Abstract
This study explores generational differences in investment behavior, focusing on the moderating roles of risk tolerance and technology adoption in shaping financial decisions across asset types. Generation Z, having entered adulthood during the COVID-19 pandemic, exhibits a more risk-averse approach compared to Generation Y and Generation X, favoring safer, liquid assets amidst economic uncertainty. The study confirms that as Gen Z’s risk tolerance increases, their preference shifts towards moderate-risk investments like gold, though high-risk assets remain largely unattractive. In contrast, technology adoption plays a more significant role for Millennials (Gen Y), enhancing their engagement with both safe and moderate-risk investments, while having minimal impact on Gen Z’s behavior. These findings provide valuable insights for financial policymakers and institutions, emphasizing the need for targeted strategies that align with the unique preferences of each generation. Tailoring financial policies and investment products to generational needs can foster sustainable economic development by encouraging engagement across diverse market segments.
Keywords
Introduction
Since the economic reforms initiated in the 1980s, China has witnessed significant wealth creation, establishing itself as one of the largest global economies. Investment has been a crucial driver of this growth, fundamentally influencing both national economic management and the global financial system (Wei et al., 2014). China’s economic and financial landscape is characterized by volatility, exacerbated by sudden policy changes (Bagh et al., 2023), a decline in foreign direct investment (FDI), and market stresses from recent insolvencies in the real estate sector (Zhang et al., 2023). The COVID-19 pandemic further disrupted export manufacturing and heightened geopolitical tensions, complicating economic stability (Guo et al., 2022). Restrictions on capital outflows limit investment opportunities (Asiedu & Lien, 2004), making the behavior of Chinese investors worthy of thorough investigation. Additionally, the immaturity of financial institutions, coupled with limited regulatory oversight and cultural skepticism towards formal lending mechanisms, adds complexity to the investment landscape (He & Wei, 2023; Yao et al., 2021).
Despite the robust growth of the Chinese economy, gaps remain in understanding how generational cohorts approach investment under shifting economic conditions. Prior research has not sufficiently addressed the moderating roles of risk tolerance and technology adoption in shaping these generational investment patterns, particularly in the post-COVID-19 era. This study seeks to fill these gaps by investigating the interplay between risk tolerance, technology adoption, and generational differences in financial decision-making, emphasizing their critical implications for financial policy and investment strategies.
Understanding how different generations approach risk in this dynamic environment is vital for comprehending market behavior, shaping policies for sustainable economic growth, and guiding businesses in attracting investment aligned with national objectives. Risk tolerance influences individuals’ willingness to pursue uncertain yet rewarding investments, while technology adoption enhances access to financial tools, facilitating informed decision-making. By examining the interplay of these factors across generational cohorts, this study highlights the importance of designing financial strategies and products that cater to diverse preferences. For example, Generation Z’s risk-averse tendencies underscore the need for financial education programs and investment options emphasizing stability and moderate returns. Additionally, financial institutions can leverage Millennials’ inclination toward digital tools by offering intuitive robo-advisors and savings apps that combine accessibility with personalized recommendations, fostering greater participation in both safe and moderate-risk investments.
In the post-COVID-19 era, understanding the differences in investment behavior between generations has become increasingly relevant (Jan et al., 2022), particularly as risk tolerance and technology adoption emerge as critical factors shaping financial decisions. Risk tolerance directly influences an individual’s willingness to engage in uncertain but potentially rewarding investments (Grable, 2000; Hanna et al., 2001), while technology adoption facilitates access to diverse financial tools and platforms, enabling faster, data-driven decision-making (Agarwal & Mazumder, 2013; Venkatesh et al., 2016). This study’s novelty lies in its focus on Generation Z, which, due to its emergence into adulthood amid the pandemic, demonstrates unique behaviors such as prioritizing financial security and safe assets over volatile investments. These traits contrast sharply with Generation X and Millennials, who exhibit more calculated risk-taking behaviors.
This study specifically examines the impact of risk tolerance and technology adoption on generational responses to risk. Generation Z, which entered adulthood during the COVID-19 pandemic, presents a unique case. With limited financial buffers and unstable employment opportunities, this cohort exhibits greater aversion to risk compared to Generation X and Millennials (Gen Y) in China (Weinbrenner, 2023). The experiences of Gen Z underscore the challenges of managing risk without the financial security enjoyed by older generations, making it essential to investigate how they adapt their financial decisions in response to uncertainty.
By addressing these dynamics, this study significantly contributes to understanding generational shifts in investment behaviors and their implications for policy design and financial product development. Policymakers can leverage insights into generational risk tolerance to create targeted financial education programs, while financial institutions can design products that meet the needs of risk-averse and digitally engaged younger investors. Additionally, the moderating role of technology adoption offers a pathway to enhance engagement across cohorts, emphasizing tailored digital tools for Millennials and Gen Z.
Focusing on generational differences in risk response provides valuable socio-economic insights. Each generation approaches risk with distinct strategies influenced by their economic experiences, financial resources, and use of digital financial tools (Antwi & Naanwaab, 2022). For instance, during heightened uncertainty, Gen Z is more likely to prioritize safe, liquid assets as a precautionary measure (Pašiušienė et al., 2023). Their limited financial experience may compel them to prioritize stability over potential returns. In contrast, older generations, having navigated previous downturns, may demonstrate greater confidence in undertaking calculated risks, leveraging accumulated wealth to pursue more volatile investment opportunities (Antwi & Naanwaab, 2022).
Understanding these generational differences has significant implications for financial policy and market dynamics. Identifying patterns in generational approaches to risk allows policymakers to design targeted financial strategies and assists institutions in developing products that resonate with generational preferences (Lusardi & Mitchell, 2014). Moreover, examining the moderating roles of risk tolerance and technology adoption provides practical insights for business leaders (Venkatesh et al., 2016) enabling them to anticipate market trends and attract necessary investments to support economic development.
This paper addresses these gaps by posing key research questions: How do risk tolerance and technology adoption influence generational investment behaviors? What implications do these findings hold for financial policy and economic stability in a post-pandemic world? By integrating these inquiries, the study advances the understanding of generational differences in risk management, offering actionable insights for policymakers and practitioners.
The remainder of this paper is structured as follows: the literature review section explores prior studies on generational differences in risk management strategies, types of financial assets, and the roles of risk tolerance and technology adoption as moderating factors. The hypotheses section introduces an integrated framework of predictions, followed by the methodology employed to test these hypotheses. Finally, the paper concludes with a discussion of the findings, implications, and limitations, offering insights for policymakers and practitioners aiming to address generational differences in risk management within financial behavior.
