The sharing economy and the antecedents of resource sharing intentions: Evidence from a developing country

Abstract

As the sharing economy continues to diffuse in the global sphere, this paper seeks to understand the relevant behavioral antecedents for individuals to supply resources in technology-enabled platforms, especially in environments where resource scarcity subsists and the cultural landscape varies. Explicitly, we examine the impact of a set of belief factors and the individual-level collectivistic orientation on the core constructs of the Theory of Planned Behavior. A web-based survey was carried out in the Philippines, and a total of 365 valid responses were analyzed using partial least squares structural equation modeling. Results show that attitude and perceived behavioral control strongly drive the consumers’ intention to share resources in the sharing economy. Attitude is markedly preceded by altruistic and social tenets traditionally attached to sharing practices, while perceived behavioral control is endorsed by perceived trust and ease of use. Also, this work recognizes the attributions of culture to behavior as evidenced by the significant positive effect of collectivism towards the subjective norm. However, the potency of subjective norms on behavioral intention is argued. The study findings offer relevant insights to existing and prospective sharing economy platform providers in fostering peer-provider participation in the less developed world.

Keywords:

• Collaborative consumption
• Collectivism
• Peer-provider
• Peer-to-peer sharing
• Sharing economy
• Theory of planned behavior

PUBLIC INTEREST STATEMENT

The rapid advances in technology and the social transition towards access-based consumption gave birth to the phenomenon known as the “sharing economy.” It embraces the idea of supplying and accessing idle resources among people through digital platforms. A rapid rise in demand for shared services has become evident in developing countries like the Philippines. Yet, crucial to sustaining the phenomenon, in the long run, requires information on the factors driving peoples’ tendency to supply idle resources in the sharing platforms. This study revealed that the belief to help others in need, overall trust beliefs, and the perceived social rewards and pleasure from sharing precede the individual’s positive attitude and resource sharing intentions in the digital platforms. Hence, promoting the altruistic, social, and hedonic nuances and establishing the integrity of transactions could be a plausible strategy for existing and prospective platform providers to entice more suppliers into the platform.

1. Introduction

The concerted rise in individual incomes and hyper-consumption have produced volumes of wastes that much have ended up dumped in landfills and causing substantial impacts to the environment. Nevertheless, the growing precedence towards sharing over the ownership of resources is deemed to counter the issue and may reduce the rapid depletion of scarce natural resources. This type of consumption backs the premise of the sharing economy, which refers to a socio-economic system that features access-based consumption in technology-driven platforms (Gerwe & Silva, 2020). It anchors on the idea of a commodity exchange or gift giving (Belk, 2007). It is also presumed to contravene the traditional consumerist view on the ownership and accumulation of resources as it underscores the efficient utilization of idle capacities (Belk, 2010; Botsman & Rogers, 2011; Frenken & Schor, 2017). According to Möhlmann (2015), the term “sharing economy” is analogous to collaborative consumption. Other similar labels include “collaborative consumption”, “accessed-based consumption”, “the mesh” and “connected consumption” (Codagnone & Martens, 2016). Meanwhile, Eckhardt et al. (2019) synthesize a definition suggesting that it is “a scalable socio-economic system that employs technology-enabled platform to provide users the temporary access to tangible and intangible resources that may be crowdsourced” (p. 3). In a typical sharing economy configuration, the platform provider set-ups a system depicting a two-sided marketplace that facilitates the exchange processes between the resource provider (peer-provider) and users (Hawlitschek et al., 2018).

Since the recognition of the phenomenon, research investigations have grown. From existing literature, sharing economy participation has been dominantly attributed to its economic benefits (Belk, 2007; Böcker & Meelen, 2017; Lang, 2018; So et al., 2018; Tran & Filimonau, 2020), hedonic benefits (Albinsson et al., 2019; Becker-Leifhold, 2018; Hwang & Griffiths, 2017; So et al., 2018), social incentives (Barnes & Mattsson, 2016; Hawlitschek et al., 2018; Lee & Chow, 2020; Roos & Hahn, 2017), trust (Boateng et al., 2019; Hallem et al., 2019; Hawlitschek et al., 2018; Laurenti & Acuña, 2020; Tussyadiah & Pesonen, 2016) and environmental sustainability (Hallem et al., 2019; Hamari et al., 2016; Laurenti & Acuña, 2020; Lee & Chow, 2020; Roos & Hahn, 2017). Nevertheless, some empirical studies still contradict the preceding arguments. Gazzola et al. (2019) assert that consumers’ behavioral patterns are distinct and context-dependent. That is, the degree of influence on the identified factors may vary across the population. Other related studies shared similar concerns and noted caution on the generalizability of their findings (Boateng et al., 2019; Kim, 2019; Mittendorf, 2018; Yan et al., 2019; Zhang et al., 2019). Meanwhile, prior empirical works have mainly concentrated on the demand side of shared mobility and accommodation modalities in the urban areas of advanced economies (Bakker & Twining-Ward, 2018; Hawlitschek et al., 2018; Mont et al., 2020). Kuah and Wang (2020) specify that the extant investigations on consumers’ involvement in circular economy practices encompassing collaborative consumption focused on Western contexts despite its promising developments in Asia. Retamal (2019) argued that behavioral patterns in sharing economy vary in different socio-economic contexts. Hence, it is imperative to capture a different viewpoint in environments where resource scarcity subsists. Presumably, the sharing economy brings the opportunity to ease the access of previously inaccessible resources. Piff et al. (2010) likewise claim that people in lower socio-economic class manifest more remarkable prosocial behavior as they hold greater values of compassion and egalitarianism. Hence, they may demonstrate a higher propensity to participate in the sharing economy as resource providers. Meanwhile, Belk (2007) reiterates that the act of sharing is shaped by culture. In support, Akhmedova et al. (2020) advocate that the difference in sharing economy practices is attributable to cultural diversity. Previous studies strongly recommend to consider cultural influences on sharing economy behavior to strengthen and validate extant findings (Agarwal & Steinmetz, 2019; Belarmino et al., 2019).

Therefore, this work anchors on the theory of planned behavior to explain the relevant perceptions and belief factors driving the attitude to behavior, subjective norm, perceived behavioral control, and the intention to offer resources in sharing or collaborative platforms from the perspective of a developing country—the Philippines. Usage of the theory is extensive in literature; however, its dynamics may vary in different socio-economic and cultural environments. Hence, the study adds knowledge on the particularities behind the underlying causes of behavior among potential peer-providers with distinct socio-economic backgrounds and cultural orientations. In addition, as prior works largely neglected the cultural attribution of consumer behavior, the study parses the interaction of Collectivism on the elemental constructs of the TPB, namely the attitude and subjective norm. Specifically, it attempts to operationalize the construct using the CVSCALE proposed by Yoo et al. (2011), a culturally invariant measurement tool that measures Hofstede’s cultural dimensions at the individual level. Advancing the understanding of the behavioral mechanisms behind the supply intentions in sharing or collaborative platforms is crucial for its sustainability in the long run. Likewise, it is essential to identify pathways to exploit the concepts’ promises in less advanced economies such as in 1) the ease of access to resources (Benoit et al., 2017), 2) promote sustainable consumption, 3) economic development, 4) entrepreneurship and regulation and 5) business formalization (Retamal & Dominish, 2017).

