Ecological Awareness and Pro-Fintech to Exploring Generation Z's Behavior in Using Fintech

Ecological Awareness and Pro-Fintech to Exploring Generation Z's Behavior in Using Fintech | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ecological Awareness and Pro-Fintech to Exploring Generation Z's Behavior in Using Fintech Hasan Mukhibad, Fachrurrozie Fachrurrozie, Indah Anisykurlillah, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8974785/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Recent literature linking pro-FinTech and sustainability performance emphasizes the perspective of corporations as FinTech service providers. This study provides empirical evidence of the relationship between perceptions of ecological awareness and pro-FinTech (the behavioral intention to use FinTech). The research data consisted of 319 students in Indonesia who were categorized as Generation Z. The data were analyzed using SEM-PLS. This study reports that Ecological awareness increases pro-FinTech. We separate ecological awareness indicators into economic, environmental, and social categories and find that both environmental and economic awareness have a positive impact on pro-FinTech attitudes. However, economic awareness does not influence pro-FinTech. This study is beneficial for users and financial institutions in increasing the number of FinTech users. FinTech service developers are advised to innovate to develop solutions that continuously improve sustainability. Financial institutions are advised to raise public awareness of FinTech's positive effects, as this is essential to fostering a positive perception that FinTech can improve sustainability. Environmental performance Financial Technology Theory of Technology Use Behavior Ecological Modernization Sustainability Performance Figures Figure 1 1. INTRODUCTION Recently, regulators worldwide have reported that economic activities contribute to environmental degradation, which in turn exacerbates climate change, in an effort to improve welfare. Human activities that rely on machines and increase consumption have depleted natural resources and exacerbated environmental damage [ 1 ]. This ecological damage is evidenced by global warming, increased environmental pollution, and the decline of flora and fauna. Increasing global warming further exacerbates the ecological system, leading to extreme weather conditions, including cyclones and droughts [ 2 ]. Thus, ecological damage can threaten the sustainability of human and corporate life. Generally, to mitigate environmental damage, governments and environmental organizations, such as the World Wide Fund for Nature and the Global Sustainability Standards Board (GSSB), encourage individuals and corporations to develop strategies and implement plans to ensure sustainable development. Likewise, the UN designed the Sustainable Development Goals (SDGs) as a global initiative agreed upon by member countries to end poverty, protect the planet, and ensure prosperity for all by 2030. Sustainable development is a development process that integrates environmental, social, and economic aspects into development strategies. Sustainable development initiatives focus on reducing pollutant emissions and waste, as well as minimizing the use of natural resources to preserve them for future generations [ 2 ]. J. Zhou & Jin [ 3 ] stated that corporations play a vital role in improving environmental quality and promoting sustainable development. This argument underlies prior literature evaluating how companies contribute to sustainability. For example, Rahman, Zahid, and Al-Faryan (2023) assessed the performance of 255 non-financial companies registered in Pakistan and reported an ESG performance of 23.10. Yadav and Prashar [ 5 ] and Veeravel et al. [ 6 ] reported that companies in India achieved ESG performance scores of 33.43 and 23.28, respectively. Li and Jia [ 7 ] used companies in 26 countries with an average sustainability performance of 31.01. Meanwhile, Knight et al. [ 8 ], Ullah et al. [ 9 ], and Sajan et al. [ 10 ] evaluated sustainability performance in small and medium enterprises (SMEs). Thus, previous studies evaluated corporate sustainability performance using various models they developed. We extend previous research by evaluating individual ecological awareness (e.g., perceptions of users and customers) in relation to pro-FinTech use, which remains limited. Joshi and Rahman [ 1 ] reported that consumer households are responsible for 40% of environmental damage. Individual concern for ecological awareness issues is manifested in the purchase of environmentally friendly products [ 11 ] [ 12 ]. Prior research reports that individual consumers have a positive attitude toward environmental issues and a commitment to purchasing eco-friendly products [ 11 ] [ 13 ]. Unfortunately, the limited number of green products on the market still leaves individuals with a limited role in global ecological awareness [ 1 ]. Meanwhile, companies use technology-based production and service processes as the primary agents of sustainability performance [ 14 ]. Technology can reduce energy consumption and carbon emissions and help reduce landfill waste. Moreover, Zhu et al. [ 15 ] argued that adopting digital technologies streamlines green production techniques, thereby optimizing business operations and enhancing the efficiency of product design and manufacturing processes. The breadth of sustainable technology companies that can adopt it is the basis for our study, which focuses on consumer-facing financial technology services. The first contribution of our research is to extend the previous literature by demonstrating the influence of individual commitment, across environmental, social, and economic dimensions, on their behavior when using FinTech. Following Siddik et al [ 16 ], ecological awareness is based on environmental, social, and economic dimensions. Second, our study builds upon prior studies that employ two main theoretical approaches: system acceptance theory and behavioral psychology. For example, Srivastava et al. [ 17 ], Bajunaied et al. [ 18 ], and Amnas et al. [ 19 ] used the Unified Theory of Acceptance and Use of Technology (UTAUT) model, Rahadian and Thamrin [ 20 ], and Shaikh et al. [ 21 ] used the Technology Acceptance Model (TAM) in explaining the intensity of customers using the system. Meanwhile, Irimia-Diéguez et al. [ 22 ] and [ 23 ] employed behavioral psychology theories, specifically the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB), to explain the intensity of FinTech use. Niswah et al. [ 24 ] combined the TAM and the theory of behavioral psychology, specifically TPB, to explain the behavior of using FinTech. Both theoretical approaches are used because FinTech is a financial service that uses an information system. To the best of our knowledge, studies on the influence of ecological awareness on the behavior of FinTech users, while controlling for the theory of system acceptance and psychological factors, remain limited. The results of our study are presented in this paper and divided into five crucial interrelated parts. The second part presents the theoretical background that underlies the logic of thinking and hypothesis development. The method section presents the population, sample, sampling technique, measurement indicators for variables, and data analysis methods. The fourth section presents the study's results. In this section, we present the model's feasibility analysis, the descriptive variables, the data test results, and the discussion. Finally, in the fifth section, we present a summary, practical recommendations, research limitations, and recommendations for further research. 2. THEORY AND HYPOTHESIS 2.1. Determinants of pro-FinTech Use Factors: Consumer Perception Many researchers have explored the intensity of customer use of FinTech. We conclude that they use two approaches. The first approach is the UTAUT. Srivastava et al. [ 17 ] studied the intensity of users using digital payment FinTech among Generation Y and Z users in India. They developed a model combining TAM and UTAUT. They reported that the intensity of FinTech use is influenced by customer satisfaction, effort expectancy, and performance expectancy. Moreover, they noted that customer satisfaction positively affects the frequency of FinTech payment use. Bajunaied et al. [ 18 ] used 361 FinTech users from Jeddah, Saudi Arabia, as research participants. They modified the UTAUT model as the primary research model. The results showed that performance expectancy, effort expectancy, facilitating conditions, and privacy enablers had a significant, positive impact on users' behavioral intentions toward FinTech services. Amnas et al. [ 19 ] integrated UTAUT2 by adding the trust variable. They reported that performance expectancy, effort expectancy, social influence, habit, price value, and supportive conditions significantly affect users' intention to use FinTech services. Their findings support UTAUT 2. Additionally, their study found that users' trust in FinTech influences their intention to use it. Rahadian and Thamrin [ 20 ] and Shaikh et al. [ 25 ] developed TAM to explain customer intensity in using the system. However, they reported different findings. Rahadian and Thamrin (2023) reported that perceived ease of use influenced perceived usefulness but did not affect FinTech use. However, Shaikh et al. [ 25 ] reported that perceived ease of use, perceived usefulness, and consumer innovation all influenced the intensity of users' FinTech use. The second approach is psychological theory. Irimia-Diéguez et al. [ 26 ] employed the TPB approach to explain the behavior of 300 FinTech users in southern Spain. They found that attitude, subjective norm, and perceived behavioral control positively influence the intensity of their FinTech use. They support the TRA and TPB theories to explain consumer behavior when using FinTech services. Al-Afeef et al. [ 23 ] developed a model based on TRA and TPB to describe the intensity of FinTech use among 192 Jordanian citizens. They found that subjective norms, perceived behavior, perceived risk, and perceived trust are crucial in increasing customers' use of FinTech. 