The Impact of the Digital Sharing Economy on the Saving and Investment Patterns of Generation Z in Iran

Introduction

The digital sharing economy, as an emerging paradigm, has influenced the financial behaviors of Generation Z and transformed traditional patterns of saving and investment. This economy, built upon digital platforms such as ride-hailing services, short-term accommodation rentals, and freelancing platforms, enables access to resources without requiring ownership and thus increases the tendency toward collaborative consumption (Güçlü et al., 2023). Studies indicate that Generation Z, as a digitally native generation, is more dependent on these platforms than previous generations, and this dependence may affect their financial decision-making (Bai et al., 2025). However, these developments not only create opportunities for saving but also bring challenges such as a reduced propensity for long-term saving. Therefore, examining the impact of the digital sharing economy on the saving and investment styles of Generation Z provides an avenue for a deeper understanding of their financial behaviors and serves as an introduction to analyzing their predictive factors.

In this study, the term digital sharing economy refers to collaborative access systems that enable individuals to temporarily use goods and services without ownership, facilitated by digital platforms that coordinate peer-to-peer exchange (Rojanakit et al., 2025). This definition focuses on access‑based consumption rather than on the broader platform economy. it distinguishes cases like ride‑hailing, accommodation sharing, and freelance service matching from e‑commerce or conventional online marketplaces.

Furthermore, sharing economy platforms, by enabling access to services without ownership costs, may shift consumption patterns from purchasing to renting, thereby influencing budgeting and saving behaviors. This introduction, therefore, highlights the need for deeper exploration of the impacts of this economy on financial behaviors and directs attention toward identifying research gaps.

The digital sharing economy, with its emphasis on resource-sharing, can shift saving styles from traditional to more flexible patterns. Bärö et al. (2022) argue that individual factors such as attitudes and trust toward platforms increase participation in the sharing economy. In Iran, where personal economic factors such as monthly income play a key role, the sharing economy may reinforce saving habits, particularly when accompanied by high digital accessibility (Akbari et al., 2023). These results underscore the importance of examining dependent variables such as saving style and provide a bridge toward analyzing its impact on investment behavior.

The relationship between the digital sharing economy and saving and investment behavior can be derived from an integration of behavioral economics, the Theory of Planned Behavior, and the Technology Acceptance Model (Ajzen, 1991; Davis, 1989). Within the framework of behavioral economics, engagement with sharing platforms shapes the decision‑making patterns of Generation Z. This influence operates through mechanisms such as mental accounting, risk perception, and social comparison (Weber et al., 2002). Specifically, the reduction of ownership costs on sharing platforms is cognitively encoded as transferable savings a form of mental accounting that links cost avoidance to increases in short‑term saving. From the perspective of the Theory of Planned Behavior, the interaction among attitude, subjective norms, and perceived behavioral control explains why and how the sharing economy influences financial intentions; Reduced access barriers and lower entry costs directly enhance perceived behavioral control in digital investment domains, while trust‑based attitudes often reinforced on sharing platforms through reputation and rating systems stabilize saving intentions (Ajzen, 1991).

At the same time, the technology acceptance model explains the cognitive mechanisms underlying these processes. In this model, the perceived usefulness of sharing tools plays an important role; the perceived ease of use of digital services also enhances users' sense of financial efficacy. The result is that riskier investment styles are encouraged, and younger users see technology as a path to financial opportunities rather than simply a consumption tool. At the same time, psychological trust and positive attitudes core elements of the Technology Acceptance Model and of platform‑based interactions stabilize saving patterns by creating highly predictable and reliable conditions that reinforce risk‑averse preferences (Davis, 1989).

Taken together, this indicates that the digital sharing economy influences saving and investment not solely through correlation, but through motivational (attitudinal), cognitive (cognitive appraisal), and control‑related (perceived control) pathways.

In the domain of investment, the digital sharing economy creates new opportunities for Generation Z, albeit accompanied by risks. Studies indicate that digital platforms, by providing online investment tools such as fintech services, increase risk-taking tendencies among young individuals (Asi et al., 2022). For example, Güçlü et al. (2023), in their comparison of developing countries such as Turkey and Canada, emphasize the role of psychological factors such as risk propensity in participation within the sharing economy. In Iranian studies, Khalkhali et al. (2022) compared retail investment patterns across different generations and found that Generation Z tends to favor high-risk digital investments. These findings suggest that the sharing economy may drive a shift in investment styles from conservative to innovative approaches.

The conceptual model of this study is constructed around the latent variable of the digital sharing economy, influenced by predictive factors such as attitude toward sharing, trust in platforms, financial risk-taking propensity, digital accessibility, and personal economic status. This model, adapted from studies such as Davlembayeva et al. (2020), posits that these factors can increase the level of engagement in sharing services and ultimately affect saving and investment styles. In Iran, domestic studies such as Aznab et al. (2020) emphasize the role of user experience on sharing platforms, aligning with the proposed model.

The main objective of this research is to examine the impact of the digital sharing economy on the saving and investment styles of Generation Z in Iran. This objective includes identifying predictive factors such as attitude, trust, risk-taking, digital accessibility, and economic status, and analyzing their relationships with the dependent variables. Based on the conceptual model, the study aims to test hypotheses predicting the relationships between these factors and engagement with the sharing economy. A secondary objective is to propose policy recommendations to foster sustainable financial behaviors among Generation Z.

Research Background

Domestic Studies

Domestic research in the field of the digital economy demonstrates the rapid expansion of sharing platforms in Iran, which have significantly influenced the consumption behaviors of younger generations. Rahmani et al. (2023) emphasized the role of the digital economy in enhancing the productivity of small businesses, noting that such platforms facilitate access to resources while also introducing challenges such as digital inequality. These findings are consistent with Kazemi and Amini (2022), who examined the investment patterns of Generation Z in digital platforms and found that young individuals are inclined toward innovative financial options. Such studies highlight the importance of behavioral factors within the sharing economy and provide a basis for analyzing its effects on saving behaviors.

In the context of Generation Z’s financial behavior, local studies have primarily focused on factors that influence their retention and engagement in financial markets. Akbari et al. (2023) identified the determinants influencing Generation Z’s persistence in the capital market and emphasized the role of compensation systems in shaping their financial decisions a concept that may overlap with aspects of the sharing economy. Support for this perspective can be found in Asi et al. (2022), who analyzed the financial literacy of retail investors in Iran’s stock market and revealed that younger investors require enhanced digital education for effective risk management. These examinations highlight existing gaps in the literature, directing attention toward exploring the influence of sharing platforms on investment behavior.

The sharing economy in Iran often encounters cultural and economic challenges that domestic scholars have explored in detail. Sharaf et al. (2021) examined the impact of sharing platforms on the saving behavior of Generation Z and found that such platforms may enhance short-term saving while potentially reducing long-term saving tendencies. This finding is reinforced by Aznab et al. (2020), who studied user experiences on Instagram and their role in shaping the brand image of sharing platforms, emphasizing the importance of digital trust. Consequently, these studies also highlight the psychological dimensions of adopting the sharing economy, bridging this topic with research on digital transformation.

Moradi and Hedayati (2018) proposed a model for Iran’s transition toward a digital economy, underscoring the importance of digital infrastructure in facilitating resource sharing. These perspectives reflect the potential of the sharing economy to transform financial patterns and the necessity of examining predictive factors such as digital accessibility. Domestic studies have also focused on entrepreneurship and value creation within the sharing economy. Kordanayij et al. (2022) identified the prerequisites for co-creation of value in sharing-based startups and emphasized the role of mixed-method analyses for exploring such factors. These results are in line with Rahmani et al. (2023), who designed a localized digital marketing model for online small businesses and highlighted the role of sharing platforms in attracting Generation Z. Collectively, these studies underscore the importance of innovation in financial behaviors and lay the foundation for analyzing aspects such as personal economic status.

Ultimately, domestic research consistently emphasizes intergenerational differences in economic behaviors, which may be directly related to the dynamics of the sharing economy.

