Madhuchhanda Das Aundhe

Jang Bahadur Singh

N Ramesh
email: nramesh23@gmail.com

M Vimalkumar

Adoption of Digital innovation in Crop Insurance -A Data Analytics based benchmarking study of Samrakshane portal in Karnataka

Keywords: Samrakshane, Agri-insurance, Digital Innovation, E-Gov portal

Agriculture in India is prone to a lot of uncertainties and different types of risks. Crop insurance is a mechanism hedge against such contingencies. Though many governments in the past had introduced various crop insurance schemes, the penetration rate was shallow, and there were no private players. Thus, the current Govt introduced PMFBM, which can be a game-changer. Karnataka went a step ahead and implemented an end to end technology platform -Samrakshane -to promote and manage the crop insurance. Samrakshane, being an innovative technology platform enabling the complete automation of crop insurance adoption. The objective of this study is to understand and measure the innovation adoption impact of the crop insurance scheme. In this short paper, we present a conceptual outline of our ongoing research.

Introduction

Agriculture is the most climate sensitive sector, and India is among the most vulnerable countries in South Asia, due to the dependence of the majority of its population on this sector for their livelihood [START_REF] Swain | Crop insurance for adaptation to climate change in India[END_REF]. Agriculture contributes to 22 percent of GDP, provides 58 percent of employment, sustains 69 percent of population, produces all the food and nutritional requirements of the nation, important raw materials for some major industries, and accounts for about 14 percent of exports [START_REF] Swain | Crop insurance for adaptation to climate change in India[END_REF]. The agricultural activity is carried out on 60% of the total land area of India, 60% of which is dependent on seasonal rains for its irrigation [START_REF] Etonline | What crop insurance? India's farmers have no clue about the covers Centre doles out for them -The Economic Times[END_REF]. Thus, agricultural sector is recognized as a national priority by the government of India and constitutes a politically articulate and powerful constituency wooed by practically political parties of all hues. Agriculture is inherently a risky venture. Agricultural risk and fluctuations pose the biggest threat to the large majority of Indian farmers who have small and marginal land holdings (less than 5 acres of land). Risks in agriculture can be classified as:

• Idiosyncratic risks that usually affect only individual farms or firms (e.g., plant and animal pests and diseases, illnesses of the owner or laborers); • Covariate risks that affect many enterprises simultaneously (e.g., major droughts or floods, fluctuating market prices).

The high propensity of covariate risks in rural areas is a major reason that informal risk management arrangements break down and that formal locally based financial institutions are hesitant to provide commercial loans for agriculture [START_REF] Skees | New approaches to crop yield insurance in developing countries[END_REF][START_REF] Skees | Conceptual and Practical Considerations for Sharing Catastrophic/Systemic Risks[END_REF]. Jaffee and Siegel [START_REF] Jaffee | Rapid Agricultural Supply Chain Risk Assessment: A Conceptual Framework[END_REF] list all possible sources of risk potentially affecting agricultural value chains. They identify seven categories: weather-related risks (especially those related to hail and wind damage and to high humidity or excess rain), natural disasters like floods, tsunami, earthquakes can affect agricultural supply chains for multiple growing seasons and/or production cycles, biological and environmental risks refer to presence of certain plant pests or livestock diseases, market-related risks existing for inputs and outputs and for the critical services that support supply chains, such as finance and logistics, agricultural supply chains increasingly face risks related to logistics and infrastructure, managerial and operational risks, which are closely associated with human judgment and response, public policy and institutional risks also have systemic impacts on decision making and productivity, as well as on market options. Traditionally developing countries have been using long-standing tools like interventions by governments, management of strategic food reserves, implementation of price stabilization schemes, heavily subsidized crop insurance, and credit guarantee programs for managing risks. These approaches to risk management can be further articulated as ex-ante or ex-post strategies.

Agricultural Risk Management: Individual & Institutional

One of the key objectives (globally) of agricultural risk management is stabilisation of the incomes of farmers. It is not only the low income of the farmer, but the fluctuations in income that cause hardships by affecting the stability between income and consumption requirements during the periods of crop loss or failure. This stabilisation, at an individual farmer level can happen through various ways -(i) managing the agricultural supply chain, (ii) extending the market, (iii) agricultural research (individual experimentation and innovation), (iv) crop insurance. Risks related to logistics and infrastructure are addressed by improving the distribution network for the supply of inputs, and improving the agricultural extension system. Market related risks are mitigated through support/ procurement prices provided by the government. Crop insurance, as a risk management alternative, provides cover only against yield risk and hence income fluctuations arising from fluctuations in yield. Where income variability is due to price fluctuations or due to untimely or irregular supply of inputs and information, crop insurance is not the solution. Therefore, crop insurance serves as a means to mitigate weather related risks, natural disasters, and biological and environmental risks. The financial and other capabilities involved in the first three ways of risk management mentioned above may be daunting for small and marginal farmers. Therefore, crop insurance is, seemingly, important for stabilisation of incomes of small and marginal farmers [START_REF] Prabhu | Crop Insurance: The International Experience[END_REF]. Crop Insurance is an ex-ante action that may transfer certain risks to other parties (i.e. insurance agencies). Another perspective of managing risk, consists of risk minimising practices generally adopted by farmers are diversification of resources -vertical, horizontal and temporal-and adjustments within the cropping systems. The vertical diversification refers to the broader baskets of agricultural assets, i.e. animal / poultry farming that the farmer adopts to mitigate his croprelated risks. The horizontal and temporal aspects refer to both spatial and crop diversification, and these are widely used in semi-arid regions. The studies by the International Crops Research Institute for the Semi-Arid topics (ICRISAT) has proved the superiority of crop diversification over spatial diversification in reducing the income instability of farmers. However, even the usefulness of crop diversification was limited as it failed to adequately protect the farmers from risk. Adjustments within the cropping system, e.g. inter-cropping of sorghum and pearl millet, also proved ineffective as was evident from the positive and significant correlation between their yields. Since the risk minimising practices are largely related to the yield of the crops, crop insurance serves as an important risk mitigation mechanism. It has a more important role to play in countries where traditional strategies (e.g. price stabilisation schemes, supply chain management etc.) to manage risks are inefficient or ineffective. Crop insurances may, further, be of two typesmultiple risk or specific risk. The fact that multiple-risk crop insurance is not a commercially viable proposition and that its existence depends on liberal subsidies from the government is further underlined by the situation in Australia. Farm incomes in Australia are notoriously volatile. The insurance of crops is undertaken by large insurance bodies in the private sector which offer insurance cover against specific risks such as hail and fire. Lloyd and Mauldon recount the failure of various attempts made in Australia to introduce multiplerisk insurance on a commercial basis [START_REF] Prabhu | Crop Insurance: The International Experience[END_REF]. Besides, contributing to income stabilisation, the provision of insurance may enable the farmers to adopt riskier innovations e.g. modern agronomic practices, riskier crops. An indirect benefit of crop insurance may be on the agricultural labour market where the incomes of labourers may stabilise as small and marginal farmers will no longer be compelled to join the ranks of agricultural labourers and push down the wage rate following a failure of crops. Given that crop insurance has an important role to play in the agricultural sector of developing countries, it is important to understand its impact on farmers. The effectiveness of crop insurance in India has been a bit dubious, given the very low rate of its adoption by farmers. Further, from an insurance agencies' point of view, Binswanger [START_REF] Binswanger-Mkhize | Is there too much hype about index-based agricultural insurance?[END_REF] points to the information asymmetry, moral hazard and adverse selection problems faced by them. The indexed approach minimises the transaction costs based on the individual basis. Incentive and information problems which are similar to both credit and insurance services act as severe obstacles to actuarial calculations. Hence, not many private insurance companies are keen to provide farmers cover for their crop yield.

Indian Scenario

In India, the efforts to introduce crop insurance date back to the years immediately after attaining independence when some state governments evinced an interest in introducing the scheme. But the schemes, since 1985 when the Comprehensive Crop Insurance Scheme (CCIS) was launched, have not been gaining the expected traction among the farmers. The Gross Cropped Areai (GCA) covered under crop insurance -touched 28% in 2016-17, then dropped to 26% in 2017-18, has remained far below the target of 50% projected by 2018-19 [START_REF] Rajeev | Where Do We Stand? Crop Insurance in India[END_REF]). This is despite several attempts at changing the schemesfrom individual based ( 72-78) to area-based ( 79-84 : Pilot Crop Insurance Scheme-PCIS); making Crop Insurance mandatory for the loanee-farmers since 1985 ; fully subsidized scheme ( 97-98 -Experimental Crop Insurance Scheme -ECIS); share croppers were covered for the first time in NAIS-1999 -2016 ; farm income , rather than cost of cultivation, was insured ( 2003-04 : Farm Income Insurance Scheme -FIIS) ; WBCIS based on the deviation in the rainfall ( 2007till date) ; Private sector was encouraged to participate and immediate partial payment to affected farmers was introduced (Modified NAIS , 2010-16) and finally PMFBY wherein Premium rates were lowered( 2.5 % to 1.5% for Kharif , rabiremains at 2% ) and use of technology was emphasized and covered more risks such as prevented sowing and post-harvest losses.