2.2. Ecological Modernization Theory, Sustainability Performance, and Financial Technology Technological advancements marked the Industrial Revolution, and the use of new energy sources significantly contributed to economic progress and welfare. During this period, factory growth, supported by advances in transportation and communication, led to greater industrial efficiency. However, the impact of this industrial activity increased pollution and environmental degradation, which, in turn, negatively affected sustainable growth. During this pre-industrial period, society sought to modernize and reorganize industrial society globally, within the geo and biosphere, by developing and implementing the latest knowledge-based technologies [ 16 ]. This effort is reorganizing the industrial world to continue improving economic welfare while supporting the earth's existence and sustainable development. Ecological modernization theory (EMT) posits that environmental problems stemming from industrial activities can be mitigated through industrial modernization, which leverages modern knowledge-based technology to facilitate sustainable growth [ 27 ]. The implementation of technology can reduce resource use, pollution, and emissions, thereby positively impacting environmental sustainability. EMT emphasizes advancing knowledge and technology through discovery and innovation to implement cleaner technologies in industry [ 28 ]. This approach requires no decline in industrial productivity, but instead increased industrial effectiveness while maintaining ecosystem and environmental sustainability, and supporting sustainable performance in the long term. Thus, EMT supports integrating economic development and environmental quality, thereby promoting sustainability. 2.3. Individual Ecological Awareness is a Factor of Pro FinTech Use Intensity Previous studies that examine the relationship between pro-FinTech use and sustainability performance are based on the perspective of entities or business actors, specifically FinTech providers. In the TPB theory approach, the primary factor influencing individual behavior is attitude. Attitude is related to positive or negative beliefs about carrying out certain behaviors. Attitude is determined by an individual's beliefs about the consequences of performing a behavior (behavioral beliefs), which are weighed against the results of the consequence evaluation (outcome evaluation). An individual will intend to use FinTech when they believe it is a positive action that provides benefits to users. Ryu [ 29 ] highlights the perceived benefits, referring to user evaluations of the positive results of using FinTech. The benefits received by FinTech users primarily concern non-monetary benefits. In a broad context, non-monetary benefits concern their participation in supporting sustainability performance [ 30 ]. Hiew et al. [ 31 ] used sustainable information society theory to explain the relationship between FinTech usage and environmental, social, and economic performance. They reported a correlation between FinTech usage and sustainability performance. FinTech supports the adoption of low-carbon production technologies, renewable energy use, and green financing, thereby enhancing sustainability performance [ 32 ]. Hence, we develop the hypothesis: H1: Ecological awareness positively influences intention to use FinTech. The TPB theory posits that a person's behavior is driven by their intention to act. Intention to use FinTech is the most reliable indicator of behavioral intention to use FinTech because it reveals the effort that individuals are willing to exert to consider specific actions [ 26 ] [ 33 ]. Intention to use FinTech refers to an individual's readiness to engage in a specific FinTech behavior. Al-Afeef et al. [ 23 ], [ 34 ] Ojiaku et al. (2024), and Fife-Schaw et al. [ 35 ] report that intention to use FinTech is vital in increasing the behavioral intention to use FinTech. H2: Intention to use FinTech has a positive impact on the behavioral use of FinTech Based on the logical framework and hypothesis, we developed the research model in Fig. 1 . Source: Author's own work 3. METHODS We distributed the research questionnaire online and sent it to respondents via WhatsApp. We provided an introduction to the research's target respondents, the purpose of the study, the rights and obligations of the respondents, a statement of guarantee from the researcher that the data obtained will be used only for this research, and a consent form for respondents. The self-administered questionnaire was voluntary after participants provided consent to participate in the study. This method was chosen because we were unable to access the list of all FinTech users in Indonesia. The data search was conducted for 4 months (April to August 2024) and obtained 319 respondents. The intention to use FinTech (hereafter INTENFIN) was measured using three indicators adopted from Venkatesh et al. [ 36 ]. The Behavioral to use FinTech (hereafter USEFIN) variable was measured using three indicators adopted from Amnas et al. [ 19 ]. The Ecological awareness variable (hereafter ECAW) is classified into three supporting components: environmental sustainability awareness (hereafter ENSP), social sustainability awareness (hereafter SOCSP), and economic sustainability awareness (hereafter ECSP) [ 16 ]. Following Sajan et al. [ 10 ] and Paulraj [ 37 ], we employ four ENSP indicators to assess awareness of the benefits of FinTech for environmental sustainability, focusing on business waste, energy, and material use. ECSP is measured by the economic sustainability awareness that FinTech use is beneficial for reducing material purchases, energy costs, and waste treatment [ 10 ] [ 37 ]. ECSP is measured by the individual awareness that using FinTech is beneficial because it has positive effects on community health and safety, environmental impact, occupational health and safety, awareness of claims and rights of people in the community served [ 10 ] [ 37 ]. All variables are measured using a Likert scale (1 = = strongly disagree, 2 = = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). Previous studies have utilized system acceptance theory (TAM or UTAUT) to explain FinTech usage behavior. Additionally, some researchers have emphasized the TPB as a means to explain FinTech usage behavior. To minimize the results due to omitted variables, we use perceived usefulness (PU) and perceived ease of use (PEOU) (as components of TAM) as control variables. Several studies report that TPB is a crucial factor in influencing FinTech usage behavior [ 24 ] [ 26 ]. We also use attitude (ATT), subjective norm (NS), and Perceived Behavioral Control (PBC) as control variables (all three are components of TPB). Our research model is presented in Fig. 1 . This study extends the TPB model by incorporating a belief factor that FinTech enhances sustainability performance as a determinant of individual behavior using FinTech. Additionally, we use TAM as a control variable to assess the impact of FinTech intensity on behavior. We use PLS-SEM for data analysis because it is an exploratory or an extension of existing structural theory [ 38 ]. We employed the PLS-SEM method, implemented in Warp-PLS, to analyze the primary data. 4. RESULTS AND DISCUSSION 4.1. Respondent Profile Table 1 presents the profile of the research respondents, including their gender and age, drawn from a research sample comprising 319 students from universities in Indonesia. In most of the research samples, 60.184% are female. Only 39.185% of respondents are male. Table 1 also reports the age distribution of respondents, indicating that the dominant age group is 18–20 years old (69.2%). However, some respondents are over 27 years old (4.075%). The varying ages of respondents affect their ability to use information technology. This finding strengthens our argument for using variables derived from the theory of respondent acceptance of the system as control variables. Table 1 Profile Respondent Gender Frequency Age Frequency Male 125 (39.18%) < 18 4 (1.254%) Women 194 (60.82%) 18–20 221 (69.279%) 21–23 47 (14.734%) 24–26 34 (10.658%) 27–28 13 (4.075%) Source: Author's own work Warp-PLS software presents several indicators for evaluating model fit and quality indices, and the standard score has been determined. First, indicators for model fit tests use the average path coefficient (APC), average R-squared (ARS), and average adjusted R-squared (AARS). The model yielded APC, ARS, and AARS scores of 0.165 (P < 0.001), 0.288 (P < 0.001), and 0.282 (P < 0.001), respectively. The P-values for APC, ARS, and AARS are < 0.05, indicating that the model meets the indicators of a goodness-of-fit model (Kock, 2019). Second, the average block VIF (AVIF) is a model-fit and quality index that can be used to assess multicollinearity in PLS models. The model test results reported AVIF and AFVIF scores of 1.910 and 1.879, respectively, indicating that our model is ideal [ 39 ]. Third, the goodness of fit (GoF) or Tenenhaus GoF is a global fitness measure. The test results produced a GoF score of 0.424. A GoF score of more than 0.36 indicates a high score and that the observed data are consistent with the assumed statistical model [ 40 ] [ 39 ]. Table 2 presents Composite reliability coefficients, Cronbach's alpha, and VIFs. The minimum loading factor score is 0.723 (NS_3), indicating that the score is acceptable. Loading factors describe the relationship between a factor and the observed variable. Table 2 also presents composite reliability coefficient