International Studies

The digital sharing economy, as an innovative model, has transformed both consumer and financial behaviors across different societies and has had a profound impact on younger generations. International research indicates that this economy, emphasizing temporary access to resources, can shift saving patterns from ownership-oriented toward sustainable consumption, while simultaneously introducing challenges such as increased dependency on platforms (Davlembayeva et al., 2020). Support for this perspective is found in the study by Güçlü et al. (2023), which compared Turkey and Canada and identified motivational factors such as individual attitude and trust as key predictors of participation in the sharing economy particularly in developing countries where Generation Z is more dependent on technology. These findings emphasize the importance of examining long-term effects of this model on financial behavior and provide a foundation for analyzing psychological factors.

Psychological and social factors play a central role in the adoption of the sharing economy and can influence investment styles. Bärö et al. (2022) examined individual characteristics such as risk-taking tendencies and interpersonal trust as the primary bases of engagement in sharing platforms, concluding that these factors guide financial decision-making toward innovative options. This idea is reinforced by Davlembayeva et al. (2020), who analyzed social and psychological dimensions in sharing exchanges and found that outcomes like satisfaction and loyalty affect sustainable economic behaviors. Therefore, these studies stress the need to integrate behavioral aspects into sharing economy models, bridging them with considerations of sustainability and innovation.

Sustainable innovation within the sharing economy, particularly in the context of the circular economy, creates opportunities to enhance saving patterns but also introduces supply chain risks. Bai et al. (2025) investigated the role of sustainable innovation in mitigating supply chain disruption risks in the digital economy, arguing that sharing platforms facilitate resource circulation and enhance financial savings. Similarly, Champkins and Bocken (2025), through a case study on resale platforms, demonstrated that such models generate economic value through circular exchange mechanisms and can redirect investment patterns toward environmentally driven options. This synthesis underscores the potential of the sharing economy in fostering sustainable financial behaviors and guides attention toward the challenges of collaborative consumption.

The darker sides of collaborative consumption can negatively affect long-term saving tendencies. Huang et al. (2024) explored negative aspects such as the perceived loss of ownership and found that these factors decrease consumer satisfaction and ultimately reshape financial patterns. This perspective aligns with the study by Srisathan and Naruetharadhol (2025), which examined the consumer open-innovation paradox in sustainable consumption, emphasizing the discrepancies between intention and actual behavior. Such research highlights gaps in understanding the psychological consequences of the sharing economy and opens avenues for further exploration of digital applications.

In the tourism sector, digital sharing economy platforms are reshaping consumption behaviors and can influence saving and investment patterns Naderpajouh et al. (2024) examined the use of digital sharing platforms in European tourism and found that they shape consumer behavior and influence digital policy, particularly among younger users. Supporting evidence is provided by Rojanakit et al. (2025), who investigated sharing economy platforms in emerging Thai markets and emphasized the move beyond traditional ownership models. Hence, these studies underscore the importance of cultural context in the sharing economy and connect it to dynamic system analyses.

Dynamic system models in the sharing economy contribute to waste management and social development while promoting sustainable investment patterns. Ranjbari et al. (2024) proposed a dynamic simulation model for food-sharing platforms, demonstrating that these systems improve waste management within the circular economy and generate economic savings. Similarly, Torrent-Sellens (2024) analyzed digital transformation and data-driven economies from a European perspective, highlighting progress toward communal advancement. These views underline the potential of the sharing economy to transform financial behaviors and link it to organizational and collaborative contexts.

The organization of competitive tensions within the sharing economy influences ecological projects and can complicate investment strategies. Naderpajouh et al. (2024) studied the organization of such tensions in collaborative consumption and found that project ecologies can foster innovation but require careful management. Additionally, Nicol et al. (2024) explored corporate incubation for platform growth in the circular economy and emphasized the challenges of scaling up such ventures. These studies focus on the organizational dimensions of the sharing economy and pave the way for applications in agriculture and insurance sectors.

Digital knowledge sharing in agriculture and insurance can strengthen financial behaviors within the sharing economy. Ollerenshaw et al. (2025) studied the implementation of digital tools for knowledge-sharing among Australian agricultural groups, showing that these tools enhance collaboration and enable cost savings. Furthermore, Wu et al. (2025) analyzed the participation of Chinese platform workers in occupational injury insurance and highlighted the associated insurance challenges. These results emphasize the significance of financial security within the sharing economy and connect to the examination of circular economy principles.

Integrating circular economy principles with the Theory of Planned Behavior in collaborative consumption shapes consumer intentions and influences saving patterns. Tanveer et al. (2025) examined this integration in the context of Airbnb and found that user experiences promote sustainable behaviors. Likewise, Wang and Hou (2025) investigated the impact of artificial intelligence on labor income share within the digital economy and highlighted the challenges of income inequality. Together, these studies emphasize the need for supportive policies and lead toward the synthesis of international research findings.

In conclusion, the international literature emphasizes diverse factors psychological, sustainability-related, and digital within the sharing economy that can transform the saving and investment styles of Generation Z. Studies such as Bai et al. (2025) and Huang et al. (2024) highlight both positive and negative aspects, showing that while the sharing economy offers opportunities for financial innovation, it also requires deliberate risk management. This synthesis identifies gaps such as limited focus on Generation Z in developing contexts, thereby presenting potential directions for future research.

Theoretical Foundations

The conceptual model is grounded in integrated principles of behavioral economics and technology adoption theory. Saving and investment behaviors are viewed as interrelated outcomes of both psychological and technological antecedents that determine how individuals form financial intentions and translate them into action. Technological factors provide the enabling environment, psychological factors supply the motivational drive, and demographic context moderates these linkages. This theoretical framework clarifies why these three variable groups were selected as antecedents: they collectively shape financial decision‑making under digital conditions, reflecting the coexistence of cognitive, technological, and socio‑structural determinants.

A. Examining the Dynamics of Usage Levels and Types of Services in the Digital Sharing Economy

The digital sharing economy, by emphasizing the level of usage and the type of services offered, directs the consumption behaviors of younger generations toward access-oriented models and has the potential to transform financial patterns. Research indicates that the usage level of services such as ride-sharing or freelancing platforms not only facilitates access to resources but also influences economic decision-making. This can be observed in the study by Güçlü et al. (2023), which, through an examination of consumer motivations in developing countries such as Turkey, demonstrates that the type of service selected whether for entertainment or housing rental is shaped by cultural and economic factors and can enhance short‑term saving tendencies. Similarly, Ranjbari et al. (2024), in their dynamic simulation model of food‑sharing platforms, emphasize that usage frequency (e.g., daily or monthly) optimizes resource management and promotes sustainable behaviors. These findings align with Rojanakit et al. (2025), who, in their study of emerging markets in Thailand, identified the type of service as a key factor in moving beyond traditional ownership structures, noting that financial and transportation services can stimulate innovative investment behaviors.

The duration of interaction with these platforms acts as an evolutionary factor that determines the sustainability of participation in the sharing economy and can shift investment styles toward digital options. Bärö et al. (2022) examined individual characteristics influencing participation and found that long‑term engagement (more than five years) enhances trust and cultivates sustainable economic behaviors. This concept is consistent with the study by Mendieta Aragón et al. (2025), who explored the use of tourism‑based sharing platforms in Europe and concluded that continuous interaction influences digital policy development.

B. Revealing Attitudes Toward Sharing: Psychological Drivers in the Digital Economy

Attitude toward sharing, as a key psychological factor, facilitates the acceptance of the sharing economy and can shift saving styles from traditional ownership to temporary access. Davlembayeva et al. (2020) examined social and psychological factors within sharing exchanges and concluded that a positive attitude toward resource sharing enhances satisfaction and loyalty while promoting cost-saving behaviors. This perspective is supported by Güçlü et al. (2023), who, by comparing developed and developing countries, identified attitude as the primary predictor of participation, asserting that willingness to share reinforces sustainable consumption. Similarly, Tanveer et al. (2025) integrated the principles of the circular economy with the Theory of Planned Behavior and found that a positive attitude toward access rather than purchase shapes consumer intentions on digital platforms. In domestic research, Aznab et al. (2020) analyzed Instagram users’ experiences in shaping the brand image of sharing platforms and emphasized the role of attitude in building trust.

