Technology Acceptance Model

The Technology Acceptance Model (TAM) proposes that beliefs about usefulness and ease of use are essential elements in determining user attitude towards using a new technology [Davis, 1989; Malhotra and Galletta, 1999; Kleijnen et al., 2004]. The theoretical foundation for TAM is based on Fishbein and Ajzen’s theory of reasoned action (TRA) model [Fishbein and Ajzen, 1975].

TRA is a widely studied model in social psychology [Malhotra and Galletta, 1999; Kwon and Chidambaram, 2000; Pedersen, 2003]. It attempts to explain why people behave as they do in situations of ‘reasoned action’ by identifying causal relations between beliefs, attitudes, intentions and behaviour [Kwon and Chidambaram, 2000; Barnes and Huff, 2003; Pedersen, 2003]. Attitude is defined as the individual’s positive or negative feelings about enacting a target behaviour [Uzoke et al., 2006]. TRA is illustrated in Figure 4.1.

The TRA has the following components [Fishbein and Ajzen, 1975; Malhotra and Galletta, 1999].: 1. Actual behaviour: According to TRA a person’s performance in a specified behaviour is

A model for representing the motivational and cultural factors that influence mobile phone usage variety 60 2. Behavioural intention (BI): BI is jointly determined by the person’s attitude (A) and the subjective

norm (SN) concerning the behaviour in question, with relative weights estimated by regression [Davis et al., 1989]:

SN

A

BI

=

+

3. Attitude towards behaviour (A): A person’s attitude towards behaviour is determined by their salient beliefs (bi) about the consequences of performing the behaviour multiplied by the

evaluation (ei) of those consequences.

∑

=

e

b

A

n∈N

4. Subjective norm (SN): Subjective norm refers to the social pressure exercised on the person to either enact or not enact the behaviour [Kwon and Chidambaram, 2000] and is expressed as the sum of all the person’s normative beliefs (nbi), which consists of the perceived expectations of

specific significant individuals or groups’ reaction, multiplied by the person’s motivation to comply (mci).with these expectations:

∑

=

mc

nb

SN

n∈N

Figure 4.1: Diagrammatic representation of the TRA adapted from [Davis et al., 1989]

TRA is a general model and it does not specify the active beliefs for a specific behaviour. Therefore a researcher using TRA has to identify the beliefs that are relevant for subjects regarding the behaviour under investigation. For example, if TRA is applied to mobile phone use, people’s beliefs regarding the benefits or liabilities of mobile phone use have to be identified by the researcher.

Beliefs and Evaluations Normative Beliefs and Motivation to Comply Attitude towards Behaviour (A) Subjective Norm (SN) Behavioural Intention (BI) Actual Behaviour

A model for representing the motivational and cultural factors that influence mobile phone usage variety 61 The Technology Acceptance Model (TAM) is a special case of TRA for modelling technology adoption in organisations [Pedersen, 2003]. TAM, as illustrated in Figure 4.2, includes six concepts [Davis et al., 1989; Malhotra and Galletta, 1999; Urbaczewski et al., 2002]:

1. External variables (EV): External variables influence perceived usefulness (PU) and perceived ease of use (PEOU or PEU), for example demographic variables (as discussed in Chapter 3).

2. Perceived usefulness (PU): Perceived usefulness is defined as ‘the extent to which a person believes that using the system will enhance his or her job performance’ [Venkatesh and Davis, 2000].

3. Perceived ease of use (PEU): perceived ease of use is ‘the extent to which a person believes that using the system will be free of effort’ [Venkatesh and Davis, 2000].

4. Attitudes towards use (A): Attitude towards use is defined as ‘the user’s desirability of his or her using the system [Malhotra and Galletta, 1999]. Perceived usefulness (PU) and perceived ease of use (PEU) are the sole determinants of attitude (A) towards the technology system. Perceived usefulness and perceived ease of use is determined by external variables (EV) and attitudes toward use (A) can therefore be defined as:

A = PU + PEU + EV

5. Behavioural intention (BI): Attitude (A) combined with perceived usefulness (PU) predict behavioural intention (BI):

BI = A + PU

6. Actual use: Behavioural intention (BI) in turn predicts actual use.

The attitude towards adopting a technology is believed to be the result of personal and social influences and the fact that TAM does not account for social influence is a limitation [Davis et al., 1989; Malhotra and Galletta, 1999].

Figure 4.2 : Technology Adoption Model (TAM) [[Davis et al., 1989]

External Variables (EV)

Perceived usefulness (PU)

Perceived Ease of Use (PEU) Attitude toward use (A) Behavioural Intention to use (BI) Actual system Use

A model for representing the motivational and cultural factors that influence mobile phone usage variety 62 TAM is noted as one of the most influential models in technology adoption research and represents an important theoretical contribution towards understanding information system usage and information system acceptance behaviour [Malhotra and Galletta, 1999]. While the TAM model is mainly applied to explaining the adoption of technology within organizations, the constructs of the model are meant to be fairly general and universal to different types of computer systems and user populations [Malhotra and Galletta, 1999]. However, it has also been criticised for its shortcomings. For example, the attitude towards adopting a technology is believed to be the result of personal and social influences and the fact that TAM does not account for social influence is a limitation [Malhotra and Galletta, 1999].

