Unified Theory of Acceptance and Use of Technology - Research Paper Example

This study is to analyze the extent to which the UTAUT (Unified Theory of Acceptance and use of Technology) model has captured, the factorsaffecting the use of smartphone .The research model also based on UTAUT model was hypothesized and empirically tested. Gender, age, experience and voluntariness were set as factors affecting performance expectancy, effort expectancy, social factors and the facilitating conditions. Survey was done with 90 people who had used or had an idea what a smartphone entails. Results were obtained and necessary conclusions made. The results provide an exploratory factor analysis of UTAUT model, demonstrating reliability and validity of the scales of the stated constructs, confirming if the model is a valuable measurement tool for evaluating the users’ intention of use of smartphones. Background to the study Controversies have come up lately over which smartphone is the best to use. Depending on the purpose that one would like to put his smartphone into, preferences are different for different people. According to the article, Blackberry versus iPhone: Which is better for business users? Articles by Hunter Skipworth (2010), show that businessmen have favored Blackberry for long very an iPhone. However, it is clear that the Apple has made an effort to introduce an enterprise feature which is more likely to make it the favorite for many whose attitude is futurists. With iPhone gaining stability due to its fast software update, Blackberry too has its own outstanding merits that make it marketable still. It is for these and many more reasons that this study was done to analyze the factors determining intention of use of a particular smartphone and not the other one. Introduction Smartphones have become very popular amongst the people of all ages and gender today due to their advanced software, access to the Internet and computer processing capabilities.Smartphones,over time, have been pursued for either business or consumer purposes since they can be considered as a portable computer. However, choosing the most appropriate device that suits ones needs is not easy. The intention to acquire, use and keep a smart phone is studied and a conclusion is done by analyzing is done by the return on investment (ROI) of a blackberry deployment in the communication systems category. Various major features of Smart phones were investigated to come up with conclusive factors about what really is the key influence of people’s intentions to use smart phones. These features include the keyboards, speed, screen sizes and resolutions software updates, hardware simplicity, battery life, screen type and many more. The questions of study considered are: 1. What makes a certain smart phone the preference for many? 2. What is the difference in intentions and actual usage of different smart phones? 3. How do these intentions relate with 1 above? UTAUT Model Development This study is to illustrate that the Unified Theory of Acceptance and use of Technology (UTAUT) model captures the factors impacting upon the intention and actuality of the use of smart phones. Its aim is to explain users’ intentions to choose and use a certain information system and the factors that affect the usage intentions. It proves the general factual and theoretical basis for understanding a users’ attitude and acceptance of using an information system. People always have a certain attitude towards a new technology based on factors like experiences, routines, and even .habits (Bandura, 1986). Fig1: The UTAUT model. Source: Taylor & Todd, 1995. The model was developed by reviewing constructs of eight models that had been used to explain behavior of using Information Systems and Technology. These eight constructs that were consolidated are; Theory of Reasoned Action(TRA) (Fishbein & Ajzen, 1975) and Technology Acceptance Model (TAM) ),Motivational Model (MM) ) (Davis et al.,1992), the Theory of Planned Behavior (TPB), a combined theory of planned behavior/technology acceptance model, Social Cognitive Theory, Innovation Diffusion Theory (IDT), and model of personal computer use(Rogers, 1995). UTUAT has gained popularity since it integrates factors that have been used in these earlier models and has improved on their limitations. UTAUT has distilled the key factors and contingencies related to the prediction of behavioral intention to use of a Smartphone in organizational contexts (Davis, 1989). These models are criticized for having a relatively low explanatory power in terms of behavioral Intention of use, which is about 40% only and yet UTAUT has its explanation power ranging between 69%-70%. After the development UTAUT many people have tried to enhance the adjusted R2, explanatory power, by integration or by the constructs. Such authors include Chau, 1996; Kim and Narasimhan, 2005 and Wang et al., 2006; Wu and Wang, 2005; but no effective results were obtained. Venkatesh et al., (2003) recommends four constructs as the key