Green Computing Adoption: Understanding the Role of Individual,

Social, and Organizational Factors

Fahima Akter Anni

1 a

, Muhammad Rezaul Islam

2 b

, Farzana Sadia

1 c

, Mahady Hasan

1 d

and M. Rokonuzzaman

Department of Software Engineering, Independent University Bangladesh, Dhaka, Bangladesh

Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, Bangladesh

Department Electrical and Computer Engineering, North South University, Dhaka, Bangladesh

Keywords:

Green Purchase Intention, Environmental, Conscious Issues, Environmentally Friendly Product, Conventional

Products, Production Process, Green IT.

Abstract:

This study investigates the factors inﬂuencing the attitudes of software developers and IT professionals towards

Green Information Technology (GIT) in Bangladeshi IT/software ﬁrms and examines their impact on engage-

ment in green computing practices. Data was collected from 130 participants. A thorough literature review

was conducted. The ﬁndings highlight key individual factors that inﬂuence employees’ attitudes, including

awareness, knowledge, and perception of environmental issues. The study demonstrates the connection with

GIT attitudes and behavior modiﬁcation, especially through stated usage of green computing methods. Data

analysis conﬁrms 5 out of 8 hypotheses and reveals the complexity of the relationships between the constructs.

The report promotes the adoption of Green IT technology by software companies in Bangladesh and highlights

the signiﬁcance of an organized ofﬁce system integrating Green IT. Limitations include a relatively small sam-

ple size and the multidimensional nature of the relationships between the constructs. The ﬁndings can assist

software companies in addressing customer concerns about the performance and functionality of green com-

puting practices, ultimately promoting sustainable computing practices in the IT sector of Bangladesh.

1 INTRODUCTION

Green growth strategy that seeks to increase perfor-

mance and productivity by fostering resource con-

sumption and output that is sustainable within compa-

nies and society is known as green information tech-

nology (GIT) (Gazzola et al., 2019), (Przychodzen

et al., 2018). Green IT provides more effective re-

source management by maximizing resource use, de-

creasing waste and emissions, and improving recy-

cling rates. However, because of the low sturdi-

ness of IT devices and speciﬁc production and dis-

posal processes, worries about negative effects have

emerged, such as increased electricity usage by com-

panies (Asadi et al., 2019). Additionally, the carbon

footprint of IT hardware and software is greater than

even the waste produced by the aviation sector (Asadi

https://orcid.org/0009-0008-7189-7974

https://orcid.org/0009-0004-5721-0276

https://orcid.org/0009-0005-1895-1044

https://orcid.org/0000-0002-9037-0181

et al., 2019), (Mishra et al., 2014). The growing need

for sustainable practices in the production and use of

IT has prompted the development of the ﬁeld of Green

IT within the ﬁelds of computer science and informa-

tion systems (Jenkin et al., 2011), (Melville, 2010).

Given the prevalent trend of industrial processes

over-shadowing their environmental impact, green

purchase intention” refers to the tendency and readi-

ness of consumers who prioritize environmental and

ethical issues to choose environmentally friendly

items instead of conventional ones. Green IT aims

to reduce the indirect ecological effects of IT oper-

ations through environmentally responsible PC de-

sign, manufacturing, use, and disposal. Govern-

ments and the public are putting increasing pres-

sure on businesses to reduce their ecological footprint

(Paill

e et al., 2014), (Zibarras and Coan, 2015). From

the perspective of Bangladesh, Lack of awareness or

knowledge about green computing among potential

customers can make it challenging for software com-

panies to market their products or services. If poten-

tial customers do not understand the beneﬁts of green

Anni, F., Rezaul Islam, M., Sadia, F., Hasan, M. and Rokonuzzaman, M.

Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors.

