Students’ Green Information Technology Behavior: Beliefs and Personality Traits

Journal Pre-proof Students’ Green Information Technology Behavior: Beliefs and Personality Traits

Mohammad Dalvi-Esfahani, Zohre Alaedini, Mehrbakhsh Nilashi, Sarminah Samad, Shahla Asadi, Majid Mohammadi PII:

S0959-6526(20)30453-4

DOI:

https://doi.org/10.1016/j.jclepro.2020.120406

Reference:

JCLP 120406

To appear in:

Journal of Cleaner Production

Received Date:

31 May 2019

Accepted Date:

02 February 2020

Please cite this article as: Mohammad Dalvi-Esfahani, Zohre Alaedini, Mehrbakhsh Nilashi, Sarminah Samad, Shahla Asadi, Majid Mohammadi, Students’ Green Information Technology Behavior: Beliefs and Personality Traits, Journal of Cleaner Production (2020), https://doi.org/10. 1016/j.jclepro.2020.120406

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

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Students’ Green Information Technology Behavior: Beliefs and Personality Traits Mohammad Dalvi-Esfahani1,*, Zohre Alaedini2, Mehrbakhsh Nilashi3,*, Sarminah Samad4, Shahla Asadi5, Majid Mohammadi6 1 Mohammad

Dalvi-Esfahani (Corresponding author) Higher Education Institute of Al-Mahdi Mehr Isfahan, Isfahan, Iran E-mail: [email protected]

2 Zohre

Alaedini Higher Education Institute of Al-Mahdi Mehr Isfahan, Isfahan, Iran E-mail: [email protected]

3 Mehrbakhsh

Nilashi (Corresponding author) 1- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam 2- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam E-mail: [email protected]

4 Sarminah

Samad Department of Business Administration, Collage of Business and Administration, Princess Nourah bint Abdulrahman University, Saudi Arabia E-mail: [email protected]

5

Shahla Asadi Department of Software Engineering & Information System, Faculty of Computer Science & Information Technology, University Putra Malaysia, Selangor, 43400, Malaysia E-mail: [email protected]

6 Majid

Mohammadi Ragheb Isfahani Institute of Higher Education, Isfahan, Iran E-mail: [email protected]

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Students’ Green Information Technology Behavior: Beliefs and Personality Traits Abstract Adoption of green information technology (Green IT) as an initiative of pro-environmental behavior is quite sparse among Malaysian students. Due to the need to integrate personality traits in environmental behavioral studies, the current study deriving from the planned behavior theory attempts to investigate the influence of attitudinal factors on students’ pro-ecological behavioral intention to practice Green IT. Extremely few studies explored the moderating role of personality traits in the context of Green IT adoption and thus this study attempts to fill the current research gap. The personality traits of openness, agreeableness and conscientiousness were included in the research model as moderating variables. A number of 262 pieces of data were collected from students. Based on the partial least squares approach and bootstrapping method, the results revealed that except social norms, other variables greatly influenced the intention to practice Green IT. Moreover, the moderating effects analysis showed that the personality trait of conscientiousness was the only trait that significantly moderated the relationships in the proposed model. Keywords – Green IT; Theory of planned behavior; Openness; Conscientiousness; Agreeableness 1

Introduction

Green information technology (Green IT) has gained global traction, with a wide range of stakeholders having become environmentally conscious. Significant planning efforts have been made in limiting energy consumption, carbon footprints, and curbing wastage to further protect the environment through various green ideas, initiatives and policies. A wealth of online articles and academic studies are dedicated toward discussing such issues and providing green solutions. Green-related issues range from personal efforts (e.g., two-sided printing with small size fonts) and printing only when required to community-based actions (e.g., offsetting carbon footprints by tree planting), green policies (e.g., green buildings; Omer, 2008), virtualization, cloud computing, and renewable energies (e.g., Hardin-Ramanan, Chang, & Issa, 2018; Lee & Cheng, 2018; Alem Molla & Cooper, 2014). Such ideas and actions reflect the need for attaining a sustained healthy environment via green practices. Among the most crucial sustainable practices is the application of Green IT due to the wide use of IT in various business sectors to enhance the performance of businesses and their outcomes. Higher education institutes also use information and communications technology (ICT) to improve their business processes, deliver their services and further enhance their managerial procedures (da Silva Junior et al., 2019). The increased use of IT in businesses led to a concern in economic and environmental perspectives related to energy consumption issues (Przychodzen, Gómez-Bezares, & Przychodzen, 2018b). Since then, the term Green IT (green computing) has been introduced. Green IT is defined as the process of manufacturing, using and disposing all related IT artifacts with consideration of eco-friendly issues. The definition focuses on both increasing energy efficiency and enhancing the sustainability of the environment by reducing the pollution emitted from IT artifacts. Green practices of individuals rely on internal beliefs and values regarding the sustainability of the environment (Steg, Perlaviciute, van der Werff, & Lurvink, 2012; Stern, Kalof, Dietz, & Guagnano, 1995). Hence, the effectiveness of Green IT practice by individuals heavily depends on understanding the intention on pro-environmental and the behavior in using IT resources more sustainably. However, the majority of previous studies focuses adoption of Green IT linked to organizations’ perspectives (e.g., Ainin, Naqshbandi, Dezdar, & Quantity, 2016; Dalvi-Esfahani, Ramayah, & Nilashi, 2017; Hintemann & Clausen, 2016; Linehan & Fisher, 2018; Thomas, Costa, & Oliveira, 2016), and few studies investigated individuals’ intention and behavior and associated factors in the adoption of Green IT (Dalvi-Esfahani, Shahbazi, & Nilashi, 2018; Dezdar, 2017). One of the concerns for environmentalists is to understand the perceptions of students toward global climate change and how they react in the face of environmental sustainability (Chuvieco, Burgui-Burgui, Da Silva, Hussein, & Alkaabi, 2018; Dagiliūtė, Liobikien ė , & Minelgait ė , 2018a, 2018b; Stough, Ceulemans, Lambrechts, & Cappuyns, 2018; Yu & Yu, 2017). Wachholz, Artz, and Chene (2014) reported that while students’ environmental concerns are appreciated, they are still unaware the root causes of environmental degradation and its consequences. Despite the favorable attitude toward environmental sustainability among the students, they may not be aware of the behavioral components that are influential in shaping their attitudes and result in sustainable outcomes (Whitley, Takahashi, Zwickle, Besley, & Lertpratchya, 2018), which reflects the existing gap between the current knowledge, eco-friendly behaviors and attitudes. Although education remains an integral aspect in encouraging

