Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 1201-1218 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: Zhanbaihui@hotmail.com Extending the theory of planned behavior to identify students’ investment intentions in internet money market funds Zhan Baihui1*, Ku Maisurah Ku Bahador2, Rafeah Mat Saat3 1Tunku Puteri Intan Shafinaz School of Accountancy, Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah Darul Aman, Malaysia; Zhanbaihui@hotmail.com (Z.B.). 2Institute for Strategic and Sustainable Accounting Development (ISSAD), Tunku Puteri Intan Shafinaz School of Accountancy, Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah Darul Aman, Malaysia; kumaisurah@uum.edu.my (K.M.K.B.). 3Accounting Information System Research and Development Institute (AISRED), Tunku Puteri Intan Shafinaz School of Accountancy, Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah Darul Aman, Malaysia; Rafeah@uum.edu.my (R.M.S.). Abstract: This study investigates students’ investment intentions in Internet Money Market Funds (IMMFs) in the Chinese context by extending the Theory of Planned Behavior (TPB) to include factors or dimensions such as functional value, efficiency value, emotional value and social value. A quantitative research approach was used, taking data from a sample of university students in China. The results revealed that attitude, subjective norms, perceived behavioral control, and perceived value significantly influence students’ investment intentions in IMMFs. In addition, perceived value, including functional value, efficiency value, emotional value and social value, also positively affects investment intentions among university students, emphasizing the importance of students’ perceptions of usefulness, ease of use, emotional satisfaction and social benefits derived from investing. in IMMFs. These findings contribute to the advancement of theory and practical applications in the internet investment domain, especially in the context of the investment market in China. Keywords: Internet money market funds, Investment intention, Perceived value, Theory of planned behavior. 1. Introduction The global financial turmoil caused by the COVID-19 pandemic has prompted investors to withdraw from bond funds and equity funds, seeking refuge in money market funds. As a result, the Money Market Fund (MMF) has emerged as an alternative as a wealth management tool, offering avenues beyond traditional bank deposits and various securities investment funds (Yang et al., 2021). MMFs are recognized as an investment platform with high security, liquidity, and stability. This investment platform can earn returns similar to those of large investors at reduced risk by pooling the capital of small investors and using it to purchase instruments with higher liquidity and stability (Makadok & Walker, 1996). Furthermore, this surge in investment inflow into MMFs reflects renewed investor interest and confidence in this asset class (Wang, 2020). The financial landscape in China has seen a profound transformation, largely driven by the rapid expansion of Internet-based financial services. The country is at the forefront of technological advancements, aided by relatively loose financial regulations to meet growing financial demands. It was proven in June 2023: 76.4 percent of the Chinese population has used the Internet (Thomala, 2023). This surge underlines the great potential for the development of internet finance as a catalyst for financial transformation. Internet finance, characterized by the use of the Internet to conduct traditional business transactions, represents the systemic integration of technology and finance aimed at delivering financial services to investors (Zheng, 2014). This innovative financial platform not only addresses the 1202 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate growing investment needs of individuals but also streamlines processes, reduces intermediary costs, saves time, and increases the liquidity of social funds (Li, 2021). Since the emergence of “Yu’e Bao” in 2013, Internet finance has exerted significant influence on Money Market Funds (MMFs) in China. Yu’e Bao, which operates as an Internet Money Market Fund (IMMF), has reshaped the landscape by integrating Internet technology and finance, thereby expanding the functionality of MMFs and introducing a variety of innovative wealth management products (Liu, 2018). This transition has pushed IMMFs to the forefront of the wealth management sector in the Internet financial market, with an operating model that emphasizes low risk and high liquidity, unlike traditional MMFs (McLoughlin & Meredith, 2017). However, despite the abundance of investable assets held by the middle- and low-income population, the vast majority of these funds remain underutilized, mainly due to the high investment thresholds imposed by traditional commercial banks, which favor high-net-worth individuals (CMB, 2023). This trend underscores the urgent need to address wealth inequality and increase investment diversification, especially given the prevailing preference among Chinese citizens for savings over investment, which can hinder long-term economic and social progress (Lu, 2019). In China, Internet-based personal wealth management platforms such as IMMFs are becoming an increasingly important aspect of the future social development trends of university students and daily life (Chen, 2019; Hao et al., 2019). This indicates that the capital market has the potential to attract young investors to the financial jurisdiction, with the optimal age to express investment usually falling between 18-25 years (Awaluddin et al. 2023). Previous studies already been conducted on examining investment intentions through the lens of young individuals or investors (see Yusriyati et al., 2023; Nugraha & Rahadi, 2021; Samsulbahri et al., 2021; Zhao, 2019). Their participation of them in the financial market is prompted by various factors, including their investment experience, financial literacy, self-perception, and emotional and psychological factors. However, those studies did not consider Internet-based investment platforms such as IMMFs, especially in investigating the investment intention behavior among university students in China. In this paper, an extended theory of planned behavior (TPB) will be examined by integrating other elements to determine the intention among university students to invest in IMMFs. This model is needed to provide a broader set of factors that may influence decision-making processes beyond the traditional TPB constructs (Raut et al., 2018). This allows for a more accurate understanding of the underlying motivations and determinants driving young individuals’ investment behavior in the context of IMMFs. Furthermore, it appears that no studies have extended TPB theory to better understand IMMFs, which emphasize Internet-based investment platforms, particularly among young generations. The closest study was conducted by Mahardhika and Zakiyah (2020), who delved into the intentions of the young generation in stock investment using an expanded TPB framework. This study, however, concentrated on university students in China, due to the flexible features of IMMFs (low investment rate, e-wallet facilities, ability to withdraw funds from the platform at any time, etc.) (McLoughlin & Meredith, 2017), such as addressing the demands of students to generate passive income. Furthermore, a study by Wang and Ben (2022) found that young people invest more in IMMFs than older people. Since TPB was the dominant theory used in predicting an individual’s behavior is determined by their intention to engage in that behavior (Leong et al., 2023), this study’s expanded TPB to include functional value, efficiency value, emotional value, and social value to better understand and predict the investment intentions of university students in IMMFs. This addition enhances the model’s comprehensiveness by capturing the multifaceted nature of investment decision-making. In other words, additional factors will make the TPB model more relevant, complete, comprehensive and parsimonious (Leong et al., 2023). This comprehensive model offers valuable insights for financial institutions, policymakers, and educators aiming to promote financial literacy and investment among young adults. The paper is divided into the following sections. The introduction and literature review are given in the first and second sections. The methodology of the study is presented in the third section. The fourth section deals with the findings and discussion. Finally, the conclusion of the study is drawn. 1203 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate 2. Literature Review 2.1. Internet Money Market Funds (IMMFs) Internet Money Market Funds (IMMF) represent an important innovation in the financial landscape where platforms combine traditional money-based funds with Internet attributes to offer easy and low-risk investment channels, especially in products such as bank deposits and short-term bonds (Chai & Cao, 2014; Jia et al., 2021). The distinguishing feature of IMMFs compared to traditional Money Market Funds (MMFs) lies in their reliance on Internet payment platforms for transactions and the implementation of their “T+0” transaction model, ensuring higher liquidity and facilitating same- day purchase and redemption transactions (Zhao & Li, 2015). This integration with online platforms not only improves accessibility but also streamlines operations, making IMMFs very attractive to tech- savvy investors, especially among the younger generation. The flexibility of this platform allows IMMFs to offer efficient cash management tools, minimal investment rates, and ease of investment withdrawal and also allows users to buy and sell goods online (McLoughlin & Meredith, 2017). The popularity and scale of IMMFs in China underscore their importance as a subject of study, especially among young adults and university students. The existing studies are more inclined to refer to IMMFs as a type of new technology or a type of general Internet finance product, Internet wealth management or Internet financial market funds. For example, Yu’e Bao, E Fund, and Jianxin Jiaxinbao, which are three leading IMMFs, have accumulated a large product scale of over 200 billion yuan each, with Tianhong Yu’e Bao having the highest number of fund shareholders, reaching 744 million as of the end of 2022 (Wu, 2023). This widespread acceptance among investors, coupled with the convenience and accessibility offered by IMMF Internet-based transactions, underscores its relevance to a younger demographic, which is often more tech-savvy and inclined toward digital financial solutions. Understanding the investment behavior and motivation of university students towards IMMF is important for financial institutions, policymakers and educators who wish to promote financial literacy and responsible investment practices among the next generation of investors in China. 