DOI: 10.5220/0012689400003687

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2024), pages 529-536

ISBN: 978-989-758-696-5; ISSN: 2184-4895

529

computing, they may be less likely to invest in soft-

ware that supports sustainable computing practices

(Ojo et al., 2019). Many software companies may

not have the expertise or resources necessary to de-

velop and market green computing practices. This can

limit their ability to compete with other companies

that have more experience in this area. Companies

may use claims of environmentally friendly software

development practices as a marketing tactic, without

making substantial changes to their processes. This

could lead to a loss of trust and credibility among

consumers who are becoming increasingly aware of

green washing tactics (Ojo et al., 2019).

Finally, some companies may be resistant to

change and may be hesitant to adopt new technologies

and practices. This can make it difﬁcult for software

companies to promote green computing practices and

gain traction in the market. In some cases, there may

be a perception among customers that green comput-

ing practices come at the expense of performance or

functionality. Software companies may need to ad-

dress these concerns through effective marketing and

communication to ensure that potential customers un-

derstand the beneﬁts of green computing initiatives.

Based on the current situation of Bangladesh, three

research questions were presented:

1. What factors inﬂuence employees attitudes to-

wards GIT, and how do these attitudes impact

their engagement in green computing?

2. How does the organizational culture of green

management inﬂuence employees attitudes to-

wards GIT and their engagement in green

computing?

3. To what extent does social inﬂuence play a role

in shaping employees attitudes towards GIT,

and how does this affect their engagement in

green computing?

To better understand how software developers

and IT professionals views about Green Informa-

tion Technology in Bangladeshi IT/software organi-

zations, this study will look at the inﬂuence of peo-

ple, community, and organizational variables. Ad-

ditionally, it investigates how their views about GIT

play a moderating function in this relationship. The

study looks speciﬁcally at stated participation in en-

vironmentally friendly computing practices to exam-

ine the relationship between GIT attitudes and alter-

ations in behavior. 130 participants completed a sur-

vey that was used to collect the data, and a thorough

evaluation of the literature was done with an emphasis

on pertinent studies carried out by well-known writ-

ers in other nations. The main goal is to Investigate

the key individual factors that inﬂuence employees at-

Figure 1: Research Model that shows the relation between

the variables and how they shape Green Computing Prac-

tices.

titudes towards GIT, such as awareness, knowledge,

and perception of environmental issues, and examine

how these attitudes impact their engagement in green

computing practices.

This paper will contribute to the country’s adop-

tion of Green IT technology in software companies

and IT companies in the long run by helping them to

adopt a structured ofﬁce system involving Green IT.

2 RESEARCH MODEL AND

HYPOTHESIS

This research builds upon the study conducted by Ojo,

A. O., and colleagues. In their paper, they presented

a comprehensive data model, depicted in Figure 1

(Ojo et al., 2019). The model is rooted in the Belief-

Action- Outcome (BAO) framework, originally pro-

posed by Melville in 2010 (Melville, 2010). The BAO

framework suggests that GIT knowledge, Green Man-

agement Culture, and social inﬂuence have an im-

pact on Git belief, which in turn inﬂuences GIT At-

titude. Ultimately, all these factors collectively shape

the Green Computing Practices of IT companies.

2.1 Effect of GIT Knowledge on GIT

Belief and Attitude

Understanding how things work in both general and

speciﬁc contexts begins with knowledge. Individuals’

acceptance of a speciﬁc phenomenon in their minds is

included in belief. When a belief is accepted person-

ally, it takes on permanent worth and inspires peo-

ple to behave in line with it (Koo and Chung, 2014),

(Xenitidou and Edmonds, 2014). Individuals can ob-

tain the necessary knowledge to embrace and em-

brace environmentally friendly beliefs and attitudes

by participating in training, education, or gaining

experience in environmental matters (Chou, 2014).

As a result, accessing information that supports pro-

environmental principles allows individuals to culti-

ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering

530

vate environmentally conscious thoughts and actions.

• H1a. The level of employees GIT knowledge

is positively associated with their attitude to-

wards GIT.

• H1b. The inﬂuence of employees GIT knowl-

edge on their attitude towards GIT is mediated

by their GIT belief.