Literature Review
Cross Generational Research
Cross-generational research frequently employs widely accepted generational labels, as shown in Table 1. However, categorizing populations into distinct generations is not an exact science; individuals born in the last year of one generation may share more in common with those born in the first year of the next (Twenge, 2010). These generational cohorts are primarily defined by shared experiences, encompassing economic conditions, geopolitical circumstances, social dynamics, and technological advancements (Grigoreva et al., 2021).
Typical Generational Research and TAM-Based Generational Research.
When utilizing the Technology Adoption Model (TAM), it is essential to categorize generations not only by birth year but also by their life experiences and interactions with emerging technologies (Davis, 1989). For instance, Generation Z has grown up in an era of ubiquitous fast connectivity and smartphones, making communication, information access, e-commerce, and social media integral to their daily lives (Anderson & Jiang, 2018).
The entrepreneurial orientation observed within Generation Y also highlights the influence of their unique socio-economic context. In emerging economies, this generation has demonstrated innovative approaches to venture creation, emphasizing their adaptability and resilience under challenging economic conditions (Saleh & Athari, 2023). These insights align with cross-generational research, as they underscore how generational cohorts’ shared experiences, particularly in education and technology, shape their economic and entrepreneurial behaviors.
In contrast, Generation Y experienced rapid technological development during their education and early careers, transitioning from dial-up connections to 5G and from limited web content to today’s extensive digital landscape (Bennett et al., 2008). Prior generations, including Generation X, Baby Boomers, and Seniors, underwent education and early adulthood in environments with limited or no internet access and minimal mobile phone functionality (Economic, Social, and Cultural & Capital, 2020). Consequently, these older cohorts had to learn and adapt to new technologies mid-career, often facing challenges that younger generations did not encounter (Costa et al., 2019).
These distinct life experiences significantly influence how users engage with and perceive technology, resulting in the generational groups indicated in Table 1. This study adopts these categories to investigate the moderating effect of technology adoption on the relationship between generations and their investment preferences.
Generational Differences in Investment Behavior
Investment behavior varies significantly across generations, influenced by distinct life experiences, economic conditions, and psychological factors (Lusardi & Mitchell, 2014). Each generation—Generation Z, Generation Y (Millennials), and Generation X—demonstrates unique preferences, risk perceptions, and decision-making strategies (Dohmen et al., 2011).
Generation Z, born approximately between 1997 and 2010, is more risk-averse than older generations. Entering adulthood during the COVID-19 pandemic and other periods of economic volatility, they face heightened uncertainty (Nikolic et al., 2022). The pandemic disrupted job markets and caused significant financial instability, making it difficult for Gen Z to secure stable employment. Without the chance to establish a financial buffer early in their careers, they prioritize security and liquidity, favoring low-risk assets like bank accounts (Lusardi & Mitchell, 2023). These findings are consistent with evidence from the Lebanese crisis, where economic instability underscored the importance of financial literacy and self-control in mitigating adverse financial outcomes (Mawad et al., 2022). Similar to Lebanese investors during the crisis, Gen Z’s behavior reflects the precautionary savings hypothesis, prioritizing financial security in response to economic uncertainty (Carroll et al., 2000).
In contrast, Millennials exhibit moderate risk tolerance. Having experienced the 2008 financial crisis, this generation has developed a cautious approach to high-risk investments while remaining open to moderate-risk options, such as gold, to maintain a balanced portfolio (Astuti et al., 2023). Millennials are also more inclined to adopt technological advancements, such as investment apps and digital trading platforms, enhancing their financial behavior’s dynamism compared to Gen Z’s security-focused approach (Singh, 2022). Generation X demonstrates higher financial risk tolerance and greater comfort with long-term, growth-oriented investments, such as real estate and stocks. With more time to accumulate resources, this generation is willing to engage in high-risk investments, such as foreign currency markets, even amid economic fluctuations (Antwi & Naanwaab, 2022). Their relative financial stability enables them to withstand market volatility and pursue higher-risk opportunities without the same fear of loss experienced by younger generations (Fahlin & Gustafsson, 2024).
These generational differences underscore the need for targeted financial education programs to enhance decision-making during uncertain times, as demonstrated in the Lebanese context (Mawad et al., 2022). Such programs could bridge generational gaps in financial literacy and risk management, equipping each cohort to navigate unique economic challenges effectively.
Prospect Theory offers a framework for understanding these generational differences, particularly the concept of loss aversion—the tendency to experience losses more acutely than gains (Kai-Ineman & Tversky, 1979). Generation Z, with fewer financial resources and no safety net, is more loss-averse and hesitant to engage in risky behavior. Conversely, Millennials balance caution with moderate risk-taking, while Generation X leverages their financial stability to pursue high-risk investments (Mitchell et al., 2009).
Types of Financial Assets and Investment Preferences
Investment preferences are significantly influenced by the perceived risk and return potential of different financial assets, which can be categorized into safe, moderate-risk, and high-risk investments. Each type appeals to investors based on their risk tolerance, financial goals, and life circumstances (Lusardi & Mitchell, 2014). Different generations engage with these assets in distinct ways, shaped by their unique economic experiences and financial stability (Xie et al., 2022).
Safe assets, such as bank accounts, savings instruments, and certificates of deposit (CDs), are characterized by low risk and high liquidity, appealing to individuals prioritizing capital preservation (Refaeli & Achdut, 2021). For Generation Z, who entered adulthood during the COVID-19 pandemic, safe investments provide essential liquidity and protection against uncertainty (Lusardi et al., 2011). This behavior aligns with the precautionary savings hypothesis, which suggests that individuals increase savings during unstable times to guard against potential income risks (Carroll et al., 2000). Millennials (Gen Y) tend to diversify their portfolios with moderate-risk assets, while Generation X primarily utilizes safe assets to manage liquidity (Mottola, 2014). Evidence from the U.S. COVID-19 response underscores the importance of stable and swift policy measures in restoring investor sentiment during crises (Chebbi et al., 2024). These measures, by reducing market uncertainty, can indirectly reinforce preferences for safe assets, particularly among risk-averse groups such as Generation Z.
Moderate-risk assets, such as gold, offer higher returns than traditional savings instruments but entail some market risk. Gold is often perceived as a safe haven during economic volatility (Naeem et al., 2020), attracting investors seeking moderate returns with reduced exposure (Baur & Lucey, 2010). Millennials show particular interest in gold-backed exchange-traded funds (ETFs) and investment apps, leveraging technology to engage with these assets (Rosdiana, 2020). In contrast, Generation Z exhibits limited interest in traditional assets like gold, preferring more liquid and technology-integrated options.
High-risk assets, such as stocks and foreign currency (forex), offer significant return potential but come with high volatility, appealing to investors with strong risk tolerance and long-term financial goals (Astuti et al., 2023). Generation X confidently engages in these investments due to their accumulated financial experience and wealth (Fahlin & Gustafsson, 2024). Millennials also invest in stocks but tend to balance high-risk options with safer alternatives (Rahman & Gan, 2020). In contrast, Generation Z remains cautious about stock investments, influenced by market instability and economic uncertainty, while both Millennials and Gen Z show limited interest in forex trading, viewing it as too complex and volatile for their risk profiles.