Condensing the points cited above, extant studies concerning the sharing economy primarily represent the resource “users” perspective in advanced economies with highly individualistic cultural orientations. Hence, in contrast with prior works, the current study attempts to narrow the knowledge gap concerning peer providers’ (resource supplier) participation in the sharing economy, specifically in developing countries where socio-economic and cultural backgrounds vary. The study parses the influence of different perceptions, belief factors, and individual collectivistic orientation on nomological constructs of the Theory of Planned Behavior (TPB). Furthermore, the study purports that the subsisting contextual factors in the less developed economies may drive a different consumer behavioral patterns. With this, the study advances the knowledge on the validity of TPB in explaining consumer behavior in the sharing economy in a developing country context.

The remaining part of the paper contains the following: a review of the factors influencing sharing economy participation, conceptual model, research hypotheses, methodology, and results. Then, it concludes by articulating the findings, theoretical and managerial implications, limitations, and future research directions.

2. Theoretical background and hypotheses

2.1. Contextualizing participation in sharing/collaborative platforms in the Philippines

According to a Nielsen survey (Nielsen, 2014), the Philippines is one of the countries in the Asia-Pacific with the most willing participants in the sharing economy. It trails China, Indonesia, and Slovenia with the top potential users for products and services in sharing economy. While it is evident that there is a high demand for sharing economy services, the scarcity of knowledge remains as to the specificity of consumers’ engagement and the forces enabling their sharing economy participation as resource providers. This study also follows the presumption that the early collaborative consumption practices manifested in the form of “barter” in the earliest stages of the country’s development (Tiquia, 2021) may be a catalyst for sharing resources using technology-enabled platforms. Meanwhile, the country is a highly collectivistic society (Hofstede, 2020). It features “a tight social framework in which people expect their in-group (relatives, clan, organizations) to look after them, and in exchange for that, they feel they owe absolute loyalty to it” (Hofstede, 1980, p. 45). Sreen et al. (2018) claim that group goals and societal pressures in a collectivist society influence individual decisions. It could as well influence Filipino consumers’ decisions to engage as peer-providers in sharing or collaborative platforms.

More opportunities transpire as internet penetration rates continue to increase and consequently boost internet-based services in the country. Projection reveals that the digital market in Southeast Asia is up-and-coming across geographic regions (Gilchrist, 2016). Data shows that the internet economy in the Philippines is valued at 7.5 billion U.S. dollars as of 2020, with approximately 73 million internet users (Sanchez, 2020). Despite the promising prospects, the ongoing coronavirus disease 2019 (SARS-CoV-2) has plunged the growth of some sharing economy sectors. Countries have enforced disease containment measures that severely affected peoples’ mobility. The travel and tourism industry, conjointly with the sharing economy’s transportation and accommodation sectors, has experienced a collapse. Rachel Botsman, a pioneering advocate of collaborative consumption, asserts that the health crisis will enable sharing economy sectors to re-align back to their roots, focusing more on its social, frugal, and sustainable attributes (The Economist, 2020). In addition, Dolnicar and Zare (2020) believes that the demand in the accommodation sharing sector will recover; however, for-profit sharing services would likely pacify. The authors expect a re-emergence of the original ethos of accommodation sharing that centers on the social aspect and the true principles of sharing. Lastly, the global pandemic may instill long-lasting changes in consumer behavior, including the proclivities towards disruptive technologies or innovations. Hence, as consumers compel to adopt digital technologies and increase online presence (The World Bank, 2020), this may have significant implications in the sharing economy or collaborative practices in general.

2.2. The theory of planned behavior and belief factors

Ajzen’s (1991) Theory of Planned Behavior (TPB) expands from its early work on the Theory of Reasoned Action. Prior works popularly use the theory to explain consumer behavior. The theory postulates that behavioral intention or the individual’s determination to perform the behavior in question is driven by attitudes, subjective norms, and perceived behavioral control. Attitude refers to the positive or negative evaluation of behavior in question. At the same time, subjective norm relates to a person’s belief over other people’s approval or disapproval of the behavior in question. In contrast, the perceived behavioral control refers to the perceived ease or difficulty of performing the behavior in question. In a nutshell, an individual elicits a behavioral intent when he or she deems the action to produce favorable consequences (attitude). Alongside, pressures from social groups or peers (subjective norms) and the perceived ease or difficulty of performing the behavior (perceived behavioral control) exert significant influences. As individuals place attributes to objects, forms the salient beliefs (i.e., behavioral, normative, and control) that precede the fundamental elements of behavioral intention (Ajzen & Fishbein, 2000). It also transpires from a person’s past learnings, experiences, or secondhand information obtained from the subsisting environment. Identifying and understanding the underlying belief factors is critical to substantiate the effectiveness of the vital determinants of behavior to predict behavioral intention and accordingly to actual behavior. Besides, the authors emphasize the flexibility of the theory regarding its applicability in diverse behavioral contexts. It also pliant as the addition of new explanatory variables are permitted that may seem to enhance the variance in explaining behavior. Following the main elaborations of TPB, the study hypothesizes:

H₁: Attitude positively influences the intention to share resources in the sharing economy platforms

H₂: Perceived behavioral control positively influences the intention to share resources in the sharing economy platforms

H₃: Subjective norm positively influences the intention to share resources in the sharing economy platforms

Botsman and Rogers (2011) highlights the economic, social, and environmental implications of the phenomenon as predominant drivers of users’ participation. The authors also noted that the trust shared amongst people serves as a new currency driving the development of the sharing economy across the globe. Other studies have offered a meaningful plethora of findings; however, the spotlight points to the users or the demand-side of sharing economy services (Karlsson & Dolnicar, 2016; Wilhelms et al., 2017; Wirtz et al., 2019). Concurrently, the determinants of behavior are still contrasting. For sharing economy models to function appropriately, there is a need to emphasize peer-providers and eventually consolidate knowledge to capture a complete picture of both perspectives and design effective strategy prescriptions accordingly. Evidence suggests the role of economic incentives in the involvement of peer-providers that the sharing economy offers (Böcker & Meelen, 2017; Karlsson & Dolnicar, 2016; Tussyadiah, 2016). Peer-providers can monetize the underutilized capacities of their resources through sharing via online platforms, hence, allowing greater productivity of supposedly idling resources (Valente et al., 2019). Huang and Kuo (2020) associate the term “micro-entrepreneurs” with peer-providers as they generate monetary benefits from their assets or skills. Popular sharing economy platforms such as Uber and Airbnb have offered these income opportunities, enticing people to share their transport vehicles and living spaces even with strangers. Despite some firm evidence on the dominating influence of economic benefits on the resource providers’ model, some studies still oppose these results (Alzamora-Ruiz et al., 2020; Urbonavicius & Sezer, 2019). With this, perceived economic benefits or or the expectation of monetary rewards form sharing out resources may encourage peer provider participation. Hence, the study posits that:

H_1a: Perceived economic benefits positively influence the attitude towards resource provision in the sharing economy platforms