factors that impact on the intention and actuality of use of any information systems such as Smart phones. These constructs are moderated by age, gender, experience, and voluntariness of use (Ajzen, 1991).The constructs include: First, performance definition which is the degree to which a person believes that a certain information system will help him achieve a good performance in his work. Second, effort expectancy which is the level of ease that comes as a result of using an information system .This is moderated by gender, age, and experience, where the effects are commonly found on females, older workers and those with less experience. The third one entails social influence, which is the degree to which a person believes how much others believe that he should use a certain information system. This construct is noticeable only in compulsory settings. All the four moderating variables, gender, age, voluntariness and experience affect this construct such that social influence is non-significant in data analysis without these moderators. The major effects of social expectancy are found mainly in women, older workers, and those that have limited experience. Finally there is the facilitating condition which is the extent to which an individual believes that there is an organizational and technical infrastructure that is in place to support the information system. These four moderators include gender, age, experience and voluntariness of use (Venkatesh et al., 2003). Gender: Past studies confirm that women mostly driven by ease of usage and subjective facts while men are mostly driven by usefulness. Age: Old workers prefer subjective facts as compared to young workers, and are more likely to be driven by ease of usage (Venkatesh et al., 2003). Age actually controls almost all relationships. Experience: Effort is more in the less experienced users of information systems Venkatesh and Davis, (2000). Actually, the experienced are driven by usefulness while the less experienced have ease of use as their driving factor and on getting some experience with the technology; they explore its benefits much more easily. Voluntariness of Use: With reference to the UTAUT model, Venkatesh and Davis, (2000) tried to explore this as the ability of an individual to voluntarily use a smart phone without being forced. A good example of explaining these moderators is to relate the perceived usefulness and intention to use a smartphone. This highly varies with gender and age whereby it significantly more on the young people and the male workers The effort expectancy ease on the intention of using a smartphone is also highly determined by age and gender such that it is significantly more for older and female workers and these effects of ease of use decrease with experience (Igbaria et al., 1995). Intention to use Information Systems focus on characteristics of a system in relation to how much technology is accepted (Davis, 1989). Studies show that intention to actual use of a smartphone is greatly determined by perceived usefulness and a person’s behavioral attitude towards the use of a smartphone. They also concluded that effort expectancy and perceived usefulness have a positive effect on ones choice to use a smartphone. Framework and Hypothesized Relations The UTAUT model was considered to do the study and in explaining behaviors related to smartphones adoption. Four factors that gave effects to intention to the use of smartphones were considered. Below is the research framework chosen and the hypothesis set to analyze intention and actuality of the use of smartphones. Venkatesh et al. (2003) also focuses on the direct ease of use and perceived usefulness on intention of use. Therefore, a direct relationship usage behavior, and between behavioral intention is considered. From the hypothesis, when both effort expectancy and performance definition constructs are present, facilitating conditions become insignificant in predicting intention of use. Fig 2: Research framework Research Hypotheses H1: Performance definition will have a positive influence on attitude towards behavior. H2: Perceived ease of use will have a positive effect on attitude towards behavior. H3: Social peer’s influence will have a direct effect on attitude towards behavior. Social influence has a direct link between smartphone quality and usage and, an indirect effect on intentions to use through usefulness and ease of use H4: Facilitating conditions will have a positive effect attitude to behavior. H5: Performance expectancy will have a positive effect on behavior intention to use. H6: Perceived ease of use will have a direct effect on intention to and actual use. H7: Social peer’s influence will have direct positive effect on intention to and actual use. H8: Facilitating conditions will have a direct effect on intention to and actual use. H9: Attitude on behavior will have a direct influence on behavior intention to use. Empirical study For factual knowledge, the hypotheses were tested using data .This was done