Moderating Factors: Risk Tolerance and Technology Adoption
Investment behavior is shaped not only by generational differences and asset types but also by external factors, with risk tolerance and technology adoption serving as critical moderators (Lusardi & Mitchell, 2014). These factors influence perceptions of risk, return-seeking behavior, and engagement with financial markets.
Behavioral finance theories, particularly Prospect Theory, highlight loss aversion—the tendency to feel losses more intensely than gains (Kai-Ineman & Tversky, 1979). Market volatility and economic uncertainty amplify these psychological responses, prompting behavioral adjustments (Hirshleifer, 2001). Moreover, advancements in digital finance tools are reshaping investor engagement by lowering barriers to entry and enhancing participation (Agyei-Boapeah et al., 2022).
Risk tolerance, defined as an individual’s comfort with uncertainty, significantly affects investment decisions. It determines whether investors opt for safe, moderate, or high-risk assets and influences their reactions to market fluctuations (Lusardi et al., 2011). Individuals with low risk tolerance prefer safe investments, while those with higher tolerance engage with moderate- to high-risk assets (Rahman, 2020).
This moderating effect varies by generation. Generation Z exhibits heightened sensitivity to risk due to limited resources, while older generations, such as Generation X, can better withstand market volatility and pursue higher-risk investments (Fahlin & Gustafsson, 2024). These differences suggest that risk tolerance impacts how generations navigate asset types during uncertain times (Mitchell et al., 2009).
Technology adoption also influences investment behavior. The rise of investment apps and digital trading platforms has made investing more accessible, especially for younger generations (Bennett et al., 2008). Millennials integrate these tools into their strategies, enhancing engagement with various asset types (Xu et al., 2022). For Generation Z, technology is a standard expectation rather than an advantage (Anderson & Jiang, 2018). This familiarity does not necessarily increase participation in high-risk investments due to enduring psychological barriers (Yazdanparast & Alhenawi, 2022). Research indicates Millennials gain more from technology adoption, which enhances their market exploration and informed decision-making (Xu et al., 2022). Overall, technology adoption appears to moderate behavior more effectively for moderate-risk assets, with less impact on high-risk investments across generations.
Conceptual Model
Conceptual model is explained in Figure 1.
Conceptual model.
Hypothesis Development
Generational Differences in Investment Behavior
Investment behavior varies across generations due to psychological tendencies, economic conditions, and financial life stages. Behavioral finance theories explain why younger generations, particularly Generation Z, exhibit more cautious investment behavior than older counterparts. These theories explain how risk perception and resource availability shape financial decision-making across generational lines, offering a structured lens to explore differences in risk tolerance (Lusardi & Mitchell, 2014; Stolper & Walter, 2017).
Prospect Theory (Kahneman & Tversky, 2013) emphasizes loss aversion—the tendency to feel losses more acutely than gains. This theory predicts that individuals with limited financial resources, such as Generation Z, will display heightened sensitivity to potential losses, leading to risk-averse behavior. Generation Z’s limited financial buffers make them particularly prone to prioritizing capital preservation, favoring low-risk investments that minimize the possibility of losses (Arora & Kumari, 2015).
The Precautionary Savings Hypothesis (Lugilde et al., 2019) complements this perspective by positing that individuals increase savings and favor safer investments during periods of economic instability to guard against potential income shocks. For Generation Z, this behavior has been reinforced by the economic disruptions of the COVID-19 pandemic, which increased their focus on financial security and liquidity (Zwanka & Buff, 2021). The Lifecycle Hypothesis (Modigliani, 1966) further explains how investment preferences evolve over time, suggesting that younger individuals prioritize conservative investments as they navigate early financial demands, such as education and career development (Mottola, 2014).
In contrast, older generations, such as Generation X, tend to engage more confidently with high-risk investments due to greater accumulated wealth and financial experience. The additional resources of Generation X allow them to absorb potential losses and pursue long-term growth opportunities in speculative markets (Thomas et al., 2024). Social and economic environments also play a role: Millennials, for example, developed a cautious but adaptable approach after recovering from the 2008 financial crisis. In contrast, Generation Z continues to face ongoing economic uncertainties, further reinforcing their conservative focus on liquidity over speculative growth.
By integrating these theoretical insights, this study aims to understand how risk tolerance varies across generations in response to unique economic experiences and life stages. Specifically, the heightened loss aversion and resource limitations of Generation Z are expected to result in more risk-averse financial behavior compared to Generation Y and Generation X. Based on these theoretical insights, the following hypothesis is proposed:
Influence of Risk Tolerance
Risk tolerance is a key determinant of investment behavior, shaping how individuals navigate the spectrum of asset types. Behavioral finance theories, such as Prospect Theory (Kahneman & Tversky, 2013), suggest that individuals’ willingness to accept risk depends on their sensitivity to potential losses and the perceived rewards of risk-taking. Those with lower risk tolerance prioritize capital preservation, aligning with safer investments that minimize exposure to uncertainty (Lusardi & Mitchell, 2014). For Generation Z, the heightened loss aversion described by Prospect Theory amplifies this tendency, as their limited financial resources and lack of a safety net intensify their focus on security.
As risk tolerance increases, investors are more likely to transition toward moderate-risk assets, such as gold, which provide a balance between potential returns and manageable risks. This behavior aligns with the concept of risk-reward optimization, as moderate-risk assets often serve as a stepping stone for those beginning to explore riskier investments (Baur & Lucey, 2010). However, the Lifecycle Hypothesis (Modigliani, 1966) explains why Generation Z remains cautious in these transitions. Still in the early stages of financial accumulation, they face competing priorities such as education and debt repayment, which limit their ability to fully embrace risk, even as their tolerance grows (van Lierop, 2023).
For high-risk investments, such as stocks and foreign currency, the influence of risk tolerance diminishes. These markets typically attract investors who already possess a baseline comfort with uncertainty, rather than those adjusting their preferences incrementally (Kahneman & Tversky, 2013). For Generation Z, the combination of economic instability and a lack of financial security limits their participation in such volatile markets. In contrast, older generations, with greater financial buffers and experience, are better positioned to engage in high-risk investments, reflecting the greater financial stability outlined by the Lifecycle Hypothesis.
