A Study on Green IT Enablers for Saudi Arabian Consumer Purchasing Behaviour Using Structural Equation Modelling




Sania Khan (1)
Mohammed Shahid Ahamed Khan (2)
D. Ravinath (3)



(1) Sania Khan, Ph.D Scholar,
Gitam School of International Business (GSIB)
GITAM University, India
(2) Mohammed Shahid Ahamed Khan, Sales Manager - IT Infrastructure,
Computer Support House Company Ltd,
Riyadh, Kingdom of Saudi Arabia
(3) Dr. D. Ravinath, Associate Professor
Gitam School of International Business (GSIB)
GITAM University, India

Correspondence:
Sania Khan
Gitam School of International Business (GSIB)
GITAM University, India
Email: saniakhan05@gmail.com

Abstract

In the growing IT services and huge investments on IT infrastructure procurement in Saudi Arabia compared to other GCC countries in Middle East, it was identified fifteen enabling factors relevant to socioeconomic and environmental issues for corporate green IT purchasing. This study attempts to validate and test the interrelationships empirically among these enablers using confirmatory factor analysis and structural equation modelling respectively with a survey conducted in Riyadh, Kingdom of Saudi Arabia. For this a hypothesised model was proposed by postulating the impact of key factors on consumers' green purchasing behaviour. Results suggest that the proposed model well fits with the data and majority of the hypothesised relations have positive influence on green purchasing behaviour except power consumption, performance, e-wastage disposal, global warming and eco-labeling and certifications failed to support the study indicating lack of awareness on these issues among the consumers. This study contributes for the industry, academia and also Saudi government to formulate new business strategies in promoting green IT products and to develop new regulations towards environment protection. Correspondingly implications and future research directions are also presented.

Key words: Structural Equation Modelling, SEM, Consumer Purchasing Behaviour, Confirmatory Factor Analysis, Green Computing, Green IT Enablers, Green Purchasing Behaviour, Sustainable Procurement, Interrelationship Study.




Introduction
IT, being an integral part of every organisation and also due to the growing mergers and acquisitions and online trading system has drastically increased the IT infrastructure. The need to operate IT equipment results in huge power consumption, increased carbon emissions and massive e-waste generation consequently causing serious health problems to human life which most people don't realise. In fact all of these problems are due to the disruptive consumption pattern of the consumers. Green IT which is synonymous to 'green computing' or 'sustainable IT' has been found to be an emerging area in the present IT management and is able to eliminate all these problems and is also attracting the interest of corporate IT buyers, IT vendors and manufacturers. Green IT which was introduced in 2007 has become popular among developed countries. Today though the green IT considerations are beginning to have an importance in consumer and business purchasing decisions; the holistic approach of green IT is not clear from the consumer viewpoint to the present IT market.

Green IT was primarily researched from the corporate perspective and its influence on consumers' purchasing behaviour is unknown so far. With the absence of any existing model that explains the impact of green IT enablers on corporate consumers' green purchasing behaviour, the present study contributes in identifying the key enabling factors and develops them into a conceptual hypothesised model to interpret the empirical interrelationships among them and understand the impact of those influencing factors on consumers' green purchasing behaviour. This helps to recognise the present purchasing pattern of green IT products in Saudi Arabian corporate firms with an informed purchasing decision and also explains how eco-friendly purchasing can help in improving sustainable organisations.

This paper is organised into many sections. The introduction is followed by a brief review of literature on green IT and consumer green purchasing behaviour. The model development of green IT consumer purchasing behaviour and the formulation of hypotheses are well explained below. The questionnaire development, data collection and survey conducted followed by pilot and main study, are discussed. Further the hypotheses testing and the results of model fit followed by major findings and discussions are documented. Finally the inferences drawn from the study, limitations and directions for further research are presented at the conclusion section.

Literature Review
Since 2007, Green IT has been found to be a new area of research study and has focused mainly from the corporate view while consumer studies have been given less importance (Velte et al., 2008; Molla, 2008). As the term green IT indicates green criteria related to power consumption, e-waste disposals and carbon footprints are expected to have an impact on the purchasing pattern of IT products too.

Most consumers realise their consumption pattern have a direct affect on the environment and have even changed their consumption and production methods (Christopher et al., 2008; Chan T.S., 1996; and Noushin et al., 2010). Environmental concern is not only valid for consumer products but also applicable for industrial products and services. In order to reduce the impact of such industrial products the European Union has established the Kyoto protocol in 1997 for reducing industrial carbon emissions worldwide (Butner et al., 2008). From the research study conducted by Gartner Inc., in 2007 it is recognised that IT industry accounts for 2% of global warming through its carbon emissions and advised IT product manufacturers and vendors regarding government restrictions to reduce their carbon emissions and hazardous substances during the production process (Simon Mingay, 2007 and Hugh Wareham, 2009). Most of the firms trust embracing green IT initiatives will widely address business sustainability and hence implementing these policies through CSR strategy in their operations (Chandrasekhar Ramasastry, 2009) and 55% of European enterprises have already positioned such strategies (nlyte software, 2011).

