Adoption of Innovations by Individuals within Organizations: An Australian Study

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1 Asia Pacific Management Review 13(2) (2008) Abstract Adoption of Innovations by Individuals within Organizations: An Australian Study Majharul Talukder*, Howard Harris, Gido Mapunda Division of Business, School of Management, University of South Australia, Australia Accepted 17 March 2008 Despite much research on organizational adoption of innovation, little is currently known about the adoption of innovation by individual employees within organizations. Organizational innovations that need to be incorporated in work processes of an organization are of little value if they are not adopted by employees. The purpose of this study is to investigate the determinants of innovation adoption and provide a new theoretical framework that addresses the adoption decision by individual employees within an organization. Using data collected from an Australian organization, the study develops and tests an enhanced model of innovation adoption to investigate a wide range of factors affecting individuals adoption of innovation in an organizational context. The study is based on a sample comprised of 275 academic and administrative staff across several departments of the University of South Australia. After data collection, correlation matrix, Analysis of Variance (ANOVA) and multiple regressions were applied to conduct data analysis to test a proposed enhanced mode, which is largely supported and validated, accounting for 53% of the variances in usage. Finally, theoretical and practical implications are discussed. Keywords: Innovation adoption, individual employees, organizational, individual and social factors. 1. Introduction 1 Technology offers the potential for substantially improving the performance of organizations. However, performance gains are often obstructed by users unwillingness to accept and use available systems. Due to the persistence and importance of this problem, explaining users acceptance of technology has been a long time study issue in organizations and information systems research (Davis, 1989). According to Frambach et al. (1998) environmental conditions increasingly force organizations to innovate and bring new products to the market. Since only a fraction of new products are successful, a thorough understanding of factors underlying the innovation adoption decisions by potential adopters is necessary. Similarly, Bhattacherjee and Sanford (2006) state that understanding IT acceptance is important because the expected benefits of information technology (IT) usage, such as gain in efficiency, effectiveness, or productivity, cannot be realized if individual users do not accept these systems for task performance. Thus, understanding the factors influencing user * Corresponding author. majharul.talukder@unisa.edu.au 463

2 acceptance of IT in the workplace have long been a concern of scholars and practioners (Sherif et al., 2006; Venkatesh et al., 2000). Employees must use an innovation to realize the intended benefits. Therefore, it is important to examine the adoption of innovation by individual employees within organizations because if there is no acceptance among employees, the desired benefits can not be realized and the organization may eventually abandon the technology. People, by nature will resist change unless they can be convinced that they can directly benefit from the change (Ajzen, 1991). New technologies are rapidly replacing old ones by providing more powerful tools, efficiency and speed for users. Their adoption can be successful, however, only when employees accept and effectively use them. Consequently, an organization should understand the acceptance process and the factors that are essential in making this process effective (Lee et al., 2006). Although technology adoption has been studied extensively, drivers of adoption and research on individual innovation acceptance in an organizational context remains limited (Frambach and Schillewaert, 2002). Relevant literature indicate that we know relatively little about the ways in which individuals adopt and the factors that influence individual adoption of innovation (Bhattacherjee, 1998; Frambach and Schillewaert, 2002; van Everdingen and Wierenga, 2002; Venkatesh and Davis, 2000). This means further research is required regarding the role of organizational, individual and social processes affecting individual adoption of innovation (Frambach and Schillewaert, 2002; Schepers and Wetzels, 2007; Yi et al., 2006b). This study is designed to fill that gap. Specifically, the study focuses on determining the relative importance of organizational, individual and social factors that affect individual adoption of innovation. The identification of these factors is important to organizations that seek to create a work environment that is conducive to individual implementation of innovation and thereby gain the expected benefits from innovation. The study seeks to answer the following research questions: (a) What is the impact of organizational influence such as training, managerial support and incentives on individual adoption of innovation? (b) Do individual factors such as perceived usefulness, personal innovativeness, prior experience, image and enjoyment of innovation affect individuals adoption of innovation? (c) What is the impact of social factors such as peers and social network on individual adoption of innovation? 2. Theoretical framework The theoretical framework for this study is based on the Theory of Reasoned Action, the Technology Acceptance Model and the conceptual framework provided by Frambach and Schillewaert (2002). For a more detailed discussion of these models see Talukder et al. (2007b). The models are explained below. 2.1 Theory of Reasoned Action (TRA) Theory of Reasoned Action (TRA) is a widely studied model from social psychology which is concerned with the determinants of conscious intended behavior. The foundation of TRA conceptual framework is provided by the distinction between beliefs, attitudes, intentions and behaviors (Al-Gahtani and King, 1999). According to TRA, a person s performance of specific behavior is determined by their behavioral intention to perform the behavior and behavioral intention is jointly determined by two basic determinants: the person s attitude toward the specific behavior and subjective norms concerning the behavior in question (Ajzen and Fishbein, 1980). Individuals are usually more likely to perform a behavior if they possess positive attitude toward this behavior and vice versa (Kwok and Gao, 2006). Attitude simply refers to a person s judgment that performing the behavior is good or bad, that they are in favor of or against performing the behavior (Ajzen and 464