These theoretical frameworks highlight how risk tolerance shapes generational investment behaviors, emphasizing distinct patterns of engagement with safe, moderate-risk, and high-risk assets. Based on this reasoning, the following hypotheses are proposed:
Role of Technology Adoption
Investment behavior is increasingly shaped by technology adoption, which has transformed how individuals engage with financial markets. The Technology Acceptance Model (TAM) (Davis, 1989) provides a theoretical foundation for understanding this influence, positing that perceived ease of use and usefulness drive the adoption of digital tools. These tools, such as investment apps, robo-advisors, and online trading platforms, reduce barriers to market participation, particularly for younger generations. However, the impact of technology adoption varies across asset classes and generations due to differences in technological familiarity, economic priorities, and psychological factors (Lusardi & Mitchell, 2014).
For safe assets like bank accounts and savings products, digital tools enhance accessibility and convenience. Millennials benefit significantly from financial apps and automated savings tools, which simplify engagement with safe investments (Yang et al., 2021). In contrast, Generation Z perceives these technologies as baseline functionalities, rather than value-adding innovations, which limits their influence on this cohort’s behavior. This reflects the TAM concept of perceived usefulness being less pronounced when technologies are viewed as standard utilities.
For moderate-risk investments, such as gold-backed ETFs, technology adoption has a more pronounced effect, particularly for Millennials. These tools enable easy access and diversification, aligning with the risk-reward balance sought by this generation (Kettunen & Kriikkula, 2020). However, Generation Z’s focus on liquidity and financial security limits their engagement with moderate-risk assets, even when facilitated by technology adoption. This disparity underscores how generational differences in risk perception intersect with the adoption of digital financial tools.
In the context of high-risk investments, such as stocks and foreign currency, the moderating role of technology adoption diminishes. While digital trading platforms lower barriers to participation, the psychological barriers outlined in Prospect Theory (Kahneman & Tversky, 2013), such as fear of losses, often outweigh the convenience provided by these tools. For both Millennials and Generation Z, concerns over volatility and complexity act as deterrents, limiting the impact of technology adoption on high-risk investment behavior (Vaportzis et al., 2017).
These theoretical insights suggest that technology adoption plays a differentiated role in shaping investment behavior across generations and asset types, with a stronger impact on safe and moderate-risk assets compared to high-risk investments. Based on this reasoning, the following hypotheses are proposed:
Methodology
Various empirical quantitative methods, including Cronbach’s alpha (Cronbach, 1951), ANOVA (Analysis of Variance), were employed to validate and analyze primary data collected from an online survey. These techniques ensured the reliability and robustness of the data for hypothesis testing. Additionally, moderation analysis using linear regression was conducted to explore the interaction effects of risk tolerance and technology adoption on the relationship between generational cohorts and investment behavior.
Social science researchers frequently utilize Cronbach’s alpha to assess the reliability of multi-item survey scales (Hair et al., 1998). For this study, values above 0.70 were deemed acceptable, indicating good internal consistency for the constructs of Risk Tolerance, Investment Preferences, and Technology Adoption (Hair et al., 1998).
ANOVA was employed to determine whether there are statistically significant differences in investment behavior across generational cohorts (Gen X, Gen Y, and Gen Z). This analysis tests the hypothesis that different generations exhibit distinct preferences and risk tolerance levels, providing insights into meaningful behavioral differences between groups.
To further explore how moderating variables (risk tolerance and technology adoption) affect the relationship between generations and investment behavior, moderation analysis using linear regression was conducted. This approach has several advantages: it identifies causal relationships, explores interaction effects, and ensures results are straightforward and replicable. Additionally, this method allows for assessment of whether the moderating effects of risk tolerance and technology adoption vary across generations.
The dependent variable in this study is perceived investment behavior across various financial products, including bank accounts, gold, and stocks. This variable reflects participants’ preferences and engagement levels with different financial assets. The independent variable is generational cohort, segmented into Gen X, Gen Y (Millennials), and Gen Z, each representing distinct life stages and economic experiences that influence financial decision-making processes.
Two moderating variables were included: Risk Tolerance, which measures the willingness to engage in financial decisions involving uncertainty and potential loss, and Technology Adoption, assessed using the Technology Acceptance Model (TAM). Risk tolerance influences whether individuals pursue moderate- or high-risk assets versus safer options. Technology adoption evaluates the extent to which individuals integrate digital financial tools, such as investment apps and online trading platforms, into their strategies. This aspect provides insight into how access to and familiarity with digital tools influence engagement with various financial products across generations.
Pilot Study—Investment Options
An initial pilot study was conducted with a sample of 29 MBA students to explore potential investment opportunities for inclusion in the online survey. These students were enrolled in a relatively expensive private university, indicating that their families were likely to possess some level of investable assets. Participants were asked about their own investment preferences as well as those of their parents and grandparents.
The initial results were unexpected, prompting a follow-up pilot study involving a larger sample of 68 MBA students from the same university. The combined findings from the two pilot studies highlighted a notable disparity between current and historical investment preferences, as detailed in Table 2.
Comparison of Typical Versus Currently Preferred Investment Options Based on Pilot Study Results.
At about 1% return, bank deposits are more secure storage than investment.
Stocks and Funds.
Informal or off grid investments such as personal loans and investment in non-public firms.
Based on the results, the study focused on the most widely recognized investment options—Non-Invest Bank, Gold, foreign currency, and Stock Market—as dependent variables. Real Estate was excluded to maintain a targeted analysis of financial products, ensuring a clear and concise scope for the study.
Survey Instrument and Data Collection
Data was collected by an online survey distributed through a variety of channels including social, professional and educational wechat groups during a 14-day period in July 2024. After screening the respondents and omitting incomplete responses, a sample of 458 (convenience sampling) was included in the analysis.
The survey instrument was adapted from online surveys previously applied and validated in related research. Technology Adoption was measured with using six survey items adapted from the work of Davis et al. (1989), and Al Darayseh (2023). Investor preferences was adapted from previous research of Marjerison et al. (2023), and risk tolerance was adapted from work of (Hanna et al., 2013; Rahmawati et al., 2015; Şahin & Yilmaz, 2009). All constructs were measured on a 7-point Likert scale (Likert, 1932).
An online survey as a data collection tool offers a variety of benefits, including wide geographic coverage, anonymity for respondents, a decreased bias level compared to the pressure given by in-person interviews, and, importantly, cost-saving. Easily understood and neutral language was used when the questions were adapted from the prior research to ensure clarity and participants’ understanding of the concepts. Items in the survey from which the data were derived for this study were, in turn, adapted from previous studies and used with modification for context and as part of the translation process. During the survey translation the survey items used in the previous studies were initially written in English so the survey was translated from English to Mandarin. To validate the question translation, the survey translation was checked by two native Mandarin speakers with PhDs from Western universities and then translated back into English. This approach to translation and back-translation is widely used and accepted technique frequently applied in cross-cultural research to achieve accuracy, reliability, and credibility with minimal loss of meaning. Prior to distribution, the survey was again tested on a pilot group of 10 people and subsequently tuned based on their experience. This was to ensure the accuracy of the translation, the clarity of the questions, ease of understanding, and to confirm the length of time the survey would take for the participants to complete. The survey QR code was distributed on the popular social media platform WeChat. The survey was hosted on the software application Wen Juan Xing where the data were compiled and later downloaded from. As is typical in reposting behavior in social media in China, a snowball sampling effect was observed.
