 social phobia and contamination concern, the relationship between contamination concern and purchase intention, and the moderating effect of the level of trust on contamination concern and purchase intention were explored. Finally, moderated mediation was analyzed using the research model. COVID-19 social phobia positively affected contamination concern, and the level of trust had a moderating effect on contamination concern and purchase intention. However, the causal relationship between contamination concern and purchase intention was not significant; thus, the moderating effect could not be confirmed. As the sharing economy expanded, the influence of contamination concern on consumer purchasing decisions was confirmed. To deal with this, corporate managers must proactively implement measures to increase the trust of consumers.

Keywords

COVID-19 social phobia contamination concern used-goods market secondhand market trust purchase intention

Introduction

SARS-CoV-2 (COVID-19) appeared in China in December 2019 and spread worldwide rapidly, changing people’s daily lives profoundly. A recent study by Armiya’u et al. (2024) identified inadequate social interaction as a key psychological barrier during this period. This highlights how community-scale disasters pose immediate risks to people’s psychological, physical, and social relationships (Bonanno et al., 2010), which subsequently impacts their consumption habits. Approximately one-third of consumers preferred shopping alone during the pandemic due to fear, while approximately 40% of consumers exhibited strong reactions to health threats (Veselovská, 2023). People were naturally concerned about COVID-19 due to its elevated infection and mortality rates (Lin, 2020), and since shopping involves touching products and contact between people and objects, the risk of infection impacted their buying habits. After the pandemic, consumers’ concerns about product safety increased, making this factor a crucial determinant in individual product choices (Bai, 2024). The potential for future pandemics and health risks has led consumers to prefer safer purchasing methods (Gupta & Mukherjee, 2022). Additionally, the COVID-19 pandemic brought significant changes to consumer behavior, increasing awareness of environmental issues and sustainable consumption (Wong et al., 2023). In this regard, the sharing economy can contribute to solving environmental problems by reducing resource waste and promoting circular economic practices (Meenakshi, 2023). While it serves as an alternative to enhance resource efficiency and reduce environmental pollution as a sustainable consumption model, hygiene concerns have become increasingly significant in shaping consumer behavior (Xu et al., 2022). Rising hygiene concerns can influence risk perception and perceived value, ultimately affecting the intention to use sharing services (Wei & Wang, 2025). In fact, the sharing economy temporarily stagnated during the pandemic, making safety issues more critical. Both service providers and consumers were motivated to adhere to new hygiene guidelines (Meenakshi, 2023). Depending on the level of physical contact required by different platforms—such as high-contact platforms (e.g., Airbnb) versus low-contact platforms (e.g., Uber Eats)—high-contact platforms had to modify their business models by enhancing hygiene measures and developing contactless options (Nuttah et al., 2023). Ultimately, the psychological impact of health and safety concerns shaped by COVID-19 is likely to have a long-term influence on consumer behavior (Xu et al., 2022). Recent studies have shown that the psychological impact of COVID-19 fear can be shaped by several moderating and mediating variables—such as resilience, personal meaning, personal growth initiative, and perceived controllability, which influence outcomes ranging from behavioral dependence and well-being to the adoption of preventive actions (Arslan et al., 2021; Green & Yıldırım, 2022; Yıldırım & Çiçek, 2022; Yıldırım & Özaslan, 2021). This highlights the need to identify both socially and personally relevant factors that shape consumer behavior during pandemics. As a result, building trust with consumers by aligning with changing consumption patterns that prioritize safety and a sustainable future will be a crucial challenge for businesses.

This research required a thorough examination of the consumer goods market, especially the sharing economy, in which consumer contact may be more pronounced. Consumers’ concerns have been addressed previously in the field of access-based services that are used temporarily, based on service innovation (Bardhi & Eckhardt, 2012). Although the company retains ownership, users access self-service anonymously in the short term (Schaefers et al., 2016). Due to the nature of these services, many people share them without ownership, so contamination has been pointed out as a typical problem of collaborative consumption (Gullstrand Edbring et al., 2016). For example, the introduction of reusable bottles into the design and supply chain increased due to corporate ethical considerations, but for consumers, environmentally friendly reusable bottles are not necessarily preferable, considering potential contamination (Meng & Leary, 2021). To illustrate, many people tend to buy clothes from the bottom of the stack to avoid those that have been handled (Underhill, 2009). In the past, flea markets provided a platform for trading used goods, but the emergence of the internet altered the market for used goods (Weill & Vitale, 2001) due to legal and institutional pressures (S. Taylor & Todd, 1995) and changes in neighboring communities (Gamba & Oskamp, 1994). In the post-pandemic era, as consumer anxiety related to the risk of infection has increased, trust has become a crucial factor in the online used-goods (secondhand) market. Ultimately, the fear induced by the pandemic and concerns about contamination may serve as major risk factors for the sharing economy and the secondhand market, as they alter consumers’ purchase intentions rather than reinforce environmental concerns and sustainable consumption behaviors (Wong et al., 2023). Therefore, alleviating consumer concerns and effectively building trust are key elements in promoting sustainable consumption. Despite the notable growth of customer-to-customer (C2C) platforms during the pandemic due to their functionality and ability to facilitate consumer transactions (Trehan & Sharma, 2020), research on the impact of social fear and concerns regarding contamination on the secondhand market and the role of trust in the market remains insufficient. To address this gap, this study raises two main research questions. First, the impact of the COVID-19 pandemic on consumers’ social phobia (SP) and contamination concern (CC) in the secondhand market is analyzed, examining how these factors influence purchase intention (PI) in this context. Second, the question of whether trust in the secondhand market community moderates the relationship between CC and PI is investigated. From these analyses, several changes in consumer psychology and behavior caused by COVID-19 are elucidated, particularly by examining the interaction between CC and trust and considering its role in shaping purchase intentions. This research expands the existing literature on consumer behavior in sharing economies, such as the secondhand market. Additionally, the findings provide practical implications for post-pandemic secondhand market and sharing economy platform operators, offering insights into strategies to enhance consumer trust and mitigate CC. These findings may help businesses better address consumer expectations for sustainable consumption.

Theoretical Background

COVID-19-Related Social Phobia and Contamination Concern

A phobia is a particular form of anxiety characterized by a persistent and overwhelming fear of the environment and objects (American Psychological Association [APA], 2013). Phobias often induce anxiety and have been associated with physical, mental, and mood disorders, including suicidal intent and major depression (Arpaci et al., 2020). Genetic and physiological antecedents and environmental conditions trigger specific phobias. Man-made and natural disasters can also activate phobias (APA, 2013). In the face of COVID-19, many people experienced phobic reactions (Arpaci et al., 2020). The COVID-19 phobia differs from ordinary temporary fear, and the intensity of fear or anxiety is vigorous (Arpaci et al., 2020). Considering the sudden and rapid expansion of the pandemic, ease of transmission, limited treatment options, and high virus-related mortality further increased psychopathological reactions (Rothan & Byrareddy, 2020). Fear is an adaptive emotion to allocate energy in the face of potential threats. If fear is too extreme, it may have a detrimental psychological impact on the individual (Mertens et al., 2020). However, if it is too low, it may lead to ignoring coronavirus policies and be harmful to individuals and society. Finally, although fear induces safe behavior and reduces specific threats, it can also increase anxiety (Mertens et al., 2020). A phobia differs from a simple fear; it is a disproportionate fear of objects or situations and leads to anxiety (APA, 2013). Corona phobia, the subject of interest in this study, is defined as excessive fear of exposure to COVID-19 (Arpaci et al., 2022). Studies have determined that excessive fear of COVID-19 is due to its high infection rate, uncertainty, and economic downturns (Arpaci et al., 2022). Uncertainty about the current state is the most critical factor affecting psychological risk; it disrupts daily routines and social support mechanisms and causes a sense of loss of control (Satici et al., 2020). Since coronavirus was spread by human-to-human contact, as the fear of contagion loomed, many people began working remotely, and hundreds of millions of people moved home from offices, schools, and university dormitories. Some were quarantined or isolated, and all gathering places—face-to-face classes, restaurants, bars, and exercise facilities—were restricted (Casale & Flett, 2020). This sudden reduction in opportunities for social connection created conditions of inadequate social interaction, which has been shown to exacerbate stress and undermine well-being during the pandemic (Armiya’u et al.’, 2024). Moreover, concern for others remains a frequently expressed worry (Mertens et al., 2020). Because human-to-human contact poses the most significant infection risk, gaging SP related to interpersonal relationships was an essential part of this research. SP is the anxiety felt when an individual is exposed to social situations (APA, 2013). Individuals feel stressed by the presence of other people during certain activities, including eating, using public restrooms, working, traveling on public transportation, and walking near other people (Mattick & Clarke, 1998), and this critically impacts their academic, work, and general social functioning (Aderka et al., 2012). According to research on the distance between people, social anxiety can result from people’s discomfort about being physically near others (Arad et al., 2021). Stress related to face-to-face contact is the worry that one may be infected with coronavirus through contact with others or objects. CC is an intense and persistent fear of being directly or indirectly contaminated by a person, place, or thing; thus, contamination is the result of direct or indirect contact with someone or something that is considered to be dirty, impure, infected, or harmful (Rachman, 2004). The specific ways in which a phobia of social relationships due to COVID-19 affects CC are outlined as follows.