2.2 Effect of Green Management

Culture on GIT Belief and Attitude

Having an understanding of the organizational culture

enables employees to identify the appropriate norms

and working practices that are endorsed by the com-

pany (Quan and Cha, 2010). By establishing clear

expectations regarding what is valued and how tasks

should be accomplished, the culture sets guidelines

for acceptable behavior and inﬂuences the choices

made by employees. It is crucial for the organization

to align its values and expectation with environmental

sustainability and grant employees the freedom to en-

gage in ecofriendly activities, fostering a supportive

environment. The organizational culture can either

facilitate or hinder employee commitment to green

initiatives (Govindarajulu and Daily, 2004), (Rothen-

berg, 2003).

• H2a. There is a positive correlation between

Green Management culture and employees at-

titude towards GIT.

• H2b. The impact of Green Management cul-

ture on employees attitude towards GIT is me-

diated by GIT belief.

2.3 Social Effect on GIT Belief and

Attitude

Research ﬁndings indicate that social factors play a

role in shaping users attitudes, intentions, and per-

ceptions regarding the utility of technology. The in-

ﬂuence of social elements has predominantly been

linked to users attitudes and intentions (Lewis et al.,

2003), (Venkatesh et al., 2003).

• H3a. There is a positive correlation between

social inﬂuence and employees attitude to-

wards GIT.

• H3b. The impact of social inﬂuence on employ-

ees attitude towards GIT is mediated by GIT

belief.

2.3.1 GIT Belief and Attitude

Employees perceptions of green information technol-

ogy (GIT) mirror their cognitive understanding of

GIT’s capabilities and their recognition of the impor-

tance of adhering to environmentally friendly work-

place IT practices (Molla et al., 2011).

• H4. There is a positive correlation between em-

ployees GIT belief and their attitude towards

GIT.

2.4 GIT Attitude and Green Computing

Practices

By implementing their own environmentally con-

scious computing practices, such as turning off com-

puters when idle, IT staff members can support the

company’s waste reduction and sustainability pro-

grams. Employees’ attitudes towards green comput-

ing demonstrate their dedication to assuming respon-

sibility for environmentally conscious IT equipment

usage in the workplace (Ojo et al., 2019).

• H5. There is a positive association between em-

ployees’ attitude towards GIT and their active

participation in green computing practices.

3 METHODOLOGY

The data was collected from software developers and

IT ofﬁcials from the software development and IT

companies of Bangladesh. The samples were cho-

sen randomly and a list of 12 companies was created.

Using the G-Power tool, the test group size was de-

termined. To calculate test family F testes and linear

multiple regression was chosen and number of pre-

dictors 4 was input. G-power returned a total sample

size of 129. The google survey questionnaire links

were sent through emails to the companies seeking

participation. Respondent identity was not collected

to ensure anonymity. The questionnaires were taken

from the works of Ojo, A. O., et al (Ojo et al., 2019).

Each issue was assessed using a Likert scale. Af-

ter collecting the data, responses with incomplete or

missing data were excluded. The rest of the data was

converted to ordinal value and analysed In SmartPLS-

4 shown in Figure 2. For algorithm PLS-SEM algo-

rithm and Bootstrapping was applied.

Along with the survey this research also con-

ducted extensive literature review on pre-existing

work of other renowned author’s publications in other

countries and the review is added to this paper.

Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors

531

Figure 2: SmartPLS design created based on the research

model in Figure 1, showing the loading values and relations.

4 RELATED LITERATURE

REVIEW

The literature review examines several empirical stud-

ies focused on investigating various aspects of green

computing practices among IT professionals in dif-

ferent countries, including Malaysia, Bangladesh,

and India. The studies explore the factors inﬂuenc-

ing green computing behaviours and practices, such

as green beliefs, attitudes, environmental awareness,

green IT consciousness, and perceived organizational

support.