Crop Insurance Implementation

A recent survey by the Comptroller and Auditor General of India (CAG) revealed that the coverage of farmers, particularly small and marginal ones, under the insurance schemes was very low compared to the population of farmers as per Census 2011, the report said [START_REF] Et | What crop insurance ? India ' s farmers have no clue about the covers Centre doles out for them[END_REF]. Duplicate claims by some farmers, manipulation in Crop Cutting Experiments (CCE), laxity in the exercise of due diligence by the bankers in the verification of documents pertaining to the identity and the Record of Rights (RoR), have been reported to have plagued the execution of the crop insurance schemes in India. To address these challenges the PMFBY Guidelines emphasize the "adoption of new technology for early loss assessment and better administration of scheme" [OPERATIONAL GUIDELINES, PMBFY, pp.48]. Technology initiatives like the Crop Insurance Mobile App1 are being attempted. It is widely expected that deployment of ICT, as part of e-Governance, would significantly influence the effectiveness and efficiency of administration of the Crop Insurance Schemes in realizing their key objectives and the potential to impact the lives of millions of farmers while safeguarding the interests of all the stakeholders.

The crop insurance scheme with a noble intention to protect farmers can succeed only if operational guidelines are strictly followed and cut off dates are not extended frequently as was done in Kharif 2016. One of the reasons for high actuarial premium rates quoted by the reinsurance companies was the extension of cut off dates. Moreover, timely submission of yield data of CCEs and payment of premium subsidy to insurance companies will smoothen and fasten the process of claim settlements as done by Tamil Nadu in Rabi 2016-17, Tamil Nadu when they experienced one of the worst droughts. There were allegations of data manipulations while conducting CCEs like the yield of groundnut of Rajkot district in Gujarat in Kharif 2016.

The litmus test of any crop insurance scheme, therefore, depends on quick assessment of crop damage and payment of claim into farmers' bank account. The infrastructure to make this scheme fully operational is still inadequate. Timely submission of yield data by State government to the insurance companies is necessary so that they can finalise the claims expeditiously and pay the claims to farmers. The Indian crop insurance scenario has been beset with several problems such as lack of transparency, high premium, delay in conducting crop cutting experiments and non-payment/delayed payment of claims to farmers and delay in payment of the premium subsidy by the governments to the insurance company.

Although the policy w.r.t. Indian crop insurance schemes have undergone several changes over the last 3 decades, there has been little attention given to the process aspects (cost and efficiency) of the various schemes. Therefore, the transparency, accountability and efficiency of the administrative processes of the crop insurance schemes have not seen as much improvement or reforms, as the specific features and benefits of the schemes.

Technology Adoption in Crop Insurance Implementation in Karnataka

Karnataka has been among the leading states in the adoption of crop insurance. The state has achieved significant increase in the enrollment of farmers in the crop insurance scheme-from 8.72 lakh farmers in Kharif 2015 to 20 lakh farmers in Rabi 2017. Though, the idea of automating the crop insurance scheme using ICT is not new, Karnataka developed an end-to-end software application, christened SAMRAKSHANE, meaning 'Protection', with many advanced features and was ahead in the implementation of such a software. This technology platform developed by National Informatics Centre (NIC), Karnataka, was rolled out in June 2016. Samrakshane, is a unified portal, and provides comprehensive support to the state Agriculture and Horticulture departments in managing the entire value chain of crop insurance under both Modified Weather-Based Crop Insurance Scheme (MWBCIS) & Prime Minister Fasal Bima Yojna (PMFBY)from enrolment of farmers to the final payout of insurance claims through a unified portal [START_REF] Gulati | Crop Insurance in India: Key Issues and Way Forward[END_REF]. It enables enrolment of crop insurance through multiple channels -the banks for the loanee farmers, and the Raitha Sampark Kendras (RSK) & Village Level Entrepreneurs (VLE) at the Citizen Service Centers (CSC), for the nonloanee farmers. Samrakshane is an e-governance tool to create a transparent, accountable, at the same time farmer friendly system of administration of the crop insurance schemes, with a great promise and potential to usher in radical change benefitting not only farmers, but all the key stakeholdersthe insurance agencies, banks and the government. This paper aims to study the technology adoption success of the crop-insurance schemes enabled by the digital initiative SAMRAKSHANE. Of late, Karnataka has also gone ahead and made compulsory use of mobile phones while conducting the CCE. In order to address the insurance process issues from farmers' point of view, and take care of the problems faced by the private sector insurance agencies.

Study Objectives:

The objectives of this study would be to understand and measure the innovation adoption impact of the crop insurance scheme as outlined below:

• Adoption success metrics at the level of individual farmer and village level entrepreneur (cost of service, quality of service, personal development, feeling of protection).

Methodology for Measuring Innovation Adoption Success

We propose to adopt a robust assessment strategy for adoption success evaluation [START_REF] Gallivan | Organizational Adoption and Assimilation of Complex Technological Innovations: Development and Application of a New Framework[END_REF] . The evaluation would be done at two levels -

The broader impact of Crop Insurance on the farmer community:

This Community impact is an outcome-oriented measure for Government impact, and would consist of data collected at a citizen level. The impact indices would help to understand 1. The farmers (i.e. their profiles, incomes, and farming techniques etc.) who benefit from the scheme 2. The crops (i.e. their sustainability, diversification etc.) that are supported by the scheme 3. Allied community developments (i.e. feeling of protection, development based on achievement of aspirations etc.) due to crop insurance availability

3.2

The specific impact of the software application (Samrakshane portal) on governance processes through two kinds of measures -1. Efficiency gains e.g. time taken for enrolment of Loanee / Non-loanee farmers in the scheme; time taken for claims process etc. 2. Effectiveness gains e.g. Reduction in errors in due diligence and verification of farmer records; Reduction in manipulations e.g. duplication in land records, benami enrolment; Reduction in Crop cutting experiment manipulations; Penetration of scheme; Quality of governance (transparency, accountability, participation etc)

The evaluation would be conducted in two parts as depicted in Figure 1 below. The first part of the study would consist of the use of Data Analytics on the database built by the Samrakshane project. Some indicative Analytics techniques that can be used are the following. However, the appropriate techniques will be decided upon accessing the Samrakshane historical data stored.

Descriptive (based on Statistical Analysis)

• Farmer profile (region, caste community, economic background) • Crop profiles (area, season, insured amount, premium paid) • Insurance Unit profiles 2. Prescriptive (based on Optimization)

• Trend Analysis of yield in a particular region for a particular crop can give an expected range, which can be used to identify any ambiguity during Crop Cutting Experiments. • These techniques would also help the government arrive at scientific figures for indemnity levels, average yields, threshold yield, sum insured, premium rates, seasonality discipline, coverage of risks and exclusions etc.

3 .•Figure 1 :

 31 Figure 1: Method of Adoption Success Evaluation

  • Adoption success metrics at Community level (e.g. crop sustainability, crop diversification, agronomic practices, income stability). • Adoption success metrics on Governance (efficiency and effectiveness). • Recommendations for process improvements to enhance adoption success (enrollment, claims, proposal verification and validation, additional data capture). • Recommendations for technological innovations in Crop cutting experiments. • Recommendations for successful adoption of digital technology innovations for

http://digitalindia.gov.in/content/crop-insurance-mobile-app visited on 17-01-2018, This Mobile App can be used to calculate the Insurance Premium for notified crops based on area, coverage amount and loan amount in case of loanee farmer. It can also be used to get details of normal sum insured, extended sum insured, premium details and subsidy information of any notified crop in any notified area.

The second part of the study (as shown in Fig 1) would consist of study of factors/reasons for the discovered patterns. This part of the study would be done based on primary data collected (i.e. Surveys / Interviews) from farmers, VLEs and RSKs, bank officials, and insurance company employees.

Acknowledgement

We thank the IIMA -Idea Telecom Center of Excellence for generously funding this project. We also thank NIC -Karnataka, Horticulture department and Agriculture Department for providing us with the access to the necessary data and information required for this project.

Research Progress and Expected Outcomes

The project began in April 2019 with an extensive literature survey along with contacting state officials for traces data of the protal. The objective of the literature review was to understand the types of risk faced by the farmers and the development of crop insurance schemes in India. We understood from the survey that the agriculture in India, kind of gamble and prone to multiple types of risks. Though the agriculture crop insurance to hedge against risks of farming was introduced right after the insurance, a serious step towards the implementation was took place after 1985. However, the adoption rate was shallow, and there were no private players in the crop insurance market. Thus, the current Govt introduced PMFBM, which can be a game-changer. Karnataka went a step ahead and implemented an end to end technology platform to promote and manage the crop insurance. In the literature review section, we have traced the entire evaluation of crop insurance to date. Once we understood landscape, we scheduled meeting with NIC, Karnataka and the horticulture department to understand the intricacies of the Samrakshane platform and nature of agriculture in the state. We had regular interaction with state departments, and the department was very cooperative in supporting our research. Projects investigators continuously held various meetings with state horticulture and agriculture department officials to understand the crop insurance digital solution platform Samrakshane. The both the department were involved in the implementation of the Samrakshane portal.

Subsequently, team requested National Informatics Center, NIC to provide us with the crop insurance raw data, up to the individual level enrolled to Samrakshane platform. The department honoured our request and facilitated us with the complete raw data of three years' (nine seasons). The contained around 80,00,000 enrolments during this period, with more than 40,00,000 unique land parcels being insuring. We have performed preliminary descriptive/exploratory data analysis this entire dataset, and the same is provided in the second chapter.

We had observed different types of adopters. Some of them are early adopters but didn't turn up in the later years. Some of them have adopted in the later years and stayed back. There some formers who have enrolled in all the three years. The preliminary data analysis also shows patterns in terms of agro-climatic regions and crops.

