Abstract
Purpose: In the circular economy era, where fashion rental platforms are crucial for promoting sustainable consumption, this research contributes by identifying the primary factors influencing consumer behaviour. Specifically, we investigate how sustainable fashion drivers and attitudes towards technology influence consumers’ intention to use fashion rental platforms.
Design/methodology/approach: The conceptual model focuses on how fashion innovation (FI) and the perception of the sharing and circular economy (PSCE) affect task–service fit (TSF), enjoyment (ENJ) and the intention to use the platform. A survey and partial least squares structural equation modeling were employed to test the proposed hypotheses. Data from 192 participants with an academic background in fashion were empirically analysed.
Findings/results: The results indicate that both FI and the PSCE have a significant and positive impact on TSF and ENJ, which in turn significantly influences platform use intention. This finding underscores the importance of sustainable fashion concepts as key drivers of consumer attitudes and behaviour within this emerging platform and service model.
Practical implications: This study provides strategic insights that can empower fashion rental platforms. By emphasising innovative design and environmental values, platforms can attract and retain consumers interested in FI and the circular economy.
Originality/value: This study expands the understanding of consumer behaviour on fashion rental platforms by highlighting the key role of FI and the PSCE in influencing user intentions. By integrating these innovative constructs, the study provides new insights and empirical support for developing the fashion rental industry.
Keywords: fashion rental platform; fashion innovation; perception of the sharing and circular economy; task–service fit; enjoyment.
Introduction
The maturity and ubiquity of digital technologies and the Internet have improved convenience and efficiency in people’s lives. Various emerging digital technologies have been integrated into everyday life as services, changing consumption patterns and living habits. Through new applications and appropriate process design, new business models can be established through the integration of digital technology and service innovation.
The emphasis on the United Nations’ Sustainable Development Goals by various industries worldwide, along with the development and implementation of environmental, social and governance solutions by enterprises, has become an inevitable trend. In this context, with sustainability increasingly becoming a core lifestyle value, the fashion industry has prominently highlighted its commitment to corporate social responsibility and sustainability principles in recent years. As a key livelihood industry, the clothing and fashion industry has been a pioneer in digital technology adoption and sustainable transformation. Moreover, the emphasis on environmental concerns and the continued growth of sustainable citizen awareness have reinforced public acceptance of new business models based on sustainable concepts, and various market opportunities have been created through the application of digital technologies, prompting people to actively consider establishing a sustainable lifestyle. With an increasing focus on environmental sustainability, circular economy practices and sustainable fashion have positioned fashion rental platforms as emerging options for fashion consumption (McCoy & Chi, 2022).
Fashion rental platforms embody an innovative consumption model based on recycling and leasing concepts. Such platforms have been gaining substantial traction across fashion markets on a global scale. Users of fashion rental platforms can enjoy diverse and varied fashion experiences while contributing to the reduction of clothing and resource waste. That is, this progressive business model actively promotes sustainable development within the fashion industry, aligning with the growing focus on environmental responsibility and ethical consumption practices.
One benefit of fashion rental is that subscribers can experience the joy of fashion at a reduced cost, with the seasonal relevance of products being a primary consideration for consumers. Shoppers have become more conscientious about their purchasing habits, placing greater emphasis on the environmental effects of their consumption. Fashion consumption has evolved into a model that focuses on buying less and buying better. Consumers are willing to spend more to purchase specific high-quality items that last a lifetime while renting the other items that they need. This is the optimal model for supporting and implementing sustainable fashion. Based on the principles of the circular economy, fashion rental models can help eliminate excessive waste and reduce carbon emissions.
Consumer behaviour is influenced by innovative technologies as well as by external factors. Interactions with new technologies promote changes in consumers’ consumption behaviours and habits. Accordingly, the present study investigated the intrinsic needs of the new generation of consumers regarding fashion and their attitudes towards this new economic model. The study also investigated these consumers’ effects on the functionality and enjoyment (ENJ) of fashion rental platforms, as well as their willingness to continue using fashion rental services.