2.2. Theoretical Model and Hypotheses In the social sciences, understanding the most fundamental determinants of human behavior is an important goal of many theorists and practitioners. Among theories, TPB is widely and effectively used in the study of behavior, which is used to predict more complex behaviors that are not controlled by self-awareness (Ajzen, 1991; Asare, 2015). The main aspect of TPB is the intention of the individual to perform a particular act, that is, the degree of difficulty with which the person is willing to attempt to perform a particular act and the degree of effort with which the plan is used (Ajzen, 1991; Brookes, 2023). The act is considered intentional only if it is within the control of the intention, that is when the individual can choose to perform an act at will or not at will. Behavioral intentions can be translated into actions. In this way, when a person has a positive evaluation of the completion of an action, the individuals consider it important to agree with the action and he/she is also aware that the action is under the individuals’ control; thus, that he/she will have a strong intention to act (Sansom, 2021; Brookes, 2023). Previous studies relied on the extended Theory of Planned Behavior (TPB) to explain internet investment behaviors (see Gopi & Ramayah, 2007; Bosnjak et al., 2020; Hapsari, 2021; Cheng & Sang, 2022). In the current study, four factors were assessed to determine the intentions of university students to invest in IMMFs, i.e., functional value, efficiency value, emotional value and social value. Instead of the traditional TPB, these factors provide a more flexible understanding of the factors that influence individual intentions. Functional value is related to the perception of the usefulness and benefits of investment (Kang & Yang, 2023), while efficiency value considers the ease and convenience of the investment process (Riahi & Garrouch, 2023; Jiang et al., 2022). Emotional value captures the emotional satisfaction derived from investment (Yang et al., 2021), while social value includes the influence of social factors on investment decisions (Tajeddini et al., 2022; Wu et al., 2018). This allows research findings that include both emotional and rational aspects of decision-making, deepening our 1204 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate understanding of the motivation behind investor behavior with IMMFs. Research findings through extending TPB, which is based on the same principle, to develop a model to measure the behavior of university students in China investing in IMMFs, as shown in Figure 1. The purpose of this study is to examine the intention to make investments in the IMMFs by extending TPB. The factors in the model are made up of additional factors such as functional value, efficiency value, emotional value, and social value, in addition to original factors such as attitude, subjective norms, and perceived behavioural control. In this study, each potential combination of these factors will be examined. Figure 1. Research model and hypotheses. 2.2.1. Attitude Attitude refers to an individual’s evaluation scheme for the consequences of performing certain behaviors (Athiyaman, 2002). This concept is shaped by beliefs about possible consequences, subjective norms by normative expectations, and perceived control by factors that facilitate or inhibit behavior (Ajzen, 2006). Therefore, this study examines the attitudes that influence investors’ intentions towards IMMFs. Previous studies have shown that attitude is an important predictor of intention in the context of Internet finance (Himel et al., 2021; Safari & Buzera, 2022; Akinwale & Kyari, 2022). However, IMMFs are based on traditional MMFs, a wealth management platform with the help of network tools and information technology by individuals to operate online recharge, account management and investment business behaviours (Jia et al., 2021). Despite being a part of Internet finance, IMMFs are still seen as an innovative platform, and people’s aversion to change and fear of the unfamiliar will 1205 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate negatively affect their use of IMMFs and lead to negative attitudes. Moreover, although online elements are believed to have a positive effect on internet investment, e-commerce research shows that customers are intrigued enough to complete a transaction even when they are interested in an online product (Jung & Jae, 2015). Therefore, the hypothesis H1 was set up: H1: Attitude Has a Positive Influence on the Intention to Invest in IMMFs. 2.2.2. Subjective Norms According to Ajzen and Fishbein (1980), subjective norms refer to an individual performs a specific action, feels it, and believes that what is important as the degree of pressure on people who agree or deny his/her actions. It is believed that subjective norms include external influences and interpersonal influences (Manning, 2009). Thus, it can be said that subjective norms will influence the user’s intention to use. Subjective norms can be gauged from standpoints about other’s normative expectations and motivation to comply with these expectations (Laohapensang, 2009). Therefore, the higher the subjective norm, the higher the individual’s willingness to use it. In this study, high score of subjective norms indicates the increase willingness of individual to invest in IMMFs. Previous studies have shown that subjective norms and intention have a positive relationship (Li et al, 2023; Pena-Garcia et al., 2020; Hasbullah et al., 2016; Ham et al., 2015; Hansen et al., 2004; Yoh et al., 2003). For example, Pena-Garcia et al. (2020) highlighted that subjective norms positively affect online health-related behavioral intentions, emphasizing the role of social influences in shaping individuals’ intentions to engage in online behaviors. However, the TPB has faced criticism from some researchers. It often refers to the weak (or nonexistent) relationship between subjective norms and intentions (Ham et al., 2015). This is evidenced by a study conducted by Kumar (2012) examined on purchase intention for environmentally sustainable products, revealed that subjective norms were not significantly related to purchase intention which is contrary to other studies. However, the recent finding by Irimia-Diéguez et al. (2023) predicted that subjective norms is one of factors that support the intention to adopt financial technology (FinTech). Therefore, the following hypothesis was proposed: H2: Subjective norms have a positive influence on the intention to invest in IMMFs. 