Today IT data centers are seen as major power consumers using 123,000 GWH all over the world and approximately equal to the power consumed by a small country like Poland (Koomey 2007; Schmidt et al. 2009). It was observed that power usage by such data centers doubled between 2000 and 2005, and again rose by 40% till 2010 indicating by 2015 energy costs may be higher than the equipment costs (nlyte software, 2011). Hence huge power consumption with direct affect on environment is a driving factor for green IT product buying. The commonly used computing equipment approximately consumes 62% of whole ICT energy and 25% by business communications infrastructure (Gartner, 2008). Since 1980 out of 25 billion electronic products including computers that are sold by the U.S have produced 2 million tons of e-waste where only 15-20% of them are found to be recycled (Vetter and Creech, 2008; Poniatowski, 2009). Many developing countries like India due to lack of proper consciousness on green IT practices also generated 3.3 lakh tons of e-waste by 2007 and reached 4.7 lakh tons in 2011 (Anand, 2009; Pinto, 2008; Noushin et al., 2010). Office IT infrastructure with 70% of heavy metals and 40% of lead are creating severe environmental problems through unofficial dumping procedures (Hobby et al., 2009). Despite mitigating such problems green IT also grants many economical benefits during IT operations (Dubie, 2009; and Castro, 2009). As such TCS provides a visible evidence to the world by various green IT initiatives in reducing power usage by 12.5%, generating 76MWH of solar energy, reusing of 1.5 cubic meters of water, reducing paper and printer cartridge usage by 28% and 67% respectively and putting down carbon footprints by 2% in 2008 compared to 2007 (Anand, 2009). The EPEAT product registry in the U.S has a major potency in providing superior energy efficiency standards for green IT procurement (Nordin, 2008). Today consumers and corporate firms are recognising green IT products with a genuine eco-labeling system as modest methods in accurately determining the ecological features of ICT products (Christopher et al., 2008; Hobby et al, 2009). Their attitude, desire and intention are driving them towards actual purchasing decisions with considerable choice and demand for such green products and not letting them waste stream prematurely (Tim Flannery, 2010; Robert and Nora, 2009; Hobby et al., 2009). The present demand for green electronic products is found to be 47% and expected to grow to 88% in future and vendors are taking an active part in guiding consumers and promoting products with green attributes (Grail Research, 2009; Peter Jones et al., 2008). As of now the market share for personal computers with green attributes is estimated to be 26.6% indicating green purchasing can improve sustainable strategy in an organisation's regular operations (Schmidt et al., 2010; Hugh Wareham, 2009; Nathalie, 2010). Even this ecological procurement has helped many firms and changed two-thirds of consumers during recession situations (Grail research, 2009).

Among all the developed countries South Korea has become the world leader in addressing green technology issues by strictly practicing legislative policies, managerial leadership and CSR commitments (Castro, 2009). Environmental performance with greater energy efficiency being the most common green criteria for electronic products (William et al., 2010) a wide array of such IT products are being introduced which can cut power usage up to 75%, carbon footprints by 56%, operative performance by 55%, saving commercial office space by 47% (Hobby et al. 2009; Murugesan, 2008).

Pertaining to the green IT literature there is found to exist many research gaps, however this study attempts to address and bridge some of those gaps by examining the interrelationships among the identified fifteen green IT enablers.

Conceptual Model And Hypotheses Formulation
To understand the influence of all these green IT enablers on consumers' green purchasing behaviour a conceptual framework was developed based on the hypothesised relations with some direct and indirect effects on green purchasing behaviour presenting the research questions for the study as explained below.

Environmental Consciousness
Green Purchase intention was found to be the strong factor between green consumer profile and their purchasing behaviour. The varying levels of consumer environmental consciousness initiates consumers for such intention (Tahir et al., 2011). Therefore the consumers who are more eco conscious intend to buy ecological products and services to attain sustainability and be role models for other customer groups (Roberts, 1996). As the past studies did not provide any evidence in understanding the relationship between environmental consciousness and sustainable strategy, it is intended to test relationships between them and hence they are hypothesised:

H1: Environmental consciousness will have a positive and direct effect on sustainable strategy in improving green purchasing behaviour.

Kyoto Protocol
The research conducted in understanding consumer's environmental concerns and their purchasing patterns provided implication for the government policy makers (Chan, T.S., 1996). Many studies explored that the involvement of firms green purchasing is positively associated to their importance on environmental regulatory agreements (Hokey and William, 2001). With the introduction of new regulations by European Union on reducing power consumption, e-waste disposals and eco-labeled products many in the corporate sector are making green purchasing and found to have a positive and direct association between them both with the involvement of corporate social responsibility (Tarig et al., 2010; Hokey and William, 2001; Preuss, 2001). Controversy other empirical studies proved there is no significant association among these two (Zhu et al., 2007a, b). Hence to understand the relationship between the Kyoto Protocol and corporate social responsibility it is hypothesised as:

H2: Kyoto Protocol will have a positive and direct effect on corporate social responsibility.