Trust in digital platforms, as the foundation of adoption, ensures transactional security and can also heighten the propensity for risk-taking. Lou (2024) investigated the application of data security within the sharing economy and stated that trust in the protection of personal information enhances user engagement. Likewise, Bärö et al. (2022) analyzed individual characteristics and found that interpersonal trust strengthens participation in collaborative exchanges. In the domestic context, Asi et al. (2022) explored factors influencing financial literacy and emphasized the mediating role of trust in risk management. Collectively, these perspectives highlight the importance of attitude as a determinant of financial sustainability and as a psychological foundation that fosters risk-taking tendencies.

C. Building Trust in Digital Platforms: Foundations of Secure Sharing Economies

Trust in digital platforms, as a critical factor, enhances the acceptance of the sharing economy and can direct investment styles toward innovative options. Davlembayeva et al. (2020) explored psychological factors in sharing exchanges and demonstrated that trust in the security of transactions generates positive outcomes such as satisfaction and continued engagement. Similarly, Lou (2024) investigated sensor-based data security systems and found that confidence in data protection facilitates the development and expansion of the sharing economy.

Financial risk-taking propensity, as a primary driver of innovation, encourages high-risk investments and can align closely with the dynamics of the sharing economy. Bärö et al. (2022) examined individual characteristics and concluded that a higher inclination toward risk increases participation in collaborative economic models. In domestic research, Asi et al. (2022) analyzed financial literacy in Iran’s stock market and emphasized the importance of risk management in shaping rational investment behaviors. Collectively, these perspectives underscore that trust is essential for risk-oriented behaviors and provides the groundwork for expanding digital access and fostering secure participation in modern sharing economies.

D. Embracing Financial Risk Tolerance: Catalysts for Innovative Investments in Sharing Economy Models

Financial risk tolerance, as a key behavioral factor, guides Generation Z toward innovative investment opportunities within the sharing economy and can yield higher potential returns. Bärö et al. (2022) examined individual characteristics in collaborative participation and revealed that a higher propensity for risk-taking directs financial decisions toward high-risk options. Similarly, Güçlü et al. (2023), through their cross-national analysis of consumer motivations, argued that in developing countries, risk-taking tendencies significantly increase participation in sharing-based platforms. Wang and Hou (2025) analyzed the impact of artificial intelligence on labor income and concluded that risk-taking behavior in the digital economy helps mitigate income-related challenges.

In domestic literature, Asi et al. (2022) investigated factors influencing financial literacy and emphasized the crucial role of risk tolerance in capital market performance. Likewise, Kazemi and Amini (2022) compared the investment patterns of Generation Z and found that individuals with greater risk propensity tend to prefer digital investment options.

Digital access, as a critical infrastructure of the sharing economy, enables platform utilization and can reduce socioeconomic inequalities. Moradi and Hedayati (2018) proposed a digital transition model that highlights the importance of equal access to technological resources for inclusive participation. Collectively, these viewpoints link financial risk-taking with digital accessibility, establishing an integrated foundation that connects individual economic status to innovative investment behaviors in the digital sharing economy.

E. Empowering Digital Access: Bridging Gaps in the Sharing Economy Landscape

Digital access, as a critical infrastructural factor, facilitates participation in the sharing economy and can transform the saving styles of Generation Z through enhanced Internet connectivity and widespread use of smart devices. Ollerenshaw et al. (2025) explored digital tools for knowledge sharing in the agricultural sector and found that an increased number of connected devices strengthens collaboration and promotes engagement in network-based economic activities. Similarly, Lou (2024) analyzed data security within digital ecosystems and concluded that sufficient access to secure Internet infrastructure promotes the broader adoption of sharing platforms. In domestic studies, Moradi and Hedayati (2018) proposed a model for transitioning to a digital economy, emphasizing the central role of digital infrastructure in enabling efficient resource sharing and inclusive participation.

Personal economic status determines the individual’s capacity to spend on shared services and can directly influence investment decisions. Asi et al. (2022) examined financial literacy factors and highlighted the importance of economic conditions in shaping responsible financial behavior. Collectively, these perspectives establish a strong linkage between digital access and personal economic capacity, providing a conceptual basis for understanding the interaction between demographic factors and participation patterns within the digital sharing economy.

F. Exploring Personal Economic Status: Impacts on Participation in the Collaborative Economy

Personal economic status, as a determining factor, shapes Generation Z’s ability to participate in the sharing economy and can influence investment styles based on monthly income levels. Wang and Hou (2025) examined the impact of artificial intelligence on income distribution and concluded that one’s economic condition plays a pivotal role in managing financial challenges within the digital context. In domestic literature, Akbari et al. (2023) identified retention factors for Generation Z in Iran’s capital market and emphasized the importance of compensation mechanisms and economic well-being in sustaining participation and investment engagement.

Demographic factors such as age, gender create diversity in behavioral patterns and can personalize sharing economy models. Güçlü et al. (2023) investigated cultural differences and found that education significantly increases participation in collaborative platforms. Likewise, Kazemi and Amini (2022) compared generational investment patterns and highlighted residential location as a determinant of financial engagement. Collectively, these perspectives link personal economic status with demographic attributes, forming a contextual foundation that connects individual economic capacity to saving styles and highlights differentiated participation in the digital sharing economy.

G. Decoding Demographic Factors: Shaping Behaviors in the Digital Sharing Domain

Demographic factors, as contextual variables, diversify the behaviors of Generation Z within the sharing economy and can influence investment styles based on characteristics such as age, gender, education, and place of residence. Güçlü et al. (2023) compared consumer motivations across different countries and found that gender and education significantly affect participation levels, particularly among younger generations. Similarly, Kazemi and Amini (2022), who examined generational investment patterns, reported that age and location (urban vs. rural) determine the degree of inclination toward digital engagement and investment.

Rojanakit et al. (2025) explored sharing platforms in Thailand and concluded that demographic factors especially education level shape behavioral patterns that transcend traditional ownership models. In domestic research, Akbari et al. (2023) identified key factors influencing the retention of Generation Z in financial markets, emphasizing gender and education as central determinants of participation. These findings align with Asi et al. (2022), who analyzed financial literacy in Iran and identified residential location as a factor influencing risk-taking behavior. Therefore, demographic characteristics generate financial diversity among individuals and ultimately extend their effects to saving styles, revealing their foundational role in shaping heterogeneous patterns of economic participation within the digital sharing economy.

H. Cultivating Saving Styles: Habits and Strategies in the Era of Shared Resources

Saving style, as a structured financial behavior, guides Generation Z toward saving before spending and can foster sustainable frugality within the sharing economy. Bai et al. (2025) explored sustainable innovation and concluded that consistent saving practices reduce supply chain risks and strengthen financial resilience. In the domestic context, Asi et al. (2022) examined financial literacy and emphasized saving style as an essential component for managing adverse economic conditions.

These insights correspond with the findings of Champkins and Bocken (2025), who analyzed circular value creation and conceptualized saving as an integral part of the resource circulation process. Accordingly, a systematic saving style not only ensures financial sustainability but also serves as a foundation for subsequent investment behavior.

Investment style, in contrast, represents a risk-oriented approach that favors higher-risk opportunities and aligns with the inherent dynamism of digital platforms. Kazemi and Amini (2022) compared generational investment patterns and indicated that a balanced combination of low-risk and high-risk investments can enhance innovation and adaptability within digital financial ecosystems. Thus, the evolution from saving habits to investment strategies reflects the transformative role of the digital sharing economy in redefining financial behavior among Generation Z.

I. Mastering Investment Styles: Risk and Innovation in Digital Sharing Contexts

Investment style, as a dynamic financial strategy, directs Generation Z toward high-risk and innovative options within the sharing economy, potentially enhancing returns and fostering adaptive financial behavior. Kazemi and Amini (2022) compared generational investment patterns and found that the inclination toward digital instruments particularly cryptocurrencies encourages a risk-tolerant style among younger investors. This observation is supported by Bärö et al. (2022), who investigated individual traits and concluded that a balanced combination of low-risk and high-risk investments facilitates broader market participation and diversification.

Güçlü et al. (2023) analyzed motivational drivers and determined that high-risk investment styles stimulate innovation in developing economies, aligning risk-taking with entrepreneurial creativity and digital agility. Within the domestic literature, Asi et al. (2022) explored financial literacy among Iranian users and emphasized openness to novel financial methods as a characteristic feature of Generation Z investors.