The study by Ojo, Raman, and Downe (Ojo et al.,

2019) in Malaysia reveals a direct correlation between

higher levels of green beliefs and direct attitudes to-

wards green computing practices between IT ofﬁcials.

Similarly, Molla, Abareshi, and Cooper (Molla et al.,

2014) ﬁnd that stronger green IT beliefs are positively

correlated with higher levels of pro-environmental IT

practices among IT professionals. The role of green

IT/IS innovation in promoting sustainability practices

and environmental conservation is highlighted in the

study by Jnr (Jnr, 2020) in an emerging economy.

Hossain, San, Ling, and Said (Hossain et al., 2020) in

Bangladesh reveal that higher levels of environmen-

tal awareness and green technological usage are as-

sociated with increased adoption of sustainable green

practices among manufacturing SMEs.

Ojo et al. (2018) investigate the cognitive inﬂu-

ences on perceptions and attitudes about environmen-

tally friendly information technology and organiza-

tional factors and barriers affecting green computing

practices (Ojo et al., 2018). Tan et al. (Tan et al.,

2019) examine the determinants of green computing

adoption, while Ong, Lim, and Lim (2019) and Chew,

Yong, and Ng (Ng and Ng, 2019)) analyze the in-

ﬂuence of organizational culture and environmental

concern on green computing practices, respectively.

Zhou, Liang, and Huang (Zhou et al., 2020) study the

mediating role of environmental awareness in the re-

lationship between perceived organizational support

and green computing practices.

Bhatti analyze the inﬂuence of integrate organi-

zational and innovative environmental behavior on

green computing practices, respectively (Bhatti et al.,

2021). Ramli’s study investigates the factors inﬂu-

encing Green IT adoption in Malaysia through qual-

itative methods, identifying environmental, cost, or-

ganizational, technological, and business opportunity

factors, aiming to support Malaysia’s goal of reducing

greenhouse gas emissions (Ramli et al., 2021).

Furthermore, Kaur, Yadav, and Singh (Kour et al.,

2020) focus on the role of green IT consciousness

in inﬂuencing green computing practices among IT

professionals in India. Syzdykbayeva highlights the

growing importance of green computing in response

to energy costs, global warming concerns, and reg-

ulatory pressures, particularly in Malaysian compa-

nies (Syzdykbayeva, 2009), while Buisson discusses

a study investigating the low awareness and practice

of green computing among IT workers, emphasizing

the need for environmental awareness initiatives to

promote green computing behaviors (du Buisson and

Naidoo, 2014). Paille et al. conducted an investi-

gation on the effects of HRM (human resource man-

agement) on the environment at the workforce sector

(Paill

e et al., 2014). Zibarras and Coan (Zibarras and

Coan, 2015) surveyed UK organizations to identify

HRM procedures are utilized to encourage environ-

mentally friendly behavior.

Gazzola discussed the trade-offs between going

green and going smart for sustainable development

(Gazzola et al., 2019), while Przychodzen et al. in-

vestigated the relationship between green information

technology (IT) practices and ﬁnancial performance

(Przychodzen et al., 2018). Perkins et al. highlighted

the global hazard of e-waste (Perkins et al., 2014),

and Asadi and Dahlan conducted a systematic litera-

ture review to identify research on Green IT in orga-

nizational contexts from 2007 to 2016 (Asadi et al.,

2017). Mishra et al. (Mishra et al., 2014) applied

the Theory of Reasoned Action to investigate the fac-

tors that inﬂuence the acceptance of Green IT among

employees, and Jenkin et al. proposed an agenda for

Green IT and systems research (Jenkin et al., 2011).

Melville explored how technological development

could contribute to ecological preservation. and

identiﬁed several areas where information systems

can contribute to environmental sustainability, such

as energy management, supply chain management,

and sustainable product design (Melville, 2010).

Asadi et al. analyzed the impacts of green man-

ufacturing and technology innovations on sustain-

able development (Asadi et al., 2021), and Sharma

ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering

532

Table 1: Demographic of the Respondent.

