Role of Behavioral Intention in Implementation of Green Information Systems among Malaysians

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IT's role in climate change, resource management, waste production, and public health is critical yet often overlooked. Excessive energy use in IT operations contributes to negative environmental impacts. This research focuses on analyzing the behavioral determinants influencing information and communication technology (ICT) employees' participation in Green Information System (Green IS) practices in Malaysia. Additionally, it seeks to ascertain whether Malaysian ICT employees have a clear intention of incorporating Green IS in a bid to combat the aforementioned issues and guarantee the viability of a sustainable green environment. The study surveyed over 500 ICT employees from various Malaysian companies, with 183 responses analysed. Using PLS, the research examined four Technology Readiness Index (TRI) constructs—Optimism, Innovativeness, Insecurity, and Discomfort. Results indicated that Optimism and Innovativeness positively affected attitudes towards Green IS, while Insecurity had a negative impact. Discomfort showed no notable results. The findings revealed that subjective norms significantly influence Malaysian ICT employees more than perceived behavioral control. Attitude was a full mediator between TRI components and the willingness to implement Green IS. The study provides new insights into the TRI’s application in Green IS and highlights attitude's mediating role. Ultimately, the research suggests that attitude indirectly influences the genuine intention to adopt Green IS among Malaysian ICT employees. These findings are expected to promote the broader adoption of green technologies and raise public awareness about environmental sustainability. Green IS green environment behavioral intention attitude awareness Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Green Information Technology (Green IT) is implemented as a strategic measure to promote sustainable business practices by balancing an organization's economic and environmental performance (Abu Al-Rejal, et al., 2020 ). Green Information Systems (Green IS) extends this concept to organizational processes and products, aiming to enhance energy efficiency and reduce environmental impact (Gholami, et al., 2016 ). While IT transmits data, IS integrates software and processes to achieve goals (Rainer, et al., 2020 ). Green IT and Green IS are intertwined but distinct, with Green IT serving as the foundation for Green IS (Dedrick, 2010; Esfahani et al., 2015). Understanding the attitudes and intentions of ICT employees towards Green IS implementation is crucial, given its potential impact on environmental sustainability (Asadi, et al., 2021 ). Although Green IT/IS are recognized for their potential to reduce energy consumption and improve environmental sustainability, many organizations in Malaysia remain at an early stage of awareness and adoption. Several studies have primarily focused on organizational-level implementations of Green IT and Green IS, neglecting the crucial role of individual attitudes towards Green IS implementation. While organizational resources and strategic direction are vital, limited attention has been paid to how individual traits and attitudes influence the intention to adopt Green IS (Esfahani et al., 2015; Esfahani, et al., 2020 ; Gholami et al., 2013). Existing literature reveals a gap in empirical research on Green IS, particularly concerning the merging of the Technology Readiness Index (TRI), Theory of Planned Behavior (TPB), and Theory of Reasoned Action (TRA) in understanding employees' attitudes and intentions towards Green IS implementation (Buba, et al., 2022 ; Lembcke, et al., 2021 ). This study addresses this gap by proposing a combined framework of TRI, TPB, and TRA, aiming to explore the direct and indirect effects of individual traits and attitudes on the intention to implement Green IS among ICT employees in Malaysia. By examining the behavioral antecedents of ICT employees and their attitudes towards Green IS, this research aims to contribute to a better understanding of the factors influencing the adoption of sustainable practices in the ICT sector. LITERATURE REVIEW Technology Readiness Index (TRI) The TRI model, introduced by Parasuraman (2000), serves as a valuable framework to understand individuals' propensity to adopt new technologies. TRI consists of four dimensions. Optimism reflects a positive belief in technology's ability to enhance control and efficiency, while Innovativeness signifies a tendency to adopt new technologies early. Insecurity reflects distrust in technology's security and functionality, and Discomfort denotes a perception of insufficient control over technology. These dimensions categorize users into groups based on their readiness to adopt technology. Optimism and Innovativeness serve as motivators for technology adoption, while Insecurity and Discomfort act as inhibitors. The TRI model is essential for predicting users' behaviors and attitudes towards technology-based systems, especially in the context of Green IS implementation (Parasuraman, 2000). By examining these constructs, this study aims to determine the relationship between technology readiness and attitude towards Green IS, contributing to a better understanding of factors influencing the adoption of environmentally sustainable practices in the ICT sector (Liljander et al., 2006; Mouakket & Aboelmaged, 2021 ). The following hypotheses were proposed to investigate the extent to which TRI dimensions explain variance in users' attitudes towards Green IS implementation. Theory of Reasoned Action (TRA) The Theory of Reasoned Action (TRA), initially proposed by Fishbein (1967) and further developed by Fishbein and Ajzen (1975), predicts consumers' behavioral intention to use IT (Niehaves & Plattfaut, 2010). While TRA is widely recognized in various sectors, including IT and IS, limited empirical studies have focused on Green IT/IS adoption (Ali, et al., 2021 ; Jenkin et al., 2011). Mishra et al. (2014) highlights the need to explore the integration of Green IT and TRA in organizational contexts, emphasizing the importance of understanding users' readiness and awareness toward sustainable practices. Behavioral intention, influenced