Global Warming
IT industry being responsible for 2% of carbon emission, on an average equal to annual pollution generated by the airline industry (Simon Mingay, 2007) is the main cause of global warming. Today United States, European Union, China, India, and Russia are found to be the five top most industrialised countries in the world. So the consumers who really think about environmental pollution from product usage will certainly show an interest in buying green products (Ishaswini and Saroj, 2011). Therefore it is hypothesised that:

H3: Global warming will have a positive, direct effect on green purchasing behaviour.

Corporate Social Responsibility
The green consumers who buy eco-friendly products will also expect their vendors and manufacturers to behave in the same way. So the firms intend to stay and work towards green initiatives. Such a contribution of the firm to the society is called corporate social responsibility (Forte and Lamont, 1998). A research study on consumer green purchasing gives evidence that there is no relationship between corporate social responsibility and green purchasing. However there is a need to confirm this relationship in context to green IT products. Hence it is hypothesised:

H4: Corporate social responsibility will have a positive effect on green purchasing behaviour.

Power Consumption
Green IT products which consume less power during IT operations are much more beneficial in data center operations also. It is understood these products with better energy efficiency and energy rating reduce power consumption up to 75% and improve functional performance up to 55% (William et al., 2010; Murugesan, 2008). However there is found to be no empirical relationship between power consumption and performance of a green IT product. Therefore it is hypothesised:

H5: Power consumption by green IT products will have a positive direct effect on product performance enabling green purchasing behaviour.

E-Wastage Disposal
An empirical study on product eco-labels indicated a positive and direct relationship with consumer's green purchasing behaviour showing importance on the environmental information that is available on the labels and resulted in reduced e-waste disposal (Elham and Abdul, 2011). The e-waste with heavy and rare material will generate green house gases like carbon dioxide and when disposed of will increase the effect of global warming on the earth (Sunil et al., 2013). Even the research conducted by the environmental protection agency in USA confirmed that the core recycling process also accounts for 7% to 10% of disposal waste by weight and 33% to 55% of emissions which is also applicable to green products but in a reduced manner when compared to other conventional products (Bill Smith et al., 2011). So it is understood e-wastage disposal from green IT products will also have less positive influence on global warming which cannot be expected to be zero. Hence it is hypothesised:

H6: E-wastage disposal will have a positive effect on global warming.

Financial Benefits
Most of the business organisations aim at making profits to get financial returns. The green attributes of eco-friendly IT products not only grant environmental sustainability but also provide economical sustainability indirectly through reduced cost of power consumption, less operations and maintenance cost and minimised office space cost (Castro, 2009; Nagata and Shoji, 2005 and Dubie, 2009). Recognising such financial benefits most of the corporate firms are now moving towards green purchasing in order to evaluate their business in terms of cost benefit criteria (Ann et al., 2006; Tarig et al., 2010; Ravi et al., 2005). In this context it is hypothesised to test if:

H7: Financial benefits from green IT products will have a positive effect on consumers' green purchasing behaviour.

Eco-Labeling and Certifications
The product eco-labels like TCO, Blue Angel, Energy Star and EPEAT certifications are found to be the best way in identifying the green products for both corporate purchasers and individual consumers as they protect the environment by generating reduced carbon emissions and dematerialising paperwork (Christopher et al., 2008). Hence it is understood there exists a positive association between eco-labeling and consumer's green purchasing behaviour and it is hypothesised:

H8: Eco-labeling and certifications will have a positive and direct effect on green purchasing behaviour.

On the other hand there has been found to be a substantial unawareness of green IT products among IT professionals and resulting in an increased volume of e-waste (Noushin et al., 2010). In fact the consumers are often confused about eco-labels due to an incorrect labelling system on the products. Even though they read such eco-labels it cannot be assumed that they understand the meaning of those labels completely; but these may result in e-waste disposal too (Kangun and Polonsky, 1995; Morris et al., 1995). With these alternating research findings it is necessary to confirm the relation between eco-labelling, e-waste disposal and consumer green purchasing behaviour. Therefore it is hypothesised:

H9: Eco-labelling and certifications will have a positive and direct relation with e-wastage disposal.

Psychological Factors
Even though the consumers say they want to behave ecologically, it is not actually reflecting in actions during purchasing. This is because their desire and attitude is found to be the weakest link between their intention and actual purchasing behaviour (Tim Flannery, 2010). So it is proposed to test this relationship for green IT products and it is hypothesised:
H10: Psychological factors will have a positive and direct effect on consumer demand and preferences to undertake green purchases.

