COMPUTER USAGE AMONG SECONDARY SCHOOL TEACHERS ...

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COMPUTER USAGE AMONG SECONDARY SCHOOL TEACHERS: AN ANALYSIS OF PSYCHOLOGICAL AND SOCIO-DEMOGRAPHIC VARIABLES

Thesis Submitted to University of Madras in Partial Fulfillment of the Degree of DOCTOR OF PHILOSOPHY IN EDUCATION

Investigated by M. PADMAVATHI

Under the Guidance of Dr. S. MALATHI Associate Professor of Education NKT National College of Education for Women Chennai – 600 005

NKT NATIONAL COLLEGE OF EDUCATION FOR WOMEN CHENNAI – 600 005 SEPTEMBER 2014

ii

DECLARATION

I declare that the thesis entitled “Computer Usage Among Secondary School Teachers: An Analysis of Psychological and Socio-Demographic Variables” submitted by me for the Degree of Doctor of Philosophy (Ph.D.) in Education is the record of work carried out by me during the period from 2008 to 2014 under the guidance of Dr. S. Malathi, Associate Professor of Education, NKT National College of Education for Women, Chennai and has not formed the basis for the award of any Degree, Diploma, Associateship, Fellowship, Titles in this University or any other University or other similar institution of Higher Learning.

_______________________ Signature of the Candidate (M. PADMAVATHI) Place: Date:

Chennai 1 September 2014

iii

CERTIFICATE FROM THE SUPERVISOR

I certify that the thesis entitled “Computer Usage Among Secondary School Teachers: An Analysis of Psychological and Socio-Demographic Variables” submitted for the degree of Doctor of Philosophy (Ph.D.) in Education by Mrs. M. Padmavathi is the record of research work carried out by her during the period from 2008 to 2014 under my guidance and supervision, and that this work has not formed the basis for the award of any Degree, Diploma, Associateship, Fellowship or other Titles in this University or any other University or institution of Higher Learning.

____________________________ Signature of the Supervisor with designation Place: Date:

Chennai 1 September 2014

iv ACKNOWLEDGEMENTS I take pleasure in expressing my deep sense of gratitude to Dr. S. Malathi, M.A., M.Phil., M.Ed., Ph.D., Associate Professor in Education, NKT National College of Education for Women, Chennai, for her sustained help and constructive suggestions during the entire tenure of this research study. I wish to extend my sincere thanks to Dr. A. Vasanta, Principal, and the faculty members of NKT National College of Education for Women, Chennai, for their constant support in carrying out this study. I am extremely thankful to Dr. Sr. Philomin Mary for encouraging me to take up this research study. I express my sincere gratitude to Prof. Swaminathan Pillai for his valuable suggestions during the initial stages of my research work. I will be failing in my duty if I am not placing on record my deep felt gratitude to my parents and my husband who stood by me in time of need and supported me all through my research work. My deep appreciation goes to my daughter, Ms. Apourva for her love and affection that kept me engaged in completing this research study. I thank all the principals and teachers who had spared their valuable time during the data collection. I thank all my colleagues, friends and professors of education and psychology who had gone through the questionnaire and offered valuable suggestions and guidance.

v

TABLE OF CONTENTS

CHAPTERS

TITLE

PAGE NO

1. INTRODUCTION………………………………………..

1

Need for the Study……………………………………

2

Significance of the Study……………………………..

4

Theories and models on technology Acceptance……

6

Behavioral Intention Theory…………………….

6

Theory of Reasoned Action………………………..

7

Theory of Planned Behavior……..………………..

8

Technology Acceptance Model……………….…...

8

Extended Technology Acceptance Model………….

9

Unified Theory of Acceptance and Use of Technology

10

Innovation Diffusion Theory……………………

12

Social Cognitive Theory…………………………

13

Conceptual Framework of the Study………………….

13

Objectives of the Study………………………………….

17

Operational Definition of Major Concepts……………

18

Assumptions of the Study……………………………….

21

Statement of the Problem……………………………….

22

Scope of the Study……………………………………….. 23 Limitations of the Study………………………………..

24

Chapterisation…………………………………………..

25

vi 2. LITERATURE REVIEW………………………………...

26

Review of Meta-analysis Studies …………………….

26

Perceived Usefulness and Perceived Ease of Use…...

30

Job Relevance…………………………………….……

35

Computer Complexity……………………………......

36

Subjective Norm………………………………............

37

Facilitating Conditions……………………………….

39

Computer Competence…………………………….....

42

Attitude Towards Computer Use……………………

44

Behaviour Intention………………………………......

48

Actual Use of Computers………………………….....

50

Socio-Demographic Factors…………………………..

52

Gender……………………………………………..

53

Age…………………………………………………

56

Location of School…………………………………

57

Level of Education………………………………...

58

Subject Taught…………………………................ ..

58

Computer Training…………………………………

59

Computer Experience………………………………

60

Review of Studies conducted in India………………...

61

3. METHODOLOGY…………………………………………

68

Research Design…………………………..……………

69

Population………………………………………….…...

70

Sample…………………………………………………..

71

Variables of the Study………………......................... ..

72

Formulation of Hypotheses……………….……………

73

Tools used in the Study………………………………..

80

Pre-testing of the Questionnaire………..................... ..

85

vii Reliability of the Scales….………………..…………..

86

Validity of the Scales…….……………….....................

86

Content Validity……………………………………

87

Construct Validity…………………………………

88

Factor Loadings for Computer Usefulness…….

91

Factor Loadings for Computer Ease of Use……

93

Factor Loadings for Social Influence…..………

95

Factor Loadings for Computer Competency…..

96

Factor Loadings for Facilitating Conditions……

97

Factor Loadings for Attitude Towards Computer Use 98 Factor Loadings for Behaviour Intention………

100

Factor Loadings for Actual Use of Computers…

101

Statistical Treatment of Data…………………………..

102

Conclusion……………………………………………….

103

4. DATA ANALYSIS AND RESULTS……………………….

104

Section-I: Descriptive Analysis of Teacher Characteristics 105 Section-II: Descriptive Analysis of Psychological Variables113 Section-III: Correlation Analysis……………………….… 131 Section-IV: Regression Analysis………………………..…. 137 Section-V: Differential Analysis………………………….. 155 Discussion…………………………………………………… 180 Conclusion…………………………………………………… 203 5. SUMMARY AND CONCLUSION………………….............. 204 Overview……………………………………………………. 204 Restatement of the problem……………………………..… 204 Need and Significance of the Study……………………..… 204 Conceptual Framework of the Study……………………... 205

viii Operational Definition of Major Concepts……………

206

Objectives of the Study……………………………….…

209

Hypotheses……………………………………………….

210

Methodology…………………………………………......

215

Data Analysis……………………………………….........

218

Limitations of the Study………………………………...

218

Major Findings…………………………………………..

218

Findings pertaining to descriptive analysis of teacher characteristic……………………………….

219

Findings pertaining to relationship among the variables

220

Findings pertaining to predictors of actual use of computers…………………………….

221

Findings pertaining to mean differences……………..

223

Implications of the Study…………………………….......

225

Suggestions for Further Research………………….........

230

Conclusion…………………………………………………

231

Bibliography…………………………………………………………

233

Appendix-A: Questionnaire Appendix-B: Rotated Factor Loadings Extracted

ix LIST OF TABLES TABLES

TITLE

PAGE NO

Table-3.1: Sample Distribution…………………………………..

72

Table-3.2: Summary of Reliability Statistics of Scales…….……..

87

Table-3.3: Summary of Criterion Validity Statistics of Scales…..

91

Table-3.4: Factor Loadings for Computer Usefulness…………….

92

Table-3.5: Factor Loadings for Computer Ease of Use……………

94

Table-3.6: Factor Loadings for Social Influence…………………..

95

Table-3.7: Factor Loadings for Computer Competency……………

97

Table-3.8: Factor Loadings for Facilitating Conditions…………..

98

Table-3.9: Factor Loadings for Attitude Towards Computer Use…

99

Table-3.10: Factor Loadings for Behaviour Intention……………..

100

Table-3.11 : Factor Loadings for Actual Use of Computers………..

101

Table-4.1: Frequency Percentages of Socio-Demographic Characteristics of Teachers……………………………. 106 Table-4.2: Frequency Percentages of Teachers’ Exposure to Computers 108 Table-4.3: Frequency Percentages of Actual Use of Computers…….

110

Table-4.4: Mean of Actual Use of Computers and its Dimensions….. 112 Table-4.5: Frequency Percentages of Computer Usefulness………… 114 Table-4.6: Frequency Percentages of Computer Ease of Use………… 117 Table-4.7: Frequency Percentages of Social Influence……………….

119

Table-4.8: Frequency Percentages of Computer Competency……….

121

Table-4.9: Frequency Percentages of Facilitating Conditions……….

122

Table-4.10: Frequency Percentages of Attitude Towards Computer Use

126

Table-4.11: Frequency Percentages of Behaviour Intention…………

128

Table-4.12: Mean of Psychological Variables and its Dimensions…..

129

x Table-4.13: Correlation Matrix of Independent and Dependent Variables 132 Table-4.14 (a,b,c): Correlation Matrix of Dimensions of Independent and Dependent Variables……………………... 134 Table-4.15: ANOVA for Predictors of Attitude Towards Computer Use……………………………………….. 138 Table-4.16: Regression Results for Predictors of Attitude Towards Computer Use…………………………………………….

138

Table-4.17: ANOVA for Predictors of Behaviour Intention…………. 140 Table-4.18: Regression Results for Predictors of Behaviour Intention……………………………………………. 140 Table-4.19: ANOVA for Predictors of Actual Use of Computers……. 141 Table-4.20: Regression Results for Predictors of Actual Use of Computers……………………………………… 142 Table-4.21: ANOVA for Dimensions of Psychological Variables as Predictors of Actual Use of Computers ……………………

143

Table-4.22: Regression Results for Dimensions of Psychological Variables as Predictors of Actual Use of Computers ………… 143 Table-4.23: ANOVA and Regression Results for Gender and Actual Use of Computers…………………………………

145

Table-4.24: ANOVA and Regression Results for Type of School Management and Actual Use of Computers………………….

146

Table-4.25: ANOVA and Regression Results for Location of School and Actual Use of Computers…………………………………

147

Table-4.26: ANOVA and Regression Results for Education and Actual Use of Computers…………………………………. 148 Table-4.27: ANOVA and Regression Results for Age and Actual Use of Computers………………………………...

149

Table-4.28: ANOVA and Regression Results for Computer Access at Home and Actual Use of Computers……………………….

150

Table-4.29: ANOVA and Regression Results for Computer Access at School and Actual Use of Computers………………………

150

xi Table-4.30: ANOVA and Regression Results for Computer Training and Actual Use of Computers………………………………..

151

Table-4.31: ANOVA and Regression Results for Computer Experience and Actual Use of Computers………………………………… 152 Table-4.32: ANOVA and Regression Results for Subject Taught and Actual Use of Computers…………………………………

153

Table-4.33:t-Test for Significant Difference among Teachers by Gender………………………………………….

156

Table-4.34:t- Test for Significant Difference among Teachers by their Education…………………………………..

159

Table-4.35: t- Test for Significant Difference among Teachers by Location of School………………………………

162

Table-4.36: t- Test for Significant Difference among Teachers by their Computer Access at Home………………..

165

Table-4.37: t- Test for Significant Difference among Teachers by their Computer Access at School………………..

166

Table-4.38: t- Test for Significant Difference among Teachers by their Computer Training…………………………. 168 Table-4.39:ANOVA for Significant Difference among Teachers by their Age Groups………………………………… 170 Table-4.40: ANOVA for Significant Difference among Teachers by Type of School Management……………………. 173 Table-4.41: ANOVA for Significant Difference among Teachers by Subject Taught…………………………………… 175 Table-4.42: ANOVA for Significant Difference among Teachers by their years of Computer Experience……………... 179

xii

LIST OF FIGURES FIGURE NO

TITLE

PAGE NO

Figure-1.1:

Theory of Reasoned Action………................................. 7

Figure-1.2:

Theory of Planned Behaviour…………………………

Figure-1.3:

Technology Acceptance Model……………………….. 9

Figure-1.4:

Unified Theory of Acceptance and Use of Technology…………………………………….. 11

Figure-1.5:

The Proposed Conceptual Framework………………… 15

Figure-3.1:

Factorial Design of Sample Respondents …………….. 71

Graph-4.1:

Percent Distribution of Respondents by Gender………… 106A

Graph-4.2:

Percent Distribution of Respondents by Age Group……... 106A

Graph-4.3:

Percent Distribution of Respondents by Education………. 106A

Graph-4.4:

Percent Distribution of Respondents by Location of School 106B

Graph-4.5:

Percent Distribution of Respondents by Type of School Management106B

Graph-4.6:

Percent Distribution of Respondents by Subject………….. 106B

Graph-4.7:

Percent Distribution of Respondents by Computer Access at Home..108A

Graph-4.8:

Percent Distribution of Respondents by Computer Access at School 108A

Graph-4.9:

Percent Distribution of Respondents by Computer Training…. 108A

Graph-4.10:

Percent Distribution of Respondents by Computer Experience… 108A

8

1 CHAPTER 1 INTRODUCTION Advancement in Information and Communication Technology (ICT) has virtually entered into every aspect of life, redefining the environment in which we learn, work and contribute to national development. It is widely believed that use of technology will promote economic development through increased work efficiency and productivity in an organization. Over the past two decades, efforts have been made to harness the benefits of ICT for educational purposes. It is anticipated that technology enabled teaching-learning process would lead to improved students’ achievement and teachers’ professional development. Computer-aided classroom environment would facilitate teacher tasks and assist student learning. It can aid teachers to explain abstract concepts in mathematics and science with illustrations and examples. Computers with internet connectivity would help in accessing open educational resources, there by leading to greater autonomy in learning among the teachers and the students. It can also provide access to up-to-date information, lesson plans, worksheets, diagrams, illustrations, and audio and video clippings. These resources can be stored, modified and retrieved by both students and teachers for further learning. In order to adopt the latest technologies the school education system needs revamping in various aspects that includes curriculum design; delivery mechanism; teacher concerns; student motivation; and appropriate and adequate technology infrastructure right in the classroom. In the digital era, the conventional role of teachers is in transition from knowledge givers to facilitators, constructors and creators of learning environments. This calls for equipping teachers with new skills and capabilities to use technology effectively.

2 Computers allow teachers for creative ways of teaching in their respective subjects to draw maximum benefits. Teachers can encourage collaborative learning and critical thinking among students using technology. Computers with internet facilitate teachers to be connected with the students, parents and school management. Internet connectivity enables teachers to locate relevant information on the subject taught and guide student learning. Successful

transformation

in

educational

practices

requires

teacher

acceptance and adaptation of new technology. It presupposes teachers’ positive attitude and ingenuousness towards use of technology. Support from school management, availability of resources and training in use of technology are preconditions for teachers’ effective utilization of technology. This calls for research studies on technology integration in school education in relation to teachers’ attitude, knowledge, skills, training and actual use of technology as a pedagogical tool, and student exposure to technology to enhance their learning capabilities. Present study deals with secondary school teachers’ use of computers in relation to teachers’ perception of computer usefulness and ease of use, social influence, computer competency, facilitating conditions to use computers, attitude towards computer use and intention to use computers. Need for the Study It is well recognized that the most influential change agents in educational innovations and practices are teachers. More specifically use of technology in teaching can be achieved when teachers believe in it and effectively use it in their classroom. Baylor and Ritchie (2002) argue that, “regardless of the amount of technology and its sophistication, technology will not be used unless faculty members have the skills, knowledge and attitudes necessary to infuse it into the curriculum” (p. 398).