2.2.3. Perceived Behavioral Control The degree of ease or difficulty an individual feels when he or she implements a specific behaviour is referred to as perceived behaviour control (Ajzen, 1991). Perceived behavior control occurs either for the individual, group or an organisation that involved services and ideas as well as tangible products. It may happen in various thoughtless situations (Kardes et al., 2010). Several studies have found that TPB shows that perceived behaviour control impacts consumers’ intention to use (Hagger et al., 2022; Gopi & Ramayah, 2007; Turan, 2012). In previous internet investment study, this notion was found to be a significant predictor of online trading adoption among individual investors (Singh & Malhotra, 2016). In this study, perceived behavioural control is used as the degree of ease or difficulty the individual feels when individuals, especially university students use the IMMFs apps or websites. However, there is currently no research on the perceived behavioural control of IMMFs to verify university students’ investment intentions. This leads to the following H3 hypothesis: H3: Perceived behavioral control has a positive influence on the intention to invest in IMMFs. 2.2.4. Perceived Value Based on the study of perceived value, there was a study examining the impacts of perceived value on the Internet investment platform adoption (Xie et al., 2021). This factor indicates to significantly impacts the intention to invest in internet investment platform by incorporating functional value, efficiency value, emotional value, and social value as its dimension. For example, Ye et al. (2018) study on individual’s perceived value, including dimension such economic value, attribute value, social value, experiential value, and platform functional value, shedding light on the factors influencing consumer perceptions of online financial products. Previous internet investment studies have shown that 1206 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate functional, efficiency, emotional and social values have a positive influence on the intention to make investment in internet or online platform (Riahi & Garrouch, 2023; García-Monleón et al., 2023; Kasilingam & Krishna, 2022; Ming-Sung et al., 2009; Kim & Forsythe, 2008). However, study by Senecal et al. (2002) indicates that a difficult or challenging interaction may negatively impact the functional value of the online experience. Thus, the following hypotheses, H4, H5, H6, and H7, were generated to examine this factor influencing the intention to invest in IMMFs. H4: Functional value has a positive influence on the intention to invest in IMMFs. H5: Efficiency value has a positive influence on the intention to invest in IMMFs. H6: Emotional value has a positive influence on the intention to invest in IMMFs. H7: Social value has a positive influence on the intention to invest in IMMFs. 3. Methodology 3.1. Survey Instrument The questionnaire was divided into multiple sections. The first section of the questionnaire was assessed using a nominal and a ratio scale. It refers to the profile of the respondent, which includes details like age, gender, education level, monthly income, and income source. Following that, a construct measurement questionnaire for the construct was administered. Using a 5-point Likert scale, the items and questions for each construct were adapted from previous studies, as indicated in Table 1. Table 1. Items Related to dependent variable and independent variables. Variable Items Sources of the items Attitude (ATT) ATT1: I feel investing in Internet money market funds is a valuable investment Kamins &Assael (1987) Pedersen (2005) Hsu et al. (2006) ATT2: Investing in Internet money market funds is good idea ATT3: I would feel that investing in Internet money market funds is very pleasant ATT4: In my opinion, it would be desirable to invest the Internet money market funds Subjective norms (SN) SN1: People who are important to me think investing in Internet money market funds is a wise idea Venkatesh (2000) Fu (2014) SN2: People who are important to me would think I should invest in Internet money market funds SN3: People who are important to me think that investing in Internet money market funds is a good idea SN4: People who are important to me would think I should invest Internet money market funds Perceived behavior control (PBC) PBC1: I would be able to invest in Internet money market funds Pedersen (2003; 2005) Hsu et al. (2006) PBC2: I have the knowledge to invest in Internet money market funds PBC3: I have the resources to invest in Internet money market funds PBC4: I have the ability to invest in Internet 1207 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate money market funds Functional value (FV) FV1: Internet money market funds can help me better complete my financial plan Sweeney & Soutar (2001) FV2: Internet money market funds allow me to manage money more quickly