Corporate perception
Corporate consumer's environmental consciousness alone is not enough to reinforce them towards green purchasing but their perception of green products will also have a significant role. Such a perception is found to be positive on green IT products assisting them to demand those products (Anand, 2009). Another research study states despite consumers' having a positive perception on green products, green washing had mislead them and did not influence them in green purchasing (D'Souza et al., 2006). In order to have a confirmed statement it is intended to test if:

H11: Corporate perception of green IT products will have a positive and direct effect on the consumer's demand and preferences to undertake green purchasing behaviour.

Performance
The green IT products are not only identified as durable, energy efficient but also seem to provide better IT operational performance by reducing the power consumption up to 75%, operational cost by 73%, reducing carbon footprints by 56%, improved performance by 55% and saving office space by 47% (Hobby et al., 2009; Murugesan, 2008). Despite admirable product performance they grant sustainability in terms of societal, environmental and economical aspects by no sooner entering into e-waste. Such products cannot have a zero percent impact on e-waste generation, however they will have a moderate positive affect. To understand this relationship empirically between the two it is hypothesised:

H12: Performance of eco-friendly IT products will have a positive relationship with e-wastage disposals.

Consumer Demand and Preferences
Green consumers do consider ecological features of a product during their purchasing. They, being major investors, create demand for such products through eco consciousness, attitude and intention and it puts some pressure on vendors to engage them in green initiatives during product development (Molla et al., 2009, Mulder, L., 1998; Paul Schwarz, 2008; Peng and Lin, 2008). Though the relation between consumer demand and vendors is found to be positive for general green products, it is important to test it in green IT context also. Hence it is hypothesised:

H13: Consumer demand and preferences will have a positive and direct effect on the market players in undertaking green purchasing behaviour.

Market Players
The present demand for green electronic products is 47% and still expected to grow to 88% in future with firms delivering products in line with customer demands (Grail Research, 2009). Among 252 IT professionals 32% believe green IT products are very essential and 60% state such products can be neither ignored nor so important (Paul Schwarz, 2008; Jerome et al., 2011). So the increasing demand of green IT products must be produced innovatively meeting green criteria as per the customer's expectations. Also some marketers claim they offer many essential techniques and correct the consumers towards green purchasing by changing their beliefs and attitudes and suggest to them sustainable goals (Peter Jones et al., 2008). Hence it is hypothesised to test:

H14: Market players will have a positive and direct effect on consumers' green purchasing behaviour.

Sustainable Strategy
The new regulations set up by the government advised the industries to strictly consider the affect of their daily operations on the environment or else they will need to pay carbon taxes consequently. Hence most of the industries are driven by undertaking green purchasing towards developing a sustainable strategy for the business to survive for at least for the near future (Hugh Wareham, 2009). So it is hypothesised:

H15: Sustainable strategy will have a positive and direct effect on green purchasing behaviour.

Provided with the hypothesised relations the proposed model is presented in Figure 1.

Click here for Figure 1: Model of Consumers' Purchasing Behaviour of Green IT Products Showing Hypothesised Relations

Survey Method
To test the proposed model empirically a survey was conducted using a structured questionnaire.

Questionnaire Instrument and Data Collection
A well developed questionnaire on a five-point Likert scale with 1 as 'strongly disagree', 3 as 'neutral' and 5 as 'strongly agree' was verified by 10 academic experts and corporate respondents respectively for the clarity and correct meaning of the words used, ensuring that all the measures are relevant and applicable contextually for the study without any ambiguity. All the fifteen constructs were structured with minimum three to six sub questions, representing totally 61 items.

As this study is intended to further the understanding of corporate green IT product purchasing in Saudi Arabia, the data was collected from a total of 272 firms in Riyadh out of which 82% are private sector and due to the maintenance of high confidentiality in procurement matters only 18% of the firms were observed to be public sector. The questionnaire was administered by e-mails followed by telephone calls and also in a personal visit from more than 1500 respondents across different industries. The profiles of the respondents are from IT infrastructure staff, purchasing departments and C-level executives as they involve primarily in procurement decision making. However the primary data was able to collect from 748 respondents out of which only 716 were found to be complete and useful for further data analyses.

By conducting Little's MCAR test using SPSS it was found the data is missing completely at random. However the missing data is less than 5% and hence we deleted them by case wise instead of conducting some data imputation methods (Tabachnick and Fidell, 2001). Further, as the structural equation modeling uses maximum likelihood method in estimating the parameters it is imperative to check the data for outliers and normality conditions (Hair et al., 1998 and Tabachnick and Fidell, 2001). The univariate outliers are assessed by using SPSS and multivariate outliers using Mahalanobis distance statistics D2 which ultimately identified six outliers and resulted with 710 useful data sets after their deletion. Skewness and kurtosis being the two facets in determining the normality conditions ranged between -2 and +2 indicating the data is distributed normally (Tabachnick and Fidell, 2001).