Studies also underscore the influence of social motives for sharing economy participation, particularly in the accommodation-sharing sector. The possibility to meet and communicate with new and culturally diverse individuals and the benefits arising from social interaction are driving the movement (Botsman & Rogers, 2011; Karlsson & Dolnicar, 2016). Kim et al. (2018) maintain that the prospect of establishing social relationships with other people is a gainful experience for accommodation providers. They infer that perceived social benefits shape peer providers’ attitude and intention to use peer-to-peer accommodation platforms. Böcker and Meelen (2017) corroborates the extant findings yet, emphasize the impact of social benefits not just for sharing accommodation assets but including other resources. Meanwhile, sharing economy participation deems to elicit fun and enjoyment among peer-providers. Becoming a peer-provider and its advantages induces the feeling of pleasure and satisfaction (Wilhelms et al., 2017). These feelings are described as hedonic motives and forms one’s positive attitude (Huang & Kuo, 2020) and behavioral intention (Kim et al., 2018). Alzamora-Ruiz et al. (2020) reveal the same claims and argues the dominant influence of perceived economic advantages. Hence, the study postulates that:

H_1b: Perceived social incentives positively influence the attitude towards resource provision in the sharing economy platforms

H_1c: Perceived hedonic incentives positively influence an individual’s attitude towards resource provision in the sharing economy platforms

The persistent concern towards the environment appears to fuel collaborative forms of consumption in the contemporary era. Botsman and Rogers (2010) stipulate that it helps to alleviate waste-related issues derived from hyper-consumption. Sharing resources among peers extends the product life cycle, mitigating the rapid depletion of scarce natural resources. Technological advances magnify this as it reduces transactional cost and infuses hedonic elements towards sharing practices. One’s perceived environmental benefits or the beliefs on the pro-environmental implications associated with sharing or collaborative practices is recognized by prior studies as a significant driver in stirring the participation towards the sharing economy as peer-providers (Bellotti et al., 2015; Böcker & Meelen, 2017; Sung et al., 2018). Specifically, Hamari et al. (2016) and Huang and Kuo (2020) claim that beliefs form one’s positive attitude towards sharing assets. Nevertheless, Wilhelms et al. (2017) argue environmental sustainability perception is merely a consequence emanating from sharing economy participation and not a direct behavioral motive. Given these, this research hypothesizes that:

H_1d: Perceived environmental benefits positively influence an individual’s attitude towards resource provision in the sharing economy platforms

Altruistic beliefs may set off sharing in the platform economy as it exemplifies a kind of prosocial behavior. Altruism is the act of being passionate and helpful in promoting their welfare of others (Hsu & Lin, 2008). Presumably, individuals who exhibit altruistic beliefs would likely support the idea of sharing. Offering resources on online platforms is an act to help others who are in need. A few initiatives examine the influence of peoples’ altruistic values in enabling peer-to-peer sharing. Roos and Hahn (2019) and Zhang et al. (2019) endorse the positive impact of altruistic orientation for higher participation in collaborative consumption. These argue the earlier findings of Becker-Leifhold (2018) as it infers the insignificance of altruistic orientation in the sharing of clothing. Piff et al. (2010) also connote that less affluent individuals share more as they hold greater values of compassion and egalitarianism. Hence, sharing resources via online platforms may be driven by compassion and the feeling of altruism in a less developed country like the Philippines. Also, the current work postulates that altruism poses more influence among Filipinos to share resources in the platform economy due to the early childhood upbringing, which markedly inculcates prosocial behavior (Gülseven et al., 2020; Kagitcibasi, 2005; Whiting et al., 1975). Thus, the study proposes the notion that:

H_1e: Altruistic beliefs positively influence an individual’s attitude towards resource provision in the sharing economy platforms

Perceived trust is the extent to which an individual feels secure and confident to transact with another party or an entity (McKnight & Chervany Norman, 2001). Botsman and Rogers (2010) indicate that trust is a fundamental element in collaborative consumption models. The authors observed that trust in institutions is deteriorating, yet a new form is emerging, shared among people even beyond close relations. It is through the platform’s trust-building mechanisms and reputation systems that facilitate this new currency of trust. Consumer behavior studies have recognized the role of trust for the demanders of collaborative platforms (Hawlitschek et al., 2016; Mittendorf, 2018; S. Nguyen et al., 2020). Likewise, in the case of peer-providers of accommodation assets, trust directly shapes the individual’s behavioral intention (Sung et al., 2018). Nevertheless, this finding does not agree with other research works (Huang & Kuo, 2020; Schreiner et al., 2018; Urbonavicius & Sezer, 2019). Hence, the study proposes that:

H_1f: Perceived trust positively influence an individual’s attitude towards resource provision in the sharing economy platforms

The sharing economy emerges as a phenomenon in light of technological advances. It is a disruptive innovation that enables sharing transactions between strangers through web-based or app-based technologies and the internet. Nonetheless, exploiting this innovative technology depends on the degree of acceptance among users. Perceived ease of use (PEOU) holds a predominant role in the consumer technology acceptance model (Pavlou & Fygenson, 2006; V. Venkatesh, 2000). Davis (1989) defined PEOU as “the degree to which a person believes that using a particular system would be free of effort” (p. 320). It epitomizes control belief factors shaping one’s perceived behavioral control (Ajzen, 1991; Chen et al., 2021). In addition, Pavlou and Fygenson (2006) asserted that perceived behavioral control is influenced by trust belief. They postulate that a higher perceived trust obviates the surrounding social uncertainties that diminish one’s control of a particular behavior. Hence, this study hypothesizes that:

H_2a: Perceived ease of use positively influence an individual’s perceived behavioral control towards resource provision in the sharing economy platforms

H_2b: Perceived trust positively influence an individual’s perceived behavioral control towards resource provision in the sharing economy platforms

2.3. Culture and consumer behavior: the role of collectivism

Belk (2007) asserts that the act of “sharing is a culturally learned behavior” (p. 130). It gauges the nuance of generosity, fairness, and the altruistic nature of sharing. As sharing economy emerges as a phenomenon grounded from the traditional sharing practices, the study argues that culture underscores the involvement in sharing economy practices. Prior empirical works examined the influence of culture on consumer behavior in general, yet a paucity of evidence supporting the claim in the sharing economy context remains. Besides, the previous empirical investigation suggested carrying out future research works that account for the role of culture on one’s behavior in the sharing economy (Belarmino & Koh, 2020; Paundra et al., 2020; Roos & Hahn, 2019; Schreiner et al., 2018; Yang et al., 2019).

Studies operationalizing cultural orientations often use Hofstede’s cultural framework (Soares et al., 2007). Geert Hofstede established five (5) cultural dimensions: individualism, power distance, uncertainty avoidance, masculinity, and long-term orientation. The dimensions are assigned with indexes and are used to account for cultural differences between nations. In several studies employing the framework, the country serves as a proxy for cultural comparison. Hence, it presumes that citizens in a particular country share similar cultural characteristics. Studies investigating cultural attributions towards behavior have commonly involved two or more culturally distant nationalities, however, asserted to measure cultural values at the individual level.