Thus, as the next step, we had plans of visiting the fields to explore these different patters by interacting with the farmers directly. We had scheduled the visits in late March 2020 till May 2020, during the academic break. However, due to the pandemic, we couldn't interact with the farmers, and the field work has been halted from March 2020. Hopefully, we will be able to restore the data collection from September 2020.


Monika Singh

G P Sahu
email: gsahu@mnnit.ac.in

Developing a Model for Green IS Adoption in Indian Banking and IT Industries

Keywords: Green IS, Environment Sustainability, Information Technology, TOE, UTAUT2

Numerous studies explore that, in current era, Banking and Information Technology (IT) services sector, in various developed/developing countries, either adopted or in the phase of Green IS adoption. Since countries, round the globe, have recognized the significance of Green IS and its impact over environment, it is now vital for every industry to implement Green IS and contribute in environment sustainability. This Research-in-Progress paper observes the effective Green IS implementation through carefully consideration of different Motivational, Technological, Organizational and Environmental factors. Subsequently, in this paper 16 different factors are revealed via Literature Review and Experts opinion and a research model is developed using Technological-Organizational-Environmental (T-O-E) theory and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework for effective Green IS implementation, which will be helpful for academician and practitioners in this field.

INTRODUCTION

According to Yang et al. [START_REF] Yang | Perceived fit between green IS and green SCM: Does it matter?[END_REF], the increasing energy ingestion put heavy pressure on energy production at large scale that consequently enforces heavy compression on the atmosphere. This becomes a huge problem in case of cloud data center and required abrupt vigilant action by the researcher, practitioners and users in the IT field since these cloud data centers consumes high level energy due to resource scheduling mechanism to complete the task at high priority causing low server use ratio as well as due to requirement of cooling system that is on the basis of highest significant strategy causing extreme chilling supply, escalations in maneuver cost, and huge energy waste [START_REF]The Global Risks Report 2019[END_REF][START_REF]Two Degrees of Transformation Businesses are coming together to lead on climate change[END_REF][START_REF] Yang | Perceived fit between green IS and green SCM: Does it matter?[END_REF]. According to WEF Report [START_REF]The Global Risks Report 2019[END_REF][START_REF]Two Degrees of Transformation Businesses are coming together to lead on climate change[END_REF], in the current scenario, data centers situated globally are consuming total 1.5 to 2% (two percent) of total energy and it is predicted that the consumption will rise to 140 billion kilowatt-hours per annum by 2020. Consequently, the carbon emission would be nearly 150 million metric tons yearly. Additional to this, data centers were consuming more than 1% (one percent) of the electricity in the server dense country i.e. U.S. Their combined yearly carbon emissions of 80 metric megatons of Carbon-dioxide (CO2) are approaching the carbon footprint of the Netherland and Argentina [START_REF] Andrae | On global electricity usage of communication technology: trends to 2030[END_REF]. A computer/laptop working for over or about ten hours per day emits 1200 pounds of carbon-dioxide i.e. nearly equivalent to one tenth CO2 emitted from an automobile yearly [START_REF] Bussel | Digital archiving, Green IT and environment. Deleting data to manage critical effects of the data deluge[END_REF][START_REF] Weiss | A. Can the PC go green?[END_REF]. According to Belkhir & Elmeligi [START_REF] Belkhir | Assessing ICT global emissions footprint: Trends to 2040 & recommendations[END_REF], total number of computers/laptops worldwide is expected to increase by 5.2% and will account for huge level of CO2 emission; it signifies the concern over IT ecological footprint is valid [START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF].

Wasteful computer energy

The computer is "on" while not in use [START_REF] Bolla | The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization?[END_REF][START_REF] Gesi | SMARTer 2020, The Role of ICT in Driving a Sustainable Future[END_REF][START_REF] Hovorka | IS Sustainability Research: A trans-disciplinary framework for a 'grand challenge[END_REF][START_REF] Jenkin | An agenda for 'Green' information technology and systems research[END_REF][START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF][START_REF] Soomro | Green computing: From current to future trends[END_REF][START_REF] Watson | Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community[END_REF] Wasteful Printing Often printing the redundant documents [START_REF] Bolla | The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization?[END_REF][START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF][START_REF] Sarkis | Green information systems & technologies -this generation and beyond : Introduction to the special issue[END_REF] Contamination Emission of GHGs during manufacturing, using or disposing of electronic equipment [START_REF] Bolla | The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization?[END_REF][START_REF] Cherki | Green IS Research : A Modernity Perspective[END_REF][START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF][START_REF] Soomro | Green computing: From current to future trends[END_REF] Toxicity Use of Toxic elements in manufacturing of IT equipment in mass [START_REF] Bolla | The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization?[END_REF][START_REF] Gesi | SMARTer 2020, The Role of ICT in Driving a Sustainable Future[END_REF][START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF][START_REF] Sarkis | Green information systems & technologies -this generation and beyond : Introduction to the special issue[END_REF] High Cost Electricity bill and other charges for using computers [START_REF] Bolla | The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization?[END_REF][START_REF] Jenkin | An agenda for 'Green' information technology and systems research[END_REF][START_REF] Nanath | A Sustainability Model of Green It Initiatives[END_REF][START_REF] Sarkis | Green information systems & technologies -this generation and beyond : Introduction to the special issue[END_REF][START_REF] Soomro | Green computing: From current to future trends[END_REF] Issues are enlisted in Table 1, which provides the necessity of Green IS urgently day by day. As a result, various countries agreed to noteworthy reductions in GHGs emission that are the main causes of global warming, as per Paris Agreement 2015, held at Paris in 2015, to ensure that the global warming will not exceed 1.5-2.0 ∘ C [START_REF] Pasolini | Energy and Spectrum Efficient Wireless Sensor Networks[END_REF][START_REF] Sarkis | Green information systems & technologies -this generation and beyond : Introduction to the special issue[END_REF]. Additional factors enforcing organizations to adopt Green IS are the regulations and laws created by various countries to reduce their carbon footprints including the IT sector, customers and stakeholders' awareness towards social responsibility and competitive forces [START_REF] Jung | Advances in intelligent grid and cloud computing[END_REF]. The literature review explores that research is in its initial stage and require more research in this area [START_REF] Belkhir | Assessing ICT global emissions footprint: Trends to 2040 & recommendations[END_REF][START_REF] Jenkin | An agenda for 'Green' information technology and systems research[END_REF][START_REF] Sarkis | Green information systems & technologies -this generation and beyond : Introduction to the special issue[END_REF] for environment sustainability and reduction of carbon footprints. IT/S produces GHGs and carbon footprints, but IT/S use in green ways, i. e. Green IS provide the solution of the problem and reduces carbon and GHGs emission [START_REF]The Global Risks Report 2019[END_REF][START_REF]Two Degrees of Transformation Businesses are coming together to lead on climate change[END_REF]. Capra and Merlo [START_REF] Capra | Green IT: Everything Starts from the Software, European Conference on Information Systems[END_REF], had defined Green IS under three areas: 1) Power competency of IT, 2) Eco-companionable life cycle management of IT, and 3) IT as a facilitator of green governance. Dedrick [START_REF] Dedrick | Green IS: concepts and issues for information systems research[END_REF], had categorized Green IS into four main dis-ciplines: 1) Blueprint, 2) Development, 3) Utilization and, 4) Disposal of IT equipment e.g. PCs, servers and other associated peripheral devices to gain minimal or no impact on the environment. According to Trimi and Park [START_REF] Trimi | Green It: Practices of Leading Firms and Ngos[END_REF] Green IS referring to the objective of achieving energy efficiency and reduction in carbon emissions through "Greening of IT" i.e. creating Green IS products and reuse, refurbish and through "Greening by IT" as using IT to reduce environmental impacts.

In

CSF(S) FOR PRACTICING GREEN IS ADOPTION

The literature review in the area of CSFs for Green IS adoption/acceptance is limited in the present scenario. From the accessible articles/reports it is explored that different authors (like [START_REF] Bokolo | Development of a Green ICT Model for Sustainable Enterprise Strategy[END_REF], [START_REF] Bokolo | A proposed model for green practice adoption and implementation in information technology based organizations[END_REF], [START_REF] Hernandez | Exploring the Factors to Green IT Adoption in SMEs in the Philippines[END_REF], [START_REF] Raisinghani | Green information systems for sustainability[END_REF], [START_REF] Thomson | Antecedents of Green IT adoption in South African higher education institutions[END_REF] etc.) has worked on the alike factors, however, categories under various themes (like economic, environmental, descriptive, injunctive etc.). at managerial level and advised framework that highpoints three groups of factors that affect Green IS acceptance are: Invention, Organizational and Ecological Factors. 5. Molla [START_REF] Molla | GITAM: A Model for the Adoption of Green IT[END_REF] developed Green IS Adoption Model (GITAM) based on existing innovation and adoption models e.g TOE model. To address the shortcomings of UTAUT, Venkatesh et al. [START_REF] Venkatesh | Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology[END_REF] developed UTAUT2, including three new constructs: hedonic motivation, price value, and habit. The literature review in the area of frameworks adopted/used for research in the area of Green IS and its' adoption unearths that T-O-E framework is the maximum used framework used by authors; however, most of the research are the qualitative research studies. Additionally, this is also unearthed that UTAUT2 is minimal times (almost not) used by any author in this area especially in the context of Indian Banking and IT industries. Consequently, this research work is based on both the theories i.e. T-O-E framework and UTAUT2 theory in order to identify the CSFs for effective and efficient adoption of Green IS in Indian services sector with special reference to Indian Banking and IT industries as well as to develop a research model for the same.