measures of the internal consistency of a set of items and reports a Composite reliability coefficient of at least 0.776. This score indicates that the items consistently measure the same construct. The minimum Cronbach's alpha and AVE scores in Table 2 are 0.724 and 1.567, respectively. These acceptable scores indicate that the latent construct effectively explains the indicators' variance. Table 2 Descriptive Statistic Variables/ Construct Mean Std. Dev. Loading Factors Composite reliability coefficients Cronbach's alpha AVE VIFs USEFIN_1 4.14734 0.74832 0.897 0.841 0.740 0.724 USEFIN_2 3.08777 0.98342 0.883 USEFIN_3 2.279 1.17651 0.868 Total 3.17137 1.24671 INTENFIN_1 4.14107 0.45782 0.808 0.872 0.780 0.833 INTENFIN_2 4.14107 0.5626 0.859 INTENFIN_3 4.07524 0.55569 0.832 Total 4.11912 0.5279 ENSP_1 3.9279 0.80357 0.801 0.835 0.835 0.792 2.519 ENSP_2 4.0721 0.58099 0.826 ENSP_3 4.04702 0.65468 0.743 Total 4.01567 0.68819 ECSP_1 4.15361 0.62815 0.878 0.881 0.797 0.844 2.485 ECSP_2 3.99373 0.65873 0.774 ECSP_3 4.18182 0.6129 0.877 Total 4.10972 0.63828 SOCSP_1 3.90909 0.66438 0.789 0.871 0.777 0.832 2.193 SOCSP_2 3.73981 0.65733 0.874 SOCSP_3 4.06583 0.57631 0.831 Total 3.90491 0.64713 PEOU_1 4.36991 0.55613 0.806 0.841 0.715 0.799 1.986 PEOU_2 4.20063 0.49259 0.850 PEOU_3 3.94044 0.59312 0.737 Total 4.17032 0.57605 PU_1 4.50784 0.51313 0.870 0.781 0.781 0.835 1.899 PU_2 4.47962 0.53675 0.871 PU_3 4.37931 0.5749 0.760 Total 4.45559 0.54443 ATT_1 3.87147 0.64849 0.791 0.876 0.788 0.838 2.410 ATT_2 4.10031 0.51024 0.877 ATT_3 4.01881 0.56046 0.846 Total 3.99687 0.58305 NS_1 3.62069 0.84135 0.724 0.817 0.701 0.728 1.567 NS_2 3.34483 0.98106 0.743 NS_3 3.62696 0.79426 0.812 NS_4 3.90282 0.59871 0.723 Total 3.62382 0.83803 PBC_1 4.29467 0.52095 0.801 0.776 0.788 0.838 1.892 PBC_2 4.02508 0.48822 0.844 PBC_3 4.03135 0.56548 0.869 Total 4.11703 0.54012 Source: Author's own work Table 2 presents the descriptive statistics for all variables. The ecological awareness variable is measured in three dimensions: ENSP, ECSP, and SOCSP. Table 2 reports that the average ENSP score is 4.01567, with a standard deviation of 0.68819. The average scores of ECSP and SOCSP are 4.18182 and 3.90491, respectively. These findings indicate that respondents consider economic sustainability awareness the most crucial ecological awareness dimension for users. In addition, the average ENSP score is 4.04702, indicating that users perceive FinTech's impact on environmental sustainability awareness as higher than its impact on social sustainability awareness. Table 2 reports that the average INTENFIN score is 4.11912, with a standard deviation of 0.5279. Respondents consider the intensity of FinTech usage to be high. However, their actual use of FinTech produces an average value of 3.17137. This score indicates that their actual behavior of using FinTech is lower than its intensity. 4.2. Discussion Table 3 presents the results of the SEM test, providing empirical evidence to support the research objectives. Panel A presents the results of the SEM test of hypotheses 1 and 2. Panel B presents the results of the SEM test, dividing the ecological awareness indicators into three categories: environmental (ENSP), social (SOCSP), and economic sustainability awareness (ECSP). Table 2 (Panel A) reports that sustainability performance has a coefficient of 0.187 and a p-value < 0.001. We report that ecological awareness significantly impacts behavioral intention to use FinTech. The study supports the theory of ecological modernization, which posits that the decline in environmental quality resulting from economic progress can be mitigated by implementing the latest knowledge-based technologies [ 16 ]. Implementing new technology in financial services reduces energy consumption, pollution, and emissions while enhancing resource efficiency, supporting sustainable growth [ 27 ]. FinTech is a technological innovation that improves and automates various financial processes, services, and products [ 41 ]. FinTech promotes low-carbon production technology, renewable energy consumption, and green financing, thereby supporting sustainable performance [ 32 ]. Individuals using FinTech are seen as key agents of ecological sustainability and responsible investment. FinTech providers can utilize adequate organizational resources to achieve a competitive advantage and superior performance in environmentally friendly practices, thereby supporting ecological performance [ 42 ] [ 43 ] [ 14 ] [ 8 ]. The positive impact of ecological awareness enhances customer perceptions, which in turn influence their behavioral intention to use FinTech. Following Ryu [ 29 ], customers highlight the monetary and non-monetary benefits they perceive from using FinTech. In our study, non-monetary benefits relate to customer participation in supporting ecological awareness through FinTech [ 30 ] [ 31 ]. Based on testing each ecological awareness indicator, Table 3 (Panel B) reports that ENSP has a coefficient of 0.106 and a P-value of 0.027. The results of this test indicate that customer environmental sustainability awareness has a positive and significant effect on behavioral intention to use FinTech. This finding is consistent with the TPB, which holds that an individual's evaluation and attitude towards a behavior are determined by the belief that it has positive benefits. Adopting FinTech is a positive step because it can reduce energy consumption, pollution, and the resources required for traditional financial transactions. Following Wang et al. [ 44 ], the integration of FinTech has led to the emergence of sustainable financial instruments, such as green bonds, which are designed to support environmental conservation and renewable energy initiatives, thereby reducing environmental damage and promoting sustainable development. FinTech is a natural fusion of finance and technology, encompassing data analysis, risk assessment, and insurance, enabling individual financial institutions to respond to environmental threats, such as climate change, and adopt environmentally friendly practices [ 45 ]. In contrast, Table 3 (Panel B) reports that SOCSP has a coefficient of -0.005 and a P-value of 0.462. This finding suggests that social sustainability awareness does not significantly influence their intention to use FinTech. The impact of social sustainability awareness on FinTech adoption stems from FinTech's potential to promote socially responsible investments by enabling the use of capital [ 16 ]. Additionally, FinTech contributes to building a more just and equitable society [ 46 ]. However, demonstrating the impact of social sustainability awareness is difficult given the breadth of FinTech use. Social sustainability awareness is also a somewhat vague term that is often used to cover a wide range of things. From a societal perspective, corporate social performance is any positive action taken to improve the welfare of people around them. The broad context of social performance makes users' perceptions of FinTech's support for social sustainability awareness vague. Subsequently, this does not affect their behavioral intention to use FinTech. Table 3 (Panel B) also reports that ECSP has a coefficient of 0.111 and a P-value of 0.022. These results suggest that user perceptions of economic sustainability awareness are associated with an intention to use FinTech. FinTech is the digitalization of finance through new technology, generating income and creating new opportunities to improve the quality of financial services provided to customers. This innovation offers affordable banking services closer to consumers, making transactions easier and more efficient, thereby saving resources. Several works of literature have provided evidence that implementing FinTech has a positive impact on economic sustainability awareness, as it reduces transaction costs [ 14 ] [ 47 ]. These results support the TPB, which suggests that the belief that engaging in the behavior benefits oneself and one's environment enhances the customer's intention to engage in that behavior. Behavioral intention to use FinTech has a coefficient of 0.189 and a P-value < 0.001. Our study finds that behavioral intention to use FinTech has a positive and significant impact on actual FinTech usage. The results support the TPB, indicating that the intensity of certain behaviors positively affects the likelihood of engaging in them. Our results extend the study by Gopi and Ramayah [ 48 ] on the behavioral intention and actual usage of Internet stock trading. 