Next Article in Journal
Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach
Next Article in Special Issue
Host–Guest Interaction and Sustainable Consumption Behaviour on Sharing-Accommodation Platforms: Using a Big Data Analytic Approach
Previous Article in Journal
Comparative Studies of Thermal Conductivity Determination in Synthetic Wood with Recyclable Waste Content Using an Experimental Design Approach
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty?

by
Gonzalo Llanos
1,
Nataly Guiñez-Cabrera
2,*,
Katherine Mansilla-Obando
1,*,
Esteban Gómez-Sotta
1,3,
Paulo Buchuk
1,
Matías Altamirano
1 and
Marcelo Alviz
1
1
Faculty of Economics and Business, Universidad Finis Terrae, Santiago 7501015, Chile
2
Department of Business Management, Faculty of Business Sciences, Universidad del Bío-Bío, Chillán 3810189, Chile
3
Faculty of Education and Social Sciences, Universität Vechta, 49377 Vechta, Germany
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(21), 15482; https://doi.org/10.3390/su152115482
Submission received: 24 August 2023 / Revised: 9 September 2023 / Accepted: 28 September 2023 / Published: 31 October 2023
(This article belongs to the Special Issue Sharing Economy and Sustainability)

Abstract

:
This study presents a consumption model tailored for emerging markets beyond BRICS, which considers the advantages of a sharing economy service and its impact on user behavioral intention. Moreover, it integrates moderating variables to enhance the understanding of consumer behavior toward adopting collaborative consumption services, making two significant contributions to the existing literature. By drawing data from 270 customers from Chile and utilizing variance-based structural equation modeling along with partial least squares techniques that use SmartPLS, the research findings highlight that convenience and enjoyment play the most pivotal roles in influencing consumer behavioral intention. Additionally, the study reveals that gender significantly moderates the relationship between convenience and purchase intention. Overall, this research sheds light on the potential of sharing economy services in emerging markets, providing valuable insights into consumer preferences and behavior, which can prove beneficial for businesses and policymakers alike.

1. Introduction

Over time, technological advancements have significantly influenced consumers’ behaviors regarding their needs and preferences. Historically, owning a product was the prevailing method for consumers to access it, but Matzler et al. [1] highlight that an increasing number of individuals now prefer paying for temporary access whether by financial gain, practical usefulness, a feeling of belonging, or even sustainability considerations [2]. Consequently, numerous firms have entered the market, leveraging digital technologies and disruptive business models to embrace collaborative consumption, displacing traditional market leaders through their innovative practices that promote temporal access to goods and services, rather than ownership.
The rising popularity of collaborative consumption models has led to extensive research from various perspectives [3]. However, there remains room for further development, particularly in distinguishing concepts like the sharing economy and collaborative consumption in the context of emerging markets [3,4]. To address this gap, this study aims to explore the following research question: Which relevant aspects may help to explain consumers’ behavior and motivations that incentivize their participation in collaborative consumption in emerging markets during uncertain times?
To address the research question, we employ quantitative techniques on a sample of 270 Uber customers in an emerging market country (Chile). Additionally, we adopt Bardhi and Eckhardt’s [5] definition of collaborative consumption, emphasizing an exchange where transactions occur as a transfer of ownership and are mediated by the market. Moreover, following Benoit et al.’s [4] framework, we recognize the participation of three key players: platform providers, peer service providers, and customers, all facilitated through market mechanisms.
Previous studies have investigated collaborative consumption and related phenomena. For instance, Bardhi and Eckhardt [5] defined car sharing as access-based consumption and studied Zipcar through 40 interviews in Boston, MA, USA. Kim et al. [6] explored the factors influencing users’ trust in the service platform using Airbnb guest interviews in South Korea. More recently, Luri Minami et al. [3] studied consumer behavior differences between sharing economy and collaborative consumption with 400 participants in Brazil. Additionally, Kim and Yoon [7] examined social capital as a motivation for collaborative consumption participation in South Korea, surveying 235 respondents. However, most of these studies were conducted in stable settings, including developed markets and large BRICS countries.
Thus, this study aims to contribute to the literature by investigating collaborative consumption motivation within Uber in an emerging market, specifically in Chile during times of political uncertainty. Even if Uber is a well-known company used as an example in various research [3,4,6,8]. Chile serves as an excellent research sample for various reasons. It is an emerging market where Uber has established itself as the leading transportation company, displacing traditional cabs and giving rise to follower companies like Beat or Cabify, creating high adherence to collaborative service. Moreover, Chile has been used as a recognized sample of emerging markets in several studies [9,10] due to its economic stability, growth potential, and attractive investment destination for international companies.
However, Chile has been grappling with a trifecta of political, social, and economic uncertainties since October 2019. These contexts of uncertainty form the backdrop against which our study unfolds. To provide a more nuanced perspective, the economic uncertainty is related to two pivotal events: the after-pandemic economic condition and a social outburst followed by a “Constituent convention” tasked to write entirely a new constitution. Concerning the post-pandemic economic uncertainty, a technical report authored by Montt et al. [11] for the International Labor Organization, a subsidiary of the UN, characterized the situation as an unprecedented economic crisis in Chile’s recent history, exerting profound ramifications on the labor market. Furthermore, real GDP growth slowed to 2.4 percent in 2022 as reported by the World Bank. This evidence underscores the essence of economic uncertainty, which denotes a situation characterized by an uncertain or unpredictable economic outlook. Even before the economic tumult brought about by the COVID-19 pandemic, Chile was navigating the repercussions of a social outburst that ignited in October 2019. In November 2019, a significant milestone was reached when most political parties signed an accord aimed at fostering peace and initiating the process of drafting a new constitution. From March to August 2022, the constituent process unfolded, seeking to propose a fresh constitutional framework. Notably, on 4 September 2022, a general election marked the rejection of the proposed constitution. Consequently, the period encompassing our study was characterized by economic policy uncertainty that manifested as economic fluctuations attributable to the unpredictability of fiscal, regulatory, and monetary policies, as elucidated by Al-Thaqeb et al. [12]. Moreover, in line with Cuervo-Cazurra et al. [13], the conceptualization of political uncertainty as the potential for significant transformation in the political landscape, it is crucial to highlight that the present study occurred during a phase of political uncertainty in Chile, which included the possibility of the replacement of the Senate. This unique situation provides a unique context to study consumer behavior since previous studies indicate that high levels of uncertainty may lead to changes in financial and consumption decisions [12]. Moreover, in this context, we adopt the economic crisis concept proposed by Lindblom and Lindblom [14], which refers to a time when a country experiences significant economic and financial difficulty that affects society broadly and deeply. Hence, this research study emphasizes the importance of a collaborative orientation as an exchange system that gains significance during times of uncertainty. People in such situations promote collaborative consumption for economic savings, environmental responsibility, and alternative income sources [15].
Theoretical hypotheses are tested by analyzing the motivations of Chilean consumers to participate in these services, differentiating between extrinsic (EM) and intrinsic (IM) motivations under the theory of self-determination [16]. The study identifies latent constructs of each motivation in participants in this type of exchange. Empirically, the research surveyed 270 participants to develop and test a conceptual model on extrinsic and intrinsic motivations moderated by perceived service factors using the variance-based structural equation modeling with partial least squares (PLS).
The findings suggest that individuals primarily choose Uber for its convenience factor. However, enjoyment, security, and environmental orientation are also significant motivators for consumers of this service. Furthermore, people perceive Uber as a sustainable service that helps conserve natural resources and is ecologically favorable. Finally, gender and education play moderating roles, where gender affects the relationship between convenience and behavior, and education level influences the link between environmental perception and behavior.