Pilot Study
To check for the reliability conditions and identify the poorly performing items, a pilot study was conducted by considering 50 responses. The results presented the acceptable range of both item-to-total correlations above 0.3 (Spector, 1992) and the Cronbach coefficient alpha more than 0.70 (Nunnally, 1978) indicating no deletion of any items from the data. Hence the analysis was proceded for main study.

Main Study
The descriptive statistics in the main study were presented by calculating the mean score and standard deviation for all the 61 items. The item-to-total correlation as a measure of correlations between each item and the total score of the scale were above 0.3 (Spector, 1992) and Cronbach alpha measuring the internal consistency of each construct ranging between 0.920 and 0.992, which is well above 0.7 (Nunnally, 1978) indicating good reliability conditions. In order to establish the construct validity and confirm the factor structure of each item both the exploratory and confirmatory factor analysis were conducted.

Results of Exploratory Factor Analysis
The exploratory factor analysis of consumers' purchasing behaviour of green IT products was conducted by using SPSS as presented in Table 1. All the items were subjected to principal component analysis with varimax rotation method and only the items with factor loadings more than 0.55 representing the correlation between the items and its underlying construct and a cross loading less than 0.45 were considered valid on each construct (Hair et al., 1992). All the items were loaded on the constructs on which they were hypothesised and extracted into fifteen factors depending on the Eigen value greater than one explaining enough total variance each representing a unique factor. KMO as a measure of sampling adequacy was observed to be 0.868 and with a significant Bartlett's test of sphericity verified the factorability conditions. Exploratory factor analysis was considered as a preliminary attempt and does not focus on advanced test of validity. To address this issue confirmatory factor analysis was conducted to confirm the factor structure established by exploratory factor analysis.

Results of Confirmatory Factor Analysis
Before making any attempt to analyse the full structural model, a preliminary step to test the validity of measurement model through confirmatory factor analysis was conducted. The factor loadings of all the indicators in measurement model were above 0.70 (Hair et al., 1998). By observing the squared multiple correlations greater than 0.50, standardised residual covariance less than 2.58 (Joreskog and Sorbom, 1988) and modification indices, the initial measurement model was modified (Hair et al., 2006). However the measurement model with minor modification was found to fit the data well and obtained X2 1.639 at p < 0.001 while the other fit indices were GFI=0.904; AGFI=0.890; TLI, NFI and CFI, all well above 0.95 (Hu and Bentler, 1999) and RMSEA, RMR and SRMR are 0.030, 0.005 and 0.0143 respectively. Convergent and discriminant validity represents two aspects in determining the construct validity. The factor loadings, composite reliability (CR) of each construct were above the upper threshold value of 0.70 and average variance extracted (AVE) are also above 0.5. Hence the good factor loadings and CR value greater than AVE provides evidence of convergent validity (Fornell and Larcker, 1981; Bagozzi and Yi, 1988). The AVE analysis of the measurement model, by comparing the square root of AVE of each latent factor with the standard correlation coefficient between the two constructs as represented in below Table 2 demonstrates the fulfillment of discriminant validity test.

Click here for Table 1: Rotated Component Matrix

Click here for Table 2: AVE Analysis and Factor Correlations among Constructs

Hypotheses Testing and Overall Model Fit
The hypothesised model with fifteen paths predicting green IT products consumer purchasing behaviour was tested using structural equation modeling. The structural model was found to be reasonably fit with the observed data. However the structural model with very weak to strong relationships is well fitted with the data as shown in Figure 2. The X2 statistics is highly sensitive to large sample size (Joreskog and Sorbom, 1989) so the X²/df (relative chi-square value) was observed as 2.328 at p < 0.001 which fell below the recommended value of 0.30. The GFI and AGFI were reported to be 0.852 and 0.840 respectively falling within the acceptable range where other fit indices such as CFI, TLI and NFI are above 0.95 (Hu and Bentler, 1999) also RMSEA and RMR were 0.043 and 0.041 respectively.

Click here for Figure 2: Final Structural Model for Green IT Consumers' Purchasing Behaviour

Findings And Discussions
A total of fifteen hypothesised path relations were tested, out of which twelve paths were supported and the other three paths due to their insignificant values were not supported by the study. Enabler sustainable strategy was found to be the highest predictor of green purchasing behaviour and power consumption, performance, global warming, e-wastage disposal and eco-labeling and certifications observed to have no significant affect on green purchasing. It is assumed consumers possess lack of awareness on these issues. The rest of the enablers also showed very weak to weak associations between them as hypothesised and also on green purchasing behaviour indicating there need to put a lot of effort in educating the consumers about eco-friendly purchasing. The structural model identified 60% of very weak, 6.7% of weak, 6.7% moderate, 6.6% strong and 20% of insignificant relationships. The overall model has explained 46% of variance in green purchasing behaviour. The consumers who are well educated and employed in a managerial role understand the real meaning of sustainability and are interested in green purchasing. Also 7.3% of manufacturing, 6.9% of construction, 6.6% of IT/ITES, 5.2% of educational and 4.5% of healthcare industry's respondents stated they already implemented green IT initiatives and preferring green IT products during their procurement process.

