Unified theory of acceptance and use of

Unified Theory of Acceptance and Use of Technology (UTAUT) A Decade of Validation and Development Mohammad I Ahmad M.S., MAIS Alexandria Univ., Egypt, PGD IT, Amity Univ India mohammad.ibraheem.ahmad@gmail.com  determine factors that contribute to enhancement of integration of information systems with operations within organizations These research efforts resulted in a great number of factors Among these factors system use gains researchers interest [2] As shown in [3], current research efforts regarding System use results in two main research streams; User Satisfaction and Technology Acceptance At the beginning, researchers’ attention was drawn to User Satisfaction which is defined as "the sum of one’s feelings or attitudes toward a variety of factors affecting the situation" [2] pp 192 On the other hand, based on general assumption that the relationship between the use of information technology and the performance is positive, it was pointed out in [4] that researchers examined many factors ( related to individuals, organization , and technology ) to specifying main factors that affect system use behavior These efforts result in Technology Acceptance as surrogate of system use Researchers in IS field proposed models, which are derived from Social psychology field, concern with Behavioral Intention as a possible theoretical foundation for system use determinants [5] Technology Acceptance Models tried to identify and investigate Behavioral Intensions of users As a matter of fact, there is a variety of Technology Acceptance models and theories Although they are similar in concepts and variables, they differ in methodologies and interpretation of the phenomenon (Technology Acceptance) Unified Theory of Acceptance and Use of Technology (UTAUT) was introduced by [1] as an accumulation of various research efforts represented in different models and theories of Technology Acceptance The UTAUT is considered as a trial to unify terminology of variables of different models and theories of Technology Acceptance Development of this theory has many facets These are addressed in this paper This paper is organized as follows Section II represents theoretical foundation of UTAUT Section III reviews IT literature adoption for UTAUT Validation Section IV represents the first facet of UTAUT development, New Constructs Section V represents the second facet of UTAUT development, New Moderators Section VI represents the final facet of UTAUT development, Model Extensions and Integration with other Models Finally, Section VII shows Conclusions Abstract— This Paper aims to review IT adoption literature concerning validation and development of Unified Theory of Acceptance and Use of Technology (UTAUT) In [1] UTAUT was introduced as an accumulation of various research efforts represented in different models and theories of Technology Acceptance The UTAUT is considered as a trial to unify terminology of variables of different models and theories of Technology Acceptance The theory was established on four theoretical constructs representing determinants of Use Behavior or the Intention to Use, which play essential roles as surrogates of Technology Acceptance These constructs are: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions In addition to these variables the theory considers also Moderating Factors which moderate the relations between various constructs and Intention to Use The Moderators are Gender, Age, Experience, and Voluntariness of use It has been a decade since UTAUT was first introduced by [1] Since then numerous studies either empirically validate or theoretically contribute to the theory Some of them empirically validate the theory (either in western countries or across different cultures) or even replicate results of [1] Theory validation and adaptation to be applied in different settings are briefly discussed A literature review revealed three major trends of theoretical contributions to the theory: suggestion of New Constructs, suggestion of new Moderating Factors, and Finally Theory Extensions and Integration with other theories and models These research trends are addressed in this Paper Index Terms—UTAUT, Technology Acceptance, IT adoption, Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions I INTRODUCTION W hile organizations sought to meet the growing needs of information, especially in the modern business environment which is characterized by risk and uncertainty, they adopt information technology to enable them to meet their information needs It has been well established, in IT adoption literature, that IT won't be effective unless it is used Since the seventies of twentieth century, researchers search for and In proceedings of Fourth International Conference on ICT in our lives 2014 ―Information Systems Supporting Decision Making” (ISSN 2314-8942), Information Systems and Computer Science Department, Faculty of Commerce, Alexandria University, Alexandria, Egypt, December 20-22, 2014 II THEORETICAL FOUNDATION OF UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) Driven by a motivation to unify these research efforts in Technology Acceptance literature, UTAUT was introduced and developed by [1] a decade ago, based on eight Technology Acceptance competing models These models and theories are the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned Behavior (TPB), a model combining the Technology Acceptance Model and the Theory of Planned Behavior (C-TAM-TPB), the model of PC utilization, the Innovation Diffusion Theory (IDT), and the Social Cognitive Theory (SCT) The theory was established on four theoretical constructs representing determinants of Intention to Use or Usage Behavior, which play essential roles as surrogates of Technology Acceptance These constructs are: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions In addition to these variables the theory considers also moderating factors which moderate the relations between various variables and Intention to Use The Moderators are Gender, Age, Experience, and Voluntariness of use Fig illustrates UTAUT showing Theoretical Constructs, Moderators and interrelations Extrinsic Motivation, which is derived from Motivational Model (MM) introduced by [6], is defined as ―The perception that users will want to perform an activity because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself‖ [1] pp 428 