Reiterating some previous works, Iran et al. (2019) examined the reasons for participation in collaborative fashion consumption in culturally different countries—Iran and Germany. Through a holistic view of culture was adopted, the findings reveal the various degrees of influence among factors on collaborative fashion consumption. Davidson et al. (2018) offer support to the previous claim and explicitly point out the cross-cultural differences in the degree of influence of materialism on one’s participation in sharing economy. Studies parsing the influence of culture on consumer behavior acknowledge the role of collectivism. Collectivism denotes a tight social framework in which people expect their in-group (relatives, clan, organizations) to look after them, and in exchange for that, they feel they owe absolute loyalty to it” (Hofstede, 1980, p. 45). People in collectivist societies recognize the relevance of interdependence and social relationships. Extant evidence suggests that collectivistic societies demonstrate a higher propensity towards prosocial behavior or helping behavior to others (Lampridis & Papastylianou, 2017). On this note, as sharing practices in collaborative platforms could be a manifestation of a prosocial act, the current work presumes that collectivistic orientation can drive consumer’s attitudes, subjective norms, and intention to participate in the sharing economy as resource providers. The work of Gupta et al. (2019) took a deeper look at the cultural ascriptions of peer-to-peer sharing participation by operationalizing cultural orientation at the individual level. The findings stress the direct role of collectivism and masculinism on the individual’s renting and renting-out propensities. However, its interlinkages within the TPB configuration in the supply of resources in technology-driven sharing platforms remain scarce. A few investigations have accounted this for in other consumer behavior contexts. For instance, cultural orientation influences attitude and subjective norms in green purchase behavior (T. N. Nguyen et al., 2017). Collectivism influences subjective norms while long-term orientation significantly impacts environmental attitudes. Similarly, Higueras-Castillo et al. (2019) recognize the considerable role of collectivism in forming attitudes and pro-environmental behavior. Liobikienė and Bernatonienė (2017) also indicate other cultural orientations suchlike power distance and uncertainty avoidance to exert indirect influence towards green purchase behavior via subjective norms. Nevertheless, Sreen et al. (2018) assert that the increase in liberalization, globalization, and urban development narrows cultural differences in societies worldwide. Hence, the current study hypothesize that:

H_1g: Collectivism positively influence the attitude towards resource provision in the sharing economy platforms

H_3a: Collectivism positively influence subjective norms in the context of resource provision in the sharing economy platform

3. Methodology

3.1. Survey design and data collection procedure

Based on the research framework presented in Figure 1, a web-based survey was designed before data collection. Multi-item measures for each latent construct were adapted from the previous studies and modified to fit under the overarching theme of the study (Table 1). The indicators are measured using a 5-point Likert scale (i.e., 1—strongly disagree and 5—strongly agree). The survey items are framed in English as it is considered an official language in the Philippines. Notably, rather than examining the influence of all cultural orientations, this work solely used the dimension of Collectivism based on Yoo et al. (2011) metrics in an attempt to secure a more parsimonious model. Extant shreds of evidence support the role of Collectivism on consumer behavior in general, hence, merits its inclusion in the research model. More importantly, based on Hofstede’s cultural dimension index, the Philippines strongly features a highly collectivistic society compared to other economically advanced countries in the west. Furthermore, the team sought assistance from a marketing professor and two doctoral students to assess the clarity and comprehensibility of the questionnaire. They cautioned on the length of the survey instrument, which can lead to large amounts of unengaged responses and the possibility of high common method variance. With this, the researchers decided to distribute demographic queries in the different parts of the questionnaire to serve as attention prompts that reduce the probability of unengaged responses. They also suggested using simple terms over technical terms or jargons in the querries (e.g., access-based/collaborative consumption → sharing). After a few modifications, we pre-tested the questionnaire to 30 respondents who had prior experience sharing resources on online platforms to spot further issues and ensure that the questions were understandable. There were no other comments on the questionnaire aside from its length; thus, the actual survey in Google forms followed.

Figure 1. Model specification anchored on the theory of planned behavior

Table 1. Measurement items of the constructs

Construct	Measurement Items	Source
Behavioral intention (INT)
INT1	If the circumstances allow it, I will also share in the future.	(Ajzen, 1991; Bucher et al., 2016)
INT2	I may share with others in the future.
INT3	I intend to share with others in the future as well.
Attitude (ATT)
ATT1	I find sharing in an online peer-to-peer sharing platform to be a wise move.	(Ajzen, 1991; Bucher et al., 2016; Hamari et al., 2016; Roos & Hahn, 2017)
ATT2	All things considered, I think sharing through sharing economy platform is a positive thing.
ATT3	All things considered, I think participating in the sharing economy is a good thing.
ATT4	Overall, sharing goods and services within an online peer-to-peer sharing platform makes sense.
Subjective norm (SN)
SN1	People who are important to me think that I should share in online sharing platform	(Ajzen, 1991; Hawlitschek et al., 2018; Roos & Hahn, 2017)
SN2	People who influence my behavior think that I should share in online peer-to-peer sharing
SN3	People whose opinions I value prefer that I share resources through sharing economy platform
Perceived behavioral control (PBC)
PBC1	I would be able to share in the sharing economy	(Hawlitschek et al., 2018; Taylor & Todd, 1995; Morris Davis Venkatesh & Davis, 2003)
PBC2	Using the online sharing platform is entirely within my control
PBC3	I have the resources and the knowledge and the ability to share in sharing economy platform
Altruism (ALT)
ALT1	I share resources because I feel compassion toward people in need	(Konrath & Handy, 2018)
ALT2	People should be willing to help others who are less fortunate
ALT3	I share because I am concerned about those less fortunate than myself
Perceived social incentive (SOC)
SOC1	Sharing resources via online platforms allow me to have fun with others.	(Gazzola et al., 2019; Van der Heijden, 2004)
SOC2	Sharing resources via online platforms make me feel like part of a community.
SOC3	Sharing resources via online platforms allow me to gain unique social experiences through meeting interesting people.
Perceived hedonic benefits (HED)
HED1	Using sharing economy platform is fun.	(Tran et al., 2019; Venkatesh & Xu, 2012)
HED2	Sharing through sharing economy platform is enjoyable.
HED3	Sharing through sharing economy platform is very entertaining.
Perceived economic benefits (ECO)
ECO1	I share through sharing economy platform because it pays well.	(Bucher et al., 2016; Fota et al., 2019; Gazzola et al., 2019; Tussyadiah, 2015)
ECO2	Online sharing allows me to make money from something I own.
ECO3	Earning extra money is an important factor when sharing resources on an online sharing platform
ECO4	Sharing resources via online platforms is a good way to supplement my income.
Environmental sustainability concern (SUS)
SUS1	Sharing resources through sharing economy platform helps save natural resources.	(Fota et al., 2019; Hamari et al., 2016)
SUS2	Sharing through sharing economy platform is a sustainable model of consumption.
SUS3	Sharing through sharing economy platform is ecological.
SUS4	Sharing through sharing economy platform is environmentally friendly.
Perceived trust (TRU)
TRU1	Sharing economy platform provides a robust and safe environment.	(Fota et al., 2019; Gefen, 2000; Mittendorf et al., 2019; Schreiner et al., 2018)
TRU2	I believe that the online peer-to-peer sharing system is trustworthy.
TRU3	Even if not monitored, I’d trust people with who I share my resources.
TRU4	I generally trust other people using my resources
TRU5	I feel less risk on damage and/or loss of the shared good in sharing economy platform
Perceived ease of use (PEOU)
PEOU1	Learning to operate the sharing economy platform would be easy for me	(Morris Davis Venkatesh & Davis, 2003)
PEOU2	I would find ways to get the sharing economy platform to do what I want it to do
PEOU3	My interaction with the sharing economy platform would be clear and understandable
PEOU4	I would find the sharing economy platform easy to use
Collectivism (COL)
COL1	Individuals should sacrifice self-interest for the group.	(Yoo et al., 2011)
COL2	Group loyalty should be encouraged even if individual goals suffer.
COL3	Group success is more important than individual success.
COL4	Group welfare is more important than individual rewards.

