In identifying readiness to adopt data mining techniques a major issue is both the willingness and capability of the work force to accept technology. Human resources primarily on their readiness toward accepting data mining technology can be argued to be the major issue to consider when undertaking or adopting new technology within any organisation (Dahlan et al., 2002, Wah & Abu Bakar, 2002). In this study issues of technology readiness and acceptance by those who actually use and
Organisational culture Organisational structure Technology People/Human resources Political directive Knowledge transfer performance Knowledge Assets
implement such technology will be important. For example, Wah and Abu Bakar (2002) tested end-users (warehouse administrators and decision makers) influence on the implementation of data mining tools. They found that the end-user played an imperative role in the successful implementation of data mining tools. They found issues related to the end-user (lack of knowledge about data mining and lack of required expertise) to be significant factors affecting the decision in adopting data mining.
An exploratory study done by Dahlan et al., (2002) addressed the readiness of employees in adopting data mining technologies. A Data Mining Readiness Index (DMRI) was used to gain a better understanding of the employees’ Data Mining Readiness (DMR). Using this index a higher score indicated that the employee was likely to be more effective in a data mining-support role. Contextual variables (organisation, cultural and strategic) that contribute to the employees’ DMR were used in their study. In developing the model for their study they incorporated change management issues, the organisation readiness model, technology acceptance model (TAM) and analytical capability model.
Figure 3.8 illustrates the dependent variable (employees’ DMR) and the independent variables (business strategy, users’ skills and experience, organisational and culture, and technology and data quality) were used in their study.
Figure 3.8: Data mining readiness framework adapted from Dahlan et al., (2002)
Independent Variables
Clarity of the business Strategy User’s skills & experience Organisation & culture Technology & Data quality
Optimism (OPT) Innovativeness (INN) Discomfort (DIS) Insecurity (INS) Dependent Variable Employees’ Data Mining Readiness
Four dimensions were used in measuring readiness. These were (1) Optimism: A positive view of technology and a belief that it offers people increased control, flexibility, and efficiency in their lives. (2) Innovativeness: A tendency to be a technology pioneer and thought leader. (3) Discomfort: A perceived lack of control over technology and a feeling of being overwhelming by it. (4) Insecurity: Distrust of technology and scepticism about its ability to work properly (Parasuraman, 2000). These four dimensions were divided into two domain feelings or beliefs about technology. Parasuraman (2000) included these in the technology readiness index (TRI) he developed. Technology readiness is defined as ‘people’s propensity to embrace and use new technology for accomplishing goals in home life and at work’ (Parasuraman, 2000, p.308).
The Technology acceptance model (TAM) is a useful theoretical model in understanding and explaining the behaviour towards Information Technology (IT) implementation. Statistically reliable results have revealed the tools to be a good model (Ndubisi & Jantan, 2003, Legris et al, 2003) in measuring technology acceptance. Most of the literature on Information Technology adoption focuses on acceptance models which relates to perceptions and beliefs to attitudes, behavioural intention and technology usage (Dahlan et al., 2002, Ndubisi & Jantan, 2003, Legris et al., 2003, Zain et al., 2004).
Two specific behavioural beliefs (perceived usefulness and perceived ease-of-use) are suggested by TAM which reflects on the individual’s behavioural intention to use the technology (Legris et al., 2003, Riemenschneider et al., 2003, Amoako-Gyampah & Salam, 2004). Perceived usefulness is defined as the extent to which a potential adopter views and believes a particular technology can offer value over alternative ways of performing the same task. In other word, ‘the degree to which a person believes that using a particular system would enhance his or her job performance’ (Davis, 1989, p.320) while perceived ease-of-use refers to the degree to which a potential adopter views and expects that the usage of a particular technology will be simple and relatively free of effort.
Figure 3.9: Technology acceptance model adapted from Legris et al., (2003)
New technology utilising data mining techniques is perceived to be useful, easier to use and less complex, has a higher likelihood of acceptance and implementation by potential adopters. Moreover, positive feelings (optimism and innovativeness) increase their readiness to accept the technologies. Positive beliefs and readiness to use technology will encourage employees to adopt technology. In this study, readiness toward accepting data mining will be measured by two readiness drivers (optimism and innovativeness) and two behavioural beliefs (perceived usefulness and perceived ease to use).
Two readiness drivers suggested by the Technology Readiness Index (Parasuraman, 2000) which were used to assess attitude toward computer-based technology, was adopted in the readiness study undertaken by Dahlan et al., (2002) in Malaysia. They found these two drivers were appropriate measures to evaluate level of data mining readiness among respondents. The two perspectives of behavioural beliefs have been adopted in many technological adoption studies (see Legris et al., 2003, Riemenschneider et al., 2003, Amoako-Gyampah & Salam, 2004). Strong perceptions of usefulness and ease of use would be expected to increase the intention to adopt data mining technology for example. A combination of these two readiness drivers and two behavioural beliefs are appropriate to this study. Therefore, readiness to adopt data mining defined as the possession by the individual worker of a positive attitude, reflecting both optimism and innovativeness toward adoption or use, strong positive perceptions toward learning new skills and ease of use and to the perceived usefulness of data mining technologies.
Perceived Usefulness External Variables Perceived ease to use Attitude towards Behavioural Intention to use Actual System Use