3 Fishbein, 1980). TRA theorizes that a person s attitude toward a behavior is determined by their salient beliefs about consequences of performing the behavior and an evaluation of the outcome of that behavior (Davis et al., 1989). Behavioural beliefs are defined as an individual s subjective probability that performing the target behavior will result in consequences and the evaluation term refers to an implicit evaluative response to the consequence (Ajzen and Fishbein, 1980). Subjective norms are also a function of beliefs, but beliefs of a different kind, namely a person s belief that most people who are important to them think they should or should not perform the behavior (Ajzen and Fishbein, 1980). These beliefs underlying a person s subjective norm are called normative beliefs. Therefore, individuals are more likely to perform an act if they perceive the existence of greater social pressure from salient referents to perform that act (Lam et al., 2007). Ajzen and Fishbein (1980) state that from our point of view, external variables may influence the beliefs a person holds or the relative importance he attaches to attitudinal and normative considerations. However, Ajzen and Fishbein (1980) did not include external variables into their model. In addition, they did not include demographic variables into the model but suggested that they are important. Ajzen and Fishbein (1980) mention that the factors which have not been analyzed were personality characteristics such as demographic variables. There is plenty of evidence that those factors influence behavior (Ajzen and Fishbein, 1980). This study seeks to enhance the model by including external factors and demographic characteristics, which affect individual employees behavior by influencing employees attitude toward adoption. 2.2 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) which was first introduced in 1986 is an adaptation of the Theory of Reasoned Action specifically tailored for explaining user acceptance of information technology (Davis et al., 1989). TAM postulates that two particular beliefs- perceived usefulness and perceived ease of use, are of primary relevance for computer acceptance behaviors (Bruner and Kumar, 2005; Davis, 1989). Among the many variables that may influence system use, previous research suggested two determinants that are especially important. First, people tend to use or not to use an application to the extent that they believe it will help them perform their job better. TAM refers to this variable as perceived usefulness. Second, even if potential users believe that a given application is useful, they may, at the same time, believe that the system is too hard to use and that the performance benefits of usage are out-weighed by the effort of using the application. In other words, in addition to usefulness, usage is theorized to be influenced by ease of use. There have been a number of studies on technology adoption, many using TAM. Although many of these studies provide insight into the acceptance of technology or a new system using TAM, their research focused only on attitudinal and behavioral intentions as determining factors of system usage. Thompson et al. (1991) state that the model should drop behavioral intention and link attitude to actual behavior directly as we are interested in actual behavior directly. In addition, Yi et al. (2006b) state that TAM should be integrated into a more inclusive model incorporating variables related to both human and social change processes as well as the adoption of innovation. Recent studies on IT adoption are generally based on TAM or extensions to it by including one variable for instance enjoyment as one of the predictors (Chang and Cheung, 2001; Huang, 2005). Note that TAM does not test as many variables compared to the model provided by Frambach and Schillewaert (2002). Therefore, an enhanced model is required, which will include a combination of variables found in different elements of the technology adoption literature. This study develops an enhanced model combining multiple sets of factors found in the 465

4 models mentioned above in order to examine a broader perspective, which will help in understanding individuals adoption of innovation and usage levels. 2.3 Conceptual framework provided by Frambach and Schillewaert The model for this study is also based on the theoretical framework provided by Frambach and Schillewaert (2002) who argue that individual acceptance of innovation is based on perceived beliefs and affects held towards the focal innovation. These beliefs and affects are reflected in an individual s attitude towards a particular innovation. Attitude can change and be influenced and there is evidence that a person s attitudes mediate the influence of external variables and stimuli (Frambach and Schillewaert, 2002). Therefore, this model shows the effect of external influences such as organizational facilitators, personal innovativeness, and social usage, as indirect, working through attitudinal components (Frambach and Schillewaert, 2002). Frambach and Schillewaert s model indicate that individual usage of innovation not only depend on attitudes but also on management strategies, policies and actions; and these factors include training, social persuasion and organizational support. The model proposes that personal innovativeness influences individual acceptance and personal innovativeness is determined by various personal characteristics such as demographics, tenure, product experience and personal values (Frambach and Schillewaert, 2002). According to the model individual acceptance of innovations is also driven by the usage of a focal innovation within their social environment. Such social influences may stem from two sources-network externalities and peers (Frambach and Schillewaert, 2002). The Frambach and Schillewaert s conceptual model has not been tested yet and they have recommended a study testing their model. Although Frambach and Schillewaert included a long list of factors in their model compared to previous Technology Acceptance Models but they have not included several important factors such as perceived usefulness, image and enjoyment with innovation which were used in other technology acceptance studies. Consequently, a more comprehensive model is needed, which will test a wide range of factors affecting individual adoption of innovation. This study attempts to develop such a model. 2.4 Research model The main feature of this study is the development of an enhanced model based on the Theory of Reasoned Action (Ajzen and Fishbein, 1980), the Technology Acceptance Model (Davis, 1989) and the conceptual framework of individual innovation acceptance by Frambach and Schillewaert (2002) while introducing several modifications which were not in these models. The model for this study maintains the basic structure of the Theory of Reasoned Action and incorporates the elements of the technology acceptance model. However, Frambach and Schillewaert (2002) proposed a longer list of factors which determine attitude and recommended a study testing those factors. The composite model used in this study adopts that list. Figure 1 presents the proposed enhanced research model of innovation adoption for this study. The Theory of Reasoned Action (TRA) was first introduced in 1967 by Ajzen and Fishbein (1980), a widely studied model which is concerned with the determinants of conscious intended behavior. However, their analysis of behavior did not make any reference to various factors such as personality characteristics (demographic variables) and external factors and there is evidence that these factors influence individual behavior (Ajzen and Fishbein, 1980). After more than one and half decade the Technology Acceptance Model (TAM) was introduced in 1986 by Davis (1989) which is an adaptation of the Theory of Reasoned Action specifically tailored for explaining the determinants of the technology 466

