Technology Acceptance Model

To design questionnaires, especially those for user research on a particular technology, a model can be drawn upon, which is the Technology Acceptance Model (TAM). This model was firstly proposed by Fred Davis in his doctoral thesis in 1985. Davis (1985) suggests that users’ motivation can be explained by three factors: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Attitude Toward Using the system. The attitude of the user, in turn, was considered to be influenced by the other two factors, with PEOU also having a direct influence on PU. During later experimentation stages, he refined his model to include other variables and modified the relationships that he initially formulated (see the first modified version of TAM in Davis et al., 1989; the final version of TAM in Venkatesh and Davis, 1996; TAM 2 in Venkatesh and Davis, 2000). Meanwhile, other researchers have also applied and improved TAM (see the extended TAM in Venkatesh, 2000; UTAUT model in Venkatesh et al., 2003; TAM 3 in Venkatesh and Bala, 2008). TAM has evolved into a popular model in explaining and predicting system use over time and has been cited in many studies that deal with user acceptance of technology (Lee et al., 2003). For example, Schepers et al. (2005) conducted a research project in a Dutch high-technology company. They modelled and tested two leadership styles (transactional and transformational) as antecedents to PU and PEOU of new technologies. In the surveyed company, transformational leadership positively

influenced PU of the technology whereas transactional leadership did not display any significant effects. Park et al. (2009) collected data from 16 institutions in Africa, Asia, and Central/Latin America, and did a survey on using a digital library system. A path analysis revealed that PEOU of the library system had a significant impact on PU, which ultimately led to behavioural intention to use.

Van Raaij and Schepers (2008) report that the core TAM relationships hold just as well in a Chinese setting as they do in Western countries. The majority of hypothesized relationships are supported by the data. This suggests that, contrary to the findings of Straub et al. (1997) and McCoy et al. (2005), but consistent with the findings of Ong et al. (2004) and Pituch and Lee (2006), TAM does hold across cultures. Besides, Mao et al. (2005) and Schepers and Wetzels (2007) discovers that PU seems important in Western cultures, while PEOU has more relevance in non-Western studies. Obviously, different research leads to different findings. In China, Qi et al. (2009) investigated the reasons why people use mobile data services in China. They presented an extended TAM model and tested it with the data collected from 802 mobile subscribers. Their findings indicated that mobile subscribers’ PEOU had obviously positive influence on usage attitude directly. It also positively affected PU. Additionally, brand experience also had a large influence on subscribers’ attitudes towards mobile data services. This study reports that sufficient training is still an important way to secure the adoption of technology. Yoon (2009) explored the effect of Hofstede’s six dimensions of national culture on consumer acceptance of e-commerce in China. He found that uncertainty avoidance and long-term orientation had moderate effects on the relationship between trust and intention to use, and masculinity also had a moderate effect on the

relationship between PU and intention to use and the relationship between PEOU and intention to use, while power distance and individualism had no significant effects. Guo et al. (2010) researched the social network services (SNS) in China by the use of an integrated model of centrality, trust and technology acceptance. Their findings suggest that centrality, technology acceptance, familiarity and user trust are important variables in users’ intention of using e-socializing services, and SNS providers should take factors like the number of social ties, channel of service promotion and web interface design into consideration when developing strategies. A study by Wu and Chen (2017) is close to this PhD research. By integrating the technology acceptance model (TAM), task- technology fit (TTF) model, features of MOOCs and social motivation, they proposed a unified model to research Chinese users’ continued intention to use MOOCs. They recruited 252 Chinese MOOC users as participants and results show that PU and attitude are key to the continued intention to use MOOCs; PEOU, task-technology fit, reputation, social recognition and social influence have a big impact on predicting continued intention, while PU is an important mediator of the effects on continued intention; PEOU can be affected by individual-technology fit, task-technology fit, and openness; PEOU and social influence have no major influence on attitude; and PU is not affected by individual-technology and openness.

For this PhD research, the Technology Acceptance Model (TAM) was utilised for designing the attitude survey. Some concepts in TAM, such as perceived usefulness, perceived ease of use, perceived enjoyment, perceived quality, intention to accept the technology, and compensation, have been borrowed for designing the statements in the attitude survey (see Section 5.2.3). The definitions are as follows (Hu and O’Brien, 2016):

Perceived usefulness: The degree to which a person believes that machine

translated subtitles would enhance his or her job performance.

Perceived ease of use: The degree to which a person believes that using machine

translated subtitles will be free of effort.

Perceived enjoyment: The extent to which the activity of using machine

translated subtitles is perceived to be enjoyable in its own right.

Perceived quality: The perceived level of the quality of machine translated

subtitles.

Intention to accept machine translated subtitles: A person’s behavioural

intention to accept machine translated subtitles.

Compensation: The degree to which a person believes that he or she has the

ability to comprehend machine translated subtitles using additional inputs.

From the literature reviewed on TAM (e.g.: Venkatesh and Davis, 2000; Legris et al., 2003; Wu and Chen, 2016; Wu et al., 2007), it can be seen that there are rules to follow for the questions used for investigating each concept. Examples are as follows:

1) Perceived usefulness:

Using the system/application improves my performance. Using the system/application increases my productivity. I find the system/application to be useful in my job.

I believe the system/application improves my performance. 2) Perceived ease of use:

My interaction with the system/application is clear and understandable.

Interacting with the system/application does not require a lot of my mental effort.

I find the system/application easy to use. 3) Perceived enjoyment:

I think using the system/application shall be interesting. I enjoy using the system/application.

4) Perceived quality:

The system/application generally function well. I’m satisfied with the system/application. 5) Intention to use:

Assuming I have access to the system/application, I intend to use it.

Given that I have access to the system/application, I predict that I would use it. I plan to use the system/application in the future.

I would recommend the system/application to my friends if they need. 6) Compensation:

I could complete the job using the system/application if there was no one around to tell me what to do as I go.

I could complete the job if I could call someone for help if I got stuck. I could complete the job if I have a lot of time.

I could complete the job if I had just the built-in help facility for assistance.

While the literature on questionnaires reviewed in this chapter helps the researcher to structure the questionnaires and individual questions, TAM approaches help to create the ‘content’ or ‘substance’ of those questions.