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environmental literacy, factors that stretch beyond knowledge and demography determine the engagement with sustainability behaviors (Ignell, Davies, & Lundholm, 2018; Lambrechts et al., 2018). From the green IT context, T. Ahmad, Badariah, Bello, and Nordin (2013) explored the green computing awareness of Malaysian students and found that the majority of them were not aware of the term itself, factors associated with it, and its outcomes. In psychology, several theories explain the predictors of proenvironmental behaviors. Norm Activation Model (NAM; Schwartz, 1977) and the Value-Belief-Norm model (VBN; Stern, Dietz, Abel, Guagnano, & Kalof, 1999) are the two most established environmental theories of studies, and stated that the prevalence of environmental behavior is directly impacted by personal norms. Such norms, according to Schwartz and Howard (1981), ascertain the need to act in a certain way as a result of the feeling of moral compulsion. However, the theory of planned behavior (TPB; Ajzen, 1985) and reasoned action theory (TRA), as its precursor, garner widespread support and utilization in the context of environmental studies. Although it has been widely used to assess behavior, the TPB model is often criticized for disregarding moral elements (Manstead, 2000). Moral norms are typically determined by belief of an individual to carry out particular behavior, which are also related to their moral rectitude. In addition to perceived social pressure, it is also important to consider emotions of moral commitment in agreeing to or refusing to perform specific behaviors (M.-F. Chen, 2016). In fact, several studies have shown that the prediction of intentions to behave morally is significantly enhanced by moral obligation (Dalvi-Esfahani, Rahman, & Ramayah, 2017; De Groot & Steg, 2009, 2010). 1.1 Problem statement and research contribution Students comprise a large portion of university stakeholders, who also show significant willingness to support sustainable developments within campus and beyond (da Silva Junior et al., 2019; Dagiliūtė et al., 2018b). Green IT as one of the initiatives of sustainable practices within universities is less explored in developing countries (Alexander, 2019). Similarly, only few studies are conducted on Green IT adoption behavior among students in Malaysia (e.g., T. Ahmad et al., 2013; T. B. T. Ahmad & Nordin, 2014; Choon, Sulaiman, & Mallasi, 2014; Nejati & Nejati, 2013; Samuri, 2014). Furthermore, the level of Green IT awareness is still unknown among Malaysian university students. Previous studies partially investigated relationships between pro-environmental attitudinal factors, such as values, beliefs and norms, and the practice of Green IT and green computing among students. Scarce studies integrating TPB and personality traits were found on the adoption of Green IT in the Malaysian context. Moreover, most of the studies in the context of Green IT adoption were conducted from the perspective of organizations, and little is known about students’ beliefs and behaviors toward practicing green initiatives. Therefore, this paper explored the role of personality traits as the moderator, linking Green IT attitude, subjective norm, perceived behavioural control (PBC) and personal norm with the intention to practice Green IT among students in Malaysia. Understanding students’ perceptions toward the environment and how they respond to environmental issues through Green IT adoption is critically important. It is also essential for students to become familiarized with determinants that motivate younger generations to behave more pro-environmentally (Jurdi-Hage, Hage, & Chow, 2019). Moreover, the relationship between personality and behavior has been highlighted in various domains in the literature (Brick & Lewis, 2016). Additionally, the influence of personality traits that determines pro-environmental behaviors, and in particular Green IT practices, is infrequently studied. Thus, this study proposed the following research questions: What is the influence of extended TPB on students’ intention to practice Green IT? What is the moderating effect of personality traits on the intention to practice Green IT? Developing a pro-environmental behavioral model of Green IT adoption could promote sustainable behavior strategies and further change perceptions of how IT can enhance environmental sustainability. A pro-environmental behavioral model of Green IT adoption among students can promote understanding of the impact of attitudinal factors, such as attitudes, green concerns, values and norms, and further green behavioral intentions. Specifically, this study aimed to investigate the pro-environmental behavior of students toward the intention to practice Green IT by considering their personality traits as moderating factors. The model and results of the study can be used to improve sustainable developments and educational settings, which are necessary for taking effective actions toward environmental sustainability. Furthermore, the results of the study are important for practicing future green innovations within universities of developing countries, since such places are considered as great platforms for implementing green strategies.

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2 2.1

Theoretical Background Green IT, universities and students

The practice of maintaining a pollution-free environment from human activities, e.g. efficient usage of computer or household resources without environmental consequences, is referred to as green computing (Lamb, 2009). It simply means utilizing school and household resources responsibly with regards to the environment, including sourcing energy-efficient central processing units (CPUs), servers and peripherals, limiting use of resources, and safe and ethical recycling of electronic waste (ewaste). Green IT ultimately will address the economic as well as environmental implications of human endeavors, with emphasis on aspects of energy waste prevention, cost-cutting measures, carbon emission reduction, mitigating pollution due to poor e-waste management, and curbing environmental impacts of dangerous computer devices (Linehan & Fisher, 2018; Przychodzen, GómezBezares, & Przychodzen, 2018a). These converge to encourage sensible, energy-efficient, and environmentally safe practices. The daily routine of the largest ICT users, who are university and campus populations, are significantly influenced by green IT practices. Various universities across the northern hemisphere have devised awareness initiatives among students, from as small as green plans and sustainability campaigns, to as grand as constructing carbon-neutral buildings to reduce the use of energy. For example, the Copenhagen University (UCPH) in Denmark successfully constructed an energy-efficient building to serve its students. The building is completely carbon-free and powered by solar energy. The UCPH also supports a green plan, ‘Green Action’, which nurtures green ambassadors to promote environmentally sustainable behaviors among students and staff. The Universiti Teknologi Malaysia (UTM) in Malaysia also promotes Green IT behaviors by establishing policies, such as providing guidelines on e-waste management, providing special baskets to collect e-waste, and also encouraging postgraduate students to conduct research related to Green IT. Despite abundance research in green IT (e.g., Bohas & Poussing, 2016; Hardin-Ramanan et al., 2018; Junior, 2019; Linehan & Fisher, 2018), less focus was given among Malaysian students who play important role in the reduction of CO2 Hence, it is important to evaluate students’ attitudes toward green IT and establish green IT awareness, which includes the involvement of universities to adhere to green compliant practices. 2.2