2. Literature Review

Collaborative consumption has been a subject of interest in various research studies exploring new forms of exchange. However, due to its relatively recent emergence, there is still a general misunderstanding of its meaning among service providers and consumers [3]. To gain a clearer understanding of this concept, it is essential to explore the fundamental aspect that defines this new form of exchange: sharing. Belk [17] defines sharing as the act of distributing something belonging to an individual for others to use or the act of receiving something belonging to others for our use. This broad concept makes it possible to understand the essence of collaborative consumption by underlining a potential value and active recognition of possible reciprocity [18]. Belk [17] further defines collaborative consumption as the coordination of resource acquisition and distribution in exchange for payment or other compensation, excluding sharing activities where no compensation is involved, such as Couchsurfing.
Collaborative consumption embodies two crucial elements: the use of temporary, non-ownership access models for consumer goods and services, and its reliance on the Internet, particularly Web 2.0, for connection and facilitation [19]. The rise of Web 2.0 has led to the development of a hybrid economy, characterized by the coexistence of different exchange modes [20]. Collaborative consumption has become one of the most prominent forms of consumption within Web 2.0 [3] and has significantly impacted consumer trends, leading to the emergence of new business models with global reach, as exemplified by companies like Uber and Airbnb [5].
Consumers’ shift towards collaborative consumption is driven by various factors. Garcia [21] suggests that consumers are seeking alternatives to traditional ownership due to associated costs and burdens, especially with goods like cars, which incur maintenance expenses. Schor and Fitzmaurice [22] identified five motivations for participation in collaborative consumption: economic aspect, benefit, connections and social networks, emotional factors, and ecological considerations. Along the same line, Möhlmann [23] also found that consumers mainly engage in collaborative consumption for rational reasons, particularly economic benefits obtained by obtaining needed goods at low costs. Recently, Luri Minami et al. [3] distinguished aspects that act as drivers for participating in collaborative consumption, differentiating between EM and IM based on self-determination theory [24] as a theoretical lens. Additionally, Belk [17] points out that factors like global warming, increasing prices of goods, and growing environmental awareness are influencing the likelihood of participating in this type of exchange. Mazareanu [25] highlighted the significant penetration of collaborative consumption services in the market, projecting a 23% increase in global collaborative consumption, traditional leasing, and car sharing between 2013 and 2025. This situation emphasizes the importance of better understanding the factors that motivate consumers to participate in collaborative consumption.
The existing literature on potential motivations for participating in collaborative consumption serves as a basis for analyzing the market and understanding consumers’ particular motivations.

3. Theoretical Framework, and Hypotheses Development

3.1. Self-Determination Theory

Self-determination theory (SDT) resolves around human motivation, development, and well-being. It delves into various types of motivation, with a particular focus on autonomous motivation, controlled motivation, and demotivation as predictors of performance, relationship, and well-being outcomes. Additionally, SDT explores individuals’ life goals or aspirations, highlighting the distinct impact of intrinsic versus extrinsic life goals on performance and psychological health [24]. Thus, SDT allows us to assess people’s psychosocial predispositions and their compatibility with new experiences when becoming aware of alternative roles, enabling us to identify the mechanisms that generate trust and also motivate changes from being consumers to being providers in the context of the collaborative economy [26]. In essence, self-determination theory connects personality, human motivation, and optimal functioning by postulating two main types of motivation, EM and IM, both of which influence our identity and behavior [3,27,28].
In this study, self-determination theory [24] serves as a theoretical framework that is widely recognized and is employed in research concerning motivation in collaborative consumption [3,27,28]. Through this theory, we distinguish the motivations of direct relationships by examining EM and IM. Specifically, our goal is to investigate how direct relationship motivations impact consumers’ behavioral intentions.
To conduct this research, we utilize direct relationship motivations previously studied in the existing literature, carefully selected based on their implications in prior investigations. Moreover, we aim to identify potential differences in these motivations compared to what consumers in emerging markets value.

3.2. Hypotheses Development

3.2.1. Extrinsic Motivations (EM)

External regulation exists within EM, causing individuals to engage in a behavior to meet external demands or to obtain rewards, causing them to feel controlled or coerced [29]. Additionally, introjected regulation, a form of self-imposed regulation, comes into play, driven by the desire to avoid anxiety, achieve ego-enhancing accomplishments, and focus on aspects related to status and pride [29]. Therefore, acting under the influence of EM involves pursuing rewards, seeking social recognition, and finding a sense of fulfillment in response to external pressures. The constructs associated with this type of motivation, which are relevant to the current study, will be elaborated below.
  • Convenience
In this study, the term “convenience” is defined based on the relevant literature as a person’s inclination towards completing a task in the quickest possible time with the least expenditure of energy [30]. This concept is associated with utilitarian value concerning a product or service, taking into account its benefits and costs [31]. Moeller and Wittkowski [31] have previously analyzed this concept in the context of collaborative consumption, referring to convenience as the relief from the burdens of ownership, such as product selection, full payment for infrequently used items, product obsolescence, and maintenance or repair costs. Collaborative consumption offers a way to circumvent these drawbacks by providing greater flexibility, convenience, and faster and more independent access to products and services that would otherwise not be accessible [32]. Therefore, it is essential to consider that convenience is part of an extrinsic factor in consumer motivations based on self-determination theory [24]. Similarly, Bardhi and Eckhardt [5] emphasize the relevance of convenience concerning collaborative consumption and consumer motivations.
Consumers may be motivated by convenience, as highlighted by Bardhi and Eckhardt [5], when the location of the object or product is closer to the consumer’s place of residence. This proximity, as exemplified by the service offered by Uber, can create a sense of ownership for consumers. Thus, convenience orientation is linked to time and effort minimization, including the physical energy spent in the process of purchasing. Consequently, renting can be seen as a form of convenient consumption, making it a plausible orientation that motivates consumers to participate in this type of consumption [31].
Luri Minami et al. [3] also point out the consequences of ownership, such as incorrect product selection, maintenance or repair efforts, and the full cost of owning infrequently used items. They incorporated this concept as an extrinsic factor, suggesting a preference to rent assets rather than own them. Considering the existing literature on extrinsic convenience motivation, we propose the following hypothesis:
Hypothesis 1.
Perceived convenience benefit drives consumers to engage in collaborative consumption.
  • Security
Security is a crucial aspect of utilizing transportation services [33]. Particularly in the sharing economy context, two significant concerns come to the forefront. First, transactions conducted through Web 2.0 and their corresponding online payments are perceived as carrying substantial risks for consumers [34]. Second, the sharing economy, involving the use of a product without transferring ownership, is characterized by information asymmetry, raising the possibility of unmet expectations [35].
In addition to the aforementioned factors, there is an underlying risk associated with the interaction between the customer and the provider, both within and outside the sharing economy. This interaction, extensively studied in the services field, can have significant implications. While it may seem simple, as it involves the one-time transfer of a persona from one place to another, it still carries a substantial risk. While there has been an extreme risk situation associated with the use of Uber [36], there is a prevailing notion that traditional transportation services, like public transit, are comparatively riskier.
Considering these aspects, perceived security becomes a crucial factor in the utilization of the sharing economy, specifically in the case of Uber.
Hypothesis 2.
Perceived security benefit drives consumers to engage in collaborative consumption.