5. CONCLUSION Previous literature linking behavioral use of FinTech and ecological awareness emphasizes the perspective of corporations as providers of FinTech services. This study provides empirical evidence of the relationship between ecological awareness and the intention to use FinTech. We also provide evidence of the influence on behavior in using FinTech. Based on a sample of 319 respondents, we report that ecological awareness increases pro-FinTech use. Testing each ecological awareness indicator, we report that both environmental and economic sustainability awareness increase the intensity of behavior in using FinTech. However, we have not found a relationship between social sustainability awareness and the intention to use FinTech. We also report that the intention to use FinTech positively affects its actual use. The results of this study are beneficial for users, FinTech service developers, and financial institutions in increasing the number of FinTech users. FinTech service developers are advised to continually innovate to develop FinTech solutions that can positively impact sustainability. Remarkably, the role of FinTech adoption in promoting social sustainability awareness needs to be continually refined. Financial institutions are advised to increase public awareness of the positive impact of the intention to use FinTech. This socialization is crucial for fostering a positive perception of FinTech among the public, which can subsequently lead to increased behavioral use of FinTech. Finally, we recommend that users increase their use of FinTech, as their behavior has a positive impact on both the users themselves and society, ultimately improving ecological awareness and sustainability performance. Our study uses a sample mainly of Generation Z, which tends to have established technological abilities. Other researchers can explore different types of samples to expand the literature. Additionally, we modify the TPB by presenting respondents with the belief that FinTech supports sustainability. Other researchers can modify existing theories, for example, by adapting the system acceptance theory to suit the specific research context. Declarations Acknowledgements: The authors gratefully acknowledge the Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi, Indonesia, and Universitas Negeri Semarang, Indonesia, for financial support. The authors also would like to thank anonymous reviewers for their incisive comments. Authors' contributions: HM: Conceptualization, Investigation, Supervision, Writing – original draft, and Writing – review & editing. FF: Formal analysis, Funding acquisition, and Writing – original draft. IA: Methodology, Project administration, and Resources. KWJ: Formal analysis, Software, and Visualization. HY: Formal analysis and Visualization. AN: Data curation, Project administration, and Resources. AMM: Project administration, and Resources. Fundings This work was funded during the research phase and for the publication of this article by Direktorat Riset, Teknologi, dan Pengabdian Kepada Masyarakat, Direktorat Jenderal Pendidikan Tinggi, Riset dan Teknologi, Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi based on contract number 070/E5/PG.02.00.PL/2024. Data availability: The questionnaires and datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethical approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) from Universitas Negeri Semarang, Indonesia, with Protocol code 1026/UN.14/PT.01.05/X/2024; date of approval 21 October 2024. Consent to participate Informed consent was obtained from all participants in this study. Respondents were provided with comprehensive information regarding the eligibility criteria, research objectives, procedures, and data use. Digital consent was obtained, as deemed appropriate by the ethics committee, based on the research context and data collection methods. The questionnaire was distributed online to increase participant response rates. Participants were assured of their identity and the confidentiality of their responses. Consent to publish Not applicable. Competing interests The authors declare no competing interests. References Joshi Y, Rahman Z. 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J Open Innov Technol Mark Complex. 2023;9(1):1–14. 10.1016/j.joitmc.2023.100010 . Amnas MB, Selvam M, Raja M, Santhoshkumar S, Parayitam S. Understanding the Determinants of FinTech Adoption: Integrating UTAUT2 with Trust Theoretic Model. J Risk Financ Manag. 2023;16(505):1–23. 10.3390/jrfm16120505 . Rahadian A, Thamrin H. Analysis of Factors Affecting MSME in Using Fintech Lending as Alternative Financing: Technology Acceptance Model Approach. Brazilian Bus Rev. 2023;20(3):301–22. 10.15728/bbr.2023.20.3.4.en . Shaikh IM, Amin H, Noordin K, Shaikh JM. Islamic Bank Customers' Adoption of Digital Banking Services: Extending Diffusion Theory of Innovation. J Islam Monet Econ Financ. 2023;9(1):57–70. 10.21098/jimf.v9i1.1545 . Albort-Morant G, Irimia-Diéguez A, Yasin M, Liebana-Cabanillas F. Social influence or religiosity? New factors determining the adoption of Paytech services in Islamic countries, Int. J. Islam. Middle East. Financ. Manag. , no. 2021, pp. 402–421, 2025. 10.1108/IMEFM-01-2024-0011 Al-Afeef MA, Alsmadi AA, Alrawashdeh N. The behavioral intention of fintech usage: applying theory of planned behavior in Jordan, in Digital technology and changing roles in managerial and financial accounting: theoretical knowledge and practical application studies in managerial and financial accounting , 2024, pp. 15–25. 10.1108/S1479-351220240000036002 Niswah FM, Mutmainah L, Legowati DA. Muslim Millennial's Intention of Donating for Charity Using Fintech Platform. J Islam Monet Econ Financ. 2019;5(3):623–44. https://doi.org/10.21098/jimf.v5i3.1080 . Razak SHA. Zakat and waqf as instrument of Islamic wealth in poverty alleviation and redistribution: Case of Malaysia. Int J Sociol Soc Policy. 2020;40:3–4. 10.1108/IJSSP-11-2018-0208 . Irimia-Diéguez A, Velicia-Martín F, Aguayo-Camacho M. Predicting Fintech Innovation Adoption: the Mediator Role of Social Norms and Attitudes. Financ Innov. 2023;9(36):1–23. 10.1186/s40854-022-00434-6 . Khan SAR, Yu Z, Sarwat S, Godil DI, Amin S, Shujaat S. The role of block chain technology in circular economy practices to improve organisational performance. Int J Logist Res Appl. 2022;25:4–5. 10.1080/13675567.2021.1872512 . Pataki G. Ecological modernization as a paradigm of corporate sustainability. Sustain Dev. 2009;17(2):82–91. 10.1002/sd.403 . Ryu HS. What makes users willing or hesitant to use Fintech? the moderating effect of user type. Ind Manag Data Syst. 2018;118(3):541–69. 10.1108/IMDS-07-2017-0325 . Dunbar K, Sarkis J, Treku DN. FinTech for environmental sustainability: Promises and pitfalls. One Earth. 2024;7(1):23–30. 10.1016/j.oneear.2023.12.012 . Hiew LC, Lam MT, Ho SJ. Unveiling the nexus: unravelling the dynamics of financial inclusion, FinTech adoption and societal sustainability in Malaysia, J. Financ. Report. Account. , vol. ahead-of-p, no. ahead-of-print, 2024, 10.1108/JFRA-12-2023-0791 Hasan M, Hoque A, Abedin MZ, Gasbarro D. FinTech and sustainable development: A systematic thematic analysis using human- and machine-generated processing, Int. Rev. Financ. Anal. , vol. 95, no. PC, p. 103473, 2024, 10.1016/j.irfa.2024.103473 Ajzen I. The Theory of Planned Behavior, Organ. Behav. Hum. Decis. Process. , vol. 50, no. 2, pp. 179–191, 1991, https://doi.org/10.1016/0749-5978(91)90020-T Ojiaku OC, Ezenwafor EC, Osarenkhoe A. Integrating TTF and UTAUT models to illuminate factors that influence consumers' intentions to adopt financial technologies in an emerging country context. Int J Technol Mark. 2024;18(1):113–35. 10.1504/ijtmkt.2024.135674 . Fife-Schaw C, Sheeran P, Norman P. Simulating behaviour change interventions based on the theory of planned behaviour: Impacts on intention and action. Br J Soc Psychol. 2007;46(1):43–68. 10.1348/014466605X85906 . Venkatesh V, Thong JYL, Xu X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology, MIS Q. , vol. 36, no. 1, pp. 157–178, 2012, [Online]. Available: https://www.jstor.org/stable/41410412 Paulraj A. Understanding the relationships between internal resources and capabilities, sustainable supply management and organizational sustainability. J Supply Chain Manag. 2011;47(1):19–37. 10.1111/j.1745-493X.2010.03212.x . Hair JF, Ringle CM, Sarstedt M, Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a Silver Bullet PLS-SEM: Indeed a Silver Bullet, J. Mark. Theory Pract. ISSN , vol. 19, no. 2, pp. 139–152, 2014, 10.2753/MTP1069-6679190202 Kock N. Factor-Based Structural Equation Modeling with Warppls. Australas Mark J. 2019;27(1):57–63. 10.1016/j.ausmj.2019.02.002 . Alotaibi RS, Alshahrani SM. An extended DeLone and McLean's model to determine the success factors of e-learning platform. PeerJ Comput Sci. 2022;8:1–26. 10.7717/peerj-cs.876 . Mertzanis C. FinTech finance and social-environmental performance around the world. Financ Res Lett. 2023;56:1–12. 10.1016/j.frl.2023.104107 . Zhou G, Zhu J, Luo S. The impact of fintech innovation on green growth in China: Mediating effect of green finance. Ecol Econ. 2022;193:107308. 10.1016/j.ecolecon.2021.107308 . Guang Z, Abu W, Siddik B. The effect of Fintech adoption on green finance and environmental performance of banking institutions during the COVID – 19 pandemic: the role of green innovation. Environ Sci Pollut Res. 2023;25959–71. 10.1007/s11356-022-23956-z . Wang S, Sun L, Iqbal S. Green financing role on renewable energy dependence and energy transition in E7 economies, Renew. Energy , vol. 200, no. July, pp. 1561–1572, 2022, 10.1016/j.renene.2022.10.067 Kashif M, Ullah A, Ullah S, Qian N. Towards a greener future: The impact of financial technology (FinTech) and climate finance on ecological sustainability. J Environ Manage. June, 2024;370. 10.1016/j.jenvman.2024.122876 . Deng X, Huang Z, Cheng X. FinTech and sustainable development: Evidence from China based on P2P data. Sustain. 2019;11(22). 10.3390/su11226434 . Konte M, Korku G. Mobile money, traditional financial services and firm productivity in Africa. Small Bus Econ. 2023;745–69. 10.1007/s11187-022-00613-w . Gopi M, Ramayah T. Applicability of theory of planned behavior in predicting intention to trade online: Some evidence from a developing country. Int J Emerg Mark. 2007;2(4):348–60. 10.1108/17468800710824509 . Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cp\u003eRecently, regulators worldwide have reported that economic activities contribute to environmental degradation, which in turn exacerbates climate change, in an effort to improve welfare. Human activities that rely on machines and increase consumption have depleted natural resources and exacerbated environmental damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This ecological damage is evidenced by global warming, increased environmental pollution, and the decline of flora and fauna. Increasing global warming further exacerbates the ecological system, leading to extreme weather conditions, including cyclones and droughts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, ecological damage can threaten the sustainability of human and corporate life.