Prior studies

In the literature, there exist various studies investigating Green IT adoption. However, for the purpose of the current research, studies conducted at individual levels were considered for further explorations. Gholami, Sulaiman, Ramayah, and Molla (2013) in their study investigated Green information system (Green IS) adoption using senior managers of organizations through the lens of the belief-action-outcome (BAO) framework (Melville, 2010). They found that attitude and consideration of future consequences greatly described the adoption behavior. In another study, Dezdar (2017) extended TPB using the personality traits of openness and consideration of future consequences to further explore students’ Green IT adoption behavior. The author reported that students’ intention was greatly explained by TPB and personality traits. Mishra, Akman, and Mishra (2014) investigated the adoption of Green IT with the help of IT professionals through the lens of the theory of reasoned action (TRA). They found that the intention to practice Green IT was greatly demonstrated by one’s related beliefs, attitude, and level of awareness. Finally, Alemayehu Molla, Abareshi, and Cooper (2014) investigated pro-environmental IT practice using IT professionals by developing a research model based on the BAO framework. They found that IT professionals’ pro-environmental behavior was greatly reflected through their Green IT attitude, which, in turn, was significantly influenced by their Green IT beliefs. From the reviewed literature, it can be concluded that although several studies investigated individuals’ Green IT adoption behavior, less studies focused on the context of universities as well as beliefs and behaviors of students toward practicing Green IT. Moreover, no studies considered personality traits as moderating variables. 2.3

Theory of Planned Behavior

TPB posits that the actual behavior of an individual is greatly explained by their intention to perform a specific action where their positive attitude, influence of important others, and control over the behavior (i.e., perceived behavioral control) impact the intention (Ajzen, 1991). According to the theories of TRA and TPB, intention is the immediate predictor of actual behavior. In the technology adoption literature, it is asserted that investigating users’ willingness (i.e., intention) to perform a behavior has a better explanatory power than merely studying the actual behavior (Armitage & Conner, 2001; Dalvi-Esfahani, Rahman, et al., 2017; Dalvi-Esfahani, Shahbazi, et al., 2018; Dalvi-Esfahani, Wai Leong, Ibrahim, & Nilashi, 2018). Hence, for the purpose of this study, students’ intention to practice Green IT was considered as the final dependent construct, reflecting the pre-adoption

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behavior. Attitudinal behaviors are linked to positive and negative feelings of performing the behavior. In this regard, subjective norm refers to individuals’ belief in performing a behavior under social pressure. Moreover, PBC is about the perceived ease and difficulty of an individual in performing a certain behavior. Thus, strong behavioral intentions are attained when: (1) a behavior is assessed favorably, (2) one perceives they should comply with extensive social pressure, and (3) there is a sense of PBC. TPB has been widely linked with several domains to predict individuals’ behaviors, including technology adoption, politics, environmental psychology, eco-innovation adoption, and Green IT adoption (Chu & Chen, 2016; Claudy, Peterson, & O’Driscoll, 2012; Mishra et al., 2014; Paul, Modi, & Patel, 2016; Wauters, Bielders, Poesen, Govers, & Mathijs, 2010). Attitude is related to the overall favorable/unfavorable feelings of a person on a given subject (Eagly & Chaiken, 1998). Green IT attitude describes people’s norms and values toward the sustainability of the environment and the role played by IT (Alemayehu Molla & Abareshi, 2011), which include awareness and concern levels regarding the influence of IT on ecosustainability. A distinction needs to be made between human-induced climate change believers and doubters when analyzing attitude as an antecedent to pro-environment behavior. Accordingly, the attitude of those believers and doubters may be beneficial and disadvantageous, respectively, for pro-environmental behaviors and practices (Hasan & Dwyer, 2010). Steg and Sievers (2000) studied the acceptance of green policies aimed to limit the CO2 footprint and contended that the resulting distinction between various behavioral elements could be due to the attitude toward the planning and execution of such policies. In another study it was found that pro-environmental attitudes influenced positively pro-environmental behavior such as utilization of a park-and-ride amenity (De Groot & Steg, 2007), green electricity brochures used by university students (S. J. J. o. e. p. Bamberg, 2003), Green IT adoption (Alemayehu Molla, Cooper, Deng, & Lukaitis, 2009), and Green IS adoption (Gholami et al., 2013). Thus this study proposes the following hypotheses: Hypothesis 1: Green IT attitude positively influences students’ intention to practice Green IT. Hypothesis 2: Subjective norm positively influences students’ intention to practice Green IT. Hypothesis 3: PCB positively influences students’ intention to practice Green IT. 2.4

Personal norm

Although TPB has been widely used to assess individual behaviors in a variety of areas, it has been criticized for disregarding moral considerations (M.-F. Chen, 2016). Personal norm is defined as “moral obligation to perform or refrain from specific actions” (Schwartz & Howard, 1981, p. 191). Moral norms are typically determined by an individual’s belief to carry out particular behavior, which are also related to their moral rectitude. In addition to perceived social pressure, considering the moral obligation or responsibility of individual to agree or refuse performing specific behaviors is important (Chan & Bishop, 2013; M.-F. Chen, 2016; Kana 2011). In fact, several studies have shown that the prediction of intentions to behave morally are significantly enhanced by moral obligation (e.g., Dalvi-Esfahani, Rahman, et al., 2017; van der Werff, Steg, & Keizer, 2013). The importance of personal norm and its influence of one’s pro-ecological behavior has been vastly investigated in the related literature. For example, Nordlund, Jansson, and Westin (2018) reported that individuals’ moral obligation plays an important role in explaining their behavior toward the acceptance of electrical vehicles. A study on Green IS by Dalvi-Esfahani, Rahman, et al. (2017) reported that Green IS adoption was greatly explained by managers’ moral obligation (i.e., personal norm). Accordingly, by considering ethical issues and motives managers would be morally responsible to behave pro-ecologically and further adopt Green IS in their organizations to move toward sustaining the environment. Hence, for the current study, it can be hypothesized that students’ intention toward the practice of Green IT could be explained by their personal norms, then we posit: Hypothesis 4: Students’ personal norms positively influence their intention to practice Green IT. 2.5

Personality Traits and pro-environmental behavior

Personality has been increasingly used as an explanatory instrument within the information system (IS) literature (Ainin et al., 2016). IS studies confirm that personality traits influence the behavior of individuals depending on their predicament, e.g. enabling IT habits of other people to be understood (Devaraj, Easley, & Crant, 2008), as well as revealing and predicting users’ IT behavior (Li et al., 2006). Traits have predictive validity through the relationship with behaviors (Block & Block, 2006; Caspi, Roberts, & Shiner, 2005). Core personality traits are predictors of environmentalism because they are cross-culturally reliable (Robert R McCrae & Paul T Costa Jr, 1985). Currently, typical personality domains are based on the Big Five model of openness to experience, conscientiousness, extraversion, agreeableness and neuroticism (Kvasova, 2015).