by attitude and subjective norms, plays a significant role in predicting actual behavior, particularly in IT adoption contexts (Khalifa & Shen, 2008; Ramayah et al., 2010). Past research indicates that attitudes and behavioral intentions positively influence the adoption of Green IT/IS, underscoring the importance of analyzing user attitudes and behaviors within the TRA framework (Chow & Chen, 2009; Shih & Fang, 2006). Thus, this study aims to examine the attitudes and behaviors leading to Green IS adoption using the TRA model. Theory of Planned Behavior (TPB) TPB acknowledges that behavioral intention is influenced by perceived control, as stated by Ajzen (1991) and supported by Kargin et al. (2009). TPB posits that individuals' intention to perform a behavior is shaped by attitudes, subjective norms, and perceived behavioral control, reflecting their perception of their ability to execute the behavior. The strength of behavioral beliefs is influenced by favorable or unfavorable attitudes, while subjective norms are shaped by perceptions of others' expectations. Perceived behavioral control encompasses past experiences and anticipated obstacles, affecting individuals' perceived ease or difficulty in performing a behavior (Ajzen, 1991). Moreover, research conducted in Hong Kong by Chow and Chen (2009) corroborates TPB's applicability, demonstrating that attitudes, subjective norms, and perceived behavioral control directly impact intention regarding green computing. Attitude emerges as a crucial contributor to intention, highlighting the importance of creating an environment conducive to promoting actual behavior towards green computing, with perceived behavior control being a fundamental factor influencing individuals' actions (Chow & Chen, 2009). Thus, the following hypotheses were proposed: Mediation This research focuses on TRA and TPB to explore how attitudes influence the intention to adopt Green IS technologies, noting that technologies often fail due to user non-acceptance (Liu, Liao & Pratt, 2009). TRA, one of the first theories discussing users’ attitude towards technology, posits that behavioral intention is predicted by attitudes and subjective norms, but its assumption of unconstrained action limits its applicability (Ajzen & Fishbein, 1980). This led to the development of TPB, which incorporates constraints to better predict decision behavior in organizational contexts. Since attitude plays a crucial role in shaping intentions, examining it is essential for understanding the factors that lead to the successful implementation of Green IS. Numerous studies have explored the indirect relationships between variables, with attitude often overlooked as a mediator, particularly in the realm of IT and Green IS. Attitude has been tested as a mediation variable in research due to its theoretical significance in explaining how variables like technology readiness influence behavioral intentions. Mediation models elucidate the intermediate role of a variable between an independent variable and an outcome (Sarstedt, et al., 2020 ). This is supported by studies such as those by Ashrafi and Easmin ( 2023 ) looked into the mediating role of attitude between users’ perceived value and behavioural intention. Past research by Porter and Donthu (2006) demonstrates the mediating role of attitude in Internet usage determinants, while Lanseng and Andreassen (2007) link technology readiness and attitude towards adopting self-diagnosis technology. Chow and Chen (2009) establish attitude as a crucial determinant of intention in green computing. Similarly, Brahim (2016) highlights attitude's mediating role between perceived advertising value and purchase intent. These findings underscore the importance of investigating attitude's mediating role between technology readiness and intention to implement Green IS among ICT employees, forming the basis for the hypothesis in this study: This research examines the impact of personality traits, attitudes, and intentions of ICT employees in Malaysian companies toward Green IS, using TRI, TPB, and TRA. TRI, widely recognized for explaining technology readiness, correlates positively with technology-related behaviors, emphasizing the desirability to use new technology (Lin et al., 2007; Parasuraman, 2000). TPB and TRA focus on personal and external gains, including subjective norms and perceived behavioral control, making them comprehensive for assessing attitudinal influences on behavior (Ong & Musa, 2011; Aguilar-Luzon et al., 2012). TRA's foundation in predicting behavior through attitudinal and normative beliefs complements TPB's inclusion of perceived behavioral control, providing a robust framework for understanding intentions (Ajzen, 1991). Integrating TRI with other models, such as the expectation-confirmation model, has shown positive associations with continued usage (Chen et al., 2013) and significant effects of optimism and innovativeness on attitudes and perceived behavioral control (Chen & Li, 2010). Studies by Chow and Chen (2009) and Choon et al. (2014) further affirm the direct impact of attitude, subjective norms, and perceived behavioral control on intentions towards Green IS. This study combines three constructs from TPB and four from TRI to predict ICT employees' intention towards Green IS, considering the unique environmental and moral dimensions of Green IT practices, which are better captured by TRI, TPB, and TRA than by models like TAM, DOI, or UTAUT (Minton & Rose, 1997; Davis, 1989). Figure 1 depicts the theoretical model of this study. METHODOLOGY This research follows a positivist paradigm, employing a quantitative methodology. The population of this study is ICT employees working in Malaysia Digital (MD) Status active companies based on the Malaysia Digital Economy Corporation (MDEC). For this study, only 10 companies were selected by generating random numbers in an Excel spreadsheet using the randbetween(x,y) function. According to Teddlie and Yu (2007), "probability samples aim to achieve representativeness, which is the degree to which the sample accurately represents the entire population." The company names on the list will match the numbers from x to y. Data collection is carried out through onsite-administered questionnaires featuring close-ended questions. Responses were measured using a 5-point Likert