Data collection was carried out from June to August 2020 at the brim of a state public health emergency in the Philippines due to the Covid-19 pandemic. Purposive sampling procedure was utilized in the selection of target respondents. It is a non-probability sampling procedure where the sample elements are selected based on specific traits, qualities, or characteristics of the population that are of relevance to the topic of investigation. One major consideration for study participants applies to the age of respondents, ranging from 25–56 years old, as this captures the Gen Y: millennials (1981–1996) and Gen X: (1965–1980) cohorts. This is owing to the evidence from prior works acknowledging the strong propensity of these young demographic cohorts to engage in collaborative forms of consumption (Amaro et al., 2019; Gazzola et al., 2020; Hwang & Griffiths, 2017). The weblink of the survey was distributed to the target respondents via social networking sites (SNS) (e.g., Facebook, Instagram, Linked In). Initially, the researchers sent about 500 survey invitations through SNS private messages to selected individuals. On the survey landing page, we presented a brief description of the study and a statement on the confidentiality of data. Then, we took their consent to participate in the survey. Upon agreement, the page transits to a brief explanation of the term “sharing economy” and subsequently to the main survey items. From the 500 invitations, the survey turnout registers 430 responses only. Then, data screening in Microsoft Excel followed to identify and eliminate substantially incomplete and duplicated questionnaire responses. After doing so, the effective sample size is reduced to 365, which still exceeds the minimum requirements specified from the previous studies (Anderson & Gerbing, 1984; Bentler & Chou, 1987; Cohen, 1992; Hair et al., 2017; Hamari et al., 2016). Table 2 presents the demographic attributes of the sample.

Table 2. Demographic attributes of the respondents

Characteristic	N = 365
Gender			Educational Attainment
Female	258	70.70%	Bachelor’s Degree	181	49.60%
Male	85	23.30%	High School Graduate	76	20.80%
LGBTQ+	22	6.00%	Some College Degree	43	11.80%
Age			Graduate Degree	41	11.20%
below 24 years old	253	69.30%	Less than High School Graduate	24	6.60%
25–45 years old	94	25.80%
41 years old and above	18	4.90%	Employment Status
Marital Status			Student	155	42.50%
Single	320	87.70%	Employed	148	40.50%
Married	45	12.30%	Self-employed	39	10.70%
			Unemployed	23	6.30%

3.2. Data analysis

Data were analyzed using partial least squares (PLS) regression in SmartPLS 3 software. It is a structural equation modeling technique that is gaining popularity along with its covariance-based counterpart. Hair et al. (2017) specified that the analytical approach works well on exploratory/predictive investigations over theory testing/confirmation studies. As the current study aims to extend a theory and estimate the causal relationships among the variables specified in the structural model, we find it suitable to use the approach. Furthremore, PLS-SEM is less stringent on the normality assumption of data and handles smaller sample size with adequate robustness. Based on Hair et al. (2017) guide in conducting PLS-SEM, the following steps were pursued:1) model specification, 2) outer model evaluation, and 3) inner model evaluation.

4. Results

4.1. Outer model assessment

Reliability seems to be satisfactory as Cronbach’s alpha (CA), and Composite Reliability (CR) values exceed the critical threshold of 0.7 (Bagozzi & Yi, 1988; Hair et al., 2017) (Table 3). Meanwhile, both values of average variance extracted (AVE) and the indicator factor loadings (FL) exceed 0.5, suggesting adequate convergent validity (Hair et al., 2017). Meaning, the constructs are well represented by each set of indicators. For discriminant validity (Table 4), results are within acceptable limits based on the Fornell-Larcker criterion as the square root of AVE of each latent construct is larger than its correlation with other latent variables (Fornell & Larcker, 1981). Alongside, the Heterotrait-monotrait (HTMT) method further substantiates the adequacy of discriminant validity as values are below the critical threshold of 0.90 (Hair et al., 2017). Using Harman’s single factor test, common-method bias remains at the acceptable level as factor analysis indicates a cumulative variance of 46% for the first factor extracted, which is below the 50% threshold (Podsakoff et al., 2003). Table 5 reveals no multi-collinearity issues among the variables in the measurement model as the variance inflation factor (VIF) values are lower than 3 (Hair et al., 2017).

Table 3. Measurement statistics of construct scales

Construct/Indicators	FL	Construct/Indicators	FL
Behavioral intention (CA: 0.938, CR: 0.961, AVE: 0.892)		Perceived economic benefits (CA: 0.893, CR: 0.926, AVE: 0.759)
INT1	0.93	ECO1	0.79
INT2	0.96	ECO2	0.90
INT3	0.94	ECO3	0.89
Attitude (CA: 0.932, CR: 0.951, AVE: 0.830)		ECO4	0.91
ATT1	0.87	Sustainability concern (CA: 0.925, CR: 0.947, AVE: 0.817)
ATT2	0.93	SUS1	0.89
ATT3	0.93	SUS2	0.88
ATT4	0.91	SUS3	0.93
Subjective norm (CA: 0.913, CR: 0.945, AVE: 0.852)		SUS4	0.91
SN1	0.92	Perceived trust (CA: 0.885, CR: 0.915, AVE: 0.684)
SN2	0.93	TRU1	0.78
SN3	0.92	TRU2	0.85
Perceived behavioral control (CA: 0.830, CR: 0.900, AVE: 0.746)		TRU3	0.83
PBC1	0.88	TRU4	0.86
PBC2	0.84	TRU5	0.81
PBC3	0.88	Perceived ease of use (CA: 0.913, CR: 0.939, AVE: 0.792)
Altruism (CA: 0.909, CR: 0.943, AVE: 0.846)		PEOU1	0.90
ALT1	0.91	PEOU2	0.88
ALT2	0.92	PEOU3	0.90
ALT3	0.93	PEOU4	0.88
Perceived social incentive (CA: 0.884, CR: 0.928, AVE: 0.812)		Collectivism (CA: 0.873, CR: 0.913, AVE: 0.726)
SOC1	0.86	COL1	0.78
SOC2	0.93	COL2	0.84
SOC3	0.92	COL3	0.90
Perceived hedonic benefits (CA: 0.930, CR: 0.955, AVE: 0.877)		COL4	0.88
HED1	0.94
HED2	0.96
HED3	0.91

Note: FL—factor loading, CA—Cronbach’s alpha, CR—composite reliability, AVE—average variance extracted.