RESEARCH MODEL

The research model in a study creates the base-structure of the entire study [START_REF] Sekaran | Research Methods for Business: A Skill -Building Approach[END_REF]. The research model represents the relationship among the constructs and subsequently facilitates hypotheses development for testing. Research model developed for this study is shown in Figure 1. The relationships between the constructs are shown in the research. In the research model all the 16 factors (independent variables) are constructs and Age, Gender and Experience are roll out as the moderating variables. For model simplicity use behavior of consumers is abolished.

RESEARCH METHODOLOGY

Research methodology is known as systematic set of process and principles to search something. The term methodology provides set of reasonable order to researcher to attain the preset goal like facts, acumen, strategy, in- For testing research model, data/information is required to validate the framed hypotheses. After phase four the research framework divided into two different approaches -1) Quantitative approach and 2) Qualitative approach for further research tools and analysis procedure. Both the approaches will be performed simultaneously in coordination with experts of the field. The quantitative approach comprises phase five of developing hypotheses, questionnaire development and pilot testing; Phase six of survey and data collection and phase seven of hypotheses testing. On the other side, in qualitative approach, phase five comprises interview of experts and decision makers of IT departments and observation of the field area phase six of conducting case study and collecting information and phase seven comprises application of qualitative tools and techniques over the collected information and analysis of the output. Phase eight in both the approaches comprises results obtained by application of tools and techniques and analysis of the collected data/information. Phase nine will provide synthesis of the results obtained from qualitative and quantitative analysis and finally, the last step of the research framework is phase ten providing overall findings of the research recommendation for the researchers/practitioners in the same field for the future practices.

INSTRUMENT DESIGN AND VALIDATION

Following the quantitative approach, survey method would be used to collect the data from IT and Banking sector. Additionally, for qualitative analysis, interview and researcher's observation would be used for data collection. A draft copy of developed of questionnaire will put in front of various experts from industry as well as academic to recognize any latent problems and check validity and reliability. For qualitative study, the main methods used are interview and researcher' observation of the experts in the organization.

SUMMARY

This paper includes the meek investigation of CSFs for Green IS adoption in Indian services sectors with special reference to Indian Banking and IT industries. The study describes the research design of the proposed investigation with regard technology adoption using mix-method approach (qualitative as well as quantitative data analysis) and data collection techniques. This paper presents the procedures for conducting the research, which include the literature review, proposed research model and validation techniques while providing selection of model. On the basis of the research methodology research study will be conducted and empirical and non-empirical results will be provided near future.

PROJECTED SUPPORT TO PRACTICE, STRATEGY AND RESEARCH

As this study is research designed to perform imminent research and that will contribute the existing research in the area of Green IS adoption as well as will helpful for the practitioners to make/implement strategies for Green IS implementation in various organization at different regions. The investigated CSFs and validated research model will positively help decision makers/ practitioners and researchers in their future works. Consequently, they will be able to influence the society and organizations to adopt Green IS in daily practices, purchasing of Green Compliant products and help in environment sustainability. Additionally, as research contribution for researchers, this study will provide literature review and research gap/scope in the area of Green IS. The research also provides unique combination of T-O-E and UTAUT2 framework development. Explicit contributions and repercussions will be accessible after accomplishment of this study.

LIMITATIONS OF THE STUDY

Like all research work, this proposed research work will also abide by some limitations. As to conduct the study with quantitative data approach, Convenience sampling is planned to use and filling the questionnaire since: 1)

The accurate population representing sample is difficult to determine.; 2) Further, due to target respondents' hectic schedule/fear from authorities, many time they avoid to fill up the questionnaire; 3) And lastly, due to expenditure problem and limitation of interviewers' (authors) of geographically approach to conduct interviews at all target offices for qualitative study. Further, the research may face problem of generalization due to nonlongitudinal study.

2 .

 2 Chen & Chang [13] established Green IS adoption model for Taiwan' administrations on the basis of Technology-Organization-Environment (T-O-E) theory along with Perceived e-Readiness Model (PERM) and identified driving forces. 3. Thomson & Belle [62] investigated the antecedents (drivers and readiness factors) for implementation of Green IS in Higher Education Institutions (HEI) of South Africa using GITAM model established by Molla (2008) 4. Nazari & Karim [43] used both the theories T-O-E framework and DoI model together, to recognize factors inducing Green IS implementation

  termediation and revolutions. In this study, therefore, 16 CSFs i.e. HM ,HA, IO, EET, PE, EE, LA, ES, EA, OP, FC, PV, GR, IC, CP and SI identified, in order to develop the research model for the study. The study and developed research model would provide answers of the following research gap: 1. The current state-of-the-art of research in the area of Green IS adoption in organizations. 2. Various CSFs that influence Behavioral Intention to adopt Green Information Systems effectively and efficiently in Indian service sector with special reference to Indian Banking and IT industries 3. Validated model for Green Information Systems adoption in Indian service sector with special reference to Indian Banking and IT industries 4. Development of Policy Framework for Behavioral Intention to adopt Green Information Systems effectively and efficiently in Indian service sector with special reference to Indian Banking and IT industries.

Figure 1 :

 1 Figure 1: Proposed Research Model

Figure 2 :

 2 Figure 2: Research Methodology Framework

Table 1 : Need for Research in Green IS Area Reasons Context Study

 1

Table 2

 2

	reflects the 16 identified CSFs and their supporting study. Literature Review
	explored that, various authors have categorized the CSFs for Green IS effec-
	tive and efficient adoption generally in four categories: Organizational fac-
	tors, Economical Factors, Social-culture factors and Regulatory compliance
	factors. From the accessible literature review, it is explored that numerous

To conduct the research, out of total 34 studied CSFs from literature review, 16 CSFs namely: Hedonic Motivation (HM) ,Habit (HA), Interoperability (IO), Energy Efficient Technology (EET), Performance Expectancy (PE), Effort Expectancy (EE), Leadership (LA), Employee Stewardship (ES), Expert Appointment (EA), Organizational Policy (OP), Facilitating Conditions (FC), Price Value (PV), Government Regulations (GR), Incentives (IC), Competitive Pressure (CP), Social Influence (SI) and Intention to Adopt Green IS in organization'(BI) have been identified on the basis of decision made after discussion with Expert's and Academician in the field of IT/Technology adoption as well as considering the modernization (inclusion of UTAUT2 theory constructs) and feasibility of the study.

Table 2 : Critical Success Factors for Green IS Adoption

 2

	Constructs/Code	Study
	Hedonic Motivation (HM)	[2, 11, 15, 22, 28-29, 31-32, 34-36, 45, 54, 60, 67]
	Habit (HA)	[2, 21, 22, 31, 32, 36, 57, 67]
	Interoperability (IO)	[9, 13, 17, 29, 35, 38, 44, 68, 74]
	Energy Efficient Technologies	[9, 13, 19, 29, 35, 38, 48]
	(EET)
	Performance Expectancy (PE)	[2, 22, 31-33, 35-36, 48, 64, 74]
	Effort Expectancy (EE)	[2, 22, 29, 31-33, 36, 48, 64, 74]
	Leadership (LA)	[5, 9, 13, 19, 21, 30, 34-35, 38, 44, 48, 52]
	Employee Stewardship (ES)	[ 13, 21, 25, 29, 33-35]
	Expert Appointment (EA)	[9, 13, 17, 21, 25, 30, 33, 35, 44, 52]
	Organizational Policy (OP)	[9, 17, 29, 37, 48, 74]
	Facilitating Conditions (FC)	[2, 9, 13, 17, 21-22, 29-30, 31-32, 35-37, 48; 67]
	Price Value (PV)	[2, 9, 13, 22, 29, 31-33, 35-36, 48, 67, 74]
	Government Regulations (GR)	[5, 9, 11, 13, 21, 28, 29, 30, 34-35, 37, 40, 44, 48,
		74]
	Incentives (IC)	[9, 13, 29, 48]
	Competitive Pressure (CP)	[ 11, 13, 19, 28-29, 34-35, 48, 74]
	Social Influence (SI)	[2, 13, 19, 22, 29, 31-34, 36, 67, 74]

  Convenience Sampling technique and 1000 sample size selected, based on expert's suggestions and following the recommendation provided by renowned authors. In this study Statistical Package for Social Sciences (SPSS) 21 version will be used to analyze the primary data, and Structural Equation Modeling (SEM) would be used for CFA and to validate the hypothesized model. Further, to analyze the qualitative data Interpretive Structural Modeling (ISM) technique will be used. Interpretive structural modeling (ISM) can rightly be employed under such circumstances[START_REF] Salimifard | Interpretive Structural Modeling of Critical Success Factors in Banking Process Re-engineering[END_REF].


Suryanarayan Iyer

Ashis K Pani

L Gurunathan

User Adoption of eHRM -An Empirical Investigation of Individual Adoption Factors using Technology Acceptance Model

Keywords: eHRM adoption, eHRM usage behavior, TAM2

Organizations would reap the intended benefits of Electronic Human Resource Management (eHRM) implementations through its sustained usage and adoption by individuals. This study is centered on the view that actual usage behavior is critical to studying eHRM adoption and needs to be measured in the context of the intended eHRM outcomes; operational, relational and transformational. Using a 10-item scale to measure eHRM usage behavior in a research framework grounded in Technology Acceptance Model (TAM2), this study investigates the factors influencing eHRM adoption in terms of "intention to use" and "actual usage behavior". Results indicate support for most TAM2 hypotheses. The study also enriches our understanding of organizational context factors; scope of implementation influencing Image-Usefulness relationship and post implementation stage influencing Ease of use-Intention to use relationship.