5 acceptance. Yi et al. (2006b) suggest that Technology Acceptance Model should be integrated into more inclusive model incorporating variables related to both human and social processes. Further, the model also did not include demographic factors as suggested by the TRA and did not test as many variables compared to the more recent model of technology acceptance provided by the Frambach and Schillewaert (2002). Again nearly one and half decade later in 2002 a more comprehensive theoretical framework provided by Frambach and Schillewaert (2002) to assess the individual adoption of technological innovation and recommended a study testing the model. However, this model did not include several important variables which have been used in other technology adoption research using TRA or TAM. ORGANIZATIONAL FACTORS -Training -Managerial support -Incentives INDIVIDUAL FACTORS -Perceived usefulness -Personal innovativeness -Prior experience -Image -Enjoyment of innovation ATTITUDE TOWARD INNOVATION INDIVIDUAL ADOPTION OF INNOVATION SOCIAL INFLUENCE -Peers -Social network DEMOGRAPHICS -Gender -Age -Academic divisions -Occupation category -Tenure -Academic qualifications Figure 1. Enhanced model of innovation adoption Using the basic structure of TRA, TAM and conceptual framework of Frambach and Schillewaert while introducing several modification which were not in these model, this study has developed an enhanced model of innovation adoption to investigate a complete and wide range of factors affecting individuals adoption of innovation in the organizational context. This study develops a coherent model by incorporating a long list of factors and introducing demographic characteristics into the model, which were not in the TRA or TAM. In the development of the enhanced model three external variables - training, managerial support and incentives are incorporated in the model since Frambach and Schillewaert (2002) recommended they be tested in future research. These external variables were not included in TRA and TAM. Therefore, external variables training, managerial support and incentives are incorporated in the model as they directly affect individual attitude toward acceptance of innovation. In the enhanced model, five individual factors - perceived usefulness, personal innovativeness, prior experience, image and enjoyment of innovation are introduced and placed corresponding to the main belief constructs of the Technology Acceptance Model and the Theory of Reasoned Action. TAM has two belief constructs - perceived usefulness 467

6 and perceived ease of use which affect attitudes toward adoption while TRA has one belief construct called behavioral belief (beliefs and evaluation). TRA also has another different belief construct called normative beliefs under subjective norms. Framback and Schillewaert s model has two belief constructs - prior experience and personal innovativeness. These belief constructs affect individuals attitude toward adoption. This study incorporates factors found in previous models and factors found in similar studies to form an enhanced research framework for the investigation of a complete and a wide range of factors affecting individuals attitude toward usage of technological innovation. In building an enhanced model two social factors - peers and social network are incorporated. TAM did not include the Theory of Reasoned Action s subjective norms construct as it was non-significant in the specific circumstances of TAM and one of the least understood aspects of the Theory of Reasoned Action (Davis et al., 1989). However, Frambach and Schillewaert (2002) point out that organizational members will exhibit more positive attitudes if people in their social environment also use the focal innovation. Therefore, two social factors - peers and social network are included in the model as directly affecting individual attitude toward innovation. In the development of a new theoretical framework, demographic variables are added into the enhanced model. Demographic characteristics are introduced as they impact on individual attitude towards adoption of innovation. These demographic factors were not included in the Theory of Reasoned Action and the Technology Acceptance Model. Frambach and Schillewaert (2002) mention that demographic characteristics affect individual attitudes toward adoption. Therefore, demographic characteristics are included in the model. 3. Factors in the enhanced model 3.1 Organizational factors Several studies have indicated that an individual s adoption of innovation not only depends upon individual attitudes but also on management policies, strategies and actions (Peansupap and Walker, 2005). Organizations need to provide facilitating conditions which include the extent and type of support provided to individuals that influence their use of technology. Facilitating conditions are believed to include the availability of training and provision of support. Facilitating conditions have been identified as having an effect on infusion or adoption of a number of new information system innovations (Lu et al., 2005). These factors include training (Al-Gahtani and King, 1999), managerial support (Ahuja and Thatcher, 2005) and incentives (Bhattacherjee, 1998). These influences affect an individual s awareness of the functioning and application of an innovation, its usefulness and fit with the job which leads to its adoption (Frambach and Schillewaert, 2002). Organizational influence can motivate individual employees adopting an innovation. According to Igbaria et al. (1997) training promotes greater understanding, favourable attitude, more frequent use, and more diverse use of applications. Several studies have reported that training positively influences an individual s adoption of innovation (Igbaria et al., 1996; Jasperson et al., 2005). By training, educating and assisting employees when they encounter difficulties, some of the potential barriers to adoption can be reduced or eliminated (Burgess et al., 2005). Individual adoption of innovation is positively influenced by the amount of relevant formal training because such training enhances individual s belief, possession of skills and knowledge that permit successful task performance (Yuan et al., 2005). Managerial support, which includes top management s help and encouragement, allocation of resources and technical help are considered as important factors for adoption of innovation by individual employees (Cho, 2006; Igbaria, 1993). It is found that managerial 468