qualitatively whereby a qualitative analysis was done in which source material were viewed as a whole and clues are not derived from statistical probabilities Rules that are applicable to the whole source material are defined. A single exception cancels the current rule and the meanings are interpreted based on the hints in source. Facts view approach of analysis was used, whereby, a clear description was stated between the concrete world and any claims made of it (Weiwei, 2009). The respondents’ answers were examined and the results analyzed on the basis of the fact that incase of the thought that the respondent might be lying, then his response would have no value unless one believes to be able to see through him to reality. To get the whole truth from the respondents, the saturation process used in collecting information was done. Ten online interviews were done to complete the research. Methodology An online survey was conducted taking into account Dillman’s (2000) online questionnaire guidelines. The questionnaires were in form of Net Questionnaires. In this program, offered pre defined emails were sent to chosen members. For maximum results, a newsletter was sent to all panel members informing them of the upcoming research. One day later, the panel members received an email inviting them to participate in this research by filling the questionnaire. Since the program had a personal hyperlink, the program could track back answers per individual. Reminders were sent to the members until they all filled the questionnaires and sent them back the survey yet. Besides, Information was collected from different sources including Business insiders on rating scores awarded to the Apple’s iPhone and Research in Motions BlackBerry smartphones. Also, some more information was derived from a telegraph comparing the two smartphones. Finally, an article by John Brodkin was used as a source of information on BlackBerry. Results and Analysis As was in original UTAUT model’s studies, partial least squires regression was used to assess the relationships between all constructs and their moderators within the model, one dependent variable was tested at a time (Venkatesh et al., 2003). PLS, which was developed in the 1960s by Herman World, is used as an exploratory analysis tool .It is most preferable due to the fact that it is less restrictive as compared to other multiple linear regression. It is mostly useful for constructing prediction models when there is collinearity among other factors .As stated by Yu (2011), average variance extracted, composite reliability, and factor loadings, were used in assessing convergent validity and discrimination validity by examining if or not the square roots of average variance go beyond correlations between the constructs. As suggested by Venkataesh et al. (2003), the reliability was evaluated by close examination of internal consistency reliability (ICR): The factors impacting on intention of use of smartphones (Ostjan, Gregor and Marjan, 2010). On running SPSS the factor loading of the fourth item was 0.60.As expected, this item was removed because its factor loading was below 0.7. The SPSS and PLS 2.0 were run again, and results stated in the table below: Table 1: Table of average variance extracted, composite reliability, and factor loadings Construct Items Factor loading Composite reliability Average variance Performance Definition PD1 PD2 PD3 PD4 0.701 0.823 0.723 0.881 0.712 0.623 Effort Expectance EE1 EE2 EE3 EE4 0.845 0.772 0.765 0.843 0.760 0.640 Social factors SF1 SF2 SF3 SF4 0.881 0.774 0.754 0.923 0.060 0.621 Facilitating Conditions FC1 FC2 FC3 FC4 0.845 0.876 0.992 0.8220. 0.881 0.642 Behavioral Intention BI1 BI2 BI3 BI4 0.834 0.745 0.882 0.912 0.766 0.612 The above table shows, all factors considered in the measurement mode, and proves the fact that the model valid and reliable .since the factor loadings were greater than 0.7, and the average variance was beyond a threshold of 0.6 throughout, therefore, it can be regarded that measurement tool is well explanatory about potential variables. Analysis Analysis result of reliability, Correlation coefficient, and discriminated validity for the Variables. Table 2: result of reliability, Correlation coefficient, and discriminant validity for the Variables MEAN S.D CR PD EE SF FD BI USE INTENTION USE PD 4.33 2.441 0.992 0.734 EE 4.78 2.123 0.964 0.644 0.754 SF 4.356 3.004 0.845 0.744 0.665 0.863 FC 5.786 3.043 0.862 0.785 0.672 0.742 0.824 BI 5.243 2.674 0.945 0.633 0.772 0.677 0.762 0.841 Use 0.724 0.800 0.735 0.7245 0.642 0.811 AVE 6.765 2.333 0.900 0.772 0.741 0.881 0.722 0.744 0.733 0.842 Respondent’s profile: Based the collected information, out of 90 respondents, 30 were males. 50 of the respondents were 18-25 years old and 40 were 26 years old and above. 