3 Technology integration to achieve better instructional goals is still in the beginning stages in Indian school education. Both central and state governments have launched various programmes to train teachers and students in use of computers, apart from funding the schools to equip with technology infrastructure. However, sufficient information is not available in respect of the actual use of technology in schools by teachers and students for effective teaching and learning. It is an undeniable fact that it is teachers who determine when, where and how to use technological tools in the classroom environment. The energy and investment put forth in schools to implement new technologies can be productive only if teachers develop positive attitudes and intention to use technology. This explains the recurrent call for conducting research studies on technology integration in schools. This is particularly true of developing countries, where technology has lately entered the educational milieu. This need cannot be overemphasized within the Indian context, since, to date, not many published studies have examined the status of technology implementation in Indian schools and among Indian teachers. It will not be out of place here to make a mention of various initiatives taken by Government of India to integrate technology in school education. Acknowledging the tremendous potential of ICT for enhancing outreach and improving quality of education, The Ministry of Human Resources Development, Government of India, had brought out a national level document titled ‘Towards a National Policy on ICT in School Education in India, 2007’ Later, Government of India has brought out ‘Revised National Policy on Information and Communication Technology in School Education’ (2012). The revised police on provide guidelines to state governments to optimize the use of ICT in school education within a national policy framework. It aims at creating an ICT-knowledgeable society, providing free access to ICT enabled tools and resources to teachers and students and motivate the sections of the society for strengthening the school education process through appropriate utilization of ICT.

4 A number of training programmes for teachers and students initiated by the central and state governments of India are listed below, though not exhaustive: CLASS (Computer Literacy And Studies in Schools) project was first introduced in 1984 -1985 throughout the country and came to a close in 1997 – 1998. A revised CLASS Project now in operation provides for computer infrastructure for all government high schools. ICT @ school is a scheme of Government of India, which assists state governments to develop infrastructure for ICT enabled education. The Intel® Teach Program is a professional development program for teachers to effectively integrate technology in classroom teaching and learning. Microsoft’s Project Shiksha- ‘Empowering the future’ is a focused program designed to deliver comprehensive training and curriculum leadership for students and teachers in government schools. “Mahiti Sindhu” is a programme of the Government of Karnataka designed to give Computer Education and Computer aided Education free of cost to VIII, IX and X standard students in the state. Given the potential of integrating technology in school education and initiatives taken by various stakeholders in the country, an attempt is made in the present study to understand the perception, attitude and related characteristics of secondary school teachers towards use of computers and their impact on teachers’ actual use of computers in schools.

Significance of the Study The adoption of technology is a complex social developmental process built upon teacher perceptions of the technology that influences teacher intentions (Straub, 2009). In the field of education, the decision to use technology in teaching is influenced to a large extent by the attitudes and beliefs of teachers, and school management towards technology acceptance.

5 Educationists and researchers believe that technology integration in Indian schools is viewed only from the point of technology per se and fails to address the concerns of teachers and students. It is in this context the present study assumes significance. An understanding of in-service teachers’ perception of technological infrastructure facilities available, computer competency, training in computer, teachers’ attitude towards computer and intention to use computer in relation to actual use of computers would go a long way in integrating technology in school education. Research studies have suggested that use of technology in schools have an impact on educational practices, significantly changing the roles of teachers and students. Educationists have cautioned that the school teachers must be fully aware of the benefits and limitations of using technology in schools. It is also widely stated that teachers are exposed only to technology per se and teachers’ use of technology is limited to mostly administrative support in schools. There are instances where computers are used as a supplement in classrooms, mostly as power point presentations, confirming less-than-optimal use of technology as a pedagogical tool. Various research studies carried out among pre-service teacher trainees, inservice teachers and students have tried to focus on possible determinants of technology acceptance and adaptation in school education. In India context, studies on technology acceptance and adaptation among teacher trainees and in-service teachers and students are limited. More specifically, studies on reasons for acceptance or rejection of using of technology by in-service teachers are either limited or not available. Studies conducted elsewhere mainly focused on pre-service teachers’ preparedness to use technology in teaching and only a few studies have attempted to analyze the determinants of in-service teachers’ intention to use technology in schools. Various studies have identified perceived usefulness, ease of use, (Davis, 1989) computer competence (Francis-Pelton & Pelton, 1996), and computer access

6 and training (Loyd & Loyd, 1985; Knezek, Christensen & Rice, 1997) as major determinants of use of technology. It is widely accepted that unless teachers develop positive attitude towards ICT, they will not use them in their teaching practice. Studies have shown that teachers’ use of technology is affected by attitudinal, cognitive and normative assessment of factors relevant to technology, the social system, the target task and implementation context (Hu, Clark & Ma, 2003; Ma, Andersson & Streith, 2005). Thus, the present study assumes significance in addressing the in-service teachers’ use of computers as a pedagogical tool in the classroom. Such an understanding will help to identify the key determinants of use of computers in schools and provide guidelines to school administrators for successful use of computers in teaching and learning. Theories and Models of Technology Acceptance Advances in information and communication technology in terms of hardware and software, improved network and use of internet provides a platform for ‘anytime’ and ‘anywhere’ access to information. Technology acceptance refers to end-users’ willingness to employ available technology to carry out the tasks assigned effectively and efficiently. Much of the research in technology acceptance is based on theories and models from behavioural and information sciences. A review of three major theoretical foundations is presented here. Behavioral Intention Theory Technology acceptance is explained by the end-users’ intention to use technology which is determined by their behavioural factors. The intention-based theories and models that are developed and empirically tested include:

7 (a) Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975); (b) Theory of Planned Behaviour (TPB) (Aizen, 1991, Ajzen, 2006; Taylor & Todd, 1995); and (c) Technology Acceptance Model (TAM) with its extended models (Davis, Bagozzi & Warshaw, 1989; Venkatesh & Davis, 2000; Hu, Clark & Ma, 2003 ; Teo, Lee, & Chai, 2008). These models emphasize that the behavioural intention of an individual is determined by factors such as individuals’ perceptions on technology, attitudes, social influences and facilitating conditions to predict intended or actual use of technology. Theory of Reasoned Action Theory of Reasoned Action, proposed by Fishbein and Ajzen (1975), is a well-researched intention model to predict and explain behaviour across a wide variety of domains. The TRA is a general theory of human behaviour and it defines relationships among beliefs, norms, attitudes, intentions and behaviour. According to TRA, a person’s behaviour intention is jointly determined by the person’s attitude and subjective norms (Figur-1.1). Beliefs and Evaluations

Normative Beliefs and Motivation to comply

Attitude Towards Behaviour (A) Behaviour Intention (BI)

Behaviour

Subjective Norm (SN)

Figure-1.1: Theory of Reasoned Action (Fishbein & Ajzen, 1975) In Theory of Reasoned Action the first factor is the attitude towards behavior that being determined by individuals’ beliefs and evaluation of behavioural outcomes. The second factor is subjective norm being determined by individuals’ perception of what they believe others expect them to do and the strength of their motivation to comply with those expectations.

8 Theory of Planned Behavior The Theory of Planned Behaviour is an extension of Theory of Reasoned Action with the addition of a construct called perceived behavioral control that would possibly determine those factors that are beyond the control of the individual and could affect the individual’s intention and behaviour. According to Ajzen (2006) attitude is the evaluation of the performance effect of a particular behavior, subjective norms are perceptions of individuals based on other people’s opinions on whether the particular behavior should be performed and perceived behavior control is the perceptions of individuals on the essential resources necessary for performing a behavior. The Figure-1.2 provides a schematic representation of the Theory of Planned Behaviour.

Figure-1.2: Theory of Planned Behavior (Ajzen, 2006) Technology Acceptance Model Technology Acceptance Model introduced and developed by Fred Davis is an extension of Ajzen and Fishbein’s Theory of Reasoned Action. The model suggests two specific variables, perceived usefulness and perceived ease of use as fundamental determinants of user acceptance (Davis, 1989).

9 Perceived usefulness explains the user’s perception of how useful the technology is in performing the job. Perceived ease of use explains the user's perception of the amount of effort required to utilize the system or the extent to which a user believes that using a particular technology will be effortless. The basic model was improvised by introducing Attitude Towards Use as a determinant of behaviour intention to use a technology (Davis et al., 1989). The Figure-1.3 provides a schematic representation of the Technology Acceptance Model. The technology acceptance model is specifically tailored for modeling user acceptance of information technology. The goal of the model is to provide an explanation of the determinants of computer acceptance by tracing the impact of external factors on internal beliefs, attitudes and intentions (Davis et al., 1989). Perceived Usefulness Attitude Towards Use

External Variables

Behavioural Intention to Use

Actual System Use

Perceived Ease of Use

Figure-1.3: Technology Acceptance Model (Davis et al., 1989) Extended Technology Acceptance Model The original Technology Acceptance Model was improvised and extended by Venkatesh and Davis (2000) as Technology Acceptance Model-2 to explain the impact of three interrelated social forces, namely, subjective norm, voluntariness and image that may affect an individual’s opportunity to adopt or reject a new system.

10 Subjective norm refers to user’s belief of what most of his/her important others believe he/she should or should not perform the behaviour to accept the technology. Voluntariness refers to the obligatory or mandatory context in which the user is placed to accept technology. Image is defined as the degree to which accepting new technology is perceived to enhance the person’s status in one’s social system. Unified Theory of Acceptance and Use of Technology Based on review, mapping and integration of eight dominant theories and models in the field of technology acceptance research, Venkatesh, Morris, Davis and Davis (2003) have proposed Unified Theory of Acceptance and Use of Technology (UTAUT) model. The eight models whose elements are integrated in developing UTAUT include: (1) Theory of Reasoned Action (TRA), (2) Technology Acceptance Model (TAM), (3) Motivational Model (MM), (4) Theory of Planned Behaviour (TPB), (5) Combined Theory of Planned Behaviour/Technology Acceptance Model (C-TPB-TAM), (6) Model of PC Utilization (MPCU), (7) Innovation Diffusion Theory (IDT), and (8) Social Cognitive Theory (SCT). UTAUT explains that performance expectancy, effort expectancy, social influence and facilitating conditions are major determinants of behavioral intention or use behavior. The UTAUT provides a unified theoretical basis for research on information technology adoption and diffusion. It postulates four core constructs – performance expectancy, effort expectancy, social influence, and facilitating conditions as direct determinants of behavioural intention to use and ultimately use behaviour (Venkatesh et al., 2003). The theory also proposed that gender, age, experience, and voluntariness of use have moderating effect on the four determining constructs of technology acceptance. Figure-1.4 provides a schematic representation of UTAUT.

11 Performance Expectancy encompasses perceived usefulness (Davis, 1989) and other constructs regarding the usefulness of the technology and is defined as ‘‘the degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003). Effort Expectancy encompasses constructs concerning the ease of use of the technology, such as perceived ease of use (Davis, 1989), and is defined as ‘‘the degree of ease associated with the use of the system” (Venkatesh et al., 2003). Performance Expectancy

Behaviour Intention

Effort Expectancy

Use Behaviour

Social Influence Facilitating Conditions Gender

Age

Experience

Voluntariness of Use

Figure-1.4: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003). Social Influence encompasses constructs relating to norms in the social environment of the individual on his/her use of technology, e.g. subjective norms (Fishbein & Ajzen, 1975). Social influence is defined as ‘‘the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003).

12 Facilitating Conditions construct is very broad as it includes training, support, infrastructure, availability to the teachers and their knowledge of technology. This construct was distilled from perceived behavioral control (Ajzen, 1991), facilitating conditions (Thompson, Higgins, & Howell, 1991) and compatibility (Moore & Benbasat, 1991). It is defined as ‘‘the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh et al., 2003). Innovation Diffusion Theory The Innovation Diffusion Theory (IDT), proposed by Rogers, helps to understand how a new innovation is diffused and adopted by end users. He defined diffusion as “the process in which an innovation is communicated through certain channels over time, among the members of a social system” (Rogers, 1995, p. 10). The central element in the diffusion process, the innovation itself, is defined by Rogers as, “an idea, a practice, or object that is perceived as new by an individual or other units of adoption” (Rogers, 1995, p. 11). Accordingly, innovation-diffusion is explained through five adopter categories based on the characteristics of individuals’ innovativeness: (a) innovators, (b) early adopters, (c) early majority, (d) late majority and (e) laggards. Based on the attributes of the innovation, five categories of adoption were proposed: (a) relative advantage, (b) compatibility, (c) complexity, (d) trialability and (e) observability. Moore and Benbasat (1991) have modified the Innovation Diffusion theory to investigate technology acceptance by individuals. The constructs of this theory include relative advantage (whether the innovation is better than the traditional practice), ease of use, image (perception of status of the new innovation), visibility (use by others), compatibility (consistent with the values and experiences), results of new innovation, and voluntariness of use (Moore & Benbasat, 1991).

13 In the context of technology integration in schools, one would expect that a teacher is likely to use technology if it is (a) advantageous as compared to present practices of teaching, (b) consistent with pedagogy of teaching and learning, (c) easy to use, (d) possible to try in classroom, and (e) likely to enhance the teachinglearning process in schools. Social Cognitive Theory Social Cognitive Theory (SCT) developed by Bandura (1986) was modified to study technology use by Compeau and Higgins (1999). This model was framed with five core constructs: performance outcome expectations (job related performance);

personal

outcome

expectations

(individual

esteem

and

accomplishment); Self-efficacy (one‘s ability); affect (liking of technology use); and anxiety (towards technology use). Based on the social cognitive model, technology integration in schools can be studied as interaction among various factors such as availability of technology infrastructure, perception of facilities and its use by the end users, perceived selfefficacy of end users, and the perception related to possible outcome in using technology for teaching and learning in schools. Conceptual Framework of the Study Most of the studies on technology acceptance are based on theories and models from behavioural and information sciences as their conceptual framework. The technology acceptance model has been extensively used by researchers in a wide range of settings, including in the field of education (Sanchez-Franco, 2010; Teo, Lee & Chai, 2008).

14 Attempts were made to consolidate the results of Technology Acceptance Model based research studies and such meta-analysis can be found in the works of Yousafzai, Foxall and Pallister (2007a, 2007b ); Sharp (2007); King and He (2006); Ma and Liu (2004); Lee, Kozar and Larsen (2003); and Legris, Ingham and Collerette (2003). The meta-analysis of Technology Acceptance Model research studies found that the variance in the dependent variable is explained by the model is no more than 40%, suggesting the inclusion of additional antecedents of acceptance (Legris, Ingham, & Collerette, 2003), resulting in many follow-up studies focusing on model expansion or refinement. Attempts were made to apply Technology Acceptance Model in education that includes studies on online learning (Gao, 2005), WebCT (Ngai, Poon & Chan, 2007). There were studies focusing on pre-service teacher trainees’ readiness to use technology based on Technology Acceptance Model (Teo, Lee & Chai, 2008; Teo, 2009; Teo & Schaik, 2009). Recent research studies have adopted Technology Acceptance Model as most influential model to explain technology usage behaviour (Teo & Noyes, 2011; Venkatesh, 2000; Venkatesh & Davis, 2000). However, research studies using Technology Acceptance Model and its extended model such as Unified Theory of Acceptance and Use of Technology are found to be limited outside the developed countries, more particularly in Asian countries. In India, research studies using technology acceptance model as conceptual framework to study technology integration in schools is either limited or non-existent.