First, feeling disgusted by the possibility of contracting COVID-19 from person-to-person contact is considered a CC. Disgust is a reaction to substances that may cause harm (Tybur et al., 2009). Stimuli that potentially induce disgust include feces, rotten food or flesh, body fluids such as blood, sneezes, coughing, vomit, or bad breath (Coelho et al., 2020). Therefore, being physically close to a group of people at potential risk of infection during an epidemic can cause maladaptive psychological consequences, such as anxiety (Satici et al., 2020). Previous studies have shown that the disgust domain is positively associated with contamination fear (Deacon & Olatunji, 2007), and disgust sensitivity, which is a physical sensation linked to potential infection that predicts fear, has been confirmed (McKay et al., 2020).

Second, SP caused by COVID-19 leads to CC because it is related to specific avoidance and protective behavior to reduce the risk of infection. Infectious diseases can increase contamination sensitivity and ultimately act as a factor in lowering infections (Stevenson et al., 2009). It has been reported that worry about the perils of COVID-19 intensified stress and avoidance behavior (S. Taylor et al., 2020). The spread of disease and infection is tentatively linked to subsequent avoidance (Viar-Paxton & Olatunji, 2016). Health-anxious people avoid coronavirus-contaminated areas outside and enter only safe places, such as their homes. Protection motivation theory asserts that consumers are induced to engage in self-protective behaviors in situations of uncertainty and lack of control (Truong & Truong, 2022). This fear activates consumers’ defensive mechanisms, leading them to adopt safer behaviors, reduce perceived risks, and choose hygienic and reliable services (Apaolaza et al., 2022; Goyal & Verma, 2021). With technology, they can work from home, shop online, and have food delivered (S. Taylor & Asmundson, 2020). The pandemic made people pickier, avoiding touching contaminated surfaces or shaking hands (S. Taylor & Asmundson, 2020). Negative mental health effects, such as crowd avoidance, also occurred during the severe acute respiratory syndrome (SARS) health crisis and led to long-term behavioral changes (Reynolds et al., 2008). CC can increase protective behaviors against disease (Stevenson et al., 2009). A critical risk factor of COVID-19 during the pandemic was individuals’ constant worry about themselves and their families (Li et al., 2020). Many pandemic factors affecting mental health have been reported, but concerns that family, friends, and acquaintances may be infected appear to be a newly reported psychological effect (Brooks et al., 2020). In addition, overall, the use of media as a safety-seeking behavior increased due to COVID-19, and the increase in disturbing information reinforced this behavior (Garfin et al., 2020). As a result, fear of person-to-person infection minimized individuals’ engagement in risky behaviors and reinforced virus-attenuating behaviors (Harper et al., 2021).

Third, the behavior of consumers who avoid products that they believe are contaminated can further strengthen the impact of SP caused by CC. Consumers frequently open, touch, and feel packaged products during the purchasing process and then buy untouched products. In light of the law of contagion, PI may decrease for products touched by other people (Argo et al., 2006). A source (person/object) affects a recipient when they come into direct or indirect contact (Rozin & Nemeroff, 1990). Notably, according to this law, the essence can be transferred even if it is not visible, and the essence of the source can remain in the recipient even after such contact ends (Rozin & Nemeroff, 1990). People do not avoid buying used clothes because they feel no connection to unknown people, but because they believe that previous wearers have contaminated the clothes (Argo et al., 2006). Overall, SP resulting from the COVID-19 outbreak is positively associated with customers’ fear of contact.

H1.1: COVID-19 social phobia is positively related to consumers’ contamination concern

Consumer Contamination Concern and Online Purchase Intention

The reasons consumers’ CC negatively impacts online PI are summarized as follows.

First, in the case of used products that other people have touched, the intent to buy may be affected. In the retail field, the law of contagion significantly impacts consumer judgment and perception (Peck & Johnson, 2011). That is, if someone else has touched the product, consumers feel that it is unclean, and this lowers their PI (Argo et al., 2006). Consumer contamination affects decision-making, product evaluation, and the outcome (Kapitan & Bhargave, 2013). In the secondhand clothing market, consumers’ perceptions of health risks and the possibility of infection are a crucial factor in their purchasing decisions. Therefore, the transparency of clothing laundering and hygiene management influences consumer purchasing behavior (Xu et al., 2022). Information asymmetries that occur in situations such as COVID-19 make it more difficult for consumers to evaluate the health risks of products. Therefore, they try to assess the product and its resulting health risks through the presumed contamination of the product site (Grande et al., 1999). Since contamination is rarely visible (Morales & Fitzsimons, 2007), responses to contamination may be driven by what people imagine through mental imagery (Gérard & Helme-Guizon, 2018). Contamination belief is the perception that contamination has occurred through salient clues and may have permanent characteristics (Rozin & Nemeroff, 2002). Therefore, once contamination occurs, it may affect subsequent actions, judgments, and preferences, even after the cause of contamination has been removed (Huang et al., 2017). For example, contamination from contact with offensive properties lowers the intention to purchase cotton (Morales & Fitzsimons, 2007). Consumers’ PI is reduced for potentially contaminated products due to an instinctive self-preservation instinct (Grande et al., 1999). Humans can physically resist and prevent infection behaviorally, such as by avoiding sneezes (Murray & Schaller, 2016). This means that evolutionarily, if we expect certain products to harm our bodies, our behavior adapts to avoid them (Meng & Leary, 2021). Transmission usually occurs through physical contact, but contamination transmission can occur in spatial proximity. People may perceive a contamination source nearby, even without physical contact (L. R. Kim & Kim, 2011). Of course, in cases in which physical contact is expected, such as a T-shirt that touches the body, people’s caution may increase, ultimately lowering PI (Meng & Leary, 2021). In addition, consumers may feel disgust toward contaminated products when they confirm physical contact. Thus, the feeling of disgust pointed out above diminishes consumers’ evaluation and their PI (Argo et al., 2006). A decrease in PI results from contamination risk knowledge. When consumers evaluate health risks, they try to hedge against the uncertainty of limited information (Frank & Schvaneveldt, 2016). Consumer suspicion is associated with mistrust due to a lack of information from the company (Healy & Palepu, 2001). The fear of infection in situations such as COVID-19 extends negative associations between the source and target products (Morales & Fitzsimons, 2007) and will inevitably affect the evaluation of the product (Gérard & Helme-Guizon, 2018). Furthermore, cognitive processes regarding contamination may occur, particularly when people’s vividness and valence mental imagery regarding contamination are activated, and will negatively affect product evaluation and PI (Gérard & Helme-Guizon, 2018).

Second, crowding may indirectly increase the possibility of contamination of used products, thereby lowering the intention to purchase. After the pandemic, this mode of shopping has further accelerated into a complex omnichannel combining online and offline trade (Chen & Chi, 2021). Therefore, even when consumers attempt to purchase online, they become sensitive to the causes of contamination perceived in various purchasing environments. Among them, crowding is a significant variable that relates to human contact and is therefore involved in purchasing, even in the COVID-19 situation. People become more concerned about contamination the closer it is to their bodies, and the more they share space with unfamiliar users, the more they feel this concern (Hazée et al., 2019). Therefore, crowding is a crucial issue relating to infection caused by face-to-face contact. The perception of crowding varies depending on the individual’s tolerance level, pressure, and the shopping task (Eroglu & Harrell, 1986). There are two types of crowding—human and spatial. The latter infringes on freedom of movement and therefore has a more negative impact (Blut & Iyer, 2020). Customers feel stressed when they feel that space is limited. When customers perceive that the space is inadequate, the experience is unpleasant, causing anxiety or stress, while human crowding refers to the number of people interacting in a given setting (Machleit et al., 1994). In general, spatial crowding has a negative impact on customer outcomes, while human crowding can have a positive impact (Blut & Iyer, 2020). This is because the social contact of a crowd can reduce social tension and stimulate shopping. In addition, human crowding can change depending on the situation, but crowding caused by the space itself has a longer-term impact (Rompay et al., 2008). Research shows that they both affect customer satisfaction and behavioral responses (Blut & Iyer, 2020). For example, spatial crowding can have a negative impact on consumers’ outcomes. In a hedonic situation, it can limit enjoyment, and in a utilitarian situation, when the primary purpose is to buy a product, spatial crowding can interfere—it can be a negative feeling (Blut & Iyer, 2020). Preliminary studies have suggested that feeling crowded is generally caused by the perceived availability of space compared with the space one wants (Stokols, 1972). Negative evaluations and emotions arise from the threat of spatial constraints on the ability to perform certain behaviors and activities. These negative consequences occur when an individual’s control does not lead to successful coping strategies (Stokols, 1976). Spatial crowding concerns the physical space in shopping areas. Individuals experience negative feelings due to insufficient space and constraints on the available space (Stokols, 1972). In view of these findings, it was anticipated that the COVID-19 pandemic would heighten the likelihood of contagion in cramped spaces, thereby limiting people’s ability to cope, which could exacerbate the perception of overcrowding. Therefore, because the recent COVID-19 infection caused an aversion to any human contact, crowding became a problem. In the crowded environment of shopping, the sense of loss of control negatively impacts comfort (Hui & Bateson, 1991), which may also be linked to PI.

H1.2: Consumer contamination concern and online purchase intention are negatively related.