Literature review and hypothesis development
The circular economy in the closet
The concept of the sharing economy, also known as collaborative consumption, is based on the idea that individuals can rent out their resources to others for a fee, thereby enhancing the utilisation of underused resources and increasing the overall efficiency of resource utilisation (Belk, 2014; Yeganeh, 2019). This model enables goods to be purchased and used, and enables benefits to be accrued through renting, borrowing or exchanging (Henninger et al., 2019; Schor, 2016), forming an essential aspect of sustainable consumption behaviour.
Fashion rental is a shared and collaborative form of fashion consumption that involves the resale, rental or exchange of clothing and luxury goods, transforming clothing into a shareable and circular asset (Hamari et al., 2016). Consumers can access diversified wearing experiences through sharing mechanisms and clothing rentals instead of continually purchasing new clothing (Iran & Schrader, 2017), thereby reducing the burden of ownership. In addition, fashion rental is also an environmentally friendly model because it has fewer harmful effects on the environment and reduces overconsumption relative to other models. Clothing rentals can also reduce clothing disposal rates while extending the lifespan of clothing (Becker-Leifhold & Iran, 2018). From a regional perspective, Amasawa et al. (2023) showed that rental platforms can facilitate more sustainable fashion behaviour in Canada, and consumer motivations are mainly driven by affordability and novelty. Many fashion rental platforms present sustainability as part of their brand identity. This suggests that platforms which more clearly communicate their circular economy impact may be better positioned to engage sustainability-conscious users, supporting the role of perceived circular economy awareness in influencing usage intention (McCoy & Chi, 2022).
Fashion rental platforms provide consumers with a continuous supply of clothing from cloud closets, positioning themselves as a new form of clothing consumption that is both economical and environmentally friendly by focusing on consumers’ budget considerations and promoting sustainable consumption. Consumers are increasingly embracing sustainable lifestyles, prompting a shift in shopping behaviours toward a new paradigm. This trend has boosted fashion rental platforms as a business model, prompting brand transformation and the birth of new companies and reducing the effects of consumption on the environment through practical actions, thereby promoting a new era of sustainable fashion consumption.
Innovative consumption thinking
Clothing is a type of consumer product that reflects the personality and status of the wearer (Dodd et al., 2000). Fashion consciousness refers to an individual’s response to new trends and styles. Most consumers exhibit consistent demand and consumption behaviour for new-season fashions, resulting in a short life cycle for fashion products. Innovators, who are highly interested in new concepts, tend to refrain from confining themselves to a single domain (Rogers, 2002). From a consumer behaviour perspective, innovativeness can be regarded as a trait because innovators tend to purchase new products more frequently and faster relative to other consumers (Goldsmith & Hofacker, 1991).
Consumers with a high level of fashionable innovativeness tend to purchase and wear stylish clothes and persuade others to purchase popular products (Kaur et al., 2024; Rahman & Kharb, 2018). Such consumers typically exhibit the characteristics of a fashion opinion leader and can be classified as fashion innovators. Individuals with high fashion innovativeness tend to use more consumption channels and are more willing to take risks (Jebarajakirthy et al., 2021). Notably, consumers’ willingness to adopt new fashions can be determined by assessing their level of fashion innovation (FI). When consumers exhibit a higher level of FI, they are more receptive to new and popular products and consumption patterns (Park et al., 2010).
The fashion industry is one of the most progressive industries, with numerous brands consistently introducing new styles to meet the preferences of their target consumers. Fashion rental services, rooted in the principles of the sharing and circular economy, are transforming the traditional linear economic model to accommodate the preferences of innovative fashion consumers.
Task–service fit
Fashion rental platforms operate on a business model based on sustainability and the circular economy. These platforms utilise information technology. The task-technology fit (TTF) model is the primary measurement framework used by scholars to evaluate the performance and efficacy of information systems and technology. According to the TTF model, which was proposed by Goodhue and Thompson (1995), the success of an information system depends on whether its functions adequately meet its users’ task needs. Numerous studies have used the TTF theory as their primary research framework, and TTF has been examined not only as a mediating factor and a dependent variable but also as an antecedent of performance and actual use (Rahi et al., 2021).