Introduction

Electronic Human Resource Management (eHRM), a critical element of digital transformation of the Human Resource (HR) function has been a topic of considerable interest in recent times. eHRM is the e-enablement of the HR function that is posited to help organizations achieve cost reduction and efficiency improvement of the HR function, increased service responsiveness towards employees and improving HR's strategic orientation [START_REF] Lepak | Virtual HR: strategic human resource management in the 21 st century[END_REF][START_REF] Lengnick-Hall | The impact of e-HR on the human resource management function[END_REF][START_REF] Ruel | eHRM-innovation or irritation: an explorative empirical study in five large companies on web-based HRM[END_REF]. Extant literature classifies these outcomes into 3 eHRM types; Operational eHRM (cost reduction), Relational eHRM (increased service responsiveness) and Transformational eHRM (improving HR's strategic orientation)

As the world sprints towards organizations digitally connecting their people and adapting to newer ways of working (remote logins, working from home, virtual meetings, online content co-creation, etc.), the paradigm for organizations shifts towards how easy-to-use, intuitive and engaging these digital HR technologies are for their employees. Consequently, eHRM adoption takes a front seat with both researchers and practitioners alike, because it is in the holistic adoption of these technologies lies the true benefits for organizations As we trace back the last 3 decades, it is important to recognize those points of inflexion that have shaped the innovations in the eHRM space and therefore, the optimism, promise and expectations from eHRM in driving an organization's HR transformation journey [START_REF] Iyer | Understanding the eHRM promise and adoption imperatives[END_REF]. One, the rapid proliferation of the Internet and its capacity to integrate businesses; internally and externally. This e-enablement of the HR function has immense potential to help drive cost competitiveness and increase service responsiveness towards employees [START_REF] Ulrich | The changing nature of human resources : a model for multiple roles[END_REF][START_REF] Martin | Theorizing the links between e-HR and strategic HRM: a model, case illustration and reflections[END_REF]. Two, globalization of the world economy wherein market place imperatives drive organizations to focus on people and their talent -knowledge, skill, abilities and other attributes as key competitive differentiators [START_REF] Ulrich | From eBusiness to e-HR[END_REF]. As [START_REF] Lepak | Virtual HR: strategic human resource management in the 21 st century[END_REF] say, "Firms compete less on products and markets and more on competencies, relationships and new ideas". Therefore, HR innovation in terms of adapting to new competencies, newer ways of connecting people and newer ways of working through adoption of digital technologies derive prominence while continuously focusing on people productivity, engagement and their development. Three, the emergence of collaborative (wikis, blogs, discussion forums -linkedin, facebook, twitter, etc.) and workplace (Zoom, Slack, Workplace, etc.) technologies. Collaborative technologies have enabled organizations to socialize, provide avenues for employee "voice" and access to engagement platforms to share, co-create and collaborate their learnings [START_REF] Martin | Web 2.0 and human resources: groundswell or hype?[END_REF]. Workplace technologies on the other hand provide employee with the necessary productivity tools to connect remotely, work from home and seamlessly conduct business meetings, attend conference calls / webinars, knowledge sharing / brainstorming sessions and various other day-to-day work related activities. Four, the advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies, its increasing applications in HR services and people decision making. These AI / ML based technologies also known as BOTS or digital assistants help address employee queries, drive HR process efficiencies by automating repetitive tasks and are optimistically posited to provide cognitive innovations using Natural Language Processing like persona based contextual responses to employee queries [START_REF] Iyer | Understanding the eHRM promise and adoption imperatives[END_REF].

eHRM Adoption

Literature suggests that, the performance gains and strategic advantages of technological innovation are often obstructed by user willingness to accept and use the systems [START_REF] Young | The Lonely Micro[END_REF][START_REF] Bowen | The puny payoff from office computers[END_REF]. eHRM is no exception to this challenge and it has been posited that "user satisfaction" and "actual system usage" provide a more complete picture of the eHRM system's success than if either measure were applied in isolation [START_REF] Haines | Conditions for successful Human Resource Information Systems[END_REF]. In extant literature, eHRM adoption studies are in 2 categories viz. organizational adoption factors and individual adoption factors. There are a lot of studies on organizational factors influencing eHRM adoption. On the other hand, studies on individual eHRM adoption are few. Some significant studies on individual eHRM adoption have employed Technology Adoption Model [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use and user acceptance of technology[END_REF]. [START_REF] Marler | Training and Effective Employee Information Technology Use[END_REF] studied the effect of "extent of training" and "quality of training" on outcome variables such as "perceived ease of use", "perceived usefulness", "employee resources" and "intention to use" new software. They concluded that employee resources mediated the relationship between the extent of training and intention to use new technology. Raaij and Schepers (2008) tested the acceptance of virtual learning environments in China using the extended TAM 2 model. [START_REF] Marler | Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships[END_REF] added a few additional constructs like perceived organization support (POS), managerial pressure to the standard TAM constructs; perceived usefulness, perceived ease of use and attitude towards using the system.

A key point to note in these individual adoption studies is that, the outcome variable in most cases is "Intention to use" and not "Actual Usage". Even though a few studies have incorporated "Actual Use" as the outcome variable, they have measured it using a single item "On average, how much time do you spend on the system every day. ___hours and ___ minutes". In the words of [START_REF] Marler | Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships[END_REF], "including an objective measure of user behavior would enhance the validity of the results and allow us to directly examine the relationship between perceived resources and system use". eHRM is a business application with intended operational, relational and transformational goals and outcomes at the individual HR process level (e.g. recruiting, employee and manager self-service, employee portal, leave management, performance management, competency management, learning management, career & succession management). There would be differing levels of individual motivation and employees' attitude toward using the eHRM system depending on the type of eHRM outcome. Therefore, actual usage of eHRM needs to be measured at the individual submodule level (or HR process level) corresponding to the respective intended eHRM outcomes. The primary purpose of this study therefore, is to identify and explain the factors influencing user adoption of eHRM in terms of user acceptance (intention to use) and actual usage (Actual Behavior) at granular eHRM outcome level.

This study makes two important contributions; first, we have attempted to measure eHRM usage behavior in greater depth which is synergistic with the eHRM goals of cost reduction and productivity (operational eHRM), increasing service responsiveness (relational eHRM) and improving HR's strategic orientation (transformational eHRM). eHRM being a business application solution with multiple intended outcomes (Operational, Relational, Transformational), there would be differing levels of individual motivation to use the eHRM system. It is therefore, important to measure eHRM usage behavior for each type of eHRM outcome rather than measuring it at an overall level. We have operationalized the eHRM usage behavior construct with 10 items covering specific employee facing eHRM areas like employee records, employee portal, employee help desk, social collaboration and networking, goal setting and performance management, skill and competency management, career planning and training and development. Second, the research identifies and reinforces that "organizational context" should be considered when studying the factors influencing eHRM adoption [START_REF] Marler | Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships[END_REF]. This study identifies two such factors relating to organizational context, "scope of implementation" and "stage of implementation" that is suggested to be taken up in future research.

Proposed Hypothesis

We have adopted one of the most established Technology Acceptance Model (TAM2) for our study [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF]. Before testing the eHRM adoption to additional constructs in TAM 2, it is important to consider the original TAM for testing eHRM adoption. The original TAM [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use and user acceptance of technology[END_REF]) has been empirically tested in multiple settings and applications and found to be relevant thereby establishing the robustness of its theoretical foundation [START_REF] Adams | Perceived usefulness, ease of use and usage of information technology[END_REF][START_REF] Legris | Why do people use Information Technology? A critical review of the Technology Acceptance Model[END_REF]. We further rely on studies relevant to eHRM field viz., eLearning system adoption [START_REF] Selim | An empirical investigation of student acceptance of course websites[END_REF][START_REF] Ong | Factors affecting engineers' acceptance of asynchronous e-Learning systems in high-tech companies[END_REF][START_REF] Raiij | The acceptance and use of a virtual learning environment in China[END_REF] which have been empirically tested for the constructs in the original TAM viz., Perceived Usefulness (USE) and Perceived Ease of Use (EASE) which have a significant influence on an individual's intention to use (INT) a technology. According to TAM, an individual's behavioral intention to use a system is determined by two beliefs; "perceived usefulness" defined as the extent to which a person believes that using a system will enhance his or her job performance and "perceived ease of use" defined as the extent to which a person believes that the system usage be free of effort. TAM also theorizes that 'perceived usefulness" is influenced by "perceived ease of use" because all things being equal, the easier the system to use, and the more useful it will be (Venkatesh and [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF]. For example, Individuals may resist updating their training and certification details if on one hand they find that the "learning management system" is not "easy to use" (involving too many clicks, too much mandatory information to be provided, inflexible). On the other hand, even if the system were made "easy to use", their "intention to use" and "actual usage" will be negatively impacted if individuals perceive that their training history / certification status is not considered during potential assessments, career planning or creating development plans. In line with TAM, we hypothesize; H1: Perceived Usefulness of the eHRM system (USE) will have a significant positive effect on Intention to Use the eHRM system (INT). H2: Perceived Ease of Use of the eHRM system (EASE) will have a significant positive effect on Perceived Usefulness of the eHRM system (USE). H3: Perceived Ease of Use of the eHRM system (EASE) will have a significant positive effect on Intention to Use the eHRM system (INT). H4: Intention to Use the eHRM system (INT) will have a significant positive effect on actual eHRM usage behavior (USAGE).