\u003c/p\u003e \u003cp\u003eGenerally, to mitigate environmental damage, governments and environmental organizations, such as the World Wide Fund for Nature and the Global Sustainability Standards Board (GSSB), encourage individuals and corporations to develop strategies and implement plans to ensure sustainable development. Likewise, the UN designed the Sustainable Development Goals (SDGs) as a global initiative agreed upon by member countries to end poverty, protect the planet, and ensure prosperity for all by 2030. Sustainable development is a development process that integrates environmental, social, and economic aspects into development strategies. Sustainable development initiatives focus on reducing pollutant emissions and waste, as well as minimizing the use of natural resources to preserve them for future generations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eJ. Zhou \u0026amp; Jin [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] stated that corporations play a vital role in improving environmental quality and promoting sustainable development. This argument underlies prior literature evaluating how companies contribute to sustainability. For example, Rahman, Zahid, and Al-Faryan (2023) assessed the performance of 255 non-financial companies registered in Pakistan and reported an ESG performance of 23.10. Yadav and Prashar [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and Veeravel et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that companies in India achieved ESG performance scores of 33.43 and 23.28, respectively. Li and Jia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] used companies in 26 countries with an average sustainability performance of 31.01. Meanwhile, Knight et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Ullah et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and Sajan et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] evaluated sustainability performance in small and medium enterprises (SMEs). Thus, previous studies evaluated corporate sustainability performance using various models they developed.\u003c/p\u003e \u003cp\u003eWe extend previous research by evaluating individual ecological awareness (e.g., perceptions of users and customers) in relation to pro-FinTech use, which remains limited. Joshi and Rahman [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] reported that consumer households are responsible for 40% of environmental damage. Individual concern for ecological awareness issues is manifested in the purchase of environmentally friendly products [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Prior research reports that individual consumers have a positive attitude toward environmental issues and a commitment to purchasing eco-friendly products [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Unfortunately, the limited number of green products on the market still leaves individuals with a limited role in global ecological awareness [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, companies use technology-based production and service processes as the primary agents of sustainability performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Technology can reduce energy consumption and carbon emissions and help reduce landfill waste. Moreover, Zhu et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] argued that adopting digital technologies streamlines green production techniques, thereby optimizing business operations and enhancing the efficiency of product design and manufacturing processes. The breadth of sustainable technology companies that can adopt it is the basis for our study, which focuses on consumer-facing financial technology services. The first contribution of our research is to extend the previous literature by demonstrating the influence of individual commitment, across environmental, social, and economic dimensions, on their behavior when using FinTech. Following Siddik et al [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], ecological awareness is based on environmental, social, and economic dimensions.\u003c/p\u003e \u003cp\u003eSecond, our study builds upon prior studies that employ two main theoretical approaches: system acceptance theory and behavioral psychology. For example, Srivastava et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Bajunaied et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and Amnas et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] used the Unified Theory of Acceptance and Use of Technology (UTAUT) model, Rahadian and Thamrin [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Shaikh et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] used the Technology Acceptance Model (TAM) in explaining the intensity of customers using the system. Meanwhile, Irimia-Di\u0026eacute;guez et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] employed behavioral psychology theories, specifically the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB), to explain the intensity of FinTech use. Niswah et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] combined the TAM and the theory of behavioral psychology, specifically TPB, to explain the behavior of using FinTech. Both theoretical approaches are used because FinTech is a financial service that uses an information system. To the best of our knowledge, studies on the influence of ecological awareness on the behavior of FinTech users, while controlling for the theory of system acceptance and psychological factors, remain limited.\u003c/p\u003e \u003cp\u003eThe results of our study are presented in this paper and divided into five crucial interrelated parts. The second part presents the theoretical background that underlies the logic of thinking and hypothesis development. The method section presents the population, sample, sampling technique, measurement indicators for variables, and data analysis methods. The fourth section presents the study's results. In this section, we present the model's feasibility analysis, the descriptive variables, the data test results, and the discussion. Finally, in the fifth section, we present a summary, practical recommendations, research limitations, and recommendations for further research.\u003c/p\u003e"},{"header":"2. THEORY AND HYPOTHESIS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Determinants of pro-FinTech Use Factors: Consumer Perception\u003c/h2\u003e \u003cp\u003eMany researchers have explored the intensity of customer use of FinTech. We conclude that they use two approaches. The first approach is the UTAUT. Srivastava et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] studied the intensity of users using digital payment FinTech among Generation Y and Z users in India. They developed a model combining TAM and UTAUT. They reported that the intensity of FinTech use is influenced by customer satisfaction, effort expectancy, and performance expectancy. Moreover, they noted that customer satisfaction positively affects the frequency of FinTech payment use.\u003c/p\u003e \u003cp\u003eBajunaied et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] used 361 FinTech users from Jeddah, Saudi Arabia, as research participants. They modified the UTAUT model as the primary research model. The results showed that performance expectancy, effort expectancy, facilitating conditions, and privacy enablers had a significant, positive impact on users' behavioral intentions toward FinTech services.\u003c/p\u003e \u003cp\u003eAmnas et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] integrated UTAUT2 by adding the trust variable. They reported that performance expectancy, effort expectancy, social influence, habit, price value, and supportive conditions significantly affect users' intention to use FinTech services. Their findings support UTAUT 2. Additionally, their study found that users' trust in FinTech influences their intention to use it.\u003c/p\u003e \u003cp\u003eRahadian and Thamrin [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Shaikh et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] developed TAM to explain customer intensity in using the system. However, they reported different findings. Rahadian and Thamrin (2023) reported that perceived ease of use influenced perceived usefulness but did not affect FinTech use. However, Shaikh et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported that perceived ease of use, perceived usefulness, and consumer innovation all influenced the intensity of users' FinTech use.\u003c/p\u003e \u003cp\u003eThe second approach is psychological theory. Irimia-Di\u0026eacute;guez et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] employed the TPB approach to explain the behavior of 300 FinTech users in southern Spain. They found that attitude, subjective norm, and perceived behavioral control positively influence the intensity of their FinTech use. They support the TRA and TPB theories to explain consumer behavior when using FinTech services. Al-Afeef et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] developed a model based on TRA and TPB to describe the intensity of FinTech use among 192 Jordanian citizens. They found that subjective norms, perceived behavior, perceived risk, and perceived trust are crucial in increasing customers' use of FinTech.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ecological Modernization Theory, Sustainability Performance, and Financial Technology\u003c/h2\u003e \u003cp\u003eTechnological advancements marked the Industrial Revolution, and the use of new energy sources significantly contributed to economic progress and welfare. During this period, factory growth, supported by advances in transportation and communication, led to greater industrial efficiency. However, the impact of this industrial activity increased pollution and environmental degradation, which, in turn, negatively affected sustainable growth. During this pre-industrial period, society sought to modernize and reorganize industrial society globally, within the geo and biosphere, by developing and implementing the latest knowledge-based technologies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This effort is reorganizing the industrial world to continue improving economic welfare while supporting the earth's existence and sustainable development.