Implications
As the green IT marketing studies were found to be very few, this study provides significant inferences for both academia and industry who works in the green IT area. As some factors show insignificant affect on green purchasing it is recommended to conduct awareness programs by Saudi firms or by local government to educate its corporate consumers to maximise the utilisation of available resources. Finally this study helps the marketers in identifying corporate consumers who are interested in green IT product purchasing and assists them in innovating such products by giving due consideration to the customer values. However it is inferred the green purchasing currently is found to be a niche market with a wide scope to improve in the near future.

Limitations
The study attempted to understand the consumers' green IT product purchasing behaviour so these results may not be applicable to other green products, but are not limited to any electronic products. Due to the cultural barriers of gender classification at work, the majority of the IT respondents were found to be males and the study could not present gender wise green purchasing behaviour. To maintain the time frame of the study only the central part of Saudi Arabia was considered and also the public sector, to keep the confidentiality in their procurement matters did not actively participate in the study.

Scope for Further Research
A detailed review on green IT literature identified many research gaps, however this study attempted to address some of them. Despite green IT being an emerging study in IT management there is found to be no much evidence of consumer studies in this area indicating there is much more to explore. However some recommendations were provided as the scope for future research. Other studies by adding or deleting appropriate enablers in the present model can be conducted in other parts of the world to confirm these research findings. Similar study can also be conducted by considering barriers for green IT product purchasing to know what factors are inhibiting consumers from purchasing green IT products. A study on cost-benefit analysis of green IT products can assist consumers and industries to realise the benefits of these products and reinforce them towards green purchasing. By classifying these fifteen enablers under socioeconomic and environmental standards, a study on multi criterion weightages can be conducted to give the rankings for each factor based on their priority. As the consumer purchasing behaviour is dynamic in nature, a system dynamics modelling study can be conducted to understand the behaviour of these enablers over time and assist the policy makers and green IT product innovators in taking appropriate decisions. Also any research approach suggesting the best marketing strategy can be undertaken to grab the green IT consumers.

List of abbreviations

AGFI Adjusted Goodness of Fit Indices
AMOS Analysis of Moment Structure
AVE Average Variance Extracted
CDP Consumer Demand and Preferences
CFI Comparative Fit Index
CPER Corporate Perception
CR Composite Reliability
CSR Corporate Social Responsibility
ECOLC Eco-Label and Certifications
ENVC Environmental Consciousness
EPEAT Electronic Product Environmental Assessment Tool
EWD E-Wastage Disposal
FB Financial Benefits
GFI Goodness of Fit Indices
GPB Green Purchasing Behaviour
GW Global Warming
KYPL Kyoto Protocol
MKTP Market Players
NFI Normed Fit Index
PCON Power Consumption
PER Performance
PSYF Psychological Factor
RMR Root Mean Square Residual
RMSEA Root Mean Square Error of Approximation
SPSS Software Package for Social Sciences
SRMR Standardised Root Mean Square Residual
SSTG Sustainable Strategy
TCO Tjänstemännens Central Organisation
TLI Tucker Lewis Index