Job-Fit, which is derived from Model of PC Utilization (MPCU), is defined as ―The extent to which an individual believes that using [a technology] can enhance the performance of his or her job‖ [7] pp 128 Relative advantage, which is derived from Innovation Diffusion Theory (IDT), is defined as ―The degree to which an innovation is perceived as being better than its precursor‖ [8] pp 195 Outcome Expectations, which is derived from Social Cognitive Theory (SCT), is defined as ―perceived likely consequences of using computers‖ [9] pp 147 As declared in [1] Performance Expectancy remains significant and is the strongest construct as a predictor of Intention among Technology Acceptance Models and Theories It was expected (from a theoretical Point of view) that Although Gender, and Age moderate the relationship between Performance Expectancy and Intention to Use, Studies have shown recently that taking into account the Gender factor alone results in misleading results unless the Age factor is taken into account too [1] Therefore it is expected in [1] that both Gender and Age moderates the impact of expected performance Effort Expectancy (EE) Effort Expectancy is ―The degree of ease associated with the use of the system‖ [1] pp 450 There are three key variables derived from Technology Acceptance Models match Effort Expectancy according to [1]: Perceived Ease of Use, which is derived from Technology Acceptance Model (TAM \ TAM 2), is defined as ―The degree to which the prospective user expects the target system to be free of effort‖ [5] pp 985 Complexity, which is derived from Innovation Diffusion Theory (IDT) and Model of PC Utilization (MPCU), is defined as ―The degree to which an innovation is perceived as relatively difficult to understand and use‖ [8] pp 195, [7] pp 128 Ease of use, which is derived from Innovation Diffusion Theory (IDT), is defined as ―the degree to which an innovation is perceived as being difficult to use‖ [8] pp 195 Fig Unified Theory of Acceptance and Use of Technology, Source [1] pp 447 A Theoretical constructs of UTAUT and their moderators: Performance Expectancy (PE) Performance expectancy is ―the degree to which an individual believes that using the system will help him or her to attain gains in job performance‖ [1] pp 447 There are five key variables derived from Technology Acceptance Models match Performance Expectancy according to [1]: Perceived Usefulness, which is derived from Technology Acceptance Model ( TAM \ TAM ), Combined TAM and TPB (C–TAM–TPB), is defined as ―The prospective user's subjective probability that using a specific application system will increase his or her job performance within an organizational context‖ [5] pp 985 Again, similarities between some of these variables are pointed out in [1] As explained in the performance Expectancy, Gender and Age are expected to moderate the relationship between Effort Expectancy and Intention to Use Furthermore, Experience is expected to moderate this relationship also Accordingly, it is expected in [1] that the effect of Effort Expectancy on Intention will be stronger for Reference [6] as cited in [1] pp 428 women, particularly younger women, at early stages of experience dealing with the system Acceptance Models match Facilitating Conditions according to [1]: Perceived Behavioral Control, which is derived from Theory of Planned Behavior (TPB), Decomposed Theory of Planned Behavior (DTPB), Combined TAM and TPB (C–TAM–TPB), is defined as ―individual perception of the presence or absence of requisite resources and opportunities‖ [11] pp 175 According to [12] it reflects perceptions of internal and external constraints on behavior, and Includes Self-efficacy, resource facilitating conditions, and technology facilitating conditions Facilitating Conditions, which is derived from Model of PC Utilization (MPCU), is defined as ―Objective factors in the environment that observers agree make an act easy to accomplish.‖ [7] pp 129 Compatibility, which is derived from Innovation Diffusion Theory (IDT), is defined as ―The degree to which an innovation is perceived as being consistent with the existing values, needs, and past experiences of potential adopters‖ [8] pp 195 Social Influence (SI) Social Influence is ―The degree to which an individual perceives that important others believe he or she should use the new system‖ [1] pp 451 There are three key variables derived from Technology Acceptance Models match Social Influence according to [1]: Subjective Norm, which is derived from Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM \ TAM 2), Theory of Planned Behavior (TPB), Decomposed Theory of Planned Behavior (DTPB), Combined TAM and TPB (C–TAM–TPB), is defined as ―The person's perception that most people who are important to him think he should or should not perform the behavior in question‖ [5] pp 984 Social Factors, which is derived from Model of PC Utilization (MPCU), is defined as ―The individual’s internalization of the reference group’s subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations‖ [7] pp 126 Image, which is derived from Innovation Diffusion Theory (IDT), is defined as ―The degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system‖ [8] pp 195 As pointed out in [1] if the model does not include Effort Expectancy as a predictor of Intention, Facilitating Conditions will have high predictive power of Intention to use However, in the presence of both of Performance Expectancy and Effort Expectancy constructs, it is expected for Facilitating Conditions to be nonsignificant in predicting Intention to Use It is expected in [1] that Age and experience moderates the relationship between Facilitating Conditions and Intention to use As Experience increase, this effect will be stronger especially in the older ages Many studies (e.g [5], [10]) pointed out the complex role of Social Influence in Technology Acceptance They pointed out that it is subject to a wide range of conditional influences It has an effect on individual behavior through three mechanisms; Compliance, Internalization, and Identification While the last two mechanisms relate to changing and modifying individual's beliefs structure and/or lead to the individual's response to potential gains from the situation or social status, the Compliance mechanism leads to changing individual's intention as a response to social pressures i.e An individual comply with the