Moderating Effect of Trust Between Contamination Concern and Purchase Intention

The social uncertainty about online vendors is high because their behavior is usually not guaranteed or monitored (Reichheld & Schefter, 2000). Therefore, trust becomes a factor that reduces risk among inexperienced online consumers (Gefen et al., 2003). Because vendors have the advantage in online transactions, trust becomes more relevant (Jarvenpaa et al., 2000). Trust is an essential aspect of e-commerce because traders are in a position to engage in opportunistic behavior (Reichheld & Schefter, 2000). Trust between buyers and sellers reduces perceived risk and directly increases PI (Gefen, 2000). As a result, trust becomes a strong predictor of PI. For example, in Facebook’s C2C community, consumer transactions are greatly influenced by trust. Viewing profiles and interacting with photos, videos, and reviews in Facebook Messenger resolves information uncertainty (Valenzuela et al., 2009). In fact, unlike traditional channels, making payments through authentication in the real world can build trust among community users and increase PI (Trehan & Sharma, 2020).

In this study, trust in an online community is mediated between CC and online PI for the following reasons.

First, differences in trust, depending on the perceptions of the ingroup and outgroup, influence online PI. In virtual communities, people communicate with one or more others. Because posts are public, trust is generalized and becomes collective (Ridings et al., 2002). Research results show that consumers have greater CC when they share objects with unfamiliar people (Hazée et al., 2019). They are more concerned about contamination the closer it is to their bodies and the more they share it with unfamiliar users (Hazée et al., 2019). Bias or prejudice against outgroups became more pronounced as the COVID-19 pandemic intensified (Luca et al., 2022). Moreover, during and after the pandemic, consumers increasingly adopted specific preventive measures due to social pressure or norms defined as pandemic subjective norms by Singh et al. (2024). Disgust, as mentioned earlier, is induced not only by contagion risk, which is a basic appraisal theme, but also has a socio-moral theme that arises from the loss of community norms (Tapias et al., 2007). Since contamination has a strong and persistent connection with disgust regarding contact with people, places, or things that are directly or indirectly perceived as harmful (Rachman, 2004), in the context of a pandemic, disgust sensitivity is related to disease avoidance and fear of contamination. At the mechanism level (Olatunji et al., 2007), outgroup bias may intensify disgust sensitivity (Luca et al., 2022). In other words, people with higher disgust sensitivity worry more about contamination, which leads to negative attitudes toward outgroups (Hodson et al., 2013). Ultimately, contamination-based disgust during the COVID-19 global pandemic may have been a form of passive or indirect aggression toward outgroups (Luca et al., 2022). Perceptions of the outgroup and disease avoidance are linked to specific emotions, such as disgust and fear of contamination. In particular, according to self-categorization theory (Turner et al., 1987), people consider familiar people extensions of themselves. The theory posits that disgust separates groups and creates prejudice against outgroups (K. Taylor, 2007). Familiar people are not viewed as a potential threat of contamination (Reicher et al., 2016). When the CC of COVID-19 is intense, boundaries between ingroups and outgroups may arise. Consumers strive for mutual relationships, develop attachments, interact with others, and become positively immersed, focusing on familiarity, which is a prerequisite for building trust (Shin, 2014). Such tendencies lead naturally to differences in consumer behavior (Schaefers et al., 2016).

Second, differences in trust within an online community resulting from differences in social capital, such as social connections and a sense of belonging, may affect online PI. Social capital strengthens trust and promotes transactions within the community. Minimal trust is required for an actual purchase (Trehan & Sharma, 2020). There is a clear relationship between trust and the exchange of information. As trust between members in a virtual space increases, the exchange of information among members increases. This information relies on the honesty inherent in people and their intention to help each other. Within such communities, members share information to solve problems (H. Y. Lee et al., 2014). From the perspective of social capital, users’ emotional connections can further activate community-level platforms and influence purchasing in the C2C community (Trehan & Sharma, 2020). Social capital is the sum of relational resources and is linked to the development of social networks (Adler & Kwon, 2002). A byproduct of social capital is trust (Woolcock, 1998). For example, new members form trust while communicating with existing members (Trehan & Sharma, 2020). Likewise, a virtual community can become a community with trust that extends beyond physical space in social networks or social relationships (Hiltz & Wellman, 1997). Online communities connect people who feel that they are the core of the community and benefit from it (McMillan, 1996). Through social capital, users increase their sense of belonging and PI (Trehan & Sharma, 2020). They feel comfortable sharing personal information over the computer (Sproull et al., 1991); when others post their personal information, it demonstrates mutual trust and encourages others to do so. This can generate trust (Ridings et al., 2002). Many communities are based on shared interests and trust in one’s personal information. For example, in the Facebook C2C community, the social capital of a virtual community is strengthened when users offer guidance and feedback (Trehan & Sharma, 2020). Such shared norms, reinforced by community managers, simulate the environment of a neighborhood. This further strengthens social capital and affects members’ PI. Sharing experiences increases social capital, and sharing emotions not only through simple text but also in photos and videos builds trust. The consequent social capital and emotional connections enhance PI (Trehan & Sharma, 2020). In a brand community, as individuals become closer, they help each other resolve consumer issues through active interaction (Wiertz & de Ruyter, 2007). In this way, customers make better purchasing decisions and resolve uncertainties through C2C communication (Adjei et al., 2010). Likewise, as CC about COVID-19 persists, people seek online purchasing centered on community with accumulated social capital that allows active interaction. Obviously, loneliness resulting from prolonged isolation can critically impact psychological well-being (Stickley & Koyanagi, 2016). Lockdowns diminished social well-being by reducing face-to-face social contact and broader connections (Kaniasty & Norris, 1993). The duration of quarantine also impacts—the longer it is, the more stressful it becomes (Bezerra et al., 2020). Conversely, the perception of coronavirus may increase social support and reduce loneliness, perhaps because the spread of the virus increased social cohesion (Courtet et al., 2020). As mentioned above, it is essential to understand the social impact of pandemics and lockdowns because social belonging and support are critical to the ability to recover and cope with the threats faced (Kaniasty & Norris, 1993). Similarly, Armiya’u et al.’ (2024) identified social support as a key facilitator of mental health during the COVID-19 pandemic. The need to belong is a particularly important human motivator. Consumers use innovative and resilient ways to meet these needs. Therefore, virtual gatherings apply technology for social connection while adhering to physical distancing requirements (Kirk & Rifkin, 2020). A sense of community promotes social connection, serves as a buffer against stress, and helps people cope with mental and physical challenges (Jetten et al., 2012). A shared threat increases people’s sense of community by acknowledging their identity and increasing their affective ties (Greenaway & Cruwys, 2019).

Third, trust in reliable communities may be high amid false information caused by COVID-19. When people lose control of the real world, they try to compensate through fantasy (Douglas et al., 2017). COVID-19 is a large-scale event that is not easily understood, affecting the entire world and simultaneously leaving many uncertainties. Therefore, people may rely on a conspiratorial worldview, distrusting expert advice (Vindegaard & Benros, 2020). This is because contradictory knowledge about complex facts generates a false sense of the causal process and exacerbates fear and suspicion. (Brooks et al., 2020). Public health is based on public trust, and if people receive incorrect information, they may be less willing to comply with measures such as social distancing (Fuller, 2020). The increase in media reports, government briefings, and discussions led to mixed messages (Balog-Way & McComas, 2020). Fully communicating uncertainty in risk information is the way to do the least damage to trust (Balog-Way & McComas, 2020). To cope with risks in uncertain circumstances, establishing regular practices together can secure transparency effectively (Boholm, 2019). Therefore, even in online communities relating to COVID-19, consumers prefer highly trustworthy information that filters out misinformation. As the pandemic intensified, the need for clear, honest, and valid information to counter uncertainty became the most effective means of preventing the spread of COVID-19 (Finset et al., 2020).

H2: Trust in an online community moderates the relationship between consumer contamination concern and online purchase intention; when trust is high, the negative impact is measurably less than when it is low.

Herein, the indirect effects of COVID-19 SP on PI through CC are expected to weaken as trust increases. Thus, a third hypothesis is proposed:

H3: Trust moderates the relationship between contamination concern and purchase intention. That is, the negative relationship between COVID-19 social phobia and purchase intention caused by contamination concern is weaker in users who have higher trust.

Moderated Mediation Model

H3 was assessed employing a moderated mediation model (Muller et al., 2005). First, this study examined whether CC mediates the relationship between COVID-19 SP and online PI. Second, the study examined whether the impact of CC on online PI varies depending on the level of trust in the seller. Overall, a moderated mediation model was constructed by combining H1–H3, as shown in Figure 1.

Figure 1.

Research model diagram.

Methods

Participants

A total of 283 participants in the United States who exhibited interest in online shopping during the COVID-19 period were recruited through the crowdsourcing platform MTurk via CloudResearch (www.cloudresearch.com; M_age = 38.57, SD 12.53; female = 51.9%, n = 147). The study was classified as exempt from full IRB review due to minimal risk. Prior to beginning the survey, participants were clearly informed about the study’s purpose (online shopping behavior after the COVID-19 pandemic), along with its duration, compensation, anonymity, and voluntary nature. Consent to participate was considered granted upon survey submission.

Procedures

Upon agreeing to participate in the study, participants identified a scenario of purchasing products in a used-goods market under COVID-19 conditions (see Appendix 1). The confirmed scenarios were divided into high and low confidence levels (N_high = 139, N_low = 144). To verify the difference in trust level, trust was measured with three questions, including “Even if not monitored, I’d trust the community of sellers to do the job right” (Gefen, 2000; Mayer et al., 1995). The difference in trust was significant between the two groups (Cronbach’s α = .96; M_high = 4.32, M_low = 3.72, t(281) = 3.44, p < .01). Next, participants answered in the following order: COVID-19 SP, CC, and PI. Finally, vaccination status, gender, and age were answered.