scale, a method recognized for its ease of use and clarity for respondents (Drumm, et al., 2022). Table 1 outlines the research design elements utilized in this study. Table 1: Research design elements. DATA ANALYSIS SmartPLS 4.0 was utilized in this study to develop and analyze the proposed model. The data analysis process in SmartPLS comprises of the measurement model evaluation and structural model evaluation (Hair et al., 2020; Urbach & Ahlemann, 2010). Measurement Model Evaluation Prior to investigating the relationships among the constructs, it is essential to evaluate the measurement model to confirm its reliability and validity (Cheung et al., 2023; Hair et al., 2020). Indicator reliability is evaluated by examining the factor loading values. According to Hair et al. (2011), a general rule is to consider factor loadings of 0.7 or higher acceptable. However, for exploratory research designs, Chin (2010) suggests that factor loadings between 0.5 and 0.6 are deemed satisfactory. In this research, the initial evaluation showed that several constructs did not achieve the recommended levels for average variance extracted (AVE), which should be above 0.5. To address this, items with low factor loadings were eliminated to improve both internal consistency reliability and convergent validity (Hair et al., 2021). Table 2 depicts the factor loadings that meet the recommended threshold, thereby confirming the reliability of the indicators. As for internal consistency reliability, it is deemed to be adequate when the composite reliability (CR) is valued above 0.7. Convergent validity, on the other hand, is deemed to be satisfactory when the AVE is valued above 0.5. Table 2 confirms that both internal consistency reliability and convergent validity are satisfactory. Figure 2 depicts the measurement model of the study. Table 2: Factor loadings of items, CR, and AVE values. To assess discriminant validity, the Fornell-Larcker criterion was applied. Discriminant validity is determined by examining correlations between overlapping measures to identify the degree to which items distinguish between constructs (Ramayah et al., 2018). Table 3 confirms that discriminant validity is adequate. Table 3: Fornell-Larcker criterion in assessing discriminant validity. Structural Model Evaluation Figure 3 depicts the structural model generated using SmartPLS 4 after performing a non-parametric bootstrapping with a sample size of 5000. Table 4 shows the path coefficients of the structural model. For the beta value to significantly influence the research model, it needed to be at least 0.1, while the t-statistic value had to exceed 1.645. Optimism, innovativeness, and insecurity showed significant relationships with attitude. However, discomfort did not show a significant relationship with attitude. The same goes to the relationship between PBC and intention. However, attitude and subjective norm both showed significant relationships with intention. Table 4: Path coefficients. The study also examined whether attitude mediated the relationship between the independent variables and intention to implement Green IS. The results of the mediation testing are presented in Table 5. The analysis suggests that attitude mediates the relationship between optimism, insecurity, and innovativeness and intention to implement Green IS. However, discomfort was not supported in mediation testing. Table 5: Mediation analysis. DISCUSSION The study findings indicate that among the four constructs of TRI, optimism and innovativeness are positively associated with attitude, while insecurity exhibits a significant negative relationship with attitude (Parasuraman, 2000; Walczuch et al., 2007). Discomfort did not yield significant results. These results highlight the significant contribution of personality to the attitudes of ICT users, influencing their readiness and intentions towards technology adoption. Additionally, personality characteristics measured in the TRI significantly impact attitudes, subsequently affecting intentions towards Green IS implementation (Parasuraman, 2000). Optimistic ICT users tend to view IT and IS positively and voluntarily engage with them, while innovative users are less critical and more comfortable with technology (Ye, et al., 2020 ). The lack of significant discomfort effects may be attributed to users' technical proficiency, while insecurity leads to a less favorable perception of IS (Parasuraman, 2000). Overall, these findings suggest that technology readiness influences ICT users' attitudes. The findings indicate that attitude plays a significant role in influencing ICT users in Malaysia towards the intention to implement Green IS. It underscores the crucial position of attitude in the decision-making process regarding Green IS among ICT users. This study emphasizes the importance of attitude in fostering intention towards technology, aligning with previous research by Sadaf et al. (2012) and Hamari & Koivisto (2013), which also highlighted the impact of attitude on technology adoption. Similarly, Mishra et al. (2014) found that workplace environment influences individuals' attitudes towards Green IT acceptance, while Teo and van Schaik (2012) concluded that attitude has a dominant effect on behavioral intentions. Positive attitudes towards Green IS among ICT users ultimately lead to an intention to implement Green IS (Choon, Sulaiman, & Mallasi, 2014). The study findings suggest that subjective norm has a stronger influence on ICT employees' intention towards Green IS in Malaysia compared to perceived behavioral control. This indicates that social influence from peers and the workplace environment plays a significant role in shaping intentions towards Green IS activities (Rochelau, 2013; Zhang, et al., 2020 ). Conversely, perceived behavioral control does not significantly affect intention, as ICT users typically feel they have sufficient control over Green IS practices (Rocheleau, 2013; Sadaf et al., 2013). The study found that ICT employees' attitude significantly mediates the relationship between optimism, innovativeness, and insecurity with their intention to adopt Green IS. However, discomfort did not show such a mediation effect, likely due to the high level of technological literacy among ICT employees, leading to a perception of technology resilience. This underscores