3.2.2. Intrinsic Motivations (IM)

IM can be defined as the driver to explore the environment and experience enjoyment in an activity without seeking external rewards. The activity itself serves as a source of gratification. The constructs associated with this type of motivation, relevant to this study, are detailed below.
  • Enjoyment
Individuals may engage in collaborative consumption platforms because they find it enjoyable and meaningful, providing them with a fulfilling way to interact with others. Enjoyment is considered a fundamental intrinsic factor in human motivation [24] and is closely linked to the concept of perceived hedonic value [3]. It represents finding purpose and pleasure in the activity itself, characterized by feelings of fun, joy, and excitement. Enjoyment significantly influences attitude and behavioral intention toward collaborative consumption [27]. Service designs, like social networking services, that promote relatedness play a crucial role in intrinsically motivated use, such as seeking enjoyment [27].
For many consumers, the experiential aspect of consumption holds great importance. The non-ownership model in collaborative consumption allows individuals to access experiential products or services for a limited time, during which they can enjoy the product or service as long as it generates positive emotions and enjoyment [31]. Similarly, Luri Minami et al. [3] describe enjoyment as an element of intrinsic motivation, defining it as the pleasure experienced while engaging in an activity, encompassing emotions like fun, joy, and excitement.
Given the very essence of collaborative consumption platforms, which offer experiences that fulfill consumers’ needs through digital and experiential trends, it is anticipated that consumers of collaborative consumption value enjoyment as a driving force in their decision to participate. Therefore, based on the existing literature on IM, we propose the following hypothesis:
Hypothesis 3.
Perceived enjoyment benefit drives consumers to participate in collaborative consumption.
  • Environment
Several authors have noted that environmental orientation is relevant to participation in collaborative consumption, but it may not hold the same significance as enjoyment [27]. According to Bardhi and Eckhardt [5], consumers are generally more concerned about their interests and well-being rather than the environment. Similarly, Schaefers [37] found that, while respect for the environment in carpooling is seen as a positive side effect, it is not the dominant motive. Da Costa Coelho and Abreu Romero [38] also commented that sustainability is often an outcome of participating in collaborative consumption, rather than the primary motivation.
However, Luri Minami et al. [3] state that collaborative consumption platforms facilitate the reuse of goods and services, reducing the need to produce new items and increasing the efficiency of resources. Shared solutions are generally considered to have more environmental benefits compared to non-shared alternatives, particularly in car sharing and ridesharing, where combining material goods leads to higher resource utilization [23,39].
Collaborative consumption has been viewed as a mode of consumption that appeals to environmentally conscious consumers. Recognizing collaborative consumption as a sustainable activity can lead to increased participation in sharing platforms [27]. According to Costain et al. [40], consumers are willing to choose more expensive alternatives as long as they are environmentally friendly. Puiu [28] suggests that green consumption influences participation in collaborative consumption. Green consumption occurs when individuals behave ethically, motivated by their needs while also considering the welfare of society and the environmental impact of their consumption choices. From an economic and environmental standpoint, green consumption is considered an essential factor in the context of collaborative consumption, which generally incorporates concepts of social contribution in environmental responsibility. Therefore, building on the existing literature on environmental IM, we propose the following hypothesis:
Hypothesis 4.
Perceived environmental benefit drives consumers to engage in collaborative consumption.

3.2.3. The Moderations Role of Gender and Educational Level

  • Gender and its moderation role
When investigating consumer behavior, it is essential to evaluate other factors, such as gender roles, as they play an important role in responding to these analyses [41]. Women tend to make purchase decisions based on the quality and value of the product, as well as the comfort and emotional satisfaction it provides them [42,43]. Meanwhile, men tend to look for products that provide them with status and power, as well as those that fit and reinforce their masculine identity [44]. However, it is essential to recognize that each individual has different preferences and motivations when making purchasing decisions [45].
Gender roles are social constructs and can vary significantly from one individual to another, so one cannot generalize purchasing preferences based on gender alone. However, gender differences may influence marketing and advertising strategies, and it is feasible to assume an adaptation to specific preferences according to region, type of economy, or even form of social implications [46]. In addition, the cognitive frameworks of women and men may differ, resulting in systematic differences in their thinking and acting [47,48,49]. Likewise, it is crucial to recognize that the experiences that shape this social construction lead to understanding the world in different areas, such as ethics and risk-taking, having an even more significant effect on purchasing decisions. It is, therefore, essential to realize that gender differences do not remain unchanged but change dynamically with time, technology, and society [50].
On the other hand, wage disparities that cause men to earn more than women [51], and behaviors associated with care responsibilities at home by women, have a significant impact on the autonomy and equal opportunities between men and women [52]. It is also important to observe the lack of representation of women in the process of implementing laws that protect their rights at different levels and spaces, causing them to experience vulnerability from educational experiences regarding stereotypes and prejudices that perpetuate discrimination in gender difference [53]. It is crucial to assume that education is a structural and cultural factor that maintains equality/inequality in multiple options concerning the freedom of those involved [54], making it necessary to create educational spaces in favor of the dissolution of prejudices at an early age [55,56], as well as to advance in the adoption and adaptation of new ideas and innovations in favor of an equitable and sustainable society, which guide the circular economy and collaborative consumption [57] with the different social actors, the family, the media and community organizations.
Considering the above factors, we can suggest differences in the adoption of collaborative consumption by gender, even during forms of social and economic uncertainty [58,59]. However, we must remember that the factors that drive people to participate in collaborative consumption may vary based on individuals’ diversity because of the complexity of personal and contextual factors that articulate economic uncertainty with contexts that depend on social norms, stereotypes, resources, opportunities, and alternatives oriented to the satisfaction or inhibition of needs according to gender. This allows us to ask whether there is a relationship between collaborative consumption and the adoption behavior of men and women.
Hypothesis 5.
Female/Male gender will positively/negatively, respectively, moderate the relationship between convenience of collaborative consumption and adoption behavior.
  • Educational level and its moderation role
As mentioned, one of the fundamental characteristics of collaborative consumption is that it offers alternatives and advantages oriented to sustainability manifested in key attributes that coexist with respect for the environment, discouraging traditional consumption models that prescribe product obsolescence [60]. In this sense, education configures discourses, thematic, and disciplinary axes in standard geographic states of learning [61,62], pressuring changes in the management of companies to rethink the needs of consumers from the different formats and levels of training in the teaching and learning processes [63]. Therefore, education plays a crucial role in favor of environmental benefits articulated with economic and social understanding.
A higher level of environmental knowledge correlates significantly with a higher degree of environmentally friendly and conservation behavior [64,65]. As stated by Žalėnienė and Pereira [66], higher education contributes significantly to the implementation of the SDGs; it is considered a “change agent” that empowers sustainable development in the contexts where education governance processes are located [67,68]; it is being relevant for the (trans)formation of awareness and understanding of organizations that are oriented and adapted to educational initiatives in different territories and communities, where their multiple stakeholders must decide to coordinate and articulate between the perspectives of sustainability and the search for organizational legitimacy [69,70].
Therefore, education has the potential to influence the perceptions of its environments and contexts, organizing the social processes that occur in it. Education also addresses the perceptions of consumers and their consumption patterns [62], preparing the conventional economics perspective to dispose of sustainability. Thus, by identifying key competencies of the Education for Sustainable Development goals [71], such as developing appropriate pedagogies and strategies [72] and managing educational outcomes and objectives, the ambiguity and uncertainty of sustainability from economic, social, and environmental perspectives will be adjusted [73]. Then, the results of teaching and learning processes will also impact self-interest and encourage orientation towards others, organizing states that involve prosocial behavior, personal initiative, and collaborative performance [74], and completing human capabilities in favor of new market trends, technology industries, and communicational networks [75].
For its part, improving access to education and equalizing and guaranteeing quality at all levels of education will provide knowledge, competencies, and capabilities for the present and future [76], as well as improve the educational level in extracurricular activities, encouraging students to develop more competencies that facilitate collaboration, strategic thinking, collaborative problem solving, and systems thinking among others [77,78]. Then, individuals with higher exposure and education levels will have greater awareness and pro-environmental behavior, even in developing countries [79].
Meanwhile, focusing on observing the results in education will allow us to identify and differentiate (recognize), in an efficient way, the perspectives in favor of sustainability, which will bring us closer to the debate on the environmental and economic benefits of collaborative consumption. Therefore, reflecting on the educational level as a moderator in behavior is not far from the conditioning factors that manifest in collaborative consumption.
Hypothesis 6.
Higher/lower educational levels will positively/negatively, respectively, moderate the relationship between environmental recognition of collaborative consumption and adoption behavior.
Figure 1 detail the proposed framework for this study.

4. Methodology

4.1. Research Design

This study employed a quantitative research methodology based on surveys. Through this approach, we aimed to test the hypotheses by analyzing path coefficients and effect sizes [80]. Data collection was conducted using a self-administered survey.

4.2. Instrument Development

The survey questions were carefully selected from validated scales used in existing literature. The instrument was administered through Google Forms, employing a convenience sampling procedure. The survey was available from 25 May to 19 June 2022, targeting individuals with easy access and availability to participate in the sample. It was primarily disseminated through the authors’ social networks, inviting people to cordially contribute to this study that was focused on consumer perceptions. Respondents were not offered any incentives or rewards.
Regarding the questionary composition, statements about economic, enjoyment, environmental benefit, and behavioral intention were adapted from Hamari et al. [27]. The convenience theme was developed based on the work by Colwell et al. [16], while statements concerning security were derived from the study by Gu et al. [33].