Table 4. Discriminant validity tests results

Construct	ALT	ATT	COL	ECO	HED	INT	PBC	PEOU	SN	SOC	SUS	TRU
ALT	0.920	0.728	0.544	0.638	0.776	0.658	0.650	0.674	0.543	0.747	0.824	0.513
ATT	0.671	0.911	0.498	0.652	0.687	0.863	0.717	0.727	0.585	0.740	0.679	0.574
COL	0.485	0.451	0.852	0.513	0.519	0.472	0.446	0.527	0.508	0.565	0.543	0.725
ECO	0.577	0.597	0.453	0.871	0.632	0.620	0.657	0.688	0.567	0.838	0.656	0.597
HED	0.714	0.641	0.469	0.576	0.936	0.598	0.683	0.728	0.554	0.743	0.772	0.582
INT	0.608	0.808	0.430	0.568	0.560	0.944	0.688	0.675	0.572	0.668	0.605	0.496
PBC	0.568	0.636	0.386	0.563	0.604	0.616	0.864	0.803	0.786	0.722	0.637	0.637
PEOU	0.613	0.670	0.470	0.618	0.672	0.624	0.702	0.890	0.571	0.724	0.662	0.635
SN	0.495	0.540	0.456	0.507	0.511	0.529	0.689	0.520	0.923	0.634	0.533	0.635
SOC	0.674	0.674	0.498	0.746	0.674	0.609	0.623	0.650	0.570	0.901	0.691	0.590
SUS	0.757	0.631	0.490	0.598	0.717	0.565	0.561	0.608	0.491	0.628	0.904	0.581
TRU	0.470	0.532	0.639	0.532	0.536	0.460	0.557	0.578	0.574	0.528	0.537	0.827

Note: square root of AVE values—diagonal values (in bold), Construct correlations—values below the square root of AVE , HTMT ratios—values in the grey zone (above the diagonal values in bold).

Table 5. Collinearity assessment results

Construct	VIF	Construct	VIF
ALT—ATT	2.996	ATT—INT	1.736
COL—ATT	1.854	PBC—INT	2.339
ECO—ATT	2.486	SN—INT	1.967
HED—ATT	2.725
SOC—ATT	3.078	PEOU—PBC	1.503
SUS—ATT	2.968	TRU—PBC	1.503
TRU—ATT	2.070
		COL—SN	1.000

4.2. Inner model assessment

A nonparametric bootstrapping procedure with 5,000 subsamples was conducted to extrapolate and assess the path coefficients in the structural model. Table 6 summarizes the results.

Table 6. Summary of inner model results

Direct Effects	Path Coefficient	t-value	p-value	Hypothesis Supported
ATT→ INT	0.688	10.552	0.00	H1 (Supported)
PBC → INT	0.133	2.194	0.03	H2 (Supported)
SN → INT	0.067	1.270	0.20	H3 (Not Supported)
ECO → ATT	0.083	1.398	0.16	H1a (Not Supported)
SOC → ATT	0.240	3.751	0.00	H1b (Supported)
HED → ATT	0.124	2.118	0.03	H1c (Supported)
SUS → ATT	0.086	1.303	0.19	H1d (Not Supported)
ALT → ATT	0.249	3.903	0.00	H1e (Supported)
TRU → ATT	0.143	2.744	0.01	H1f (Supported)
COL → ATT	−0.019	0.389	0.70	H1g (Not Supported)
PEOU → PBC	0.227	4.742	0.00	H2a (Supported)
TRU → PBC	0.571	11.218	0.00	H2b (Supported)
COL → SN	0.456	9.527	0.00	H3a (Supported)
Specific Indirect Effects
ECO → ATT → INT	0.057	1.417	0.16
SOC → ATT → INT	0.165	3.696	0.00
HED → ATT → INT	0.085	2.125	0.03
SUS → ATT → INT	0.059	1.275	0.20
TRU → ATT → INT	0.098	2.632	0.01
ALT → ATT → INT	0.171	3.800	0.00
COL → ATT → INT	−0.013	0.388	0.70
TRU → PBC → INT	0.030	2.107	0.04
PEOU → PBC → INT	0.076	2.039	0.04
COL → SN → INT	0.031	1.229	0.22
	R^2	Adjusted R^2
SN	20.80%	20.60%
PBC	52.80%	52.50%
ATT	58.00%	57.20%
INT	67.20%	67.00%

The structural model explains 67.20 percent of the total variance of intention to share resources in the sharing economy platforms. The prediction of behavioral intention following an expanded TPB model adopted in this research produces a robust R² value, concurring to the upper bound R² figure (i.e.,> 20%) set-out for most consumer behavior studies (Rasoolimanesh et al., 2017). In addition, the R² values for the other endogenous variables, attitude and perceived behavioral control are 58% and 52.8%, respectively. Consistent with the original configuration of TPB, attitude pose the highest significant impact to behavioral intention (β = 0.688 T = 10.522, P < 0.01), followed by perceived behavioral control (β = 0.133 T = 2.197, P < 0.05). However, the observation contests the presumed significant relation between subjective norm and behavioral intention. Further analysis bare which factors significantly impact the core elements of behavioral intention in the original TBP model. Findings show that altruistic belief (β = 0.249 T = 3.903, P < 0.001), perceived trust (β = 0.143 T = 2.744, P < 0.05), perceived social incentive (β = 0.240 T = 3.751, P < 0.01) and perceived hedonic incentive (β = 0.124 T = 2.118, P < 0.05) exerted positive and significant effects towards attitude to behavior, supporting H1b, H1c, H1e and H1f respectively. It also appears that attitude fully mediates the relationship between the said belief factors and intention to share resources in the platform economy through attitude. Meanwhile, the analysis supports H2a and H2b as perceived ease of use (β = 0.227 T = 4.742, P < 0.01) and trust (β = 0.571 T = 11.218, P < 0.01) exerted a significant and positive impact on perceived behavioral control. Also, the presumed positive influence of Collectivism as a cultural driver of behavior is only significant on subjective norms, supporting H3a (β = 0.456 T = 9.527, P < 0.01). The study did not support H1a, H1d, and H1g due to the insignificant impact of perceived economic benefits, perceived environmental benefits, and collectivistic orientation on attitude and behavioral intention.

5. Discussion and implications

The rise of the sharing economy challenges traditional business models as it leverages technological advances in responding to social changes. The shift from ownership to access-based consumption underpins its strong traction in the contemporary world. Nevertheless, its infancy further requires research initiatives to fill the subsisting research gaps. Primarily, the paucity of knowledge on peer-provider participation in emerging countries establishes the grounds of this research investigation. The study contributes explicitly to theory by predicting the relevant perceptions and belief factors driving the attitude to behavior, subjective norm, perceived behavioral control, and the intention to offer resources in sharing economy platforms from the perspective of a developing country. Emphasis on individual-level cultural attribution of sharing economy participation is neglected mainly in prior works. Therefore, the study also serves as an initial attempt that exposes the relationship of an individual’s collectivistic orientation on the elemental constructs of the theory of planned behavior, namely the attitude to behavior and subjective norm.

Consistent with the extant theoretical claims, the findings reveal that attitude and perceived behavioral control pose a significant positive effect on behavioral intention. Attitude remains the most critical parameter for behavioral intention (Ajzen, 1991; Sutton, 2012). It signifies that the performance of the desired behavior largely depends on one’s positive evaluation of such behavior. Hence, facilitating an affirmative assessment towards sharing resources in the online platforms is a powerful way to entice more peer-providers. The result also supports the significant role of perceived behavioral control in dictating the desired behavior, which further justifies its inclusion in the original TPB configuration. Upon the perception of adequate knowledge, skills, or resources, individuals grasp complete control of their behavior such that it enhances their confidence to perform the desired behavior. In this study, increasing the willingness to share resources in the sharing platforms can transpire by equipping prospective users with the relevant resources to transact in the platform confidently.