the importance of attitude in influencing ICT users' intentions towards Green IS adoption, highlighting the need for proactive efforts to change perceptions and attitudes towards Green IS before widespread implementation. This aligns with previous research indicating that factors such as age, education, income, and race influence attitudes and beliefs about technology use (Brahim, 2016). CONCLUSION The study underscores how advancements in IT technologies and systems influence the implementation of Green IT/IS, crucial for sustainable business practices. By integrating personality traits from TRI, TRA, and TPB, the study reveals attitude as a significant mediating factor positively affecting the intention to implement Green IS among ICT professionals. Moreover, the findings highlight the importance of attitude and subjective norms in driving intention towards Green IS, contributing to a better understanding of technology's role in shaping a sustainable future and emphasizing the readiness of ICT employees in Malaysia to embrace Green IS initiatives. This study's focus on ICT companies in the Selangor region of Malaysia means its findings are based on surveys from this specific area. While the model used could be applied elsewhere, cultural differences and regional values may affect the generalizability of the results, which might also evolve as Green establishments become more prevalent. This research, which examines attitudes, behaviors, and readiness toward Green IT/IS, can be extended to other sectors and management levels to influence employee adoption and implementation of green practices. Future studies could also explore the role of organizational culture and environmental campaigns in promoting eco-sustainability, with potential replication in other regions to enhance national economic and sustainable development efforts. Declarations Author Contribution S.V. and S.G. wrote the main manuscript text, M.R wrote the Discussion and Conclusion, M.J. prepared all the figures and tables. All authors reviewed the manuscript. References Abu Al-Rejal H, Udin Z, Hassan M, Sharif K, Al-Rahmi W, Al-kumaim N. 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Technol Forecast Soc Chang. 2020;157:120066. https://doi.org/10.1016/j.techfore.2020.120066 . Zhang H, He J, Shi X, Hing Q, Bao J, Xue S. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability. 2020;12(7):2891. https://doi.org/10.3390/su12072891 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4916510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":354984093,"identity":"072864d3-f777-4e10-b0ea-6ac6715043d4","order_by":0,"name":"Sharmini Gopinathan","email":"data:image/png;base64,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","orcid":"","institution":"Taylor’s University","correspondingAuthor":true,"prefix":"","firstName":"Sharmini","middleName":"","lastName":"Gopinathan","suffix":""},{"id":354984094,"identity":"7fc5d26d-d31d-421c-991b-2f511f3a66d1","order_by":1,"name":"Segaran Veeraya","email":"","orcid":"","institution":"Asia Pacific University","correspondingAuthor":false,"prefix":"","firstName":"Segaran","middleName":"","lastName":"Veeraya","suffix":""},{"id":354984095,"identity":"e90e59e3-c7bd-45ce-ab99-39cba9ef9229","order_by":2,"name":"Murali Raman","email":"","orcid":"","institution":"Asia Pacific University","correspondingAuthor":false,"prefix":"","firstName":"Murali","middleName":"","lastName":"Raman","suffix":""},{"id":354984096,"identity":"e3d406c4-9e2f-4dda-b4f4-0fb6509bba6c","order_by":3,"name":"Manimekalai Jambulingam","email":"","orcid":"","institution":"Taylor’s University","correspondingAuthor":false,"prefix":"","firstName":"Manimekalai","middleName":"","lastName":"Jambulingam","suffix":""}],"badges":[],"createdAt":"2024-08-15 02:01:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4916510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4916510/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-00873-y","type":"published","date":"2025-02-25T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64919919,"identity":"49e3f14f-a53c-4bc9-8edb-9a2aec7e1d02","added_by":"auto","created_at":"2024-09-20 11:30:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68920,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4916510/v1/ae338b3a3d1956184a9a3f77.png"},{"id":64919917,"identity":"8776549d-bf38-4428-9c5e-e6f7df201a7f","added_by":"auto","created_at":"2024-09-20 11:30:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132129,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4916510/v1/779968c45fb58d958578322f.png"},{"id":64920240,"identity":"e2424484-b7e2-4d81-a4fc-3c091390f3ba","added_by":"auto","created_at":"2024-09-20 11:38:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109319,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4916510/v1/d49a99488d46cc3b0f26322d.png"},{"id":77622539,"identity":"d024c83c-c7fd-4d54-a73d-41ab672fc5df","added_by":"auto","created_at":"2025-03-03 16:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":790760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4916510/v1/25d6c32c-eadc-411a-8375-8d50dd4b7078.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of Behavioral Intention in Implementation of Green Information Systems among Malaysians","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGreen Information Technology (Green IT) is implemented as a strategic measure to promote sustainable business practices by balancing an organization's economic and environmental performance (Abu Al-Rejal, et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Green Information Systems (Green IS) extends this concept to organizational processes and products, aiming to enhance energy efficiency and reduce environmental impact (Gholami, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While IT transmits data, IS integrates software and processes to achieve goals (Rainer, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Green IT and Green IS are intertwined but distinct, with Green IT serving as the foundation for Green IS (Dedrick, 2010; Esfahani et al., 2015). Understanding the attitudes and intentions of ICT employees towards Green IS implementation is crucial, given its potential impact on environmental sustainability (Asadi, et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough Green IT/IS are recognized for their