Measure Items Responses Responses (%)

Education CSE, EEE, Self-taught 113, 15, 2 87%, 12%, 2%

Project IT Support, Mobile App, Database Management, 4, 8, 11, 90, 17 3%, 6%, 8%, 69%, 13%

Web Development, Software Development

Experience 0-2, 2-5, 5-10, 10+ Years 71, 50, 6, 3 55%, 38%, 5%, 2%

Company Size Midsize, Startup, Large Corporation 61, 60, 9 47%, 46%, 7%

Table 2: AVE and CR.

Measure Composite Reliability Average Variance Extracted(AVE)

GIT Belief 0.89 0.619

GIT Knowledge 0.868 0.523

Green IT Attitude 0.893 0.736

Green Management Culture 0.889 0.618

Social Inﬂuence 0.913 0.839

et al. provided a strategy for the implementation

of environmentally friendly information technology

in manufacturing enterprises (Sharma et al., 2022).

Meidute-Kavaliauskiene et al. researched the im-

plications of open innovation for green invention in

complicated environmental environments (Meidute-

Kavaliauskiene et al., 2021), and Kour et al. inves-

tigated the effect of green practices on the ﬁnancial

performance of Indian automobile businesses (Kour

et al., 2020). The limiting inﬂuence of IT capability

in the connection between green innovation and busi-

ness sustainable development was also investigated

by Li et al. (Li et al., 2022). Therefore, the literature

review on green computing practices among IT pro-

fessionals identiﬁes gaps in research, including lim-

ited studies in developing countries, a lack of focus on

speciﬁc IT professional roles, and the need for com-

parative and longitudinal studies. Additionally, there

is a limited exploration of technological solutions and

a lack of emphasis on the outcomes and impacts of

green computing practices.

Overall, the literature review highlights the impor-

tance of various factors, including green beliefs, atti-

tudes, environmental awareness, green IT conscious-

ness, and perceived organizational support, in shap-

ing green computing behaviours and practices among

IT professionals in different countries. The ﬁndings

from these empirical studies provide valuable insights

for understanding the complex dynamics and chal-

lenges associated with green computing practices in

the ﬁeld of Information Technology.

5 DATA AND RESULT ANALYSIS

5.1 Validation of Measurement Model

130 individuals in total took part in the research, and

table 1 shows some demographic information about

them. The majority of the respondents (approxi-

mately 86%) have a background in Computer Science

Education, while 12% come from an Electrical Engi-

neering background, and 2% are self taught. In terms

of employment, the respondents are mainly engaged

in various IT related roles, including IT support, Mo-

bile app development, Database management, Web

Development, and Software Engineering. Regarding

their professional experience, according to the ﬁnd-

ings, 38% of those surveyed have 2–5 years of exper-

tise, compared to 0–2 years for 55% of respondents. 5

percent of respondents said they had ﬁve to ten years

of expertise, and 2 percent said they had more than

ten years.

Overall, the ﬁndings indicate that the sample con-

sists predominantly of individuals with a background

in Computer Science Education, working in diverse

IT roles, and varying levels of professional experi-

ence. When analyzing the study’s ﬁndings, these as-

pects must be kept in mind.

In order to evaluate the convergent and discrim-

inant validity of the reﬂective variables, several as-

sessments were conducted. Convergent validity was

evaluated based on factor loadings shown in Table 3,

and CR and AVE shown in Table 2.

To evaluate discriminant validity, the correlations

between variables were compared with the square

root of the average variance extracted (AVE) for the

respective variables. The results, as presented in Ta-

ble 2, indicate that all constructs met the cut-off cri-

teria. This means that the AVE values were greater

than 0.5, and the CR values exceeded 0.7, indicating

good internal consistency. Additionally, the CR val-

ues were greater than the AVE values, further support-

ing the discriminant validity of the constructs.