FV3: Internet money market funds allow me to manage money more conveniently FV4: Internet money market funds give me more choices in financial management Efficiency value (EFFV) EFFV1: Learning to invest Internet money market funds product is easy for me Parasuraman (2000) Bourdeau et al. (2002) EFFV2: I don't need to spend a lot of energy in investing Internet money market funds EFFV3: I know every step in the process of investing Internet money market funds EFFV4: For me, Internet money market funds are easy to invest Emotional value (EMOV) EMOV1: Internet money market funds would make me want to invest it Bourdeau et al. (2002) EMOV2: Internet money market funds are one that I would feel relaxed about investing EMOV3: Investing Internet money market funds would make me feel good EMOV4: Investing in Internet money market funds would give me pleasure Social value (SV) SV1: Investing in Internet money market funds would help me to feel acceptable Sweeney & Soutar (2001) SV2: Investing in Internet money market funds would improve the way I am perceived SV3: Investing in Internet money market funds would make a good impression on other people SV4: Investing in Internet money market funds would give its owner social approval Intention (INT) INT1: I think positively towards investing in Internet money market funds Jarvenpaa et al., (2000) Fu (2014) INT2: I think I have intentions to invest in Internet money market funds INT3: I predict that I would invest in Internet money market funds INT4: I intend to be a heavy user of Internet money market funds 3.2. Sample Characteristics and Data Collection This study’s target respondents were university students from seven universities in Songjiang University Town in Shanghai, China. University students have been chosen because their exposure to a dynamic economic landscape may influence students’ perceptions and attitudes towards online investment opportunities and have a significant impact on future financial development, which is the pillar of the country’s future economic development. In addition, Shanghai, being a major financial center in China, provides a conducive environment for studying financial decision-making among 1208 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate university students, especially concerning innovative financial products such as IMMFs (Zheng et al., 2020). The proximity to financial institutions and exposure to a dynamic economic landscape may influence students’ perceptions and attitudes towards online investment opportunities. The list of the seven universities is as below: Table 2. The sample size of students in Songjiang university town. No. Name of the university Number of students Percentage student in the university Sample size 1 Shanghai international studies university 9688 9.0% 35 2 Donghua university 23,401 21.8% 84 3 East China university of political science and law 15,420 14.4% 55 4 Shanghai university of international business and economics 12,258 11.4% 44 5 Shanghai lixin university of accounting and finance 18,410 17.2% 66 6 Shanghai university of engineering science 24,000 22.4% 86 7 Shanghai institute visual arts 4000 3.8% 15 Total 107,177 100% 385 Prior to commencing data collection, a pre-test of the questionnaire was conducted with a sample comprising 56 respondents. The analysis indicated that each construct exhibited Cronbach’s alpha value above 0.70, indicating favorable reliability. Furthermore, all terms within the questionnaire demonstrated standard factor loadings surpassing 0.50, thus confirming sufficient validity. In this study, questionnaires were formulated using China’s professional online questionnaire platform, WenJuanXing, and disseminated to the selected seven universities situated in Shanghai’s Songjiang University Town via various channels and links including university forums, WeChat, and QQ, among others. A total of 404 questionnaires were obtained, of which 15 multivariate outliers were detected and eliminated based on Mahalanobis distance calculations. After screening and cleaning, only 389 questionnaires were retained, with an effective response rate of 96.3%. Therefore, this exceeds the threshold identified by Krejcie and Morgan (1970) for the required sample size, which was estimated to be 385 valid responses. Thus, this study’s response rate considered achieved and satisfactory for social science researchers (Sekaran & Bougie, 2016). 4. Results 4.1. Respondent Profile The respondent profile from this study is shown in Table 3. The distribution is almost equal by gender, with fewer males (51.2%) than females (48.8%). In terms of age, the majority were between the ages of 18 and 22 (57.3%), followed by those between 23 and 27 (41.4%), and only a small percentage were 28 and above (1.3%). Bachelor’s degree holders (71.0%) constitute the largest percentage of respondents, followed by diploma holders (14.4%), postgraduates (8.7%), and certificate holders (5.9%). According to the respondents’ monthly income, the majority of students (72.5%) earn between RMB 1,001 and 2,000, followed by 13.9% who earn RMB 2,001 and 3,000 (13.9%), and 4.6% who earn RMB 3,000 and above. 