In one study of smart mobile devices and applications, the device-functional fit model (Negahban & Chung, 2014) was proposed, extending the TTF theory to evaluate mobile application performance and highlighting the importance of fit theory in technology application contexts. Whether they are used to improve user performance or meet consumer needs, technologies and functions can be regarded as reflections of services. The technology fit theory has been further extended to establish the task-service fit (TSF) model (Fang, 2017).
For the fashion rental service platform examined in the present study, TSF can be used to evaluate the platform’s ability to meet user needs for FI and the circular economy, facilitated by the recommendation and personalisation technologies of the online platform. Accordingly, the following hypotheses were proposed:
H1: Fashion innovation positively influences task-service fit.
H2: Perception of the sharing and circular economy positively influences task-service fit.
Drivers of enjoyment in fashion rental
The ENJ derived from shopping can be defined as the pleasure consumers experience during the shopping process and related activities, such as fashion-oriented consumption behaviours (Beatty & Ferrell, 1998; Mohan et al., 2013). The engagement and experience of consumption and shopping are closely associated with ENJ, and the ENJ derived from consumption is considerably affected by personal characteristics (Odekerken-Schröder et al., 2003; Wong et al., 2012). Consumers who find ENJ in shopping and exhibit greater dedication to consumption or shopping tend to be less conventional and more willing to innovate and fashion innovators, in particular, exhibit stronger consumption motivations and shop more frequently than other consumers (Goldsmith & Hofacker, 1991).
The concept of innovativeness can be extended to consumer behaviour to establish the concept of consumer innovativeness (Hirschman, 1980), a personal characteristic of consumers. Individuals who exhibit greater innovativeness will be more receptive to new concepts, technologies and services. Consumers with an innovative personality tend to enjoy trying out new products and services, acquiring new clothing products and exploring new fashion trends. Thus, those with a stronger inclination toward FI may use fashion rental platforms to acquire their preferred and specific fashion items, deriving pleasure from this service (Muzinich et al., 2003; Workman, 2010).
Fashion rental is recognised as an efficient method of sharing, eliminating the burden and costs associated with ownership. This consumption model is also regarded as environmentally friendly (Becker-Leifhold & Iran, 2018). Promoting awareness of sharing and the circular economy can enhance consumers’ experiences with fashion rental platforms and their services. Based on these premises, we proposed the following hypotheses:
H3: Fashion innovation positively influences enjoyment.
H4: Perception of the sharing and circular economy positively influences enjoyment.
Task–service fit and enjoyment influence platform use intention
Intention is the assessment of future behaviour that leads to a decision to act (Chennamaneni et al., 2012). Behavioural intention refers to a customer’s inclination to engage in specific behaviours, serving as a crucial indicator of a company’s ability to retain customers (Zeithaml et al., 1996). A consumer’s attitude towards a given subject influences their behavioural intention, which in turn influences their actual behaviour. Overall, behavioural intention is a key step that precedes any behavioural performance and is the primary determinant of the occurrence of actual behaviour. Behavioural intention is a primary indicator for predicting individual behaviour.
Kang et al. (2015) argued that customers’ inclination to use mobile applications for retail purposes is influenced by perceived attributes such as innovation, emotional engagement and mobile application use. Chung (2015) discovered that consumers were more likely to exhibit stronger purchase intentions when they perceived higher quality, indicating that intentions are influenced by factors influencing rational and emotional responses.
Hedonic shopping refers to the ENJ derived from shopping, involving consumers seeking happiness, fantasy and ENJ (To et al., 2007). Brown and Venkatesh (2005) also found that hedonic motivation directly and positively influences consumers’ intention to purchase. Hedonic value includes experiential benefits such as perceived ENJ, satisfying fashion needs and trying new clothing styles at lower prices. These factors contribute to an increase in consumers’ inclination to use clothing rental services (Armstrong et al., 2015; Lang et al., 2019; Mishra et al., 2021).