Results
Sampling
Table 3 summarizes the demographic characteristics of the 458 respondents, highlighting the diversity and representativeness of the sample within the study’s focus. The sample is well-distributed across generational cohorts, with Generation Z comprising the largest group (39.5%), followed by Generation Y (35.2%) and Generation X (25.3%). The gender distribution is nearly equal, with 50.2% male and 49.8% female participants. Additionally, the sample includes respondents with varied marital statuses, as 68.1% are married while 31.9% are single.
Demographic Characteristics of the Respondents (N = 458).
The educational background of participants also demonstrates diversity, with 35.2% holding a university degree, 31.4% having vocational education, and 33.4% completing high school. In terms of household net worth, the majority (39.5%) reported assets between 500,001 and 1,000,000 RMB, while others span lower and higher net worth categories. Regarding savings withdrawal timelines, over half (55.2%) of respondents anticipate withdrawing funds in 10 to 20 years, while smaller groups have shorter-term plans.
This diverse sample ensures that key variables—such as investment behavior and generational differences—are effectively captured, supporting the study’s objectives. The balanced distribution of demographic characteristics provides robust insights into the population under investigation, aligning with the study’s focus on generational cohorts and their investment preferences.
Reliability Testing
Table 4 presents the descriptive statistics for the scales used in the study, based on responses from 458 participants. Each scale was measured on a 7-point Likert scale, with 1 representing the lowest level of agreement or intention and 7 representing the highest. Among the investment options, participants showed the highest average intention to invest in bank accounts (M = 3.47, SD = 2.39) and the lowest in foreign currency (M = 2.77, SD = 2.18). Investment in gold and the stock market had similar mean scores, ranging from 3.20 to 3.24, indicating moderate levels of interest across these asset types. Risk tolerance had a mean score of 3.25 (SD = 2.39), suggesting participants generally exhibited cautious attitudes toward financial risks. Notably, technology adoption received a higher average score (M = 4.62, SD = 2.45), reflecting participants’ significant engagement with digital tools for financial activities. These descriptive statistics provide a preliminary understanding of participants’ investment behaviors, risk attitudes, and technology usage.
Descriptive Statistics for the Scales Used in the Study.
The reliability analysis, as shown in Table 5, was conducted to evaluate the internal consistency of the scales used in the study. All scales exhibit Cronbach’s alpha values greater than .9, indicating excellent reliability and suggesting that the items within each scale are highly consistent. Specifically, the scales for investment in various assets, such as bank accounts (α = .987), gold (α = .985), foreign currency (α = .982), and the stock market (α = .989), demonstrate strong internal consistency. Additionally, the scales measuring risk tolerance (α = .991) and technology adoption (α = .993) also show high reliability. These results confirm that the survey items effectively capture the intended constructs with minimal measurement error.
Reliability Analysis for the Scales Used in the Study.
Hypothesis Testing
A number of one-way analyses of variance were conducted to assess the effect of generation on the preference of investment options. The analysis results for foreign currency, one of the high-risk assets, were provided in the Appendix due to the length of the manuscript.
The first ANOVA was conducted to examine the effect of generation on the intention to invest in bank accounts. As shown in Table 6, there is a significant difference in the intention to invest in bank accounts across the three generational cohorts (F(2, 455) = 656.68, p < .01). These results support the hypothesis (H1) that generation Z will exhibit more risk-averse behavior in financial investments compared to generation Y and generation X. This finding aligns with existing literature indicating that younger cohorts, particularly those entering adulthood during periods of economic instability, tend to favor safe, liquid assets over riskier investment options (Carroll et al., 2000; Lusardi & Mitchell, 2014). The economic disruptions caused by the COVID-19 pandemic further reinforced such behaviors among Generation Z, who prioritize financial security in the face of employment instability and limited financial buffers (Spohn, 2024).
The Results of ANOVA Comparing Intention to Invest in Bank Account Across Generations.
The ANOVA results indicate that both Gen X and Gen Y exhibit relatively low levels of intention to invest in bank accounts, with mean scores of 1.57 and 2.00, respectively. In contrast, Gen Z shows a significantly higher level of interest, with a mean score of 6.01, suggesting that younger individuals prioritize safe, liquid assets like bank accounts. These results underscore how generational differences in economic experiences shape investment preferences, with Gen Z demonstrating a distinct preference for safe, liquid assets like bank accounts.
A post hoc analysis using the Games-Howell Test was conducted to determine which generational groups differed significantly in their intention to invest in bank accounts. The Games-Howell Test was chosen because it is particularly suitable for comparing means across groups with unequal variances and sample sizes, which aligns with the characteristics of the generational data in this study. Unlike other post hoc tests, such as Tukey’s HSD, the Games-Howell Test does not assume homogeneity of variances, making it a robust choice when the assumption of equal variances is violated. This ensures more reliable and accurate results when analyzing group differences. As shown in Table 7, the results reveal that Gen Z had a significantly higher intention to invest in bank accounts compared to both Gen X and Gen Y (p < .01). Additionally, while Gen Y exhibited a slightly higher intention than Gen X, this difference was also statistically significant (p < .01). These findings suggest that Gen Z prioritizes safer, liquid financial assets to a greater extent than older generations, with Gen Y showing a moderate but still significantly higher interest compared to Gen X.
Post Hoc Comparisons for Intention to Invest in Bank Account Using Games Howell Test.
The mean difference is significant at the 0.05 level.
The subsequent tables (from Tables 8 to 11) also show similar results, confirming that Gen Z exhibits more risk-averse behavior. This is evident from their lower preference for speculative assets, such as gold, and stocks, compared to other generations. The second ANOVA, as shown in Table, revealed a significant difference in the intention to invest in gold across generations (F(2, 455) = 145.85, p < .01). The results indicate that both Gen X and Gen Y exhibited moderate levels of intention to invest in gold, while Gen Z demonstrated a notably low level of intention. This result highlights that Gen Z displays a lower preference for gold, a moderately risky asset, compared to older cohorts. This behavior aligns with their risk-averse tendencies, as they are less inclined to invest in assets that carry higher levels of market uncertainty.
The Results of ANOVA Comparing Intention to Invest in Gold Account Across Generations.
Post Hoc Comparisons for Intention to Invest in Gold Account Using Games Howell Test.
The mean difference is significant at the 0.05 level.
The Results of ANOVA Comparing Intention to Invest in Stock Market Account Across Generations.
Post Hoc Comparisons for Intention to Invest in Stock Using Games Howell Test.