Social Influence Processes. TAM2 discusses two basic constructs with reference to social processes influencing behavioral intention viz., Subjective Norm (SN) and Image.

Subjective Norm. In Theory of Reasoned Action [START_REF] Fishbein | Belief, Attitude, Intention and Behavior: An introduction to Theory and Research[END_REF] and Theory of Planned Behavior (Ajzen, 1991), the theoretical underpinning for understanding the social influence on behavioral intention, Subjective Norm has been defined as "a person's perception that most people who are important to them think that they should or should not perform a behavior in question. In other words, as stated by [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF], "the rationale for a direct effect of subjective norm on behavioral intention is that people may choose to perform a behavior even if they are not themselves favorable towards the behavior or its consequences, if they believe that one or more important referents think that they should and they are sufficiently motivated to comply with the referents". We therefore, hypothesize:

H5: Subjective Norm (SN) will have a positive direct effect on perceived usefulness of the eHRM system (USE) H6: Subjective Norm (SN) will have a positive direct effect on intention to use the eHRM system (INT).

Image and Social Influence. "In a typical work environment with a high degree of interdependence with other social actors in carrying out ones duties, increased status within the group is a basis of power and influence via processes such as social exchange, coalition formation and resource allocation" [START_REF] Blau | Exchange and Power in Social Life[END_REF][START_REF] Pfeffer | Power in Organizations[END_REF][START_REF] Pfeffer | Organizations and Organizations Theory[END_REF]. "By performing behaviors that are consistent with group norms, an individual achieves membership and the associated social support. This results in increased power and influence that provides a general basis for greater productivity" [START_REF] Pfeffer | Organizations and Organizations Theory[END_REF]. As a result, an individual may perceive that using the eHRM system will lead to performance improvements indirectly due to image enhancement over and above the performance benefits attributable to the system itself. For example, individuals who are a part of specialist communities (compensation and rewards, innovation, research & development, etc.) would perceive themselves as having a better image than others for being a part of an elite group. The member individuals in addition would also perceive benefits at the prospect of collaborative learning from the elite community thereby; forming positive perceptions related to image and improved job performance (perceived usefulness). We therefore, hypothesize that:

H7: Subjective Norm (SN) will have a positive effect on image perception towards the eHRM system (IMAGE) H8: Image will have a positive effect on perceived usefulness of the eHRM system (USE).

Cognitive Instrumental Processes. TAM 2 theorizes four cognitive instrumental determinants of perceived usefulness viz., job relevance (JR), output quality (OQ), result demonstrability (RD) and perceived ease of use. These cognitive instrumental constructs have been theorized by [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF] after carefully studying multiple theoretical foundations viz. Work Motivation theory [START_REF] Vroom | Work and Motivation[END_REF], Action theory from social psychology [START_REF] Fishbein | Belief, Attitude, Intention and Behavior: An introduction to Theory and Research[END_REF] and Task-contingent decision making from behavioral decision theory [START_REF] Beach | A contingency model for selection of decision strategies[END_REF]. Based on their extensive study of the above theories, they posited that "people use mental representation for assessing the match between important work goals and consequences of performing the act of using a system as a basis for forming judgments about the use-performance contingency. We therefore, hypothesize; H9: Job Relevance (JR) will have a positive effect on the perceived usefulness of the eHRM system (USE). H10: Output Quality (OQ) will have a positive effect on perceived usefulness of the eHRM system (USE). H11: Result Demonstrability (RD) will have a positive effect on perceived usefulness of the eHRM system (USE).

Research Methods

Measures -TAM 2

For the present study, all the nine constructs of the TAM2 [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF] viz., Subjective Norm, Image, Job Relevance, Output Quality, Result Demonstrability, Perceived Usefulness, Perceived Ease of Use, Intention to Use and Actual Usage Behavior were considered as a part of the eHRM adoption model to be tested and were measured using 26 items as proposed in the original model by [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF]. The TAM 2 measures used in the study are shown in Appendix -1.

Measures -eHRM Usage Behaviour

The final outcome construct of TAM 2 viz., usage behaviour was specifically developed for the current research. Though Venkatesh and Davis (2000) have indicated that they measured "usage behaviour" by asking a single question, "On average, how much time do you spend on the system everyday? ___hours and ___ minutes", it was felt during the design phase for the current research that a mere duration of use does not capture the entire domain of eHRM actual usage behaviour. eHRM is a business application with intended operational, relational and transformational goals and outcomes at the individual HR process level (e.g. recruiting, employee and manager self-service, employee portal, leave management, performance management, competency management, learning management, career & succession management).

There would be differing levels of individual motivation and employees' attitude toward using the eHRM system depending on the type of eHRM outcome. We therefore, argue that actual usage of eHRM needs to be measured at the individual submodule level (or HR process level) corresponding to the respective intended eHRM outcomes.

Accordingly, 10 items were created for the 3 attributes of eHRM usage behavior viz., operational (2 items), relational (4 items) and transformational (4 items). These 10 items representing eHRM usage is provided in Table-4. The combinations of items borrowed from the original TAM and the 10 items created for "eHRM usage behavior" were subject to scale validity check viz., face validity and construct validity. We also subjected the scale items to other forms of robust validation checks such as discriminant, convergent and predictive validity in order to confirm construct validity [START_REF] Hardesty | The use of expert judges in scale development. Implications for improving face validity of measure of unobservable constructs[END_REF]).

Data Collection and Sampling

Primary data was collected through structured undisguised questionnaires administered to the respondents. The questionnaires were administered through individual face to face meetings or through e-mail as per the convenience of the respondents at home or in office. The 26 items from the TAM 2 scale were measured on a 5-point Likert scale (5-Strongly Agree to 1-Strongly Disagree) while the 10 items pertaining to eHRM usage behaviour were measured on a 4-point scale (0-Don't use, 1-Low usage, 2-Medium usage and 3-High usage). A total of 249 correct responses were received and considered for further analysis. It was found from the initial review of the responses that most of the organizations had implemented the following eHRM processes viz., HR automation of employee records, leave, payroll, time accounting and performance management. Therefore, in order to measure eHRM usage behavior correctly, we selected only the response of three items (u1, u2 and u8 -refer Table-4) of eHRM usage behavior corresponding to the above implementation scope for inclusion in the structural model and further analysis.

Response Bias

The existence of response bias for the present research was assessed by performing a t-test on the responses collected from 2 modes of survey viz. personal administration and email. The ttest results indicate that there is no significant difference between the responses obtained by these 2 modes of administration indicating that the responses obtained are not susceptible to the "response bias" owing to different data collection methods adopted except for 2 constructs viz., OQ and Image.

Analysis

We have applied Structural Equation Modeling (SEM), a second-generation analytical technique [START_REF] Iyer | Structural Equation modeling to test the impact of Organization Communication Satisfaction on Employee Engagement[END_REF] as the analytical tool of analysis and maximum likelihood estimation method for examining the proposed hypothesis. Taking support from the studies by various researchers who have used similar analytical tool (e.g., [START_REF] Anderson | Structured Equation Modeling in practice: a review and recommended two-step approach[END_REF], we chose a two-step analysis method. In the first step, we conduct a Confirmatory Factor Analysis (CFA) of the overall measurement model based on the correlation coefficient matrix of each factor of eHRM adoption in TAM2 and usage behaviour developed exclusively for the current study. This helps in confirming the fitness of the 9 constructs in terms of composite reliability, convergent and discriminant validity. In the second step, the structural model is further examined based on the covariance matrix and the hypotheses of the study tested for the entire eHRM adoption / usage behaviour model.

Results

Exploratory Factor Analysis and Reliability

Before proceeding with assessment of model of the present study, it was decided to test the univariate normality pattern of the study variables. Accordingly, we computed skewness and kurtosis for each of the variables of the TAM 2 model. All the variables have passed the stipulated criterion of +2 value for skewness and kurtosis [START_REF] Hair | Multivariate Data Analysis[END_REF]. Having assessed that the data collected do not indicate any sign of non-normality, we proceeded with performing an Exploratory Factor Analysis (EFA). The factor analysis (varimax rotated) results of all the 26 items for a 9-factor solution extracted 9 factors with the respective items clearly loading onto their corresponding factors. The KMO measure of sampling adequacy was 0.825 and the Bart-let's test of sphericity (χ2 value of 4765.5, df=406) was significant at 0.001 level. All the 9 factors extracted resulted in a total variance of 80.327. Upon confirming the existence of clear dimensions of TAM2 components in the EFA study, we moved on to assess the consistency of the data pertaining to each of the 9 components extracted separately. The Cronbach α reliability coefficients were greater than the acceptable limit of 0.7 for each of the TAM2 components (dimensions) as prescribed by [START_REF] Nunnally | Psychometric theory[END_REF].

Common Method Variance

Common Method Variance was not found to be a significant threat in the present research because the results of the EFA of all the study variables brought out 9 factors with 80% total variance explained with the first factor explaining 27% of the total variance (unrotated) and 12.14% of total variance (varimax rotated) fulfilling Harman's single factor solution criteria.