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Agreeableness is the desire for social compatibility and cooperating with others (McCrae & Costa Jr, 1997). It is related to empathy (Ezra M. Markowitz, Lewis R. Goldberg, Michael C. Ashton, & Kibeom Lee, 2012), which supports pro-environmental behaviors (Schultz et al., 2005). Individuals without agreeableness are more selfish and less concerned about what others are feeling (Jacob B Hirsh, 2010). Agreeable people are more caring, modest, empathetic, trusting, easy-going, gentle, and compassionate (Wuertz, 2014); thus, they are more likely to have pro-social (Gerber et al., 2011) and pro-environmental behaviors (Gholami et al., 2013; Ezra M. Markowitz et al., 2012; Milfont & Sibley, 2012; Wuertz, 2014). Goldstein, Cialdini, and Griskevicius (2008) studied towel reuse behavior in hotels and indicated that highly agreeable people were influenced by social norms. Some studies demonstrated the association between environmentalism and agreeableness (Hilbig, Zettler, Leist, & Heydasch, 2013; Ezra M Markowitz, Lewis R Goldberg, Michael C Ashton, & Kibeom Lee, 2012). Almost all of these studies suggest that higher agreeableness leads to more environmentalism (Jacob B Hirsh, 2010; Jacob B. Hirsh & Dolderman, 2007; Milfont & Sibley, 2012). Openness to experience is the capability of abstract thinking and tending to unusual experiences (McCrae & Costa Jr, 1997). It is related to productive cognitive flexibility (DeYoung, Peterson, & Higgins, 2005), and potentially coping a wide perspective on understanding the aesthetic aspects of nature's beauty and human position on a broader ecology. People with low openness have a limited and more conservative approach to nature (Jacob B Hirsh, 2010). Environmentalism has shown links to openness moderately (Brick & Lewis, 2016; Hilbig, Zettler, Moshagen, & Heydasch, 2013; Jacob B. Hirsh & Dolderman, 2007; E. Markowitz, M, L. Goldberg, R, M. Ashton, C, & K. Lee, 2012). Pavalache-Ilie and Cazan (2018) studied the personality correlates of pro-environmental attitudes and illustrated that openness would predict environmental behavior. Conscientiousness is illustrated by respect for duty, responsibility, self-discipline, and self-efficacy (Major, Turner, & Fletcher, 2006; McCrae & Costa Jr, 1997; Roccas, Sagiv, Schwartz, & Knafo, 2002). Conscientiousness leads to high impulse control, ability to postpone gratification, and following norms, which help to plan tasks and achieve goals (Gerber et al., 2011). Studies have shown that this trait is associated with social investment (Lodi-Smith & Roberts, 2007). Conscientious people carefully follow social rules and norms for environmental guidelines, whereas individuals with low conscientiousness pay less attention to environmental responsibilities. Some studies demonstrated the association between pro-environmental behavior and conscientiousness (Brick & Lewis, 2016; Hilbig, Zettler, Leist, et al., 2013; Jacob B Hirsh, 2010; E. Markowitz, M et al., 2012; Milfont & Sibley, 2012). Brick and Lewis (2016) showed that all facets of conscientiousness (i.e., diligence, organization, prudence, and perfectionism) could significantly predict emissions-reducing behavior. In addition, Nisbet, Zelenski, and Murphy (2009) showed a significant relationship between desire to nature with conscientiousness, agreeableness, and openness. Jacob B Hirsh (2010) observed that individuals with greater concerns toward the environment score more highly on openness (i.e., various experiences appreciation), agreeableness (i.e., kind, friendly, sympathetic, and tactful), and conscientiousness (i.e., being careful or diligent). Openness, Agreeableness, and Conscientiousness have been found to be substantially linked to commitment toward the environment individually and nationally (Milfont & Sibley, 2012). A study in Britain obtained a positive correlation between agreeableness and conscientiousness and attitudes toward recycling (Swami, Chamorro-Premuzic, Snelgar, & Furnham, 2011). Personality traits can play a positive role in enhancing the impact of attitudinal factors on individual’s proenvironmental behaviors (Yu & Yu, 2017). Accordingly, it can be concluded that the personality traits of Openness, Agreeableness, and Conscientiousness moderate the variables that influence students’ intention to adopt Green IT. Hence this study posits hypotheses that: Hypothesis 5: Openness positively moderates (a) the attitude-intention relationship; (b) the subjective norm-intention relationship; (c) PBCintention relationship; and (d) personal norm-intention relationship. That is, the relationships are stronger with higher level of Openness. Hypothesis 6: Agreeableness positively moderates (a) the attitude-intention relationship; (b) the subjective norm-intention relationship; (c) PBCintention relationship; and (d) personal norm-intention relationship. That is, the relationships are stronger with higher level of Agreeableness. Hypothesis 7: Conscientiousness positively moderates (a) the attitude-intention relationship; (b) the subjective norm-intention relationship; (c) PBC-intention relationship; and (d) personal norm-intention relationship. That is, the relationships are stronger with higher level of Conscientiousness.