potential to reduce energy consumption and improve environmental sustainability, many organizations in Malaysia remain at an early stage of awareness and adoption. Several studies have primarily focused on organizational-level implementations of Green IT and Green IS, neglecting the crucial role of individual attitudes towards Green IS implementation. While organizational resources and strategic direction are vital, limited attention has been paid to how individual traits and attitudes influence the intention to adopt Green IS (Esfahani et al., 2015; Esfahani, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gholami et al., 2013). Existing literature reveals a gap in empirical research on Green IS, particularly concerning the merging of the Technology Readiness Index (TRI), Theory of Planned Behavior (TPB), and Theory of Reasoned Action (TRA) in understanding employees' attitudes and intentions towards Green IS implementation (Buba, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lembcke, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study addresses this gap by proposing a combined framework of TRI, TPB, and TRA, aiming to explore the direct and indirect effects of individual traits and attitudes on the intention to implement Green IS among ICT employees in Malaysia. By examining the behavioral antecedents of ICT employees and their attitudes towards Green IS, this research aims to contribute to a better understanding of the factors influencing the adoption of sustainable practices in the ICT sector.\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eTechnology Readiness Index (TRI)\u003c/h2\u003e\n \u003cp\u003eThe TRI model, introduced by Parasuraman (2000), serves as a valuable framework to understand individuals\u0026apos; propensity to adopt new technologies. TRI consists of four dimensions. Optimism reflects a positive belief in technology\u0026apos;s ability to enhance control and efficiency, while Innovativeness signifies a tendency to adopt new technologies early. Insecurity reflects distrust in technology\u0026apos;s security and functionality, and Discomfort denotes a perception of insufficient control over technology. These dimensions categorize users into groups based on their readiness to adopt technology. Optimism and Innovativeness serve as motivators for technology adoption, while Insecurity and Discomfort act as inhibitors. The TRI model is essential for predicting users\u0026apos; behaviors and attitudes towards technology-based systems, especially in the context of Green IS implementation (Parasuraman, 2000). By examining these constructs, this study aims to determine the relationship between technology readiness and attitude towards Green IS, contributing to a better understanding of factors influencing the adoption of environmentally sustainable practices in the ICT sector (Liljander et al., 2006; Mouakket \u0026amp; Aboelmaged, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The following hypotheses were proposed to investigate the extent to which TRI dimensions explain variance in users\u0026apos; attitudes towards Green IS implementation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eTheory of Reasoned Action (TRA)\u003c/h2\u003e\n \u003cp\u003eThe Theory of Reasoned Action (TRA), initially proposed by Fishbein (1967) and further developed by Fishbein and Ajzen (1975), predicts consumers\u0026apos; behavioral intention to use IT (Niehaves \u0026amp; Plattfaut, 2010). While TRA is widely recognized in various sectors, including IT and IS, limited empirical studies have focused on Green IT/IS adoption (Ali, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jenkin et al., 2011). Mishra et al. (2014) highlights the need to explore the integration of Green IT and TRA in organizational contexts, emphasizing the importance of understanding users\u0026apos; readiness and awareness toward sustainable practices. Behavioral intention, influenced by attitude and subjective norms, plays a significant role in predicting actual behavior, particularly in IT adoption contexts (Khalifa \u0026amp; Shen, 2008; Ramayah et al., 2010). Past research indicates that attitudes and behavioral intentions positively influence the adoption of Green IT/IS, underscoring the importance of analyzing user attitudes and behaviors within the TRA framework (Chow \u0026amp; Chen, 2009; Shih \u0026amp; Fang, 2006). Thus, this study aims to examine the attitudes and behaviors leading to Green IS adoption using the TRA model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eTheory of Planned Behavior (TPB)\u003c/h2\u003e\n \u003cp\u003eTPB acknowledges that behavioral intention is influenced by perceived control, as stated by Ajzen (1991) and supported by Kargin et al. (2009). TPB posits that individuals\u0026apos; intention to perform a behavior is shaped by attitudes, subjective norms, and perceived behavioral control, reflecting their perception of their ability to execute the behavior. The strength of behavioral beliefs is influenced by favorable or unfavorable attitudes, while subjective norms are shaped by perceptions of others\u0026apos; expectations. Perceived behavioral control encompasses past experiences and anticipated obstacles, affecting individuals\u0026apos; perceived ease or difficulty in performing a behavior (Ajzen, 1991). Moreover, research conducted in Hong Kong by Chow and Chen (2009) corroborates TPB\u0026apos;s applicability, demonstrating that attitudes, subjective norms, and perceived behavioral control directly impact intention regarding green computing. Attitude emerges as a crucial contributor to intention, highlighting the importance of creating an environment conducive to promoting actual behavior towards green computing, with perceived behavior control being a fundamental factor influencing individuals\u0026apos; actions (Chow \u0026amp; Chen, 2009). Thus, the following hypotheses were proposed:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"636\" height=\"116\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eMediation\u003c/h2\u003e\n \u003cp\u003eThis research focuses on TRA and TPB to explore how attitudes influence the intention to adopt Green IS technologies, noting that technologies often fail due to user non-acceptance (Liu, Liao \u0026amp; Pratt, 2009). TRA, one of the first theories discussing users\u0026rsquo; attitude towards technology, posits that behavioral intention is predicted by attitudes and subjective norms, but its assumption of unconstrained action limits its applicability (Ajzen \u0026amp; Fishbein, 1980). This led to the development of TPB, which incorporates constraints to better predict decision behavior in organizational contexts. Since attitude plays a crucial role in shaping intentions, examining it is essential for understanding the factors that lead to the successful implementation of Green IS.