TAM 2 Measurement Model for eHRM Adoption

A first order Confirmatory Factor Analysis (CFA) was conducted to confirm the fitness of the measurement model. The measurement model test results shown in Table -1 indicate that the factor loadings of most of the items were above 0.7 (except four items where the factor loading was marginally less at 0.65 and 0.68). These items were retained because they were very critical to the study and the judges who validated the scales confirmed their relevance and importance for the study. One usage behavior item, "I use the eHRM system for my goal / key result area (KRA) setting process and entering my self-evaluation, achievements and training needs" had a factor loading of 0.59. This was deliberated upon by the researchers and primary analysis showed that this item is critical to the study since majority of the organizations who were covered in the study had implemented performance management process in their organizations and it was felt important to capture the actual usage behavior for this vital HR process. Further, removal of the item was also not significantly improving the model fit. Therefore, we retained this item in the final analysis. The critical ratio (CR) values of all the items reached the significant level (p<.001). We further analyzed the composite reliability for each of the constructs and found them all to be greater than 0.7 except for one factor, "Output Quality" which was marginally lower at 0.69. This implies that all the components exhibit a good internal consistency as the composite reliability coefficients are greater than 0.6 as recommended by Bagozzi & Yi (1990). We calculated the Average Variance Extracted (AVE) estimates for each of the factors and all of them are nearly equal to or greater than 0.5 [START_REF] Hair | Multivariate Data Analysis[END_REF]) thereby confirming the convergent validity of the factors in the model. The AVE of each construct was further compared with the squared correlations of all other construct combinations (Table -2).

The AVE of all the constructs was found to be greater than the squared correlations of the other construct combinations revealing the independent nature of each of the TAM2 constructs. Overall, the above results provide adequate evidence of convergent validity, discriminant validity and composite reliability of the model adopted for the study. The other key model fit statistics and values of the CFA, CFI = 0.914 and RMSEA = 0.074 indicate an acceptable level of model fit.

Testing TAM 2 Structural Model for eHRM Adoption -I

The results of the Structural Model for testing eHRM adoption are shown in Table -3. The TAM 2 model for eHRM adoption was tested using Structural Equation Modeling (SEM). In this study, we have simultaneously tested all the nine constructs of the TAM 2 in our structural model. The results are shown in Table -3. Looking at the results therein, we find that the overall fit using CFI criteria was 0.828, RMSEA = 0.10, χ2/df = 3.493, indicating the poor fit of the model. A perusal of the structure coefficients and their corresponding significance in Table-3 indicated that paths OQ USE, Image USE and Ease INT were not significant. This being the case, a scrutiny of factor loadings (lambda coefficients) of the corresponding items on their respective factors indicated that only item a19, The quality of the output I get from the eHRM system is high" belonging to OQ had a non-significant and poor loading of 0.07 and warranted removal from the overall model. Hence, it was decided to discard the same and re-run the model. Although we felt the need to retain atleast one item related to OQ, modification indices indicated that χ2 value would be reduced by 21.838 if the item a20 of OQ is merged with RD.

A discussion with the experts also indicated that clubbing of item a20 of OQ was meaningful as the content of a20 was similar to the contents of RD. Further discussion with experts also revealed that the perceived closeness of meaning by the respondents can be attributed to the words "output" (for items in OQ) and "results (for items in RD) and probably this is the reason why modification indices of SEM indicated the need for linking item a20 with RD. Further, this study pertains to user adoption of eHRM systems and OQ discusses that "given a choice set of multiple relevant systems, an individual would be inclined to choose a system that delivers highest quality". In the context of eHRM systems, individuals seldom have a choice of multiple eHRM systems to base a comparative preference with regard to OQ. Hence, there is a good possibility that the respondents perceived the items with reference to increasing their job performance rather than the overall quality of the system itself. We therefore, decided to remove OQ and its item a19 from the model and club item a20 with RD for the next SEM run.

Testing TAM 2 Structural Model for eHRM Adoption -II

Results of TAM2 after removal of OQ and addition of a20 with RD in the model yielded model fit indices of CFI = 0.857, RMSEA = 0.093, χ2 = 835.93 (df = 265), χ2/df = 3.154. All this indicated that removal of OQ as a separate component of TAM2 has significantly improved the model fit (Δ χ2 = 162.92, Δ df = 23). However, a perusal of structural path coefficients between Image  USE, Ease  INT and RD  USE were found insignificant. A scrutiny of modification indices revealed that the χ2 statistic can be reduced by 69.97 by linking RD to JR. Discussion on the relevance of connecting the path from RD JR led us to the reasoning that since eHRM is a business application and its adoption and usage by individuals could be purely for the purpose of performing in their current and potential jobs, users would consider as important and project only those results that are relevant to their current and future jobs. This reasoning corroborates with [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF] view that: "If a system produces effective jobrelevant results desired by the user, but does so in an obscure fashion, users of the system are unlikely to understand how useful such a system really is (p 192)." This led us to the reasoning that the results from the eHRM system would benefit individuals in either reducing their administrative burden or lead to higher productivity and flexibility due to presence of automated workflow driven self-service. Further, individuals would benefit from competency building, greater knowledge exchange and thereby advance themselves towards career growth in their respective organizations. It was hence, reasoned that Results Demonstrability has an influence on "perceived usefulness" for individuals, if realized in the context of their performance in their current jobs and building competencies towards advancement into future jobs ("job relevance"). Hence, it was decided to test the model of TAM2 with RD connected to JR as it was convincing as stated above.

Testing TAM 2 Structural Model for eHRM Adoption -III

In the third SEM run, as anticipated, we found significant impact of RD on JR with a structure coefficient of 0.6, while the relationship between RD to Perceived Usefulness was found to be insignificant. The model fit statistics also improved considerably viz., CFI = 0.902, RMSEA = 0.079, χ2 = 644.13 (df = 252), χ2/df = 2.556 (Table -3). The modification indices also did not indicate any major reduction in χ2 for further modifications to the model and hence is validated in the current study. Nonetheless, the relationship between TAM2 components viz., Image and Perceived Usefulness was again found insignificant. We analyzed this also closely and felt that since the sample population comes from organizations that have implemented the basic eHRM system components viz., employee records, employee lifecycle events, payroll, benefits, time and attendance and performance management the sample population could not perceive these transactional process as being a source of status or prestige in the organization. Probably, these results would have been different if the sample population included organizations that had implemented Integrated Talent Management Systems (especially Career and Succession Planning, Competency Management, Learning and Knowledge Management) or Web 2.0 collaboration based Social Networking initiatives in the organization. Usage of these systems are either related to an individuals' career growth (in the case of Talent Management Systems) or related to contemporary technologies (Web 2.0 collaboration and social networking) both of which could potentially drive "internalization" and "individualization" amongst individuals. The potential of these systems, especially, Web 2.0 and social networking in the context of social exchange and social interdependence is perceived as a source of power, influence and enhanced status by individuals. We therefore, argue that image will positively impact perceived usefulness for individuals from organizations that have implemented talent management systems and social networking processes.

Another TAM 2 relationship that was found non-significant was "perceived ease of use" and "intention to use". This finding was consistent with the results from recent research [START_REF] Szajna | Empirical evaluation of the revised technology acceptance model[END_REF][START_REF] Chau | Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories[END_REF][START_REF] Selim | An empirical investigation of student acceptance of course websites[END_REF][START_REF] Wu | What drives mobile commerce? An empirical investigation of the revised technology acceptance model[END_REF]. A very significant reasoning by [START_REF] Raiij | The acceptance and use of a virtual learning environment in China[END_REF] explains this phenomenon of non-significance fundamentally. They state that TAM 2 (Venkatesh and [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF] did not find any direct postimplementation effects of "perceived ease of use" on "intention to use". On the contrary, the direct effect posited in TAM 2 was in the context of pre-implementation stage. Therefore, in accordance with Raiij and Schepers (2008), we also reason that "as users gain experience with a new eHRM system, perceived ease of use becomes less profound since instrumentality overshadows concerns about the eHRM system's ease of use [START_REF] Adams | Perceived usefulness, ease of use and usage of information technology[END_REF][START_REF] Straub | Testing the technology acceptance model across cultures. A three country study[END_REF]. All the other hypotheses of the study were supported and found significant. The overall model fit statistics viz., CFI = 0.902, RMSEA = 0.079, χ2 = 644.13 (df = 252), χ2/df = 2.556 (Table -3) indicates an acceptable level of model fit [START_REF] Hair | Multivariate Data Analysis[END_REF].

Discussion

Through an in-depth study of a large sample population covering individuals from organizations across all major industries, we have understood the factors affecting eHRM adoption through one of the most accepted Technology Acceptance Model -TAM2 [START_REF] Venkatesh | A theoretical extension of the Technology Acceptance Model. Four Longitudinal field studies[END_REF]. Overall, the hypothesized model fit the data well and most of the TAM2 hypotheses were supported in the study. We did not find support for hypotheses H3, H8 and H11 though. However, a more detailed analysis led us to enrich our understanding of the role played by organizational context factors in explaining the relationships mentioned in H3, H8 and H11.

Hypotheses H3, the effect of eHRM's perceived ease of use on intention to use was not supported. On careful analysis, we realized that most of the respondents belonged to organizations where eHRM was in the post-implementation phase. Raiij and Schepers (2008) also found a similar phenomenon in their study of acceptance of virtual learning environments and reasoned that TAM2 did not find any direct post-implementation effects of "perceived ease of use" on "intention to use". [START_REF] Marler | Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships[END_REF] also found similar results in their study of Employee Self Service (ESS) technology adoption wherein they observed a marked change in the relationship between ease of use and attitude in the post implementation scenario and suggested that ease of use becomes redundant with perceived usefulness, post implementation. We make an important inference that as individuals gain experience of continuously using eHRM systems, post implementation, instrumentality overshadows concerns about ease of use. Therefore, unless new eHRM system alternatives offer significantly better ease of use as compared to what the users are already comfortable with and habituated to -"intention to use" will not be significantly impacted by "perceived ease of use". Future research should consider "stage of implementation" -an organization context while studying the relationship between ease of use and intention to use.