5.2 Hypotheses Testing

The proposed relationships between variables were

investigated by analyzing the structural model (Fig.

1). Beta (β) coefﬁcients were computed based on

Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors

533

Table 3: Measurement Scales (Factor Loading).

Construct Measurement Items Loadings

GIT

Knowledge

I know about Green IT (e.g., energy efﬁcient device, thin client, cloud computing) 0.801

I concur that opting for Green IT represents an individual choice or option 0.781

I agree that green energy has the potential to serve as a replacement for fossil fuel energy sources

(such as oil and gas) as well as nuclear power

0.706

I agree that using Green IT device can affect my company’s electricity bill 0.682

I agree about that Green IT is reliable and secure 0.697

I get enough information about programs to promote Green IT 0.663

Green IT

Attitude

I feel that green computing is a convenient thing for me to practice in the workplace 0.893

I feel that green computing is a good thing for me to practice in the workplace 0.873

I feel that green computing is a pleasant thing for me to practice in the workplace 0.857

GIT Belief

I believe that IT equipment and systems contribute to greenhouse gas emissions 0.815

I believe that IT personnel should be responsible for reducing IT’s greenhouse gas emissions 0.771

I believe IT can be used to reduce a business’s total carbon footprint (i.e., emission of carbon dioxide) 0.767

I believe that IT professionals have the ability to make substantial contributions towards assisting

businesses in addressing their carbon footprint, which refers to the emission of carbon dioxide

0.774

I believe that tackling the carbon footprint of IT systems should be a core part of the green business 0.806

Green Man-

agement

Culture

Our top management actively advocates for and supports the implementation of environmentally

conscious practices

0.832

Environmental considerations are integral to our organization’s vision and mission statements 0.811

Our top management effectively disseminates information and promotes the importance of environ-

mental management across the organization

0.752

Our top management establishes a system of punishments and penalties to enforce compliance with

environmental management policies

0.675

Our team/department budgets are allocated to address and mitigate our environmental impact 0.848

Pro Green

IT Practices

I make use of the power-saving features available on the IT devices I regularly use 0.818

I ensure to turn off my computer when it is not being used 0.717

I choose to purchase IT equipment that has been recycled for my personal use 0.682

I actively practice double-sided printing to minimize paper waste 0.86

Social

Inﬂuence

People who exert inﬂuence over my behavior strongly advocate for the adoption of Green IT practices 0.933

Individuals who hold signiﬁcant importance in my life emphasize the importance of practicing Green

0.899

Table 4: Mean, STDEV, T Values, P Values.

Measurement Items GIT Belief GIT Knowledge Green IT Attitude Green Management Culture Social Inﬂuence

GIT Belief 0.787

GIT Knowledge 0.865 0.723

Green IT Attitude 0.757 0.72 0.858

Green Management Culture 0.781 0.716 0.834 0.786

Social Inﬂuence 0.637 0.575 0.702 0.679 0.916

the path analysis, and the signiﬁcance of these coefﬁ-

cients was determined using T Statistics.

Table 2 provides the Composite Reliability (CR)

and Average Variance Extracted (AVE) values for

each construct in the study. The CR values range from

0.868 to 0.913, indicating good internal consistency

reliability, while the AVE values range from 0.523

to 0.839, suggesting satisfactory convergent validity.

These values demonstrate the reliability and validity

of the measurement scales used in the study.

Table 3 displays the factor loadings for measure-

ment items under each construct. The factor loadings

range from 0.663 to 0.933, indicating strong associ-

ations between the measurement items and their re-

spective constructs. For example, in the GIT Knowl-

edge construct, the measurement item ”I know about

Green IT” has a factor loading of 0.801, suggesting a

strong relationship between participants’ knowledge

of Green IT and this construct. Similarly, in the So-

cial Inﬂuence construct, the measurement item ”Peo-

ple who exert inﬂuence over my behavior strongly ad-

vocate for the adoption of Green IT practices” has a

high factor loading of 0.933, indicating a strong asso-

ciation between social inﬂuence and participants’ at-

titudes towards Green IT adoption. Overall, these fac-

tor loadings provide evidence of the constructs’ valid-

ity and reliability in measuring participants’ percep-

tions and behaviors related to Green IT adoption.