These findings converge with Wang and Hou (2025), who examined the influence of artificial intelligence on financial decision-making and identified risk-oriented investment patterns as essential mechanisms for addressing digital-era challenges. Consequently, the investment style not only guides financial transformation among Generation Z but also integrates theoretical fundamentals into practical outcomes, demonstrating how technological advancement and behavioral innovation jointly redefine modern investment conduct within the digital sharing economy.

Source: Research Findings

Figure 1: Conceptual Model of the Research

Research Methodology

According to Creswell’s (2014) classification, this research is an applied study, the results of which can be used in policymaking and in improving the financial behaviors of Generation Z as well as developing the digital sharing economy in Iran. In terms of the nature of data and approach, the present study is quantitative, and regarding the type of study, it is descriptive survey research conducted using a cross-sectional method; the data were collected within a specific time frame accessible to the researchers.

The research strategy in this study is survey-based. This strategy was selected due to its ability to collect standardized data from a relatively large sample, examine the relationships between variables, and generalize the results to the statistical population. It is fully consistent with the nature of the structural equation modeling approach based on PLS-SEM.

The decision to combine indicators such as Internet speed and device availability into a single latent structure is rooted in their shared theoretical function as core enablers of technological access. Both indicators represent infrastructural capacities that convert potential engagement into actual participation on digital sharing platforms. Conceptually, these elements do not reflect separate advantages but rather complementary dimensions of the same underlying technological‑readiness capability that facilitates financial participation, information access, and effective interaction within the digital sharing economy. This perspective aligns with digital‑access frameworks that conceptualize ICT access as a multidimensional construct integrating network quality with user‑side device availability, as well as with the Rhizomatic Digital Ecosystem Framework, which considers connection speed and the ability to support multiple devices as interdependent aspects of adequacy in digital access (Hollimon et al., 2025).

Statistical Population and Sampling Method

The statistical population consisted of Iranian members of Generation Z who had used at least one digital sharing‑economy platform such as Snapp, Divar, Filimo, or freelancing applications at least once. Since more than 65% of this generation resides in provincial centers and large cities, where the penetration of high‑speed Internet, access to smart devices, and active use of platforms are considerably higher, data collection was concentrated in major urban areas to ensure close alignment between the sample and the actual user base of digital sharing services.

The convenience sampling method was used. To ensure anonymity and facilitate access to diverse urban Generation Z respondents, data collection was conducted online through Porsline and various social media networks. This approach prioritized relevance. Although convenience sampling was used, the sampling frame was limited to individuals with verified experience in the sharing economy to minimize irrelevant participation.

Statistical power analysis determined the target. Using a 95% confidence level with a maximum variance of 0.25 and a margin of error of 5%, the minimum required sample size was determined to be 384 participants. The final dataset, with 384 valid responses, met this requirement. The sample consisted of 183 women (47.6%) and 201 men (52.3%), of whom 114 were high school students or had a high school diploma, 185 were undergraduate students or had a bachelor's degree, 65 were graduate students or held a master’s degree, and 20 were doctoral student.

This distribution covers the full spectrum of this group. This sample, which ranges from high school students to advanced researchers, provides a comprehensive foundation for analyzing behavioral and digital economic patterns. Particular attention was paid to demographic diversity. The significant diversity in income, education, and digital engagement mitigated common concerns about sampling bias and ensured that respondents reflected active Generation Z users in Iran’s sharing platform ecosystem. These procedures created a statistically sufficient sample. The sample closely matched the demographic and technological characteristics of the study population.

Data Collection Instrument

The primary data collection instrument was a standardized and adapted questionnaire developed based on the conceptual model of the research and using internationally validated scales. Attitude was measured with four items from the scale of Güçlü et al. (2023) (α = 0.82). Trust in digital platforms was assessed with three items adapted from Davlembayeva et al. (2020) (α = 0.88). Financial risk-taking tendency was measured using two items from the scale of Barro et al. (2022) (α > 0.70). Digital access was evaluated with two items from the studies of Willey et al. (2022) (α = 0.75). Personal economic status was measured using commonly utilized indicators in digital sharing economy studies by Güçlü et al. (2023). Saving style was assessed using four items from the scale of Dew & Xiao. (2011) (α = 0.80), and investment style was measured based on four items from Grable and Lytton (1999).

In this research, the construct of technological factors included two operational indicators: (1) the number of internet-connected smart devices, and (2) internet speed. Both indicators measure complementary dimensions of a single phenomenon digital access and technological infrastructure.

The results of the measurement model analysis in SmartPLS showed that the standardized factor loadings of both indicators were higher than 0.7, the composite reliability of this construct exceeded 0.7, and its average variance extracted (AVE) was greater than 0.5. These values, in addition to confirming convergent validity, indicate conceptual coherence between the two indicators and statistically and conceptually justify their aggregation into a single latent construct. Therefore, in accordance with the principles of first-order construct modeling in the PLS-SEM approach (Hair et al., 2021), both indicators were modeled as a single latent variable so that the assessment of their effects could be conducted in a valid and meaningful manner.

In this study, the monthly income indicator was categorized as an individual economic characteristic under demographic factors. This classification was based on the rationale that income reflects an individual’s economic capacity and purchasing power and does not have a direct relationship with digital access or technological infrastructure. In the questionnaire, this item was placed under the section on demographic factors, and in the conceptual model, it was considered as one of the defining indicators of the demographic factors construct. Unlike indicators such as the number of connected devices or internet speed, which pertain to technological access, income has a socio-economic nature and should be analyzed alongside variables such as age, gender, and education.

In this study, there are two higher-order constructs (second-order reflective constructs), each composed of multiple first-order constructs. These constructs are: Digital Sharing Economy and Psychological Factors. To estimate these constructs within the framework of structural equation modeling using the partial least squares (PLS-SEM) approach, the two-stage method was employed. This method was chosen considering the reflective nature of the first-order constructs and the need for precise estimation of the combined effects of their dimensions, in order to avoid indicator overloading and to enhance analytical accuracy.

The advantage of using the two-stage method in this study is that, by reducing the direct number of indicators in the structural model, it enables more accurate estimation of the effects of higher-order constructs on the dependent variables (Saving Style and Investment Style). Moreover, this method is more suitable than alternative approaches for models with unbalanced dimensions (i.e., one dimension having several indicators and another having only one), and it resolves the issue of unequal indicator weighting.

The data were collected online via Porsline, which ensures respondent anonymity and data security. This tool directly covers the relationships of the conceptual model and facilitates their transfer to the analytical process.

Data Analysis Method

Data analysis was performed using structural equation modeling (SEM) based on the partial least squares (PLS) approach in SmartPLS version 3. The selection of this method is justified by its advantages when dealing with medium-sized samples, non-normal data distributions, and complex models containing latent variables conditions that are common in studies of the sharing economy. The conceptual model includes reflective constructs, whose path relationships are tested. Therefore, structural equation modeling provides a framework for evaluating the quality of the model and proceeds toward assessing reliability and validity.

For model estimation, the two higher-order constructs Digital Sharing Economy and Psychological Factors were first estimated using the two-stage approach. The Digital Sharing Economy second-order construct consists of the following first-order dimensions (constructs):

• Level of use of sharing services (4 indicators: online taxi services, video streaming subscriptions, freelancing platforms, short-term housing rentals),
• Type of main service used (1 indicator: the most frequently used service type), and
• Duration of interaction with platforms.

In the first step for computing the scores of this construct, each dimension was analyzed as a first-order construct with its respective indicators. The results showed that the standardized factor loadings of all indicators were greater than 0.7 and statistically significant. In addition, Cronbach’s alpha (α) and composite reliability (CR) for all dimensions exceeded 0.7, and the average variance extracted (AVE) for all dimensions was greater than 0.5, indicating convergent validity.

In the second stage, the scores of the first-order constructs obtained from the first stage were used as the observed indicators for the higher-order construct Digital Sharing Economy. The results showed that the loadings of the dimensions on the second-order construct ranged from 0.573 to 0.849, and all were statistically significant. The composite reliability (CR) and Cronbach’s alpha (α) values of this construct were above 0.8 and 0.7, respectively. The average variance extracted (AVE) was approximately 0.6, confirming the convergent validity of the second-order construct. The Fornell–Larcker and HTMT indices further confirmed the discriminant validity of this construct relative to other constructs in the model.