References
Alemayehu Molla. GITAM: A Model for the Adoption of Green IT. 19th Australasian Conference on Information System (ACIS) proceedings, 2008; paper 64.
Alemayehu Molla, Vanessa Cooper, Siddhi Pittayachawan. IT and eco-sustainability: Developing and validating a green IT readiness model. Conference Proceedings: 13th International Conference on Information Systems, Phoenix, 2009.
Anand Siva Subramaniam. White Paper, TCS, Making IT Green - The TCS Way, 2009.
Ann, G.E., Zailani, S. and Wahid, N.A. A study on the impact of environmental management system (EMS) certification towards firms' performance in Malaysia, Management of Environmental Quality: An International Journal, 2006; 17 (1): 73-93.
Bagozzi Richard, P. and Yi, Y. On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 1988; 16 (1): 74-94.
Bill Smith, John Davis, McKenna Morrigan and Evan Johnson. Reducing Greenhouse Gas Emission through Recycling and Composting: A Report by the Materials Management Workgroup of the West Coast Climate and Materials Management Forums, reported by EPA U.S.A government, EPA 910-R-11-003, May 2011.
Butner, K., Geuder, D., and Hittner, J. Mastering Carbon Management: Balancing Trade-Offs to Optimize Supply Chain Efficiencies. Supply Chain Management, IBM Institute for Business Value, 2008; pp. 1-13.
Castro, D. Learning from the Korean Green IT Strategy. The Information Technology and Innovation Foundation Web Memo, 2009. http://www.greenbiz.com/research/report/2009/08/11/learning-korean-green-it-stratey.
Chan, T.S. Concerns for environmental issues and consumer purchase preferences: A two-country study, Journal of International Consumer Marketing, 1996; 9 (1): 43.
Chandrasekhar Ramasastry. Green IT Matters at Wipro LTD. Asia Case Research Centre, The University of Hong Kong, 2009.
Christopher Gan; Han Yen Wee; Lucie Ozanne and TzuHui Kao. Consumers' purchasing behaviour towards green products in New Zealand. Innovative Marketing, 2008; 4 (1): 93-102.
Clare D'Souza, Mehdi Taghian, Peter Lamb, Roman Peretiatkos. Green products and corporate strategy: an empirical investigation. Society and Business Review, 2006; 1 (2): 144 - 157.
Dubie, D. How to Cut IT Costs with Less Pain, Network world, 26 January, 2009; pp. 11- 40.
Elham Rahbar and Nabsiah Abdul Wahid. Investigation of green marketing tools' effect on consumers' purchase behaviour. Business Strategy Series, 2011; 12 (2): 73 - 83.
Fornell, C., and Larcker D. F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 1981; 18 (1): 39-50.
Forte, M., and Lamont, B. The bottom-line effect of greening (implications of ecological awareness). The Academy of Management Executive, 1998; 12(1): 89-91.
Gartner Inc., Sustainable IT, A Gartner Briefing, Gartner, Dublin. 2008.
Grail Research. The Green Revolution. September, 2009. Retrieved from: http://grailresearch.com/pdf/ContenPodsPdf/The_Green_Revolution.pdf
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., and Tatham, R.L. Multivariate Data Analysis, 6th ed., Upper Saddle River, NJ, Pearson Prentice-Hall, 2006.
Hair, Jr. Joseph F., Rolph E., Anderson, Ronald L., Tatham, and William C. Black. Multivariate Data Analysis. Englewood Cliffs, NJ: Prentice Hall International, 1998.
Hair, Jr. Joseph F., Rolph E., Anderson, Ronald L., Tatham, and William C. Black. Multivariate Data Analysis with Readings, New York: MacMillan Publishing Company, 1992.
Hobby, C., Rydel, N., Sjogren, E., and Williams, W. IT products - Going beyond Green - Can High performance and Sustainability Co-exist. Sustainable Systems and Technology, ISST IEEE International symposium, 2009; pp. 1-4.
Hokey Min, William P. Galle. Green purchasing practices of US firms. International Journal of Operations and Production Management, 2001; 21(9): 1222- 38.
Hu, L.T., and Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 1999; 6 (1): 1-55.
Hugh Wareham. CEO - ECO Buy, Green Purchasing in Australia, August 2009. http://www.ecobuy.org.au/uploads/documents/Green%20Purchasing%20Australia%20Report%20Summary%20Document.pdf
Ishaswini, Saroj Kumar. Pro-environmental concern influencing Green Buying: A study on Indian consumers. International Journal of Business and management, 2011; 6(6): 124-133.
Jerome K., Vanclay, John Shortiss., Scott Aulsebrook., Angus M. Gillespie., Ben C. Howell., Rhoda Johanni., Michael J. Maher., Kelly M. Mitchell., Mark D. Sterwart, Jim Yates. Customer Response to Carbon Labeling of Groceries. Journal of Consumer Policy, 2011; 34: 153-160.
Joreskog, K. G., and Sorbom, D. LISREL 7: A guide to the program and applications, Chicago: SPSS, 1988.
Joreskog, K. G. and Sorbom, D. LISREL7: A Guide to the Program and Applications, 2nd edition. Chicago: SPSS Inc., 1989.
Kangun, N. and Polonsky, M.J. Regulation of Environmental Marketing Claims: A Comparative Perspective. International Journal of Advertising, 1995;14: 1-24.
Koomey, J.G. Estimating total power consumption by servers in the U.S. and the world. Final report, Standford University, February 15, 2007.
Morris, L. A., Hastak, M. and Mazis, M.B. Consumer Comprehension of environmental advertising and labeling claims. Journal of Consumer Affairs, 1995; 29: 328 -50.