4.3. Sample and Procedure

The survey was addressed to people aged 18 and older who had used the Uber service at least once. Respondents answered 39 questions, organized into three sections, using a Likert scale to indicate their level of agreement or disagreement, with a value of 1 representing “Strongly disagree” and a value of 5 representing “Strongly agree”. This approach allowed us to measure consumers’ perceptions and gain valuable insights into the specific motivations driving their participation in this type of exchange. To avoid order bias, the questions were randomized [81].
Respondents who had not used the Uber service were automatically directed to complete the survey process. This approach aimed to gather information on the user experience and identify motivations for using the service beyond the general perceptions of individuals who were familiar with the service but who had not personally used it.
After collecting the responses, a filter was applied to eliminate respondents who reported not having used the Uber service. The final sample consisted of 270 surveys. Within the sample, 78.1% were individuals between 18 and 35 years old. Approximately 53.7% identified as female, while approximately 45.9% identified as male. In terms of education level, 51.1% reported having incomplete higher education (including students in the process of graduating), 40.7% completed higher education, and only 8.1% had completed high school. In terms of the region of residence, 88.1% reported being from the metropolitan area (Table 1).

4.4. Analytical Procedure

To conduct this explanatory–predictive study [80,82], we employed the partial least squares structural equation modeling (PLS-SEM) technique using SmartPLS. This approach involves two stages of linear equations: the measurement model and the structural model. The measurement models reveal the relationships between the constructs and their indicators while the structural model assesses the relationships between the constructs [83]. Additionally, we performed a predictive performance evaluation of the model using the holdout samples technique [84].
The choice to use the PLS-SEM technique was driven by the need for a composite measurement model for one of the constructs [85,86], and the fact the model was estimated from an explanatory–predictive perspective [80,84].

5. Results

The results for Uber indicate that a coefficient of determination, R-squared, is 0.742 for behavioral intention. This means that, initially, the benefits of comfort, security, enjoyment, and environment account for 74.2% of the variance of behavioral intention. See Table 2.

5.1. Evaluation of the Measurement Model (Outer Model)

First, the model’s reliability was assessed by considering the outer loadings, which were found to be greater than 0.7 for the reflective measurements [82]. Next, composite reliability (C.R.) and Cronbach’s alpha were examined to verify the internal consistency, and their values surpassed the commonly accepted threshold of 0.7. To test convergent validity, the average variance extracted (AVE) was also considered, and its values exceeded the commonly accepted threshold of 0.5. See Table 3.
For testing discriminant validity, we employed the Fornell–Larcker criterion [87] and the Heterotrait–Monotrait ratio of the correlation matrix (HTMT) [88]. See Table 4 and Table 5.
The evaluation of the formative construct commenced by assessing the level of multicollinearity among the items, following the approach by Henseler et al. [89]. The maximum variance inflation factor (VIF) was found to be 1.781, well below the accepted threshold of 3.3 that was suggested by [90]. For the inner model, the maximum VIF was found to be 2.997, which was below the accepted threshold of 3.3; therefore, the model is considered free of common method bias considering the full collinearity test [91]. To evaluate internal consistency, the rho indicator, with an interpretation similar to Cronbach’s Alpha, was utilized. All values were satisfactory, exceeding 0.70 as recommended by Nunnally and Bernstein [92]. See Table 3 for more details.
Subsequently, a confirmatory composite analysis was conducted to assess the nomological validity of the model, following the methodology proposed by Henseler et al. [93] and Henseler [83]. The standardized root means square residual (SRMR) obtained a value of 0.053.

5.2. Evaluation of the Structural Model (Inner Model)

The proposed model was evaluated through a comprehensive analysis that included reviewing the path coefficients between the exogenous and endogenous variables, bootstrapping of critical ratios, and predictive relevance Q2, following broadly accepted methodology [94]. To assess the significance of the relationships between constructs, bootstrapping analysis with 10,000 subsamples was conducted, utilizing the one-tailed test as described by Kock [95]. The t-values and their corresponding p-values were thoroughly examined, and all paths were found to be significant at a = 0.05, except for the moderation of education level on the relationship between environment and purchase behavior, with a p-value of 0.075.
Regarding the specific variables, convenience (B = 0.509, t = 7.435, p < 0.01), security (B = 0.143, t = 2.384, p < 0.01), enjoyment (B = 0.263, t = 4.159, p < 0.01), and environment (B = 0.130, t = 1.802, p < 0.05) together explain 74.2% of the behavior attitudinal variable. Details information about these variables and the moderation can be found in Table 6.
Furthermore, the predictive significance of the model was found to be satisfactory, with a Q2 = value of 0.675, indicating an acceptable level of predictive power. See Table 7.
Finally, we present the moderation analysis. First, gender acts as a moderator in the relationship between convenience and behavioral intention. Figure 2 illustrates that both genders exhibit relatively low levels of convenience, but women show a significantly lower reaction compared to men.
Secondly, education has a weaker moderation effect on the relationship between environmental concerns and behavioral intentions. Figure 3 indicates that individuals with lower levels of education tend to significantly reduce their behavioral intentions when they perceive less concern about the environment from the company.
Based on the results obtained, there is support for hypotheses H1, H2, H3, H4, and H5. However, regarding H6, which explored the moderation of education level on the relationship between environmental benefits and attitude towards purchasing, the results had a p-value of 0.075 but showed a contrary effect to the one proposed (see Figure 4). This contradiction might stem from increased awareness and consciousness about environmental issues at the high school level, leading to more environmentally friendly behavior. However, with higher educational levels, the impact of environmental awareness campaigns might be relatively lower.
The numbers represent the path coefficient, and the p-values are in parentheses.

6. Discussion and Conclusions

Collaborative consumption is no longer a niche trend; on the contrary, it involves millions of users and constitutes a profitable trend in which many companies invest [96]. Despite the revolution of collaborative consumption companies in the global market, surprisingly little research focuses on understanding the motivations of consumers to participate in this type of sharing and collaborative exchange, especially during times of crisis and uncertainty.
Collaborative consumption has revolutionized how consumers access products and services [1]. However, recent research by Luri Minami [3] highlighted the limited knowledge about consumers’ motivations driving participation in sharing and collaboration movements, urging the need to explore their impacts. This study was inspired to delve into consumers’ perceptions and motivations to understand what drives them to participate in collaborative consumption, focusing on the Uber service and investigating potential gender differences in the valuation of convenience.
To build the theoretical model, a state-of-the-art review identified key issues related to collaborative consumption, exploring definitions and constructs that were studied and validated by previous authors. The study utilized the motivational model presented by Deci and Ryan [24], employing self-determination theory as a lens to differentiate intrinsic and extrinsic motivations in the theoretical model.
The results demonstrated that consumers place significant value on convenience-oriented benefits, particularly in terms of external rewards (extrinsic motivations). This finding aligns with previous studies [27], indicating that consumers are strongly motivated by convenience when participating in collaborative consumption.
Furthermore, intrinsic motivations related to environmental aspects and security were also important drivers for consumers. In the case of Uber service, convenience emerged as a key motivator, in line with the research by Bardhi and Eckhardt [5]. According to the results obtained from the moderators’ variables, although a low level of convenience leads to a lower purchase intention for both genders, the effect is greater for women than for men, with a significant difference in the situation of low convenience. However, this difference dissipates when the level of convenience is high, in which case, there is no significant difference between the purchase intention of men and women. Also, consumers appreciate the practicality and efficiency of this type of service, which allows them to request transportation and have a vehicle arrive promptly at their location, avoiding the effort and waiting times associated with traditional transportation options, as mentioned by Moeller and Wittkowski [31].
The enjoyment construct also played a significant role, aligning with the study by Luri Minami et al. [3] and Hamari et al. [27], who refer to hedonic value associated with activities that generate pleasure. This result is particularly relevant given the current environmental challenges facing the planet. Consumers recognize collaborative consumption, like Uber, as a sustainable service that contributes to environmental preservation through the sharing of assets rather than acquiring new goods. This eco-friendly aspect of collaborative consumption significantly impacts consumers’ behavioral intentions. This finding is aligned with other research [28,40] and enhances collaborative consumption as a model that is valued in its environmental contribution, under its activity of sharing assets instead of acquiring new goods, generating for Uber service, efficient use of vehicles, which minimizes the need to acquire a vehicle of their own.