\u003c/p\u003e\n \u003cp\u003eNumerous studies have explored the indirect relationships between variables, with attitude often overlooked as a mediator, particularly in the realm of IT and Green IS. Attitude has been tested as a mediation variable in research due to its theoretical significance in explaining how variables like technology readiness influence behavioral intentions. Mediation models elucidate the intermediate role of a variable between an independent variable and an outcome (Sarstedt, et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is supported by studies such as those by Ashrafi and Easmin (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) looked into the mediating role of attitude between users\u0026rsquo; perceived value and behavioural intention. Past research by Porter and Donthu (2006) demonstrates the mediating role of attitude in Internet usage determinants, while Lanseng and Andreassen (2007) link technology readiness and attitude towards adopting self-diagnosis technology. Chow and Chen (2009) establish attitude as a crucial determinant of intention in green computing. Similarly, Brahim (2016) highlights attitude\u0026apos;s mediating role between perceived advertising value and purchase intent. These findings underscore the importance of investigating attitude\u0026apos;s mediating role between technology readiness and intention to implement Green IS among ICT employees, forming the basis for the hypothesis in this study:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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height=\"236\" width=\"612\"\u003e\u003c/p\u003e\n \u003cp\u003eThis research examines the impact of personality traits, attitudes, and intentions of ICT employees in Malaysian companies toward Green IS, using TRI, TPB, and TRA. TRI, widely recognized for explaining technology readiness, correlates positively with technology-related behaviors, emphasizing the desirability to use new technology (Lin et al., 2007; Parasuraman, 2000). TPB and TRA focus on personal and external gains, including subjective norms and perceived behavioral control, making them comprehensive for assessing attitudinal influences on behavior (Ong \u0026amp; Musa, 2011; Aguilar-Luzon et al., 2012). TRA\u0026apos;s foundation in predicting behavior through attitudinal and normative beliefs complements TPB\u0026apos;s inclusion of perceived behavioral control, providing a robust framework for understanding intentions (Ajzen, 1991). Integrating TRI with other models, such as the expectation-confirmation model, has shown positive associations with continued usage (Chen et al., 2013) and significant effects of optimism and innovativeness on attitudes and perceived behavioral control (Chen \u0026amp; Li, 2010). Studies by Chow and Chen (2009) and Choon et al. (2014) further affirm the direct impact of attitude, subjective norms, and perceived behavioral control on intentions towards Green IS. This study combines three constructs from TPB and four from TRI to predict ICT employees\u0026apos; intention towards Green IS, considering the unique environmental and moral dimensions of Green IT practices, which are better captured by TRI, TPB, and TRA than by models like TAM, DOI, or UTAUT (Minton \u0026amp; Rose, 1997; Davis, 1989). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the theoretical model of this study.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eThis research follows a positivist paradigm, employing a quantitative methodology. The population of this study is ICT employees working in Malaysia Digital (MD) Status active companies based on the Malaysia Digital Economy Corporation (MDEC). For this study, only 10 companies were selected by generating random numbers in an Excel spreadsheet using the \u003cem\u003erandbetween(x,y)\u003c/em\u003e function. According to Teddlie and Yu (2007), \u0026quot;probability samples aim to achieve representativeness, which is the degree to which the sample accurately represents the entire population.\u0026quot; The company names on the list will match the numbers from x to y. Data collection is carried out through onsite-administered questionnaires featuring close-ended questions. Responses were measured using a 5-point Likert scale, a method recognized for its ease of use and clarity for respondents (Drumm, et al., 2022). Table 1 outlines the research design elements utilized in this study.\u003c/p\u003e\n\u003cp\u003eTable 1: Research design elements.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"553\" 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\" alt=\"image\" height=\"357\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA ANALYSIS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmartPLS 4.0 was utilized in this study to develop and analyze the proposed model. The data analysis process in SmartPLS comprises of the measurement model evaluation and structural model evaluation (Hair et al., 2020; Urbach \u0026amp; Ahlemann, 2010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Model Evaluation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to investigating the relationships among the constructs, it is essential to evaluate the measurement model to confirm its reliability and validity (Cheung et al., 2023; Hair et al., 2020). Indicator reliability is evaluated by examining the factor loading values. According to Hair et al. (2011), a general rule is to consider factor loadings of 0.7 or higher acceptable. However, for exploratory research designs, Chin (2010) suggests that factor loadings between 0.5 and 0.6 are deemed satisfactory. In this research, the initial evaluation showed that several constructs did not achieve the recommended levels for average variance extracted (AVE), which should be above 0.5. To address this, items with low factor loadings were eliminated to improve both internal consistency reliability and convergent validity (Hair et al., 2021). Table 2 depicts the factor loadings that meet the recommended threshold, thereby confirming the reliability of the indicators. As for internal consistency reliability, it is deemed to be adequate when the composite reliability (CR) is valued above 0.7. Convergent validity, on the other hand, is deemed to be satisfactory when the AVE is valued above 0.5. Table 2 confirms that both internal consistency reliability and convergent validity are satisfactory. Figure 2 depicts the measurement model of the study.