The mean difference is significant at the 0.05 level.
A post hoc analysis using the Games-Howell test, as shown in Table 9, further identified which generations differed significantly from one another. The results demonstrate that Gen Y had a significantly higher intention to invest in gold compared to both Gen X and Gen Z (p < .01). Additionally, Gen X exhibited a significantly higher intention to invest in gold compared to Gen Z (p < .01).
The next ANOVA, as presented in Table 10, revealed a significant difference in the intention to invest in the stock market across generations (F(2, 455) = 32.15, p < .01). The results revealed that newer generations exhibit a lower intention to engage in stock market investments compared to older generations. Specifically, Gen X demonstrated the highest intention to invest in the stock market, followed by Gen Y, while Gen Z exhibited the lowest level of interest in this asset class. This finding aligns with the notion that Gen Z’s investment behavior reflects a risk-averse nature, as stock market investments are considered high-risk assets (Grable, 2000). Their preference for safer options, such as bank accounts, highlights their cautious financial outlook, shaped by economic instability and limited financial buffers during their formative years (Lusardi & Mitchell, 2014; Weinbrenner, 2023). Gen Z’s reluctance to engage with the stock market reinforces their tendency to prioritize financial security over growth opportunities in the post-COVID-19 period.
The subsequent post hoc analysis, as shown in Table 11, using the Games-Howell test, identifies which generational differences were statistically significant. The results indicate that Gen X had a significantly higher intention to invest in the stock market compared to both Gen Y and Gen Z (p < .01). Additionally, Gen Y exhibited a higher intention to invest in the stock market than Gen Z (p < .01), although this difference was less pronounced.
When combining the results from the previous tables, the data provides strong support for Hypothesis 1 (H1): Generation Z will exhibit more risk-averse behavior compared to Generation Y and Generation X. The significantly higher intention of Gen Z to invest in safer financial products, such as bank accounts, aligns with the hypothesis, indicating that this generation adopts more cautious investment behavior relative to older cohort.
A series of linear regression models were conducted to test H2a to H3c, focusing on the moderating effects of risk tolerance and technology adoption on the relationship between generation and the intention to invest in bank accounts. Linear regression models were chosen because they are well-suited for examining moderating effects by incorporating interaction terms between independent and moderating variables. This approach allows for a clear interpretation of how risk tolerance and technology adoption influence the strength and direction of the relationship between generation and investment intention. These models specifically examined the moderating role of risk tolerance and technology adoption without considering demographic control variables. The first moderation model was statistically significant, (F(8, 449) = 316.11, p < .01), explaining 84.92% of the variation in the intention to invest in bank accounts. As shown in Table 11, both risk tolerance and technology adoption exhibited significant interactions with generation, highlighting the meaningful role these factors play in shaping generational investment behavior.
The interaction between risk tolerance and Generation Y yielded a significant negative effect (β = −.16, p < .01), implying that as risk tolerance increases, Generation Y’s intention to invest in safe assets, such as bank accounts, decreases. This trend indicates that Gen Y shifts away from low-risk investments as they become more comfortable with financial risks. The negative effect was even more pronounced for Generation Z, with a stronger interaction (β = −.47, p < .01). This suggests that as Gen Z’s risk tolerance rises, their preference for bank accounts declines at a greater magnitude, consistent with the idea that they gradually move away from safe, liquid investments as they develop greater confidence in managing financial risks.
Regarding technology adoption, the results reveal a positive interaction for Generation Y (β = .39, p < .01), suggesting that higher levels of technology adoption increase Gen Y’s intention to invest in bank accounts. This reflects that Gen Y actively uses digital tools—such as online banking platforms and automated savings apps—to reinforce their engagement with safe investments. In contrast, the interaction between technology adoption and Generation Z was not statistically significant (p = .439), indicating that technology adoption does not meaningfully affect Gen Z’s intention to invest in bank accounts. This may be due to Gen Z’s familiarity with digital environments, where technology is viewed as a baseline expectation rather than a distinctive advantage shaping investment behavior.
Based on these results, H2a is supported, indicating that as risk tolerance increases, the preference for safe assets decreases across generations, with Gen Z exhibiting a particularly notable shift. This finding aligns with Black et al. (2018), who observed that individuals with higher cognitive abilities—often correlated with greater risk tolerance—are more inclined to participate in the stock market and allocate a larger proportion of their financial wealth to higher-risk assets such as stocks. Meanwhile, H3a is partially supported—although technology adoption enhances Gen Y’s engagement with safe investments, it does not have the same impact on Gen Z. These findings provide actionable insights for financial service providers, suggesting the need for generation-specific strategies that address differing attitudes toward risk and technology to better align with investors’ preferences (Table 12).
The Moderating Effect of RT and TA on the Relationship Between GN and Intention to Invest in Bank Account.
Note. Reference: Generation X.
The second moderation analysis was conducted to investigate the moderating effects of risk tolerance and technology adoption on the relationship between generation and the intention to invest in gold, a moderate-risk asset. The model was statistically significant, (F(8, 449) = 341.57, p < .01), explaining 85.89% of the variation in the intention to invest in gold. As presented in Table 13, both risk tolerance and technology adoption demonstrated significant interactions with generation, indicating their moderating role in shaping investment behavior across different cohorts.
The Moderating Effect of RT and TA on the Relationship Between GN and Intention to Invest in Gold.
Note. Reference: Generation X.
For H2b, which posits that risk tolerance will positively moderate the relationship between generation and moderate-risk assets, the results were mixed. Specifically, the interaction between risk tolerance and Generation Z was significant and positive (β = .91, p < .01), suggesting that as Gen Z’s risk tolerance increases, their intention to invest in gold also rises. However, the interaction between risk tolerance and Generation Y was not significant (p = .156), indicating that increased risk tolerance did not significantly impact Gen Y’s investment behavior in gold. Therefore, H2b is partially supported, as risk tolerance plays a more substantial role for Gen Z’s investment in moderate-risk assets but has no significant effect on Gen Y.
The analysis also assessed H3b, which suggests that technology adoption will positively moderate the relationship between generation and moderate-risk assets. The results reveal a positive and significant interaction between technology adoption and Generation Y (β = .17, p < .01), indicating that higher technology adoption increases Gen Y’s intention to invest in gold. In contrast, the interaction between technology adoption and Generation Z was not significant (p = .755), implying that technology adoption does not meaningfully influence Gen Z’s investment in gold. As such, H3b is also partially supported, as technology adoption enhances Gen Y’s engagement with moderate-risk investments, but it does not have a significant impact on Gen Z’s behavior in this context.