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Figure 1: Research model

2.6

Research model

The following three points have been taken into consideration in developing the research model based on TPB: First, instead of measuring the actual behavior of students the current study explored intention, as the immediate influential factor of actual behavior. The stronger one’s intention to perform a specific behavior, the more likely she performs the actual behavior. The strong positive correlation between intention and behavior has been reported by the scholars in the literature (Venkatesh & Davis, 2000; Venkatesh, Morris, & Ackerman, 2000). In the context of the current study few students might have experienced Green IT practically which would lead to incorrect inferences. Accordingly, it was decided to replace the actual behavior with the intention to practice. Second, despite the extensive use of TPB in the context of pro-social/pro-environmental behavior, there are some concerns regarding its incompleteness in studying such behaviors (Gifford, 2014). Consequently, in line with the variables of TPB in explaining Green IT adoption by students, we incorporated personal norms as additional proximal determinants of intention. This factor was included based on its power in explaining various environmental behaviors in the literature (e.g., De Groot & Steg, 2010; Doran, Hanss, & Larsen, 2015; Gifford & Nilsson, 2014; López-Mosquera, 2016; Weller et al., 2014). Third, while the importance of personality traits in explaining individuals’ pro-social/pro-environmental behaviors is highlighted in the literature (Jacob B Hirsh, 2010; Jacob B. Hirsh & Dolderman, 2007; Wuertz, 2014), unlike previous studies, the current study considered personality traits as moderator variables in the research model (see Figure 1). 3 3.1

Methodology Measures

Items to measure students’ intention to practice Green IT were adopted from the study by Chow and Chen (2009) with three items, with the sample item of “I will plan to practice Green IT”. Social norms were measured using five items adopted from Wunderlich (2013), with the sample item of “People whose opinions that I value prefer that I practice Green IT”. Personal norms were measured adapting items from the study by Tonglet, Phillips, and Read (2004), with the sample item “I would feel guilty if I did not practice Green IT” (totally four items). To measure PBC, authors of the current study adapted items from the research conducted by Jones (1986), with the sample item “I do not anticipate any problems in practicing Green IT in my day-to-day work”. For the personality traits measurement we have utilized the scale developed by Donnellan, Oswald, Baird, and Lucas (2006) called MiniIPIP (“International Personality Item Pool”) with four items for each personality trait. All items were measured using a 5-point Likert-scale with “1 denoting strongly disagree” and “5 denoting strongly agree”.

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Since purposive sampling was applied, respondents were filtered according to their response to the third section of the questionnaire which asked the respondents to indicate the Green IT initiatives they usually practice in their daily activities. Table 1: Demography attributes of respondents Frequency

Percent (%)

Male Female

86 176

33 67

Under 18 18-22 22-25 25-35 More than 35 Highest level of education Diploma Bachelor degree Master PhD and higher

17 98 66 43 38

7 37 25 16 15

113 73 47 29

43 28 18 11

Gender Age

3.2

Sample and Data Collection

The inclusion criterion to select respondents to participate in the current study was students who already had the experience of practicing Green IT in their daily activities. Accordingly, to determine the number of respondents, we referred to the approach proposed by Cohen (1992) for a statistical power of 80 percent. Hence, the minimum number of respondents was 80, which was also supported with the recommended minimum sample size of G*POWER software with the settings of 0.15 for f2 effect size and 0.05 for error Type I to further achieve 80% power. To collect data, 300 copies of the questionnaire were distributed among the students of Universiti Teknologi Malaysia. Totally, 262 completed questionnaires were collected and prepared for data analysis. Table 1 shows that the majority of the respondents were female (67%), aged 18-22 (73%), and had completed a diploma as their highest level of education (68%). Furthermore, Figure 2 shows that among Green IT practices, students declared that they mainly used “electronic means to take notes, communicate, and store documents”; they also selected “energy-efficient equipment or technologies”. 3.3

Data Analysis

To analyze the data, the partial least squares structural equation modeling (PLS-SEM) approach using the SmartPLS 3.0 software was applied both for the assessment of the developed questionnaire and related hypotheses. Accordingly, as suggested by Joseph F Hair and Hult (2016), the two-stage analysis approach was followed. Since the normality of data is not considered in the PLS-SEM approach, it relies on the bootstrapping procedure to assess the path-coefficients for their significance (Joe F Hair, Ringle, & Sarstedt, 2011). Hence, in the current study the bootstrapping procedure was applied to assess the developed hypotheses.

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Figure 2: Students' Green IT practices

Figure 3: Reflective measurement model assessment criteria (Hair et al., 2013)

4 4.1

Analysis and Results Measurement model assessment

The validity and reliability of the measurement model in the current study was assessed using the approach proposed by Hair , Hult, Ringle, and Sarstedt (2013) for the constructs developed reflectively. Figure 3 depicts the criteria to assess the measurement model. The results of the measurement model assessment for its validity and reliability are presented in tables 2-4. The results show that the questionnaire was well-developed and data were ready to enter for the further analysis. 4.2

Structural model assessment

To assess the structural model which focuses on the assessment of the developed hypotheses, the authors applied bootstrapping approach to test the significance of the relationships (Joseph F Hair & Hult, 2016). Hair et al. (2013) suggested to run bootstrapping using 5000 resamples which the results of the current study are exhibited in Table 5 and Figure 4. The R2 value of the intention to practice Green IT was 0.77, which following the cut-off value suggested by Cohen (1992) was acceptable. As depicted in Figure 4, all the relationships were significant with the p-values of less than 0.001 (one-tailed test), except the relationship between social norms and intentions (p-value > 0.05). Accordingly, hypothesis 1, Green IT attitude significantly impacts intention, was strongly supported (t-value = 5.61, p-value < 0.001). The relationship between social norms and intentions was assessed in hypothesis 2 which was rejected with the pathcoefficient of 0.08 (p-value > 0.05). Hypothesis 3 tested the influence of PBC on the intention to practice Green IT, and results

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revealed the strong impact of PBC on intentions with a patch-coefficient of 0.23 and a t-value of 4.48, indicating the acceptance of hypothesis 3. Finally, the influence of personal norms on intentions was investigated in hypothesis 4 and was accepted (t-value = 4.48, p-value < 0.001). Table 2: Constructs’ validity and reliability Construct Items Outer loading Cronbach’s alpha CR AVE Intention IN01 0.810 IN02 0.794 0.747 0.856 0.664 IN03 0.840 Attitude AT01 0.847 AT02 0.831 AT03 0.858 0.899 0.925 0.712 AT04 0.845 AT05 0.838 Social norm SN01 0.827 SN02 0.860 0.856 0.897 0.637 SN03 0.813 SN04 0.774 SN05 0.710 Perceived behavioral control PBC1 0.954 PBC2 0.941 0.941 0.962 0.894 PBC3 0.942 Personal norm PN01 0.884 PN02 0.898 0.916 0.941 0.799 PN03 0.894 PN04 0.899 Openness to experience OE01 0.786 OE02 0.724 0.785 0.86 0.607 OE03 0.812 OE04 0.791 AG01 0.834 Agreeableness AG02 0.848 0.876 0.915 0.728 AG03 0.867 AG04 0.863 Conscientiousness CS01 0.834 CS02 0.854 0.869 0.911 0.718 CS03 0.845 CS04 0.856 Notes: IN=Intention; AT = Attitude; SN = Social norm; PBC = Perceived behavioral control; PN = Personal norm; OE = Openness to experience; AG = Agreeableness; CS = Conscientiousness

