TECHNOLOGY READINESS AND ACCEPTANCE MODEL (TRAM)

As the influence of individual TR dimensions on technology evaluation and adoption behaviour came to be supported, scholars moved forward by investigating the aggregate effect of the individual dimensions on the form of technology readiness itself. Three such studies are described below.

Sophonthummaparn and Tesar (2007) conducted research aimed at examining the effect of technology readiness on cellular telephone users in association with their willingness to subscribe to commercial Short Message Service (SMS) services. They proposed that there are two possible channels for sending commercial SMSs to customers. Using the first channel, a company can send an SMS directly to customers using its own customer database, while the second channel is one in which a company buys services from a commercial SMS service agent. This agent would be a company that buys customer databases from various telecommunications operators which provide basic demographic information on cellular telephone subscribers. This agent will then categorise customers according to their demographic characteristics, and offer a service to a company wishing to send a commercial SMS to a specific group of customers drawn from the demographic database. It is believed that the second channel could be used for advertising and promotional purposes to attract new customers. It has been argued that the intention to use the second channel is influenced by the Technology Readiness Index of cell phone users, as illustrated in Figure 2.16.

Sophonthummaparn and Tesar (2007) were unable to find support for any aggregate TR

influence on technology adoption behaviour. Their results showed that there is no statistical difference in technology readiness scores between people who would subscribe and those who would not subscribe to commercial SMS services in this study’s sample.

Consequently, it can be said that technology readiness plays a minor role in explaining cellular telephone users’ propensity towards subscribing to commercial SMS services.

With regard to this result, the authors explained that these days most people have owned or do own a cellular telephone (98.5%) and thus know how to send and receive SMS messages. The cellular telephone has become such a common communication device that now anyone can easily obtain one. Indeed, it could be argued that the cellular telephone and its common functions are no longer considered new technology. In this situation, TRI may play a minor role in explaining such adoption.

FIGURE 2.16 A MODEL OF THE EFFECT OF TECHNOLOGY READINESS INDEX ON CELL PHONE USERS’ INTENTIONS TO SUBSCRIBE TO COMMERCIAL SMS

Source: Sophonthummaparn and Tesar (2007)

These researchers examined TR’s influence on the adoption of some relatively recent Self-Service Technologies (SSTs), including e-banking, online ticketing and electronic retailing. Their research endeavour was inspired by the proposal by other researchers positing that traits affect the adoption of SST and technology readiness is the dominant trait positively correlating with technology use.

The authors proposed that TR is positively associated with people’s attitude toward using SST. It is also correlated positively with their intention to adopt SST. Technology readiness is also positively associated with their responses to SSTs, in terms of perceived

quality, satisfaction and loyalty. In this context, optimism and innovativeness as contributors to technology readiness are positively related to all responses to technology (technology evaluation, attitude and intention to adopt), whereas discomfort and insecurity as inhibitors are negatively related to those responses.

The relationships expected between the variables were indeed found in this study, showing that TR is positively related with customer attitudes to using SST. Their analyses also confirmed that overall TR is positively associated with customers’ willingness to use SST. Liljander et al. also performed a t-test to compare the TR of adopters and non-adopters. The results revealed that adopters possess higher TR than do non-non-adopters.

However, TR explained only a small proportion of the variance in the dependent variables.

The role of TR in the adoption of new technology has also been confirmed by Lin et al.

(2007), who integrated technology readiness into TAM in the context of consumer adoption of e-service systems. By so doing, the researchers expected to improve the explanatory power of TR in the mobile technology adoption context, which had not been supported in previous research. Their model is called TRAM (Technology Readiness and Acceptance Model). The initiative to integrate TR and TAM emerged from an intuitive idea that the two models are interrelated. TAM is intended to analyse the adoption process from a particular system aspect, while TR is based on an individual’s predisposition to adopt new technologies. Lin et al.’s web-based survey results confirmed that TRAM can be used to integrate individual factors with system characteristics as the basis for analysing technology adoption as part of a more comprehensive approach. The psychological process verified by TRAM to follow a ‘TRPEOUPUUI’ mechanism bridges the contradiction between the two main opposing theories.

The research of Liljander et al. (2006) and Lin et al. (2007) has given us strong empirical findings about the role of TR in the adoption of new technology. Their studies have encouraged others to investigate the aggregate effect of TR in new technology adoption as a more realistic approach. Separating the individual dimensions’ effects on technology adoption not only adds unnecessary complexity but is also unrealistic. It was originally stated that individual dimensions will interact with each other to form overall technology readiness. Thus, it is the technology readiness that affects an individual’s evaluation of technology, rather than its individual dimensions. This understanding has led to what is known as the Technology Readiness and Adoption Model, or TRAM (Lin et al. 2007), presented below in Figure 2.17.

FIGURE 2.17 TRAM MODEL

Source: Lin et al. (2007)

Although the validity of TRAM still requires further investigation, there is a need to perform a detailed review of this model. With the inclusion of technology readiness as an individual trait factor in the model, TRAM is believed to have the potential to better predict technology acceptance. The review should include an analytical comparison with TAM since this model is a major constituent of TRAM. The results obtained can be used to establish the baseline framework for further developing a more integrative model that includes moderator variables.