impact of social influence (for those referent others who have the ability to motivate and reward the desired behavior of these individuals, and penalize unwanted behavior) only in the presence of moderating effect of voluntarily use It is expected in [1] that women tend to be more sensitive to the opinions of others and thus the effect of social Influence will be stronger for women while forming intention to use new technology As Experience increase, this effect will decline specially in the older ages As such, it is expected that there will be a complex and interactive relationships between these moderating factors leading to final moderating impact on the relationship between Social influence and Intention to use B Constructs that are not considered in UTAUT as direct determinants of Intention As pointed out in [1], literature does not consider Selfefficacy and Anxiety, although they appear to be direct determinant of Intention to Use according to Social Cognitive Theory (SCT) UTAUT does not consider them as direct determinant of Intention to Use This is because according to UTAUT, they haven't significant effect on Intention to Use Similarly, UTAUT did not take into account Attitude toward using technology, and thus is omitted, in contrast to most Technology Acceptance models and theories While Selfefficacy, and Anxiety are simple variables, Attitude toward Using technology is a complex (multi-dimension) construct which involves a composite number of variables [5], [13] Attitude Toward Using Technology Attitude toward using technology is ―individual’s overall affective reaction to using a system‖ [1] pp 455 There are four key variables derived from Technology Acceptance Models match Attitude toward using technology according to [1]: Attitude Toward Behavior, which is derived from Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Decomposed Theory of Planned Behavior Facilitating Conditions (FC) Facilitating Conditions is ―The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system‖ [1] pp 453 There are three key variables derived from Technology (DTPB), and Combined TAM and TPB (C–TAM–TPB), is defined as ―An individual's positive or negative feelings (evaluative affect) about performing the target behavior‖ [5] pp 984 Intrinsic Motivation, which is derived from Motivational Model (MM) introduced by [6], is defined as ―The perception that users will want to perform an activity for no apparent reinforcement other than the process of performing the activity per se‖ [1] pp 428 Affect toward Use, which is derived from Model of PC Utilization (MPCU), is defined as ―Feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act‖ [7] pp 127 Affect, which is derived from Social Cognitive Theory (SCT), is defined as ―An individual’s liking for a particular behavior (e.g., computer use)‖ [9] pp 148 the model (Performance Expectancy, and Effort Expectancy) Findings of [1] proved that in the presence of both of them, there is no significant direct effect of Attitude Toward Using Technology on technology use behavior C Dependent Variable: Behavioral Intention It was suggested by [1] that there is a significant positive effect of Behavioral Intention on the use of technology Table (1) summarizes results of experimental tests of [1] It has been a decade since UTAUT was first introduced by [1] Since then numerous studies either empirically validate or theoretically contribute to the theory Some of them empirically validate the theory Others theoretically contribute to UTAUT by either proposing new constructs, or moderating factors, or even extending and integrating the theory with other models These research efforts will be discussed in the next sections It was suggested by [1] that Attitude Toward Using Technology will not have a significant direct effect on intention to use unless two main constructs are excluded from III TABLE I UTAUT EXPERIMENTAL RESULTS SUMMARY A Dependent Variables Independent Variables Moderators Explanation Behavioral Intention Performance Expectancy Gender, Age Effect stronger for men and younger workers Behavioral Intention Effort Expectancy Gender, Age, Experience Effect stronger for women, older workers, and those with limited Experience Behavioral Intention Social Influence Gender, Age, Voluntariness, Experience Effect stronger for women, older workers, under conditions of mandatory use, and with limited Experience Behavioral Intention Facilitating conditions None Nonsignificant due to the effect being captured by Effort Expectancy Usage Facilitating Conditions Age, Experience Effect stronger for older workers with increasing Experience Behavioral Intention Computer Self-efficacy None Nonsignificant due to the effect being captured by Effort Expectancy Behavioral Intention Computer Anxiety None Nonsignificant due to the effect being captured by Effort Expectancy Behavioral Intention Attitude toward using tech None Nonsignificant to the effect being captured by process Expectancy and Effort Expectancy Usage Behavioral Intention None Direct effect a MODEL VALIDATION Vast majority of studies in Technology Acceptance literature empirically validate the theory (either in western countries or across different cultures) or even replicate results of [1] In this section, these issues will be addressed A Validation of Full version of UTAUT Although UTAUT didn’t consider Attitude toward using technology, besides Anxiety, and Self-efficacy constructs, as direct determinant of Behavioral Intension and system Use due to their insignificance, number of studies validates full version of UTAUT (including Attitude, Anxiety, Self-efficacy) This is because Attitudes is considered a main construct in many models and theories of Technology Acceptance Furthermore, it is also considered in these theories and models as a multidimensional construct [5], [6], [11], [12] In the context of educational technology acceptance, the full version of UTAUT was validated in [14] A total of 262 respondents from a business administration undergraduate level course at a Midwestern university were surveyed for their acceptance of Blackboard; an educational Web-based software system Results of [14] demonstrate satisfactory reliable and valid scales of the model constructs, and suggest further analysis to confirm the model as a valuable tool to evaluate the user acceptance of an information technology A modification of UTAUT was proposed by [15] to evaluate students’ acceptance of mobile computing devices Tablet PC Results showed that variables of Performance Expectancy, Effort Expectancy, Attitude toward Using Technology, Self efficacy are key components of Behavioral Intent Social Influence and Anxiety not appear to have much contribution to behavioral intent Gender, Age, Experience, and Voluntary use are considered as Moderating Conditions All Moderators, except for Experience, were found to not have any effect Experience with computers was Source [1] pp 468 Reference [6] as cited in [1] pp 428 significant relationship – the relationship between Performance Expectancy, Effort Expectancy and Behavioral Intention It is also significant between Attitude toward Using Technology and Facilitating Condition and Self-efficacy An insignificant relationship between Age and SI, FC and SE, and Industry and Attitude toward Using Technology is revealed The data also shows a significant relationship between Self-efficacy and Facilitating Condition Conclusions drawn from [19] results show that all of UTAUT relations are supported except for the moderating effect of Age and Industry shown to have a significant impact on the acceptance of technology indicated by the significance found between freshman and upper classmen in the study The modified full version of UTAUT was proposed in [16] to investigate IT based management accounting information systems use in Egypt Modified UTAUT takes into account Attitude toward using technology (as a multidimensional construct that considers both positive and negative attitudes), besides Anxiety, and Self-efficacy, as a direct determinant of Behavioral Intention and System Use On the other hand, modified UTAUT considers original moderating factors including Cultural Values Results reveal significance of all Constructs (including Attitudes, Anxiety, and Self-efficacy), while moderators are not To examine Acceptance and Usage of open access within public universities using the full version of UTAUT based open access adoption model, a survey of 544 university researchers at six public universities in Tanzania was conducted by [17] Age, Experience, Gender, Awareness and Position (Rank) of the researchers have been considered as moderators (Voluntariness was dropped as a moderator in this respect) Among the findings of [17], Attitude, Awareness, Effort Expectancy and Performance Expectancy were established as the key determinants for the researchers’ Behavioral Intentions of open access usage Similarly, Age, Awareness, Behavioral Intention, Facilitating Conditions and Social Influence were found to significantly affect researchers’ actual Usage of open access Unlike most Technology acceptance literature that utilize UTAUT, a replication of full UTAUT, with no changes or elimination of constructs, was conducted by [18] in an academic environment for educational technologies introduced to the students in higher-Education in Qatar A mixed support for UTAUT was found PE, EE, FC and Attitude towards using technology were significant determinants of technology acceptance All moderating variables had a significant moderating influence except for experience, which was surprisingly not significant The results of [18] show that UTAUT is found to be applicable to some extent in the educational setting, but might need a few modifications to fit the context In the context of e-marketing of Future Internet Ultra-LargeScale (ULS) solution development, the market acceptance of CHOReOS Project3 assessment is done in [19] using the full version of UTAUT The effects of PE, EE, SI, Self-efficacy, FC, CHOReOS products Anxiety, and Attitude towards CHOReOS products on Behavioral Intention and the Use of CHOReOS products are examined Furthermore, moderators of Gender, Age, Country, Industry, and Occupation are considered Results revealed some concepts have a more B Cross-Culture Validation Diverse cultures result in different results of Technology Acceptance [20], [21], [22], particularly in the Middle East and the Arab region [23] Validating UTAUT across diverse cultures is considered in this section Validation of UTAUT outside its original country and language of origin, over nine culturally-diverse countries was examined by [20] Results provide cross-cultural validity of Technology Acceptance with respect to UTAUT, which may be useful in providing insight into cross-cultural technology acceptance differences Use and acceptance of Web 2.0 applications was compared by [21] between American and Korean college students using UTAUT through the lens of cultural differences based on Hofstede’s Cultural dimensions While modified UTAUT considers Attitude and Anxiety, FC is dropped The study targeted six Web 2.0 applications (blogs, instant messenger, online social communities / Facebook, online video sharing / YouTube, online video and audio conference/Skype, and social virtual communities/Second Life) Results of [21] show that American and South Korean students tend to differ in their technology Acceptance levels and the Usage of Web 2.0 applications for learning Significant differences on utilization level and the Anxiety level for using them were found in numerous Web 2.0 applications Korean students responded that most Web 2.0 applications are apprehensive for them to use when compared to their counterparts in the U.S Korean students reported positive Attitudes towards Using blogs and participating in online social communities but they had high Anxiety levels in using online conferencing tools (eg., Skype) and social virtual environments (eg., Second Life) American students perceived a high difficulty level in using several Web 2.0 applications, such as social virtual environment tools, while they felt at ease in participating in online social communities (eg., Facebook) American students felt optimistic in using instant messenger and online video sharing for learning but their survey responses showed lower levels of Anxiety towards online conferencing and social virtual environments Conclusions drawn from [21] show that although Web 2.0 applications themselves are neutral but users are always affected by their cultural contexts CHOReOS is the European 7th Framework FP7-ICT-2009-5 project No 257178 (2010-2013) ―Large Scale Choreographies for the Future Internet (IP)‖ which is aimed to elaborate on new methods and tools related to Future Internet Ultra-Large-Scale (ULS) solution development based on the use of choreographies [19] Based on a survey of 1723 Turkish educational technology Factors determining the acceptance and use of infomediary4, and effects of infomediary on information search were identified in [25], based on UTAUT Results of a survey of 650 Chinese university students show that Performance Expectancy and Social Influence have positive influences on user Intention to Use infomediary; User Intention has a positive influence on Use of infomediary; and Facilitating Condition has a positive influence on Use of infomediary The study evidences that UTAUT model is applicable to measure factors that influence the success of Infomediaries users of diverse profession, geographical location, age and gender, the applicability of UTAUT was extended to Turkish culture by [22] in the context of Educational Technology Acceptance (ETA) Modified UTAUT considers Anxiety and Computer Literacy along with other UTAUT constructs Using Hofstede’s Cultural dimensions, cross-cultural differences are explored within Turkey both between regions (Istanbul area vs other regions) and between professional cultures (STEM, i.e science, mathematics, engineering and mathematics, vs non-STEM professions) Conclusions of [22] are drawn with respect to UTAUT applicability in educational practice, and to interconnections between ETA and culture Results confirm the wide applicability of the ETA concept, as conceptualized in UTAUT Using UTAUT, ICT usage by academic staff in Nigerian public universities was examined [26] Results show that Performance Expectancy, Effort Expectancy and Facilitating Condition have a significant positive influence and impact on the Behavioral Intention to accept and use ICT, by the university academic staff However the findings show that Effort Expectancy is the most influential UTAUT construct Another attempt to examine applicability of UTAUT in the context of ETA concept was done by [24] The study further attempts to integrate three of Hofstede’s culture dimensions (Individualism, Masculinity, and Uncertainty avoidance) into extended UTAUT, enlarging the picture of the relationship of ETA and culture Not only does the model consider Hofested's Cultural Values, but it considers Geographic Location (Germany - Romania) and profession (STEM - Non-STEM) as well Based on a survey of 2866 learning technology users from Germany and Romania, the model was validated by [24] Results reveal the presence of cultural differences both between countries and between professions Compared with Germans, Romanians display higher Performance Expectancy, lower Effort Expectancy, higher Social Influence, lower Perceived Facilitating Conditions, higher Computer Anxiety, higher Use Intention and lower technology Use Results also showed very weak influence of Technology Use Intention on the actual Use Behavior On the other hand, compared with STEM professionals, non-STEM professionals display lower Performance Expectancy, higher Effort Expectancy, lower Social Influence, lower Perceived Facilitating Conditions, higher Computer Anxiety, lower Use Intention and lower Technology Use Non-STEM professionals perceive their computer literacy higher than STEM professionals As for the moderating influence of Profession on the UTAUT model, there are notable differences in the influences of effort expectancy on Use Intention, and of Facilitating Conditions and Computer Anxiety on the Use Behavior Also, validity of the UTAUT model was examined by [27] in Nigeria, and confirmed its validity in developing countries in the context of educational technologies Findings of [27] show that the four constructs of UTAUT have significant positive influence and impact on the Behavioral Intention to accept and Use ICT by university academic staff Furthermore, EE and SI are found to be the most influential predictors of acceptance of ICT and Use among the four constructs of UTAUT The greatest barriers are time and technical support for staff Amended version of UTAUT proposed by [28] was used to determine factors influencing e-government services adoption and acceptance in Pakistan Results show that the factors influencing the adoption of e-government services in Pakistan are related to Ease of Use, Usefulness, Social Influence, Technological Issues, lack of Awareness, Data Privacy, and Trust Using UTAUT, adoption of established and emerging information technology in higher education classrooms was examined in [29] Findings suggest that in the context of instructors’ Use of technology for classroom purposes, the most important antecedents are Performance Expectancy, Effort Expectancy, Social Influence, and Habit with more complex effects when Gender is added as moderating factor Findings specifically indicated that the relationship between Performance Expectancy and Effort Expectancy on Intention to Use classroom IT was stronger for males, while the relationship between Social Influence and Usage was stronger for females In a university setting, female professors may be more likely to succumb to pressure to use Blackboard or other social networking technology than men, particularly if promoted by their peers and supervisors C Replication of UTAUT In this section, replication of the original UTAUT in different settings is discussed A modified version of UTAUT was proposed and examined by [23] in determining Intention to Use and Usage Behavior of desktop computer applications on a voluntary basis in Saudi Arabia Findings show overall consistency with literature except for moderating effects of Gender, rather than influence of EE and FC Infomediaries are intermediaries that aggregate information from multiple electronic commerce retailers and provide services of searching and comparison for Internet customers [25] D Systematic Review of UTAUT citations (Meta-Analysis) The originating article of the Unified Theory of Acceptance and Use of Technology (UTAUT) has been cited by a large number of studies However, a detailed examination of such citations by [30], [31] revealed that only small proportion (43articles) of these citations actually utilized the theory or its constructs in their empirical research for examining IS/IT related issues [30], [31] learning is defined as the extent to which an individual feels he or she is self-disciplined and can engage in autonomous learning [34].5 Furthermore, the model was validated by [33] in m-learning context in Taiwan Age and Gender differences of 330 respondents were examined in [33] Results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Perceived Playfulness, and Self-management of learning were all significant determinants of Behavioral Intention to Use mlearning Also results of [33] indicate that Age differences moderate the effects of Effort Expectancy and Social Influence on m-learning Use Intention, and that Gender differences moderate the effects of Social Influence and Self-management of learning on m-learning Use Intention Findings of meta-analysis conducted by [30] reveal the underperformance of theory in subsequent studies in comparison to the performance of UTAUT reported in the originating article (e.g number of empirical studies that utilized UTAUT were based on relatively very small sample size, rather than ignoring the effect of moderating variables which might be distorting the actual performance of the theory.) A theoretical modification of UTAUT was proposed by [35] As antecedent factors, Management Effectiveness (refers to characteristics that deal with organizational issues and management actions on the staff within organizations [36]) and Program Effectiveness (refers to the characteristics that deal with the services or programs provided by the organizations [36]) constructs6, towards user acceptance of Telecentre were proposed Findings of [30] ensure findings reported by [31] which raised number of arguments: (1) The majority of articles that cited UTAUT have done so as a basis for supporting an argument, or for criticizing the theory, rather than actually using the theory; (2) Many studies reported as using UTAUT actually made only partial use of it, often utilizing only a small number of constructs; (3) A number of citing articles made use of UTAUT with all constructs but without considering the use of moderating factors; and (4) There appears to be an increasing trend of using external variables and external theories together with UTAUT Based on the Service Oriented Unified Theory of Acceptance and Use of Technology (SOUTAUT), which explains 57% of variance towards acceptance and use of elibrary services, [37] explored adoption of Information Communication Technology (ICT) services in libraries in Uganda The focus of [37] was on four independent constructs of Performance Expectance, Relevance, Social Influence and Facilitating Conditions; the dependent variable of Behavior Intentions, Usage Behavior and Expected Benefits and moderator variables of Gender, Age, Experience and Awareness as the construct affect e-library services acceptance in university communities Results of [37] show that Relevance and Social Influence have significant effects on Intentions to Use e-library services in Uganda Results indicate that, in the context of e-library services, Social Influence construct was found to be one of the driving forces of Behavior Intentions to Use In [32], based on 37 selected empirical studies, a metaanalysis was conducted in order to harmonize the empirical evidence on UTAUT Findings confirm initial findings of [1] Amongst the five constructs of UTAUT, only the relationship between PE and BI is strong while others are slightly weak but significant Also the relationship between BI and UB is also reliable while the relationship between FC and UB is found to be fairly less than desired Although these efforts regarding UTAUT contribute to literature, there still number of contributions regarding development of original model even after a decade since UTAUT was first introduced IV Another theoretical modification of UTAUT was proposed by [38] specifically with regards to Nigerian Factors towards user adoption of e-commerce in Nigeria Nigerian Factors proposed by [38] are: Public Education and Awareness, Culture/Language, Cost, Power Supply, Government Regulations and Legal Issues, Accessibility, Trust/Security, and Reliability NEW CONSTRUCTS This section reviews research efforts contributing new constructs to UTAUT UTAUT was extended in [33] by adding Perceived Playfulness and Self-management of learning for the mobile learning (m-learning) context Perceived Playfulness is defined as a state of mind that includes three dimensions: the extent to which the individual (1) perceives that his or her attention is focused on the interaction with the m-learning (ie, concentration); (2) is curious during the interaction (ie, curiosity); and (3) finds the interaction intrinsically enjoyable or interesting (ie, Enjoyment) [33] Self-management of To investigate the factors and determinants of internet banking adoption in Malaysia, a new construct, Trust, was proposed by [39] besides other UTAUT’s constructs Results showed that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition and Trust were Reference [34] as cited in [33] Reference [36] as cited in [35] positively correlated (and significant) with Behavioral Intention among respondents Nevertheless, Demographic Factors were not influencing the Behavioral Intention towards internet banking adoption moderated the effects of Performance Expectancy and Perceived Financial Cost on Behavioral Intention, and the Age considerably moderated the effects of Facilitating Conditions and Perceived Self-efficacy on actual adoption Behavior UTAUT was introduced by [40] updating original UTAUT with three variables; Hedonic Motivation, Price Value, and Habit Individual Differences—namely, Age, Gender, and Experience—are hypothesized to moderate the effects of these constructs on Behavioral Intention and Technology Use Examination of UTAUT in Hong Kong provided substantial improvement in explained variance Furthermore, results of [40] confirmed the important roles of Hedonic Motivation, Price Value, and Habit in influencing technology use in UTAUT 2, which is tailored to the context of Mobile internet’s consumer acceptance and use of technology Results revealed that the impact of Hedonic Motivation (e.g., Enjoyment) on Behavioral Intention is moderated by Age, Gender, and Experience Also, the effect of Price Value on Behavioral Intention is moderated by Age and Gender Finally, Habit has both direct and mediated effects on Technology Use, and these effects are moderated by Individual Differences Drawing on both perspectives of UTAUT and Privacy Risk, user adoption of an emerging mobile service, location-based services (LBS), was examined