\u003c/p\u003e\n\u003cp\u003eTable 2: Factor loadings of items, CR, and AVE values.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"591\" 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\" alt=\"image\" height=\"305\"\u003e\u003c/p\u003e\n\u003cp\u003eTo assess discriminant validity, the Fornell-Larcker criterion was applied. Discriminant validity is determined by examining correlations between overlapping measures to identify the degree to which items distinguish between constructs (Ramayah et al., 2018). Table 3 confirms that discriminant validity is adequate.\u003c/p\u003e\n\u003cp\u003eTable 3: Fornell-Larcker criterion in assessing discriminant validity.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"420\" 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\" alt=\"image\" height=\"152\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 depicts the structural model generated using SmartPLS 4 after performing a non-parametric bootstrapping with a sample size of 5000.\u003c/p\u003e\n\u003cp\u003eTable 4 shows the path coefficients of the structural model. For the beta value to significantly influence the research model, it needed to be at least 0.1, while the t-statistic value had to exceed 1.645. Optimism, innovativeness, and insecurity showed significant relationships with attitude. However, discomfort did not show a significant relationship with attitude. The same goes to the relationship between PBC and intention. However, attitude and subjective norm both showed significant relationships with intention.\u003c/p\u003e\n\u003cp\u003eTable 4: Path coefficients.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"538\" 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\" alt=\"image\" height=\"148\"\u003e\u003c/p\u003e\n\u003cp\u003eThe study also examined whether attitude mediated the relationship between the independent variables and intention to implement Green IS. The results of the mediation testing are presented in Table 5. The analysis suggests that attitude mediates the relationship between optimism, insecurity, and innovativeness and intention to implement Green IS. However, discomfort was not supported in mediation testing.\u003c/p\u003e\n\u003cp\u003eTable 5: Mediation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"618\" height=\"224\"\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study findings indicate that among the four constructs of TRI, optimism and innovativeness are positively associated with attitude, while insecurity exhibits a significant negative relationship with attitude (Parasuraman, 2000; Walczuch et al., 2007). Discomfort did not yield significant results. These results highlight the significant contribution of personality to the attitudes of ICT users, influencing their readiness and intentions towards technology adoption. Additionally, personality characteristics measured in the TRI significantly impact attitudes, subsequently affecting intentions towards Green IS implementation (Parasuraman, 2000). Optimistic ICT users tend to view IT and IS positively and voluntarily engage with them, while innovative users are less critical and more comfortable with technology (Ye, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lack of significant discomfort effects may be attributed to users' technical proficiency, while insecurity leads to a less favorable perception of IS (Parasuraman, 2000). Overall, these findings suggest that technology readiness influences ICT users' attitudes.\u003c/p\u003e \u003cp\u003eThe findings indicate that attitude plays a significant role in influencing ICT users in Malaysia towards the intention to implement Green IS. It underscores the crucial position of attitude in the decision-making process regarding Green IS among ICT users. This study emphasizes the importance of attitude in fostering intention towards technology, aligning with previous research by Sadaf et al. (2012) and Hamari \u0026amp; Koivisto (2013), which also highlighted the impact of attitude on technology adoption. Similarly, Mishra et al. (2014) found that workplace environment influences individuals' attitudes towards Green IT acceptance, while Teo and van Schaik (2012) concluded that attitude has a dominant effect on behavioral intentions. Positive attitudes towards Green IS among ICT users ultimately lead to an intention to implement Green IS (Choon, Sulaiman, \u0026amp; Mallasi, 2014).\u003c/p\u003e \u003cp\u003eThe study findings suggest that subjective norm has a stronger influence on ICT employees' intention towards Green IS in Malaysia compared to perceived behavioral control. This indicates that social influence from peers and the workplace environment plays a significant role in shaping intentions towards Green IS activities (Rochelau, 2013; Zhang, et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, perceived behavioral control does not significantly affect intention, as ICT users typically feel they have sufficient control over Green IS practices (Rocheleau, 2013; Sadaf et al., 2013).\u003c/p\u003e \u003cp\u003eThe study found that ICT employees' attitude significantly mediates the relationship between optimism, innovativeness, and insecurity with their intention to adopt Green IS. However, discomfort did not show such a mediation effect, likely due to the high level of technological literacy among ICT employees, leading to a perception of technology resilience. This underscores the importance of attitude in influencing ICT users' intentions towards Green IS adoption, highlighting the need for proactive efforts to change perceptions and attitudes towards Green IS before widespread implementation. This aligns with previous research indicating that factors such as age, education, income, and race influence attitudes and beliefs about technology use (Brahim, 2016).