In the context of online shopping, pleasure is regarded as consumers’ perception of environmental stimulation (Mazaheri et al., 2014). When consumers experience pleasure and ENJ, their assessment of store preference is influenced (Sherman et al., 1997). On a related note, positive emotions of pleasure affect consumers’ willingness to consume (Lunardo & Mbengue, 2009). Furthermore, when a consumer’s perceived efficacy and compatibility with a technological innovation are higher, the intention of the consumer to adopt the innovative technology is stronger. A significant relationship exists between users’ perception of novelty and their willingness to adopt new technologies and online services (Kim et al., 2017).
Consumers have continued to evolve in their approach to accessing fashion information and purchasing clothing through e-commerce. By leveraging cutting-edge technologies and business models, we can create new and distinctive experiences that foster the development of innovative consumption models (Pantano & Gandini, 2017). Essentially, when a consumer’s awareness of how well a task aligns with certain technological services is greater, and their attitude towards enjoying a platform is more positive, they are more willing to use the related service platform. In summary, the present study proposed the following hypotheses:
H5: Task-service fit adaptation positively influences platform use intention.
H6: Enjoyment positively influences platform use intention.
The literature review provides a context for the circular economy in fashion, innovative consumption, the TSF (an extension of TTF) and the factors contributing to ENJ in fashion rental. The originality of this study lies in its integration of FI with the concepts of sharing and the circular economy. This approach aims to understand how these elements influence TSF, ENJ and ultimately, the intention to use fashion rental platforms.
The present study established a conceptual model incorporating five constructs: FI, perception of the sharing and circular economy (PSCE), TSF, ENJ and platform use intention (PUI). Fashion innovation and PSCE were examined as driving factors, and TSF and ENJ were investigated as mediating factors, with the aim of exploring how these constructs influence consumers’ willingness to adopt fashion rental platforms. The research model is depicted in Figure 1.
Methodology
Instrument
The measurements for FI and PSCE were derived from the studies by Zhang et al. (2019) and Rahman et al. (2014), respectively. The scale for assessing TSF was adapted from Fang (2017), and that for ENJ was derived from the research by Sun and Zhang (2006) and adjusted to fit the context of fashion rental platforms. Finally, the measurement for PUI was based on the study by Agarwal and Karahanna (2000). These constructs were assessed using a 7-point scale with endpoints ranging from 1 (strongly disagree) to 7 (strongly agree).
Data collection and sample profile
Study participants were selected on the basis of their background in fashion. They were instructed to interact with a fashion rental platform for 30 min, after which they were given a questionnaire to complete. A total of 192 individuals were recruited. Among the participants, 83.3% were women, and 16.7% were men. Most participants (183 individuals, 95%) were in the 18–24 year age group. In terms of education, the largest group comprised college students, and 98% of the participants had a background in fashion.
Data analysis and results
Partial least squares structural equation modeling (PLS-SEM) was chosen as the analytical method because it is well-suited for exploratory research, especially when the research model is complex and involves multiple constructs and indicators, along with a relatively small sample size. Partial least squares structural equation modeling is particularly effective for developing theories rather than testing them. Additionally, it provides greater statistical power and flexibility in terms of data distribution, making it robust in situations where the data may not be normally distributed (Hair et al., 2012, 2019).
Assessment of measurement models
The five latent constructs in the conceptual model were reflective variables. Confirmatory factor analysis was employed to test the reliability and validity of these constructs. Table 1 lists the indicators used. The factor loadings of the reflective indicators ranged from 0.778 to 0.915, exceeding the recommended value of 0.7. This result indicates that each construct accounted for more than 50% of the variance in each indicator, implying acceptable item reliability (Hair et al., 2019). Given these results, the indicators were regarded as having met the criteria, allowing for the subsequent analysis to be conducted.
| TABLE 1: Measurements for reflective constructs. |
Reliability refers to the consistency of a measure. For the assessment of reliability, studies have suggested that Cronbach’s alpha, as an unweighted measure, is less precise for assessing reliability because it may be excessively conservative, and that composite reliability may be overly liberal (Hair et al., 2019, 2021). To address this problem, Dijkstra and Henseler (2015) introduced a more accurate measure of rho_A that falls between Cronbach’s alpha and the composite reliability in terms of precision. In structural equation modeling (SEM), convergent reliability refers to the extent to which multiple indicators of a given construct are consistent and reliable. In the present study, all rho_A values ranged between 0.844 and 0.931, suggesting that the scales exhibited acceptable reliability (Hair et al., 2019).