15 The research of Venkatesh et al. (2003) applying Unified Theory of Acceptance and Use of Technology was originally conducted in business organizations. Later this model has been adopted in the field of education (Marchewka, Liu & Kostiwa, 2007; McCombs, 2011). Given the importance of the role of Indian school teachers to support or inhibit the integration of computer technology in the classroom the present study proposes to use Technology Acceptance Model as a conceptual framework, including its extended model, namely, Unified Theory of Acceptance and Use of Technology.

The conceptual framework of the present study consists of five major determinants of teachers’ Attitude Towards Computer Use, which in turn affects teachers’ behaviour intention and actual use of computers. A diagrammatic representation of the proposed conceptual framework of the present study is given in Figure-1.5. Computer Usefulness

Computer Ease of Use

Social Influence Computer Competency

Attitude Towards Computer Use

Behaviour Intention

Actual Use of Computers

Facilitating Conditions Gender Age Education Type of School Management Computer Access at Home Computer Training

Figure-1.5: The Proposed Conceptual Framework

Location of School Subject Taught Computer Access at School Computer Experience

16 Arrows in the proposed conceptual framework indicate hypothesized relationships between the independent and dependent variables. The relationships indicated in the proposed conceptual framework include the mediators derived from the characteristics of respondents in the present study. The conceptual framework of the present study differs from the basic model of Unified Theory of Acceptance and Use of Technology (UTAUT) in the following aspects: 1.

The present study is conducted only in voluntary setting, secondary

schools, hence, the variable voluntary/mandatory is not included; 2.

Keeping in view the cultural context of secondary school teachers in

India, the present study includes a number of mediating variables to understand its effect on various study variables; 3.

The study has used all the four constructs of UTAUT, in addition to a

fifth construct: Performance Expectancy of UTAUT is referred as Computer Usefulness which includes three dimensions. (a)

Perceived usefulness, (b) Instructional Advantage and (c) Job

Relevance. Effort Expectancy of UTAUT is referred as Computer Ease of Use which includes two dimensions: (a) Perceived Ease of Use and (b) Computer Complexity. Social Influence is retained to refer to two dimensions: (a) Subjective Norm and (b) Prestige and Image.

17 Computer Competency is a newly added construct with two dimensions: (a) Operating Skills and (b) Application Skills. Facilitating Conditions is retained to refer to two dimensions: (a) Infrastructure Support and (b) School Support; 4.

The UTAUT specified attitude as a mediating construct, while the

present study has used Attitude Towards Computer Use as an independent construct having four dimensions: (a) Anxiety, (b) Confidence, (c) Liking and (d) Enjoyment. 5.

The UTAUT specified Behaviour Intention as a dependent variable,

while the present study has used Behaviour Intention as an independent variable. 6.

The UTAUT specified Usage Behaviour as ultimate dependent

variable to be examined and the present study has considered Actual Use of Computers as the dependent variable. Objectives of the Study 1. To describe the socio-demographic profile of the teachers and their actual use of computers. 2. To analyze the relationships among teacher’s perception of use of computers, attitude towards computer use, behaviour intention and actual use of computers. 3. To establish the effect of teacher’s perception of use of computers, attitude towards computer use, behaviour intention on actual use of computers. 4. To find the effect of socio-demographic characteristics of teachers on actual use of computers.

18 5. To examine the mean differences in teacher’s perception of use of computers, attitude towards computer use, behaviour intention and actual use of computers across socio-demographic characteristics of the teachers. Operational Definition of Major Concepts Computer Usefulness is defined in the present study as the secondary school teachers’ perception of the usefulness and relevance of computer to perform their job effectively and efficiently, and as a tool for teaching and learning. Perceived Usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance’’ (Davis et al., 1989, p. 320). People tend to use or not to use an application to the extent that they believe it will enhance their job performance. In the present study it refers to the advantages in using a computer for job performance as reported by the respondents.

Instructional Advantage is defined by the researcher as the usefulness of computer for the purpose of teaching and learning as perceived by the teachers. The definition is derived from the relative advantage construct that refers to “the degree to which an innovation is perceived as being better than its precursor” (Moore & Benbasat 1991, p.195). Job Relevance in the present study refers to the extent to which teachers consider use of computers is relevant to his or her job. It is based on the definition of job-fit that refers to “the extent to which an individual believes that using a technology can enhance the performance of his or her job” (Thompson et al., 1991, p. 129). Computer Ease of Use is defined in the present study as the secondary school teachers’ perception of the ease of using computer and their own judgment of capability to use computer to achieve desired teaching and learning objectives.

19 Perceived Ease of Use refers to “the degree to which a person believes that using a particular technology will be free of effort’’ (Davis et al., 1989, p. 320). In the present study it refers to the degree of ease or difficulty in using computer as perceived by the teacher. Computer Complexity is defined as “the degree to which a system is perceived as relatively difficult to understand and use” (Thompson et al., 1991). Social Influence is defined as ‘‘the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003, p.451). Subjective Norm is defined as “the person’s perception that most people who are important to him think he should or should not perform the particular behavior in question” (Fishbein & Ajzen, 1975, p.302). In this study, subjective norm is the teachers’ perception of the degree to which the demands of the ‘important’ others would affect the teachers’ use of computers. Prestige and Image is defined by the researcher as the perceived enhancement of teachers’ status in school. The definition is derived from the image construct that refers to “the degree to which an innovation is perceived to enhance one’s image or status in one’s social system” (Moore & Benbasat 1991, p.195). Computer Competency is defined as being able to handle a wide range of varying computer applications for various purposes (van Braak et al., 2004). In the present study it refers to secondary school teachers’ self-reported skills of using a computer, such as basic computer operating skills such as save, delete and copy a file; application skills related to use of word, excel, power point.

20 Facilitating Conditions is defined as ‘‘the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh et al., 2003). In the present study it refers to the factors that are present in the school environment that exert an influence over a person’s desire to perform a task. The support received from school management and colleagues is referred to as school support and the provision of computer and related infrastructure is referred to as infrastructure support. Attitude Towards Computer Use: According to Ajzen, attitude towards the behavior refers to “the degree to which a person has a favourable or unfavourable evaluation or appraisal of the behavior in question” (1991, p.188). In the present study, attitude towards computer use is operationally defined as the teachers’ disposition towards the use of computers. Computer Anxiety refers to fear of computers or the tendency of a person to be uneasy, apprehensive and phobic towards current or future use of computers in general (Loyd & Loyd 1985). Computer Confidence refers to a person’s ability/self-reliance to use or learn about computers (Loyd & Loyd 1985). Computer Liking refers to a person’s liking of working with computers (Loyd & Loyd 1985). Computer Enjoyment refers to teachers’ perception of use of computers as playful and enjoyable (Thompson et al., 1991). Behavior Intention refers to “a measure of the strength one’s intention to perform a specified behaviour” (Fishbein & Aizen, 1975, p.288).

21 Actual Use of Computers is defined in the present study to refer to secondary school teachers’ use of computers for various purposes. According to Kellenberger and Hendricks (2008) a teacher may generally use a computer for various purposes other than for teaching and learning. Such activities may include preparation of lesson plan, class notes, test papers, and maintenance of student records and entry of marks. Thus, in the present study, the actual use of computers by the teacher has three major domains: academic use, administrative use and personal use. Assumptions of the Study It is assumed that the participants are capable of describing their perceptions and attitudes towards use of computers as requested in the study instruments. It is further assumed that the participants will be candid in their responses to the survey items. The researcher also presumes that the responses of participants involved in this random sample are representative of those teachers who work in the state curriculum schools constituting the population of the current study.

The conceptual framework for the present study is based on the assumption that the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model would be suitable to capture the psychosocial correlates of computer usage among the sample studied. Venkatesh et al. (2003) determined that behavioural intention of a user to use a technology is a determinant of use. In schools it is the teacher who makes the decision to use technology as a pedagogical tool. Hence, it is assumed that behaviour intention of teacher impacts the use of technology in a classroom.

22 Statement of the Problem The problem addressed in this study is to understand the reasons for acceptance or rejection of use of computers in schools by the secondary school teachers. For this purpose various psychological and socio-demographic variables are considered as predictors of the actual use of computers by the secondary school teachers. Acceptance of any innovation is based on the interplay of various factors, such as, belief-attitude-intention-use behavior. In this context various theories and models were developed and tested to understand the acceptance and adoption of technology by the end users. The present study uses the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model as its conceptual framework. The available literature shows that in India no studies pertaining to computer usage among school teachers had been carried out using Technology Acceptance Model as a conceptual framework.

Accordingly, using the framework of technology acceptance constructs the present study aims at examining the psychological and socio-demographic factors that affect the actual use of computers among the secondary school teachers.

Available research studies have identified several independent variables that may affect the teachers’ attitude towards computer use and intention to use computer as dependent variables. But studies on teachers’ actual use of computers and its determinants are very limited and non-existent in Indian context. Hence, the present study tries to explore the effect of various independent variables on secondary school teachers’ Actual Use of Computers.

23 The independent variables proposed in the study include secondary school teachers’ perceived Computer Usefulness, Computer Ease of Use, Social Influence, Computer Competency, Facilitating Conditions, apart from Attitude Towards Computer Use and Behaviour Intention to use computer. The study also considers the moderating effect of socio-demographic variables on both independent and dependent variables. Scope of the Study It is believed that using computer technology improves teaching and learning process in schools. Several factors play a role in the use of computers by teachers in schools. Importantly, the availability of computers and its access at workplace and at home influence one’s decision to use them. Hence, availability of computers infrastructure in schools, both hardware and software, is important in ensuring possible use of computers by the teachers. At the same time, it is very important to understand the key motivating factors that would encourage the teachers to accept and adopt computer technology for teaching and learning.

The scope of the study is to focus on the major motivating factors that allow the teachers to venture into use of computers in schools. The willingness of students to use computer and its impact on their learning and achievement is another field of interest, which is beyond the scope of this study. The study is confined to the in-service teachers working in state board secondary schools in Bangalore. It is an exploratory study aimed at examining the factors that contribute to the teachers’ actual use of computers. The study is designed to identify key determinants of use of computers by teachers and relate it to the existing literature in the field of focus.

24 The project mode technology training offered to school teachers and students in India by both government and IT- corporate would be of potential area for research and beyond the scope of this study. The study also tries to identify significant teacher characteristics that may have an impact on use of computers by the teachers. The study extends its scope by including the support offered by the school management and colleagues in encouraging the teacher to use computer. Teachers’ computer competency and computer experience are also a part of this study in examining its impact on teachers’ actual use of computers. Present study uses seven comprehensive constructs to explain teachers’ perception on use of computers, the attitude towards computer use and the intention to use computer. Analyzing the effect of these constructs on teachers’ actual use of computers would provide practical guidelines for school management and educationist to integrate technology in school education. Limitations of the Study The study is limited to the population of secondary school teachers in Bangalore districts who are working in state board schools. The sample respondents are drawn from randomly selected secondary schools located in the Bangalore districts. Hence, the study findings may not be generalisable to teachers working at various levels of school education and in other countries. Another limitation of this study is that the self-reported nature of data collected and can be an accepted limitation in research.

25 Chapterisation The thesis is presented in five chapters. Chapter One includes a brief introduction to the nature and scope of the present study, conceptual frame work, significance, objectives and limitations of the study. Various theories and models on technology acceptance are briefly discussed. The Second Chapter reviews literature in relation to the research objectives and scope of the present study. The research studies published in the area of technology acceptance and use in various countries and in India are reviewed. The studies that specifically deal with technology acceptance model and which are applied to the field of education are given prominence in the literature review. The Third Chapter discusses the methodological procedure adopted in the study. It explains the conceptual framework, sample selection, development of psychometric scales, pre-testing of tools and reliability and validity tests for psychometric scales. The Fourth Chapter presents the data analysis and interpretation of results. It examines the results of the present study in the light of the findings of similar such studies conducted elsewhere. Chapter Five discusses the implications of the present study in understanding the teachers’ computer usage and identifies factors that are significant in enhancing the teachers’ use of computers for teaching and learning. Based on the results, recommendations for technology integration at school level, policy relevance and practical difficulties to be addressed are discussed.

26 CHAPTER 2 LITERATURE REVIEW A review of both theoretical and empirical studies is of importance to develop the conceptual framework of the study. An attempt is made in this chapter to review the literature pertaining to technology acceptance and use of technology in the field of education because it facilitates in identifying the variables and their interrelationships that are of concern for the study. The review of literature in this study has been focussed on major findings, limitations and gaps in research in the use of technology in the field of education that needs to be addressed. Firstly, the meta-analysis and review studies that have summarised various research findings dealing Technology Acceptance Model are discussed. Secondly, the research studies concerning major determinants or factors that influence the computer usage are presented. Thirdly, the research studies focusing on the moderating effect of various socio-demographic factors in technology acceptance and usage are reviewed. Finally, the studies that are conducted in India in the field of education related to technology acceptance and use are reviewed to place in context the importance of the present study. Review of Meta-analysis Studies Most of the research studies on technology acceptance are based on a variety of theoretical foundations, such as Innovation Diffusion Theory, Behaviour Intention Theory that includes Theory of Reasoned Action, Theory of Planned Behaviour and Technology Acceptance Models and Social Cognitive Theory.

27 In the beginning, research studies have applied these theories and models in corporate and business organisations and further development has led to their application in the field of education. The literature review presented here is confined to technology acceptance model and its extensions as applicable to the field of education. The Technology Acceptance Model (TAM) proposed by Davis et al. (1989) addresses the issue of how users come to accept and use a technology. The model proposed two specific variables, namely, perceived usefulness and perceived ease of use, to be fundamental determinants of user acceptance of technology. The study finds that perceived usefulness is more strongly related to system use rather than perceived ease of use. Further the regression analysis of variables in the study suggests that ease of use is an antecedent to usefulness rather than a direct determinant of use of computers. In recent years additional variables were added to the technology acceptance model that led to emergence of TAM2 (Venkatesh & Davis, 2000) and UTAUT model (Venkatesh et al., 2003). The additional variables include subjective norm, voluntariness, image, job relevance, computer compatibility, computer skills and self-efficacy, apart from demographic factors like, gender, age, education and computer training. Over the years attempts have been made to consolidate the findings of various research studies through meta-analysis and review of research studies that applied TAM and its extended models. Legris et al. (2003) have reviewed the articles that are based on TAM, published from 1980 to the first part of 2001 and consulted more than 80 articles and considered 22 studies for the meta-analysis. The review found TAM to be a useful theoretical model helping to understand and explain user acceptance of technology.

28 Most of the studies reviewed have found that intention of using computers is determined largely by perceived usefulness and to a lesser extent by attitude. In eleven out of 22 research studies reviewed, the use of computers was measured through respondents’ self-reporting. The authors suggested for inclusion of external variables to strengthen the explanatory power of perceived usefulness and ease of use in predicting the intention and use of computers.

Lee et al. (2003) traced the origin and development of technology acceptance model and its extended model. The authors have done a meta-analysis of 101 studies based on TAM and found that TAM is the most influential and commonly employed theory for describing an individual’s acceptance of information systems. The effect of perceived usefulness on behaviour intention and use of technology is found to be strong and significant. The perceived ease of use is found to be a weak predictor, but mediates its influence through usefulness. The technology usage behaviour was generally measured through self-reported answers. The questions were related to frequency of use, amount of time spent in using computer, actual number of days/weeks the computer is used and the purpose for which computer is used. The review had also analysed the influence of various external variables, such as compatibility, computer anxiety, self-efficacy, enjoyment, computing support, and computer experience. King and He (2006) had carried out a meta-analysis of 88 TAM studies. They found that the behaviour intention as dependent variable is mainly determined by perceived usefulness, while perceived ease of use had only mediating effect through usefulness.