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe study underscores how advancements in IT technologies and systems influence the implementation of Green IT/IS, crucial for sustainable business practices. By integrating personality traits from TRI, TRA, and TPB, the study reveals attitude as a significant mediating factor positively affecting the intention to implement Green IS among ICT professionals. Moreover, the findings highlight the importance of attitude and subjective norms in driving intention towards Green IS, contributing to a better understanding of technology's role in shaping a sustainable future and emphasizing the readiness of ICT employees in Malaysia to embrace Green IS initiatives. This study's focus on ICT companies in the Selangor region of Malaysia means its findings are based on surveys from this specific area. While the model used could be applied elsewhere, cultural differences and regional values may affect the generalizability of the results, which might also evolve as Green establishments become more prevalent. This research, which examines attitudes, behaviors, and readiness toward Green IT/IS, can be extended to other sectors and management levels to influence employee adoption and implementation of green practices. Future studies could also explore the role of organizational culture and environmental campaigns in promoting eco-sustainability, with potential replication in other regions to enhance national economic and sustainable development efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.V. and S.G. wrote the main manuscript text, M.R wrote the Discussion and Conclusion, M.J. prepared all the figures and tables. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu Al-Rejal H, Udin Z, Hassan M, Sharif K, Al-Rahmi W, Al-kumaim N. Green Information Technology Adoption Antecedence: A Conceptual Framework. In: Saeed F, Mohammed F, Gazem N, editors. Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing. Volume 1073. 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Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability. 2020;12(7):2891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su12072891\u003c/span\u003e\u003cspan address=\"10.3390/su12072891\" 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":true,"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":"Green IS, green environment, behavioral intention, attitude, awareness","lastPublishedDoi":"10.21203/rs.3.rs-4916510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4916510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSignificant advancements in Information Technology (IT) have improved work processes, efficiency, and business operations, but these advancements bring environmental sustainability issues. IT's role in climate change, resource management, waste production, and public health is critical yet often overlooked. Excessive energy use in IT operations contributes to negative environmental impacts. This research focuses on analyzing the behavioral determinants influencing information and communication technology (ICT) employees' participation in Green Information System (Green IS) practices in Malaysia. Additionally, it seeks to ascertain whether Malaysian ICT employees have a clear intention of incorporating Green IS in a bid to combat the aforementioned issues and guarantee the viability of a sustainable green environment. The study surveyed over 500 ICT employees from various Malaysian companies, with 183 responses analysed. Using PLS, the research examined four Technology Readiness Index (TRI) constructs\u0026mdash;Optimism, Innovativeness, Insecurity, and Discomfort. Results indicated that Optimism and Innovativeness positively affected attitudes towards Green IS, while Insecurity had a negative impact. Discomfort showed no notable results. The findings revealed that subjective norms significantly influence Malaysian ICT employees more than perceived behavioral control. Attitude was a full mediator between TRI components and the willingness to implement Green IS. The study provides new insights into the TRI\u0026rsquo;s application in Green IS and highlights attitude's mediating role. Ultimately, the research suggests that attitude indirectly influences the genuine intention to adopt Green IS among Malaysian ICT employees. These findings are expected to promote the broader adoption of green technologies and raise public awareness about environmental sustainability.\u003c/p\u003e","manuscriptTitle":"Role of Behavioral Intention in Implementation of Green Information Systems among Malaysians","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 11:30:09","doi":"10.21203/rs.3.rs-4916510/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-17T04:30:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T12:01:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T09:40:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T19:59:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198516304247751149694281040323160994089","date":"2024-09-06T14:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301335648102109537037679292393576970644","date":"2024-09-03T14:37:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59326019695719288521241816701375475482","date":"2024-09-02T10:54:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-01T14:18:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T11:11:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T11:10:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2024-08-15T01:59:47+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":"f818f338-7f5f-4b4d-903d-252224773cef","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:02:35+00:00","versionOfRecord":{"articleIdentity":"rs-4916510","link":"https://doi.org/10.1007/s43621-025-00873-y","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-02-25 15:58:01","publishedOnDateReadable":"February 25th, 2025"},"versionCreatedAt":"2024-09-20 11:30:09","video":"","vorDoi":"10.1007/s43621-025-00873-y","vorDoiUrl":"https://doi.org/10.1007/s43621-025-00873-y","workflowStages":[]},"version":"v1","identity":"rs-4916510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4916510","identity":"rs-4916510","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}