Green IT Practices in Business: Energy Efficiency & Waste Management

Green IT practices in the business sector
Andrea Mory1 and Diego Cordero2 and Silvana Astudillo3 and Ana Lucia Serrano3
1
Universidad de Las Islas Baleares, Cra. de Valldemossa, km 7.5, Palma de Mallorca España
[email protected]
2
Universidad Católica de Cuenca, Av. de Las Américas y Tarqui, Cuenca, Ecuador
[email protected]
3Universidad de Cuenca, Av. 12 de Abril, Cuenca, Ecuador
{silvana.astudillo,ana.serrano}@ucuenca.edu.ec
Abstract. The purpose of this study is to determine the level of application that
the company has in terms of green IT practices. In this context, the study begins
with a theoretical conceptual review, which serves to identify the different
variables involved in Green Computing and to support the nine research
hypothesis. The relationship between the identified constructs (hypothesis)
generates a model of structural equations; the operationalization of the variables
results in questionnaire with sixty indicators to determine the situational status
of various businesses with regard to green information technologies; the
instrument is applied to forty seven informants from various organizations in the
city of Cuenca in Ecuador. The model is evaluated with the software product
Smart PLS; finally, with the results provided from the analysis of the model, it’s
concluded that: "organizational strengths , policies, procedures( FO )" have a
positive influence on "applications used (AP)", " energy efficiency (EE),"print
management and paper use ( IP )" and “treatment and disposal of technological
waste ( RT)". On the other hand, it is concluded that “used applications( AP)",
" energy efficiency (EE )" and "organizational strengths, policies, procedures
( FO)", "print management and paper use( IP)"and "treatment and disposal of
technological waste( RT)", have no influence on "green IT in the organization(
GIT)".
Keywords: Green Computing, Energy Efficiency, Structural Equations
1
Introduction
This study comprises an analysis of Green IT practices, also known as Green
Computing (GC), within the business sector of the city of Cuenca in Ecuador. The main
objective is to determine which of the practices currently implemented have a direct
link to the standards of low consumption and efficiency of computer resources. It’s
important to determine the weighting of each of the variables in order to determine
those most relevant within the different organizations.
The problem of research from an ecological point of view determines the
nonexistence of practices and techniques in organizations that make use of green
information technologies in a way that contributes to the protection of the environment.
2
To this end, a review of the literature is carried out on the implications of green IT,
green computing, green practices, reference models, experiences, strategies, application
techniques on hardware and software in certain organizations and an investigation into
the current environmental protection standards in the region and the country.
2
Literature Review
2.1
Theorical Framework
IT power control, PC supply management, remote conferencing, remote computing and
reuse of IT equipment are all initiatives currently being adopted, involving cost
reduction and environmental protection [1]. Another important piece of information on
environmental impact is paper consumption in companies [2] [3]. Authors like
Rodríguez and Carreño [4] indicate that there has been an increase in energy
consumption within organizations for some years now, due to the development of new
technology and services offered. Cabarcas, Puello and Canabal [5] point out that
internet applications are now available to evaluate and select computer equipment such
as laptops that meet low power consumption standards.
A direct relationship is being built between the IT industry and climate change,
taking into account CO2 emissions, because worldwide most of them come from data
centers. In the face of this, must become an alternative to eliminate emissions, energy
consumption and waste [6]. In this sense Valdés [7] states that during the first decade
of the 21st century, organizations on all continents have made a series of efforts to
increase the socialization of Green IT; in other hand institutions are participating to the
creation of energy-efficient products [8].
Several initiatives are being implemented in Latin America and the Caribbean to
minimize the impact of climate change through IT and electronic waste management
and recycling. [9]. England & Bartczak [10] say that the drive towards ecological
sustainability in organizations has increased both at the public and private level, thus
the increase in green jobs focused on IT, but the problem is finding qualified workers
in Green IT, as a result, there is a growing demand for education in green IT.
In Ecuador, private initiatives have been implemented, especially by mobile phone
companies, which seek to recycle mobile devices. [9]. For its part, the Ecuadorian state
created a regulatory context integrating techno-political proposals and technical
aspects, to guarantee that the organizations operating in the national territory meet
energy sustainability requirements [11], [12]. In addition, the National Assembly
approved the Organic Law on Energy Efficiency in March 2019 [13].