Measures

The questionnaire was developed using items that were validated and applied in previous research (see Appendix 2). Each item was scored by the participants based on a seven-point Likert scale (1, strongly disagree; 7, strongly agree), except the scores for CC, which ranged from 1 (not worried at all) to 7 (very worried).

COVID-19 Social Phobia

COVID-19 social phobia is defined as a persistent fear of social situations due to the perceived risk of infection during the pandemic. COVID-19 SP was measured using three items developed by Arpaci et al. (2020). An example is, “After the coronavirus pandemic, I feel extremely anxious when I see people coughing.” Cronbach’s α was .84.

Contamination Concern

Contamination concern refers to consumers’ discomfort or aversion arising from the perception that shared goods may have been physically tainted by previous users. CC was measured using four items developed by Hazée et al. (2019). An example is, “To what extent would you be concerned about someone else touching the product you were going to use?” Cronbach’s α was .97.

Purchase Intention

Purchase intention refers to a consumer’s deliberate willingness and readiness to engage in transactions. PI was measured using three items developed by Trehan and Sharma (2020). An example is, “Given the chance, I would consider transacting with these communities in the future.” Cronbach’s α was .96.

For the factor analysis, a varimax rotation was conducted to ensure the validity of the measures. Excluding two of the five questions measuring COVID-19 SP, three questions were used in the analysis (Cronbach’s α = .84). As a result, the three measures contributed to 87.70% of the variance (factor loading, 0.58–0.98), and all of the items exhibited acceptable validity.

Analytical Strategies

Descriptive statistics and normality checks were conducted, and common method bias was assessed using Harman’s single-factor test. A moderated mediation model was tested using PROCESS Macro (Model 14; Hayes, 2017) with 5,000 bootstrapped samples. The model examined CC as a mediator between SP and PI, and trust as a moderator on the CC–PI path. Age, gender, and vaccine status were included as controls.

Results

Descriptions and Correlations

The research variables were first tested for normality. All measures of the skewness (−0.335 to 0.854) and kurtosis (−0.996 to 0.225) were below the cutoff for acceptability. Then, the Mahalanobis distance was used to check for multivariate outliers. No participants were removed because of the multivariate outlier results, resulting in a final sample of 283 participants. To determine whether there were any demographic control variables that should be included in the model for testing, the demographic characteristics (i.e., age, gender, vaccine status) of the bivariate correlation variables were examined. Correlations were assessed using the demographic and research variables. Finally, age, gender, and vaccine status were included as control variables in the subsequent analyses.

In correlating, COVID-19 SP and CC showed the expected correlation, but the correlation between CC and PI was not significant. This may be because the relationship was viewed by combining the two results of the scenario according to the level of trust, and a detailed interpretation of the correlation results was confirmed based on regression analysis. Table 1 presents the means, standard deviations, and bivariate correlations determined for the study variables.

Table 1.

Means, Standard Deviation, Correlation, Alpha Reliabilities.

Variables	M	SD	1	2	3	4	5
1. COVID19 social phobia	4.14	1.63	(0.84)^c
2. Contamination concern	3.23	1.73	0.65^**	(0.97)^c
3. Purchase intention	4.30	1.49	−0.01	−0.08	(0.96)^c
4. Age	38.57	12.53	−0.04	−0.05	−0.00
5. Vaccination status^a			−0.13^*	−0.12^*	−0.01	−0.00
6. Gender^b			0.10	−0.03	−0.05	0.12^*	0.03

Vaccination status: No = 0, Yes = 1.

Gender: 1 = “male”, 2 = “female”.

Values on the diagonal are Cronbach’s alphas.

p < .05; ^**p < .01 (two-tailed).

Common Method Bias

Considering that the analysis relied on self-reported data, Harman’s single-factor test was performed to determine whether the results were influenced by common method bias. All measures were examined by employing an exploratory factor analysis, assuming that common method variance, or a majority of the covariance, is attributed to either a single factor or a general factor (Podsakoff et al., 2003). Without rotation, the analysis indicated that a four-factor model may be more appropriate than a single-factor model. The majority of the variance could not be ascribed to a single factor; the most influential factor contributed to only 47.97% of the variance.

Moderated Mediation Model Test

The procedural validation procedure was analyzed as follows: (1) mediation role analysis for H1.1 and H1.2, (2) verification of the interaction effect of CC for H2, and (3) moderated mediation analysis for H3. PROCESS macro model 14 (Hayes, 2017) was used to simultaneously evaluate the direct, indirect, and conditional direct effects of the hypotheses. Regarding the indirect effect of X (COVID-19 SP) on Y (PI) through M (CC), the unconditional interaction effect of W (trust) was considered, and the conditional indirect effect of W on the M-Y relationship was assessed while controlling for X. Specifically, 5,000 bootstrapped resamples were used, generating a 95% bias correction and adjusted confidence intervals (CIs). CIs that excluded zero were considered to have a significant indirect effect. The two indirect effects were simultaneously evaluated using the same model applied to the conditional indirect effect. Regarding the unconditional interaction effects, the moderator variables were coded as discrete variables (Trust_low = 0; Trust_high = 1).

Table 2 presents the results and unstandardized regression coefficients. The first hypothesis stated that COVID-19 SP is positively correlated with CC (H1.1). From the analysis, COVID-19 SP and CC were significantly and positively correlated, showing that CC increased as COVID-19 SP increased (B = 0.69, t = 14.20, p < .01, CI [0.59, 0.79]). Thus, H1.1 was supported. Next, CC was not significantly associated negatively with PI (B = 0.03, t = 0.30, p = .76, 95% CI [−0.14, 0.19]). Thus, H1.2 was rejected. Then, the second hypothesis proposed the conditional direct effect of trust on the CC-PI relationship (H2). This was supported by the results, revealing that trust significantly moderated the relationship (B = −0.23, t = −2.27, p = .02, CI [−0.43, −0.03]).

Table 2.

Bootstrapping With Moderated Mediation Model.

Variables	M	SD	1	2	3	4	5
1. COVID19 social phobia	4.14	1.63	(0.84)
2. Contamination concern	3.23	1.73	0.65^**	(0.97)
3. Purchase intention	4.30	1.49	−0.01	−0.08	(0.96)
4. Age	38.57	12.53	−0.04	−0.05	−0.00
5. Vaccination status			−0.13^*	−0.12^*	−0.01	−0.00
Mediator variable model with contamination concern as dependent variable R² = 43%, F (4, 278) = 52.62, p < .01
Variables		B	SE	t	p	LLCI	ULCI
Constant		.65	0.34	1.93	.06	−0.01	1.31
COVID19 SP		.69	0.05	14.20	.00	0.59	0.79
Vaccination status		−.15	0.19	−0.79	.43	−0.52	0.22
Age		.00	0.01	−0.34	.73	−0.01	0.01
Gender		−.31	0.16	−1.94	.05	−0.62	0.01
Dependent variable model with purchase intention as dependent variable R² = 6%, F (7, 275) = 2.65, p = .01
Constant		3.89	0.41	9.53	.00	3.09	4.70
COVID19 SP		.05	0.07	0.70	.48	−0.09	0.19
Contamination concern		.03	0.08	0.30	.76	−0.14	0.19
Trust		1.30	0.37	3.48	.00	0.56	2.03
Trust × contamination concern		−.23	0.10	−2.27	.02	−0.43	−0.03
Vaccination status		−.03	0.21	−0.13	.90	−0.44	0.39
Age		.00	0.01	−0.24	.81	−0.02	0.01
Gender		−.20	0.18	−1.11	.27	−0.55	0.15
Conditional effect of trust as the moderator
Low trust		.03	0.08	0.30	.76	−0.14	0.19
High trust		−.21	0.08	−2.47	.01	−0.37	−0.04

Figure 2 illustrates this interaction, showing differences in PI at high and low levels of trust. Overall, used-goods market users reported the highest levels of PI when they also reported high levels of trust. Even when trust was high, PI increased when CC was low, and the direction of PI decreased as CC increased (B = −.21, t = −2.47, p = .01, CI [ −0.37, −0.04]). However, when trust was low, the relationship between CC and PI was not significant (B = .03, t = 0.30, p = .76, CI [−0.14, 0.19]).

Figure 2.

The conditional effect of trust as a moderator.

Herein, the mediation of PI was predicted to be affected by COVID-19 SP through CC (H3). First, the direct effect of COVID-19 SP on PI was not significant (B = .05, t = 0.70, p = .48, CI [−0.09, 0.19]). Next, the conditional indirect effect according to the level of moderator trust was confirmed. The indirect effect of COVID-19 SP on PI through CC was significant under high trust conditions (B = −.14, CI [−0.27, −0.03]) and not significant under low trust conditions (B = .02, CI [−0.13, 0.16]). Finally, the moderated mediation test result was insignificant (index = −0.16, BootSE = 0.08, CI [−0.33, 0.00]). Based on these results, H3 is rejected (see Table 3).

Table 3.

Conditional Indirect Effect of Trust on Research Model.