by [43] Results indicated that Usage Intention is affected by both enablers such as Performance Expectancy and inhibitors such as Perceived Risk Besides four constructs of UTAUT and Perceived Risk, there exist other enablers such as Satisfaction and inhibitors such as Switching Cost Although, UTAUT's original constructs are used in [44] as a research framework to examine the factors affecting the Intention to Use educational (classroom) technology by 517 MBA students in Indian universities, The study contributed to the body of literature of Technology Acceptance by decomposing both SI and FC While SI is decomposed into Subjective Norms (The person’s perception that most people who are important to him think he should or should not perform the behavior in question [44]) and Image (The degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system [44]), FC is decomposed into Facilitating Conditions-Direct (FCD) and Facilitating Conditions–Support (FCS) Results reveal that Behavioral Intention was primarily predicted to a larger extent by Ease of Use and Subjective Norm while Perceived Usefulness, Image, Facilitating Conditions Support and Facilitating Conditions Direct had a lesser effect on it Based on UTAUT, [41] proposed model of telecentres7 acceptance which considers Management Effectiveness and Program Effectiveness as theoretical constructs along with original UTAUT’s constructs Furthermore, this modified UTAUT based telecentre model also considers Gender and Age and Ethnicity as moderators The results of a survey of 203 respondents in six telecentres in Nigeria showed four core determinants of Performance Expectancy, Social Influence, Management Effectiveness and Program Effectiveness significantly influence intention towards telecentre acceptance Two core determinants, Behavioral Intention and Facilitating Conditions, were found to significantly determine User Acceptance Gender, Age and Ethnicity were found to moderate the relationships between the latent variables Upon investigation of factors influence E-Health Acceptance in Thai government hospitals, IT Knowledge was proposed by [45] as a new UTAUT construct Results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and IT Knowledge are significant Although Age and Gender are not significant, Experience and Voluntariness of Use are Significant The original UTAUT lacks of considering trust-based and economy-based constructs, which may results in a limitation of UTAUT [42] Thus an extended UTAUT model was proposed by [42] to explore what affects consumers to adopt mobile banking, upon diffusion of 3G smart cell phones in Taiwan Model proposed by [42] considers Perceived Credibility, Perceived Financial Cost, Perceived Self-efficacy constructs along with UTAUT's original constructs Results of [42] indicate that Individual Intention to adopt mobile banking was significantly influenced by Social Influence, Perceived Financial Cost, Performance Expectancy, and Perceived Credibility, in their order of influencing strength The Behavior was considerably affected by Individual Intention and Facilitating Conditions As for moderating effects of Gender and Age, [42] discovered that Gender significantly Appendix I shows summary of new Constructs proposed in UTAUT literature Contributions to UTAUT regarding new moderators will be addressed in the next section V MODERATING FACTORS This section reviews research efforts contributing new moderators to UTAUT Based on UTAUT, 3G mobile telecommunication services acceptance and use was examined in Taiwan by [46] taking into consideration Education as a new moderator along with original UTAUT’s moderators The theoretical modification of UTAUT proposed by [35] accounts for new moderators: Ethnicity, and Location While examining acceptance and use of e-library services in Uganda using SOUTAUT, the moderating effect of Awareness Telecentres are physical space that provides public access to information and communication technology to some members of underserved communities [41] (The degree an individual knows the existence of something, and its availability for his/her use [37])8 was taken into consideration by [37] Results show that the effects of Social influence and Relevance on Behavior Intention to Use electronic library services are moderated by Gender, Age, Experience and Awareness such that it is more salient to younger women for Social Influence and experienced males for Relevance construct Also, the effect of Facilitating Conditions on usage is moderated by Age, Experience and Awareness Appendix II shows summary of new moderating factors proposed in UTAUT literature Contributions to UTAUT regarding extension and integration with other models and theories will be addressed in the next section VI In this section, research efforts contributing to UTAUT regarding extension and integration with other models and theories will be discussed A theoretically adapted version of the UTAUT was presented in [47] to assess the users’ acceptance and technology adoption of specific e-Trial Systems modules of the electronic Primary Care Research Network (ePCRN) within the framework of Clinical Trial Management Systems (CTMS) In [47], new moderating factors for the model were introduced in the context of validating an eligibility criteria tool for primary care and community-based clinical research These moderating factors include the following dimensions [47]: - Individual Factors dimension condensed the four original UTAUT moderators (age, gender, experience level, and voluntariness to use the system) under one dimension It also added (speciality) to see if it would make any difference - Anxiety dimension (Individual’s psychological and/or habitual readiness to adapt change) - Adaption Timeline dimension (allowing enough time for an individual to completely absorb change) A Model Extension In an attempt to link UTAUT constructs to related Technology Acceptance Model constructs, [48] investigates the acceptance and usage of ICT by the University of Port Harcourt academic staff, using UTAUT and other TAM constructs Results of [48] show that the most influential UTAUT construct is Effort Expectancy (EE), which is related to Perceived Ease of Use (PEOU) The most influential construct outside UTAUT model is Attitudes towards Use of technology The paper certifies that Anxiety about ICT does have an impact on the academicians The study confirms