This reflective construct represents the level and diversity of user participation and the temporal experience in the digital sharing economy, indicating that more diverse and prolonged interactions can have significant effects on the financial styles of Generation Z.

The second-order construct of Psychological Factors consists of the following first-order dimensions (constructs):

• Attitude toward sharing (4 indicators),
• Trust in sharing platforms (3 indicators), and
• Financial risk-taking tendency (2 indicators).

For this variable as well, all indicator loadings on the first-order constructs ranged between 0.858 and 0.949, and were statistically significant. The CR and α values for all three dimensions were above 0.8, and their AVE values exceeded 0.5, confirming internal consistency and convergent validity.

After computing the scores of the three first-order constructs, these scores were entered into the model as the observed indicators of the second-order construct. The results showed that the loadings of the dimensions on the higher-order construct were as follows: Attitude (0.898), Trust, and Risk-Taking, all greater than 0.7 and statistically significant. Discriminant validity was confirmed using both HTMT and Fornell–Larcker criteria.

This second-order construct integrates three key psychological dimensions associated with financial engagement in the digital sharing economy, reflecting the combined role of a positive attitude, trust in digital platforms, and a willingness to accept risk in predicting modern financial behaviors.

Results

In this section, the results of data analysis collected from 384 participants of Generation Z in Iran are presented using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The analysis was conducted based on SmartPLS3 software and includes the evaluation of the measurement model and the structural model. Bootstrapping with 5,000 subsamples was used to compute inferential statistics. The results of the path coefficients are presented in Figure 2. The structural model was evaluated through path coefficients, T-statistics, P-values, and 95% confidence intervals. The coefficients represent standardized linear relationships.

Source: Research Findings

Evaluation of the Quality and Validity of the Measurement Model in the Present Study

In this study, the measurement model was thoroughly assessed in terms of reliability, validity, and bias control to ensure the quality of data and constructs. Internal consistency reliability was examined using Cronbach’s alpha and Composite Reliability (CR), both of which exceeded 0.70 for all constructs particularly for key constructs such as Investment Style and Trust indicating adequate internal stability and consistency.

Convergent validity was confirmed through the calculation of the Average Variance Extracted (AVE), which was greater than 0.50, and factor loadings above 0.70 for all items. Discriminant validity was established using the Fornell–Larcker criterion and the HTMT ratio, both of which were below 0.85, indicating satisfactory conceptual and statistical distinction among the constructs. Furthermore, the analyses showed that the square root of the AVE for each construct was greater than its correlations with other constructs, which further supports discriminant validity.

To ensure the absence of multicollinearity problems, the VIF index was calculated for all items and was reported to be less than 3 for all cases, indicating the lack of significant multiple covariance. At the level of content validity, the questionnaire was reviewed by five experts in digital economics, and a pilot test was conducted on 30 participants; the feedback obtained led to minor revisions of some items. Altogether, these procedures demonstrated that the measurement model possesses the required quality and validity in terms of reliability, validity, and lack of bias for subsequent analyses.

Source: Research Findings

Figure 2: Research model estimation results

Operational Definition of Variables, Reporting, and Interpretation of Coefficients

Saving Style (First Dependent Variable)

Saving style, as a multidimensional construct, represents individuals’ behavioral patterns in storing financial resources. This variable was operationalized through four key items.

Prioritization of saving in financial decisions: This item measures the extent to which an individual values saving compared to other financial goals. Individuals who obtain high scores place saving at the top of their financial priorities and set aside a portion of their income before any expenditure or investment.

Discipline and regularity in saving: This dimension refers to the stability of saving behavior over time. High scores indicate individuals who, regardless of income fluctuations, regularly (monthly or weekly) save a specific amount.

Stability of behavior under critical conditions: This item measures the resistance of saving behavior to financial shocks and consumption temptations. Individuals with high scores refrain from using their savings even in difficult economic conditions or when faced with attractive consumption opportunities.

Prevention of unnecessary expenses: This dimension indicates the individual’s ability to identify and avoid unnecessary expenditures. High scores represent individuals who evaluate the necessity of each purchase before spending and avoid impulsive buying.

The measurement scale is based on a five-point Likert scale (from completely disagree = 1 to completely agree = 5). Higher scores (4–5) indicate an orderly, purposeful, and conservative saving style, associated with characteristics such as long-term planning, resistance to consumption temptations, and prioritization of financial security. In contrast, lower scores (1–2) represent an irregular, reactive, and consumption-oriented saving style, characterized by lack of planning, temporary and unstable saving behavior, and preference for consumption over saving.

Investment Style (Second Dependent Variable)

Investment style, as a behavioral pattern in the allocation of financial resources to profit-generating activities, is measured through four fundamental components:

Tendency toward low-risk investment (reverse-scored): This item measures the individual’s tendency to choose low-risk investment instruments such as bank deposits or government bonds. Considering the reverse scoring, individuals who express strong agreement with this item obtain lower scores on the investment style scale.

Level of financial risk tolerance: This dimension indicates the individual’s readiness to accept risk in exchange for higher returns. High scores represent individuals who are willing to expose part or all of their capital to potential risk in order to achieve greater returns.

Interest in financial innovations: This item assesses the individual’s interest in and readiness to use modern investment instruments such as cryptocurrencies, startups, or crowdfunding platforms. High scores indicate rapid acceptance of innovations and a willingness to experiment with new methods.

Degree of portfolio diversification: This dimension reflects the individual’s strategy in distributing capital among different assets. High scores represent individuals who diversify their investments across several asset classes (stocks, gold, real estate, currency).

The score range is from 1 to 5, where higher values (4–5) represent a bold, innovative, and diversified investment style characterized by high-risk acceptance, interest in emerging markets, and active short-term trading. Lower scores (1–2) indicate a conservative, traditional, and concentrated investment style characterized by preference for security over return, adherence to conventional methods, and long-term passive investing.

Analysis and Interpretation of Path Coefficients

Technological Factors: The Driving Force Behind Transformation in Investment Behavior

This coefficient, which represents the strongest positive effect in the entire model, indicates that technological factors (including the number of smart devices connected to the Internet and the quality of access to high-speed Internet) exhibit a strong relationship with investment style, with a standardized path coefficient of 0.508. More precisely, each one standard deviation increase in the composite index of technological factors leads to a 0.508 standard deviation increase in the tendency toward risk-taking and innovative investment. These factors alone explain 25.8% of the variance in investment style (R² = 0.508² = 0.258).

An increase in the number of smart devices (such as smartphones, tablets, laptops) owned by the individual enables uninterrupted and pervasive 24-hour access to trading platforms. This constant access fundamentally transforms the investment pattern from a passive (buy-and-hold) approach to an active (day-trading) approach. The individual can, at any time and place, monitor their portfolio status, respond to market news, and execute instant transactions.

Moreover, high-speed Internet provides real-time access to market information, online analyses, and trading signals. This capability allows the investor to react to price changes within fractions of a second, which is particularly crucial in highly volatile markets such as cryptocurrencies. Fast Internet access also facilitates participation in algorithmic trading and the use of trading bots.

In addition, digital platforms with user-friendly interfaces, interactive tutorials, and the possibility of starting with minimal capital have eliminated traditional obstacles to entry into financial markets. An individual who previously needed to physically visit a brokerage to buy stocks can now invest in global markets with just a few clicks.

Furthermore, the digital environment, with features such as gamification (game-like trading experiences), the continuous display of others’ profits, and targeted advertisements, intensifies the inclination toward risk-taking. Colorful charts and trading competitions stimulate emotions and lead to an increase in both the number and volume of transactions.

Finally, technological factors make it possible for ordinary individuals to use advanced tools such as financial leverage, margin trading, options, and futures contracts. These instruments, once exclusively accessible to professional investors, are now available with a few clicks naturally increasing both risk exposure and portfolio volatility.

The effect of technological factors on saving style is positive but weak. This coefficient indicates that each one standard deviation increase in technological factors leads to a 0.142 standard deviation increase in saving style. Technological factors explain only 2% of the variance in saving style (R² = 0.142²). This limited effect, compared to the strong effect on investment (3.6 times weaker), is noteworthy.