7 support is associated with greater adoption and usage and lack of organizational support is considered as a critical barrier to the adoption and effective utilization of new innovation (Lee et al., 2005). Incentives are often considered powerful motivators of employee behavior in adopting an innovation (Nilakant and Rao, 1994). Managers must provide individual employees either incentives such as commissions, recognition and praise for adoption and penalties such as threat and demotion for non adoption of innovation (Bhattacherjee, 1998). Thus, there are three organizational factors such as training, managerial support and incentives have influence on the individual s adoption of innovation. 3.2 Individual factors According to Lewis et al. (2003), individual factors are one of the important determinants of adoption of technological innovation. Several studies found that individual factors such as perceived usefulness, personal innovativeness, prior experience, image and enjoyment of innovation have stronger influence on individual s adoption of technological innovation (Davis, 1989; Lewis et al., 2003). Studies have shown perceived usefulness is one of the strongest predictor and remains significant at all point of measurement (Venkatesh et al., 2003). If an individual thinks that the new system usage will enhance the efficiency and effectiveness or offer greater control over the job, innovation is more likely to be adopted (Lee, 2004). Organizations try to influence their employees attitudes towards adoption of an innovation. However, some individuals more readily adopt certain innovations than others. Frambach and Schillewaert (2002) mentioned that personal innovativeness is the innate tendency of a person to adopt an innovation. Innovativeness may influence perception regarding a new technology (Yi et al., 2006a). Prior experience and familiarity with technology reduces anxiety and provide confidence (Fuller et al., 2006). Hill et al. (1987) assert that previous experience positively influences behavioral intentions. In a typical work environment, with high degree of interdependence with other employees in carrying out their duties, increased image and status within the group is important to many individuals. An individual may thus perceive that using new technology will lead to improvement in his or her image in job performance (Venkatesh and Davis, 2000). According to Venkatesh and Brown (2001) individual adoption decision is influenced by hedonic outcomes. Hedonic outcome is described as pleasure driven from the consumption or use of innovation (Babin et al., 1994). Thus, there are five individual factors - personal innovativeness, prior experience, image and enjoyment of innovation that influence an individual s adoption of innovation. 3.3 Social factors Employees adoption of innovation is driven by their social environment. Innovation usage by others in the employees social environment is likely to play an important role in adoption of innovation. Social influence is the extent to which members of a social group influence one another s behavior in adoption (Konana and Balasubramanian, 2005; Venkatesh and Brown, 2001). It is perceived pressure and influence that peers feel to adopt an innovation and this influence is exerted through messages and signals that help to form perceptions of the value of a technology or activity (Fulk and Boyd, 1991). Ajzen and Fishbein (1980) refer to such influence as normative beliefs about the appropriateness of the adoption of innovation. According to this perspective, employees may adopt a new technology not because of its usefulness but because of the perceived social pressure. Such pressure may be perceived as coming from individuals whose beliefs and opinions are important, including peers and people who are in social networks (Igbaria et al., 1996). Abrahamson and Rosenkopf (1997) mentioned that it is largely internal influence that potential adopters exert on each other that persuade them to adopt. 469

