1. PRESENTED BY: Isaac Momanyi Maonga: D61/79546/2012 Anthony Njogu: D61/75282/2012 Bernard Njenga: D61/62047/2013 Brian Tetea: D61/67521/2011 Rose Waeni: D61/79063/2012

2. Davis ( 1986 ) developed the Technology Acceptance Model which deals more specifically with the prediction of the acceptability of an information system. The purpose of this model is to predict the acceptability of a tool and to identify the modifications which must be brought to the system in order to make it acceptable to users. This model suggests that the acceptability of an information system is determined by two main factors:  perceived usefulness  perceived ease of use.

3.  Perceived usefulness is defined as being the degree to which a person believes that the use of a system will improve his performance.  Perceived ease of use refers to the degree to which a person believes that the use of a system will be effortless.

4.  Technology Acceptance Model postulates that the use of an information system is determined by the behavioral intention, but on the other hand, that the behavioral intention is determined by the person’s attitude towards the use of the system and also by his perception of its utility.  According to Davis, the attitude of an individual is not the only factor that determines his use of a system, but is also based on the impact which it may have on his performance. E.g. : if an employee does not welcome an information system, the probability that he will use it is high if he perceives that the system will improve his performance at work

5. Original TAM Proposed by Fred Davis(Davis,1986,p.24.)

6. The Main finding in this is both perceived usefulness and perceived ease of use were found to have direct influence on behavioral intention, thus eliminating the need for attitude construct.

7. Final Version of TAM : (Venkatesh & Davis),1996.p.453

8. Venkatesh and Davis (2000) developed TAM2 by adding social influences (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use) to predict the adoption of an information technology. Venkatesh and Davis (2000) used the construct of subjective norm to capture social influences. TAM2 suggests that in mandatory contexts, subjective norm has a direct effect on intention through the mechanism of compliance. If an individual perceives that an important social actor has the ability to punish nonbehavior or reward behavior, the social influence of compliance effect will occur (French and Raven, 1959; Kelman, 1958; Warshaw, 1980). In voluntary contexts, social influences can influence intention indirectly through the mechanism of internalization and identification. Internalization refers to the process when an individual incorporates the important referent’s belief into his or her own belief structure (Kelman, 1958; Warshaw, 1980). Identification means that an individual can gain a membership in a social group or achieve a higher status within the group by performing a behavior (Blau,1964; Kelman, 1958; Kiesler and Kiesler, 1969; Pfeffer, 1982). TAM2 theorizes that there are four cognitive instrumental determinants of perceived usefulness: job relevance, output quality, result demonstrability, and perceived ease of use. TAM2 retains perceived ease of use from TAM as a direct determinant of perceived usefulness.

10.  Subjective norm: Person's perception that most people who are important to him think he should or should not perform the behavior in question.  Image: The degree to which use of an innovation is perceived to enhance one's status in one's social system.  Job relevance: Individual's perception regarding the degree to which the target system is relevant to his or her job.  Output quality: The degree to which an individual believes that the system performs his or her job tasks well.  Result demonstrability: Tangibility of the results of using the innovation.  Voluntariness: The extent to which potential adopters perceive the adoption decision to be non-mandatory.

11.  The UTAUT aims to explain user intentions to use an IS and subsequent usage behavior. The theory holds that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants of usage intention and behaviour (Venkatesh et. al., 2003).

13.  Performance Expectancy is the degree to which the user expects the system will help him or her to attain gains in job performance.  Effort Expectancy: the degree of ease associated with the use of the system.  Social Influence: the degree to which an individual perceives that important others believe that he or she should use the new system.  Facilitating Conditions: Ventakesh et al(2003) find that the influence of facilitating conditions on usage is moderated by age and experience of the individual. Its defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system.

14.  The theory of Planned behaviour assumes that individuals are rational decision makers. Individuals assess perceived behaviour control using a method similar to the expectancy-value model. For each in a set of control beliefs, individuals multiply the belief’s strength by perceived power of the control factor.  The concept was proposed by Icek Ajzen to improve on the predictive power of the theory of reasoned action by including perceived behavioural control. It is one of the most predictive persuasion theories. It has been applied to studies of the relations among beliefs, attitudes, behavioral intentions and behaviors in various fields such as advertising, public relations, advertising campaigns and healthcare.  The theory states that attitude toward behavior, subjective norms, and perceived behavioral control, together shape an individual's behavioral intentions and behaviors.

