Breaking the Mold: An Educational Perspective on Diffusion of Innovation/Unified Theory of Acceptance and Use of Technology
By Jing Liu
Unified Theory of Acceptance and Use of Technology (UTAUT) is an acceptance and adoption model created by Venkatesh, Morris, Davis and Davis in 2003. Coming from the field of business and management and four different universities (University of Maryland, University of Virginia, University of Minnesota and University of Arkansas), these researchers were able to create a model that studies common people's adoption decision and innovation behaviors. For example, this model can be used to analyze adult learners' adoption behaviors of a new e-learning app; it is also suitable to be used in corporate environments if one wants to know the degree of employee motivation when adopting a new software.
Liu’s chapter introduces Venkatesh’s original study and discusses constructs and mediate factors of this theory. The application of UTAUT to investigate the acceptance of e-learning in workplace and academic environments is discussed as well as UTAUT strengths and limitations.
- 1 Venkatesh's Original Study
- 2 Application of UTAUT to Investigate the Acceptance of e-Learning
- 3 UTAUT: Strengths and Limitations
- 4 References
Venkatesh's Original Study
Unified Theory of Acceptance and Use of Technology (UTAUT) model combines eight different models (Venkatesh, Morris, Davis, & Davis, 2003), which are:
- the theory of reasoned action,
- the technology acceptance model,
- the motivational model,
- the theory of planned behavior,
- a model combining the technology acceptance model and the theory of planned behavior,
- the model of PC utilization,
- the innovation diffusion theory, and
- the social cognitive theory.
Venkatesh and his associates created UTAUT based on their identification of certain factors they considered to be significant in affecting a person’s decision on whether or not to adopt a particular new technology.
Four Important Constructs
Within the heated debate about factors that have influenced adoption, Venkatesh（2003）picked out seven constructs from the eight models as important facilitation reasons for which a person will accept a cutting-edge technology. Finally they had been summarized into four constructs: performance expectancy, effort expectancy, social influence and facilitating conditions. Performance expectancy, effort expectancy and social influence are direct determinant of behavioral intention, while facilitating conditions are direct determinants of user behavior.
Performance expectancy refers to the estimate of adopter for the potential job benefit that the use of technology may bring. And this kind of estimate is composed of the perceived usefulness of the technology, extrinsic motivation to use the technology, usefulness of the technology to job-fit, relative advantages of the technology over others, and outcome expectancy. To be more specific about these five constructs that are included in performance expectancy, the construct of perceived usefulness of the technology measures the extent to which a person considers using a particular technology and will improve his or her job performance; extrinsic motivation pays attention to the outer drive, such as improved job performance, pay or promotions, to use a particular technology; for job-fit, it stresses the function aspect of technology in upgrading an individual’s job performance; the construct of relative advantage deals with the benefit that the new technology may bring compared with what has already been achieved by former systems; the construct of outcome expectancy focuses on the consequence of behavior which can be split into job-related performance expectations and personal expectations that relate to individual goals. In application, items used in estimating performance expectancy include: “I would find the system useful in my job”, “Using the system increases my productivity,” as well as “If I use this system, I will increase my chance of getting a raise," etc. (Venkatesh et al., 2003).
Effort expectancy is similar to the notion of perceived usefulness of technology described in Technology Acceptance Model (TAM). It consists of three constructs: perceived ease of use, complexity, and ease of use, which derive from a previous study. The construct of perceived ease of use aims at testing the extent to which a user considers it spare effort to use a particular technology; the construct complexity defines a situation in which people think of the new system as a comparably more complex tool to understand and use; the construct of use of ease is the degree to which using an innovation is perceived as being difficult to use. Effort expectancy construct plays a significant role in both voluntary and mandatory usage contexts, but never as important in a second round because the users who utilize the technology for a second time or further are familiar with the manipulation process already. In practice, items used to estimate effort expectancy include: “My interaction with the system would be clear and understandable,” “It would be easy for me to become skillful at using the system,” and “I would find the system easy for me” (Venkatesh et al., 2003).
Social Influence Construct
Social influence construct describes the situation in which an individual considers to adopt a particular technology because of other people’s suggestion. It is a compound of subjective norm construct, social factor construct and image construct. Subjective norm construct refers to a situation in which a person’s decision about whether to adopt an innovation depends on other people whose idea deemed to be important to him or her. Social factor construct defines that an individual makes decisions of adoption of a technology under the influence of the whole social situation. Image construct focuses on testing the degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system. These three constructs can also be identified as “compliance,” “internalization” and “identification” (Venkatesh & Davis, 2000; Warshaw, 1980). Usually, to measure the effect of social influence, researchers utilize items like: “People who influence my behavior think that I should use the system," “People who are important to me think that I should use the system,” and “In general, the organization has supported the use of the system” (Venkatesh et al., 2003).