1209 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate Table 3. Descriptive statistics for respondent profiles. Demographic attributes Frequency Percent % Gender Male 199 51.2 Female 190 48.8 Age 18 - 22 223 57.3 23 - 27 161 41.4 28 and above 5 1.3 Education Certificate 23 5.9 Diploma 56 14.4 Bachelor 276 71.0 Postgraduate 34 8.7 Monthly income RMB 0 - 1,000 35 9.0 RMB 1,001 - 2,000 282 72.5 RMB 2,001 - 3,000 54 13.9 RMB 3,000 and above 18 4.6 Source of income Parents 369 28.0 Scholarship 233 17.7 Education loan 176 13.4 Part time jobs 336 25.5 Investment 203 15.4 Access to knowledge Internet 301 26.9 Magazine 102 9.1 Newspapers 140 12.5 Financial books 250 22.3 Friends/parents 121 10.8 Courses in FM 192 17.1 Others 14 1.3 Experience in investing in IMMFs Yes 228 58.6 No 161 41.4 Invested in IMMFs platforms P2P 36 9.3 Yu’e bao 224 57.6 Li Caitong 151 38.8 Others 8 2.1 IMMFs investment experience time Below 6 months 82 36.0 6 - 12 months 103 45.2 1 - 2 years 31 13.6 More than 2 years 12 5.3 Expected returns and risk appetite 1210 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate Expect substantial capital growth and are willing to take significant investment risk 45 11.6 Expect to generate more income, can bear greater investment risk 78 20.1 Expect to generate certain income, can bear certain investment risks 244 62.7 No risk tolerance, no loss accepted 22 5.7 Most of the respondents’ main source of income is from their parents (28%). Additionally, it was also found that 25.5% of university students worked part-time jobs to support themselves during their studies. It is interesting to note that 15.4% of the respondents’ income was generated by investments. This demonstrates that the current generation has started to venture into the investment sector in order to earn income while they are still in universities. When it came to how respondents learned about IMMFs, the majority obtained it through the internet (26.9%), followed by financial books (22.3%). In addition, most of respondents (57.6%) invested in IMMF using the Yu’e Bao platform, with Li Caitong placing second with a percentage of 38.8%. 4.2. Measurement Model Assessment The objective of assessing the measurement model is to ascertain that the items effectively measure the intended construct, thereby reflecting the instrument’s reliability and validity. The measurement model was assessed in terms of the reliability of the individual items, internal consistency reliability, convergent validity and discriminant validity, as recommended by Henseler et al. (2009) and Hair et al. (2017). 4.2.1. Individual Item Reliability Test The first step in evaluating a reflective measurement model is to examine how much of the fluctuation in each indicator is explained by its concept, which indicates indicator reliability (Hair et al., 2019). To get the explained variance of an indicator, the bivariate correlation between the indicator and the construct, known as the indicator loading, has to be squared. An indicator’s commonality may be seen via its indication of dependability. According to Hair et al. (2019), indicator loadings greater than 0.708 are recommended since they demonstrate that the construct explains over 50% of the indicator’s volatility, providing appropriate indication dependability. In other words, indicators with loadings between 0.40 and 0.708 should be eliminated to increase the dependability of the internal consistency or the convergent validity beyond the suggested threshold value (Hair et al., 2021). Strong item loading is indicated by the item loading for all outer loadings of the build, which varies from 0.768 to 0.973. 4.2.2. Internal Consistency Reliability Internal consistency reliability is the degree of relationship between indicators assessing the same notion (Hair et al., 2019). The dependability of the construct may be assessed using Cronbach’s alpha and composite reliability. Both reliability requirements are often required to be greater than 0.70 (Hair et al., 2019). For example, in exploratory research, dependability levels between 0.60 and 0.70 are considered “acceptable,” and values between 0.70 and 0.90 are classified as “satisfactory to good.” It is challenging to interpret values more than 0.90 (and more than 0.95), as this implies that the indicators are redundant, which reduces the construct validity (Diamantopoulos et al., 2012). If the dependability is 0.95 or above, the individual elements are redundant, which indicates that they do not need more diversity to guarantee the reliability of multi-item constructs (Hair et al., 2019). Table 4 shows that all composite dependability values are more than 0.7 and less than 0.95. It suggests the presence of internal consistency dependability. 1211 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate Table 4. Measurement model analysis Cronbach alpha and composite reliability (CR) results for this study. Cronbach’s alpha Composite reliability (rho_a) Average variance extracted (AVE) ATT 0.828 0.829 0.660 EFFV 0.876 0.881 0.733 EMOV 0.809 0.816 0.636 FV 0.895 0.903 0.763 INT 0.818 0.819 0.647 PBC 0.856 0.876 0.704 SV 0.875 0.885 0.731 SN 0.881 0.898 0.741 4.2.3. Convergent Validity Convergent validity is the extent to which a notion converges to explain the fluctuation of its indicators (Hair et al., 2011). The convergent validity of a notion is evaluated using the average variance extracted (AVE) for all indicators for each component. Therefore, for the AVE to be deemed acceptable, it must be 0.50 or above, meaning that the construct must explain at least 50% of the variation of the indicators that comprise the construct (Hair et al., 2021). In Table 4, the AVE value was more significant than 0.50, showing solid levels of convergent validity for each of the components. 