Variables	M	SD	1	2	3	4	5
1. COVID19 social phobia	4.14	1.63	(0.84)
2. Contamination concern	3.23	1.73	0.65^**	(0.97)
3. Purchase intention	4.30	1.49	−0.01	−0.08	(0.96)
4. Age	38.57	12.53	−0.04	−0.05	−0.00
5. Vaccination status			−0.13^*	−0.12^*	−0.01	−0.00
6. Gender			0.10	−0.03	−0.05	0.12^*	0.03
Conditional indirect effect as a function of Trust : COVID19 SP → contamination concern → purchase intention
Value of trust	Effect	BootSE		BootLLCI		BootULCI
Low trust	0.02	0.07		−0.13		0.16
High trust	−0.14	0.06		−0.27		−0.03

Discussion

The purpose of this study was to examine how COVID-19 SP and CC influence purchase intention PI in the secondhand market, and whether trust moderates this relationship. The findings showed both consistent and inconsistent results with prior research, as discussed in detail below.

First, it was found that the SP of consumers in the C2C community increased CC during the COVID-19 pandemic. This indicates that fears arising from interpersonal contact may have extended to concerns about the products exchanged in such markets. SP, as previously defined, refers to anxiety that emerges when interpersonal distance is reduced (Mattick & Clarke, 1998). The present findings suggest that COVID-19 intensified this form of anxiety by introducing an added risk of infectious disease, thereby broadening the scope of SP to include CC. This observation is consistent with Yıldırım and Özaslan (2021), who showed that heightened perceptions of COVID-19 severity contributed to increased anxiety levels. Moreover, in the secondhand marketplace, where prior ownership is especially salient, these concerns appear even more pronounced.

Second, the hypothesis that CC reduces online PI was not supported. This outcome differs from prior findings in offline contexts, where CC has been shown to lower purchase likelihood (Argo et al., 2006). A possible explanation is that, unlike offline environments where crowding and physical contact cannot be avoided, online platforms can manage such issues technologically, thereby minimizing direct interpersonal exposure (Blut & Iyer, 2020). As a result, concerns about contact may not directly translate into lower PI in online markets. In addition, the intensity of CC may vary depending on the type of product; for example, a bicycle may elicit weaker concerns than a T-shirt that comes into direct contact with the body. Importantly, the findings also indicate that trust within the community moderates this relationship. Consumers with higher trust appeared more sensitive to CC, which in turn influenced their willingness to purchase, whereas those with lower trust were already skeptical or indifferent, resulting in a weaker effect. This demonstrates that community trust remains an important factor in the link between CC and PI.

Third, the effect of CC on PI varied depending on the level of trust within the community. When trust was high, PI remained higher despite CC. In contrast, when trust was low, the conditional effect was not significant, suggesting that reducing CC alone was insufficient to increase PI. Notably, the conditional effect reached statistical significance only under high trust conditions, emphasizing that community trust served as a critical moderating factor in buffering the negative influence of CC during the COVID-19 crisis. This moderating role of trust parallels the findings of Green and Yıldırım (2022), who demonstrated that personal growth initiative mitigated the negative impact of fear of COVID-19 on life satisfaction, demonstrating the importance of protective resources in shaping psychological and behavioral outcomes.

Fourth, the indirect effect of COVID-19 SP on PI through CC was not significant overall. However, the pattern differed when the analysis considered trust levels. When trust was high, the mediating role of CC became significant, indicating that consumers reflected more strongly on their CC in their purchase decisions under conditions of trust. In contrast, when trust was low, the mediating effect was not significant, suggesting that PI was less responsive to variations in CC. These results emphasize that the mediating pathway from SP to PI operates effectively only when community trust is present, which indicates that trust is a key boundary condition in explaining consumer behavior during the COVID-19 crisis.

Implication, Limitations and Future Research

Implication

The findings suggest several actionable insights for businesses responding to pandemic-driven shifts in consumer behavior, as follows.

First, because the SP caused by COVID-19 affects purchasing behavior, corporate managers must deal with the fact that perceptions of infection and contamination lower sales performance. Specifically, managers must understand individuals’ fears of COVID-19 and use psychometric tools to appropriately respond (Ahorsu et al., 2022). Taking human psychological functions into account and linking them with strategies to relieve contamination fears gradually, as advised by healthcare professionals (S. A. Lee, 2020), may help counteract COVID-19 anxiety. In particular, designing consumer experiences that emphasize a sense of safety can help enhance trust as a way to alleviate fear (Veselovská, 2023). A communication strategy under COVID-19 conditions must differentiate consumer groups that are highly susceptible to COVID-19 phobia. For example, highly sociable extroverts may find it difficult to comply with social distancing, while more disciplined people readily accept prevention measures (Carvalho et al., 2020). Research results suggest that such differences in inclination correlate with CC. In this regard, Arslan et al. (2021) showed that personal meaning can mediate the psychological burden of pandemic suffering, suggesting that fostering meaning and purpose may also support consumer resilience and healthier purchasing behavior.

Second, managers handling products in used-goods markets should consider the impact of COVID-19 SP on CC. In the sharing economy, CC issues have been identified as an obstacle to business expansion (Bardhi & Eckhardt, 2012). As COVID-19 spread, CC became a problem needing prompt resolution. Addressing it in the sharing economy required innovation to overcome consumers’ cognitive obstacles (Martin et al., 2016). Individual differences in attitudes about contamination vary. For example, people’s feelings about the residue remaining on a secondhand product differ. Residue sensitivity refers to perceived contamination from the previous owner (Kapitan & Bhargave, 2013) and has implications for online communities in the sharing economy. Residue sensitivity goes beyond simple touch or aversion; it is directly related to market adaptation of consumers who enter the secondhand or refurbished market. They may feel that the product has been contaminated by previous users (Kapitan & Bhargave, 2013). This perceived contamination also affects sellers’ reputations as an inference of the product’s quality (Ou et al., 2006). In market research, the issue of residue sensitivity is considered in social commerce situations in which human exchanges are central (Kapitan & Bhargave, 2013). To communicate more effectively with consumers, managers in the secondhand market should provide traceability for their products to counteract residue sensitivity.

Third, a community operation plan must be created to increase the level of trust in the community. In consumer-centered C2C secondhand markets, the level of trust may be altered by ingroup–outgroup perceptions. To increase the level of trust in consumer-centered communities, policies that emphasize the similarities of consumers participating in the sharing economy should be considered (Hazée et al., 2019). In particular, to foster consumer trust and encourage recommendation exchange among consumers, a secondhand market platform should be designed based on pandemic subjective norms formed during the pandemic (Singh et al., 2024). Connecting through the internet is a proactive coping strategy that reduces loneliness and social isolation (Moore & March, 2022). Likewise, focusing on online communities can be seen as a desire to make up for the lack of human relationships and social connections. In the context of COVID-19, consumers tend to seek alternatives for safer shopping. Therefore, trust must be secured by establishing online commerce with a high sense of connection to reinforce human relationships within the community. Furthermore, long-term consumer trust in secondhand product platforms can be strengthened by conducting eco-friendly information campaigns in the community targeting consumers who have continued to show interest in environmentally sustainable consumption after the pandemic (Wong et al., 2023). Ultimately, this may increase consumers’ PI even under COVID-19′s influence when ineffective and inadequately filtered information caused confusion (Balog-Way & McComas, 2020). Because pandemic fog occurred due to the rapid evolution of knowledge and mixed messages (Fuller, 2020), consumers’ questions related to trust and their purchase-related behavior took on greater importance.

Limitations

This research model has several limitations. First, it did not fully consider variables for individual differences, such as tendencies to worry and general health anxiety. Overexposure to COVID-19-related media or higher levels of perceived threat (loved ones at risk, low-risk control) may increase fears (Mertens et al., 2020). Conversely, people’s sense of efficacy, which can be enhanced through education, becomes an active coping resource (Olesen et al., 2020) to reduce phobias and, in turn, lower CC. Alongside efficacy, resilience (Yıldırım & Çiçek, 2022) and personal meaning (Arslan et al., 2021) may also serve as protective psychological resources. In this case, the situation may become less distressing (Sica et al., 2021).

Second, due to the nature of the research, consumer metrics may be affected by the proximity, frequency, or severity of infection experienced by test participants (Stevenson et al., 2009) or by the extent of their exposure to sensationalist news or dire warnings about the risks. During the pandemic’s peak, environmental factors may have strongly influenced response levels (Asmundson & Taylor, 2020). For example, measurements taken during the most severe expansion of COVID-19 could have been altered by a temporary increase in contamination sensitivity.

Third, response bias may have occurred because this study used a self-reporting scale. In other words, if it is difficult to fully imagine a situation based on social desirability or the reported scenario, this can affect the validity of the measured data (Tull et al., 2020).

Fourth, this study employed a convenience sampling method available from MTurk via CloudResearch to recruit respondents interested in the survey and research scenarios.

Future Research

In future research, even if nonprobability sampling is used due to practical considerations, referring to existing studies and considering a purposive sampling method that more strictly meets specific criteria (e.g., frequency of use of secondhand transaction platforms) is strongly recommended to increase the validity and generalizability of the study (Suleman et al., 2019).

In addition, sensitivity to or intolerance of uncertainty might be considered variables in individual differences that directly affect CC and lead to compulsive behavior (Samuels et al., 2021). Future research should also diversify the moderating factors of trust. In this study’s scenario, to control trust levels, the seller’s trustworthiness was manipulated by physical proximity to the consumer. In the same context, to examine more precisely how social relationships influence community trust, future research should investigate whether policies such as social distancing and lockdown measures exacerbated social inequality by discriminating against specific social groups (Sibley et al., 2020). Distinguishing between ingroups and outgroups can alter consumers’ sense of belonging, which may, in turn, impact community trust and influence purchasing decisions.