A facilitating condition discusses the role that organizational and technical infrastructures play in the innovation adoption decision of an individual. It is made up of three different constructs, which are perceived behavioral control, facilitating conditions, and compatibility. Perceived behavioral control includes an individual’s self-efficacy, resource facilitating conditions, and technology facilitating conditions. Facilitating conditions give more detailed information about the surrounding environment, including both technical aspect and rule aspect, which may enhance or retard innovation acceptance for individuals. Compatibility construct mainly refers to the compatibility of the innovation with already existing values, needs, and experiences of potential adopters. Items to measure a facilitating condition’s effects are usually: “I have the resources necessary to use the system," “The system is not compatible with other systems I use,” and “A specific person (or group) is available for assistance with system difficulties” (Venkatesh et al., 2003).
Besides the four main constructs, there are another four moderators which are gender, age, experience and voluntariness of experience. Although they are not determinant factors compared with performance expectancy, effort expectancy, and social influence and facilitating condition, they can execute effect on using behavior through affecting those four determinant constructs.
Gender will moderate performance expectancy, effort expectancy and social influence. As research indicates, men tend to have higher performance expectancy than women because they incline to be task-oriented and task achievement is important to them (Minton & Schneider, 1980). And this instinct derives from gender roles and socialization. Also, previous studies suggest that effort expectancy is more significant to women than to men (Bem & Allen, 1974; Bozionelos, 1996). Gender roles contribute to this difference between men and women (Lynott & McCandless, 2000; Motowidlo, 1982; Wong, Kettlewell & Sproule, 1985). As for the social influence, women tend to be more sensitive to others’ opinions than men do so that social influence is more salient in adopting technology to women than to men (Miller, 1976; Venkatesh et al., 2000).
Age, as another important mediator factor, will impact all the main constructs. For performance expectancy, younger people tend to be more attracted by extrinsic rewards than older people. Effort expectancy is a more salient factor among older people than younger people to adopt an innovation (Morris & Venkatesh, 2000). Also, older people are more likely to place increased salience on social influence, with the effect declining with experience (Morris & Venkatesh, 2000). Furthermore, in the facilitating condition part, older people are more subjective to environmental setup because their way of learning is more passive and based on experience.
Experience will make a difference on adopter’s effort expectancy, social influence and facilitating condition. For people who have little experience with a new system, effort expectancy is more a salient factor in predicting behavioral intention. On the contrary, if the experience is in a later stage, effort expectancy will not exert much effect on behavioral intention. Also, the social influence has particular effect on behavioral intention during the early stages of experience, while its effect will fade as people’s experience about the new technology evolves into later stage (Agarwal & Prasad, 1997; Taylor & Todd, 1995a). As to facilitating condition, it will become a more important factor to behavioral intention as experience with the new systems increases so that impediments toward sustainable usage can be removed (Bergeron, Rivard & De Serre, 1990).
Voluntariness of use can only mediate social influence’s effect on behavioral intention. Social influence will exert its influence to fullness under mandatory context because it has direct effect on intention, while it spends more effort to impact behavioral intention under voluntary context (Venkatesh & Davis, 2000).
Application of UTAUT to Investigate the Acceptance of e-Learning
There are many research studies using UTAUT theory to study the innovation acceptance process of adopters. From the perspective of testing target, these researches involve innovations from commercial products to educational technologies. From aspect of testing context, these research studies' focuses vary from large organization, such as international cooperation, to small business and educational institutions. From the standpoint of cultural difference, some studies test about UTAUT theory in different countries from Asia to Europe.
Research relevant to the education area mainly focuses on e-learning, which is a very popular way of studying these days among young people. There is plenty of research aimed at finding out the reasons why people adopt or reject e-learning.
The Acceptance of e-Learning in Workplace
A study on the acceptance of e-learning in the workplace in South Korea was conducted using UTAUT theory (Yoo, Han, & Huang, 2012). This research targeted the exploration of intrinsic and extrinsic motivation behind the acceptance of e-learning by young employees. The researchers selected a mid-size food service company in South Korea as sample site and used a survey which was composed of a 7-point likert scale covering categories such as performance expectancy, effort expectancy, attitude, social influence, facilitating condition, anxiety and the intention to use e-learning. Among those items, performance expectancy, social influence, and facilitating conditions had been classified as extrinsic motivation; while effort expectancy, anxiety and attitude towards e-learning had been regarded as intrinsic motivation. Results showed that intrinsic factors such as effort expectancy and attitudes towards e-learning had big positive effects on behavioral intention of use while anxiety had tremendous negative effect on behavioral intention of use. On the other hand, extrinsic factors such as facilitating conditions does little with behavioral intention to use e-learning. Therefore, this study concluded that extrinsic motivation on e-learning in the workplace did not immediately or independently affect intention to use e-learning among employees.