4.2.4. Discriminant Validity Henseler et al. (2015) propose assessing discriminant validity using correlations’ heterotrait- monotrait ratio (HTMT). A discriminant validity deficiency would be indicated by an HTMT score greater than 0.90. However, a lower, more conservative threshold value, like 0.85, is recommended when ideas are more unlike. To do this, this research needs to ascertain whether, at a significance level of 5%, the upper bound of the 95% confidence interval is less than 0.85. According to Table 5 for the structures, all achieved values are below the cutoff point of 0.85, meaning that the HTMT values fall within a reasonable range. Table 5. Discriminant validity by Heterotrait-Monotrait ratio (HTMT). ATT EFFV EMOV FV INT PBC SV SN ATT EFFV 0.499 EMOV 0.543 0.681 FV 0.484 0.742 0.69 INT 0.748 0.72 0.72 0.696 PBC 0.4 0.517 0.498 0.447 0.747 SV 0.452 0.674 0.661 0.679 0.66 0.458 SN 0.451 0.445 0.487 0.437 0.674 0.286 0.406 4.3. Structural Model Analysis When two or more predictors are correlated, a phenomenon known as multicollinearity occurs, and the standard error of the coefficients will rise as a result. Higher standard errors indicate the possibility that any or all of the independent variable coefficients deviate considerably from 0 (Daoud, 2017). Most often, an indicator known as a variance inflation factor (VIF) is used to detect multicollinearity (O’brien, 2007). There was no evidence of common method bias in any of the VIF values less than 5 (attitude 1212 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate 1.421, efficiency value 2.183, emotional value 1.938, functional value 2.182, perceived behavior control 1.357, social value 1.885, subjective norms 1.342). The assessment of structural models primarily looks at the model’s predictive power and the relationship between structures (AlNuaimi et al., 2021). A two-tailed 0.05 p-value bootstrapping approach using 5,000 subsamples was used (Onofrei et al., 2022; Streukens & Leroi-Werelds, 2016). Testing hypotheses, the coefficient of determination (R²), effect size assessment (f²), the blindfolding- based cross-validated redundancy measure (Q²), and determining its strength are all important considerations when analyzing a structural model. Figure 2 below displays the findings from the bootstrap structural model used for this study. Figure 2. Bootstrap structural model results. Table 6 shows that the results of the structural model, attitude, subjective norms, perceived behavioral control, Functional value, Efficiency value, Emotional value, and Social value are well correlated with university students’ investment intention. This confirms that H1, H2, H3, H4, H5, H6, H7 are supported. In this study, f ² is another tool used to examine the interrelationship of variables, and the results show that all paths have a small influence on intention. 1213 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate Table 6. Hypothesis testing results. Hypothesis Relation Beta Std. dev. T values P values Effect size ( ) Findings H1 ATT -> INT 0.257 0.035 7.291 0.000 0.055 Supported H2 SN -> INT 0.255 0.038 6.788 0.000 0.002 Supported H3 PBC -> INT 0.332 0.034 9.783 0.000 0.037 Supported H4 FV -> INT 0.109 0.037 2.967 0.003 0.006 Supported H5 EFFV -> INT 0.094 0.041 2.267 0.023 0.057 Supported H6 EMOV -> INT 0.070 0.035 2.016 0.044 0.096 Supported H7 SV -> INT 0.078 0.034 2.285 0.022 0.002 Supported 4.4. Assessment of Prediction Relevance (Q²) Coefficient of determination (R²) values are often used to assess the endogenous variables in-sample predictive power (Hair et al., 2017). 72.1% of the variances in intention could be explained by the framework. Calculating the Q² value is another method for evaluating the prediction accuracy of the PLS path model (Geisser, 1974; Stone, 1974). For Intention, the Q² value was above 0.25 (Q²=0.459), indicating that the model had high predictive significance. Furthermore, to prevent model misspecification, Henseler et al. (2014) offer the standardised root mean square residual (SRMR) as a goodness of fit measure for PLS-SEM. This study’s SRMR value, which is 0.046, less than 0.08, is a good fit (Hu & Bentler, 1998). 5. Discussion This study presents an extension of the TPB to investigate the investment intentions of university students in IMMFs in China. By integrating additional factors such as functional value, efficiency value, emotional value, and social value into the traditional TPB framework, this study offers a comprehensive model for understanding the motivations and determinants driving young individuals’ investment behavior in the context of IMMFs. The research findings provide a deeper understanding of the factors influence investment behavior in IMMFs. Specifically, the study demonstrates the significant influence of attitude, subjective norms, perceived behavioral control, and perceived value (functional, efficiency, emotional, and social) on the intention to invest in IMMFs among university students in China. It shows that attitude towards IMMFs significantly influences university students’ investment intention (H1 supported). This suggests that students’ positive evaluation of IMMFs and their perceived benefits play a crucial role in shaping their intention to invest in this investment platform. It also shows that a 1% change in attitude can increase the intention to invest by 0.257%. This is in line with previous studies, which state that attitude is an important predictor of intention in the context of Internet finance (Himel et al., 2021; Safari et al., 2022; Akinwale & Kyari, 2022). This positive relationship indicates that students who have a favorable attitude towards IMMFs are more likely to express an intention to invest in them. This implies that students’ perceptions of the benefits, usefulness, and overall desirability of IMMFs play a crucial role in shaping their investment intentions. Therefore, efforts to promote investment in IMMFs among university