29 Sharp (2007) had reviewed articles published between 2001 and 2005 on TAM. The review found mixed results for the effect of perceived usefulness and perceived ease of use on user acceptance of technology. The review had also shown that the results of user acceptance of technology vary in voluntary and mandatory settings. Further, the respondents’ attitude had no significant effect on technology usage behavior. However, it is suggested that attitude can be a potential variable for further research in applying TAM as the conceptual framework. Yousafzai et al. (2007a) carried out a literature review of 145 papers published on TAM and identified areas of further research in applying TAM. Apart from perceived usefulness, ease of use and intention to use, the authors have emphasized the importance of including mediating variables such as attitude towards system use, characteristics of users and type of use of computers. Followed by this the authors have conducted a meta-analysis of 95 studies that have based their research on TAM (Yousafzai et al., 2007b). The review investigated the effect of various variables on self-reported technology usage behavior in mandatory and voluntary settings using TAM. It had also examined the role of attitude and the issue of measuring usage based on intention and actual usage of technology. The authors find that the relationship between usefulness-intention and attitude-intention are positive and strongly correlated. Further, usefulness and ease of use are found to exert a positive influence on attitude towards use of computers. In summary, review of meta-analysis studies reveals that the there exists a positive and direct relationship between: usefulness-attitude; ease of use-attitude; ease of use-usefulness; usefulness-intention; attitude-intention. However, further research is needed to examine the possible relationship between usefulness-actual use; ease of use-actual use; and attitude-actual use. The present study makes an attempt in this direction.

30 Keeping in view the objectives of the present study, an attempt is made to review selected research studies that have dealt with acceptance and use of technology among pre-service and in-service teachers across various countries. For the present study, such a literature review would facilitate in identifying key determinants of acceptance and usage of computer among the school teachers. Perceived Usefulness and Perceived Ease of Use Perceived usefulness refers to the degree to which an individual believes that using a particular system would enhance job performance (Davis et al., 1989). In the present study perceived usefulness refers to the advantages in using computers as perceived by the respondents. Perceived ease of use refers to the degree to which a person believes that using a particular technology will be free of effort (Davis et al., 1989). While users may believe that technology is useful, they may be, at the same time, perceive it to be too difficult to use and that the benefits of usage do not justify the amount of effort needed to use the technology. In the present study perceived ease of use refers to the degree of ease or difficulty in using computer as perceived by the respondents. In their Technology Acceptance Model, Davis et al. (1989) suggest that “ease of use” is the second most important innovation factor determining individuals’ attitudes and subsequent acceptance of technology. It is suggested that the impact of perceived ease of use of technology on intention to use is mediated through perceived usefulness and is expected to decrease with an increase in experience (Davis et al., 1989). Yuen and Ma (2002) used TAM as the framework to determine the gender differences in technology use among 186 pre-service teachers that includes 25%

31 females and 75% males, who have joined for one-year full time teacher education programme at University of Hong Kong. Analysis of variance reveals no significant mean difference in gender, teaching experience, computer training, perceived usefulness, ease of use, intention to use and computer usage.

The study indicated that perceived usefulness has a direct and significant effect on the pre-service teachers’ intention to use computers and mediates its effect on self-reported computer usage. The ease of use has shown positive and significant effect on usefulness, whereas its effect on intention to use computers is nonsignificant. In terms of gender difference, the effect of ease of use on usefulness is greater for male rather than female pre-service teachers. Further, effect of ease of use on intention to use is greater for females than for males. In summary, the study has confirmed TAM as a suitable model with usefulness and ease of use as key determinants of intention to use computers. Hu et al. (2003) have conducted a longitudinal study of technology acceptance among 130 public school teachers in Hong Kong who have attended fourweek intensive computer course in Microsoft Power Point Programme. The authors find that the proposed model was supported by the data and the model has shown satisfactory results in explaining user acceptance of technology. Perceived usefulness is identified as a critical determinant of user acceptance and its influence appears to increase as individuals become more experienced. The perceived ease of use had only a limited direct effect on intention to use power point presentation, before and after training. The authors have stated that in conformity with TAM, ease of use is found to exert significant influence on usefulness and appeared to be stronger with increase in user experience of technology.

32 Ma et al. (2005) conducted a research on 84 pre-service teachers in Sweden to investigate the user acceptance of computer technology. The study finds that the preservice teachers’ perceived usefulness of computer technology had a direct significant effect on their intention to use it. But, student-teachers’ perceived ease of use had only an indirect significant effect on intention to use. D’Silva (2007) carried out a research study among 318 secondary school teachers in Malaysia to identify the factors influencing the actual usage of computer. The study established a significant positive effect of perceived usefulness and ease of use on the actual use of computers.

Kumar, Rose and D’Silva (2008a) conducted a study to understand the relationship between actual use of computers and technology acceptance constructs. The study was carried out among the secondary school teachers in Malaysia. The results have shown that perceived usefulness and ease of use are the two major determinants of actual use of computers by the secondary school teachers. It is interesting to note that, in terms of magnitude of the relationship, the ease of use had higher magnitude as compared to usefulness on actual use of computers.

Teo, Su Luan and Sing (2008) in their study compared the future intention of 495 Singapore and Malaysian pre-service teachers in technology acceptance and use. The study has used technology acceptance model for testing. In both samples perceived usefulness and perceived ease of showed a significant effect on intention to use computer in future. Teo and Schaik (2009) have tried to understand the computer acceptance among a sample of 250 pre-service teachers in Singapore. The study had used The Technology Acceptance Model (TAM) as a framework. Data were analyzed using structural equation modeling approach.

33 The study had also tried to find out the extent of explanation provided by TAM and Unified Theory of Acceptance and Use of Technology (UTAUT) in predicting computer acceptance among pre-service teachers. The results of this study offer evidence that perceived usefulness has a direct effect on behavioral intention towards computer acceptance. However, the ease of use had only indirect effect on intention to use computers. Pynoo, Devolder, Tondeur, van Braak, Duyck, and Duyck (2011) carried out a study among secondary school teachers to examine the acceptance and use of a digital learning environment. The study has drawn on the UTAUT as its theoretical framework. The results of the study confirmed direct and significant effect of the performance expectancy (perceived usefulness) on use of Smart school technology. Adiguzel, Capraro and Willson (2011) tried to explore special education teachers’ acceptance of handheld computer use based on the modified version of the technology acceptance model. The authors have added a variable ‘dependability’ to the existing constructs: perceived usefulness, perceived ease of use, subjective norms and intention to use. The results showed a good fit of the overall model. The study confirmed significant direct effect of perceived usefulness and perceived ease of use as two major determinants of use of handheld computer by the special education teachers. Further, ‘dependability’ as an additional variable has mediated its effect on intention to use through usefulness. Ataran and Nami (2011) conducted a study among high school teachers of Iran. The data was collected from the teachers before and after an intensive 4-week training program on Microsoft PowerPoint. The perceived usefulness was found to have a direct and significant effect on intention to use. The perceived ease of use had a significant effect on usefulness, while it had no statistically significant effect on intention to use. After training, the magnitude of positive effect of ease of use on usefulness had increased.

34 Chang, Lieu, Liang, Liu and Wong (2011) studied the use of overhead projectors by university teachers in Taiwan by using modified TAM. Overall, model fit was excellent and the data showed satisfactory explanatory power. The study confirmed that perceived usefulness had direct effect on intention to use technology. However, the effect of ease of use on intention to use overhead projectors is not significant and its effect is observed only through the usefulness. Chiou (2011) in his research study attempts to investigate the extent of effect of perceived usefulness, perceived ease of use and computer attitude on the intention to use Web 2.0 applications among 125 pre-service teachers at Midwestern University. The results show that perceived usefulness is a major predictor of intention to use, while the perceived ease of use had no significant effect on intention to use Web 2.0 applications by the pre-service teachers. Wong, Osman, Goh and Rahmat (2013) carried out a study among Malaysian student- teachers to validate and test the Technology Acceptance Model (TAM).

Data were collected from 302 respondents and were analysed using confirmatory factor analysis (CFA), and structural equation modelling (SEM). The overall model explained 37.3% of the variance in intention to use technology among student-teachers. Perceived usefulness is found significant on attitude towards computer use and behavioural intention. Perceived ease of use is found significant in influencing the perceived usefulness, but not attitude and intention to use computers. Wong, Teo and Russo (2013) studied the factors affecting the studentteachers’ use of computers in Malaysia using TAM as its conceptual model. Overall, the model had accounted for 36.8% of the variance in behavioural intention to use computers. The results of the study demonstrated a significant positive influence of perceived usefulness and perceived ease of use on behavioural intention to use computer in teaching and learning.

35 The above studies show that (a) Technology Acceptance Model is a robust model to understand the user acceptance and adaptation of technology; (b) perceived usefulness exerts a moderate effect on attitude and strong positive effect on intention to use technology; (c) the effect of perceived ease of use on attitude and intention to use is either indirect through usefulness or statistically non-significant; and (d) perceived usefulness has a direct effect on self-reported computer usage and the mediating effect of intention to use on computer usage is found to be moderate.

Job Relevance It is generally stated that a teacher is likely to consider a technology to be useful when it is relevant to his or her job. Many researchers have explained that relevance of computer technology for teachers toward their jobs is a significant factor in the implementation of computers in education. The present study considers job relevance as one of the dimensions of computer usefulness. Here, the research studies pertaining to job relevance and job-fit within the framework of technology acceptance model are reviewed.

A longitudinal study was conducted by Venkatesh and Davis (2000) to develop and test TAM2 to assess the external factors that influence perceived usefulness in voluntary and mandatory setting. The study has included job relevance as an external variable in the model. The study has found that job relevance in interaction with output quality (organizations job-goals) exhibited a statistically significant effect on perceived usefulness of use of computers.

A longitudinal study conducted by Hu et al. (2003) on teachers attending an intensive 4-week training program on Microsoft PowerPoint revealed that job relevance was one of the key determinants of perceived usefulness, both at commencement and at the end of training.

36 Kumar, Rose and D’Silva (2008c) conducted a study among 318 secondary school teachers in Malaysia. The results showed positive linear relationship between job relevance and actual use of computers. It is inferred that the actual use of computers is influenced by the degree of its relevance to the associated job. Ataran and Nami (2011) conducted a study in high school teachers of Iran on the factors affecting the intention to use power point presentation. The study had used TAM as a basis and added job relevance, education, subjective norm as external variables. Results of the study found to be consistent with the TAM factors for explaining behavior intention. The study indicates that the job relevance and level of education have substantial influence on the teachers’ technology acceptance. The study of Chang et al. (2011) on university teachers in Taiwan reported that job relevance exhibited only a moderate effect on perceived usefulness with a path coefficient of 0.19. The authors have suggested that in-service training programmes should emphasize the usefulness and relevance of use of technology. In summary, Job relevance is found to be an important variable in understanding the perceived usefulness that determines the intention and use of computers. Computer Complexity In order to adopt a technology, its nature and complexity is also relevant for the end-user. In addition to the effect of ease of use of technology, studies have shown that the technology complexity will have a bearing on attitude and intention to use technology. According to Thompson, Higgins and Howell (1991) technological complexity refers to the degree to which a system is perceived to be relatively difficult to understand and use. In general, it is suggested that greater the perceived complexity of an innovation, the less will be its rate of adoption (Rogers, 1995).

37 Some researchers have pointed out that computer complexity acts as a barrier to teachers’ use of the new technology. Albirini (2006) in his study, used computer complexity as one of the dimensions of computer attributes. He found a moderate relationship between attitude of teachers and computer complexity. A study carried out among 239 pre-service teachers in Singapore (Teo, 2010), shows that computer complexity has a statistically significant effect on ease of use and attitude towards use of computers though the magnitude of influence is more for ease of use than for attitude. This confirms to the fact that if a technology is perceived as less complex then it will be perceived by end-user as easy to use and lead to a positive attitude. Subjective Norm Subjective norm refers to an individual user’s perception about opinions or suggestions of the significant referents concerning his or her behavior. In the present study, subjective norm refers to the secondary school teachers’ perception of the extent of influence exerted by the significant others in making a decision to use computer. The significant others include, colleagues, school management and people who are considered to be important by the respondent. Subjective norm has been empirically tested and found to have a significant direct (Mathieson, 1991; Taylor & Todd, 1995) or indirect effect (Venkatesh & Davis, 2000) in predicting an individual’s intention to use computer technology. The study of Marcinkiewicz and Regstad (1996) was the most influential study in early years that dealt with the direct influence of subjective norm on computer use. The study finds that subjective norm is a key predictor of computer use, in addition to self-competence, perceived relevance and innovativeness. The significant others identified by the authors under the subjective norm include the principal, colleagues, pupils and professional body.

38 Hu et al. (2003) in their study among public school teachers in Hong Kong observed a changing role of subjective norm. According to this longitudinal study, subjective norm is a significant factor influencing the initial user acceptance of computer and then diminishes in its importance as the individuals gain additional knowledge and experience in using computer.

Ma et al. (2005) in their study among student-teachers in Sweden observed that subjective norm did not have any direct or indirect effect on their intention to use computers.

Teo, Lee and Chai (2008) in their study among 239 pre-service teachers in Singapore found that the subjective norm has a significant influence on the perceived usefulness and attitude towards using computer. The authors have also noted that the effect of subjective norm is acceptable among sample respondents under mandatory setting and may not be true in case of voluntary setting.

Kumar et al. (2008c) in their study among the secondary school teachers in Malaysia found no significant relationship between subjective norm and actual use of computers. Teo and Schaik (2009) included subjective norm as an additional variable within the TAM to assess its influence on intention to use technology by the preservice teachers of Singapore. The study found that subjective norm had no significant influence on perceived usefulness or on attitude towards computer or on behavior intention to use computer. The study of Lee, Cerreto and Lee (2010) carried out among 134 middle and high school teachers in the Republic of Korea had used the theory of planned behavior as its theoretical model. According to the authors, effect of subjective norm on teachers’ intention to use computer found to be moderate, while attitude towards computer emerged as a strong determinant of intention to use computers.

39 Adiguzel et al. (2011) in their study among the special education teachers found that subjective norm had no significant direct or indirect effect on perceived usefulness and intention to use handheld computer. Thus, the authors have concluded that the respondents’ initial decision to accept or reject the use of handheld computer is not governed by the opinion of their colleagues. The study carried out by Ataran and Nami (2011) showed that subjective norm had a direct effect only on perceived usefulness at the time of commencement of 4-week training in power point presentation. Subjective norm had no effect on intention to use either at the beginning or at the end of the training programme. Chang et al. (2011) conducted a study among the professors teaching at public and private universities in Taiwan. The results of the study show that the subjective norm was a non-significant factor in user acceptance of overhead projector, suggesting that teachers are not influenced by their colleagues’ opinions or suggestions. In summary, the influence of colleagues, school management and significant others defined as subjective norm is either minimal or non-existent in determining the user acceptance and adoption of technology. This is true in case of voluntary setting, but may exert some influence on perceived usefulness and intention to use technology in mandatory working conditions, where the views of others would be more important.