The inherent contradiction between technology and traditional saving is one of the reasons for this weak effect. The nature of digital technologies with characteristics such as speed, diversity, and excitement contradicts the essence of saving, which requires patience, discipline, and a long-term perspective. Users, instead of storing resources, prefer to employ them in short-term investment opportunities.

Moreover, the hidden costs of technology are also considerable. Maintaining and updating multiple devices, paying for high-speed Internet, purchasing applications and various subscriptions consume a portion of income that could otherwise be allocated to saving. These peripheral expenses are often overlooked; however, their cumulative impact is significant.

Furthermore, the stimulation of digital consumerism is an important factor in moderating this coefficient. Technology intensifies consumerism by creating easy access to online stores, targeted advertising based on user behavior, and generating new needs. Discount messages, personalized offers, and one-click purchasing reduce the psychological barriers to buying and make saving more difficult.

In addition, the lack of a direct relationship between Internet speed and the motivation to save contributes to this moderation. Unlike investment, which requires quick reactions, saving is a slow and gradual process that does not directly depend on Internet speed. High-speed Internet more effectively stimulates transaction-oriented and short-term behaviors than the long-term accumulation of resources.

Finally, the competition between saving and investment is also significant. Easy access to investment opportunities leads individuals, instead of retaining funds in low-yield savings accounts, to invest them in volatile markets. This substitution results in a reduction of traditional saving.

Psychological Factors: The Supporting Pillars of Saving Behavior

The highest positive effect in the model pertains to psychological factors. Psychological factors (including trust in digital platforms, a positive attitude toward the sharing economy, and the level of financial risk tolerance) exhibit a strong relationship with saving style, with a path coefficient of 0.443. This finding indicates that each one standard deviation increase in the psychological factors index leads to a 0.443 standard deviation increase in the tendency toward regular and purposeful saving. These factors explain 19.6% of the variance in saving style (R² = 0.443²).

As a second-order construct, psychological factors represent a synergistic combination of three fundamental components: trust in digital platforms, attitude toward the sharing economy, and financial risk tolerance. In the domain of saving, these factors exert the strongest positive influence. Trust in the security and stability of digital platforms strengthens the sense of confidence in conducting financial transactions and creates motivation for the long-term use of cost-effective services. This institutionalized trust stabilizes resource-saving behavior and forms the foundation for regular saving habits.

Furthermore, a positive attitude toward the sharing economy leads individuals to recognize the intrinsic value of frugality and optimal resource utilization. This attitude is accompanied by concepts such as sustainability, smart consumption, and waste reduction all of which align with the philosophy of saving. Individuals with a positive attitude perceive saving not as a restriction but as an intelligent choice.

Additionally, contrary to common belief, risk-tolerant individuals are not necessarily opposed to saving. They view saving as a tool for creating initial capital to seize future opportunities. This type of saving is purposeful and strategic rather than a passive accumulation of resources.

On the other hand, trust, positive attitude, and balanced risk tolerance interact to form a positive cycle. Trust leads to increased usage; successful usage enhances the positive attitude; and a positive attitude raises the readiness for calculated risk-taking.

Moreover, unlike external factors (such as financial incentives), psychological factors generate intrinsic and lasting motivations. When an individual saves for personal reasons and based on their own beliefs, the likelihood of continuing this behavior even under difficult conditions is significantly higher.

The Effect of Psychological Factors on Investment Style

The effect of psychological factors on investment style is positive but weak. Each one standard deviation increase in psychological factors results in a 0.120 standard deviation increase in investment style. These factors explain only 1.4% of the variance in investment style (R² =0.120²). This effect, which is 3.7 times weaker than their strong impact on saving, indicates the distinct role these factors play.

Although trust in economic and investment platforms facilitates user engagement with them, it does not necessarily imply acceptance of high financial risks. Many users, even when confident in the security and reliability of these platforms, still adopt a cautious approach to investing and refrain from entering areas associated with high financial risk.

This cautious attitude is often accompanied by a positive view toward the sharing economy a perspective that focuses more on concepts such as balance, sustainability, and optimal consumption rather than speculative and high-risk behaviors. Such an outlook directs individuals toward balanced and sustainable investments rather than aggressive choices that may yield quick but unstable profits.

In cases where trust is combined with risk tolerance, it usually results in a form of calculated risk-taking. Individuals under such conditions accept only those risks about which they have sufficient knowledge and whose potential outcomes and consequences they can trust.

Moreover, positive psychological factors lead many individuals to prioritize capital preservation over the pursuit of rapid growth. This approach, which emphasizes long-term and sustainable development, fundamentally differs from the nature of short-term, high-risk investments that focus primarily on immediate profits.

Demographic Factors as Structural Constraints of Modern Financial Behaviors

Demographic factors (including age, gender, level of education, income level, and place of residence) have a moderately to strongly negative relationship with investment style, with a path coefficient of −0.318. More precisely, each one standard deviation increase in the composite index of demographic factors leads to a 0.318 standard deviation decrease in the tendency toward high-risk investments. These factors explain 10.1% of the variance in investment style (R² = 0.318²).

As age increases, the investment time horizon shortens, and the remaining time to compensate for potential losses diminishes. An individual who loses 50% of their capital at age 20 has forty years to recover, whereas the same person at age 35 acts more cautiously. Additionally, with increasing age, responsibilities such as marriage, children, home purchase, and securing the family’s future become priorities, all of which require a higher degree of financial security.

Moreover, the older segment of Generation Z (aged 28–30) has experienced financial crises such as the cryptocurrency market crash in 2018 and the severe fluctuations of the Iranian stock market in 2020, events that have made them more cautious. Alongside this, the willingness to learn new financial tools and to embrace innovation declines with age. The technical complexities of cryptocurrencies or DeFi systems appear intimidating to older individuals.

Overall, demographic factors function as structural constraints that limit the tendency toward innovative, high-risk financial behavior. As these factors intensify (particularly age, family responsibilities, and income stability), the preference for secure, low-volatility investments grows stronger among Generation Z in Iran.

Higher education plays an important role in reducing uninformed investment risks, because it leads to an increase in financial knowledge and a better understanding of concepts such as systematic risk, diversification, and the time value of money. This awareness prevents individuals from emotional decision-making and behaviors similar to gambling and directs them toward adopting logical and analytical approaches. As a result, instead of relying on rumors and emotions, educated individuals usually make use of fundamental and technical analyses and choose options with moderate risk. In addition, higher education increases the likelihood of studying and examining the history of financial markets and past crises, such as the 2008 financial crisis, which brings important lessons in the field of caution and risk management. Also, individuals with higher education often have access to jobs with fixed and stable income, and this factor reduces their need to accept high risks to earn additional income.

The combination of factors such as higher age, advanced education, and medium to high income creates the strongest inhibitory effect on the tendency toward high-risk investments; because this group of individuals is neither faced with immediate financial pressure nor inclined to gamble with their assets. Although each of these characteristics alone can lead to caution, when they exist simultaneously, their effect increases cumulatively and multiplicatively. Under such conditions, individuals, due to their stable demographic position, develop a greater inclination to maintain the current state and have less motivation to create change through accepting high risks.

The effect of demographic factors on saving style is a moderately negative one. Demographic factors, with a path coefficient of −0.233, have a moderate negative relationship with saving style. This finding indicates that each one standard deviation increase in demographic factors leads to a −0.233 standard deviation decrease in the tendency toward regular saving. These factors explain 5.4% of the variance in saving style (R² = 0.233²).

The high inflation rate in Iran causes the value of savings to decrease rapidly. Young people prefer to spend their money or invest it in assets that keep pace with inflation. On the other hand, the existence of high-return investment options (even if they are risky) makes traditional saving with low interest unattractive. Moreover, Generation Z has grown up in the era of digital advertising and social networks, which promote consumption as a means of expressing identity. Finally, economic and political instability leads young people to prefer living in the present rather than saving for an uncertain future.

The level of education has a complex and sometimes contradictory effect on individuals’ financial behavior and saving ability. Those with low educational attainment often have limited awareness regarding the importance of saving and financial management concepts; consequently, they may spend their resources without regular planning and face greater economic problems in the long term. In contrast, individuals with higher education, although more capable in terms of financial knowledge, have higher expectations regarding quality and standard of living, which in itself can lead to increased expenses and reduced saving capacity. In addition, many graduates, especially at young ages, face student loan debts or educational-related costs, the repayment of which becomes a priority and limits the possibility of allocating resources to saving.