\u003c/p\u003e \u003cp\u003eEcological modernization theory (EMT) posits that environmental problems stemming from industrial activities can be mitigated through industrial modernization, which leverages modern knowledge-based technology to facilitate sustainable growth [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The implementation of technology can reduce resource use, pollution, and emissions, thereby positively impacting environmental sustainability. EMT emphasizes advancing knowledge and technology through discovery and innovation to implement cleaner technologies in industry [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This approach requires no decline in industrial productivity, but instead increased industrial effectiveness while maintaining ecosystem and environmental sustainability, and supporting sustainable performance in the long term. Thus, EMT supports integrating economic development and environmental quality, thereby promoting sustainability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Individual Ecological Awareness is a Factor of Pro FinTech Use Intensity\u003c/h2\u003e \u003cp\u003ePrevious studies that examine the relationship between pro-FinTech use and sustainability performance are based on the perspective of entities or business actors, specifically FinTech providers. In the TPB theory approach, the primary factor influencing individual behavior is attitude. Attitude is related to positive or negative beliefs about carrying out certain behaviors. Attitude is determined by an individual's beliefs about the consequences of performing a behavior (behavioral beliefs), which are weighed against the results of the consequence evaluation (outcome evaluation). An individual will intend to use FinTech when they believe it is a positive action that provides benefits to users.\u003c/p\u003e \u003cp\u003eRyu [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] highlights the perceived benefits, referring to user evaluations of the positive results of using FinTech. The benefits received by FinTech users primarily concern non-monetary benefits. In a broad context, non-monetary benefits concern their participation in supporting sustainability performance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hiew et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] used sustainable information society theory to explain the relationship between FinTech usage and environmental, social, and economic performance. They reported a correlation between FinTech usage and sustainability performance. FinTech supports the adoption of low-carbon production technologies, renewable energy use, and green financing, thereby enhancing sustainability performance [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Hence, we develop the hypothesis:\u003c/p\u003e \u003cp\u003eH1: Ecological awareness positively influences intention to use FinTech.\u003c/p\u003e \u003cp\u003eThe TPB theory posits that a person's behavior is driven by their intention to act. Intention to use FinTech is the most reliable indicator of behavioral intention to use FinTech because it reveals the effort that individuals are willing to exert to consider specific actions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Intention to use FinTech refers to an individual's readiness to engage in a specific FinTech behavior. Al-Afeef et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Ojiaku et al. (2024), and Fife-Schaw et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] report that intention to use FinTech is vital in increasing the behavioral intention to use FinTech.\u003c/p\u003e \u003cp\u003eH2: Intention to use FinTech has a positive impact on the behavioral use of FinTech\u003c/p\u003e \u003cp\u003eBased on the logical framework and hypothesis, we developed the research model in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Author's own work\u003c/p\u003e \u003c/div\u003e"},{"header":"3. METHODS","content":"\u003cp\u003eWe distributed the research questionnaire online and sent it to respondents via WhatsApp. We provided an introduction to the research's target respondents, the purpose of the study, the rights and obligations of the respondents, a statement of guarantee from the researcher that the data obtained will be used only for this research, and a consent form for respondents. The self-administered questionnaire was voluntary after participants provided consent to participate in the study. This method was chosen because we were unable to access the list of all FinTech users in Indonesia. The data search was conducted for 4 months (April to August 2024) and obtained 319 respondents.\u003c/p\u003e \u003cp\u003eThe intention to use FinTech (hereafter INTENFIN) was measured using three indicators adopted from Venkatesh et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The Behavioral to use FinTech (hereafter USEFIN) variable was measured using three indicators adopted from Amnas et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Ecological awareness variable (hereafter ECAW) is classified into three supporting components: environmental sustainability awareness (hereafter ENSP), social sustainability awareness (hereafter SOCSP), and economic sustainability awareness (hereafter ECSP) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Following Sajan et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and Paulraj [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], we employ four ENSP indicators to assess awareness of the benefits of FinTech for environmental sustainability, focusing on business waste, energy, and material use. ECSP is measured by the economic sustainability awareness that FinTech use is beneficial for reducing material purchases, energy costs, and waste treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. ECSP is measured by the individual awareness that using FinTech is beneficial because it has positive effects on community health and safety, environmental impact, occupational health and safety, awareness of claims and rights of people in the community served [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All variables are measured using a Likert scale (1\u0026thinsp;=\u0026thinsp;=\u0026thinsp;strongly disagree, 2\u0026thinsp;=\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;agree, 5\u0026thinsp;=\u0026thinsp;strongly agree).\u003c/p\u003e \u003cp\u003ePrevious studies have utilized system acceptance theory (TAM or UTAUT) to explain FinTech usage behavior. Additionally, some researchers have emphasized the TPB as a means to explain FinTech usage behavior. To minimize the results due to omitted variables, we use perceived usefulness (PU) and perceived ease of use (PEOU) (as components of TAM) as control variables. Several studies report that TPB is a crucial factor in influencing FinTech usage behavior [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We also use attitude (ATT), subjective norm (NS), and Perceived Behavioral Control (PBC) as control variables (all three are components of TPB). Our research model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis study extends the TPB model by incorporating a belief factor that FinTech enhances sustainability performance as a determinant of individual behavior using FinTech. Additionally, we use TAM as a control variable to assess the impact of FinTech intensity on behavior. We use PLS-SEM for data analysis because it is an exploratory or an extension of existing structural theory [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. We employed the PLS-SEM method, implemented in Warp-PLS, to analyze the primary data.\u003c/p\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Respondent Profile\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the profile of the research respondents, including their gender and age, drawn from a research sample comprising 319 students from universities in Indonesia. In most of the research samples, 60.184% are female. Only 39.185% of respondents are male. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also reports the age distribution of respondents, indicating that the dominant age group is 18\u0026ndash;20 years old (69.2%). However, some respondents are over 27 years old (4.075%). The varying ages of respondents affect their ability to use information technology. This finding strengthens our argument for using variables derived from the theory of respondent acceptance of the system as control variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProfile Respondent\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125 (39.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (1.254%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e194 (60.