6.1. Theoretical Contributions

The present study tests a motivational model focused on collaborative consumption as a form of exchange, examining both extrinsic and intrinsic motivations using the theoretical lens of self-determination theory [24]. Through this approach, important theoretical contributions are made by analyzing consumer perceptions and generating insights into the motivations driving consumers to participate in collaborative consumption, specifically in the case of Uber. It is worth noting that most existing motivational studies in the literature have primarily focused on direct relationships and their impact on behavioral intention. In contrast, our study emphasizes the direct relationship constructs, compiling and validating constructs previously evaluated in other research [3,27], to provide a deeper understanding of their relationship with behavioral intention.
Furthermore, the consideration of moderators has been relatively overlooked in previous studies, leaving unanswered questions about individual differences that may influence the adoption of sharing economy services. As a result, our study contributes to advancing knowledge in this line of research, shedding light on the role of gender and educational level as potential moderators in the context of collaborative consumption.

6.2. Managerial Implications

This research makes valuable contributions to management, particularly in the context of collaborative consumption businesses. The finding reveals essential intrinsic and extrinsic motivations that play a crucial role in collaborative consumption. Consequently, companies such as Uber and similar platforms can utilize these results to refine their marketing strategies and attract new consumers. Emphasizing aspects of convenience, enjoyment, and environmental impact can be instrumental in attracting and retaining customers. Additionally, other collaborative consumption companies can gain insights from these findings to better understand the factors influencing consumers’ behavioral intentions.
Furthermore, effectively communicating the benefits of convenience and environmental responsibility can lead to competitive advantages for these businesses. It is recommended to invest in marketing strategies that transparently showcase the convenience of their service, highlighting the unique experiences they offer to consumers. It is particularly vital to consider the perceptions of women when assessing convenience ratings. Simultaneously, special care should be taken to communicate the environmental benefits genuinely, ensuring that the company is not perceived as engaging in “greenwashing” practices.

6.3. Limitations and Future Research

Like all research studies, this work has certain limitations that should be acknowledged. First, the analyses were conducted based on a cross-sectional sample, which means that it does not capture changes or trends over time. Consequently, it does not account for possible shifts in the economy that might impact the availability or quality of the service, nor does it consider other social, cultural, or political factors that could influence the finding.
Second, the study did not account for cultural differences in its measurements, potentially overlooking social norms that might influence how participants perceive the variables under study [97]. Future research could consider incorporating variables such as nationality of origin, time spent in the country of residence, and other cultural factors to gain a more comprehensive understanding.
Thirdly, the study focused on a specific service company, such as Uber, but it is essential to acknowledge that consumers’ responses might also be influenced by other competitors in the market. These factors could have an undetermined impact on the participants’ assessment and behaviors.
Finally, user experience in terms of time and frequency of usage could be an influential factor that warrants consideration in future research. Individuals with limited or infrequent use of the service may have different perceptions compared to those who use it regularly, leading to varying levels of appreciation for the service.
In conclusion, while this study provides valuable insights, addressing these limitations in future research can lead to a more comprehensive and nuanced understanding of consumer motivations and behaviors in the context of collaborative consumption services.