High income is not necessarily equivalent to greater saving, and in many cases, it can even have a reverse effect on financial behavior. A phenomenon known as lifestyle inflation causes the level and volume of expenses to rise alongside income growth sometimes to the extent that a large portion or even all of the income is spent on daily and luxury expenditures. Many high-income individuals also tend toward conspicuous consumption, meaning they allocate a substantial part of their income to luxury goods, foreign travel, or expensive experiences to display their social status, rather than retaining it in the form of long-term investment or saving.

Furthermore, high income can create a kind of false sense of security, in which an individual assumes the ability to earn money at any time, and therefore feels no need to create financial reserves or save for the future.

Living in metropolitan areas is inherently associated with high costs; from heavy housing prices and high transportation costs to elevated entertainment and leisure expenses all these factors leave only a small portion of income available for saving. In addition to financial pressure, social pressure also plays an important role in urban environments, as individuals often seek to align their lifestyle and consumption level with those of their peers and surroundings, even if this increases expenses and reduces saving capacity.

Moreover, the abundance of shopping centers, restaurants, and recreational facilities in large cities provides easy access to consumption options and significantly heightens the temptation to spend, which ultimately serves as yet another obstacle to forming long-term saving and investment habits.

The Digital Sharing Economy: A Catalyst for Saving, Not Investment

The digital sharing economy, with a path coefficient of 0.069, has a very weak relationship with investment style. Each one standard deviation increase in the use of sharing economy services results in only 0.069 standard deviation increase in investment style. This factor explains less than 0.5% of the variance in investment style (R² = 0.069² ≈ 0.005). This negligible effect indicates that sharing economy platforms play a very limited role in altering investment behaviors.

The sharing economy and investment are two concepts with entirely different natures, and this fundamental difference makes their relationship particularly in Iran highly limited and indirect. The sharing economy, such as using services like Snapp or Uber, essentially focuses on optimizing consumption and utilizing others’ assets, whereas investment seeks wealth creation through ownership of productive and profitable assets.

Decisions in the context of the sharing economy are generally short-term and instantaneous, aiming to satisfy immediate needs or reduce current expenses. In contrast, investment requires planning, long-term commitment, and risk acceptance to achieve future returns.

Moreover, users of sharing economy platforms perceive themselves as smart consumers rather than investors, and this mindset naturally prevents the transfer of saving-oriented behaviors into the realm of investment.

The impact of the sharing economy on investment, if it emerges at all, usually follows an indirect and weak path. The savings generated through the use of such services are, in most cases, very small and limited, rarely reaching the threshold necessary to initiate a significant investment. Even in instances where such savings do occur, the majority of them are immediately spent on other expenses such as entertainment services or consumer purchases, and thus they do not transform into long-term investments. Furthermore, a large portion of users do not even consider the possibility of investing these small amounts, and there is no mechanism in place to direct these freed-up funds toward financial markets.

Another major reason for this lack of connection is the absence of a direct bridge between sharing economy platforms and financial markets. Applications that provide sharing services, such as Snapp, and investment platforms, such as brokerages or investment funds, are completely separate from one another. So far, no infrastructure or program has been developed in Iran to integrate these two sectors.

In contrast to some Asian examples such as the Grab platform, which also offers financial and investment services Iranian companies active in the sharing economy have focused entirely on solving users’ daily problems, such as transportation or accommodation, and in practice, play no role in generating or developing wealth for them.

The digital sharing economy has a moderate positive effect on saving style. Each one standard deviation increase in the use of sharing economy services results in a 0.184 standard deviation increase in the tendency to save. This factor explains 3.4% of the variance in saving style (R² = 0.184² ≈ 0.034). This effect is 2.7 times stronger than its impact on investment, suggesting that the sharing economy contributes more to the formation and reinforcement of saving behaviors rather than to direct wealth creation.

This relatively stronger effect can be explained through several key mechanisms:

1. Direct and tangible savings generated by the use of sharing services—such as reduced costs of car ownership or the elimination of associated expenses free up significant amounts of money. Moreover, pay-per-use systems increase cost transparency and help users manage their resources more effectively.
2. The shift in mindset from ownership to access frees individuals from consumerist patterns. Users learn to pay for function and need (e.g., transportation) rather than asset possession, which reduces the pressure to purchase expensive items and enables more regular saving practices.
3. The sharing economy can also strengthen positive financial habits. Budgeting for service use, continuous price comparison, and access to precise spending data foster financial discipline and greater awareness of consumption patterns, which can be generalized to other areas of financial management.
4. Finally, the cumulative effect of savings manifests over the long term. Small daily amounts saved gradually accumulate into significant sums over months and years, increasing motivation to continue these behaviors. Many individuals even open separate accounts to retain these savings, which effectively constitutes a form of structured saving.

Discussion

The findings of this study reveal a complex pattern of interrelated influences among various factors affecting the financial behaviors of Generation Z within the context of Iran’s digital sharing economy. The analysis of standardized path coefficients indicates that technological factors, with the strongest coefficient (β = 0.508), play a determinant role in shaping risk-taking investment behaviors. This finding not only confirms but also deepens the insights of Kazemi and Amini (2022) regarding Generation Z’s inclination toward innovative digital investments. The present study demonstrates that this inclination is not merely an intrinsic tendency, but an instrumental action that depends heavily on access to technological infrastructure.

Consistent with the findings of Moradi and Hedayati (2018) who emphasized the importance of digital infrastructure in the transition to a digital economy this study specifically quantifies the enabling role of technology in activating investment behaviors. Broad access to smart devices and high-speed internet has not only eliminated physical barriers to financial market participation but also fostered a new paradigm of active and short-term investment among young Iranians.

In contrast, psychological factors, with a path coefficient of β = 0.443, are identified as the strongest predictors of saving behaviors. The prominence of these psychological dimensions including attitude, trust, and risk tolerance confirms the importance of mental and perceptual aspects in sustaining long-term financial behavior. This finding aligns fully with international studies by Davlembayeva et al. (2020) and Bärö et al. (2022), which emphasized the role of individual and psychological characteristics in the adoption of the sharing economy.

However, the present research extends this relationship to the domain of saving, showing that savings derived from participation in the sharing economy evolve into a habitual and sustainable saving pattern only when accompanied by a positive attitude and trust in the digital ecosystem.

This clear duality between the drivers of investment and saving challenges previous findings and highlights a crucial distinction: while investment behavior emerges primarily as a technology-driven phenomenon, saving behavior remains rooted in psychological foundations.

An important and unexpected finding is the inhibitory role of demographic factors in both investment (β = -0.318) and saving (β = -0.233) behaviors. These significant negative coefficients contradict the conventional belief that financial maturity is associated with older age and higher education, thereby revealing a profound generational divide. This insightful result indicates that the youngest members of Generation Z, even with lower levels of education, are at the forefront of adopting innovative financial behaviors within the digital economy. This phenomenon can be interpreted within the framework of the digital natives theory, referring to a generation that has grown up with technology and consequently faces far fewer barriers to entry into financial platforms compared to preceding generations. The paradox observed here lies in the fact that the very factors expected to drive innovative financial behaviors, in practice, lead to more cautious attitudes due to increased awareness of risks and market complexities.

Older, more educated members of Generation Z often consider financial independence and face rising living costs (such as housing and inflation) that outpace their entry-level professional income. Unlike younger members who live with their parents, this group bears the full brunt of economic instability. Furthermore, Iran’s high inflation rate encourages immediate consumption or “expediency spending” rather than traditional savings, as the value of the currency rapidly depreciates. Thus, higher education and age, while increasing earning potential, simultaneously expose these individuals to structural economic pressures that suppress the savings rate. These socio-economic realities explain why demographic progress, while expected to increase financial capacity, may instead suppress conservative saving tendencies in the Iranian context.