8 4. Methodology This empirical study examined the extent to which individuals within an organization, the University of South Australia, have adopted a specific technology. The study examined the use of selected advanced features of Microsoft Outlook (such as calendar applications). Microsoft Outlook will be a good test because it has been widely used for many years, and is the university s preferred application but it has many advanced features beyond the core ( ) function. There is a large variation in the usage of these advanced features, and the university is anxious to increase the use of them. The primary procedure for obtaining data for this study was through an online survey questionnaire. The online questionnaire was available to full-time and part time academic and administrative (professional) staff of the university. These include four academic faculties (divisions) of the University of South Australia and administrative staff who are not in the divisions (faculties) - Business; Education, Arts and Social Sciences; Health Science; and Information Technology, Engineering and Environment. The questionnaire is based on the research model described earlier in this paper and includes questions designed to measure each of the ten factors shown to affect attitude and hence usage. The first stage after the design of the questionnaire was to obtain IT experts opinions and comments regarding the questionnaire, sentence structure relevant to the type of questions and technical aspects of the questionnaire. This was followed by a pilot study in which the research instruments were pre-tested to identify and modify the items which the respondents tended to misinterpret, skip over or answer improperly. The purpose of this pilot study was to examine the validity and the reliability of the instrument. The questionnaire was then distributed to all staff using the university s weekly all-staff , and followed up with invitations to participate in the study to individual schools or disciplines. The online survey tool used required respondents to log in using their university password, thus preventing multiple submission and ensuring the integrity of the sample. To test the proposed model, multiple regression analysis was performed on the collected data. Multiple regression analysis, a form of general linear modeling, is a multivariate statistical technique that can be used to analyze the relationship between a single dependent (criterion) variable and several independent (predictor) variables (Hair et al., 1998). Each independent variable was weighted by the regression analysis procedure to ensure maximal prediction from the set of independent variables. The weights denote the relative contribution of the independent variables to the overall prediction and facilitate interpretation as to the influence of each variable in making the prediction. The set of weighted independent variables will form the regression variate (also referred to as the regression equation or regression model), a linear combination of the independent variables that best predicts the dependent variable (Hair et al., 1998). In the context of this study, with ten independent variables, the regression model can be shown in the following equation: Y=b 0 +b 1 X 1 +b 2 X 2 +b 3 X 3. +b 10 X 10 where, Y, the dependent variable is a measure of the usage of the advanced features; b 0 is a constant; b 1, b 2, b 3...b 10 are coefficients; and X 1, X 2, X 3 X 10 are the ten independent variables shown in the model to affect usage. 5. Results The study was interested in more accurately predicting the level of new technology usages by individual employees in the organization in an attempt to develop a theoretical construct that brings individual innovation adoption issues into a coherent model and promote management awareness, understanding and support in technology adoption by individual employees. To this end, multiple regression analysis was used to provide an objective means of assessing the predictive power of a set of independent variables. The 470

9 following ten variables were included as independent variables: training, managerial support, incentive, perceived usefulness, personal innovativeness, image, prior experience, enjoyment with innovation, peers, social network and usage as dependent variable. The sample of 275 observations meets the proposed guideline for the ratio of observations to independent variables with a ratio of 15 to 1 (Hair et al., 1998). This sample ensures that there is no danger of overfitting the results and helps to validate the results to ensure the generalizability of the findings to the entire population. 5.1 Demographic information about respondents This section contains the analysis of demographic data and explains the descriptive statistics, which is used to describe information about a population or sample in the form of frequency. Demographic characteristics include gender, age, division, classification, position (employed as) and academic qualification. Male respondents constitute 36% whereas female respondents make up 64%. This distribution of gender reflects the university s total employee distribution, which is that female employees outnumber male employees. Nineteen percent of all employees belong to the age group and 20% to the age group. Those aged make up 31% of the respondents; whereas respondents aged 51 to 60 constitute 24%. Respondents aged 60 and above make up only 5%. Twenty four percent of the respondents are from the Division of Business and 25% are from the Division of Education, Arts and Social Sciences. The Division of Health science constitutes the highest respondent 26% whereas respondents from the Division of Information Technology, Engineering and Environment make up 12%. Respondents not in a division represent 13%. The majority of the respondents (65%) are academic staff whereas 35% are professional (administrative) staff. Almost 78% of the respondents are full time employees and 22% are part time employees. The survey results reveal that 31% of the respondents are PhD holders and 20% of respondents have a master s degree. Eleven percent of the respondents have graduate diplomas whereas 22% of respondents are bachelor degree holders. Only 6% of them have undergraduate diplomas. This information shows that most of the respondents are middle age, moderate to highly educated, academic staff and full time employees. Such information may prove significant when the adoption and usage level of these respondents are investigated. 5.2 Inter-correlations among study variables A correlation analysis was conducted to find out the relationship among the study variables. The correlations among all research variables are presented in Table 1. Pearson s correlations coefficients (r) of the various variables were significant at level of From the Pearson s r analysis, the correlation matrix table shows that there is a significant, positive correlation between dependent and independent variables. Table 1 shows that usage is significant and positively related with three organizational variables such as training (r =.196, p<.01), managerial support (r =.475, p<.01) and incentives (r =.624, p <.01). The data shows that the level of adoption or usage is significant and positively related to five individual or personal factors such as perceived usefulness (r =.658, p <.01), personal innovativeness (r =.331, p <.01), prior experience (r =.279, p <.01), image (r =.528, p <.01) and enjoyment with innovation (r =.546, p <.01). The analysis also demonstrate that usage is also significant and positively related to two social factors such as peers (r =.215, p <.01) and social network (r =.367, p <.01). The data also indicates that attitude has strong and positive correlation (r =.727, p <.01) toward the usage. 471