Author Contributions

Conceptualization, G.L., M.A. (Matías Altamirano) and M.A. (Marcelo Alviz); methodology, G.L.; software, G.L.; validation G.L.; formal analysis, G.L.; investigation, G.L., M.A. (Matías Altamirano) and M.A. (Marcelo Alviz); resources, G.L.; data curation, G.L., M.A. (Matías Altamirano) and M.A. (Marcelo Alviz); writing—original draft preparation, G.L., K.M.-O., E.G.-S. and P.B.; writing—review and editing, G.L., N.G.-C., K.M.-O., E.G.-S. and P.B.; visualization, G.L.; supervision, G.L.; project administration, G.L.; funding acquisition, N.G.-C. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Universidad of Bío-Bío.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. Uber company had no role in the design of this study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, and in the decision to publish the results. Also, the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Matzler, K.; Veider, V.; Kathan, W. Adapting to the sharing economy. MIT Sloan Manag. Rev. 2015, 56, 71–77. [Google Scholar]
  2. Li, H.; Wen, H. How is motivation generated in collaborative consumption: Mediation effect in extrinsic and intrinsic motivation. Sustainability 2019, 11, 640. [Google Scholar] [CrossRef]
  3. Luri Minami, A.; Ramos, C.; Bortoluzzo, A.B. Sharing economy versus collaborative consumption: What drives consumers in the new forms of exchange? J. Bus. Res. 2021, 128, 124–137. [Google Scholar] [CrossRef]
  4. Benoit, S.; Baker, T.L.; Bolton, R.N.; Gruber, T.; Kandampully, J. A triadic framework for collaborative consumption (cc): Motives, activities and resources & capabilities of actors. J. Bus. Res. 2017, 79, 219–227. [Google Scholar] [CrossRef]
  5. Bardhi, F.; Eckhardt, G.M. Access-based consumption: The case of car sharing. J. Consum. Res. 2012, 39, 881–898. [Google Scholar] [CrossRef]
  6. Kim, J.; Yoon, Y.; Zo, H. Why people participate in the sharing economy: A social exchange perspective. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS), Singapore, 5–9 July 2015. [Google Scholar]
  7. Kim, E.; Yoon, S. Social capital, user motivation, and collaborative consumption of online platform services. J. Retail. Consum. Serv. 2021, 62, 102651. [Google Scholar] [CrossRef]
  8. Pouri, M.J.; Hilty, L.M. The digital sharing economy: A confluence of technical and social sharing. Environ. Innov. Soc. Transit. 2021, 38, 127–139. [Google Scholar] [CrossRef]
  9. Buchuk, D.; Larrain, B.; Muñoz, F.; Urzúa, F.I. The internal capital markets of business groups: Evidence from intra-group loans. J. Financ. Econ. 2014, 112, 190–212. [Google Scholar] [CrossRef]
  10. Silva, F.; Majluf, N. Does family ownership shape performance outcomes? J. Bus. Res. 2008, 61, 609–614. [Google Scholar] [CrossRef]
  11. Montt, G.; Ordóñez, F.; Silva, I.; Velasco, J. Chile: Impacto de la COVID-19 Sobre los Mercados de Trabajo y la Generación de Ingresos; OIT: Santiago, Chile, 2020. [Google Scholar]
  12. Al-Thaqeb, S.A.; Algharabali, B.G.; Alabdulghafour, K.T. The pandemic and economic policy uncertainty. Int. J. Financ. Econ. 2022, 27, 2784–2794. [Google Scholar] [CrossRef]
  13. Cuervo-Cazurra, A.; Duran, P.; Arregle, J.-L.; van Essen, M. Host Country Politics and Internationalization: A Meta-Analytic Review. J. Manag. Stud. 2023, 60, 204–241. [Google Scholar] [CrossRef]
  14. Lindblom, A.; Lindblom, T. De-ownership orientation and collaborative consumption during turbulent economic times. Int. J. Consum. Stud. 2017, 41, 431–438. [Google Scholar] [CrossRef]
  15. Kaushal, L.A.; Prashar, A. Determinants of service consumer’s attitudes and behavioural intentions towards sharing economy for sustainable consumption: An emerging market perspective. J. Glob. Inf. Technol. Manag. 2022, 25, 137–158. [Google Scholar] [CrossRef]
  16. Colwell, S.R.; Aung, M.; Kanetkar, V.; Holden, A.L. Toward a measure of service convenience: Multiple-item scale development and empirical test. J. Serv. Mark. 2008, 22, 160–169. [Google Scholar] [CrossRef]
  17. Belk, R. You are what you can access: Sharing and collaborative consumption online. J. Bus. Res. 2014, 67, 1595–1600. [Google Scholar] [CrossRef]
  18. Bucher, E.; Fieseler, C.; Lutz, C. What’s mine is yours (for a nominal fee)—Exploring the spectrum of utilitarian to altruistic motives for internet-mediated sharing. Comput. Hum. Behav. 2016, 62, 316–326. [Google Scholar] [CrossRef]
  19. Carroll, E.; Romano, J. Your Digital Afterlife: When Facebook, Flickr and Twitter Are Your Estate, What’s Your Legacy? New Riders Publishing: Berkeley, CA, USA, 2010. [Google Scholar]
  20. Belk, R.W.; Eckhardt, G.M.; Bardhi, F. Handbook of the Sharing Economy; Paperback Edition; Edward Elgar Publishing: Cheltenham, UK, 2021. [Google Scholar]
  21. Garcia, H. Consumption 2.0. Futurist 2013, 47, 6. [Google Scholar]
  22. Schor, J.B.; Fitzmaurice, C.J. Chapter 26: Collaborating and Connecting: The Emergence of the Sharing Economy. In Handbook of Research on Sustainable Consumption; Edward Elgar Publishing: Cheltenham, UK, 2015; pp. 410–425. [Google Scholar]
  23. Möhlmann, M. Collaborative consumption: Determinants of satisfaction and the likelihood of using a sharing economy option again. J. Consum. Behav. 2015, 14, 193–207. [Google Scholar] [CrossRef]
  24. Deci, E.L.; Ryan, R.M. Self-determination theory: A macrotheory of human motivation, development, and health. Can. Psychol./Psychol. Can. 2008, 49, 182–185. [Google Scholar] [CrossRef]
  25. Mazareanu, E. Value of the Global Sharing Economy, 2014–2025; Statista: Hamburg, Germany, 2022; Available online: https://www.statista.com/statistics/830986/value-of-the-globalsharing-economy/ (accessed on 12 October 2022).
  26. Lang, B.; Kemper, J.; Dolan, R.; Northey, G. Why do consumers become providers? Self-determination in the sharing economy. J. Serv. Theory Pract. 2022, 32, 132–155. [Google Scholar] [CrossRef]
  27. Hamari, J.; Sjöklint, M.; Ukkonen, A. The sharing economy: Why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 2016, 67, 2047–2059. [Google Scholar] [CrossRef]
  28. Puiu, A.-I. Motivations of young consumers to participate to collaborative consumption. J. Soc. Econ. Stat. 2020, 9, 43–55. [Google Scholar] [CrossRef]
  29. Deci, E.L.; Ryan, R.M. The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychol. Inq. 2000, 11, 227–268. [Google Scholar] [CrossRef]
  30. Morganosky, M.A. Cost-versus convenience-oriented consumers: Demographic, lifestyle, and value perspectives. Psychol. Mark. 1986, 3, 35–46. [Google Scholar] [CrossRef]
  31. Moeller, S.; Wittkowski, K. The burdens of ownership: Reasons for preferring renting. Manag. Serv. Qual. Int. J. 2010, 20, 176–191. [Google Scholar] [CrossRef]
  32. Oliveira, T.; Barbeitos, I.; Calado, A. The role of intrinsic and extrinsic motivations in sharing economy post-adoption. Inf. Technol. People 2022, 35, 165–203. [Google Scholar] [CrossRef]
  33. Gu, H.; Zhang, T.; Lu, C.; Song, X. Assessing trust and risk perceptions in the sharing economy: An empirical study. J. Manag. Stud. 2021, 58, 1002–1032. [Google Scholar] [CrossRef]
  34. Zhu, G.; So, K.K.F.; Hudson, S. Inside the sharing economy. Int. J. Contemp. Hosp. Manag. 2017, 29, 2218–2239. [Google Scholar] [CrossRef]
  35. Mittendorf, C. Create an uber account? An investigation of trust and perceived risk in the sharing economy. J. Cust. Behav. 2017, 16, 281–307. [Google Scholar] [CrossRef]
  36. Rogers, B. The social costs of uber. Univ. Chic. Law Rev. Online 2015, 82, 6. [Google Scholar] [CrossRef]
  37. Schaefers, T. Exploring carsharing usage motives: A hierarchical means-end chain analysis. Transp. Res. Part A Policy Pract. 2013, 47, 69–77. [Google Scholar] [CrossRef]
  38. Da Costa Coelho, P.F.; Romero, C.B.A. Determinantes de diferentes tipos de consumo colaborativo. Rev. Adm. FACES J. 2019, 18, 72–87. [Google Scholar]
  39. Böcker, L.; Meelen, T. Sharing for people, planet or profit? Analysing motivations for intended sharing economy participation. Environ. Innov. Soc. Transit. 2017, 23, 28–39. [Google Scholar] [CrossRef]
  40. Costain, C.; Ardron, C.; Habib, K.N. Synopsis of users’ behaviour of a carsharing program: A case study in toronto. Transp. Res. Part A Policy Pract. 2012, 46, 421–434. [Google Scholar] [CrossRef]
  41. Prakash, V.; Flores, R. A study of psychological gender differences: Applications for advertising format. ACR N. Am. Adv. 1985, 12, 231–237. [Google Scholar]
  42. Kim, J.O.; Forsythe, S.; Gu, Q.; Jae Moon, S. Cross-cultural consumer values, needs and purchase behavior. J. Consum. Mark. 2002, 19, 481–502. [Google Scholar] [CrossRef]
  43. Yoon, Y.S.; Lee, J.S.; Lee, C.K. Measuring festival quality and value affecting visitors’ satisfaction and loyalty using a structural approach. Int. J. Hosp. Manag. 2010, 29, 335–342. [Google Scholar] [CrossRef]