Other aspects of trust that consumers consider when assessing trustworthiness should also be explored. For example, in platform design, B2C-based platforms have been shown to reduce CCs more effectively through hygiene management than do C2C-based platforms (N. L. Kim et al., 2023). This suggests that consumers are more likely to feel greater trust in secondhand trading platforms when hygiene and safety standards are clearly defined. Similarly, governments can increase consumer trust by incentivizing platforms that promote environmental responsibility and implementing measures such as the certification of cleanliness and increased transparency of information (Nuttah et al., 2023). This shows the significant role public policy can play in strengthening trust in the secondhand market.

As sharing economy platforms refine their operational strategies, research has confirmed that the reliability and authenticity of reviews have a significant impact on user trust and decision-making (Meenakshi, 2023). Providing transparent transaction information in the secondhand market may help to assess and differentiate various levels of consumer trust. Such future research will facilitate a more precise evaluation of the impact of diverse trust-moderating factors on consumer purchase decisions in the sharing economy and on secondhand product trading platforms.

Conclusion

This study confirms that COVID-19-induced SP increases consumers’ CC, with CC negatively affecting PI only when trust levels are high, while no significant effect is observed when trust levels are low. Furthermore, trust moderates the relationship between CC and PI, as consumers with high trust are more influenced by CC, leading to lower PI, whereas those with low trust are largely unaffected. These findings contribute to the theoretical understanding of consumer trust in the sharing economy and offer practical insights for used-goods market platforms to enhance trust and mitigate CC. By implementing effective trust-building strategies, businesses can foster a more sustainable sharing economy, even during crises such as the pandemic.

Footnotes

Appendix 1

Appendix 2

Trust (1 = Strongly disagree, 7 = Strongly agree)

Even if not monitored, I’d trust the community of sellers to do the job right.

I trust the community of sellers.

I believe that the community of sellers is trustworthy.

Covid-19 social phobia (1 = Strongly disagree, 7 = Strongly agree)

After the coronavirus pandemic, I feel extremely anxious when I see people coughing.

After the coronavirus pandemic, I actively avoid people I see sneezing.

Following the coronavirus pandemic, I have noticed that I spend extensive periods of time cleaning my hands.

Contamination concern (1 = Not worried at all, 7 = Very worried)

To what extent would you be concerned about someone else touching the product you were going to use?

To what extent would you worry about using this product if someone else had touched it?

I would be concerned about getting a disease when using this product.

Touching this product would be a concern to me.

Purchase intention (1 = Strongly disagree, 7 = Strongly agree)

I am likely to purchase the cell phone from this seller.

I am likely to purchase products from this seller in the future.

I am likely to recommend this seller to my friends.

ORCID iD

Myungsuh Lim

Ethical Considerations

All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 2013 revision of the Declaration of Helsinki. The study was deemed exempt from full IRB review due to minimal risk.

Consent to Participate

Informed consent was obtained from all participants prior to data collection; consent was implied through the submission of the survey.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

References

Aderka

I. M.

Hofmann

S. G.

Nickerson

Hermesh

Gilboa-Schechtman

Marom

(2012). Functional impairment in social anxiety disorder. Journal of Anxiety Disorders, 26(3), 393–400.

Adjei

M. T.

Noble

S. M.

Noble

C. H.

(2010). The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the Academy of Marketing Science, 38, 634–653.

Adler

P. S.

Kwon

S.-W.

(2002). Social capital: Prospects for a new concept. Academy of Management Review, 27(1), 17–40.

Ahorsu

D. K.

Lin

C. Y.

Imani

Saffari

Griffiths

M. D.

Pakpour

A. H.

(2022). The fear of COVID-19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 20, 1537–1545.

American Psychological Association (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM-5. APA.

Apaolaza

Paredes

M. R.

Hartmann

García-Merino

J. D.

Marcos

(2022). The effect of threat and fear of COVID-19 on booking intentions of full board hotels: The roles of perceived coping efficacy and present-hedonism orientation. International Journal of Hospitality Management, 105, 103255.

Arad

Shamai-Leshem

Bar-Haim

(2021). Social distancing during a COVID-19 lockdown contributes to the maintenance of social anxiety: A natural experiment. Cognitive Therapy and Research, 45(4), 708–714.

Argo

J. J.

Dahl

D. W.

Morales

A. C.

(2006). Consumer contamination: How consumers react to products touched by others. Journal of Marketing, 70(2), 81–94.

Armiya’u

A. Y.

Yıldırım

Muhammad

Tanhan

Young

J. S.

(2024). Mental health facilitators and barriers during covid-19 in Nigeria. Journal of Asian and African Studies, 59(2), 354–376.

10.

Arpaci

Karataş

Baloğlu

(2020). The development and initial tests for the psychometric properties of the COVID-19 Phobia Scale (C19P-S). Personality and Individual Differences, 164, 110108.

11.

Arpaci

Karatas

Baloglu

Haktanir

(2022). COVID-19 phobia in the United States: Validation of the COVID-19 Phobia Scale (C19P-SE). Death Studies, 46(3), 553–559.

12.

Arslan

Yıldırım

Leung

M. M.

(2021). Mediating effect of personal meaning in the prediction of life satisfaction and mental health problems based on coronavirus suffering. Frontiers in Psychology, 12, 638379.

13.

Asmundson

G. J. G.

Taylor

(2020). Coronaphobia revisted: A state-of-the-art on pandemic-related fear, anxiety, and stress. Journal of Anxiety Disorders, 76, 102326.

14.

Bai

(2024). Adolescent cosmetic consumption: Exploring societal influences, safety concerns, and post-pandemic market shifts. In: Proceedings of the 3rd international conference on financial technology and business analysis.

15.

Balog-Way

D. H. P.

McComas

K. A.

(2020). COVID-19: Reflections on trust, tradeoffs, and preparedness. Journal of Risk Research, 23(7-8), 838–848.

16.

Bardhi

Eckhardt

G. M.

(2012). Access-based consumption: The case of car sharing: Table 1. Journal of Consumer Research, 39(4), 881–898.

17.

Bezerra

A. C. V.

Silva

C. E. M. D

Soares

F. R. G.

Silva

J. A. M. D

. (2020). Factors associated with people's behavior in social isolation during the COVID-19 pandemic. Ciencia & Saude Coletiva, 25, 2411–2421.

18.

Blut

Iyer

G. R.

(2020). Consequences of perceived crowding: A meta-analytical perspective. Journal of Retailing, 96(3), 362–382.

19.

Boholm

. (2019). Risk communication as government agency organizational practice. Risk Analysis, 39(8), 1695–1707.

20.

Bonanno

G. A.

Brewin

C. R.

Kaniasty

Greca

A. M.

(2010). Weighing the costs of disaster: Consequences, risks, and resilience in individuals, families, and communities. Psychological Science in the Public Interest, 11(1), 1–49.

21.

Brooks

S. K.

Webster

R. K.

Smith

L. E.

Woodland

Wessely

Greenberg

Rubin

G. J.

(2020). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet, 395(10227), 912–920.

22.

Carvalho

L. F.

Pianowski

Gonçalves

A. P.

(2020). Personality differences and COVID-19: Are extroversion and conscientiousness personality traits associated with engagement with containment measures? Trends in Psychiatry and Psychotherapy, 42, 179–184.

23.

Casale

Flett

G. L.

(2020). Interpersonally-based fears during the COVID-19 pandemic: Reflections on the fear of missing out and the fear of not mattering constructs. Clinical Neuropsychiatry, 17(2), 88–93.

24.

Chen

Chi

(2021). How does channel integration affect consumers’ selection of omni-channel shopping methods? An empirical study of US consumers. Sustainability, 13(16), 8983.

25.

Coelho

C. M.

Suttiwan

Arato

Zsido

A. N.

(2020). On the nature of fear and anxiety triggered by COVID-19. Frontiers in Psychology, 11, 581314.

26.

Courtet

Olié

Debien

Vaiva

(2020). Keep socially (but not physically) connected and carry on: preventing suicide in the age of COVID-19. Journal of Clinical Psychiatry, 81(3), 15527.

27.

Deacon

Olatunji

B. O.

(2007). Specificity of disgust sensitivity in the prediction of behavioral avoidance in contamination fear. Behaviour Research and Therapy, 45(9), 2110–2120.

28.

Douglas

K. M.

Sutton

R. M.

Cichocka

(2017). The psychology of conspiracy theories. Current Directions in Psychological Science, 26(6), 538–542.

29.

Eroglu

Harrell

G. D.

(1986). Retail crowding: Theoretical and strategic implications. Journal of Retailing, 62, 346–363.

30.

Finset

Bosworth

Butow

Gulbrandsen

Hulsman

R. L.

Pieterse

A. H.

Street

Tschoetschel

van Weert

(2020). Effective health communication - A key factor in fighting the COVID-19 pandemic. Patient Education and Counseling, 103(5), 873–876.

31.

Frank

Schvaneveldt

S. J.

(2016). Understanding consumer reactions to product contamination risks after national disasters: The roles of knowledge, experience, and information sources. Journal of Retailing and Consumer Services, 28, 199–208.

32.