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221 (69.279%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (14.734%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (10.658%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (4.075%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author's own work\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWarp-PLS software presents several indicators for evaluating model fit and quality indices, and the standard score has been determined. First, indicators for model fit tests use the average path coefficient (APC), average R-squared (ARS), and average adjusted R-squared (AARS). The model yielded APC, ARS, and AARS scores of 0.165 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.288 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.282 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The P-values for APC, ARS, and AARS are \u0026lt;\u0026thinsp;0.05, indicating that the model meets the indicators of a goodness-of-fit model (Kock, 2019). Second, the average block VIF (AVIF) is a model-fit and quality index that can be used to assess multicollinearity in PLS models. The model test results reported AVIF and AFVIF scores of 1.910 and 1.879, respectively, indicating that our model is ideal [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Third, the goodness of fit (GoF) or Tenenhaus GoF is a global fitness measure. The test results produced a GoF score of 0.424. A GoF score of more than 0.36 indicates a high score and that the observed data are consistent with the assumed statistical model [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents Composite reliability coefficients, Cronbach's alpha, and VIFs. The minimum loading factor score is 0.723 (NS_3), indicating that the score is acceptable. Loading factors describe the relationship between a factor and the observed variable. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also presents composite reliability coefficient measures of the internal consistency of a set of items and reports a Composite reliability coefficient of at least 0.776. This score indicates that the items consistently measure the same construct. The minimum Cronbach's alpha and AVE scores in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are 0.724 and 1.567, respectively. These acceptable scores indicate that the latent construct effectively explains the indicators' variance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables/ Construct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoading Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComposite reliability coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVIFs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSEFIN_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSEFIN_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSEFIN_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINTENFIN_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINTENFIN_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINTENFIN_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.07524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.11912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSP_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSP_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.04702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECSP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.15361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECSP_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECSP_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.18182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOCSP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOCSP_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.73981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOCSP_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.06583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.94044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.17032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.47962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.37931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.45559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.87147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNS_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.62069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNS_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.34483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNS_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.62696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNS_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.62382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.29467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.03135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.11703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSource: Author's own work\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics for all variables. The ecological awareness variable is measured in three dimensions: ENSP, ECSP, and SOCSP. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports that the average ENSP score is 4.01567, with a standard deviation of 0.68819. The average scores of ECSP and SOCSP are 4.18182 and 3.90491, respectively. These findings indicate that respondents consider economic sustainability awareness the most crucial ecological awareness dimension for users. In addition, the average ENSP score is 4.04702, indicating that users perceive FinTech's impact on environmental sustainability awareness as higher than its impact on social sustainability awareness.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports that the average INTENFIN score is 4.11912, with a standard deviation of 0.5279. Respondents consider the intensity of FinTech usage to be high. However, their actual use of FinTech produces an average value of 3.17137. This score indicates that their actual behavior of using FinTech is lower than its intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Discussion\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the SEM test, providing empirical evidence to support the research objectives. Panel A presents the results of the SEM test of hypotheses 1 and 2. Panel B presents the results of the SEM test, dividing the ecological awareness indicators into three categories: environmental (ENSP), social (SOCSP), and economic sustainability awareness (ECSP). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Panel A) reports that sustainability performance has a coefficient of 0.187 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. We report that ecological awareness significantly impacts behavioral intention to use FinTech. The study supports the theory of ecological modernization, which posits that the decline in environmental quality resulting from economic progress can be mitigated by implementing the latest knowledge-based technologies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Implementing new technology in financial services reduces energy consumption, pollution, and emissions while enhancing resource efficiency, supporting sustainable growth [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e\u003cimg 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width=\"609\" height=\"445\"\u003e\u003c/p\u003e\u003cp\u003eFinTech is a technological innovation that improves and automates various financial processes, services, and products [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. FinTech promotes low-carbon production technology, renewable energy consumption, and green financing, thereby supporting sustainable performance [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Individuals using FinTech are seen as key agents of ecological sustainability and responsible investment. FinTech providers can utilize adequate organizational resources to achieve a competitive advantage and superior performance in environmentally friendly practices, thereby supporting ecological performance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The positive impact of ecological awareness enhances customer perceptions, which in turn influence their behavioral intention to use FinTech. Following Ryu [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], customers highlight the monetary and non-monetary benefits they perceive from using FinTech. In our study, non-monetary benefits relate to customer participation in supporting ecological awareness through FinTech [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on testing each ecological awareness indicator, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Panel B) reports that ENSP has a coefficient of 0.106 and a P-value of 0.027. The results of this test indicate that customer environmental sustainability awareness has a positive and significant effect on behavioral intention to use FinTech. This finding is consistent with the TPB, which holds that an individual's evaluation and attitude towards a behavior are determined by the belief that it has positive benefits. Adopting FinTech is a positive step because it can reduce energy consumption, pollution, and the resources required for traditional financial transactions. Following Wang et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the integration of FinTech has led to the emergence of sustainable financial instruments, such as green bonds, which are designed to support environmental conservation and renewable energy initiatives, thereby reducing environmental damage and promoting sustainable development. FinTech is a natural fusion of finance and technology, encompassing data analysis, risk assessment, and insurance, enabling individual financial institutions to respond to environmental threats, such as climate change, and adopt environmentally friendly practices [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Panel B) reports that SOCSP has a coefficient of -0.005 and a P-value of 0.462. This finding suggests that social sustainability awareness does not significantly influence their intention to use FinTech. The impact of social sustainability awareness on FinTech adoption stems from FinTech's potential to promote socially responsible investments by enabling the use of capital [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, FinTech contributes to building a more just and equitable society [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, demonstrating the impact of social sustainability awareness is difficult given the breadth of FinTech use. Social sustainability awareness is also a somewhat vague term that is often used to cover a wide range of things. From a societal perspective, corporate social performance is any positive action taken to improve the welfare of people around them. The broad context of social performance makes users' perceptions of FinTech's support for social sustainability awareness vague. Subsequently, this does not affect their behavioral intention to use FinTech.