  44. Parent, M.C.; Cooper, C. Masculinity threats influence evaluation of hypermasculine advertisements. J. Soc. Psychol. 2020, 160, 282–292. [Google Scholar] [CrossRef]
  45. Grohmann, B. Gender dimensions of brand personality. J. Mark. Res. 2009, 46, 105–119. [Google Scholar] [CrossRef]
  46. Ashraf, R.; Albert, N.; Merunka, D.; Khan, M.A. Consumer involvement with corporate ads vs product ads: A cross-national study. Asia Pac. J. Mark. Logist. 2020, 32, 322–342. [Google Scholar] [CrossRef]
  47. Eagly, A.H.; Carli, L.L. The female leadership advantage: An evaluation of the evidence. Leadersh. Q. 2003, 14, 807–834. [Google Scholar] [CrossRef]
  48. Gupta, V.K.; Mortal, S.; Chakrabarty, B.; Guo, X.; Turban, D.B. Cfo gender and financial statement irregularities. Acad. Manag. J. 2020, 63, 802–831. [Google Scholar] [CrossRef]
  49. Ridgeway, C.L. Framed by Gender: How Gender Inequality Persists in the Modern World; Oxford University Press: Oxford, UK, 2011. [Google Scholar]
  50. Vlačić, B.; Corbo, L.; e Silva, S.C.; Dabić, M. The evolving role of artificial intelligence in marketing: A review and research agenda. J. Bus. Res. 2021, 128, 187–203. [Google Scholar] [CrossRef]
  51. Bennedsen, M.; Simintzi, M.E. Tsoutsoura and D. Wolfenzon. Do firms respond to gender pay gap transparency? J. Financ. 2022, 77, 2051–2091. [Google Scholar] [CrossRef]
  52. Tang, C.S. Innovative technology and operations for alleviating poverty through women’s economic empowerment. Prod. Oper. Manag. 2022, 31, 32–45. [Google Scholar] [CrossRef]
  53. Pescosolido, B.A.; Perry, B.L.; Krendl, A.C. Empowering the next generation to end stigma by starting the conversation: Bring change to mind and the college toolbox project. J. Am. Acad. Child Adolesc. Psychiatry 2020, 59, 519–530. [Google Scholar] [CrossRef]
  54. O’Connor, P.; White, K. Gender Equality in Higher Education: The Slow Pace of Change. In Gender, Power and Higher Education in a Globalised World; Springer International Publishing: Cham, Switzerland, 2021; pp. 1–23. [Google Scholar] [CrossRef]
  55. Agustin, M.; Djoehaeni, H.; Gustiana, A.D. Stereotypes and Prejudices in Young Children. In Proceedings of the 5th International Conference on Early Childhood Education (ICECE 2020), Online, 15–16 October 2020. [Google Scholar] [CrossRef]
  56. Tabassum, N.; Nayak, B.S. Gender stereotypes and their impact on women’s career progressions from a managerial perspective. IIM Kozhikode Soc. Manag. Rev. 2021, 10, 192–208. [Google Scholar] [CrossRef]
  57. Suchek, N.; Fernandes, C.I.; Kraus, S.; Filser, M.; Sjögrén, H. Innovation and the circular economy: A systematic literature review. Bus. Strategy Environ. 2021, 30, 3686–3702. [Google Scholar] [CrossRef]
  58. De Medeiros, J.F.; Marcon, A.; Ribeiro, J.L.D.; Quist, J.; D’Agostin, A. Consumer emotions and collaborative consumption: The effect of COVID-19 on the adoption of use-oriented product-service systems. Sustain. Prod. Consum. 2021, 27, 1569–1588. [Google Scholar] [CrossRef]
  59. Małecka, A.; Mitręga, M.; Pfajfar, G. Segmentation of collaborative consumption consumers: Social identity theory perspective. Int. J. Consum. Stud. 2022, 46, 2445–2465. [Google Scholar] [CrossRef]
  60. Ertz, M.; Durif, F.; Arcand, M. A conceptual perspective on collaborative consumption. AMS Rev. 2019, 9, 27–41. [Google Scholar] [CrossRef]
  61. Adomßent, M.; Fischer, D.; Godemann, J.; Herzig, C.; Otte, I.; Rieckmann, M.; Timm, J. Emerging areas in research on higher education for sustainable development—Management education, sustainable consumption and perspectives from central and eastern europe. J. Clean. Prod. 2014, 62, 1–7. [Google Scholar] [CrossRef]
  62. De los Rios, I.C.; Charnley, F.J.S. Skills and capabilities for a sustainable and circular economy: The changing role of design. J. Clean. Prod. 2017, 160, 109–122. [Google Scholar] [CrossRef]
  63. Al-Nuaimi, S.R.; Al-Ghamdi, S.G. Sustainable consumption and education for sustainability in higher education. Sustainability 2022, 14, 7255. [Google Scholar] [CrossRef]
  64. Barth, M.; Godemann, J.; Rieckmann, M.; Stoltenberg, U. Developing key competencies for sustainable development in higher education. Int. J. Sustain. High. Educ. 2007, 8, 416–430. [Google Scholar] [CrossRef]
  65. Sepasi, S.; Rahdari, A.; Rexhepi, G. Developing a sustainability reporting assessment tool for higher education institutions: The university of california. Sustain. Dev. 2018, 26, 672–682. [Google Scholar] [CrossRef]
  66. Žalėnienė, I.; Pereira, P. Higher education for sustainability: A global perspective. Geogr. Sustain. 2021, 2, 99–106. [Google Scholar] [CrossRef]
  67. Niedlich, S.; Bauer, M.; Doneliene, M.; Jaeger, L.; Rieckmann, M.; Bormann, I. Assessment of sustainability governance in higher education institutions—A systemic tool using a governance equalizer. Sustainability 2020, 12, 1816. [Google Scholar] [CrossRef]
  68. Findler, F.; Schönherr, N.; Lozano, R.; Reider, D.; Martinuzzi, A. The impacts of higher education institutions on sustainable development. Int. J. Sustain. High. Educ. 2019, 20, 23–38. [Google Scholar] [CrossRef]
  69. Leal Filho, W.; Amaro, N.; Avila, L.V.; Brandli, L.; Damke, L.I.; Vasconcelos, C.R.; Hernandez-Diaz, P.M.; Frankenberger, F.; Fritzen, B.; Velazquez, L.; et al. Mapping sustainability initiatives in higher education institutions in Latin America. J. Clean. Prod. 2021, 315, 128093. [Google Scholar] [CrossRef]
  70. Haack, P.; Rasche, A. The legitimacy of sustainability standards: A paradox perspective. Organ. Theory 2021, 2. [Google Scholar] [CrossRef]
  71. Rieckmann, M. Learning to transform the world: Key competencies in Education for Sustainable Development. Issues Trends Educ. Sustain. Dev. 2018, 39, 39–59. [Google Scholar]
  72. Barth, M.; Rieckmann, M. Academic staff development as a catalyst for curriculum change towards education for sustainable development: An output perspective. J. Clean. Prod. 2012, 26, 28–36. [Google Scholar] [CrossRef]
  73. Ruhul Amin, M.; Amin, N.A. Benchmarking learning outcomes of undergraduate business education. Benchmarking Int. J. 2003, 10, 538–558. [Google Scholar] [CrossRef]
  74. Jahanshahi, A.A.; Brem, A.; Bhattacharjee, A. Who takes more sustainability-oriented entrepreneurial actions? The role of entrepreneurs’ values, beliefs and orientations. Sustainability 2017, 9, 1636. [Google Scholar] [CrossRef]
  75. Hernandez-de-Menendez, M.; Morales-Menendez, R.; Escobar, C.A.; McGovern, M. Competencies for industry 4.0. Int. J. Interact. Des. Manuf. 2020, 14, 1511–1524. [Google Scholar] [CrossRef]
  76. Giangrande, N.; White, R.M.; East, M.; Jackson, R.; Clarke, T.; Saloff Coste, M.; Penha-Lopes, G. A competency framework to assess and activate education for sustainable development: Addressing the UN sustainable development goals 4.7 challenge. Sustainability 2019, 11, 2832. [Google Scholar] [CrossRef]
  77. Dinning, T. Articulating entrepreneurial competencies in the undergraduate curricular. Educ. Train. 2019, 61, 432–444. [Google Scholar] [CrossRef]
  78. Rieckmann, M.; Mindt, L.; Gardiner, S. Education for Sustainable Development Goals: Learning Objectives; UNESCO: Online, 2017. [Google Scholar]
  79. Ilie, C.; Fornes, G.; Cardoza, G.; Mondragón Quintana, J.C. Development of business schools in emerging markets: Learning through adoption and adaptation. Sustainability 2020, 12, 8448. [Google Scholar] [CrossRef]
  80. Henseler, J. Partial least squares path modeling: Quo vadis? Qual. Quant. 2018, 52, 1–8. [Google Scholar] [CrossRef]
  81. Schwab, D. Measurement foundations: Validity and validation. Res. Methods Organ. Stud. 2005, 25–37. [Google Scholar]
  82. Hair, J.F. Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; SAGE Publishing: Los Angeles, CA, USA, 2019. [Google Scholar]
  83. Henseler, J. Partial Least Squares Path Modeling. In Advanced Methods for Modeling Markets; Leeflang, P.S.H., Wieringa, J.E., Bijmolt, T.H.A., Pauwels, K.H., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 361–381. [Google Scholar]
  84. Shmueli, G.; Ray, S.; Velasquez Estrada, J.M.; Chatla, S.B. The elephant in the room: Predictive performance of pls models. J. Bus. Res. 2016, 69, 4552–4564. [Google Scholar] [CrossRef]
  85. Rigdon, E.E. Rethinking partial least squares path modeling: In praise of simple methods. Long Range Plan. 2012, 45, 341–358. [Google Scholar] [CrossRef]
  86. Sarstedt, M.; Hair, J.F.; Ringle, C.M.; Thiele, K.O.; Gudergan, S.P. Estimation issues with pls and cbsem: Where the bias lies! J. Bus. Res. 2016, 69, 3998–4010. [Google Scholar] [CrossRef]
  87. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  88. Hamid, M.R.A.; Sami, W.; Mohmad Sidek, M.H. Discriminant validity assessment: Use of fornell & larcker criterion versus htmt criterion. Present. J. Phys. Conf. Ser. 2017, 890, 012163. [Google Scholar] [CrossRef]