10 Table 1. Inter-correlations among study variables Variables Usage Traini. Mans. Incent. Useful. Innov. Experi Image Enjoy. Peers Social. 1. Usage 1 2. Training.196** 1 3. Managerial support.475**.569* 1 4. Incentive.624**.191**.423** 1 5. Usefulness.658**.253**.424**.809** 1 6. Innovativeness.331** **.313** 1 7. Experience.279**.229**.157**.318**.332**.348** 1 8. Image.528**.352**.504**.627**.622**.230**.290** 1 9. Enjoyment.546**.230**.360**.726**.707**.255**.274**.616** Peers.215**.259**.385**.331**.300** **.333** Social net.367**.326**.496**.471**.438** **.596**.458**.547** Attitude.727**.256**.488**.780**.789**.227**.304**.683**.786**.361**.533** ** Significant at the 0.01 level (2-tailed); n=275 The table shows correlations among dependent and independent variables ranged from r =.196 to r =.658 and correlations among all variables ranged from r =.053 to r =.809, indicating no multicollinearity problems among the variables. The simplest and most obvious means of identifying collinearity is an examination of the correlation matrix for the independent variables and the presence of high correlations (generally.90 and above) is the first indication of substantial Collinearity (Hair et al., 1998). 5.3 Reliability and validity of the instruments A reliability test was performed to determine the internal consistency of the instruments. The reliability coefficient in the form of Cronbach s alpha for dependent and independent variables are presented in Table 2. Table 2 represent means, standard deviations and reliability coefficients for all independent and dependent variables. The reliability scores of the independent and dependent variables range from.70 to.96. The scales show good reliability with Cronbach s alphas greater than.70. The higher reliability range indicates high internal consistency of the collected data. The diagnostic measure is the reliability coefficient that assesses the consistency of the entire scale, with Cronbach s alpha being the most widely used measure. The generally agreed upon lower limit for Cronbach s alpha is.70, although it may decrease to.60 in exploratory research (Hair et al., 1998). According to DeVellis (2003), reliability values between.70 to.80 are considered respectable while reliability values between.80 to.90 are considered very good. As all of the reliability scores for the independent and dependent variables exceed.70, the instruments are regarded reliable in measuring individual s adoption of innovation across independent variables. 472

11 Variables Table 2. Variable means, standard deviations and scale reliability Mean Standard deviation Cronbach s alpha Organizational factors Training Managerial support Incentive Individual factors Perceived usefulness Personal innovativeness Prior experience Image Enjoyment with innovation Social influence Peers Social network Usage (Dependent variable) Estimation of the regression model and assessment of overall model fit Meeting the assumptions of regression analysis is essential to ensure that the result obtained will be truly representative of the sample and the analysis obtained the best result possible. The data was tested to see if there were any serious violations of the assumptions that could be detected and corrected. Several assumptions are addressed for the variables are linearity, outliers, normality, multicollinearity, homoscedasticity/ heteroscedasticity and independence of residuals. For linearity, scatterplots of the individual variables did not indicate any nonlinear relationships between dependent variables and independent variables. Test for multicollinearity shows that there is no problem of multicollinearity. Regression analysis was conducted and the result of the regression model is shown in Table 3. The result of the regression model explained 53.1 percent of the variance in usage. In Table 3, R square (R²) is the correlation coefficient squared (.729²=.531) also referred to as the coefficient of determination. This value indicates the percentage of total variation of Y (dependent variable) that is explained by the independent variables or predictor variables. In this case, 53.1% of the variance in usage or the individual acceptance can be explained by training, managerial support, incentive, perceived usefulness, personal innovativeness, image, prior experience, enjoyment of innovation, peers and social network variables. Cohen (1988) suggests that an R² of.15 indicates moderate variance and an R² of.35 indicates a large amount of variance explained (as cited in Ahuja and Thatcher, 2005, p.450). The result of F-statistics represents the contribution to prediction accuracy as a result of fitting the model relative to the inaccuracy that still exists in the model. The model is a significantly better predictor of dependent variable than intercept alone: F (10, 264) =29.912, p < From the data it can be concluded that the model is highly significant (p < 0.001) in predicting the outcome variable. Unstandardized coefficient B values indicate the individual contribution of each predictor to the model. If the significant B values are replaced into the equation (Y=b 0 +b 1 X 1 +b 2 X 2 +b 3 X 3. +b 10 X 10 ), then the empirical model can be written as: U = b 0 + b 1 TR+ b 2 MS+ b 3 IN+ b 4 PU+ b 5 PI+ b 6 PE+ b 7 IM+ b 8 EI+ b 9 PR+ b 10 SN Where: 473