The Adoption of e-Learning in Academic Institutions
Another research study using UTAUT theory to explain web-based learning/e-learning adoption behavior was led by two Taiwan researchers (Chin & Wang, 2008). However, this research focused on learners' continuance of using web-based learning under the context of educational institution which provided online courses for both full-time and part-time students, quite different from the research in South Korea as introduced above. This research adapted UTAUT theory to its theme-technology adoption continuance. It set up 14 pairs of relationships in total, including:
- performance expectancy and continuance intention,
- effort expectancy and performance expectancy,
- effort expectancy and continuance intention,
- computer self-efficacy and effort expectancy,
- computer self-efficacy and continuance intention,
- social influence and continuance intention,
- facilitating condition and continuance intention,
- attainment value and continuance intention,
- utility value and continuance intention,
- intrinsic value (playfulness) and continuance intention,
- social isolation and continuance intention,
- anxiety and continuance intention,
- delay in response and continuance intention,
- risk of arbitrary learning, and
- continuance intention.
The results indicated that performance expectancy and utility value had almost the same effects on continuance intention for part-time students who had limited time for study; social influence and facilitating conditions, social isolation and delay in response had little effect on users' intention to continue use of web-based learning; the total influence of performance expectancy, effort expectancy, computer self-efficacy, social influence and facilitating conditions was only 46.6% on continuance intention. The implication here was that intrinsic values, such as effort expectancy and positive subjective task value, could drive learners to continue taking web-based courses.
The Use of Educational Portal in Developing Countries
In the study of Maldonado, Khan, Moon and Rho, the situation of acceptance of educational portal had been put under a closer observation in context of developing countries (Maldonado, Khan, Moon & Rho, 2010). It explored the effects that e-learning motivation, social influence and facilitating condition had on Peruvian students’ use of Peru EDUCA e-learning portal (MinEdu, 2007b; BFPE, 2008). The researchers adjusted the UTAUT model minimally by substituting the constructs of performance expectancy and effort expectancy with e-learning motivation, which was defined as “a student’s tendency to find an e-learning system useful, easy to use, and try to derive the intended academic benefits from it” and “is composed of items adopted from the motivation, performance, and effort expectancy constructs” (Maldonado et al., 2010, p. 70). Furthermore, in considering the social and economic situation in Peru, Maldonado, Khan, Moon and Rho (2010) listed region and gender as moderators instead of the original ones in Venkatesh’s study because regional culture and gender role may exert bigger influences on students in Peru (Eamon, 2004). After data analysis, the researchers came to the conclusion that e-learning motivation and social influence both had a significant and positive influence on behavior intention, and behavior intention had a positive influence on use behavior, which in turn would positively affect e-learning motivation; while region had a negative interacting effect with the social influence, which had effect on intention behavior; facilitating condition had no obvious influence on intention behavior (Maldonado et al., 2010).
UTAUT: Strengths and Limitations
As a product generated from experience of previous technology adoption theories, Unified Theory of Acceptance and Use of Technology is a comparably complete model.
First of all, its explanatory power in technology using behavior is up to 70%, a much higher rate than other technology acceptance theories (Wu, Tao & Yang, 2008, p. 928). With such accuracy and broad application in explaining technology adoption behavior, UTAUT model surpassed other theories and became a better choice for researchers in the area of technology using behavior.
Secondly, its usage is not limited to mono industry but can be extended to industry such as mobile commerce (Xiao, 2006), online learning (Zeng, 2005), as well as medical surgery equipment (BenMessaoud, Kharrazi & MacDorman, 2011) and clinical decision support system (Jeng & Tzeng, 2012).
The limitation of Unified Theory of Acceptance and Use of Technology model is its inflexibility to adapt to different contexts. As Gahtani, Hubona and Wang (2007) reported in their research about information technology acceptance in Saudi Arabia, which is a middle-east country, cultural difference of Saudi Arabia from that of a typical western country became an obstacle for them to use UTAUT to analyze worker’s adoption of computers in Saudi Arabia. Workers in Saudi Arabia had different work-related values from that of workers in western countries thanks to Arab cultural beliefs which formed resistance to information technology, and this difference had negatively interacted with social influence and hence exerted negative influence on workers’ acceptance of IT.
Also in the research on students' acceptance of educational portal in Peru, Maldonado, Khan, Moon and Rho (2010) had to do some adjustment on moderators such as experience, voluntariness and age to region. Because according to Trichenor’s theory (1970), the higher the social-economic status the faster and easier people can acquire political and scientific knowledge including technology. And in Peru, the different levels of social-economic status can be classified based on three regions.
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