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Table 3: Discriminant validity (cross-loadings) Agreeableness Attitude Conscientiousness AG01 AG02 AG03 AG04 AT01 AT02 AT03 AT04 AT05 CN01 CN02 CN03 CN04 IN01 IN02 IN03 OE01 OE02 OE03 OE04 PBC1 PBC2 PBC3 PN01 PN02 PN03 PN04 SN01 SN02 SN03 SN04 SN05

0.834 0.848 0.867 0.863 0.622 0.634 0.653 0.580 0.599 0.512 0.548 0.538 0.590 0.650 0.586 0.670 0.304 0.311 0.348 0.346 0.634 0.647 0.638 0.679 0.678 0.722 0.692 0.431 0.432 0.445 0.371 0.356

0.623 0.678 0.584 0.613 0.847 0.831 0.858 0.845 0.838 0.624 0.579 0.661 0.681 0.666 0.607 0.675 0.350 0.307 0.360 0.389 0.593 0.621 0.621 0.673 0.671 0.722 0.680 0.433 0.412 0.423 0.403 0.360

Table 4: Discriminant validity (Fornell-Larker) AG Agreeableness 0.853 Attitude 0.733 Conscientiousness 0.646 Intention 0.781 Openness to experience 0.421 Perceived behavioral control 0.677 Personal norm 0.775 Social norm 0.512

Table 5: Structural model assessment Hypothesis Relationship Path coefficient H1 0.332 AT → IN 0.075 H2 SN → IN H3 0.230 PBC → IN H4 0.363 PN → IN Notes: * Significant at 1% level (one-tailed test) ** Not significant (> %5)

Intention

Openness to experience

0.667 0.681 0.648 0.667 0.696 0.673 0.702 0.644 0.648 0.627 0.640 0.611 0.632 0.810 0.794 0.840 0.398 0.307 0.427 0.438 0.671 0.694 0.693 0.704 0.724 0.745 0.733 0.448 0.432 0.433 0.400 0.371

0.417 0.352 0.342 0.323 0.410 0.391 0.400 0.356 0.356 0.372 0.326 0.318 0.336 0.460 0.348 0.434 0.786 0.724 0.812 0.791 0.295 0.323 0.309 0.445 0.468 0.460 0.493 0.367 0.434 0.388 0.345 0.307

0.571 0.540 0.536 0.556 0.613 0.600 0.640 0.665 0.651 0.834 0.854 0.845 0.856 0.575 0.583 0.651 0.303 0.242 0.321 0.359 0.537 0.563 0.536 0.652 0.638 0.678 0.652 0.372 0.337 0.316 0.319 0.287

Perceived behavioral control 0.585 0.596 0.544 0.583 0.565 0.563 0.575 0.512 0.514 0.469 0.460 0.512 0.516 0.606 0.563 0.604 0.256 0.200 0.297 0.256 0.954 0.941 0.942 0.617 0.596 0.601 0.639 0.353 0.343 0.333 0.355 0.309

Personal norm

Social norm

0.662 0.695 0.651 0.637 0.667 0.676 0.663 0.612 0.619 0.639 0.589 0.611 0.646 0.688 0.635 0.664 0.408 0.324 0.411 0.466 0.646 0.652 0.648 0.884 0.898 0.894 0.899 0.449 0.427 0.397 0.384 0.337

0.452 0.432 0.435 0.428 0.434 0.447 0.462 0.437 0.367 0.354 0.350 0.364 0.322 0.457 0.439 0.385 0.309 0.334 0.418 0.376 0.392 0.428 0.383 0.420 0.476 0.419 0.480 0.827 0.860 0.813 0.774 0.710

AT

CS

IN

OE

PBC

PN

SN

0.844 0.750 0.798 0.454 0.648 0.768 0.510

0.847 0.741 0.399 0.577 0.733 0.410

0.815 0.510 0.726 0.813 0.523

0.779 0.327 0.522 0.463

0.945 0.686 0.424

0.894 0.502

0.798

R2

t-value

p-value

Significance

Result

0.77

5.613 1.325 4.479 5.548

0.000 0.083 0.000 0.000

* * *

Supported Not Supported Supported Supported

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Figure 3: Structural model assessment

4.3

Moderating effects assessment

It was hypothesized that the relationships between the exogenous variables (i.e., attitudes, social norms, PBC, and personal norms) and the endogenous variable (intentions to adopt Green IT) were moderated positively by the personality traits openness, agreeableness, and conscientiousness. To assess the moderating effects, the current study followed the method suggested by Joseph F Hair and Hult (2016) and applied the product indicator approach. To test the significance of the moderating relationships, a bootstrapping procedure with 1000 resamples was run. Table 6 summarizes the results. The results show that hypothesis 5 which posited the positive moderating impact of openness on the relationships toward intentions was rejected. While the interaction terms were positive for the moderating effect of openness on AT → IN, PBC → IN and PN → IN, the bootstrapping analysis revealed no significant moderating effect of openness on these relationships. For hypothesis 6 which explored the moderating impact of conscientiousness on the relationships with intentions, the results of moderating effects assessment supported the hypothesis. The interaction term procedure and the bootstrapping approach revealed that the moderating impact of conscientiousness on the relationships AT → IN, PBC → IN and PN → IN was significant. Finally, the results of moderating effect assessment showed that the personality trait agreeableness significantly moderated the relationship between PCB and the intention to adopt Green IT, while the other two relationships (with positive values of the interaction terms) were found not to be significant.