These findings emphasize the nuanced ways in which both risk tolerance and technology adoption influence investment behavior across generations. Specifically, Gen Z appears to adjust their behavior towards moderate-risk investments as their risk tolerance increases, while technology adoption plays a more substantial role for Gen Y in promoting engagement with such investments. These insights highlight the importance of tailoring financial strategies to account for generational differences in both attitudes toward risk and the use of technology in financial decision-making.
The third moderation analysis was conducted to examine the moderating effects of risk tolerance and technology adoption on the relationship between generation and the intention to invest in the stock market. The model was statistically significant, (F(8, 449) = 428.42, p < .01), explaining 88.42% of the variation in the intention to invest in the stock market. However, as shown in Table 14, the interactions between generation and both risk tolerance and technology adoption were not statistically significant. Specifically, for Generation Y, the interaction with risk tolerance (p = .681) and technology adoption (p = .148) did not reach statistical significance. Similarly, for Generation Z, the interactions with risk tolerance (p = .599) and technology adoption (p = .944) were also not significant.
The Moderating Effect of RT and TA on the Relationship Between GN and Intention to Invest in Stock Market.
Note. Reference: Generation X.
These results indicate that neither risk tolerance nor technology adoption significantly moderates the relationship between generation and intention to invest in the stock market. Therefore, both H2c (risk tolerance does not significantly moderate the relationship between generation and high-risk assets) and H3c (technology adoption does not significantly moderate the relationship between generation and stock market investments) are supported.
The findings highlight that while individual preferences toward stock market investments may vary, these preferences are not meaningfully influenced by interactions between generational differences and levels of risk tolerance or technology adoption. This suggests that stock market participation is not strongly shaped by these factors across generational cohorts.
Robustness Check
To further confirm the first hypothesis (H1), we conducted a linear regression analysis as a robustness check. In this analysis, the dependent variable was the intention to invest in each type of financial assets, while the independent variable was a binary variable representing Gen Z (1 for Gen Z and 0 otherwise) to explore their distinct investment behavior. Additionally, a series of demographic variables, including gender, marital status, educational level, household net worth range (reflecting economic status), and the timeline for withdrawing money from savings (reflecting future financial intentions), were included as control variables.
The results, presented in Table 15, corroborate the earlier findings: Gen Z demonstrates a significantly higher propensity to invest in safer assets, such as bank accounts (β = 2.22, p < .01), while showing a lower intention to invest in speculative assets like gold (β = −1.00, p < .01), and stocks (β = −1.10, p < .01) compared to other generations.
The Results of Robustness Check.
p < .05. ***p < .01.
These results reinforce the conclusion that Gen Z exhibits a strong preference for safe financial products, likely driven by their risk-averse nature. Conversely, their lower interest in high-risk assets aligns with their cautious financial outlook (Lusardi & Mitchell, 2014). Building on these findings, the subsequent hypotheses (H2a–H3c) were tested using moderation analysis to further explore the impact of risk tolerance and technology adoption on generational differences in investment behavior. To further validate the findings from the tests of hypotheses H2a through H3c, an additional series of linear regression analyses were conducted, incorporating demographic variables such as those presented in Table 14 as control variables. The results of these analyses remained consistent with the previously reported findings (Tables 11–13), confirming the robustness of the original conclusions. However, due to constraints on the length of the paper, these additional results are not included in the main report.
Discussion
Theoretical Contribution
This study contributes to the growing body of literature on generational differences in investment behavior by integrating established behavioral finance theories—such as Prospect Theory, the Precautionary Savings Hypothesis, and the Lifecycle Hypothesis—with empirical insights about risk tolerance and technology adoption. These frameworks offer a comprehensive understanding of how psychological tendencies, economic conditions, and digital access shape generational approaches to financial decision-making.
First, Prospect Theory (Kai-Ineman & Tversky, 1979) explains why Generation Z exhibits heightened loss aversion compared to older cohorts. Gen Z’s lack of financial buffers makes them more sensitive to potential losses, resulting in a preference for low-risk investments, such as bank accounts. This aligns with the study’s findings that Generation Z prioritizes financial security, especially during economic uncertainty.
Second, the Precautionary Savings Hypothesis supports the notion that Gen Z’s cautious investment behavior is a response to entering adulthood during the COVID-19 pandemic. The pandemic’s economic disruptions have reinforced their preference for liquid, safe assets to mitigate short-term risks rather than pursuing long-term growth through higher-risk investments. The study confirms that as their risk tolerance increases, Gen Z gradually shifts from safe to moderate-risk assets, such as gold, while avoiding high-risk investments.
Third, the Lifecycle Hypothesis highlights the evolution of investment behavior across life stages (Modigliani, 1966). Gen Z’s current financial conservatism aligns with the early stages of financial planning, where individuals prioritize capital preservation. This contrasts with the behavior of Generation X, which, having accumulated more wealth, engages more confidently in high-risk investments like stocks and foreign currency.
By combining these theoretical frameworks with empirical evidence, this study enhances our understanding of how risk tolerance and technology adoption interact with generational differences to influence investment behavior across asset types. These insights have practical implications for financial institutions and policymakers, who can use them to design targeted strategies that align with the unique needs and preferences of each generation.
Practical/Managerial Contribution and Policy Implications
The findings of this study highlight the importance of tailoring financial policies and investment products to align with the distinct behaviors and preferences of different generations. Policymakers can leverage insights into generational risk tolerance to create targeted financial education programs and incentives. For example, since Generation Z tends to exhibit a more risk-averse approach, promoting financial literacy around the benefits of long-term investments could encourage them to diversify beyond safe assets like bank accounts (Lusardi & Mitchell, 2014). For example, our findings reveal that Generation Z, particularly in the post-COVID-19 period, exhibits a strong preference for stable assets like bank accounts. This indicates a need to promote financial literacy that emphasizes the advantages of diversified, long-term investment strategies. Such initiatives could help shift their focus beyond safe assets while maintaining their sense of financial security (Lusardi & Mitchell, 2014). Financial institutions can also introduce investment products that offer a balance of security and moderate returns to meet the unique needs of younger investors, fostering engagement without overwhelming them with high-risk options.
In terms of risk tolerance, our results suggest that it negatively moderates the relationship between generation and safe assets. This finding underscores the potential for tailored financial education to address risk perceptions directly, particularly for risk-averse cohorts like Gen Z. By highlighting the calculated benefits of moderate-risk investments, policymakers can help mitigate excessive conservatism in financial decision-making. For instance, emphasizing the role of diversification and compound growth in achieving long-term financial goals could encourage a more balanced investment approach.
In addition, technology adoption plays a critical role in shaping how different generations interact with financial markets, particularly for Generation Y. Policymakers and business leaders must acknowledge that Millennials are more inclined to leverage digital tools, such as robo-advisors and automated savings apps, while our findings reveal that technology adoption does not significantly moderate the relationship between generation and high-risk assets. This insight suggests that while technology can enhance financial accessibility, it alone may not be sufficient to overcome generational risk-averse tendencies. Policymakers should therefore focus on integrating technology with personalized guidance and education to build trust and confidence in engaging with high-risk investments. Developing intuitive digital platforms with features that cater to younger users’ demand for seamless access and financial security could boost participation in safe and moderate-risk investments.