Nevertheless, the influence of subjective norms on behavioral intention is found insignificant. The study infers that sharing through a technology-enabled platform may still be an eccentric practice as envisaged by people in society in general. In essence, one’s affirmative opinion on the practices in the sharing economy holds less weight to stir others to the same as the concept’s positive acumen in the bigger society remains limited. A second explanation lies in the individualistic nature of the behavior (Earle et al., 2020; Kothe & Mullan, 2015) since the decision to share an expensive asset or property solely depends on the owner to a large extent. It may also appear that the high perceived behavioral control and attitude may seem to diminish the influence of subjective norms towards behavioral intention. The recent work of La Barbera and Ajzen (2020) observed this distinctive dynamic on the nomological configuration of the theory of planned behavior. The authors asserted that the relevance of subjective norm tends to diminish when the impact of attitude becomes more apparent due to a greater perceived behavioral control.

A closer look at the underlying determinants of attitude exposes the significant and positive impact of perceived social benefits, perceived hedonic benefits, altruistic beliefs, and perceived trust. It further substantiates the relevance of the antecedents described above in shaping one’s attitude and intention to engage in the sharing economy as a peer-provider. A distinct result points to the highest impact of altruistic belief on attitude. It opposes the most agreed-upon claims on the dominance of economic benefit as a driver for consumption and supply of shared goods and services (Böcker & Meelen, 2017; Hamari et al., 2016; Tussyadiah, 2016; Wilhelms et al., 2017). It upholds the prosocial nature of the act of sharing as it elicits the feeling of compassion and the willingness to help others who are in need. Kuroishi and Sawada (2019) recognized this manifestation among Filipinos during the early recovery stage after natural calamities. Considering that the study took place during the Covid-19 pandemic, it may seem that sharing resources would help those adversely affected individuals.

Another logical explanation points to the individual’s early upbringing in a typical Filipino household. The assumption of responsibilities such as caring for family members deems to inculcate social sensitivity and promote prosocial behavior (Gülseven et al., 2020; Kagitcibasi, 2005; Whiting et al., 1975). Moreover, the sharing economy offers the possibility to engage with people, even to absolute strangers, while deriving a socially and hedonically gainful experience. The study recognizes the role of these derived experiences in generating a positive evaluation towards the supply of resources in the sharing economy, thereby confirming the findings of Böcker and Meelen (2017), Huang and Kuo (2020), and Kim et al. (2018). Another crucial factor is the impression of trustworthiness surrounding the transactions in the sharing economy. It substantiates the prior work of Hawlitschek et al. (2018), Kim et al. (2018), and S. Nguyen et al. (2020) ascertaining the role of trust in sharing economy models. The perception of ample platform security and the confidence towards the proper use of shared resources are some aspects to consider to encourage resource provision. Concurrently, extant evidence shows that security and consumer protection concerns partly undermine digital adoption in the Philippines (The World Bank, 2020). Chiu et al. (2017) asserted that mobile banking services in the country remain poor; however, ascertaining trust can significantly influence future usage. Along this line, participation in the sharing and collaborative platforms can accelerate as trust perception improves. Furthermore, as the incidence of the Covid-19 pandemic embraces the importance of digital technologies, this may as well propel the adoption of sharing economy and collaborative consumption in the future.

Prior works broadly recognized the economic and environmental implications of the sharing economy as it generates economic incentives and helps reduce waste and carbon footprint. These features reveal to support the involvement among users and peer-providers (Botsman & Rogers, 2011; Lai & Ho, 2020; Schor, 2016). Nevertheless, the study contends the significant positive impact of perceived economic and environmental benefits on individuals’ attitudes and intentions. It appears that monetary returns are least expected in exchange for the shared resources in general, supporting the claims of Alzamora-Ruiz et al. (2020) and Urbonavicius and Sezer (2019). Ostensibly, it is only the leading companies in accommodation and ride-sharing sectors (e.g., Airbnb, Grab) that are pronouncing income opportunities to prompt people’s engagement. However, this also entails acquiring capital-intensive assets that people can hardly own in a developing country like the Philippines. Offering monetary returns in exchange for the shared resources is considered by Belk (2014) as a pseudo-sharing practice, which erodes the pro-environmental and sustainable nature of collaborative consumption. It is pushing people to acquire more resources, hence disputing the original essence of the sharing economy since its inception (Botsman & Rogers, 2011). The insignificant impact of environmental perceptions implies that sharing in the platform economy is still rooted in social tenets shrouding over the traditional act of sharing. People’s awareness of the environmental consequences of being involved in collaborative practices may still be ambiguous. The sharing economy in the Philippines may still be at its early stages; hence further advocating its environmental benefits may instill awareness among people in the country. In the same vein, as consumers gain more experiences in the sharing economy, they may progress their pro-environmental perceptions (Wilhelms et al., 2017).

The findings also substantiate the role of perceived ease of use and perceived trust towards perceived behavioral and behavioral intention. As with prior studies in online systems, the study result corroborates to findings of Chen et al. (2021), Hansen et al. (2018), and Pavlou and Fygenson (2006). Indeed, the need to operate a technology-enabled platform is a feature that separates the sharing economy from its traditional forms. The ease of use or the user-friendliness of the platform enhances one’s behavioral control towards sharing in the platform economy in the future. It is most evident in young demographic cohorts given their proficiency and acceptance of modern technologies (Abrams & von Frank, 2014), including those in the sharing economy (Godelnik, 2017; Hwang & Griffiths, 2017). Given this, they may discern simplicity in the processes involved in operating the sharing platform. In addition, related studies recognized the significant role of perceived trust among sharing economy users (Boateng et al., 2019; Hawlitschek et al., 2018; Lee & Chow, 2020; Tussyadiah & Pesonen, 2018). This study likewise asserts its role over peer-provider participation. It appears that overall trust perception reduces the perceived uncertainty and the behavioral difficulty to accomplish sharing economy transactions. Hence, it is imperative to build confidence over the platform and in the users of shared goods/services to encourage prospective peer-providers.

Hofstede (2001) proposed several dimensions of national culture and studies adopted them in several consumer studies. Evidence recognized the effect of collectivism in e-commerce (Choi & Geistfeld, 2004) and green purchase behavior (T. N. Nguyen et al., 2017), renewable energy adoption (Higueras-Castillo et al., 2019), and technology acceptance (Srite & Karahanna, 2006). Collectivism is primarily manifested in the form of concern with others, including the sharing of tangible and intangible resources (Hui & Triandis, 1986). Though prior works articulate the preceding variable relationship in other consumer behavior contexts, the study result maintains that the relation is valid in the sharing economy participation context. It implies that individuals who manifest deep concern for others and recognize in-group relevance will be more receptive to others’ opinions and will more likely behave following the in-group norms. Nevertheless, subjective norms did not show a significant and positive effect on sharing intentions, arguing extant contentions. Also, contrary to the claims of Higueras-Castillo et al. (2019), Perfili et al. (2019), and Srite and Karahanna (2006), the analysis shows a negative relationship between Collectivism on attitude to behavior, although found insignificant.