Facilitating Conditions Facilitating conditions are external environmental factors that are likely to exert an influence on individuals’ decision or desire to perform a task. In the context of schools, various factors are identified that would probably work either as barriers or as facilitators of user acceptance of technology. It includes technical support, adequate technology equipment and software, support from colleagues and school administration.

40 Studies concerning the effect of end-users’ perception of facilitating conditions in acceptance and use of technology in schools are reviewed here. It is assumed that efficient and effective use of technology in schools depends largely on the availability of hardware and software, and the equity of access to resources by teachers and students. Mumtaz (2000) in the review of literature on factors affecting the use of ICT in the classroom reported that near absence or lack of access to resources, quality of software and hardware, ease of use, incentives to change, support in the school, school and national polices and formal computer training as main obstacles in using technology. Butler and Sellbom (2002) investigated the barriers to technology use and found that unreliability of technology and limited technical support as problems for technology use.

Knezek and Christensen (2002) found that teachers’ access to technology tools has a major impact on the use of computers by the teachers. Isleem (2003) reported a moderate correlation between accessibility and level of computer use in his study. Lim and Khine (2006) in their study among the primary and junior college teachers, elaborated on the barriers that made the use of computers less effective in Singapore schools. The barriers to ICT integration in schools cited by the authors include lack of access to computers, lack of support from peers, inadequate technical support, outdated computers and time pressure in covering the curriculum. A study of Abu Samak (2006) carried out among teachers in Jordan mention that lack of sufficient time in the school affects computer use. The study of Ertmer, Ottenbreit-Leftwich and York (2006) among exemplary technology-using teachers concluded that intrinsic factors such as confidence and

41 commitment, as opposed to extrinsic factors such as resources and time are deciding factors in the use of technology for teaching and learning. However, the authors stated that in early stages, extrinsic barriers in using technology such as technical support, as well as administrative and peer support needs to be strengthened. A study by Sim and Theng (2007) on secondary school mathematics and science teachers from Malaysia found that factors such as sufficient time, technical support and training to integrate ICT in teaching would enhance use of ICT in classrooms. Schrum, Shelley & Miller (2008) in a study on teacher educators in the USA observed that successful technology use is attributed to the access to computers. Cavas, Cavas, Karaoglan, and Kisla (2009) in their study among 1071 science teachers in Turkey found that computer ownership and computer access at school had greatly contributed towards positive attitude of science teachers in using computers. The study also emphasized the need for training in pedagogical use of computers to translate the positive attitude into actual use of computers.

Teo and Schaik (2009) in their study among pre-service teachers of in Singapore based on technology acceptance model found that facilitating conditions influence perceived ease of use. The availability of guidance and specialized instruction to use computers had a positive impact on teachers’ perceived ease of use of computers. Agnes and Wallace (2010) conducted an in-depth interview among select teachers from 4-schools in Western Cape, Kenya. The study had analysed the factors that facilitate or hinder teachers’ use of computers in schools. The results of the study showed that insufficient ICT training, school administrative restrictions on time of use, access to computer lab, inadequate technical support and insufficient number of computers for teachers’ use as the reasons for teachers’ use of computers.

42 Pynoo et al. (2011) conducted a study among 90 secondary school teachers in Belgium using UTAUT as the base model to analyse the teachers’ acceptance and actual use of digital learning environment. The results of the study showed a weak relationship between facilitating conditions and the actual use. The authors have found that facilitating conditions have only an indirect effect on user acceptance mediated through usefulness and ease of use. Tezci (2010) in a study among 1540 primary school teachers in Turkey found that access and duration of internet connectivity affect the attitude and use of computers among the respondents.

Mwalongo (2011) conducted a study among 74 teachers in Tanzania. The study had made a qualitative analysis of the effect of teachers’ perception of facilitating conditions such as access, training, competence and ICT resources on teachers’ use of ICT. The results have shown that the frequency of use of ICT resources is influenced by access and competence of use influenced by training. From the above studies one can assume that facilitating conditions such as access to computers, support from colleagues and school administration, training and technical support in use of computers are found to influence the use of technology. However, studies have confirmed that the influence of facilitating conditions is mostly mediated through usefulness and ease of use. Computer Competence In the present study, computer competency refers to the secondary school teachers’ self-reported ability to perform a given task on computer. Studies related to the effect of computer proficiency or competency on user acceptance and actual use of computers are reviewed here.

43 Pelgrum (2001) in a multinational study that involved teachers from 26 countries found that teachers’ lack of knowledge and skills was the second most inhibiting obstacle to the use of computers in schools.

Knezek and Christensen (2002) found that teachers’ competence with computer technology is the principal determinant of effective classroom use by students. Isleem (2003) reported that computer expertise (competence) was found to be the strongest predictor of computer use by Ohioan technology education teachers. Albirini (2006) in a study of Syrian English teachers believed that higher computer competence may foster the positive attitudes of teachers and eventually result in their use of computers in the classroom. Hsu (2010) conducted a survey among 3729 teachers from 334 schools in Taiwan.

The study examined the relationship between teachers’ technology

integration ability and usage. The results showed a positive correlation between selfperceived ability and the frequency of use of technology. Al-ruz and Khasawneh (2011) conducted a study among 1008 pre-service teachers in Jordan to understand the views of respondents on modeling of technology in teacher education. The study found that teachers’ technology proficiency has direct and positive impact on their technology integration efforts. Above studies reveal that teachers’ knowledge and skills in using computers influence their attitude and intention to use computers. It also had a direct effect on the actual use of computers.

44 Attitude Towards Computer Use Any initiatives of technology integration in school education depend strongly on the support and attitudes of teachers involved. Ajzen (2006) described attitude as a predisposition to respond favorably or unfavorably to an object, person, or event. Positive computer attitudes are expected to foster computer integration in the classroom. Research shows that the success of technology use in the educational settings largely depends on the teachers attitudes toward technology use. Teachers’ attitudes are considered as a major predictor of the use of new technologies in the educational settings. An attempt is made here to review the research studies that have focused on teachers’ attitude towards using technology and the factors that determine their attitude. Davis et al. (1989) in their study made a comparison of theory of reasoned action model and technology acceptance model to arrive at key determinants of intention to use technology. The study was carried out among MBA students before and after training. In both models attitude towards using technology was used as a mediating variable that affects intention to use technology. The study reported that perceived usefulness to a large extent and ease of use to some extent exerts influence on attitude. Further, the effect of attitude on intention to use was weak. Venkatesh (2000) in his extended model of TAM suggested that computer anxiety, an affective dimension of attitude, as one of the factors that determine ease of use of technology in the beginning stages and it has only an indirect effect on intention to use technology.

45 Venkatesh and Davis (2000) found that attitude is significant only when performance and effort expectancy, referring to the usefulness and ease of use of technology, are not involved in the study. The authors have hypothesized that anxiety did not have a direct effect on behavioural intention. Yildrim (2000) observed that if teachers perceive that computers are not fulfilling their own or their students’ needs, they are likely to resist any attempts to use computers in the teaching and learning process. The author stressed that it is unlikely for teachers with negative attitudes towards computers to be able to encourage their students to use computers. In a study among the Malaysian teacher educators conducted by Ridzuan, Sam and Ahmad (2001) report that liking (sub-dimension of attitude) was positively related to computer use while anxiety (sub-dimension of attitude) was negatively related to computer use. Christensen (2002) conducted a study of a public elementary school in north Texas on introducing of information technology for classroom practice. The results of the study showed that training improve attitude of teachers and reduce anxiety. Lokken, Cheek and Hastings (2003) in a study on the impact of intensive training found that older teachers exhibited higher levels of computer anxiety and had less confidence in technology. There was a significant negative relationship between computer use and anxiety after two week training. Albirini (2006) investigated the attitudes of EFL teachers in Syrian high schools toward technology in education, indicated that teachers had positive attitudes toward technology use in education. Rainey, McGlothlin, and Miller (2007) in their investigation on school counselors’ attitude towards using technology found that usefulness of computers influences the attitude.

46 Hermans, Tondeur, van Braak, and Valcke (2008) conducted a study among primary teachers to understand their educational beliefs as antecedents of computer use. The results showed that computer attitude has a positive and significant effect on use of computers among the primary teachers of Turkey.

Jegede (2008) in a study among 467 Nigerian pre-service teachers found that their attitude and use level of computers are significantly related. The attitude of teachers were studied based on five constructs of which behavioural factor and perceived control factor had higher influence as compared to defense factor perceived usefulness and affective component. Kumar et al. (2008c) in his study among the Malaysian secondary school teachers found that teachers’ attitude towards computers had a statically significant positive relationship with the overall actual use of computers. Teachers use of computers was higher for administrative purpose, followed by academic and lastly for personal needs. Teo (2008) in a study examined the attitudes of 139 pre-service teachers in Singapore towards use of computers. The study had used a Likert-type questionnaire to assess the computer attitudes with four factors: affect (liking), perceived usefulness, perceived control, and behavioural intention to use the computer. Based on correlation analysis, the study demonstrated a significant relationship between computer attitude and years of computer use, level of confidence and by the subject areas of pre-service teachers. Gender and age had no significant influence on respondents’ attitude towards the use of computers.

47 Teo, Lee and Chai (2008) had made a study on pre-service teachers’ attitude towards technology. The study found that perceived usefulness is an antecedent of attitude and shows significant influence on attitude towards computer use. Perceived usefulness is believed to exert direct influence on intention to use and attitude towards computer use. Cavas et al. (2009) in their study among Turkish science teachers found that a majority of the teachers showed positive attitude towards ICT in education. The study found no gender difference in teachers’ attitude towards ICT. Among the age groups, the younger Turkish science teachers have more positive attitudes and significantly differ from the teachers in older groups. Teachers’ computer experience found to influence the attitude of teachers towards ICT. Agbatogun (2010) conducted a study among 454 Nigerian teachers reported that computer anxiety is the significant and potent predictor of teachers’ attitude towards Interactive Computer Technology.

Al-Zaidiyeen, Mei and Fook (2010) carried out a study among 650 teachers in Jordanian rural secondary schools to investigate the level of ICT use for educational purposes. The results of the study showed a positive attitude of teachers towards ICT. Further, a significant positive correlation between teachers’ level of ICT use and their attitudes towards ICT was found. Tezci (2010) attempted to study the attitude and knowledge of teachers and their ICT use among 1540 primary school teachers in Turkey. The results showed that teachers’ attitudes towards both the Internet and computers are at a medium level. However, their levels of attitude towards computers are lower than those towards the Internet.

48 Pynoo et al. (2011) found that the secondary school teachers’ attitude towards digital environment is determined by usefulness of technology. But ease of use of technology had no statistically significant effect on teachers’ attitude towards the use of technology.

Larbi-Apau and Moseley (2012) conducted a study among teaching faculty in higher education in Ghana found that the affective component was the highest contributor of computer attitude. Wong et al. (2013) reported that student-teachers’ attitude towards computer use is determined by the perceived usefulness and attitude has a significant influence on intention to use computers. Further, perceived ease of use did not have a significant influence on student-teachers’ attitude towards computer use. From the reviews presented above, we can assume that attitude is determined largely by perceived usefulness and to a lesser extent by ease of use. In turn, attitude has an effect on intention to use. The attitude of end-user of technology is found to be influenced by computer training. Gender and age had mixed results in influencing the attitude, although, younger age group seemed to have a positive attitude towards the use of technology in schools. Behaviour Intention Most of the studies on acceptance of technology have considered end-users’ behaviour intention to use technology as the dependent variable. A number of independent variables are used to determine their direct and indirect effect of intention to use, which includes: perceived usefulness, perceived ease of use, selfefficacy, subjective norm and attitude towards technology.

49 Apart from these key independent variables, other variables of importance that mediates its effect on intention to use technology include: computer complexity, computer competency, facilitating conditions and socio-demographic characteristics such as gender, age, prior computer training, years of computer experience. Selected studies are reviewed to bring out significant determinants of intention to use computer among pre-service and in-service teachers. The research studies upon behaviour intention indicate that: (1) end-users’ computer usage can be predicted reasonably well from their intentions; (2) endusers’ perceived usefulness of computer is a major determinant of intentions to use; and (3) end-users’ perceived ease of use has a limited effect on intentions to use computer (Davis et al., 1989; Yuen & Ma, 2002; Ma et al., 2005). Studies have shown that intention to use computers is largely depend on (1) perceived usefulness of a given technology; (2) perceived ease of use of a given technology has only an indirect effect on intention to use; (3) jointly perceived usefulness and ease of use, the two key variables of TAM, are found to be the major determinants of intention to use computers (Teo, Su Luan & Sing, 2008; Teo & Schaik, 2009; Adiguzel et al., 2011; Ataran & Nami, 2011; Chang et al., 2011; Chiou, 2011).

Studies focusing on the effect of subjective norm on intention to use had produced mixed results. The effect of subjective norm found to be (a) moderately significant in the early stages of training and adoption of technology; (b) started diminishing with increase in computer experience (Hu et al., 2003; Teo, Lee & Chai, 2008, Lee et al., 2010). On the contrary, studies have shown that (a) subjective norm had no direct effect on their intention to use computers; (b) subjective norm had no mediating or

50 indirect effect on intention to use computers through any of the key determinants of technology acceptance model; (c) the non-existence of effect of subjective norm on intention to use is more true in case voluntary as against mandatory setting of technology use (Ma et al., 2005; Kumar et al., 2008c; Teo & Schaik, 2009; Adiguzel et al., 2011; Ataran & Nami, 2011; Chang et al., 2011). The research studies show that (a) perceived usefulness has a direct and significant effect on intention to use computers; (b) perceived ease of use has only moderate and indirect effect on intention to use computers; (c) subjective norm and facilitating conditions have only limited moderating effect on intention to use through perceived usefulness. Actual Use of Computers Studies related to actual use of computers by teachers were not many. Studies tested only the attitude and behavior intention of teachers towards computers.

Al-Khaldi, & Al-Jabri, (1998) measured the actual use of computers in terms of frequency of use, diversity of packages used and intensity of class related computer use.

Yuen and Ma (2002) studied attitude and the factors influencing the use of computers among secondary school teachers. They measured the instructional use of computers by the frequency of use of computers in (1) the classroom, (2) preparing teaching material, (3) extra-curricular activities, and (4) students using computer for homework. The results indicate that behavioural control followed by general computer usefulness had significant effect on the usage of computers.

51 D’Silva (2007) studied the actual use of computers and identified the best-fit model using the step-wise multiple regression. The best model explained 54.5% of variance in actual use of computers. The most significant predictors of actual use of computers were perceived ease of use, followed by perceived usefulness, job relevance, computer compatibility, and attitude. Overall, the study found that the actual use of computers among MSE secondary school teachers was on the moderate level. There was a significant difference in means of actual use of computers by the main subject taught and training in computer usage. Hermans et al. (2008) measured classroom use of computers by the frequency of use: never, once a term, monthly, weekly and daily. Gender, computer experience and general computer attitudes were found to have a significant effect on the use of computers in the classroom.

Kumar et al. (2008b) studied computer usage on actual use of computers among secondary school teachers. The actual use of computers was measured in terms of frequency of use of computers for teaching and learning, administrative, and personal tasks. The study reports that despite government efforts, the use of computers was found to be moderate and teachers have not fully utilized the investment made in technology. It was reported that MSE secondary school teachers who were actually using computers are science teachers followed by mathematics teachers. Relatively, very few English language teachers seem to use computers. A study carried out in Tanzania (Mwalongo, 2011) examined teachers’ use of ICT for teaching, administration, professional development and personal tasks. The use of computers was measured by the frequency of use. Results indicate that the frequency of use of ICT was influenced by its access.