12 U=Usage TR=Training MS=Managerial support IN=Incentives PU=Perceived usefulness PI=Personal innovativeness PE=Prior experience IM-Image EI=Enjoyment with innovation PR=Peers SN=Social network b 0 =Intercept (constant) b 1, b 2.b 10 =Coefficients Independent variables Table 3. Summary of results of regression analysis Unstand. coef. B Standard coef. β T R square F Sig (Constant) Training Managerial support Incentive Usefulness Innovativeness Prior experience Image Enjoyment Peers Social network Note: Significant at p < 0.05 level The variables which significantly affect the model are: training, managerial support, perceived usefulness and personal innovativeness. However, if enjoyment of innovation is taken away from the analysis as it is not a significant and highly related with incentives (r =.726, p <.01) then incentive becomes significant and R² remains almost unchanged (R²=.529). The correlation between these two variables creates some problem for incentives to become significant although correlation.70 is not a problem and is not considered as collinearity (Hair et al., 1998). With this analysis one more variable becomes significant making a total of five significant variables in the model. This analysis is also supported by stepwise regression model. The standard multiple regression shows that perceived usefulness is an important predictor (β =.327, t(275) = 4.213, p < 0.001) followed by managerial support (β =.299, t(275) = 5.035, p < 0.001), incentives (β =.156, t(275) = 2.041, p < 0.042), personal innovativeness (β =.130, t(275) = 2.738, p < 0.007) and training (β = -.122, t(275) = , p < 0.022). All are statistically significant. From the hierarchical regression analysis results show that gender (male and female), employment status (full time and part time), classification (academic and administrative staff) and academic disciplines have an impact on adoption. Other variables are not statistically significant and they are not contributing much for the overall prediction. All the assumptions have been met and the model could be generalized to the population of the study. The non-significant variables can be further investigated through in-depth interviews. 474

13 6. Discussion of findings The Theory of Reasoned Action and the Technology Acceptance Model did not include external factors in their model but stated that external factors play a key role in affecting an individual s behavior. In this study we have incorporated three external factors described as organizational factors and extended the previous models to develop an enhanced model to examine their effect on individual s attitude toward adoption and usage. Based on the findings, this study has shown the importance of external factors in explaining the adoption of technological innovation by individual employees in the organization. The regression analysis data demonstrated that training is significant but surprisingly, it was negatively related. However, from the Pearson correlation analysis, the data showed that training has positive and significant relationship with usage (r =.196, p <.01). This means training significantly affects usage and if training increases the level of usages also increases. Training helps individual employees to understand and learn about a new technology and its features. Findings showed that training and educational programs should aim to increase awareness of potential applications and benefits of using technology. Training should be provided for employees to improve their competency of using new technology, especially during the early stage of implementation of new technology in the organization. The study found that management support and incentives are important factors for a smooth process of adoption. When individuals perceive strong managerial support, allocation of sufficient resources and some kind of personal benefits behind the use of a new technology, they are likely to develop a positive attitude toward the technology and its usage. Findings also suggest that managers need to carefully pay attention on exhibiting commitment to provide support and resources for adoption of a technological innovation. An organization may nurture in their employees a positive attitude to use technology by preplanned support activities such as allocation of resources and provision of some incentives or benefits that employees may receive by using the technology such as recognition, increased autonomy and greater job security. Unlike TRA or TAM where they have used only one to two variables under belief construct in their models, this study tested five variables under belief construct defined as individual factors and found significant support for those variables. This study found that perceived usefulness has a strong effect on usage. The study also revealed that there is a strong positive relationship between usefulness and usage because individuals adopt a technology when they see that it will give them much value and will be worthy considering the time they will spend on it. In order to increase the adoption of a new technology, an organization should emphasize the benefits, functionality and advantages of using the technology. The result indicated that personal innovativeness affects individual s adoption behavior. While individual employees who are innovative will be more inclined to use the technology and organizations can utilize them as important agents of change. This is because they are likely to show a favorable attitude toward the new technology and can influence others to create a positive attitude toward the acceptance of new technology. Prior experience was not supported in the regression analysis but has positive and significant relationship in the Pearson correlation analysis (r =.279, p <.01). Individual employees who have experience with a similar technology show more confidence and less fear with new technology, and it is relatively easy for them to adopt a new technology when they are familiar with it. Findings from Pearson correlation showed that image and enjoyment of innovation have positive and significant relationship with usage (r =.528, p <.01 and r =.546, p <.01 respectively) although regression analysis did not show their significance. Both factors are important in explaining usage behavior. Being good at a certain technology helps individual employees to see themselves as capable and 475