Table 4 provides key statistical values for mea-

surement items related to different constructs in the

study. The mean scores for GIT Belief, GIT Knowl-

edge, Green IT Attitude, Green Management Cul-

ture, and Social Inﬂuence are 0.787, 0.865, 0.757,

0.781, and 0.637, respectively. Standard deviations

(STDEV) range from 0.575 to 0.723, indicating vari-

ability in responses. T values and P values demon-

strate the signiﬁcance of relationships between con-

structs, with lower P values suggesting stronger evi-

dence for hypotheses. Notably, the P value for Social

Inﬂuence is 0.91, indicating a higher level of signiﬁ-

cance compared to other constructs.

Table 5 provides insight into the discriminant va-

ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering

534

Table 5: Discriminant Validity.

Hypothesis Description Original

sample

T statistics P values Comments

H1a GIT knowledge has a direct impact on Green IT attitude 0.156 1.625 0.104 Not Supported

H1b The progression from GIT knowledge to GIT belief and its

subsequent impact on GIT attitude

0.61 8.513 0 Supported

H2a Green Management Culture has a direct impact on GIT Atti-

tude

0.51 5.543 0 Supported

H2b The connection between the culture of Green Management, the

belief in GIT, and the subsequent attitude towards GIT

0.278 4.091 0 Supported

H3a Social Inﬂuence has a direct impact on GIT Attitude 0.207 3.275 0.001 Supported

H3b The inﬂuence of social factors on GIT belief and its impact on

GIT attitude

0.098 1.867 0.062 Not Supported

H4 GIT Belief has a direct impact on Green IT Attitude 0.092 0.862 0.389 Not Supported

H5 GIT Attitude has a direct impact on Green Computing Prac-

tices

0.739 11.114 0 Supported

lidity of the constructs, with diagonal values being

higher than the corresponding inner values. However,

it is worth noting that two of the inner values were

lower than the diagonal values, suggesting some po-

tential overlap between those constructs. It presents

the results regarding the support for the hypotheses

based on the data. Hypotheses with p-values below

0.05 are considered supported by the data, while hy-

potheses with p-values above 0.05 are deemed unsup-

ported. Upon analysis, it was found that Hypothe-

ses H1a (0.104), H3b (0.062), and H4 (0.389) had

p-values higher than 0.05, indicating that they were

not supported by the data. Conversely, Hypotheses

H1b, H2a, H2b, H3a, and H5 exhibited p-values be-

low 0.05, meeting the expected criteria and therefore

being supported by the data. In conclusion, out of the

total of eight hypotheses tested, ﬁve were supported

by the data, while three were not. These ﬁndings pro-

vide insights into the relationships between the vari-

ables under investigation and offer important implica-

tions for the study’s overall results.

6 CONCLUSION

This paper tries to highlight the relationship be-

tween individual, social, and organizational factors

on software developers’ and IT professionals’ atti-

tudes toward Green Information Technology (GIT) in

Bangladeshi IT/software ﬁrms. The analysis proved 5

out of the hypotheses were correct. Some of the limi-

tations of this paper are lack of participants. At most

only 12 companies were found willing to provide

data. Data from more companies could help reach

a better representation of the population. The rela-

tionship between the constructs are multidimensional

and complex. But this paper hopes to help achieve

better green adoption policies for Bangladesh. Some-

times customers may have concerns about the perfor-

mance or functionality of green computing practices

(Venkatesh et al., 2003). By investigating the relation-

ship between GIT attitudes and behavioural change,

it can provide evidence to alleviate these concerns.

Software companies can use these ﬁndings in their

marketing efforts to assure potential customers that

adopting green computing practices does not compro-

mise software performance or functionality.

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