  89. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The Use of Partial Least Squares Path Modeling in International Marketing. In New Challenges to International Marketing; Sinkovics, R.R., Ghauri, P.N., Eds.; Advances in International Marketing 20; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 277–319. [Google Scholar]
  90. Roldán, J.L.; Sánchez-Franco, M.J. Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research. In Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems; Mora, M., Gelman, O., Steenkamp, A.L., Raisinghani, M., Eds.; IGI Global: Hershey, PA, USA, 2012; pp. 193–221. [Google Scholar]
  91. Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. e-Collab. 2015, 11, 1–10. [Google Scholar] [CrossRef]
  92. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994. [Google Scholar]
  93. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common beliefs and reality about pls: Comments on Rönkkö and Evermann. Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef]
  94. Hair, J.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); SAGE: Newcastle upon Tyne, UK, 2022. [Google Scholar]
  95. Kock, N. One-tailed or two-tailed p values in pls-sem? Int. J. e-Collab. 2015, 11, 1–7. [Google Scholar] [CrossRef]
  96. Botsman, R.; Rogers, R. What’s Mine Is Yours: The Rise of Collaborative Consumption, 1st ed.; HarperBusiness: New York, NY, USA, 2010. [Google Scholar]
  97. Minton, E.A.; Tan, S.J.; Tambyah, S.K.; Liu, R.L. Drivers of sustainability and consumer well-being: An ethically-based examination of religious and cultural values. J. Bus. Ethics 2022, 175, 167–190. [Google Scholar] [CrossRef]
Figure 1. Proposed framework for the study. Source: author’s own elaboration.
Figure 1. Proposed framework for the study. Source: author’s own elaboration.
Sustainability 15 15482 g001
Figure 2. Gender moderation. Women are in red and men are in green.
Figure 2. Gender moderation. Women are in red and men are in green.
Sustainability 15 15482 g002
Figure 3. Education moderation. The greater educational level is in green, smaller educational level is in red.
Figure 3. Education moderation. The greater educational level is in green, smaller educational level is in red.
Sustainability 15 15482 g003
Figure 4. Results of the hypotheses. Note: Abbreviations: CON, Convenience; SEC, Security; ENJ, Enjoyment; ENV, Environment; BEH, Behavior; GEND, Gender; and EDUC, Education. In the outer model: outer weights/loadings and p-values. In the inner model path coefficients and p-values.
Figure 4. Results of the hypotheses. Note: Abbreviations: CON, Convenience; SEC, Security; ENJ, Enjoyment; ENV, Environment; BEH, Behavior; GEND, Gender; and EDUC, Education. In the outer model: outer weights/loadings and p-values. In the inner model path coefficients and p-values.
Sustainability 15 15482 g004
Table 1. Demographic information of the sample.
Table 1. Demographic information of the sample.
Sample Size (N)270
FrequencyPercentage
Age range
18–25 years old11743.3%
26–35 years old9434.8%
36–45 years old2710.0%
46–55 years old197.0%
Older than 55 years old134.8%
Gender
Female14553.7%
Male12445.9%
Other10.4%
Education
High school228.1%
University studies (incomplete)13851.1%
University studies (completed)11040.7%
Source: author’s own elaboration.
Table 2. R Square.
Table 2. R Square.
Confidence
Intervals
Original Sample (O)Sample Mean (M)5.0%95.0%
BEH0.7420.7540.6960.806
Source: author’s own elaboration.
Table 3. Reliability Estimates and Convergent Validity Statistics.
Table 3. Reliability Estimates and Convergent Validity Statistics.
ConstructVariableItem DescriptionMeanStandard DeviationOuter WeightsOuter LoadingsVariance Inflation Factor (VIF)Cronbach’s AlphaComposite
Reliability (rho_a)
Composite
Reliability (rho_c)
The Average
Variance Extracted (AVE)
Behavior intentionBEH1I hope to continue to use Uber often in the future.4.1111.1230.3110.8892.790.8890.8910.9230.75
BEH2I see myself using Uber more frequently3.7331.0230.290.88 2.714
BEH3I see myself increasing the use of services like Uber’s service.3.7331.0380.2720.8642.395
BEH4I may participate more frequently in companies similar to Uber in the future.3.6841.0460.2810.832.023
ConvenienceCON1Using the Uber service, I can make a profit with less effort.4.0851.1170.4530.8651.693N/AN/AN/AN/A
CON2Uber’s service meets my needs4.3780.860.310.7541.46
CON3Using Uber’s service, I receive benefits in a reasonable amount of time.4.2410.9220.4310.8691.781
EducationEDUCEducational level1.430.639111N/AN/AN/AN/A
EnjoymentENJ1I think Uber’s service is nice3.8560.8460.2930.7561.5070.8270.830.8850.659
ENJ2I think Uber’s service is exciting2.7481.0630.3080.842.122
ENJ3I think Uber’s service is fun2.8151.0450.2930.8272.06
ENJ4I think Uber’s service is interesting3.6931.0530.3380.8221.723
EnvironmentENV1Uber’s service helps save natural resources2.7811.0220.2380.8432.290.8770.8830.9110.673
ENV2Uber’s service is a sustainable consumption mode (appropriate use of resources and minimization of waste and pollution).3.2591.2080.260.8732.909
ENV3Uber’s service is environmentally friendly (it contributes to society, improves the economy and people’s quality of life).3.0851.0490.2320.7611.707
ENV4Uber’s service is energy efficient2.970.9920.220.7611.703
ENV5Uber’s service is environmentally friendly3.1781.0980.2680.8562.627
GenderGENDGender0.4590.498111N/AN/AN/AN/A
SecuritySEC1Uber’s service cares for and protects my physical integrity and personal items.3.6590.9710.3210.7771.5240.7960.8160.880.71
SEC2My privacy is protected when using the Uber service.3.1960.9480.4250.8691.822
SEC3Services provided through an intermediary company (such as Uber) are much safer.3.6191.0320.4350.8771.86
Note. BEH stands for behavior intention; CON for convenience; EDUC for education; ENJ for enjoyment; ENV for the environment; GEND for gender; SEC for security; and N/A for not applicable.
Table 4. Discriminant validity based on Heterotrait–Monotrait ratio HTMT.
Table 4. Discriminant validity based on Heterotrait–Monotrait ratio HTMT.
BEHEDUCENJENVGENDSECGEND × CONEDUC × ENV
BEH
EDUC0.03
ENJ0.8670.076
ENV0.7740.0340.891
GEND0.270.0610.2080.192
SEC0.790.0440.8280.810.245
GEND × CON0.3970.0110.4130.3890.1990.297
EDUC × ENV0.0550.020.0390.0480.0490.0930.057
Table 5. Discriminant validity based on the Fornell-Larcker criterion.
Table 5. Discriminant validity based on the Fornell-Larcker criterion.
BEHEDUCENJENVGENDSEC
BEH0.866
EDUC0.0281
ENJ0.747−0.0690.812
ENV0.686−0.0040.760.82
GEND0.256−0.0610.1910.1851
Note. The square root of variance shared between the constructs and their measures (AVE) are in bold. Off-diagonal elements are the correlations among constructs.
Table 6. Latent variable correlations: Mean, STDEV, T values, p values.
Table 6. Latent variable correlations: Mean, STDEV, T values, p values.
Original Sample (O)Sample Mean (M)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p Values
CON → BEH0.5090.5080.0687.4350
EDUC → BEH0.0610.060.0292.150.016
ENJ → BEH0.2630.2620.0634.1590
ENV → BEH0.130.1270.0721.8020.036
GEND → BEH0.1470.1440.0582.5310.006
SEC → BEH0.1430.1480.062.3840.009
GEND × CON → BEH−0.238−0.2310.0653.6370
EDUC × ENV → BEH−0.052−0.050.0361.4420.075
Table 7. PLS prediction results.
Table 7. PLS prediction results.
PLS-LM
Q2 PredictPLS-SEM RMSEPLS-SEM MAELM RMSELM MAERMSEMAE
BEH10.590.7220.5430.7090.5220.0130.021
BEH20.5140.7160.5350.7320.543−0.016−0.008
BEH30.420.7940.5880.8060.578−0.0120.01
BEH40.4790.7590.550.7840.56−0.025−0.01
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Llanos, G.; Guiñez-Cabrera, N.; Mansilla-Obando, K.; Gómez-Sotta, E.; Buchuk, P.; Altamirano, M.; Alviz, M. Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty? Sustainability 2023, 15, 15482. https://doi.org/10.3390/su152115482

AMA Style

Llanos G, Guiñez-Cabrera N, Mansilla-Obando K, Gómez-Sotta E, Buchuk P, Altamirano M, Alviz M. Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty? Sustainability. 2023; 15(21):15482. https://doi.org/10.3390/su152115482

Chicago/Turabian Style

Llanos, Gonzalo, Nataly Guiñez-Cabrera, Katherine Mansilla-Obando, Esteban Gómez-Sotta, Paulo Buchuk, Matías Altamirano, and Marcelo Alviz. 2023. "Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty?" Sustainability 15, no. 21: 15482. https://doi.org/10.3390/su152115482

APA Style

Llanos, G., Guiñez-Cabrera, N., Mansilla-Obando, K., Gómez-Sotta, E., Buchuk, P., Altamirano, M., & Alviz, M. (2023). Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty? Sustainability, 15(21), 15482. https://doi.org/10.3390/su152115482

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

Article Metrics

Back to TopTop