For validity, convergent validity was measured using the average variance extracted (AVE) for all indicators within each construct. In the present study, all AVE values were higher than the minimum acceptable threshold of 0.5, indicating that each construct explained more than 50% of the variance of its indicators (Hair et al., 2021).
The Fornell-Larcker criterion is widely used by researchers to assess discriminant validity (Hair et al., 2021), and an alternative method, the Heterotrait-Monotrait (HTMT) ratio of correlation, has also been recommended for assessing discriminant validity. In the present study, both the Fornell-Larcker criterion and HTMT ratio were used. As revealed in Table 2, the correlation coefficient between constructs (off-diagonal) was less than the square root of the AVE (in bold), meeting the Fornell-Larcker criterion and indicating acceptable discriminant validity (Hair et al., 2021). Additionally, the HTMT ratio (shaded grey) was less than 0.85 for conceptually different constructs, indicating discriminant validity (Hair et al., 2021; Henseler et al., 2015).
Assessment of the structural model
The primary evaluation criteria used for the structural model are the influence level and significance of the path coefficients, the effect size measured by f2, and the explanatory power measured by R2 (Hair et al., 2019). Predictive power was assessed using Q2predict values of the latent variable. Q2predict values were calculated using PLSpredict. Firstly, we assessed the collinearity issues of the structural model. The results revealed that the variance inflation factor values among predictor constructs were lower than the threshold of 3.0, indicating the absence of collinearity issues (Hair et al., 2021).
In the partial least squares (PLS) structural equation modeling, the individual path coefficients can be standardised beta coefficients in ordinary least squares regressions, and path coefficients can be used to measure the effect of exogenous variables on endogenous variables. The present study employed bootstrapping with 5000 resamplings for hypothesis testing, and the corresponding p-value was used to assess the significance of the hypotheses. Hair et al. (2019) stated that the p-value should be smaller than 0.05. As presented in Table 3, all the effect directions of the structural paths were positive, validating the theoretically assumed relationships between latent variables.
| TABLE 3: Assessment of the structural model. |
For the drivers of sustainable fashion, FI (β = 0.207, p = 0.002, f2 = 0.054) and PSCE (β = 0.455, p = 0.000, f2 = 0.262) both had positive significant effects on TSF, with small and medium effect sizes, respectively. Therefore, H1 and H2 were supported, with these relationships accounting for 32.0% of the variance in TSF (R2 = 0.320). In addition, ENJ was significantly and positively influenced by FI (β = 0.209, p = 0.004, f2 = 0.065) and PSCE (β = 0.455, p = 0.000, f2 = 0.449), with the effect sizes of FI and PSCE being small and large, respectively. Thus, H3 and H4 were supported, with these relationships explaining 42.8% of the variance in ENJ (R2 = 0.428).
For the motivators of attitudes towards technology, TSF (β = 0.349, p = 0.000, f2 = 0.168) and ENJ (β = 0.469, p = 0.000, f2 = 0.303) had positive and significant effects on PUI, with the effect sizes of TSF and ENJ being moderate. Therefore, H5 and H6 were supported, with both variables explaining 54.0% of the variance in PUI (R2 = 0.540). The full results of the conceptual model are presented in Figure 2.