14 knowledgeable among co-workers and there is a certain amount of status that goes with being an expert in the area. It creates a positive image for them and gives them a special sense of pride. Organizations which intend to introduce a new technology should recognize individual s expertise and knowledge in the area. This will motivate others and facilitate greater adoption among employees. Organizations which aim for a greater use of technology should emphasize not only the advantages of adopting technology, but also the features which make individual employees enjoy using technology. Organization should strive to include fun elements in the system or if it is already there, they need to inform employees because many employees prefer enjoyable and entertaining system to use. From the regression analysis it was found that both social factors - peers and social network are non-significant. However, Pearson correlation showed that peers and social network have positive and significant relationship with usage (r =.215, p <.01 and r =.367, p <.01 respectively). Individuals will more likely create a positive attitude toward a new technology when they receive help from peers. Peers usage of a technology triggers adoption and learning about the new technology via interpersonal contacts and observing others usage of the technology. Organizations should create a supportive social environment among peers to encourage productive use of technology. Organizations can emphasize to its employees to provide greater support to peers and particularly to the individuals who are newly employed. Similarly, individual employees are influenced by social network to adopt or at least to investigate a new technology, since hearing from friends makes individual employees not want to be left behind. Instead, they want to be knowledgeable about the new technology. With the help of Internet and virtual communities, individuals are able to get much information and consequently be influenced by technology users around the world. Individuals may want to imitate what their social network is using or may want to be in the technological edge, which provokes them to adopt and use the similar technology. Incorporating and testing demographic characteristics in the enhanced model in this study is another extension of pervious Technology Acceptance Models. Ajzen and Fishbein (1980) and Davis (1989) did not include demographics characteristics in their TRA and TAM models but mentioned demographics characteristics as important in explaining usage behavior. The study has discovered that demographic variables influence attitude toward adoption. The study also found that female, professional (administrative) staff and full time employees have greater influence on attitude toward adoption and usage of technological innovation compared to male, academic staff and part time employees. Employees in science and technology disciplines have a greater influence on adoption and usage compared to employees in business or social sciences disciplines. Demographic characteristics such as age and educational qualification did not show any significant impact on attitude toward adoption and usage. Data in this study suggests that organization should not allocate resources on training and other efforts to educate or encourage adoption based on age or educational qualifications. Findings indicated that age and educational qualifications do not make any significance difference in adoption of technological innovation. Employees in all ages and educational backgrounds understand the importance of technology and use technology in their daily activities. Organizations also need to design training and other educational programs, which will motivate employees who are in the disciplines of business and social sciences toward adoption and usage of technological innovation. This research evaluated the relative importance of various factors in the adoption of innovation by individuals in an organization. Surveys and interviews showed that the adoption of the advanced features of an program was influenced most by the perceived usefulness of the innovation and the extent of managerial support. 476

15 7. Conclusion From a theoretical point of view, this research has served to broaden our understanding of the factors affecting new technology adoption from the perspective of individual employees within an organization. This research is a response to the call for a more in-depth and comprehensive research on the ways in which individuals adopts innovation and the factors that influence individual employees adoption of innovation. The main theoretical contribution of this research is the development of the enhanced model of innovation adoption. This enhanced model of innovation adoption is particularly useful for understanding the adoption of innovation by individual employees in comparison with TRA, TAM or Frambach and Schillewaert s (2002) model as it examines a complete and wide range of factors affecting individuals adoption of innovation in an organizational context. The enhanced model is clear, simpler, with three steps of processes and easy to understand. The model developed in this study is more effective than TRA, TAM or Frambach and Schillewaert s model in technology acceptance in explaining individual s adoption of technological innovation. The proposed enhanced model of innovation adoption will move the technology acceptance and adoption model into the next generation model. In fact, this enhanced model can remain for a long time testing technology acceptance and adoption because as the organizational, individual and social or demographics situation changes over time and new factors emerge in future, they can be added and fitted into the boxes identified as organizational, individual, social factors or in the demographics characteristics and test them in any organization anywhere in the world. This study represents a careful and systematic effort to examine the enhanced model of innovation adoption by individual employees within an organization. It incorporates a number of features including a combination of variables found in different IT and management literature, a moderately large sample size, actual measure of behavior and a realistic setting, which is significant to the study. However, the study is not without limitations. The study encompasses a single Australian educational institution. The same research carried out in another setting might generate a different result as organizational, interpersonal and social factors could vary according to cultural context. Further, since the data were collected from a single education institution in Australia, the findings may not be generalizable to other organizations or countries with different environmental settings and factors. However, the outcome of this study has been the production of superior model of innovation adoption then previous models. This is not say it is perfect but for the time being this is the best model on offer. The study derived a set of ten variables, not all would be equally important in specific organizational settings. In addition, the study may have ignored some moderating effects related to the considered variables. Future research may incorporate more moderating effects. Other variables such as organizational size, age, and structure, that may affect attitudes and usage, should also be included in future studies. It might also be useful to examine the relationship between the adoption of technological innovation and organizational performance. Further qualitative studies could also be conducted to see the impact of social factors such as peers and social network. Future work could use longitudinal research to explore similar issues. Longitudinal studies should be undertaken to fully investigate the casual effect of various factors and their relationships over time. Further research studies may address the difference of technological innovation between developed and developing nations. A cross cultural study on the adoption of innovation will help us understand the attitudinal and perceptual differences between countries. It is possible to extend the research into other industry settings such as manufacturing or production industries. It is also possible to conduct similar 477