16. SUMMARY OF EVALUATION From: J Biomed Inform. Author manuscript; available in PMC 2011 February 1.Published in final edited form as: J Biomed Inform. 2010 February; 43(1): 159. Published online 2009 July 15. doi: 10.1016/j.jbi.2009.07.002

17. Interpretivism  Technology acceptance mode uses interpretivism paradigm. The model explains how users come to accept and use technology and the impact of external factors on internal beliefs, attitudes and intentions.

18.  TAM has been used to explain and predict the use of a system and Technology.  The future research direction is defined as follows:  First, keep going with the extension of Technology Acceptance Model.  Second, use the extension model of technology acceptance to assess the state-of-the-art technologies contexts including mobile service, cloud computing applications, ubiquitous computing applications and so on.

19.  TAM has proven to be a useful theoretical model in helping to understand and explain user behavior in the information system implementation.  It has been tested in many empirical researches and the tools used with the model have proven to be of quality and to yield statistically reliable results.

20.  Mobile Money transfer – M-Pesa services success story in Kenya  M-pesa is a mobile-phone based money transfer and micro-financing service for Safaricom and Vodacom, the largest mobile network operators in Kenya and Tanzania. Currently the most developed mobile payment system in the world, M-Pesa allows users with a national ID card or passport to deposit, withdraw, and transfer money easily with a mobile device

21.  Mobile Money transfer – M-Pesa services success story in Kenya  M-pesa is a mobile-phone based money transfer and micro-financing service for Safaricom and Vodacom, the largest mobile network operators in Kenya and Tanzania. Currently the most developed mobile payment system in the world, M-Pesa allows users with a national ID card or passport to deposit, withdraw, and transfer money easily with a mobile device

22. M-pesa has been widely accepted in Kenya and other countries are trying to adopt a similar model of money transfer. The success behind M-pesa technology has been due to:  Trust – users were comfortable with the reliability of the system.  Network effect – The value of the customer of a payment system depends on the number of people connected to and actively using the network.  Simultaneous increase in the number of M-pesa dealers and customers.  Strong Safaricom brand.

23.  Mobile Banking  E-Banking  E-learning portal – UON  Agency Banking

24.  Journal of Business Research 59 (2006) 999–1007- Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics: Research found that age, education, income and race are associated differentially with beliefs about the Internet, and that these beliefs influence a consumer's attitude toward and use of the Internet. The research extended TAM in two ways. First, it included perceived access barriers among the key beliefs about a technology that influence its use. Second, it added four key demographic constructs as external variables to the TAM (i.e., age, education, income and race). The model suggests that the TAM's belief variables are differentially relevant to consumers with different demographic profiles and serve to mediate the relationships between demographic variables and attitude toward the Internet.

25.  Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e- Learning. Educational Technology & Society, 12 (3), 150–162. The general structural model, which included e-learning self efficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use e- learning, was developed based on the technology acceptance model (TAM). The result proved TAM to be a good theoretical tool to understand users’ acceptance of e-learning.  This study proposed an integrated theoretical framework of university students’ e-learning acceptance and intention to use based mainly on the technology acceptance model (TAM). The objectives of the study were to analyze the relationship of university students’ intention to use e-learning with selected constructs such as their attitude, perceived usefulness, perceived ease of use, self-efficacy of e-learning, subjective norm and system accessibility, and to develop a general linear structural model of e-learning acceptance of university students that would provide a school manager or an educator with implications for better implementing e-learning.

26.  International Journal of Accounting Information Systems 10 (2009) 214–228. Information technology acceptance in the internal audit profession: Impact of technology features and complexity.(HyoJeong Kim, Michael Mannino, Robert J. Nieschwietz Business School, University of Colorado Denver, Denver, CO 80202, United States) The research used TAM for technology acceptance among Internal Auditors and tested the model using a sample of internal auditors provided by the Institute of Internal Auditors (IIA). System usage, perceived usefulness, and perceived ease of use were tested with technology features and complexity. Through the comparison of TAM variables, it was found that technology features were accepted by internal auditors in different ways. The basic features such as database queries, ratio analysis, and audit sampling were more accepted by internal auditors while the advanced features such as digital analysis, regression/ ANOVA, and classification are less accepted by internal auditors. As feature complexity increases, perceived ease of use decreased so that system usage decreased. Through the path analysis between TAM variables, the results indicated that path magnitudes were significantly changed by technology features and complexity. Perceived usefulness had more influence on feature acceptance when basic features were used, and perceived ease of use had more impact on feature acceptance when advanced features were used.