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Table 6: Moderating effects assessments Hypothesis Relationship Moderator: Openness to experience H5a AT → IN H5b PCB → IN H5d PN → IN Moderator: Conscientiousness H6a AT → IN H6b PCB → IN H6d PN → IN Moderator: Agreeableness H7a AT → IN H7b PCB → IN H7d PN → IN Notes: * p-value < 0.001 ** p-value < 0.01 *** p-value < 0.05

5

Interaction term

t-value

p-value

Result

0.028 0.000 0.009

1.012 0.006 0.293

0.311 0.995 0.770

Rejected Rejected Rejected

0.072 0.114 0.087

2.624 4.125 2.701

0.009** 0.000* 0.007**

Supported Supported Supported

0.035 0.056 0.039

1.299 2.114 1.364

0.194 0.035*** 0.173

Rejected Supported Rejected

Discussion

Several studies investigated how to change the detrimental behavior of people toward the environment through the lenses of various theories and models (e.g., Jugert et al., 2016; Klein, Heck, Reese, & Hilbig, 2019; Moon, Mohel, & Farooq, 2019; Pavalache-Ilie & Cazan, 2018; Tagkaloglou & Kasser, 2018; Yu & Yu, 2017). In accordance with the results of these studies, young generations have been found as the most important and critical stakeholder (de Leeuw, Valois, Ajzen, & Schmidt, 2015). The generation that burdens the negligence of past and present. This generation is considered as a powerful engine toward changing the behavior. However, several studies reported that, while some young people react positively to environmental threads and act responsibility to enhance the situation, others behave negatively with no interest to ascribe responsibilities (Doherty & Clayton, 2011; Sanson, Wachs, Koller, & Salmela-Aro, 2018). Furthermore, studies reported that, despite the higher degree of pro-ecological attitude in young generations, they are less committed to behave pro-environmentally than the older ones (Grønhøj & Thøgersen, 2012). Accordingly, exploring behavior of young adults gains practical importance, since understanding of what motives them to practice green initiatives would result in making more sustainable future (de Leeuw et al., 2015; Sanson et al., 2018). The present study investigated the pro-environmental behavior of students toward practicing of Green IT through the lens of TPB. The results of the study confirmed the suitability of the TPB in environmental studies, and specifically Green IT adoption context. Inclusion of moral obligation (i.e., personal norm) to the research model resulted in a greater proportion of variance explanation. The results showed that students’ attitude, their perceived control, together with personal norm were significantly impacting the behavioral intention of practicing Green IT. However, it was reported that the influence of subjective norm was insignificant. Environmental studies reported that the more attitude of individuals are positive toward the environmental sustainability, the more they are ascribed to take corrective actions (Y. Chen, Shi, & Chow, 2016; Dalvi-Esfahani, Shahbazi, et al., 2018). The rejection of the hypothesis related to the relationship between subjective norm and the intention, the finding is in contradiction with the results reported by Yoon (2018) and de Leeuw et al. (2015). The authors highlighted that descriptive norm, perceived as what most others do in a given situation, is a significant determinant in explaining individual’s practicing of pro-environmental behavior. One possible explanation to this rejection would be related to the important factor in environmental studies, that is, culture. Eom, Kim, Sherman, and Ishii (2016) conducted a study to investigate the role of culture in the link between environmental concerns and pro-environmental actions. In their study, they have compared two types of nation cultures (i.e. individualistic vs. collectivistic) and found that social norms are a significant predictor of acting proenvironmentally in collectivistic nations. The difference between the result of the current study regarding the impact of social norm and the study by Yoon (2018) might be explained by the difference in their culture settings (Malaysia vs. South Korea). The importance of PBC in explaining students’ intention to practice Green IT is consistent with previous findings of Asadi, Hussin, and Dahlan (2018) and Dalvi-Esfahani, Mehrbakhsh, Azizah Abdul, Amir Hossein, and Nor Hidayati (2015) supporting the important role of PBC in practicing Green IT and Green IS. In the environmental studies PBC also found as an important factor in explaining various behaviors such as green purchases (Liobikien ė , Mandravickait ė , & Bernatonien ė , 2016), carbon reduction (M.-F. Chen, 2016), and recycling (Park & Ha, 2014). The structural model assessment revealed the strong power of personal norms in explaining the variance of the intention to practice Green IT. According to the theories of NAM and VBN, the

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main predictive variable of pro-environmental behavior among individuals is their moral obligation (i.e., personal norm) to take corrective actions (Dalvi-Esfahani, Rahman, et al., 2017; Dalvi-Esfahani, Shahbazi, et al., 2018; Fornara, Pattitoni, Mura, & Strazzera, 2016; Han, 2015; Nordlund et al., 2018). This result indicates that the willingness to retain the environment and attain environmental sustainability is considered a significant factor for Green IT practice by the individuals, beyond the monetary benefits of using IT. The analysis of the moderating effects showed that the moderating effects of openness to experience and agreeableness on the relationships AT → IN, PBC → IN and PN → IN were not significant. Thus, hypothesis H5 and H7 were rejected. While several studies supported the importance of openness and agreeableness in explaining pro-environmental behaviors (e.g., Brick & Lewis, 2014; Jacob B Hirsh, 2010; Jacob B. Hirsh & Dolderman, 2007; Klein et al., 2019; Ezra M. Markowitz et al., 2012; Pavalache-Ilie & Cazan, 2018), the result of this study showed the opposite. According to Klein, Hilbig, and Heck (2017) one possible explanation would be the nature of the current study which is estimating individuals’ attitudinal factors and behavioral intention as the predictors of their pro-environmental behavior to adopt Green IT. Klein et al. (2017) suggested to study the actual behavior of people while investigating their environmental behaviors associated with their personality traits. In this study, factors of attitude, PBC, personal norm and intention were investigated as the determinants of Green IT practice intention which supports the lack highlighted by Klein et al. (2017). The interaction term procedure and the bootstrapping approach revealed the significant moderating effect of conscientiousness on the relationships AT → IN, PBC → IN and PN → IN, hence, hypothesis 6 was supported. The result supports the findings revealed in the studies by Yu and Yu (2017), Milfont and Sibley (2012), and Jacob B Hirsh (2010) who reported that attitudes and moral obligations of individuals to take corrective actions toward the environment are more powerful for the ones with higher degree of Conscientiousness. People with the personality of Conscientiousness may consider their act (i.e. practicing Green IT) useful to the performance of their job or the environment which consequently will magnify their beliefs and further their intention. Other researchers also demonstrated that conscientiousness was related to environmentalism (Fraj & Martinez, 2006; Ezra M. Markowitz et al., 2012; Milfont & Sibley, 2012). Conscientiousness is the tendency to be responsible, organized, and selfdisciplined and also to stand for rules and norms (Robert R McCrae & Paul T Costa Jr, 1985). This trait is also related to a higher future time perspective (Zimbardo & Boyd, 2015), which, in turn, is associated with environmental engagement (Arnocky, Milfont, & Nicol, 2013; Milfont & Sibley, 2012). People with future time perspective usually pay attention to outcomes of their behaviors and attempt to plan for better future consequences, including environmental ones (Milfont & Sibley, 2012). People with high conscientiousness carefully follow any kind of social guidelines, and the tendency ‘to do the right thing’ is reflected in their pro-environmental behaviors as well (Jacob B Hirsh, 2010). Further, conscientiousness is associated with the willingness to be a ‘good citizen’. Therefore, conscientious individuals perform pro-environmental behaviors because they consider them as socially acceptable behaviors. Self-efficacy and responsibility are the characteristics of conscientious people, which lead to environmentally friendly actions (S. Bamberg & Möser, 2007; Stern, 2000). Self-discipline, aiming for achievement, and acting dutifully may explain their greater involvement in pro-environmental behaviors, as a citizenship duty. Logically, responsibility and self-discipline relate to conservation behaviors. Additionally, conscientious individuals have a strict commitment to act on their personal attitudes and control their functioning. Thus, it is expected that for these individuals, Green IT attitude, personal norms and perceived behavioral control are associated with the intention to practice Green IT. However, people with less conscientiousness do not feel the compulsion to behave according to their own attitudes and norms; thus, attitudes and personal norms logically do not relate to intentions and behaviors in them. Human behavior is highly influenced by diverse, complex factors and any effect of norms and attitudes is likely to be moderated and mediated by individual differences (e.g., personality traits). 6