Fuller

(2020). How Much Should the Public Know about Who Has Coronavirus? The New York Times. March 30, 2020. Retrieved October 05, 2023 from https://www.nytimes.com/2020/03/28/us/coronavirus-data-privacy.html?referringSource=articleShare

33.

Gamba

R. J.

Oskamp

(1994). Factors influencing community residents' participation in commingled curbside recycling programs. Environment and Behavior, 26(5), 587–612.

34.

Garfin

D. R.

Silver

R. C.

Holman

E. A.

(2020). The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychology, 39(5), 355–357.

35.

Gefen

(2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725–737.

36.

Gefen

Karahanna

Straub

D. W.

(2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

37.

Gérard

Helme-Guizon

(2018). Within a hair's breadth of buying the product: The impact of tangible and intangible bodily cues of contamination: The role of disgust and mental imagery. Applied Cognitive Psychology, 32(5), 537–549.

38.

Goyal

Verma

(2021). “Do it yourself effect on restaurants – The pandemic effect: Driven by the fear appeal theory. Journal of Hospitality and Tourism Management, 48, 76–87.

39.

Grande

Bjørnstad

Wilson

Hanley

(1999). Assessment of consumer risk attitudes and behaviour related to countermeasures and radioactive contamination of food. In: Contract deliverable of the CESER project (FI4P-CT95-0021). EU Nuclear Fission Safety Programme. University of Stirling, Stirling, Scotland.

40.

Greenaway

K. H.

Cruwys

(2019). The source model of group threat: Responding to internal and external threats. American Psychologist, 74(2), 218–231.

41.

Green

Z. A.

Yıldırım

(2022). Personal growth initiative moderates the mediating effect of COVID-19 preventive behaviors between fear of COVID-19 and satisfaction with life. Heliyon, 8(6), e09729.

42.

Gullstrand Edbring

Lehner

Mont

. (2016). Exploring consumer attitudes to alternative models of consumption: Motivations and barriers. Journal of Cleaner Production, 123, 5–15.

43.

Gupta

A. S.

Mukherjee

(2022). Long-term changes in consumers' shopping behavior post-pandemic: An exploratory study. International Journal of Retail & Distribution Management, 50(12), 1518–1534.

44.

Harper

C. A.

Satchell

L. P.

Fido

Latzman

R. D.

(2021). Functional fear predicts public health compliance in the COVID-19 pandemic. International Journal of Mental Health and Addiction, 19, 1875–1888.

45.

Hayes

A. F.

(2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.

46.

Hazée

Van Vaerenbergh

Delcourt

Warlop

. (2019). Sharing goods? Yuck, no! An investigation of consumers’ contamination concerns about access-based services. Journal of Service Research, 22(3), 256–271.

47.

Healy

P. M.

Palepu

K. G.

(2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. Journal of Accounting and Economics, 31(1-3), 405–440.

48.

Hiltz

S. R.

Wellman

(1997). Asynchronous learning networks as a virtual classroom. Communications of the ACM, 40(9), 44–49.

49.

Hodson

Choma

B. L.

Boisvert

Hafer

C. L.

MacInnis

C. C.

Costello

(2013). The role of intergroup disgust in predicting negative outgroup evaluations. Journal of Experimental Social Psychology, 49(2), 195–205.

50.

Huang

J. Y.

Ackerman

J. M.

Newman

G. E.

(2017). Catching (up with) magical contagion: A review of contagion effects in consumer contexts. Journal of the Association for Consumer Research, 2(4), 430–443.

51.

Hui

M. K.

Bateson

J. E. G.

(1991). Perceived control and the effects of crowding and consumer choice on the service experience. Journal of Consumer Research, 18(2), 174–184.

52.

Jarvenpaa

S. L.

Tractinsky

Vitale

(2000). Consumer trust in an Internet store. Information Technology and Management, 1, 45–71.

53.

Jetten

Haslam

Alexander

S. H.

(2012). The social cure: Identity, health and well-being. Psychology Press, Taylor & Francis Group, Oxford.

54.

Kaniasty

Norris

F. H.

(1993). A test of the social support deterioration model in the context of natural disaster. Journal of Personality and Social Psychology, 64(3), 395–408.

55.

Kapitan

Bhargave

(2013). Navigating residue sensitivity in the used goods marketplace. Psychology and Marketing, 30(4), 305–317.

56.

Kim

L. R.

Kim

N. S.

(2011). A proximity effect in adults' contamination intuitions. Judgment and Decision Making, 6(3), 222–229.

57.

Kim

N. L.

Jin

B. E.

Kim

T. H.

(2023). Negative and positive contamination in secondhand fashion consumption: Does culture matter? International Marketing Review, 40(6), 1509–1530.

58.

Kirk

C. P.

Rifkin

L. S.

(2020). I'll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Business Research, 117, 124–131.

59.

Lee

H. Y.

Ahn

Kim

H. K.

Lee

(2014). Comparative analysis of trust in online communities. Procedia Computer Science, 31, 1140–1149.

60.

Lee

S. A.

(2020). Coronavirus Anxiety Scale: A brief mental health screener for COVID-19 related anxiety. Death Studies, 44(7), 393–401.

61.

Lin

C. Y.

(2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior, 3(1), 1–2.

62.

Wang

Xue

Zhao

Zhu

(2020). The impact of COVID-19 epidemic declaration on psychological consequences: A study on active Weibo users. International Journal of Environmental Research and Public Health, 17(6), 2032.

63.

Luca

A. I.

Podina

I. R.

Boian

(2022). Contamination fear in the time of the COVID-19 pandemic: A moderated mediation quasi-experimental model of the effect of disgust on outgroup bias towards diaspora. Current Psychology, 41, 7402–7415.

64.

Machleit

K. A.

Kellaris

J. J.

Eroglu

S. A.

(1994). Human versus spatial dimensions of crowding perceptions in retail environments: A note on their measurement and effect on shopper satisfaction. Marketing Letters, 5, 183–194.

65.

Martin

Gustafsson

Choi

(2016). Service innovation, renewal, and adoption/rejection in dynamic global contexts. Journal of Business Research, 69(7), 2397–2400.

66.

Mattick

R. P.

Clarke

J. C.

(1998). Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy, 36(4), 455–470.

67.

Mayer

R. C.

Davis

J. H.

Schoorman

F. D.

(1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.

68.

McKay

Yang

Elhai

Asmundson

G. J. G.

(2020). Anxiety regarding contracting COVID-19 related to interoceptive anxiety sensations: The moderating role of disgust propensity and sensitivity. Journal of Anxiety Disorders, 73, 102233.

69.

McMillan

D. W.

(1996). Sense of community. Journal of Community Psychology, 24(4), 315–325.

70.

Meenakshi

(2023). Post-COVID reorientation of the sharing economy in a hyperconnected world. Journal of Strategic Marketing, 31(2), 446–470.

71.

Meng

M. D.

Leary

R. B.

(2021). It might be ethical, but I won't buy it: Perceived contamination of, and disgust towards, clothing made from recycled plastic bottles. Psychology and Marketing, 38(2), 298–312.

72.

Mertens

Gerritsen

Duijndam

Salemink

Engelhard

I. M.

(2020). Fear of the coronavirus (COVID-19): Predictors in an online study conducted in March 2020. Journal of Anxiety Disorders, 74, 102258.

73.

Moore

March

. (2022). Socially connected during COVID-19: Online social connections mediate the relationship between loneliness and positive coping strategies. Research Square, 1, 1–20.

74.

Morales

A. C.

Fitzsimons

G. J.

(2007). Product contagion: Changing consumer evaluations through physical contact with “disgusting” products. Journal of Marketing Research, 44(2), 272–283.

75.

Muller

Judd

C. M.

Yzerbyt

V. Y.

(2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89(6), 852–863.

76.

Murray

D. R.

Schaller

(2016). The behavioral immune system: Implications for social cognition, social interaction, and social influence. Advances in Experimental Social Psychology, 53, 75–129.

77.

Nuttah

M. M.

Roma

Lo Nigro

Perrone

(2023). The short- and long-term impacts of COVID-19 pandemic on the sharing economy: Distinguishing between “symptomatic” and “asymptomatic” platforms. Journal of the Knowledge Economy, 15(2), 9238–9287.

78.

Olatunji

B. O.

Williams

N. L.

Tolin

D. F.

Abramowitz

J. S.

Sawchuk

C. N.

Lohr

J. M.

Elwood

L. S.

(2007). The Disgust Scale: Item analysis, factor structure, and suggestions for refinement. Psychological Assessment, 19(3), 281–297.

79.

Olesen

Gyrup

H. B.

Troelstrup

M. W.

Marloth

Mølmer

(2020). Infection prevention partners up with psychology in a Danish hospital successfully addressing staffs fear during the COVID-19 pandemic. Journal of Hospital Infection, 105(2), 377–378.

80.

W. M.

Abratt

Dion

(2006). The influence of retailer reputation on store patronage. Journal of Retailing and Consumer Services, 13(3), 221–230.

81.

Peck

Johnson

J. W.

(2011). Autotelic need for touch, haptics, and persuasion: The role of involvement. Psychology and Marketing, 28(3), 222–239.

82.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J.-Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

83.

Rachman

(2004). Fear of contamination. Behaviour Research and Therapy, 42(11), 1227–1255.

84.