Perhaps the most surprising finding concerns the marginal impact of the digital sharing economy on financial behaviors. Contrary to initial expectations, this paradigm exerts only a slight effect on investment (β = 0.069) and a weak effect on saving (β = 0.184). This divergence has a clear economic rationale: the use of sharing services directly reduces expenses and frees up financial resources for saving, but it lacks a direct link to the investment ecosystem. This finding suggests that despite the significant growth in the use of sharing services in Iran, such platforms have yet to function as catalysts for a comprehensive transformation in financial behavior.

Although the path coefficients related to the digital sharing economy are statistically significant in this study, the practical magnitude of their impact remains limited. Distinguishing between statistical significance and practical significance becomes particularly important in the Iranian economic context; as relationships that are statistically confirmed do not necessarily lead to substantial behavioral outcomes. This phenomenon can be explained within the framework of a set of macroeconomic and socio‑technological factors. These factors limit the actual capacity of the sharing economy. As a result, its ability to shape the saving and investment patterns of Generation Z becomes constrained.

First, structural and chronic inflation, along with the continuous depreciation of the national currency in Iran, has shortened the financial planning horizon of young individuals and leaves little room for converting the modest savings generated through sharing platforms into productive investments. In such an unstable environment, economic benefits derived from cost savings benefits that in other countries may contribute to capital accumulation are largely absorbed by the need to offset daily living expenses, thereby minimizing their behavioral impact.

Second, limited financial literacy and digital literacy among a substantial segment of users reduces their ability to strategically benefit from sharing services. Although many users employ these platforms in a consumptive manner, they often lack the skills required to translate this participation into informed financial and investment decisions. This aligns with the findings of the present study. The results show that psychological factors have a stronger influence on saving behavior. Technological factors play a more decisive role in shaping investment styles. These effects are more substantial than the direct influence of the sharing economy itself.

Third, socio‑economic disparities and unequal digital access including variations in Internet quality, unequal distribution of smart devices, and the high cost of maintaining stable connectivity restrict the sharing economy’s capacity to actively improve the financial behaviors of Generation Z. Under such conditions, the contribution of the sharing economy remains largely at the level of facilitating consumption, rather than serving as a catalyst for financial transformation.

Therefore, the effect of the digital sharing economy is statistically supported; However, its magnitude remains limited. This limitation reflects the broader economic and social landscape of Iran. These conditions prevent participation in sharing services from becoming a driving force for meaningful financial change.

The weak influence of technological factors on saving (β = 0.142) is also noteworthy and debatable. It appears that access to money and technology places Generation Z at a crossroads between digital consumerism and saving, with psychological factors tipping the balance in favor of saving. This finding underscores that the mere development of technological infrastructure, without considering psychological dimensions, cannot lead to a comprehensive transformation of financial behavior.

Taken together, these findings present a multifaceted picture of the financial behavior of Iran’s Generation Z, in which technology and psychology play distinct yet complementary roles, while structural barriers remain intact. This pattern highlights the necessity for policymakers and financial service developers to adopt a holistic and integrated approach one that simultaneously advances technological infrastructure, strengthens psychological foundations, and designs innovative solutions to overcome demographic constraints.

Conclusion

This study, aimed at exploring the impact of the digital sharing economy on the financial behaviors of Iran’s Generation Z, opens a new window into understanding the complexities of financial conduct in the digital age. The findings go beyond merely confirming or rejecting the initial hypotheses, revealing a multilayered pattern of interactions among technology, psychology, and socio-economic structures that presents significant theoretical and practical challenges for researchers and policymakers alike.

The most important theoretical contribution of this research lies in identifying the fundamental duality in the drivers of financial behavior. While technological factors act as the driving force behind risk-taking investments (β = 0.508), psychological factors play a decisive role in shaping stable saving behavior (β = 0.443). This finding challenges traditional paradigms that view financial behavior as a homogeneous phenomenon, highlighting the need to rethink theoretical models of financial behavior within the context of the digital era.

This apparent dichotomy between investment and savings drivers, where investment is driven by technology while savings is rooted in psychological factors, challenges traditional, monolithic models of financial behavior. This finding can be more effectively articulated through the lens of recent cross‑cultural and intergenerational financial literature. The dominance of technological factors in shaping investment behavior is consistent with the financial gamification hypothesis observed in global studies; For example, a comprehensive report by the CFA Institute (2023), which compares Generation Z investors across the United States, Canada, the United Kingdom, and China, shows that this cohort’s entry into investing is heavily mediated by digital interfaces, social media, and fin‑influencers, making the medium (technology) the primary driver of the act (investment). Unlike older generations, investment decisions among Generation Z follow a different pattern. Older generations often rely on long‑term security concerns and traditional advisory relationships. In contrast, Generation Z shows intense engagement with digital applications. They also prefer digital assets such as cryptocurrencies. Conversely, the finding that saving behavior continues to be rooted more in psychological factors (trust and attitude) than in technology reflects the cultural persistence theory of saving behavior proposed by Costa‑Font et al. (2018). Their research shows that while investment tools rapidly evolve through technological innovations, saving tendencies remain deeply embedded in cultural norms and individual risk preferences that extend beyond mere technological access.

Thomas et al. (2024) conducted a comparative generational analysis in India. They found that Generation Z shows the highest risk tolerance for investments, which is a technology‑enabled characteristic. However, their fundamental financial security still depends on psychological comfort and perceived stability. In this sense, they are similar to both Generation Y and Generation X. Therefore, the duality observed in our study reflects a broader generational paradox: Generation Z uses technology to take risks (investment), yet depends on psychological foundations to reduce uncertainty (saving).

The discovery of the inhibitory role of demographic factors reveals yet another dimension of complexity. Contrary to conventional expectations, the youngest members of Generation Z with the lowest levels of formal education emerge as the pioneers of digital financial transformation. This paradoxical finding extends the concept of digital natives beyond the realm of technical skills into the domain of financial behaviors, demonstrating that the generational gap in finance is far deeper and more complex than previously assumed.

The limited role of the digital sharing economy in transforming financial behavior (β = 0.069 for investment and β = 0.184 for saving) serves as a reminder that technological innovation alone does not guarantee behavioral transformation. Despite their impressive quantitative growth, these platforms have not yet evolved into the primary catalysts for financial change. This finding underscores the importance of strategically and purposefully designing digital financial services that go beyond mere digitalization to generate meaningful behavioral value.

From a practical perspective, the findings of this study offer clear guidance for policymakers and financial institutions. First, there is a need to adopt differentiated approaches to promote investment and saving. While the development of technological infrastructure can act as a strong stimulus for investment, fostering a saving culture requires investment in educational programs and building trust in the financial ecosystem. Second, the redesign of financial products and services must align with the unique characteristics of Generation Z, reducing entry barriers while ensuring security and sustainability. Third, it is crucial to build bridges between sharing economy platforms and traditional financial services to convert short-term savings into long-term financial behaviors.

Policymakers can use these findings to design targeted programs to improve digital financial literacy and promote trust in online financial ecosystems. Platform developers can integrate micro-savings or investment features into sharing apps, allowing users to turn small savings into capital accumulation. Educational institutions can also incorporate digital finance education into youth curricula to foster responsible financial participation. These measures collectively translate the theoretical insights of this study into actionable strategies for sustainable financial behavior among Generation Z.

The limitations of this study, including its focus on the geographical context of Iran and the use of cross-sectional data, pave the way for future research. Longitudinal studies that can track the evolution of financial behaviors over time, cross-cultural comparative analyses, and deeper examinations of the underlying psychological mechanisms are among the promising directions for further investigation. Moreover, exploring the role of mediating variables such as digital financial literacy and the impact of social networks on financial behaviors could enrich our understanding of this complex phenomenon.

Ultimately, this study demonstrates that the financial transformation of Generation Z in Iran is a multifaceted process shaped not solely by technology or socio-cultural change, but by the intricate interaction among these factors. Success in steering this transformation toward sustainable and constructive financial behaviors requires a deep understanding of these complexities and the design of intelligent interventions that simultaneously address technological, psychological, and structural dimensions. It is hoped that the findings of this research represent a meaningful step toward achieving this important goal.

Ethical Considerations and Limitations

The study was conducted in full accordance with ethical principles: anonymity was ensured, and the data were used exclusively for research purposes. Overall, this comprehensive methodological approach enabled a rigorous test of the conceptual model and laid the groundwork for the results section.