the validity of the UTAUT model in the field situation of a developing country’s educational system The study also certifies that some of the academicians are still having the fear of using ICT for their teaching and learning In the same manner, the two theories UTAUT and TAM are compared by [49] in prediction of behavior and intensity of Internet usage among 122 employees of BKCU Kalimantan (Credit Union) Indonesia, including identifying the most dominant perceptions that influence decisions to adopt the internet ICT types examined are the mobile phones, Personal Computers and the Internet Results of [49] show that UTAUT outperformed TAM Highest discriminating power is reported to UTAUT variables; internet Anxiety and internet Selfefficacy Internet Usage is lower compared with personal computers and mobile phones among employees of the credit union The decision of internet adoption is dominantly influenced by the perceptions about internet, especially internet Anxiety, internet Self-efficacy, and Personal Innovativeness Another theoretical modification of UTAUT proposed by [38] towards user adoption of e-commerce in Nigeria, considers Income and Education as new moderators besides original UTAUT’s moderators A UTAUT based open access adoption model proposed by [17], considers Awareness and Position (Rank) of the researchers as new moderators along with original UTAUT’s moderators (Voluntariness was dropped as a moderator in this respect because it is only relevant when technology usage is mandatory) Results reveal that Age, Experience, Gender and Position (Rank) of the researchers have been established as important moderators In addition to its moderating effects, Age was also established to directly affect both researchers’ Behavioral Intention and Usage of open access, while Awareness was found to have an effect only on the latter In [50], relationship between constituent components of UTAUT has been studied with regard to the acceptance of new technology of Electro-Slag Remelting (ESR) in Esfarayen Steel Industry Complex using FUZZY DEMATEL Technique FUZZY DEMATEL technique was used by [50] to identify key success factors of UTAUT Diagram of the factors was obtained by [50], and factors were placed in two groups, the cause group including PE and EE and the effect group including SI, FC, and Voluntarines of use The factors of cause group are called ―key success factors‖ As claimed by [50], concentrating on these key success factors can settle facing problems and restrictions with relation to the acceptance of new technologies by the users The market acceptance of Future Internet ultra-large-scale (ULS) solutions was examined by [19] using UTAUT The effects of PE, EE, SI, Self-efficacy, FC, Anxiety, and Attitude on Behavioral Intention and Use are examined Furthermore, moderators of Gender, Age, Country, Industry, and Occupation are considered Results show that all of UTAUT relations are supported except for the moderating effect of Age and Industry EXTENSION AND INTEGRATION WITH OTHER MODELS Reference [45] as cited in [37] Fig Comprehensive e-business quality model based on UTAUT Source: [Cody-Allen and Kishore 2006], pp 86 B Model Integration with other models and Ability, while System Quality is considered direct determinant of both System Satisfaction and Integrity A comprehensive model was proposed by [51] of quality based on UTAUT in the context of e-business systems This proposed model brings together existing quality constructs, satisfaction constructs, technology acceptance mechanisms, and trust literature, in order to tie all of these research streams together as antecedents for intention to use and usage of e-business systems Thus, E-business quality model (proposed model) extends the UTAUT with E-quality, Trust, and Satisfaction constructs as shown in Fig A further contribution to Technology Acceptance literature is Individual-Technology-Organization- Environment (I-TOE) as suggested by [52] by integrating UTAUT with Technology-Organization-Environment (TOE) paradigm Furthermore, the framework was complemented with new variables of technology risk, technology task fit, organization readiness and top management commitment Fig illustrates I-TOE Conceptual Framework of CAATTs Adoption based on both UTAUT and TOE paradigm Substantial contribution of [52] is discrimination between Individual perspective in Acceptance (which is represented by UTAUT2), and Organizational perspective (which is represented by TOE) Organizational perspective includes Technological Influence (Technology Cost-benefit, Technology risk, and Technology-Task Fit), Organizational Influence (Top Management Support, Organization's Readiness, and organization size), and Environmental Influence (Technology Environment Complexity) The new paradigm of I-TOE was used by [52] to investigate the acceptance of CAATTs in audit firms Computer-AssistedAuditing Techniques and Tools (CAATTs) are audit technologies that allow IT audit work to be performed efficiently, effectively and reduce audit time.I-TOE framework contributes to professional audit firms that need to measure CAATTs acceptance for the advancement of audit profession E-business quality model considers UTAUT constructs (PE, EE, Intension to Use, and Use), while FC, and SI are dropped E-business quality model considers Trust Constructs which includes Trusting Beliefs which is determined by Ability, Integrity, and Benevolence Trusting Beliefs are proposed in E-business quality model as direct determinants to both PE and Intension to Use Furthermore, E-business quality model considers Satisfaction Constructs (Information Satisfaction and System Satisfaction) as direct determinants of UTAUT constructs Mainly, Information Satisfaction is considered direct determinant of PE, while System Satisfaction is considered direct determinant of EE Finally, E-business quality model considers Quality Constructs (Information Quality and System Quality) as direct determinants of Satisfaction Constructs Mainly, Information Quality is considered direct determinant of both Information Satisfaction 10 REFERENCES [1] [2] V Venkatesh, M Morris, G Davis, and F Davis, ―User Acceptance Of Information Technology: Toward A Unified View,‖ MIS Quarterly, vol 27, no 3, pp 425-478, 2003 P Legris, J Ingham, and P Collerette, ―Why Do People Use Information Technology? 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