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Panel B) also reports that ECSP has a coefficient of 0.111 and a P-value of 0.022. These results suggest that user perceptions of economic sustainability awareness are associated with an intention to use FinTech. FinTech is the digitalization of finance through new technology, generating income and creating new opportunities to improve the quality of financial services provided to customers. This innovation offers affordable banking services closer to consumers, making transactions easier and more efficient, thereby saving resources. Several works of literature have provided evidence that implementing FinTech has a positive impact on economic sustainability awareness, as it reduces transaction costs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These results support the TPB, which suggests that the belief that engaging in the behavior benefits oneself and one's environment enhances the customer's intention to engage in that behavior.\u003c/p\u003e \u003cp\u003eBehavioral intention to use FinTech has a coefficient of 0.189 and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Our study finds that behavioral intention to use FinTech has a positive and significant impact on actual FinTech usage. The results support the TPB, indicating that the intensity of certain behaviors positively affects the likelihood of engaging in them. Our results extend the study by Gopi and Ramayah [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] on the behavioral intention and actual usage of Internet stock trading.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003ePrevious literature linking behavioral use of FinTech and ecological awareness emphasizes the perspective of corporations as providers of FinTech services. This study provides empirical evidence of the relationship between ecological awareness and the intention to use FinTech. We also provide evidence of the influence on behavior in using FinTech. Based on a sample of 319 respondents, we report that ecological awareness increases pro-FinTech use. Testing each ecological awareness indicator, we report that both environmental and economic sustainability awareness increase the intensity of behavior in using FinTech. However, we have not found a relationship between social sustainability awareness and the intention to use FinTech. We also report that the intention to use FinTech positively affects its actual use.\u003c/p\u003e \u003cp\u003eThe results of this study are beneficial for users, FinTech service developers, and financial institutions in increasing the number of FinTech users. FinTech service developers are advised to continually innovate to develop FinTech solutions that can positively impact sustainability. Remarkably, the role of FinTech adoption in promoting social sustainability awareness needs to be continually refined. Financial institutions are advised to increase public awareness of the positive impact of the intention to use FinTech. This socialization is crucial for fostering a positive perception of FinTech among the public, which can subsequently lead to increased behavioral use of FinTech. Finally, we recommend that users increase their use of FinTech, as their behavior has a positive impact on both the users themselves and society, ultimately improving ecological awareness and sustainability performance.\u003c/p\u003e \u003cp\u003eOur study uses a sample mainly of Generation Z, which tends to have established technological abilities. Other researchers can explore different types of samples to expand the literature. Additionally, we modify the TPB by presenting respondents with the belief that FinTech supports sustainability. Other researchers can modify existing theories, for example, by adapting the system acceptance theory to suit the specific research context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi, Indonesia, and Universitas Negeri Semarang, Indonesia, for financial support. The authors also would like to thank anonymous reviewers for their incisive comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHM: Conceptualization, Investigation, Supervision, Writing \u0026ndash; original draft, and Writing \u0026ndash; review \u0026amp; editing. FF: Formal analysis, Funding acquisition, and Writing \u0026ndash; original draft. IA: Methodology, Project administration, and Resources. KWJ: Formal analysis, Software, and Visualization. HY: Formal analysis and Visualization. AN: Data curation, Project administration, and Resources. AMM: Project administration, and Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded during the research phase and for the publication of this article by \u003cem\u003eDirektorat Riset, Teknologi, dan Pengabdian Kepada Masyarakat, Direktorat Jenderal Pendidikan Tinggi, Riset dan Teknologi, Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi\u003c/em\u003e based on contract number 070/E5/PG.02.00.PL/2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaires and datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) from Universitas Negeri Semarang, Indonesia, with Protocol code 1026/UN.14/PT.01.05/X/2024; date of approval 21 October 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants in this study. Respondents were provided with comprehensive information regarding the eligibility criteria, research objectives, procedures, and data use. Digital consent was obtained, as deemed appropriate by the ethics committee, based on the research context and data collection methods. The questionnaire was distributed online to increase participant response rates. Participants were assured of their identity and the confidentiality of their responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJoshi Y, Rahman Z. Factors Affecting Green Purchase Behaviour and Future Research Directions. Volume 3. 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Int J Emerg Mark. 2007;2(4):348\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/17468800710824509\u003c/span\u003e\u003cspan address=\"10.1108/17468800710824509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Environmental performance, Financial Technology, Theory of Technology Use Behavior, Ecological Modernization, Sustainability Performance","lastPublishedDoi":"10.21203/rs.3.rs-8974785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8974785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent literature linking pro-FinTech and sustainability performance emphasizes the perspective of corporations as FinTech service providers. This study provides empirical evidence of the relationship between perceptions of ecological awareness and pro-FinTech (the behavioral intention to use FinTech). The research data consisted of 319 students in Indonesia who were categorized as Generation Z. The data were analyzed using SEM-PLS. This study reports that Ecological awareness increases pro-FinTech. We separate ecological awareness indicators into economic, environmental, and social categories and find that both environmental and economic awareness have a positive impact on pro-FinTech attitudes. However, economic awareness does not influence pro-FinTech. This study is beneficial for users and financial institutions in increasing the number of FinTech users. FinTech service developers are advised to innovate to develop solutions that continuously improve sustainability. Financial institutions are advised to raise public awareness of FinTech's positive effects, as this is essential to fostering a positive perception that FinTech can improve sustainability.\u003c/p\u003e","manuscriptTitle":"Ecological Awareness and Pro-Fintech to Exploring Generation Z's Behavior in Using Fintech","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 12:27:45","doi":"10.21203/rs.3.rs-8974785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T14:30:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T15:19:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T06:16:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T03:07:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T13:38:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297074727469962079414886264360000211870","date":"2026-04-05T10:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T15:25:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152031630127757416898339734512442430642","date":"2026-04-04T14:59:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56045324837918226759648507126580527437","date":"2026-04-04T07:04:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109937958791286267556022765758630262759","date":"2026-04-03T12:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297625143693630769398319215476885487365","date":"2026-04-03T09:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150925415209192305815615020573844838575","date":"2026-04-03T01:14:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T17:38:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T11:25:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T14:27:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-20T14:15:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"222c53c3-1e5e-4ab3-bf62-befd97a99705","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T09:27:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 12:27:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8974785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8974785","identity":"rs-8974785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}