Contributions The contributions of the study are twofold which are described in the following subsections. 6.1. Theoretical contributions

Theoretically, the results of the study contribute to the Green IT literature by investigating the impact of personality traits as moderating variables. Furthermore, while vastly explored, the study also provides theoretical contribution by extending TPB using the personal norm variable as one of the well-established constructs in environmental studies. Moreover, the study contributes to the literature through collecting data from the population of students, who have recently gained a lot of attention

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from sustainability researchers (Chuvieco et al., 2018; Cruz et al., 2018; Moon et al., 2019). The results of this study also append support for the usefulness of TPB as a pro-environmental and pro-social theory in studying Green IT practice. 6.2. Practical contributions One of the important practical contributions of the study relates to differences in students’ personality traits toward behaving pro-environmentally. Universities can segment students according to their personalities to further develop their focused programs to enhance students’ pro-ecological views. Furthermore, scientists have found that to alter one’s attitude and behavior toward the environment, policymakers and decision-makers should understand people’s personality traits to further tailor environmental suggestions and proposals. Furthermore, as recommended by several studies (Cruz et al., 2018; Innes et al., 2018; Stough et al., 2018), universities and institutions are advised to include environmental concerns and courses to their curriculum. The study conducted by Dagiliūtė et al. (2018b) showed that considering a separate course about environmental sustainability would enhance concerns of students toward the environment, which would further impact their pro-environmental behavior. 7

Conclusions

This study extended TPB and utilized traits of openness, agreeableness, and conscientiousness as moderating variables to investigate Green IT practice among Malaysian students. The results revealed that the intention to practice Green IT was significantly determined by students’ attitude, PBC, and personal norm. Moreover, the assessment of effects of the moderating variables showed that conscientiousness was the only trait significantly moderating the relationships toward behavioral intention. The findings of this research revealed no significant relationship between SN and the students’ intention to adopt Green IT. Some limitations are observed in this study, which are suggested to be considered for future studies. First, the setting at which the study was conducted was in Malaysian higher learning education. Since Malaysia is considered an early stage in initiation and adoption for Green IT and the concept of Green IT is still quite new to them, the results of this study may be unable to provide accurate generalization to other culture settings. Moreover, while the respondents were students other business sectors who want to use outcomes of this research are required to be more careful, since the factors possibly will dissimilar by the types of industries. Some factors that may influence the intention to practice Green IT such as culture, environmental values, and demography differences could be explored in future studies. References Ahmad, T., Badariah, T., Bello, A., and Nordin, M. S. (2013). Exploring Malaysian university students ’ awareness of green computing. GSTF International Journal on Education, 1(2), 92-102. Ahmad, T. B. T., and Nordin, M. S. (2014). University Students' Subjective Knowledge of Green Computing and ProEnvironmental Behavior. International Education Studies, 7(2), 64-74. Ainin, S., Naqshbandi, M. M., Dezdar, S. J. Q., and Quantity. (2016). Impact of adoption of Green IT practices on organizational performance. 50(5), 1929-1948. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior: Springer. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. Alexander, A. H. (2019). An Empirical Investigation on the Awareness and Practices of Higher Education Students in Green Information Technology: Implications for Sustainable Computing Practice, Education, and Policy. International Journal of Social Ecology and Sustainable Development (IJSESD), 10(2), 1-13. Armitage, C. J., and Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐analytic review. British journal of social psychology, 40(4), 471-499. Arnocky, S., Milfont, T. L., and Nicol, J. R. (2013). Time Perspective and Sustainable Behavior: Evidence for the Distinction Between Consideration of Immediate and Future Consequences. Environment and Behavior, 3(6), 1-27. Asadi, S., Hussin, A. R. C., and Dahlan, H. M. (2018). Toward Green IT adoption: from managerial perspective. International Journal of Business Information Systems, 29(1), 106-125. Bamberg, S., and Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of environmental psychology, 27(1), 14-25. Bamberg, S. J. J. o. e. p. (2003). How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. 23(1), 21-32.

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Credit authorship contribution statement Mohammad Dalvi-Esfahani: Conceptualization, Methodology, Investigation, Software, Formal analysis, Writing - original draft, Writing - review & editing. Zohre Alaedini: Writing - original draft, Writing review & editing. Mehrbakhsh Nilashi: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Supervision. Sarminah Samad: Writing - review & editing. Shahla Asadi: Methodology, Writing - review & editing. Majid Mohammadi: Methodology, Writing - review & editing.

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Students’ pro-ecological behavior of Green IT practice was investigated The behavior was investigated through the lens of theory of planned behavior Moderating impact of Agreeableness, Openness, and Conscientiousness were explored Personal norm, attitude and PCB were found significant factors Conscientiousness was the only personality trait moderating the relationships