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FIGURE 2: Evaluation of the structural model. |
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The empirical model’s predictive quality was evaluated using PLSpredict (Shmueli et al., 2016). The Q2predict values of TSF (0.298), ENJ (0.407) and PUI (0.341) were greater than zero (Table 3). Because these values were positive, the corresponding prediction error was smaller than the naive mean value prediction, indicating that the constructs achieved satisfactory predictive power within the empirical model (Shmueli et al., 2019). Furthermore, predictive power was assessed using manifest variable predictions (i.e. items of endogenous constructs) by using PLSpredict (Hair, 2021). For most TSF items, the root mean square error values obtained through PLS-SEM were smaller than those obtained through linear modeling, indicating a moderate predictive capability (Table 4). Additionally, for the indicators of ENJ and PUI, all the root mean square error values obtained through PLS-SEM were smaller than those obtained through linear modeling. Thus, those constructs can be interpreted as exhibiting strong predictive power (Hair et al., 2019).
| TABLE 4: Summary of the manifest variable predictions. |
Mediation analysis
Mediation effects were assessed using a non-parametric bootstrap procedure in line with modern PLS-SEM practices. Recent methodological literature recommends direct assessment of the indirect effect using bootstrapping methods, because of its higher statistical power and fewer assumptions (Preacher & Hayes, 2008; Zhao et al., 2010).
To examine the hypothesised full mediation effect of technology attitude variables (TSF and ENJ) between sustainable fashion variables (FI and PSCE) and new service intention (PUI), we applied the non-parametric bootstrapping technique using 5000 resamples in SmartPLS 4. As suggested by Preacher and Hayes (2008), significance was assessed based on the 95% bias-corrected confidence interval (CI) of the indirect effect. The mediation effect is considered statistically significant if the CI does not include zero. As shown in Table 5, the direct path from FI to PUI was not included in the model, as the mediation structure assumed full mediation. The results confirmed a significant indirect effect of FI on PUI through TSF (β = 0.072, t = 2.525, p < 0.001), with a 95% bias-corrected CI of (0.024, 0.136), which does not include zero. Similarly, the indirect effect of FI on PUI through ENJ, along with the mediation effects of TSF and ENJ between PSCE and PUI, also demonstrated significant indirect effects. As the structural model was defined without the direct path connecting the sustainable fashion variables (FI and PSCE) and the new service intention variable (PUI), these results support the existence of a full mediation effect. This indicates that the influence of sustainable fashion variables (FI and PSCE) on the new service intention variable (PUI) is fully transmitted through technology attitude variables (TSF and ENJ).
| TABLE 5: Summary of the mediation analysis. |
Discussion and implications
The present study identified the driving factors of sustainable fashion in the context of fashion rental services, as well as explored system attitudes towards fashion rental services and the willingness to use this new type of service platform. Its findings provide valuable insights into the potential benefits of fashion rental platforms, clarifying their role in promoting sustainable fashion. Sustainability has become a key emphasis in the apparel industry. During the promotion of sustainable fashion and the transition to a more sustainable path, adjustments in consumer mindsets and the development of new business models can create new value, indicating that economic development and sustainability are not necessarily in conflict. With the sharing and circular economy framework, fashion rental services have emerged as a new market opportunity.
This research suggests that the target customers of fashion rental platforms exhibit two main characteristics. Firstly, they tend to be fashion innovators with a high level of fashion consciousness. Secondly, they tend to understand and recognise the concepts of the sharing and circular economy. Sustainable fashion awareness serves as a driving factor, with technology platforms and services acting as intermediaries. Service-task fit refers to whether a system’s services effectively meet user needs, which was confirmed in the case of fashion rental platforms, where the sense of pleasure was mainly derived from the products and shopping services provided by such platforms. Furthermore, this study proposes that sustainable fashion, including FI and recognition of the sharing and circular economy, serves as a key precursor to the promotion of users’ attitudes toward technology and their willingness to use fashion rental services. In the fashion rental context, sustainable fashion awareness drives attitudes towards technology, and it enhances users’ willingness to use fashion rental platforms by improving service-task fit and promoting enjoyable platform experiences.