Pollution is defined as the process of releasing pollutants into the environment that
cause adverse changes, which takes the form of chemical nuclei or energy such as noise,
heat or light, where the pollutants and components may be substances, foreign energies
3
or natural contaminants [14], [15], [16]. Life cycle of the equipment or IT resources,
is defined as the time that the device provides a contribution to the institution without
causing loss of time or quality, this time is predetermined by the manufacturer, but can
be altered by the conditions of use and the deterioration suffered by the interaction of
the environment. [17].
For, Garcia [18] the life cycle is comprised of a series of sequential and interrelated
stages of a product system, from the acquisition or generation of raw materials to their
disposal. It is advisable to follow the 3Rs model (reduce, reuse and recycle) to reduce
the volume of technological waste [19]. The processes that comprise green
technologies are: virtualization, cloud computing, client server technology, efficient
data centers, cluster computing, use of renewable energy, and recycling of electronic
components [20].
Green information systems (Green IS) refer to the ecological and efficient use of
computer resources, cellular telephony and other information media with the aim of
reducing environmental impact as much as possible and increasing economic viability
[21]. Pinto, [22] defines technological scrap as those old electronic devices that are at
the end of their useful life, such as computers, televisions, telephones, mobile phones,
among others, that have been disposed of. When electronic waste is disposed of without
any control, negative impacts on the environment and health will [23]. Then, IT
equipment recycling and disposal programs can create habits of responsible disposal of
computer equipment at the end of its life or reuse if it could be [24]. IT processes,
policies, procedures and standards that impact organizational behavior; end-user
computing, usage standards for the appropriate use of end-user computing equipment
[25].
3
3.1
Materials and Methods
Methodology
The research is of an exploratory type, since it allows a general vision to be given, with
respect to a certain reality, in addition the green it theme has been little explored and
recognized in the environment; it aims to determine trends and identify potential
relationships between the variables or constructs identified . It is also descriptive and
correlational since it seeks to determine the way in which green it practices are carried
out in the organizations analyzed. At the same time, the degree of relationship or
association existing between two or more variables is determined (hypothesis
determination), prior to measuring the variables and then, by means of correlational
hypothesis tests and the application of statistical techniques, the correlation is estimated
4
3.2
Hypothesis and Model
With the determination of the parameters in the theory, the following study hypotheses
are put forward.
H1:
H2:
H3:
H4:
H5:
H6:
H7:
H8:
H9:
The applications used (AP) influence green IT in the organization (GIT).
Energy efficiency (EE) influences green IT in the organization (IWG).
Organizational Strengths, Policies, Procedures (FO) influence the applications used (AP).
Organizational strengths, policies, procedures (FO) influence energy efficiency (EE).
Organizational strengths, policies, procedures (FO) influence green IT in the
organization (GIT).
Organizational strengths, policies, procedures (FO) influence print management and
paper use (IP).
Organizational strengths, policies, procedures (FO) influence the treatment and disposal
of technological (RT) waste
Print management and paper use (IP) influences green IT in the organization (GIT).
The treatment and disposal of technological waste (RT) influences green IT in
the organization (GIT).
The set of hypotheses gives rise to the formation of a structural equation model,
which is made up of six constructs or variables as shown in Fig 1.
Fig. 1. Structural model
3.3
Instrument for information gathering
In order to design the questionnaire, questions were developed and selected from the
review of the literature, and the instrument was subjected to expert judgment, in order
to analyze and refine the quality of the texts. The instrument contains 60 questions
(indicators), resulting from the operationalization of the variables or constructs of the
5
model; it is evaluated through a Likert scale from 1 to 4, where: 1, never; 2, rarely; 3,
often; 4, almost always; 5, always, in reference to the digital competencies model.
3.4
Procedure
Data processing is executed as follows: authorization to gather information, given by
the directors of the organizations, following the motivation of the research group.
Disposition of the database of all informants. Selection of most suitable informants.
Application of the questionnaire via the web to the selected actors; for this purpose, the
e-mail address where the instrument built with Google Drive is located is sent. The
information survey is carried out between February and April 2020. The data from the
web-based survey are resident in Excel / In SPSS V.20, the survey is formatted; the
Excel data is migrated to SPSS, to obtain descriptive statistics from the respondents.
The structural model is implemented with the Smart PLS 3.1.9 software. The data in
Smart PLS 3.1.9 is imported from SPSS. With the model generated in Smart PLS 3.1.9,
statistics are generated that correspond to structural models.
4
Results
4.1
Validity and Reliability of the Measurement Model
Here it is validated whether the theoretical concepts are supported through the observed
variables, the values are broken down in Table 1.