Over the years several researchers have used the TAM model to explain the attitudes and behaviours of information system users. The following studies are noted for applying and extending the TAM:

• As mentioned before, TAM does not account for social influence in the adoption and utilization of new information systems. Perceived ease of use and perceived usefulness are the only determinants of attitude towards using the technology and behavioural intention to use the technology. In addressing this problem, Malhotra and Galetta [1999] established a theoretical and empirical base for the introduction of social influence through the processes of internalization, identification and compliance with the TAM model. According to their findings, users’ attitudes are directly affected by social influence, while behavioural intentions are indirectly affected.

• Urbaczewski et al. [2002] did research on finding the predictors of use and ease of use in new system acceptance. They propose the addition of culture as a variable that might determine the success or failure of an innovation.

• Meso et al. [2005] applied TAM to study mobile information and communication technology

(mobile ICT) in the least developed countries, specifically sub-Saharan Africa. In addition to the traditional TAM factors of usefulness and ease of use, they found that easier access and greater reliability of the technology contribute significantly towards greater confidence and hence greater use of mobile ICTs.

• Uzoke et al. [2006] used the TAM model to investigate infrastructural, management and

behavioural factors that impact on e-commerce development. They found that infrastructural and management factors exerted considerable influence over the organizational decision to adopt e- commerce while behavioural aspects had a minimal impact.

Considering the studies mentioned, it can be deduced that social and cultural factors influence technology adoption [Malhotra and Galletta, 1999; 2002] though these factors are not modelled by the TAM. Furthermore, the TAM model is based on the assumption of the availability of basic infrastructure and organisational context for the adoption of new technology. If this is not the case then conditions facilitating infrastructure become important in technology adoption.

Venkatesh [2003] developed the Unified Theory of Acceptance and Use of Technology (UTAUT) model to explain user intentions to use an information system and subsequent usage behaviour. The UTAUT was developed through a review and consolidation of the constructs of the following models

A model for representing the motivational and cultural factors that influence mobile phone usage variety 63 (theory of reasoned action, technology acceptance model, motivational model, theory of planned behaviour, a combined theory of planned behaviour/technology acceptance model, model of PC utilization, innovation diffusion theory and social cognitive theory).

According to UTAUT [Venkatesh, 2005], performance expectancy, effort expectancy, social influence and facilitating conditions are the four key constructs that determine usage intention and behaviour. Gender, age, experience, and voluntariness (i.e. the degree to which use of the innovation is perceived as being of free will) are mediating factors in the impact of the key constructs on usage intention and behaviour. An important contribution of UTAUT is to distinguish between mediating factors and determining factors.

The following studies applied the TAM to mobile phones or mobile phone features:

• Kwon and Chidambaram [2000] applied the TAM model to mobile phone adoption. They found

that perceived ease of use significantly affected users' extrinsic and intrinsic motivation, while apprehensiveness about cellular technology had a negative effect on intrinsic motivation.

• Lee et al. [2002] studied user acceptance of the mobile internet and found that social influence and self-efficacy variables significantly affect perceived usefulness and perceived ease of use respectively, while perceived ease of use and perceived usefulness influence actual usage frequency.

• Pedersen [2003] studied the adoption behaviour of early adopters of mobile commerce services. According to his findings, the TAM model should be extended to include both subjective norm and behavioural control norm in order to improve model fit and add explanatory power. Behavioural control includes two components: resources, e.g. time, and financial resources, and self-efficacy. The latter refers to the users’ confidence in their own ability to enact behaviours or use a service. Behavioural control relates to both intention to use and actual use. It reflects the internal and external constraints on behaviour.

• Teo and Pok [2003b] studied the adoption of WAP-enabled mobile phones among Internet users and found that attitudinal and social factors like perceptions of relative advantage (usefulness), risk and image play a significant role in influencing intentions to adopt a WAP-enabled mobile phone.

• Kleijnen et al. [2004] investigated consumer acceptance of wireless finance and found that the variables of perceived cost, system quality and social influence correlated significantly with attitude towards use. The variables of age, computer skills, mobile technology readiness and social influence proved to have moderating effects in the mobile phone usage context.

• Roberts [Roberts, 2004] studied factors in corporate adoption of mobile phones and found security, reliability, digital standards and web connectivity to be the most important technology adoption factors, with customer service the most important non-technology factor.

A model for representing the motivational and cultural factors that influence mobile phone usage variety 64 • Given that cultural factors are encompassed in the social factors, the finding that social factors

influence mobile phone adoption [Peterson, 1994; Teo and Pok, 2003b] provides justification for investigating cultural factors as an influence in mobile phone usage variety

• The importance of infrastructural factors in mobile phone adoption [Kleijnen et al., 2004] means that infrastructural factors will have to be taken into account during the design of this research, e.g. selection of participants with access to similar infrastructure, etc.

TAM therefore provides a useful reference point for the issues to investigate when considering the factors that influence mobile phone usage, although it must be borne in mind that TAM models technology

adoption, while this research seeks to model mobile phone usage. Considering the components of the TAM

as depicted in Figure 4.2, external variables encompass demographic variables as was discussed in Chapter 3. Social and cultural influences (as discussed in Chapter 3) are not a component of the TAM, though they are a component of the TRA on which the TAM is based and were found relevant to technology adoption by several researchers. This supports the aim of this study, namely to investigate cultural factors in mobile phone usage.