At the same time, regulatory frameworks must adapt to the evolving landscape, ensuring consumer protection in digital financial services while maintaining accessibility. These tailored policies and strategies can help attract the necessary investment to fuel economic growth, especially during periods of uncertainty.
Limitations Future Research
This study offers valuable insights but has several limitations that open pathways for future research. First, the reliance on survey-based data means the findings reflect perceived intentions and behaviors rather than actual outcomes. Self-reported preferences may not fully align with real investment behavior. Future studies could address this by using experimental designs or secondary data from financial institutions to validate findings with actual behavioral data.
Second, the focus on Chinese consumers limits the generalizability of the results beyond China. China’s unique economic and regulatory environment may not reflect financial behaviors in other countries. Future research could conduct cross-country comparisons using similar surveys to explore cultural differences in generational investment behavior and examine how regulatory and cultural factors shape these behaviors in diverse contexts.
Third, the study did not account for differences in investment behavior before and after the COVID-19 pandemic. Since the pandemic significantly impacted global financial markets and risk perceptions, comparing pre- and post-pandemic behaviors could provide deeper insights. Future research could address this by collecting longitudinal data or focusing on evolving behaviors post-pandemic to better understand changes in investment patterns over time.
Finally, while the sample included diverse generational cohorts, the use of convenience sampling limits generalizability. Online data collection may have favored digitally active individuals, potentially underrepresenting older generations like Generation X. Future research could employ mixed-method approaches, combining online and offline data collection, or use probabilistic or stratified sampling to enhance representativeness. Additionally, while the study suggested refining technology adoption measures, time constraints prevented the inclusion of detailed indicators such as fintech app usage. Future studies could develop more comprehensive tools to better capture the role of technology in financial decision-making.
Conclusion
This study underscores the importance of generational differences in investment behavior within China’s evolving financial landscape. Compared to Generation Y and Generation X, Generation Z is more risk-averse, favoring safer and more liquid assets amid economic uncertainty. This cautious approach reflects broader trends in China, where younger generations face heightened investment challenges due to factors like fluctuating bank lending rates, real estate market volatility, and the lingering economic effects of COVID-19 (Hou et al., 2022).
Real estate has traditionally been a dominant investment vehicle in China, reflecting cultural preferences for security and long-term wealth preservation (Rust, 2022). However, recent market instability caused by oversupply and demographic shifts, such as declining birth rates, has pushed younger generations toward more diversified and liquid investments, including savings accounts and low-risk assets.
The rise of internet finance, such as digital banks and online investment platforms, has further transformed investment behavior. Millennials (Gen Y) have leveraged these tools to diversify into medium-risk assets like gold-backed ETFs, making their participation in financial markets more dynamic. In contrast, despite their technological familiarity, Generation Z continues to prioritize stability and liquidity, shaped by socioeconomic pressures and uncertainty.
Key factors such as economic volatility, technological advancements, and cultural attitudes help explain these generational differences. While technology adoption has a greater influence on Generation Y’s investment behavior, Generation Z’s preferences are more strongly driven by risk tolerance, leading to a cautious shift toward moderate-risk assets.
These findings offer valuable insights for policymakers and financial institutions in China, providing a basis for designing tailored financial policies and investment products. Understanding generational behaviors and the factors influencing them enables business leaders and policymakers to anticipate market trends, attract investments, and support sustainable economic development.
Footnotes
Appendix
The next ANOVA, as presented in Table A1, revealed a significant difference in the intention to invest in foreign currency across generations (F(2, 455) = 352.47, p < .01). The results show that both Gen X and Gen Z had relatively low levels of intention to invest in foreign currency, while Gen Y exhibited a markedly higher level of interest in this asset class. This result underscores that Gen Z, like Gen X, demonstrates a low preference for foreign currency, a high-risk asset. This aligns with Gen Z’s overall risk-averse behavior, as they are less inclined to engage in investments characterized by significant volatility and complexity. Compared to Gen Y, who shows a higher interest in foreign currency investments, Gen Z’s cautious financial approach further emphasizes their tendency to prioritize safety and avoid exposure to high-risk financial products. One possible explanation for Gen X’s lower intention to invest in foreign currency is that, as the oldest cohort in this study, they may be less familiar or comfortable with this type of asset. Foreign currency markets require a level of engagement with global financial trends and digital platforms, which may not align with the investment habits or preferences developed by Gen X (Venkatesh et al., 2003).
A post hoc analysis using the Games-Howell test, as displayed in Table A2, was conducted to identify significant differences between generations. The results demonstrate that Gen Y had significantly higher intention to invest in foreign currency compared to both Gen X and Gen Z (p < .01). However, no significant difference was found between Gen X and Gen Z regarding their intention to invest in foreign currency (p = .934).
Another moderation analysis examined the moderating effects of risk tolerance and technology adoption on the relationship between generation and the intention to invest in foreign currency, a high-risk asset. The model was statistically significant, (F(8, 449) = 478.99, p < .01), explaining 89.51% of the variation in intention to invest in foreign currency.
As shown in Table A3, the interaction between risk tolerance and Generation Y was significant and negative (β = −.86, p < .01), indicating that higher risk tolerance reduces Generation Y’s intention to invest in foreign currency. This suggests that even though Generation Y has a higher overall interest in foreign currency investments, greater comfort with risk actually discourages them from participating in this highly volatile market. In contrast, the interaction between risk tolerance and Generation Z was not significant (p = .987), suggesting that risk tolerance does not meaningfully influence Gen Z’s decision to invest in foreign currency. This aligns with the idea that Gen Z’s aversion to such volatile markets remains consistent, regardless of their risk tolerance levels.
Regarding technology adoption, neither the interaction with Generation Y (p = .159) nor Generation Z (p = .812) was statistically significant. This finding indicates that technology adoption does not moderate the relationship between generation and the intention to invest in foreign currency for either cohort. These results suggest that while digital tools might enhance access, they do not overcome the psychological and market-related barriers associated with foreign currency investments for either Gen Y or Gen Z.
Therefore, H2c is partially supported: risk tolerance does not affect Gen Z’s behavior toward foreign currency investments but has a significant impact for Gen Y. H3c is supported, confirming that technology adoption does not moderate the relationship between generation and high-risk assets, including foreign currency, for any generation.
Ethical Considerations
For data collected anonymously, posing no risk, without any funding, and gathered previously, no IRB statement is required according to university policy.
Consent to Participate
Informed consent was obtained from all subjects involved in the study.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
Not applicable.