Hypothesis H8, the effect of "image" on "perceived usefulness" was not supported. On careful analysis, we realized that the sample population belonged to organizations who had implemented only the core eHRM components like employee records, lifecycle events, payroll, time and attendance and performance management. These organizations had not implemented the other eHRM processes like integrated talent management (career and succession planning, learning and development) and Web 2.0 technologies (social networking, collaboration). Though we had not included this aspect in our main hypothesis, we probably would have got support for the "image" to "perceived usefulness" relationship if the sample organizations had implemented integrated talent management, social networking and web 2.0 collaboration. The potential of these systems, especially, in the context of social exchange and social interdependence is perceived as a source of power, influence and status by individuals. We therefore, suggest that future research focus on the relationship between "image" and "perceived usefulness" in the context of "implementation scope and coverage". Hypothesis H11, the effect of "results demonstrability" to "perceived usefulness" was also not supported. We carefully, analyzed the results and found a link between "results demonstrability" and "job relevance". This led us to probe further into its significance, though we had not hypothesized the relationship. We infer that eHRM is a business application and individuals' perception of its usefulness would largely depend on its ability to demonstrate results that are relevant in the context of an individual's performance in their current or potential jobs. Therefore, results demonstrability in the context of "job relevance" makes more meaning in the study of its relationship with perceived usefulness. This probably explains our finding of significant support for the "results demonstrability" linkage with "job relevance".

Contributions and Implications

This study makes two significant contributions. First, this research reinforces the organizational context angle to be considered when studying the factors influencing eHRM adoption [START_REF] Marler | Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships[END_REF]. In this study, we have identified two such factors of organizational context -"scope of implementation" (effect of Image on perceived usefulness) and "stage of implementation" (effect of ease of use on intention to use). Second, we have called for measuring "eHRM usage behavior" in a detailed manner rather than measuring it at an overall level. We argue that eHRM is a business application with differing outcomes at operational, relational and transformational stages of eHRM evolution. Organizations who implement eHRM are at various stages of maturity in terms of eHRM adoption and hence, measuring eHRM usage behavior at an overall level does not seem logical. There seems to be a clear gap in literature in the way eHRM usage is measured. As a first step, therefore, we have attempted to create a scale to measure eHRM usage behavior with a 10 point scale spanning the three eHRM types stated in literature -operational, relational and transformational). Our 10 point eHRM usage scale focuses on individuals (in effect we can say Employee Self-Service -ESS) and covers employee records, leave, mobility, internal job postings (Operational eHRM), employee portal, helpdesk, Web 2.0 collaboration, social networks (Relational eHRM) and competencies, goal setting, performance reviews, career planning, learning and development (Transformational eHRM).

Though we created this 10-point scale and administered it all the respondents, we got responses only for 3 items which covered employee records, leave and performance management. This was because most of the organizations from where we got individual responses had not implemented the other eHRM processes -a clear case of organizational context of "implementation scope". Therefore, though we could not test the entire 10-point scale, we believe we have initiated a serious discussion on measuring eHRM usage behavior more objectively and in greater detail. Future research should identify a more comprehensive and innovative sampling procedure to cover those organizations which have implemented all the eHRM types to test eHRM adoption and in particular, eHRM usage behavior. Using the eHRM system improves my performance in my job (a2) Using the eHRM system in my job increases my productivity (a3) Using the eHRM system enhances my effectiveness in my job (a4) I find the eHRM system to useful in my job EASE (a5)

My interaction with the eHRM system is clear and understandable (a6)

Interacting with the eHRM system does not require a of my mental effort (a7) I find the eHRM system easy to use (a8) I find it easy to get the eHRM system to do what I want it to do SN (a9)

People who influence my behavior think that I should use the eHRM system (a10)

People who are important to me think that I should use the eHRM system IMAGE (a14)

People in my organization who use the eHRM system have more prestige than those who do not (a15)

People in my organization who use the eHRM system have a high profile (a16) Using the eHRM system is a status symbol in my organization JR (a17)

In my job, usage of the eHRM system is important (a18) In my job, usage of the eHRM system is relevant OQ (a19)

The quality of the output I get from the eHRM system is high (a20)

I have no problem with the quality of the eHRM system's output RD (a21) I have no difficulty telling others about the results of using the eHRM system (a22) I believe I could communicate to others the consequences of using the eHRM system (a23)

The results of using the eHRM system are apparent to me (a24R) I would have difficulty explaining why using the eHRM system may or may not be beneficial INT (a25)

Assuming I have access to the eHRM system, I intend to use it (a26)

Given that I have access to the eHRM system, I predict that I would use it

Table- 4 .

 4 p<.001 ** p <.01 * p <.05 Items for measuring Actual eHRM Usage Behavior # eHRM process u1 I use the eHRM system to view and update my personnel records like change of address, marital status, contact details, dependent / beneficiary information and tax declarations O u2 I use the eHRM system to apply for leave, compensatory off, applying for change of department or location or any other job related attribute. O u3 I use the eHRM system to review and apply for jobs through Internal Job postings and refer job postings to my friends under the company's employee referral scheme R u4 I use the eHRM -employee portal to check the company directory and organization structure and to update myself on company communication, policies & guidelines R u5 I use the eHRM -Help desk functionality to log in requests for resolution of any job related issues, for eg. Leave regularization, payroll corrections, performance rating related, or any other personal or job related issues. R u6 I participate in "in-company" eHRM Web 2.0 collaboration initiatives (eg. blogging, discussion boards) by actively voicing my opinions and sharing my knowledge and experience. R u7 I use the eHRM system to update my skills and competencies and review my competency improvement areas T u8 I use the eHRM system for my Goal / KRA setting process and entering my selfevaluation, achievements and training needs T u9 I carefully review the career paths available on the eHRM system and work my manager to design development plans to help me grow up the career ladder T u10 I use the eHRM system for all my training needs eg. Enrolling into a course, launching and attending the course, entering feedback and cost details, etc. T O -Operational eHRM, R -Relational eHRM, T -Transformational eHRM Appendix 1. TAM 2 measures used in the study Item Description USE (a1)

Table 1 .

 1 Constructs & Indicators of measurement items of eHRM adoption -First Order

	Measurement Item	Factor	Standard	Error	CR	Construct Alpha* AVE
		Loading	Error	Variance	Value
	USE					0.93	0.76
	(a1)	0.91	0.10	0.82	14.84
	(a2)	0.93	0.09	0.86	15.19
	(a3)	0.90	0.10	0.80	14.68
	(a4)	0.74	0.05	0.55
	EASE					0.86	0.61
	(a5)	0.73	0.07	0.54	11.56
	(a6)	0.82	0.07	0.67	12.95
	(a7)	0.79	0.07	0.62	12.52
	(a8)	0.78	0.09	0.61
	SN					0.92	0.85
	(a9)	0.88	0.06	0.77	15.15
	(a10)	0.96	0.07	0.93
	IMAGE					0.87	0.70
	(a14)	0.68	0.07	0.47	12.24
	(a15)	0.93	0.06	0.86	17.81
	(a16)	0.87	0.05	0.76
	JR					0.88	0.79
	(a17)	0.91	0.08	0.82	15.29
	(a18)	0.87	0.06	0.75
	OQ					0.69	0.53
	(a19)	0.80	0.12	0.63	8.77
	(a20)	0.65	0.11	0.43
	RD					0.88	0.66
	(a21)	0.78	0.05	0.61	16.01
	(a22)	0.83	0.05	0.69	17.93
	(a23)	0.92	0.11	0.85
	(a24R)	0.68	0.06	0.46	12.73
	INT					0.89	0.81
	(a25)	0.88		0.77
	(a26)	0.92	0.08	0.84	14.51
	USAGE					0.72	0.47
	(u1)	0.77	0.13	0.59
	(u2)	0.68	0.13	0.46	7.79
	(u8)	0.59	0.11	0.35	7.28
	Fit Index
	Chi-Square (df)	623.39	263.00
	CFI	0.914
	RMSEA	0.074
	(*)Composite Reliability

Table - 2

 - . Mean, Standard Deviation, Squared Correlations and AVE for the model constructs compared with squared correlations of other constructs Values in bold shown diagonally in the table indicate the Average Variance Extracted. Values shown below the diagonal are squared correlations.

		Mean SD
			USE	EASE SN	IMAGE JR	OQ	RD INT USAGE
	USE	4.12	0.704 0.757
	EASE	4.05	0.644 0.338	0.608
	SN	3.55	0.860 0.151	0.089	0.846
	IMAGE	2.37	1.028 0.034	0.010	0.225 0.695
	JR	3.90	0.848 0.286	0.124	0.095 0.078	0.788
	OQ	3.62	0.784 0.229	0.287	0.062 0.001	0.348 0.531
	RD	3.73	0.720 0.104	0.090	0.040 0.110	0.362 0.371 0.656
	INT	3.93	0.708 0.113	0.061	0.172 0.264	0.194 0.028 0.206 0.807
	USAGE	2.72	0.664 0.000	0.001	0.003 0.099	0.030 0.000 0.126 0.032 0.466

Table - 3

 - . Structural Model for testing eHRM Adoption using TAM2

	Model	Standardized Coefficient	χ2	df	χ2/df	CFI	RMSEA
	Model -1		1005.855	288	3.493	0.828	0.1
	SN ----> Image