52 In summary, only a few studies have considered the actual use of computers as a dependent variable, while most of the existing literature on technology acceptance considered intention to use as a proxy for technology usage.

The actual use of computers by the end-user is measured using one or more aspects of use, namely, frequency of use (daily, weekly, monthly); intensity of use (hours), type or purpose of use (academic, administrative, personal); type of software/package or technology used. It is found that the research studies on computer usage have used either self-reported data from the end-users or used the log-time of end-users’ registered computers. The review of literature shows that computer usage is determined by perceived usefulness, job relevance, computer compatibility and attitude, while ease of use affects usage to a lesser extent. The computer usage is found to differ by years of computer experience. Science and mathematics teachers use computers in the classroom more often than other subject teachers. Female teachers tend to use computers more often than male teachers for academic and administrative tasks in the school. Male teachers are found to use computer more often than female teachers for personal use. Socio-Demographic Factors Research studies have tried to analyse the effect of various background variables on technology acceptance and use that include gender, age teaching experience, computer ownership, computer training and experience, determine of teaching and policy on ICT. A select studies dealing with such mediating variables are reviewed here.

53 Gender Gender is the most investigated mediating factor in technology acceptance and adoption. Gender difference in perceived usefulness, ease of use, attitude towards computer use, intention to use computers and self-reported use of computers technology has been discussed here. Kellenberger and Hendricks (2000) found no difference in the use of computers between male and female pre-service teachers studying at a southwestern Ontario university in Canada. Venkatesh and Morris (2000) identified a significant difference between female and male in introducing a system for information retrieval. They found that men emphasized more on perceived usefulness in determining behavioral intention to use, while women regarded perceived ease of use as a more significant factor in determining behavioral intention to use. Yuen and Ma (2002) conducted a study among 186 pre-service teachers of Hong Kong to examine the computer acceptance. Based on path analysis, the study finds that the influence of ease of use on perceived usefulness is much greater for men than for women student-teachers. On the contrary, the effect of ease of use on intention to use is relatively greater for women than for men teachers. It is important to note that perceived usefulness was not found to have significant influence on the intention of men teachers’ to use computers. A study conducted in Turkey by Asan (2003) showed that gender has a significant relationship to familiarity with computer technologies. Jamieson-Proctor, Burnett, Finger and Watson (2006) conducted a study on teachers’ integration of ICT in schools in Queensland State. Responses from 929 teachers indicated that female teachers were integrating technology into their teaching less than their male counterparts.

54 In a research conducted by Kay (2006), indicated that male teachers had relatively higher levels of computer attitude and ability before computer implementation, but there was no difference between males and females regarding computer attitude and ability after the implementation of the technology. He claims that quality preparation on technology use can help lessen gender inequalities.

In a study among the Jordanian English language teachers (Aub Samak, 2006) found no significant difference among men and women in their attitude towards using computer. An outcome of the study of Agbatogun (2010) established that gender as a variable did not contribute to the prediction of teachers’ attitude towards Interactive Computer technologies. A study was carried out by Hermans et al. (2008) on the primary school teachers’ educational beliefs on classroom use of computers. According to this study, the average difference in classroom use of computers was higher for males than for females. In a study among the Malaysian secondary school teachers Kumar et al. (2008b) found that the mean computer usage for females is slightly higher than for males. Female teachers tend to use computers more for academic and administrative work while males used computer for personal work. The t-test results showed that there exists no statistically significant difference among male and female teachers in overall computer usage and by type of use of computers. A study of pre-service teachers in Singapore by Teo (2008) showed no significant difference in the attitude of male and female teachers. The study has used affective, perceived usefulness, perceived control and behaviour intention as the four components of attitude towards using computer. A multivariate analysis demonstrated no significant difference among male and female teachers for all the four components of teachers’ attitude towards the use of computers.

55 The study by Yukselturk and Bulut (2009) reported that gender gap has reduced over the past years, presently, a greater number of females than males have used internet and web 2.0 technologies. Tezci (2010) studied primary school teachers’ attitude and knowledge towards using ICT in Turkey. The results of the study show male teachers have higher level of ICT use, knowledge and internet attitude as compared to female teachers. But there was no significant difference in computer attitude among male and female teachers. In a study, Bakr (2011) ascertained that there exists no significant difference in attitude towards use of computers by male and female teachers in Egypt. Hafeez, Khattak and Gujjar (2011) found no significant difference in gender with regard to competency in computer usage. Female English language teachers have greater interest and attitude towards learning computer as compared to males. Female teachers have significantly better views on Computer Aided Instruction. The results of various research studies discussed above show mixed results with respect to the effect of gender on perceived usefulness, ease of use, attitude and intention to use computers. The effect of gender on actual use of computers differs. Based on the review, it would be of research interest to test the proposition that: (a) perceived usefulness of computers is stronger among females than males; (b) influence of attitude towards computer use upon intention to use is stronger for females than males; (c) influence of perceived ease of use of computers upon the perceived usefulness is stronger for males than females.

56 Age Different experiences of different age groups may influence one’s attitude towards computers. Many studies were conducted to find the relationship between teachers’ age and their computer attitude.

Blankenship (1998) found that age was the most important demographic variable affecting computer use and attitudes. Davis (1989) found a significant correlation between teachers’ attitudes and age. Many studies have shown that the senior and more experienced teachers are less receptive towards computers compared to the younger ones (Young, 2000). Spiegel (2001) investigated the attitudes and the usage habits of secondary school teachers at four public schools in the Netherlands. The findings of the study revealed that age was not significantly correlated with attitudes towards computers. Lokken, Cheek and Hastings (2003) in their study on impact of intensive training found that older teachers exhibited higher levels of computer anxiety and had less confidence in technology. Jamieson-Proctor et al. (2006) found that teacher resistance to change was more among older teachers is evident in Queensland. This may be because older teachers may have only a limited exposure to computers and their usage. For them learning to use a computer in the classroom is a new skill and may result in different attitudes towards ICT. On the other hand young teachers may have been exposed to computers as part of their school and college. Kumar et al. (2008b) found no significant difference in the age of teachers and the use of computers in Malaysian teachers. The study indicates that younger teachers are more enthusiastic in using computers but the senior and more experienced teachers were not far behind.

57 A study by Lau and Sim (2008) on Malaysian teachers indicated that elderly teachers were eager to adopt ICT in schools. They were receptive to ICT and reported a higher use of ICT use in teaching.

Wahab (2008) examined the relationship between teachers’ attitudes, emotions, beliefs, outside influences on the use of computers. He found that younger teachers who had less teaching experience and higher education qualifications than their older colleagues demonstrated more positive attitudes towards ICT.

The results of a study by Cavas et al. (2009) show statistically significant difference between teachers’ age and attitudes. Young Turkish science teachers in the age group 20-35 have more positive attitudes than the higher age group. There appears to be conflicting results in the literature with respect to age as a factor related to attitudes towards and use of computers. Most often supported findings of influence of age are: (a) senior teachers tend to be more anxious and less confident in using computers; (b) younger teachers’ perceived computers are more useful than senior teachers; (c) younger teachers have a positive attitude towards computer; (d) younger age group teachers have better scores in use of computers as against older age group. Location of School Generally, school location often tells about the general welfare of the area in which it is located, and may also show the amount of support that schools receive from local people. Isleem (2003) found that teachers in urban schools in Ohio have less positive attitudes and less level of computer use than those in suburban schools.

58 Level of Education It seems reasonable that the higher the educational level, the more familiarity an individual may have with the new technologies. This may entail more positive attitudes towards ICT. This hypothesis has been supported in different educational contexts.

Subject Taught Over the years, computer usage issues related to various subjects taught have been debated in the literature. Computers have been identified useful, particularly with mathematics and science subjects as the teachers teaching these subjects realize its inherent utilitarian value. There are studies conducted on the use of technology in science, mathematics and English. Hennessy, Ruthven and Brindley (2005) in their study in the UK found that science teachers were most positive about the educational benefits of using computers. Newa (2007) found no significant difference between the secondary school teachers in different academic streams, viz. Language, Science and Mathematics and Social Sciences was found with respect to the attitude towards Information and Communication Technology and its different areas. D’Silva (2007) in his study of the factors influencing the actual usage of computer among secondary school MSE teachers in Malaysia held the opinion that computer use is more among science teachers compared to mathematics and English teachers. Kumar et al. (2008b) found that the highest numbers of the MSE secondary school teachers who are actually using computers are Science teachers followed by Mathematics teachers. Relatively, very few English language teachers seem to use computers. The authors believe that this could be due to the availability of illustrated packages in Mathematics and Science.

59 Lau and Sim (2008) tested the mathematics and science teachers in secondary schools in Malaysia and found that teachers’ competency as the reason for the positive attitude. Cavas et al. (2009) worked on science teachers’ attitude and reported that teacher’s computer attitude is influenced by computer experience.

Hafeez et al. (2011) studied the attitude of English university teachers in Pakistan. Female English language teachers were found to have greater interest and attitude. The reviews bring out the following major findings with respect to the subject taught and using computers: (a) Science and mathematics teachers have positive attitudes towards computer use as compared to language teachers; (b) Science and Mathematics teachers find computers more useful and easy to use than other teachers; (c) Science and Mathematics teachers used computers more frequently for academic purpose than other teachers. Computer Training The relationship between teachers’ attitudes toward ICT and their computer training is well documented in the literature. Research supports the idea that the biggest obstacle to teachers using technology in their classrooms is the lack of adequate teacher training (Yildirim, 2000). Pelgrum (2001) in his study argued that large scale innovations require large-scale teacher training indicates the importance of in-service training of teachers for successful integration of technology. Lokken et al. (2003) in their study of impact of intensive training found a statistically significant difference in liking and confidence to use computers. Angers and Machtmes (2005) stated that teachers who received 11 or more hours of curriculum-integration training said that they are better prepared to integrate technology into their classroom lessons than teachers who received no such training.

60 Lau and Sim (2008) found that those who received either prior to and on the job training recorded a higher competency in ICT. Teachers who have used ICT extensively in their daily routines still indicate high training and support needs which signify the requirement of periodic in-service training of teachers.

Agnes and Wallace (2010) reported that insufficient ICT training limits the educators to use the technology for teaching confidently and effectively. Tezci (2010) reported that those who had prior computer training had higher levels of use than those who did not receive any training. Mwalongo (2011) in his study of teachers of Tanzania found that the frequency of use of ICT is influenced by training. In summary, most of the studies emphasized the importance of computer proficiency and training that significantly affect the attitude and use of computers. Computer Experience The experience of teachers in using computers is expected to reduce their computer anxiety leading to positive attitude towards computer use and intention to use computers.

Yildirim (2000) found a significant correlation between prior computer experience and attitude towards computer use, both of which significantly affect teacher competence with computers. Lokken et al. (2003) carried out a study among high school teachers and found that computer experience was not significantly correlated with the overall attitude of teachers towards computers. The study also found that computer experience had no influence on any of the four dimensions of attitude: anxiety, confidence, liking and usefulness of use of computers. Teachers’ computer technology experience and teachers’ perceived computer competence would affect perceived ease of use (Ma et al., 2005).

61 Cavas et al. (2009) found that science teachers’ attitude toward ICT differs with computer experience. The study finds that prior computer experience is one of the important factors that affect teachers’ attitudes toward ICT in education. They also found that teachers who have experience of five years and up have more positive attitudes than teachers with less experience. In summary, computer experience is found to influence ease of use and attitude towards computer use in majority of the studies. Review of Studies conducted in India In India, studies on user acceptance of technology, more specifically, acceptance and use of computers among pre-service and in-service teachers are either limited. Nanjappa and Lowther (2004) conducted a study on influence of computer self-efficacy on technology integration beliefs among 267 school teachers in Mumbai, India. The teachers expressed strong belief about the impact of technology integration in schools, but teachers’ own readiness to adopt technology in the classroom is weak. The study finds a weak but positive significant relationship between teacher technology beliefs and computer self-efficacy. Pulist (2005) conducted an exploratory study among science teachers and principals of schools in Delhi. The study finds that a majority of female teachers had internet connection at home as against male teachers. It is important to note that a majority of teachers reported that they were able to use internet in the classroom for 1-3 hours a week. The study reveals that majority of secondary school science teachers believe that they are able to get relevant and useful material on the Internet for their teaching. Based on the responses from school principals, it is found that

62 most of the schools have adequate computers for teacher and student use, including internet access, mostly of dial-up connection. Among the teachers who have received training in web-based technology, a majority of them were in the younger age group. Most of the teachers used text, graphics and e-mail on the web while their use of audio and video streaming are very low. Physics and Chemistry teachers use internet material more often than Mathematics and other subject teachers. Some of the barriers to effective use of web-based technology as reported by the teachers and principals include: limited bandwidth and slower Internet connection, poor audio/video components, pressure of curriculum on teachers, lack of motivation and technical expertise and insufficient school budget for integrating technology in schools. Rishikesh (2005) has conducted an evaluation of the Computer Aided Learning Program (CALP) in three districts of the state of Karnataka. The programme deals with training of government higher primary school heads, teachers and children under CALP. The findings in the report have highlighted that there is a lack of use of computers among the teachers and students. There are instances where no monitoring and maintenance of equipment are done leading to underutilisation or non-utilisation of resources. Among the teachers who have received the training, a considerable number of teachers used the facilities in computer aided classroom only to view a few CDs, mostly at the start of the academic session, but no computer aided teaching and learning takes place in these schools. Annaraja and Joseph (2006) conducted a study on the teacher trainees in Kerala to find their attitude towards ICT. The attitude of teacher trainees was assessed by their responses to questions on use of computers for enjoyment, computers anxiety, and computer avoidance/acceptance. The study shows that 68% of the teacher trainees have high levels of attitude towards ICT and remaining 32%

63 have moderate level of attitude towards ICT. The study found no significant difference between male and female teacher trainees in their attitude towards ICT. In terms of background variables, it is found that education, occupation and family income of parents had no significant impact on teacher trainees’ ICT-attitude. Rajasekar and Vaiyapuri Raja (2007) studied computer knowledge and attitude of 670 higher secondary school teachers in the Cuddalore district of Tamil Nadu. A majority of the teachers (83%) had a low level of computer knowledge. Significant difference in computer knowledge is observed between females against males; teachers working in urban rather than rural schools and teachers with postgraduate qualification as against graduation. No significant difference in computer knowledge is observed between the government and private schools. It was reported that 60.40% of the teachers had relatively favorable attitude towards computers. No significant difference in the attitude of teachers was observed for gender, rural-urban schools, government-private schools and secondary grade postgraduate teachers. A survey conducted by Uniyal and Pandey (2008) on teachers of Uttarakhand, observed that teachers who are above 40 years of age and teachers with 20 years of experience and above showed a favorable opinion towards the use of computers, but used less in the classroom. The study also reported that there is no difference in opinion towards the use of computers between the male and female teachers, but difference was found between the rural and urban teachers. The study found that in spite of the availability of computers in schools, teachers were not using them for teaching and learning. Funde and Dhondge (2011) conducted a study among student-teachers who were pursuing diploma in teacher education programme and teacher educators to

64 elucidate their views on integrating ICT into teacher training. The study indicates that the respondents have shown a strong desire for the integration of ICT into teacher education but they encountered many barriers to it. The major barriers of ICT integration into teacher training as reported by the student-teachers include: lack of training, lack of confidence and competency, lack of access to computers in practice teaching schools, lack of motivation among teacher educators and negative attitude of teacher educators in using ICT for practice teaching. Gulati and Dang (2011) had conducted a study among 200 primary school teachers of Sangamner region in the state of Maharashtra to analyse their perspective and awareness level in relation to use of computers. Problems faced by the respondents in the use of computers in schools are also discussed. The study finds that the teachers have low levels of computer knowledge and lack in functional computer literacy. Gender and years of teaching experience are found to influence teachers’ knowledge and awareness of computer use. Lack of infrastructure, lack of computer training and crowded classroom are cited as some of the immediate problems faced in integrating computers in schools. The study of Naaz (2011), conducted in Aurangabad found that teacher trainees have a positive attitude towards computer technology. Nair and Das (2011) reviewed 45 research studies that deal with acceptance of technology among teachers using TAM. The analysis projects that TAM is a robust model and can be used in assessing technology acceptance among teachers. The review states that studies that considered actual computer usage as dependent variable is limited, but this can be an important dependent variable for further research that applies TAM. Most often used determinants of technology acceptance studies among teachers are perceived usefulness, perceived ease of use, attitude towards the use and intention to use.