students should focus on enhancing their positive attitudes towards these funds by highlighting their benefits, features, and potential returns. Findings show that subjective norms play a significant role in influencing university students’ intention to invest in IMMFs. Subjective norms, which reflect social influences and normative expectations, exert a positive impact on investment intention, supporting H2. According to the results of H2, students’ investment decisions regarding IMMFs are not solely based on individual perceptions or preferences but are also influenced by social factors and peer pressures. Despite some criticism directed towards the TPB regarding the weak relationship between subjective norms and intentions in 1214 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate certain contexts, recent research, such as that by Irimia-Diéguez et al. (2023), has reaffirmed the significance of subjective norms in supporting intentions, particularly in the adoption of FinTech. This study also revealed that perceived behavioral control significantly influences investment intention among university students, hence H3 is supported. The data analysis also indicates that a 1% change in perceived behavioral control can increase the intention to invest into the IMMFs by 0.332%. This is consistent with the theory of TAM, which states that perceived behaviour control impacts consumers’ intention to use (Hagger et al., 2022; Gopi & Ramayah, 2007; Turan, 2012). Furthermore, in previous internet investment study, this factor was found to be a significant predictor of online trading adoption among individual investors (Singh & Malhotra, 2016). Thus, this study suggests the importance of students’ perceived behavioral control over the investment process in influencing their willingness to invest in IMMFs. This study revealed that perceived value, encompassing functional value, efficiency value, emotional value, and social value, has a significant positive impact on investment intention among university students (H4, H5, H6, H7 supported). The findings suggest that students’ perceptions of the usefulness, ease of use, emotional satisfaction, and social benefits associated with investing in IMMFs, play a vital role in determining their investment intentions. Previous studies have also proven that the importance of functional, efficiency, emotional and social values can influence individuals’ intentions to invest in online financial products (Xie et al., 2021; Ye et al., 2018; Riahi & Garrouch, 2023; García-Monleón et al., 2023; Kasilingam & Krishna, 2022; Ming-Sung et al., 2009; Kim & Forsythe, 2008). These findings underscore the need for financial institutions and educational institutions to focus on enhancing students' perceptions of the value proposition offered by IMMFs. Efforts should be directed towards highlighting the functional benefits, simplifying the investment process, emphasizing emotional satisfaction, and showcasing the social benefits associated with investing in IMMFs. 6. Conclusion This study makes a significant contribution to the literature by extending the TPB to comprehensively examine students’ investment intentions in IMMFs. By integrating additional factors/dimensions such as functional value, efficiency value, emotional value and social value into the TPB framework, this study offers a deep understanding of the relationship between existing TPB factors and additional factors that influence investment decisions among students’ university in China. This can help stakeholders such as capital market and regulatory agencies to identify the characteristics of investors and their investment tendencies which can help in recognising current market segments. The findings of this study also have practical implications for financial institutions, policy makers and educators who wish to foster financial literacy and responsible investment practices among young adults in China. By revealing the motivations and determinants underlying student investment behavior in IMMFs, this study provides valuable insights that can inform the development of targeted interventions and educational initiatives aimed at empowering students to make informed financial decisions. Overall, this study contributes to the advancement of theory and practical application in the internet investment domain, especially in the Chinese context, thereby enriching the existing body of knowledge and laying the foundation for future research efforts in this field. However, this study has several limitations. There may be issues in the distribution of the questionnaire to the respondents and potentially affect the generalizability of the findings. In addition, the scope of the study is limited to the factors proposed by TPB, which may ignore other relevant variables that may influence students' investment intentions in IMMFs. Therefore, future research should consider using systematic probability sampling methods and develop the model by integrating theories and additional factors to provide a more comprehensive understanding of investment behavior among university students in the context of IMMFs. 1215 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1201-1218, 2024 DOI: 10.55214/25768484.v8i6.2224 © 2024 by the authors; licensee Learning Gate Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Ajzen, I. (1991). The Theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991; 50:179– 211. [2] Ajzen, I. (2006). Constructing a theory of planned behavior questionnaire. [3] Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (p. 278). 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