Mulder, Lettemieke. Green Purchasing: does it make sense? Electronics and the Environment. Proceedings of the IEEE International Symposium, 1998; pp.123-128.
Nagata, S. and Shoji O. Green Process Aiming at Reduction of Environmental Burden. Fujitsu Science and Technology Journal, 2005; 41(2): 251- 58.
Nathalie Bachour and Larry Chasteen. Optimising the Value of Green IT Projects within Organisations, IEEE, 2010.
Nlyte software. Enabling the Green Data Center: Drivers, Benefits and Technology Enablers, White Paper 102, 2011. Retrieved from: www.thedatachain.com/materials/nlyte_software_wp102.pdf?
Nordin, H. Your Computer and the Climate. TCO Development, Stockholm, 2008.
Noushin Laila Ansari., Mahfuz Ashraf., Bushra Tahseen Malik and Helena Grunfeld. Green IT awareness and Practices: result from a field study on mobile phone related e-waste in Bangladesh. IEEE International Symposium on Technology and Society, 2010; pp.375-383.
Nunnally, J. C. Psychometric Theory, New York: McGraw-Hill, 1978.
Paul Schwarz. Green TECH Plus, 2008. A report from HANSA GCR www.hansagcr.com
Peng, Y.S. and Lin, S.S. Local responsiveness pressure, subsidiary resources, green management adoption and subsidiary's performance: evidence from Taiwanese manufactures. Journal of Business Ethics, 2008; 79(1): 199-212.
Peter Jones, Colin Clarke-Hill, Daphne Comfort and David Hillier. Marketing and Sustainability. Marketing Intelligence & Planning, 2008; 26(2): 123-130.
Pinto, V.N. E-waste hazard: The impending challenge. Journal of occupational and environmental medicine, May, 2008; 12(2): 65-70.
Poniatowski, M. Foundations of Green IT: Consolidation, Virtualisation, and ROI in the data center. United States: Prentice Hall, 2009.
Preuss, L. In dirty chains? Purchasing and greener manufacturing. Journal of Business Ethics, 2001; 34 (3): 345-59.
Ravi, V., Shankar, R., and Tiwari, M.K. Productivity improvement of computer hardware supply chain. International Journal of Productivity and Performance Management, 2005; 54 (4), 239-55.
Robert R., Harmon and Nora Auseklis. Sustainable IT Services: Assessing the Impact of Green Computing Practices. PICMET Proceedings, 2009; pp. 1707-1717.
Roberts, J.A. Green Consumers in the 1990s: Profile and implications for advertising. Journal of Business Research, 1996; 36: 217-231.
San Murugesan. Harnessing Green IT: Principles and Practices. IT Professional, IEEE Computer Society, 2008; 10 (1): 24-33.
Schmidt, N.H., Schmidtchen, T., Erek, K., Kolbe, L.M., Zarnekow, R. Influence of Green IT on Consumers' Buying Behaviour of Personal Computers: Implications from a Conjoint Analysis. 18th European Conference on Information Systems, 2010; pp.1-10.
Schmidt, N-H., Erek, K., Kolbe, L.M., and Zarnekow, R. Towards a Procedural Model for Sustainable Information Systems Management. Proceedings of the 42th Hawaii International Conference on System Sciences (HICSS-42) (Sprague, R.H.Ed.). USA, Hawaii, 2009.
Simon Mingay. Green IT: The New Industry Shock Wave. Gartner Research, 2007. http://www.gartner.com/newsroom/id/503867
Spector, P. E. (Ed.)., Summated Rating Scale Construction: An Introduction, University Papers Series on Quantitative Applications in the Social Sciences Series No. 07-082, Newbury Park, CA: Sage Publications, 1992.
Sunil J.V., Kavitha T. C. and Krishna B.S.V.R. Climate Change and Sustainable Development Through Carbon Dioxide Utilisation. Business Sciences International Research Journal, 2013; 1 (1): 154-158.
Tabachnick, B. and Fidell, L. Using multivariate statistics, (4th ed.), Boston, MA: Allyn and Bacon, 2001.
Tahir Albayrak., Meltem Caber., Luiz Mutinho and Ram Herstein. The influence of skepticism on green purchase behaviour. International Journal of Business and Social Science, 2011; 2 (13): 189-97.
Tarig Khidir ElTayeb., Suhaiza Zailani., Krishnaswamy Jayaraman. The examination on the drivers for green purchasing adoption among EMS 14001 certified companies in Malaysia. Journal of Manufacturing Technology Management, 2010; 21 (2): 206 - 25.
Tim Flannery. Faculty of Business and Economics, The gap between consumer attitude and purchase behaviour of eco-friendly products. Macquarie University, Sydney, Australia, October 2010; pp.1-9.
Velte, T.J., Velte, A.T. and Elsenpeter, R. Green IT: Reduce your Information System's Environmental Impact While Adding to Bottom Line. New York: McGraw Hill Companies, 2008.
Vetter, T. and Creech, H. The ICT Sector and the Global Connectivity System: A sustainable development overview. The International Institute for Sustainable Development, IISD, Winnipeg, 2008; 15 (11/12): 1041-52.
http://www.iisd.org/pdf/2008/ict_global_con_sd.pdf. [Accessed: Dec, 10, 2009].
William Young, Kumju Hwang, Seonaidh McDonald and Caroline J. Oates. Sustainable Consumption: Green Consumer Behaviour When Purchasing Products. Journal of Sustainable Development, 2010; 18: 20-31.
Zhu, Q., Sarkis, J. and Lai, K. H. Green supply chain management: pressures, practices and performance within the Chinese automobile industry. Journal of Cleaner Production, 2007b; 15 (11/12): 1041-52.
Zhu, Q., Sarkis, J. and Lai, K. H. Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers. Journal of Environmental Management, 2007a; 85 (1): 179-89.