65 Panigrahi (2011) conducted a study among 100 senior secondary school teachers of Faridabad district in Haryana. The study focused on understanding the teachers’ perception towards ICT. The study reported that there is no significant difference in the teachers’ perception towards utilization of ICT with regard to gender, location of the school and teaching subject of the teachers. Bhalla (2012) studied the barriers in the use of computers for teachinglearning among the teachers of Kendriya Vidyalaya schools in New Delhi. The findings revealed that insufficient time for planning, preparing, and presenting computer-based instructions as the most important barrier. Various other barriers listed in order of descending importance were: access (hardware), access (software), support, training and competence. Dasari and Mallu (2012) conducted a study in Andhra Pradesh among the tribal pre-service teachers and found that the respondents had a positive attitude towards using computers. The results showed no gender and sub-community differences in their computer attitude. Kareem and D’Souza (2012) conducted a study to assess the use of computers among 201 secondary school teachers in the city of Bangalore, state of Karnataka. Knowledge of Word Processing is reported by a majority of teachers, while familiarity with problem solving software received the lowest response. Under usage of ICT for teaching-learning, report writing received the highest score as against using ICT for conducting quiz. Teachers of various subjects responded that they very rarely use ICT as a teaching tool, except the computer subject teachers. A majority of teachers access internet for downloading teaching and reference material but rarely used for communicating with students and as an online assessment tool.

66 Kulkarni (2012) studied the attitude of secondary school teachers in using new technologies in Northern Goa. The sample was drawn from150 secondary school teachers working in 45 schools. Study shows that there is no difference in attitude by gender, or experience but significant difference was noticed with respect to age, computer ownership and computer experience of the respondents. Nair and Das (2012) conducted a study to assess attitude towards using computers among 220 mathematics teachers of government high schools in Kerala. All the respondents had hands-on training in the use of software for teaching mathematics. The study shows that 70% of the teachers are familiar with the use of computers and capable of using software for teaching mathematics. Perceived ease of use was found to influence perceived usefulness and attitude towards the use of IT tools in teaching mathematics. The effect of perceived usefulness on attitude is not significant and this may be due to the fact that the use of IT tools is mandatory among the sample schools. Narasimhan (2012) studied the attitude towards using ICT among 120 secondary school English subject teachers in Srikakulum district in Andhra Pradesh. The respondents showed a positive attitude towards using information and communication technology in teaching English. Bhalla (2013) investigated Central School teachers’ use of computers. The findings revealed that teachers mostly used computers to update subject knowledge and teaching skills, develop lesson plans, for advanced information and to prepare question banks. They also used computers for showing visuals in the class, displaying students’ work on school-website, preparing question papers, and drill. They hardly used computers for presenting a complete lesson, students’

67 presentations, tutorials, communicating with parents, publishing homework, giving tests to students either offline or online, maintaining students’ records, and individualized instructions. The study indicated computer mediated teaching was not successfully used by the teachers. In summary, available studies have shown a positive attitude towards the use of technology among high school and secondary school teachers. Most of the studies have used descriptive statistics. The studies have shown significant differences in attitude by gender, location of the school, subject taught, age and educational qualification of teachers.

In general, higher mean score is observed in attitude towards use of technology for female, urban, younger age group and post graduation qualified teachers as against their respective counterparts. Only Nair and Das (2012) explicitly used technology acceptance model in their study and reported a significant influence of perceived ease of use on using technology among the mathematics teachers. The review of literature clearly exhibits the need to carry out research on technology acceptance in the field of school education, more specifically among teachers in India, since such studies are limited. Research studies that analyse the role of perceived usefulness, perceived ease of use, subjective norm, computer competency and facilitating conditions in affecting teachers’ attitude towards use of computers in classroom in India is found to be the need of the hour. Further, research studies to understand key determinants of teachers’ intention to use and actual use of computers are of importance for successful integration of technology as a pedagogical tool in Indian schools. It is equally important to include various socio-demographic characteristics of teachers to assess their impact on teachers’ acceptance of technology in Indian schools.

68 CHAPTER 3 METHODOLOGY

Methodology defines the overall approach to research process that states the theoretical underpinnings, research design, procedure for drawing of sample respondents, defining the independent, mediating and dependent variables, collection of data and statistical methods applied for analysis of data. Available research studies on acceptance and adaptation of technology in the field of education are mostly concerned with attitude towards computer use and intention to use computers. In the area of school education, most of the studies focused on pre-service teachers and a few studies on in-service teachers. Studies dealing with computer usage or actual use of computers as dependent variable among in-service teachers are limited elsewhere and non-existent in India. Hence, the present study is designed to explore factors that might influence teachers’ computer usage in schools. More specifically, the present study sought to understand the major determinants of actual use of computers among secondary school teachers in India.

The considers secondary school teachers’ actual use of computers as dependent variable and a number of related psychological and socio-demographic factors as independent variables. This chapter outlines the research design, sample selection, development and testing of tools for data collection, stating of hypothesis and data analysis techniques proposed to be used in the study.

69 Research Design In India, research studies on teachers’ perception, attitude and intention to use computers are very few and more specifically, relating these variables to teachers’ actual use of computers is lacking. Hence, the present study is designed as an exploratory cross-sectional survey among the secondary school teachers in India. The study is designed to elucidate the interaction between psychological and sociodemographic factors and self-reported actual use of computers among teachers. Computer Usage is considered as the dependent variable and it is measured in terms of the frequency of use of computers by the teachers for various purposes. The independent variables include both psychological and socio-demographic factors. The basic research design used in the study is diagrammatically represented below: External Factors

Perception

Attitude

Intention

Behaviour

At psychological level, the study addresses teachers’ perception measured in terms of perceived computer usefulness, computer ease of use, social influence, computer competency and facilitating conditions as independent variables. To understand the teachers’ perception, attitude towards computer use and intention to use computers are used as mediating variables, whereby they are used both as dependent and independent variables. In order to understand the effect of socio-demographic variables the study includes gender, age, education, location of school, types of management, teaching subject, computer access at home, computer access at school, computer training and years of computer experience as moderator variables.

70 Most of the studies on technology acceptance and adaptation have examined the effect of user perception of technology on intention to use, except for a few research studies that have tried to examine the actual use of computers as dependent variable. Thus, this study attempts to bridge the research gap by establishing the relationship between the psychological, socio-demographic factors and computer usage among the secondary school teachers in India. The study has adopted Technology Acceptance Model (Davis et.al., 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to develop the conceptual framework. The UTAUT is a robust model trying to provide a unified view of eight well known models in the field, including TAM. The authors of UTAUT have tested the model for technology acceptance at both voluntary and mandatory settings. As the present study is carried out in secondary schools where use of technology by the teachers is voluntary, a modified version of the basic UTAUT is used in the present study. The study has used exploratory study design by applying cross-sectional survey method to collect data. A structured questionnaire was developed and distributed to the respondents. The responses are self-reported by the sample respondents under each question or item with required instruction or direction to be followed. Population The present study is carried out in the state of Karnataka. The target population is state board secondary school teachers belonging to Bangalore districts, as it happens to be the hub of IT industry. The convenience of having access to large number of schools to get adequate sample also governed the researcher to conduct the survey in Bangalore districts. The researcher has considered three types of school management, namely, government, government aided and private schools as the universe for drawing the sample respondents.

71 Sample The state board secondary schools located in North, South and Rural Bangalore districts as provided by the Department of Public Instructions (Govt. of Karnataka, 2011) are divided by type of school management resulting in three lists: government, government aided and private schools. The study has proposed a factorial design of 3 X 2 to draw the sample respondents and presented below as a flow chart in Figure-3.1. For the present study, from each type of school management, 20 schools are selected using random sampling method. As a result a total of 60 schools are covered under this study. From all the selected schools those teachers who are handling class-8, class-9 and class-10 are covered in the present study as sample respondents. Secondary School Teachers N=578

Government Schools N=148

Govt. Aided Schools N=174

Male N=52

Male N=58

Female N=96

Female N=116

Private Schools N=256

Male N=61

Female N=195

Figure-3.1: Factorial Design of Sample Respondents In all the study has covered 633 secondary school teachers for data collection through

self-administered

questionnaires.

After

scrutinizing

the

filled-in

questionnaires for consistency of information and missing data, the responses of 578 secondary school teachers are considered for the study. The Table-1 presents number of secondary school teachers considered in the present study distributed by their type of school management and gender.

72 Table-3.1: Sample Distribution Type of School Management Gender Government School Govt. Aided School Private School Total Male

52

58

61

171

Female

96

116

195

407

Total

148

174

256

578

All the sample schools were contacted and permission was taken from the school principals for the study. Self-administered questionnaires were distributed to all the teachers who are handling class-8, class-9 and class-10. Teachers were explained the purpose and need of the study, before distributing the questionnaires. The data collection took four months: October 2011-January 2012 to cover all the 60 sample schools. A total of 633 secondary school teachers returned the questionnaires.

The entire 633 filled-in questionnaires were scrutinized for missing information. It was observed that 55 questionnaires were incomplete, especially for psychometric scales. It had amounted to rejection of 8% of total respondents as missing data cases. After omitting the respondents with missing data, responses received from 578 secondary school teachers were considered for the analysis. Variables of the Study The variables considered for the present study are divided into three groups: dependent, independent and moderator variables. Dependent Variable: Actual Use of Computers is measured on a Likert-type 5-point scale to understand the computer usage among the respondents.

73 Independent Variables: It includes seven psychological variables as predictors of dependent variable. The information pertaining to each of the following seven psychological variables is measured on a Likert-type 5-point scale: 1. Computer Usefulness 2. Computer Ease of Use 3. Social Influence 4. Computer Competency 5. Facilitating Conditions 6. Attitude Towards Computer Use 7. Behaviour Intention Moderator Variables: They include socio-demographic variables pertaining to respondents’ characteristics. 1. Gender

:

Male / Female

2. Age

:

Number of Completed Years

3. Education

:

Graduate / Post Graduate

4. Location of School

:

Rural / Urban

5. Type of School Management

: Government / Govt.Aided /Private

6. Subject Taught :

Science/Mathematics/Social/English/Kannada

7. Computer Access at Home

:

Yes / No

8. Computer Access at School

:

Yes / No

9. Computer Training

:

Yes / No

10. Computer Experience

:

Number of Years

Formulation of Hypotheses Present study involves a network of relationships among a number of independent variables and one dependent variable. This has necessitated the formulation of hypotheses with reference to relationships between perception, attitude, intention and computer use among the teachers.

74 Various research studies have tried to establish the difference in perception, attitude and intention towards computer across user characteristics such as gender, age, education and computer training and experience. This has led to formulation of null hypotheses with regard to differences in perception, attitude, intention and computer use across the socio-demographic variables.

To examine the major determinants of computer usage, hypotheses are formulated with respect to the direction and strength of relationship among independent and dependent variables. Hypothesis with reference to relationships Hypothesis-1: Relationship among Independent and Dependent Variables There is a significant relationship between computer usefulness, computer ease of use, social influence, computer competency, facilitating conditions, attitude towards computer use, behaviour intention and actual use of computers among teachers. Hypotheses with reference to predictors of criterion variable Hypothesis-2: Predictors of Attitude Towards Computer Use There exists a significant contribution of predictor variables: computer usefulness, computer ease of use, social influence, computer competency and facilitating conditions on the criterion variable: attitude towards computer use. Hypothesis-3: Predictors of Behaviour Intention to Use Computer There exists a significant contribution of predictor variables: computer usefulness, computer ease of use, social influence, computer competency, facilitating conditions and attitude towards computer use on the criterion variable: behaviour intention. Hypothesis-4: Predictors of Actual Use of Computers There exists a significant contribution of predictor variables: computer usefulness, computer ease of use, social influence, computer competency, facilitating conditions, attitude towards computer use and behaviour intention on the criterion variable: actual use of computers.

75 Hypotheses with reference to Mean Differences Hypothesis-5: Gender difference There is no significant difference between the mean scores of male and female teachers in their: 5.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 5.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 5.3) Social Influence: a) subjective norm; b) prestige and image 5.4) Computer Competency: a) operating skills; b) application skills 5.5) Facilitating Conditions: a) school support; b) infrastructure support 5.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 5.7) Behaviour Intention 5.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use Hypothesis-6: Education There is no significant difference between the mean scores of graduate and post graduate teachers in their: 6.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 6.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 6.3) Social Influence: a) subjective norm; b) prestige and image 6.4) Computer Competency: a) operating skills; b) application skills 6.5) Facilitating Conditions: a) school support; b) infrastructure support 6.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 6.7) Behaviour Intention 6.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use

76 Hypothesis-7: Location of School There is no significant difference between the mean scores of rural and urban school teachers in their: 7.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 7.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 7.3) Social Influence: a) subjective norm; b) prestige and image 7.4) Computer Competency: a) operating skills; b) application skills 7.5) Facilitating Conditions: a) school support; b) infrastructure support 7.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 7.7) Behaviour Intention 7.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use Hypothesis-8: Computer Access at Home There is no significant difference between the mean scores of teachers with respect to computer access at home and their: 8.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 8.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 8.3) Social Influence: a) subjective norm; b) prestige and image 8.4) Computer Competency: a) operating skills; b) application skills 8.5) Facilitating Conditions: a) school support; b) infrastructure support 8.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 8.7) Behaviour Intention 8.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use

77 Hypothesis-9: Computer Access at School There is no significant difference between the mean scores of teachers with respect to computer access at school and 9.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 9.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 9.3) Social Influence: a) subjective norm; b) prestige and image 9.4) Computer Competency: a) operating skills; b) application skills 9.5) Facilitating Conditions: a) school support; b) infrastructure support 9.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 9.7) Behaviour Intention 9.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use Hypothesis-10: Computer Training There is no significant difference between the mean scores of teachers with respect to computer training and 10.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 10.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 10.3) Social Influence: a) subjective norm; b) prestige and image 10.4) Computer Competency: a) operating skills; b) application skills 10.5) Facilitating Conditions: a) school support; b) infrastructure support 10.6) Attitude Towards Computer Use: a) Computer Anxiety; b) Computer Confidence; c) Computer Liking; d) Computer Enjoyment 10.7) Behaviour Intention 10.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use