Table 1. Model reliability analysis
Parameter: Individual reliability
Compliance values
Values obtained
The values of the loads are analyzed
The values obtained in the model show that some
(λ) with respect to their own variable,
indicators are lower than 0.6 so they were
this value must be greater than 0.707.
eliminated, resulting in the final model.
Parameter: Reliability of each variable or construct
Compliance values
Values obtained
It evaluates Cronbach's alpha and the
The values obtained check the internal consistency
composite reliability of the construct,
of all the latent variables or constructs.
should mark a value greater than 0.7.
Parameter: Convergent validity
Compliance values
Values obtained
This parameter rates the AVE value,
The results confirm that the indicators effectively
which should be set higher than 0.5.
measure the construct
Parameter: Discriminant validity
Compliance values
Values obtained
The square root of AVE must be greaThis criterion is not fully met in all constructions.
ter than or equal to the correlation between the same construct.
Cross load check
Compliance values
Values obtained
6
The correlation value between an
indicator and its construct must be
greater than the correlation value
between the indicator and another
construct.
4.2
This parameter is also met for most indicators,
except for only an EE13, which implies that each
construct is different from the others.
Assessment of the Structural Model
This part seeks to demonstrate the relational hypotheses of the model (see Table 2)
Table 2. Correlation analysis between variables
Parameter: Index R2
Compliance values
Values obtained
The predictive capacity of the model is The values obtained from the R2. In the case of the
measured, for this the value of R2 must GIT construct, it is clear that the AP, IP, EE, RT
be greater than 0.1
constructs have adequate predictive power
Parameter: Effect ƒ2
Compliance values
Values obtained
In terms of value ƒ2 The only relationships that meet
these parameters are those established between the
FO organizational strengths, but not with respect to
the GIT variable.
Parameter: Standardized path coefficients
Compliance values
Values obtained
It evaluates the impact of a latent variable on a dependent construct. Acceptable values are those above 0.15
The value of these standardized
coefficients must exceed a minimum of
0.2
With regard to the path coefficients, the accepted
values are those that are positive, however according to the statistics, it is necessary that these have
a value greater than 0.2, giving as valid results the
same correlations obtained in ƒ2
Parameter: Bootstrapping analysis
Compliance values
Values obtained
This process identifies the standard error The bootstrapping results indicated in Table 3
and the Student t-coefficients. For a once again show that the correlations between FO
coefficient to be considered significant it and the other constructs are significant, since they
must exceed the value of 1.96. Further- have a Student t-value greater than 1.96 and a pmore, to give a hypothesis as valid the value less than 0.05.
value of p must be lower than 0.05.
Table 3.
H1:AP -> IWP
H2:EE -> GIT
H3:FO -> AP
Relations between constructors
β
Standard
error
0.087
0.088
0.297
0.387
0.656
0.084
T Student
statisticians
P value
Result
0.292
0.224
0.770
0.822
Rejected
Rejected
7.809
0.001
Accepted
7
H4:FO -> EE
0.565
0.080
7.069
0.001
Accepted
H5:FO -> GIT
0.062
0.457
0.225
0.132
0.274
3.314
0.784
0.005
Rejected
Accepted
0.601
0.175
0.138
0.078
0.227
0.284
7.302
0.774
0.486
0.001
0.440
0.625
Accepted
Rejected
Rejected
H6:FO -> IP
H7:FO -> RT
H8:IP -> IWP
H9:RT -> GIT
5
Conclusions
The identification of the variables present in the application of green IT practices within
organizations is fulfilled. The research determined the situational scenario of four
companies in terms of the level of involvement with these practices. With this
information, a structural model was structured which made it possible to investigate the
behavior of the parameters or constructs and predict how the variations on these affect
the model in general.
The model aims to evaluate the factors involved in the perception that the staff has
in relation to the achievement of adequate practices for green IT; the proposed factors
involved are: the organizational strengths including policies, procedures and standards;
the efficient consumption of the equipment used during the usual procedures; the
printing and paper handling service; the applications or software aimed at reducing the
high energy consumption of the IT equipment; and finally the plans for the reuse and
treatment of the equipment that has fulfilled its useful life.
Reliability tests on constructs (variables) showed that some indicators (questions)
were not significant within the model, so some of these were eliminated in order to
structure a more reliable model for statistical analysis. Of the 60 initial indicators only
27 remained, after the model was adjusted, the same reliability test showed the validity
of the model through the fulfilment of several parameters.
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