80 students used mobile smartphones devices at with highest percentage using 50 Blackberry and 30 using iPhones. Below is the demographic profile. Profile Classification Frequency Gender Male 60 Female 30 Age 18-25 50 26 and above 40 Smartphone experience Blackberry 50 iPhone 30 Table 3: Demographic profile From the sampling, the various factors considered for choosing one type of a smartphone over the other are recorded in the table below and their scores as well, Intention for choice and use BlackBerry (cumulative score) Iphone (cumulative score) Ease in synchronizing 0 3 Best apps and games 0 4 Speed 1 5 Sturdiness 2 5 Notification system 3 5 Battery life 4 5 Camera quality 5 6 Ease in sharing content 5 8 Email notifications speed 8 8 Table 4: Intentions of using a smartphone Besides this comparison based on the functionality of the two types of smartphones, other factors put into consideration were the features of the phones Feature BlackBerry IPhone Software update Slow Fast Screen size Small Big Screen resolution High Low Data entry Optical mouse and Qwerty keyboard Touchscreen Operating system Complex Simple Email program Reliable Unreliable Battery Replaceable Irreplaceable Business features Highly reliable Just reliable Table 5: Features of consideration when choosing a smartphone The return of investment (ROI) experienced on BlackBerry u depends on various factors such as their role in the organization and the purpose for which they are using the blackberry. BlackBerry ROI continues to increase due to increases in personal productivity due to its many favorable features stated above and also due to growth in workflow efficiency which is likely influenced by higher percentages of staff with BlackBerry phones thus creating improved economy. Conclusion In this part, discussions cover the findings in respect to the variables in the UTAUT model: effort expectancy, performance definition, social influences, and their relationship with their moderators on intention and actual use of smartphones. The results indicate that users with knowledge and different experience in smartphones use have same interpretation of effort expectancy and performance expectancy. However, social factors are not interpreted the same way among different users. Facilitating conditions are also not comparable for different users of smartphones with difference in experience and frequency of usage (Venkatesh & Zhang, 2010). In accordance to models drawing from psychological theories, which argue that an individual’s behavior is predictable and influenced by individual intention to use, UTAUT contended and proved behavioral intention to have significant influence on smartphones usage (Venkatesh et al., 2003). Given that the goal of businesses is to attract more consumers to adopt their devices. The effect of performance definition on behavioral intention is moderated by age and gender, such it is stronger for men and mostly, younger men. The effect of effort expectancy on behavioral intention will be moderated by experience, gender, age, and, such that the effect is more for younger women, and particularly at early stages of their experience in using smartphones. The effects of social peer factors influence on behavioral intention are moderated by all the four moderators, such that the effect is more for older women, in mandatory settings in and at the early stages of gaining experience in use of smartphones. Facilitating conditions have minimal effects on intention to use smartphones. A conclusion can be drawn confidently that the research approach taken in this analysis resulted to the following three facts concerning the replication of the UTAUT model. First, the coefficient factor analysis clearly proved construct validity of the UTAUT model, although some items had to be omitted. Second, the items of all scale obtained a very high reliability and validity, with a low inter-items correlation value. Third, from the four indicators of behavioral intention of use of smartphones, the factors came out as being well-suited to the correlations in this study between the various variables. Therefore, this study simply provides enough evidence that UTAUT model is an adequately reliable and valid instrument to measure the use behavior of any information technology. Limitations Note should be taken that Venkatesh et al. (2003) did the studies across many technologies, organizations, functions, business unlike this study which was done over a short period of time and using few samples thus the measure of how much the model captures intentions of use of an Information System which is a smartphone in this case, cannot be similar to that of his. References Ajzen, I. 1991, The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. Bandura, A. 1986, Social foundations of thought and action: a social cognitive theory: Prentice Hall, Englewood Cliffs, NJ. Chau, P.Y.K., 1996a, Empirical assessment of a modified technology acceptance model. Journal of Management Information Systems, Vol 13 no. 2, pp.185–204. 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Venkatesh, V. and Zhang, X., 2010, Unified theory of acceptance and use of technology: U.S. vs. China, Journal of Global Information Technology Management vol.13 no.1 pp. 5-27. Read More