In the pragmatic sense, the results imply that platform providers may need to mainstream the social nuances of the sharing economy to foster consumers’ participation as peer providers. For instance, advocating it as a platform that channels support for people in need may actuate an individual’s altruistic belief and sharing intentions. Also, embedding platform features that enhance the quality of social interaction and fun (e.g., gamification) may be a compelling strategy to entice peer-provider participation. The integrity of transactions transpiring in the sharing platform is deemed essential; hence, platform providers must assure the public of their system security and privacy protection measures. There is also a need to reinforce the currently adopted trust-building mechanisms (i.e., reviews or ratings) to infuse one’s confidence with other people using the shared resources. For instance, damages on shared tangible resources after use need resolves to protect peer providers’ rights should be available. Platform providers should also espouse the ease and comfort associated with using their web or app-based platform. They can also target the younger generation due to their proficiency in modern technology use. However, encouraging participation from older generations who can hold excess resource capacities may need more enlightenment on the platform use. Collectivistic orientation endorses the salience of interpersonal influences to one’s behavior, yet, the pervasiveness of tech-driven sharing or collaborative models in the Philippines remains limited. Nevertheless, as actual usage accelerates, covering a significant portion of the populace may accordingly increase the potency of social influences, including the pro-environmental perceptions towards the sharing economy in general.

Meanwhile, other countries (e.g., Southeast Asia) which may share similar contextual characteristics with the Philippines in terms of the level of maturity of the sharing economy models, digital competitiveness, cultural orientation, and prosocial tendencies may draw from our findings in the design of plausible strategies to encourage more people to share their resources in technology-enabled platforms. The Philippines and its citizens’ distinct and innate tendency to extend help and manifest concern for the needs of others and the community (Ang, 1979) brace the act of sharing as a custom practice; hence, emphasis points to the promotion of the social nuances and enhancement of trust-building features to incite more peer-providers. Ultimately, insights in this research may help advance the economic, social, and environmental promises of the sharing economy and befit as an avenue to attain a more sustainable future across the globe.

6. Conclusion and limitations

The research presents a distinctive behavioral pattern among potential peer-providers in the technology-driven sharing platforms in a developing country. The study presents varying degrees of influences of the selected antecedents to one’s attitude, subjective norm, perceived behavioral control, and the intention to share resources in online sharing platforms. In a developing country where the sharing economy is at its early stages, it appears that peer-provider participation remains shrouded over the altruistic and social tenets emanating from the traditional concept of sharing. Also, its novelty and less widespread recognition in society still undermine the potency of interpersonal influences. Meanwhile, as with other technology-enabled systems budding in less-developed economies, overall trust remains a core determinant to propel one’s affirmation and control over sharing resources in the online platforms in the future. People in ordinary social class and the young generations may still not have the possessions (e.g., cars, apartments) to take advantage of economic incentives offered by for-profit sharing economy platforms like Airbnb and Grab. At the same time, a poor understanding of its environmental consequences remains. Hence, within the study context, the economic and pro-environmental features of the sharing economy as dominant drivers of consumer and peer-provider participation are opposed. This research is also in line with the contention that embedding cultural values in theoretical models of consumer behavior may capture a better picture of the phenomena under investigation (Srite & Karahanna, 2006). The study found that collectivism exhibit a significant positive effect on the subjective norm, thus endorsing empirical findings in other consumer behavior contexts. Relevant insights are also proposed to existing and prospective platform providers to stimulate people to share their resources through their online sharing platforms.

Though this exploratory work contributes to the ongoing discourse around the sharing economy, we recognize several limitations that future research needs to address. First, the study examines the behavioral antecedents towards sharing of resources in general. Underlying causes of sharing behavior may display different dynamics when the examination points towards a specific resource type. Researchers may pursue works that dissect behavioral patterns based on a specific type of resource. Second, the present work solely looks at behavioral intention over actual behavior. Arguments prevail as to the translatability of behavioral intentions to actual behavior; thus, future studies can examine the relationship of both variables. Third, the study sample is represented mainly by Gen Z and Gen Y cohorts. Despite their high propensity to adopt modern technologies and their potential to back the sharing economy, their sharing intentions limit their capacity to share-capital intensive resources. The acquisition of assets to be shared in for-profit sharing economy platforms may not be financially feasible. Global sharing economy firms offering monetary returns to peer-providers are reputed to engage in pseudo-sharing practices (Belk, 2014), which in turn erodes the concept’s original essence. As people own more resources accordingly increases resource capacities and magnifies the possibilities of environmental rebound effects. Future investigations can improve the sample’s composition if the focal point directs towards the sharing in prominent and for-profit sharing economy platforms. Fourth, the present work only examines a selected group of antecedents argued to influence behavioral outcomes in the sharing economy. Likewise, the probe on the cultural attribution of behavior only accounts for the cultural dimension of collectivism as it is deemed relevant according to previous studies in another consumer behavior context. Future research can integrate other determinants of behavior and include other dimensions of culture operationalized at the individual level. Lastly, this work recognizes that the findings may not be generalized in all contexts. Certain limitations and potential bias arise from self-reported online surveys; hence, future research can adopt approaches that further limit response consistency effects and subsequently reduce common method variance and response bias. Scholars can carry out complementary qualitative research as well to acquire an in-depth account of the inferred relationship parameter relationships. Moreover, as this investigation was conducted in the middle of a crisis, there is a need to continuously track behavioral patterns and validate the current findings, especially in the post-pandemic world.

CRediT author statement

Mark Ratilla: Conceptualization, Methodology, Software, Writing - Original Draft Sandeep Kumar Dey: Methodology, Software, Formal analysis Miloslava Chovancová: Resources, Supervision, Project administration, Funding acquisition

Acknowledgements

The authors are thankful to the Internal Grant Agency of FaME through Tomas Bata University in Zlin No. IGA/FaME/2020/002 (The impact of digital transformation on customer behavior and firm’s sustainable performance) and IGA/FaME/2021/003 (Consumer behaviour and performance management of firms in a competitive digital world) for financial support to carry out this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Tomas Bata University in Zlin [IGA/FaME/2020/002 and IGA/FaME/2021/003].

Notes on contributors

Mark Ratilla

Ing. Mark Ratilla is a Ph.D. candidate in the Department of Management and Marketing at the Tomas Bata University in Zlin, Czech Republic. His research interest spans sharing economy participation, e-commerce, and cross-cultural consumer behavior. His current research intends to contribute to the sharing economy’s ongoing discourse and leverage its advantages in the developing world.

Sandeep Kumar Dey

Sandeep Kumar Dey is a Ph.D. candidate in the Department of Business Administration at the Tomas Bata University in Zlin, Czech Republic. His research interest centers around sustainable consumption in the tourism and hospitality industry.

Miloslava Chovancová

doc. Ing. Miloslava Chovancová, CSc. is an associate professor at the Department of Marketing, Tomas Bata University in Zlin, Czech Republic. She also chairs the academic senate of the Faculty of Economics and Administration in the same university. Her research focus covers the fields of cross-cultural consumer behavior, brand management, and entrepreneurship. She has an excellent track record in publishing several research outputs in reputable journals.