78 Hypothesis-11: Age There is no significant difference among the mean scores of teachers with respect to age and 11.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 11.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 11.3) Social Influence: a) subjective norm; b) prestige and image 11.4) Computer Competency: a) operating skills; b) application skills 11.5) Facilitating Conditions: a) school support; b) infrastructure support 11.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 11.7) Behaviour Intention 11.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use Hypothesis-12: Type of School Management There is no significant difference among the mean scores of teachers with respect to type of school management and 12.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 12.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 12.3) Social Influence: a) subjective norm; b) prestige and image 12.4) Computer Competency: a) operating skills; b) application skills 12.5) Facilitating Conditions: a) school support; b) infrastructure support 12.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 12.7) Behaviour Intention 12.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use

79 Hypothesis-13: Subject Taught There is no significant difference among the mean scores of teachers with respect to subject taught and 13.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 13.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 13.3) Social Influence: a) subjective norm; b) prestige and image 13.4) Computer Competency: a) operating skills; b) application skills 13.5) Facilitating Conditions: a) school support; b) infrastructure support 13.6) Attitude Towards Computer Use: a) computer anxiety; b) computer confidence; c) computer liking; d) computer enjoyment 13.7) Behaviour Intention 13.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use Hypothesis-14: Computer Experience There is no significant difference among the mean scores of teachers with respect to years of computer experience and 14.1) Computer Usefulness: a) perceived usefulness; b) instructional advantage; c) job relevance 14.2) Computer Ease of Use: a) perceived ease of use; b) computer complexity 14.3) Social Influence: a) subjective norm; b) prestige and image 14.4) Computer Competency: a) operating skills; b) application skills 14.5) Facilitating Conditions: a) school support; b) infrastructure support 14.6) Attitude Towards Computer Use: a) Computer Anxiety; b) Computer Confidence; c) Computer Liking; d) Computer Enjoyment 14.7) Behaviour Intention 14.8) Actual Use of Computers: a) academic use; b) administrative use; c) personal use

80 Tools used in the Study The present study has used structured self-administered questionnaire to collect the data from the sample respondents. Apart from the information on respondents’ characteristics, the following psychometric scales are used to collect information on the study variables

Psychometric Scales Computer Usefulness

Dimensions Perceived Usefulness Instructional Advantage Job Relevance Computer Ease of Use Perceived Ease of Use Computer Complexity Social Influence Subjective Norm Prestige and Image Computer Competency Operating Skills Application Skills Facilitating Conditions School Support Infrastructure Support Attitude Towards Computer Computer Anxiety Use Computer Confidence Computer Liking Computer Enjoyment Behaviour Intention Behaviour Intention Computer Usage Academic Use Administrative Use Personal Use

Authors Davis, et.al. (1989) Developed by the researcher Thompson, et al. (1991) Davis, et.al. (1989) Thompson, et al. (1991) Taylor, S. & Todd, P.A. (1995) Developed by the researcher Developed by the researcher Developed by the researcher Loyd, B.H. & Gressard, C.P. (1984) Loyd, B.H. & Loyd, D.E. (1985) Developed by the researcher Davis, et.al. (1989) Developed by the researcher

The questionnaire is divided in to three sections. A brief description is presented here. Section–A:

Information on Socio-demographic Variables: It includes

information pertaining to gender, age, education, location of school, type of school management, subject taught, computer access at home, computer access at school, computer training and years of computer experience.

81 Section–B:

Information on Psychological Variables: It includes seven

psychometric scales, of which six are constructed using 5-point Likert-type statements while one scale on computer competence is constructed using 4-point Likert-type statements. The respondents were asked to circle their choice for each statement that ranges from Strongly Agree (5), Agree (4), Undecided (3), Disagree (2) to Strongly Disagree (1). The negatively worded statements are reverse coded while entering the data. The responses are reduced to a mean score ranging from 1to 5, with highest score indicating favourableness of respondents with respect to their perception, attitude and intention to use computers. Following is the detailed description of the constructs used in the study. Computer Usefulness The researcher has developed a total of 34-items with reference to the teachers’ perception on usefulness of computer. It consists of three dimensions: perceived usefulness, instructional advantage and job relevance. (a) Perceived Usefulness: The statements on perceived usefulness proposed by Davis et al. (1989) are reviewed and modified to develop perceived usefulness dimension with 11 statements. (b) Instructional Advantage: A review of literature was carried out on professional development of teachers, pedagogical use of computers, and teacher and student understanding of computers for teaching-learning. This has helped the researcher to develop statements on instructional advantage with 16-items. (c) Job Relevance: The statements on Job-fit proposed by Thompson et al. (1991) are reviewed and re-worded to develop job relevance dimension with seven statements.

82 Computer Ease of Use The computer ease of use consists of 26-statements on teachers’ perceived easiness and their judgments of capabilities in using computer. It has two dimensions: perceived ease of use and computer complexity. (a) Perceived Ease of Use: The perceived ease of use scale proposed by Davis et al. (1989) is adopted with required modification and re-wording to develop perceived ease of use dimension with 14-statements. (b) Computer Complexity: According to Thompson et al. (1991) computer complexity refers to “the degree to which a system is perceived as relatively difficult to understand and use”. The computer complexity scale proposed by Thompson et al. (1991) is adopted with required modification to develop computer complexity dimension with 12-statements. Social Influence The statements under this construct are developed to understand the role of school, colleagues and socially significant people who might influence the respondents’ perception on use of computers. It has a total of 24-items divided into two dimensions: subjective norm and prestige and image. (a) Subjective Norm: For the subjective norm, the present study has relied on the work of Taylor and Todd (1995) and developed 13-statements for subjective norm dimension (b) Prestige and Image: The statements for prestige and image are designed by the researcher after due review of literature in this field. In all, prestige and image dimension has 11-items.

83 Computer Competency For effective utilisation of any technology the knowledge and skill of endusers play a greater role. The researcher has developed a total of 16-statements to access the general computer competency of the respondents with two dimensions: operating skills with seven statements; and application skills with nine statements. The study uses 4-point Likert-type scoring of statements, ranging from highly competent (4), moderately competent (3), less competent (2) to not competent (1). Facilitating Conditions The statements under this construct focus on teachers’ perception of conditions that facilitate or hinder use of computers. According to Theory of Reasoned Action and Theory of Planned Behaviour, this construct is referred to as perceived behaviour control dimension that determines one’s intention to use technology. In the present study the facilitating conditions is represented by 14 statements under two dimensions: school support with nine statements; and infrastructure support with five statements. Based on the statements from the work of Moore and Benbasat (1991), the researcher had developed this scale with due modification and additional statements to meet the objectives of the present study. (a) School Support: The school support dimension deals with teacher perception on how far technical, training and colleagues support facilitate in use of computers. (b) Infrastructure Support: The infrastructure dimension includes statements on the availability of computer hardware and software support.

84 Attitude Towards Computer Use The statements under this construct have reference to teachers’ attitude towards computer use. It consists of four dimensions: computer anxiety, computer confidence, computer liking and computer enjoyment. According to Francis, Katz and Jones (2000) Computer Attitude Scale developed by Loyd and Gressard “is one of the most frequently used instruments to assess computer-related attitudes among pre-service and in-service teachers” (p.149). It had three subscales: anxiety, confidence and liking. Based on a review of statements on attitude towards computer developed by Loyd and Loyd (1985) the researcher has developed teachers’ attitude towards computer use scale. In the present study, enjoyment is added to attitude scale as a dimension. Thus, the attitude towards computer use scale has four dimensions: computer anxiety with 10 statements; computer confidence with 10 statements; computer liking with 12 statements and computer enjoyment with 8 statements. Behaviour Intention The behaviour intention scale proposed by Davis et.al. (1989) was adopted in the present study with required modification and additions. It consists of eight statements on teachers’ intention to use (or continue to use) computers in future. Section – C: Actual Use of Computers: Based on the review of related literature that had dealt with computer usage, the researcher has developed teachers’ actual use of computers scale. It has 19 statements and respondents are requested to mark their response by circling Likerttype 5-point scale ranging from always (5), often (4), sometimes (3), rarely (2) to never (1). It consists of three dimensions representing the purpose for which teachers are actually using computers: academic use with seven statements; administrative use

85 with four statements and personal use with eight statements. In the present study, teachers’ actual use of computers is considered as dependent variable. Pre-testing of the Questionnaire Pre-test of the questionnaire ensures identifying the possible inconsistencies and ambiguities in designing questionnaire. Generally, the questionnaire should be pre-tested among a group of respondents who are similar to the sample respondents to whom the questionnaire will be eventually administered. In the present study, the pre-test of the questionnaire was conducted among 60 secondary school teachers in the city of Mysore. To ensure reliability and intern consistency of psychometric scales proposed in the present study, it is necessary to select appropriate statements in the formative stage of designing the scales. Thus, for pre-test a relatively more number of statements were included in designing the psychometric scales. The pre-test has helped the researcher to revise the questions related to teachers’ characteristics and re-look at the statements in each of the psychometric scales. Inconsistent responses and non-responses to some of the statements were noticed from the pre-test responses. Accordingly, the researcher has reduced the size of the questionnaire, revised the wordings of certain statements to minimize ambiguity and removed certain statements that were found to be repetitive. The pre-testing of the questionnaire had also guided the researcher to change the order of a few statements, avoid more negative statements and to reduce redundant questions, so as to make the questionnaire more readable.

86 Reliability of the Scales Reliability of a construct helps to assess the goodness of the measure and indicates accuracy in measurement (Sekaran, 2003). The present study has used the Cronbach’s coefficient alpha (Nunnally, 1979) to test the inter-item consistency reliability of the scales. According to Sekaran (2003) if the scales’ Cronbach’s coefficient alpha is less than 0.6 the reliability is poor; those in the range of 0.7 the reliability is acceptable; and those over 0.8 the reliability is good. The generally agreed lower limit for Cronbach’s coefficient alpha is 0.7 and may decrease to 0.6 in an exploratory research. The results of reliability test for dependent variable and the independent variables are presented in Table-3.2. The Cronbach’s coefficient alpha was calculated using IBM SPSS Statistics version-20. The scores for negative statements in the Likert-type scales are reversed. All the seven scales measuring independent variables were found to have good reliability along with their dimensions. In case of actual use of computers, the Cronbach’s alpha value is slightly lower than the other scales. More specifically for statements on personal use of computers has Cronbach’s coefficient alpha = 0.633 indicating a poor reliability, although it is an acceptable level.

Validity of the Scales Validation of a scale is an important step in any educational research that uses a construct to measure respondents’ perceptions, attitudes and behaviour.

87 In the present study the researcher has tested the validity of eight scales using two methods: (1) Content Validity and (2) Construct Validity. Table-3.2: Summary of Reliability Statistics of Scales Scales and subscales 1. Computer Usefulness a) Perceived usefulness b) Instructional advantage c) Job relevance 2. Computer Ease of Use a) Perceived ease of use b) Computer Complexity 3. Social Influence a) Subjective norm b) Prestige and image 4. Computer Competency a) Operating skill b) Application skill 5. Facilitating Conditions a) School support b) Infrastructure support 6. Attitude Towards Computer Use a) Computer Anxiety b) Computer Confidence c) Computer Liking d) Computer Enjoyment 7. Behaviour Intention 8. Actual Use of Computers a) Academic use b) Administrative use c) Personal use

Items 34 11 16 7 26 14 12 24 13 11 16 7 9 14 9 5 41 10 10 12 9 8 19 7 4 8

Cronbach’s Alpha 0.951 0.966 0.951 0.917 0.950 0.961 0.945 0.945 0.938 0.944 0.958 0.940 0.965 0.885 0.927 0.814 0.951 0.935 0.919 0.949 0.879 0.969 0.889 0.905 0.881 0.633

Reliability Result Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Good Poor

Content Validity According to Hair et al. (2006), content validity is done through the ratings by expert judges and pre-tests to assess the correspondence between the individual items and the concept being measured or studied.

88 Firstly, the researcher has pre-tested the questionnaire among a sample of 60 teachers and results are used to revise the questionnaire, including re-wording and omitting redundant items. Secondly, revised questionnaire was given to three experts: one teacher educator and one education psychologist in a teacher training college and an education technology faculty of a university education department. The experts were briefed about the objectives of the study and requested to provide their judgment on the questionnaire. The expert opinion has helped in removing certain items that were repetitive and misleading. In addition, items that are general in nature were rerewritten to clearly focus on the sample studied, namely, the school teachers. Construct Validity Construct validity examines the fit of the items in a scale in relation to variables or factors intended to be measured and how well it can explain the variance in the proposed conceptual framework. Various methods are applied to test the construct validity: composite validity based on Cronbach’s coefficient alpha of a construct, criterion validity using correlation analysis of items in a construct, convergent validity to assess the factor loadings on to the underlying construct using factor analysis, average variance extracted and discriminate validity (Hair et al. 2006). To test the construct validity of all the eight scales and its dimensions, the study has applied the following three methods: (a) Composite Validity, (b) Criterion Validity and (c) Convergent Validity. (a) Composite Validity Using Cronbach’s Coefficient Alpha The composite reliability of each construct is tested using Cronbach’s coefficient alpha. It is suggested that a construct having alpha value greater than 0.70

89 should be considered as acceptable and above 0.80 as good (Robinson et al., 1991). In the present study the Cronbach’s coefficient alpha is above 0.80 in all the scales and its dimensions, except an alpha of 0.66 for ‘personal’ dimension under the actual use of computers (Table-3.2). Given the results of Cronbach’s coefficient alpha being good, the composite validity of eight scales and their dimensions are deemed to be adequate. (b) Criterion Validity Using Item Correlation Criterion validity is a method of establishing the validity of a construct using the correlation among the items underlying the construct. It is suggested that higher the item correlations the better the validity of the construct. The validity of a scale is established by convergence of items that are indicators of a specific construct or share a high proportion of variance in common (Hair et al., 2006). It refers to medium or high correlation of items in a construct such that the construct is measuring the intended dimension or concept. Generally, for criterion validity the item-to-total correlations should exceed 0.50 and the inter-item correlations should exceed 0.30.For the present study, the recommendations of Cohen (1988) are applied to decide the range for correlation (both positive and negative): Correlation (r) = 0.10 to 0.29 Correlation (r) = 0.30 to 0.49 Correlation (r) = 0.50 and above

: small correlation : medium correlation and : high correlation

The results for criterion validity of each scale and its dimensions are presented in Table-3.3. In the present study the inter-item correlation values for each of the eight scales and their dimensions are in the medium and high levels. However, in case of certain scales it has shown small correlation due to the contribution of a particular dimension in the construct.

90 For item-to-total correlation, the values exceed 0.50 confirming high correlation levels. In some cases it is in the medium correlation (0.30 to 0.49) levels. Having achieved a moderate level of item correlations, the criterion validity for the scales and dimensions proposed in the present study are deemed to be just adequate. (c) Convergent Validity Using Principal Component Analysis The present study has tested each of the eight scales for factor loading using IBM SPSS version-20. The study has used principal component analysis to extract the components or factors under each scale. Using 5-point Likert-type each statement in the scale are scored as strongly agree=5, agree=4, undecided=3, disagree=2 and strongly disagree=1. Statements that are negatively worded are indicated by star (*) and their scores are reversed while analysing. Results of rotated factor loadings extracted for all the items of psychomctric scales are presented in Appendix-B.

As a first step in performing the principal component analysis, the Bartlett’s test of sphericity to determine the strength of relationship among the items and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is obtained. The Bartlett’s test of sphericity should be statistically significant at p