The positive relationship between FI and TSF indicates that a higher level of FI orientation positively influences TSF on fashion rental platforms, thereby supporting H1. This finding instils optimism about the future of fashion rental platforms, suggesting that individuals receptive to FI are more inclined to embrace new offerings and exhibit a greater acceptance of the diverse products available on fashion rental platforms. Additionally, fashion rental platforms can benefit from targeting such consumers with personalised recommendation services to enhance their user experience and satisfaction. Tailoring a platform’s interface and product selection to meet the needs of fashion innovators may improve task-service alignment.
Perception of the sharing and circular economy also had a positive and significant influence on TSF (H2). That is, consumers’ positive perceptions of the sharing and circular economy enhance their likelihood of finding suitable services on fashion rental platforms. This is because these consumers emphasise efficient resource utilisation and the environmental benefits associated with rental services. From a practical standpoint, platforms should highlight their contributions to sustainability and circular economy practices in their marketing strategies to attract consumers who share these values. Additionally, highlighting eco-friendly practices and sustainable benefits can improve perceived TSF.
Enjoyment was positively influenced by FI, indicating that fashion innovators tend to experience greater ENJ while using fashion rental platforms, thereby supporting H3. This is because they find excitement and pleasure in exploring new and innovative designs. This ENJ stems from the novelty and variety offered by fashion rental platforms, aligning with the desire of fashion innovators to acquire cutting-edge fashion. To enhance user ENJ, fashion rental platforms should continuously update their inventory with innovative designs and exclusive collections. Offering unique and trendsetting fashion pieces can attract fashion innovators and help maintain their engagement. Moreover, the empirical findings support the notion that PSCE significantly influences ENJ, thereby supporting H4. Consumers with a stronger PSCE tend to derive more ENJ from using fashion rental platforms because they resonate with the eco-friendly orientation of such platforms. Their sense of fulfilment and satisfaction increases when engaging with services that align with their environmental values. These platforms can improve user ENJ by communicating their commitment to sustainability and the circular economy. Therefore, building a brand image that emphasises these values can enhance user experiences and foster loyalty among environmentally conscious consumers.
The present study revealed that both TSF and ENJ significantly influenced PUI, thereby supporting H5 and H6. Task–service fit, defined as the extent to which a platform’s offerings align with the consumers’ fashion needs, is a key determinant of user intention. This finding is consistent with previous research on technology acceptance, emphasising the importance of perceived usefulness and fit in influencing user adoption (Venkatesh & Bala, 2008). To optimise TSF, platforms should focus on personalised recommendations and user-centric design, ensuring that consumers can easily find items that match their preferences.
System ENJ also plays a pivotal role in driving platform use intention. As an intrinsic motivation, ENJ enhances user engagement and satisfaction, leading to a stronger intention to use a platform (Turel et al., 2010; Van der Heijden, 2004). Thus, platforms should strive to create a user-friendly and enjoyable experience, delivering seamless and engaging user experiences that encourage repeat use.
In Taiwan, the emergence of fashion rental platforms such as AMAZE and Ohphire exemplifies the practical application of the constructs identified in this study, and the results of this study can be more clearly understood by examining how they manifest in the practices of existing fashion rental platforms. AMAZE, a rental service based in Taiwan, offers a diverse selection of clothing with home delivery, simple return logistics and a monthly subscription model. This approach reflects the importance of TSF and ENJ, as identified in this study. The platform enhances user satisfaction by minimising rental efforts and offering fashionable options for various occasions. Ohphire focuses on renting and resale of luxury fashion items, promoting sustainable fashion practices. By allowing users to rent high-end items for special occasions, Ohphire addresses the constructs of FI and perceived circular economy, encouraging consumers to participate in a sharing economy model that reduces waste and extends the lifecycle of fashion products. These platforms demonstrate how integrating technology and sustainability can create a compelling value proposition for consumers, reinforcing the relevance of our research findings.
The results indicate that public education and policy incentives are essential for policymakers and business developers in areas where the circular economy is still developing. Governments can promote sustainable fashion behaviour through subsidy programmes, awareness campaigns and tax benefits for circular fashion startups. In parallel, platform operators can collaborate with local institutions to co-create educational content that strengthens the perception of shared economy and eco-conscious consumption. These strategies can create the social and cultural foundation for fashion rental platforms to thrive beyond niche segments.