Reicher

S. D.

Templeton

Neville

Ferrari

Drury

(2016). Core disgust is attenuated by ingroup relations. Proceedings of the National Academy of Sciences, 113(10), 2631–2635.

85.

Reichheld

F. F.

Schefter

(2000). E-loyalty: your secret weapon on the web. Harvard Business Review, 78(4), 105–113.

86.

Reynolds

D. L.

Garay

J. R.

Deamond

S. L.

Moran

M. K.

Gold

Styra

(2008). Understanding, compliance and psychological impact of the SARS quarantine experience. Epidemiology and Infection, 136(7), 997–1007.

87.

Ridings

C. M.

Gefen

Arinze

(2002). Some antecedents and effects of trust in virtual communities. Journal of Strategic Information Systems, 11(3-4), 271–295.

88.

Rompay

T. J. L. V.

Galetzka

Pruyn

A. T. H.

Garcia

J. M.

(2008). Human and spatial dimensions of retail density: Revisiting the role of perceived control. Psychology and Marketing, 25(4), 319–335.

89.

Rothan

H. A.

Byrareddy

S. N.

(2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of Autoimmunity, 109, 102433.

90.

Rozin

Nemeroff

(1990). The laws of sympathetic magic: A psychological analysis of similarity and contagion. Cambridge University Press.

91.

Rozin

Nemeroff

(2002). Sympathetic Magical Thinking: The contagion and similarity ‘Heuristics. In Gilovich

Griffin

Kahneman

(Eds.), Heuristics and biases: The psychology of Intuitive Judgment (pp. 201–216). Cambridge University Press.

92.

Samuels

Holingue

Nestadt

P. S.

Bienvenu

O. J.

Phan

Nestadt

(2021). Contamination-related behaviors, obsessions, and compulsions during the COVID-19 pandemic in a United States population sample. Journal of Psychiatric Research, 138, 155–162.

93.

Satici

Saricali

Satici

S. A.

Griffiths

M. D.

(2020). Intolerance of uncertainty and mental wellbeing: Serial mediation by rumination and fear of COVID-19. International Journal of Mental Health and Addiction, 20(5), 2731–2742.

94.

Schaefers

Wittkowski

Benoit

Ferraro

(2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19(1), 3–21.

95.

Shin

D. H.

(2014). Measuring the quality of smartphones: Development of a customer satisfaction index for smart services. International Journal of Mobile Communications, 12(4), 311–327.

96.

Sibley

C. G.

Greaves

L. M.

Satherley

Wilson

M. S.

Overall

N. C.

Lee

C. H. J.

Milojev

Bulbulia

Osborne

Milfont

T. L.

Houkamau

C. A.

Duck

I. M.

Vickers-Jones

Barlow

F. K.

(2020). Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. American Psychologist, 75(5), 618–630.

97.

Sica

Caudek

Cerea

Colpizzi

Caruso

Giulini

Bottesi

(2021). Health anxiety predicts the perceived dangerousness of covid-19 over and above intrusive illness-related thoughts, contamination symptoms, and state and trait negative affect. International Journal of Environmental Research and Public Health, 18(4), 1933.

98.

Singh

Alcántara-Pilar

J. M.

Liébana-Cabanillas

F. J.

Pavluković

(2024). Does the pandemic effect still exist? A comparative analysis of online food services in India and Spain. International Journal of Consumer Studies, 48(1), e12986.

99.

Sproull

Kiesler

S. B.

(1991). Connections: New ways of working in the networked organization. MIT Press.

100.

Stevenson

R. J.

Case

T. I.

Oaten

M. J.

(2009). Frequency and recency of infection and their relationship with disgust and contamination sensitivity. Evolution and Human Behavior, 30(5), 363–368.

101.

Stickley

Koyanagi

(2016). Loneliness, common mental disorders and suicidal behavior: Findings from a general population survey. Journal of Affective Disorders, 197, 81–87.

102.

Stokols

(1972). A social-psychological model of human crowding phenomena. Journal of the American Institute of Planners, 38(2), 72–83.

103.

Stokols

(1976). The experience of crowding in primary and secondary environments. Environment and Behavior, 8(1), 49–86.

104.

Suleman

Ali

Nusraningrum

Ali

M. M.

(2019). Perceived ease of use, trust, and risk toward attitude and intention in shopping for online fashion products in Indonesia. Archives of Business Research, 7(4), 240–253.

105.

Tapias

M. P.

Glaser

Keltner

Vasquez

Wickens

(2007). Emotion and prejudice: Specific emotions toward outgroups. Group Processes & Intergroup Relations, 10(1), 27–39.

106.

Taylor

(2007). Disgust is a factor in extreme prejudice. British Journal of Social Psychology, 46(Pt 3), 597–617.

107.

Taylor

Asmundson

G. J. G.

(2020). Life in a post-pandemic world: What to expect of anxiety-related conditions and their treatment. Journal of Anxiety Disorders, 72, 102231.

108.

Taylor

Landry

C. A.

Paluszek

M. M.

Asmundson

G. J. G.

(2020). Reactions to COVID-19: Differential predictors of distress, avoidance, and disregard for social distancing. Journal of Affective Disorders, 277, 94–98.

109.

Taylor

Todd

(1995). Understanding household garbage reduction behavior: A test of an integrated model. Journal of Public Policy & Marketing, 14(2), 192–204.

110.

Trehan

Sharma

(2020). What motivates members to transact on social C2C communities? A theoretical explanation. Journal of Consumer Marketing, 37(4), 399–411.

111.

Truong

M. D.

(2022). How do customers change their purchasing behaviors during the COVID-19 pandemic? Journal of Retailing and Consumer Services, 67, 102963.

112.

Tull

M. T.

Edmonds

K. A.

Scamaldo

K. M.

Richmond

J. R.

Rose

J. P.

Gratz

K. L.

(2020). Psychological outcomes associated with stay-at-home orders and the perceived impact of COVID-19 on daily life. Psychiatry Research, 289, 113098.

113.

Turner

J. C.

Hogg

M. A.

Oakes

P. J.

Reicher

S. D.

Wetherell

M. S.

(1987). Rediscovering the social group: A self-categorization theory. Basil Blackwell.

114.

Tybur

J. M.

Lieberman

Griskevicius

(2009). Microbes, mating, and morality: Individual differences in three functional domains of disgust. Journal of Personality and Social Psychology, 97(1), 103–122.

115.

Underhill

(2009). Why we buy: The science of shopping-updated and revised for the Internet, the global consumer, and beyond. Simon and Schuster.

116.

Valenzuela

Park

Kee

K. F.

(2009). Is there social capital in a social network site?: Facebook use and college students' life satisfaction, trust, and participation. Journal of Computer-Mediated Communication, 14(4), 875–901.

117.

Veselovská

(2023). When fear is a factor: Consumer behavior changes during the pandemic. Journal of Business and Retail Management Research, 18(01), 41–48.

118.

Viar-Paxton

Olatunji

B. O.

(2016). Measurement of disgust proneness. In Meiselman

H. L.

(Ed.), Emotion Measurement (pp. 513–535). Woodhead Publishing.

119.

Vindegaard

Benros

M. E.

(2020). COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain, Behavior, and Immunity, 89, 531–542.

120.

Weill

Vitale

(2001). Place to space: Migrating to eBusiness models. Harvard Business Press.

121.

Wei

Wang

(2025). Contamination concerns and face consciousness in fashion-sharing services. Journal of Fashion Marketing and Management: An International Journal, 29(2), 269–285.

122.

Wiertz

de Ruyter

(2007). Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities. Organization Studies, 28(3), 347–376.

123.

Wong

W. M.

Nunualvuttiwong

Ngamcharoenmongkhon

(2023). Environmental concerns, sustainable consumption, and COVID-19 fear in online consumers: A research exploration. Theoretical and Practical Research in Economic Fields, 14(2), 246–257.

124.

Woolcock

(1998). Social capital and economic development: Toward a theoretical synthesis and policy framework. Theory and Society, 27(2), 151–208.

125.

Zhou

Jiang

Shen

(2022). Exploring sustainable fashion consumption behavior in the post-pandemic era: Changes in the antecedents of second-hand clothing-sharing in China. Sustainability, 14(15), 9566.

126.

Yıldırım

Çiçek

. (2022). Fear of COVID-19 and smartphone addiction among Turkish adolescents: Mitigating role of resilience. Family Journal. Advance online publication. http://doi.org/10.1177/10664807221139510

127.

Yıldırım

Özaslan

(2021). Worry, severity, controllability, and preventive behaviours of COVID-19 and their associations with mental health of Turkish healthcare workers working at a pandemic hospital. International Journal of Mental Health and Addiction, 20(4), 2306–2320.

COVID-19 Social Phobia and Customer Purchase Intentions in Used-Goods Market: The Role of Contamination Concern and Seller Trust

Abstract

Keywords

Introduction

Theoretical Background

COVID-19-Related Social Phobia and Contamination Concern

Consumer Contamination Concern and Online Purchase Intention

Moderating Effect of Trust Between Contamination Concern and Purchase Intention

Moderated Mediation Model

Methods

Participants

Procedures

Measures

COVID-19 Social Phobia

Contamination Concern

Purchase Intention

Analytical Strategies

Results

Descriptions and Correlations

Common Method Bias

Moderated Mediation Model Test

Discussion

Implication, Limitations and Future Research

Implication

Limitations

Future Research

Conclusion

Footnotes

Appendix 1

Appendix 2

ORCID iD

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References