Limitations and further research
The promotion of sustainable fashion is a long-term process that requires continuous effort. The present study confirms the viability of a service-provision model as an alternative to conventional sales models. Establishing and transitioning to new business models can be challenging. Examples of related challenges include enhancing operational quality and preventing the externalisation of costs to the environment. Additionally, we believe that product selection capabilities, supply and service are crucial for establishing and operating a successful platform service. Therefore, improving organisational agility to enhance overall performance should be a focus of future research.
While this study provides valuable insights into users’ behavioural intentions towards fashion rental platforms, certain limitations should be noted. The sample in this study consists predominantly of students with academic or professional training in fashion-related disciplines. While this ensures a certain degree of contextual familiarity and relevance, it may also introduce demographic and experiential skewness, potentially limiting the generalisability of the findings to the broader population of consumers. Future research should consider expanding the demographic scope to include consumers from diverse age groups, occupations and fashion knowledge levels to enhance external validity and cross-group comparisons. Future research is encouraged to include more diverse demographic segments to test the model’s applicability and validate findings across broader consumer profiles. Furthermore, given the shift towards sustainable fashion driven by the circular economy, strategies for promoting and educating consumers to change their established consumption patterns are a key avenue for further exploration.
Conclusion
The present study integrates two constructs, FI and PSCE, to examine the intention to use fashion rental platforms, thereby enriching the literature on consumer behaviour within a sharing economy. The promotion and development of sustainability in the fashion industry is necessary for its transformation. In addition to improving the design and materials of apparel products, addressing consumer demand for both fashion and sustainability can drive industry-wide transformation.
Innovative business models provide consumers with new options, while fashion rental services and platforms provide brands with more opportunities to engage with target consumers. The experience-centric nature of fashion rental services can foster longer-lasting relationships between brands and consumers. Fashion brands can employ product-as-a-service as a strategic tool. By providing fashion rental services, they can offer more diverse choices to meet consumers’ fashion needs, thereby increasing flexibility for consumers while reducing their risks. This strategy serves as an optimal solution for aligning the circular economy with sustainable fashion.
Brands can communicate with and educate new generations of consumers, encouraging them to rethink and redefine their needs and to adjust their values and behavioural patterns. For consumers, the benefits include reduced costs, lower expenditures, reduced waste generation and increased flexibility. This not only allows consumers to access fashion items more economically but also fosters in them a sense of participation in environmental sustainability, thereby expanding the fashion rental market. For emerging brands, this strategy can help them establish niches and expand their market presence.
For brands and apparel industry players, shifting away from short-term profit models to those that increase return rates and service reuse can cultivate more stable and long-term customer relationships, expand customer bases, enhance brand image and reduce risks. Thus, emerging brands and apparel manufacturers aiming to promote sustainable fashion products should focus not only on developing sustainable materials but also on incorporating new business models (e.g. fashion rental services). This strategy enables them to provide consumers with an additional option for sustainable fashion consumption, thereby making consumption patterns more diverse and comprehensive while enhancing brand differentiation.
In summary, through sharing and circular practices, product lifecycles can be extended. Fashion rental platforms can promote sustainable fashion concepts by combining platform technology and services, driving brands to adopt product-servitisation as a viable sustainable service solution. This encourages apparel manufacturers to design and produce high-quality, easily recyclable products, ultimately contributing to the reshaping of the fashion industry.
Acknowledgements
Competing interests
The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.
Authors’ contributions
C.-F.L. and T.-H.K. designed the conceptual model, verified the analytical methods and analysed the data. C.-F.L. and T.-H.K. contributed to the design and implementation of the research, the analysis of the results and the writing of the manuscript.
Ethical considerations
Ethical clearance to conduct this study was obtained from the LeeMing Institute of Technology.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The data supporting the findings of this study are available from the corresponding author, T.-H.K., upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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