ARISTÓTELES 141 identifica con el grado sumo de lo deseable: dicha sustancia es, pues,

The study set out to investigate a number of research questions and objectives that the research was to fulfil. Utilisation of data mining was found to be central to the framework indicating that technological, organisational, human resources and external issue were important in the decision as whether to adopt or not to adopt the technology. Readiness toward data mining technology which measured through optimism, innovativeness, perceive usefulness and perceive ease to use, contributes to the strong intention to adopt data mining technology. Individual differences in regards to readiness has provides a greater understanding in regard to characteristics of respondents. Knowledge and awareness was also found to be associated with the willingness and intention to utilise such technology. The utilisation of data mining was found to have a significant impact on the creating a better performance of the accounting information system and also improving the process of decision making.

Technological, organisational, human resource and external issues were found to be significant factors (H1) in the decision to adopt and utilise data mining. Technological, organisational and human resources were also found to be significant reasons (H2) for those who choose not to utilise data mining. Other findings from the qualitative data, indicate that good infrastructure, on going training, workshop and other awareness programs in developing human capital would assist in ensuring the successful implementation of new technology including a data mining technologies. It is a challenge to the public sector to successfully implement any new technology since it will involve many levels of implementation and various issues need to be considered for example in attempt to increase staff’s awareness and capability dealing with new innovations of technology. Continuous programs with interactive features of hand-on training besides some other strategy would be good to be considered. This is because human capital seen to be the most important factor in any technological implementation projects.

The data has revealed that the public sector does have good programs in implementing technology utilisation within departments with courses, hand-on training and awareness programs. Awareness and knowledge about such technology was found to be correlated with the intention to use data mining technology (H3). These programs have to be concurrently brought together with good leadership which reflects the views that top management are one of the major factors in rolling out a good implementation of any technology. From the survey and the interviews, it appears that top management are generally very supportive of technology developments. All public sector staff for example are required to attend professional development courses for at least 7 days per year to develop a range of skills including the use of technology. This policy reflects the concern of the Malaysian Prime Minister on the need to enhance human capital, the development of technological skills being one of them.

Although data mining technology was not widely adopted, the readiness and level of intention to adopt reflects a strong indication that the adoption of this technology is favored. Results indicated high levels of optimism, innovativeness and perceptions of

ease of use, and usefulness towards data mining technology. It also has confirmed that optimism and innovativeness are the key drivers to data mining readiness which was also found by Dahlan et al., (2002). In the process of developing a model for this study a combination of Dahlan’s et al., (2002) data mining readiness framework and elements of the Technology Acceptance Model (TAM) were adopted to measure technological readiness. These two models were found to offer a good foundation from which to develop a new perspective on measuring readiness among workers or public servants towards the adoption of new technology. Further analysis on readiness, the study found that differences in gender, job function and utilisation groups make no difference in the readiness of public sector department staff toward this technology (H4, H6 and H8). However, experience in the Accounting Information System (AIS), and the level of education are reflected in different levels of readiness toward the adoption of data mining technology (H5 and H7).

The summary shown in Figure 7.1 both summarises and demonstrates the interaction between the variables that were investigated in this study. This summary draws together each of the hypotheses demonstrating how they have been used to achieve the objectives of this study and offer insights into the utilization of, and attitudes toward data mining in the Malaysian public sector.

Figure 7.1

Framework for understanding the relationships between variables in the utilisation of data mining47

Identified within the thesis factors that influence the decision to utilize data mining technology were discussed. These were discussed as technological, organizational, human resource and external influences, and technological, organizational, and human resource reasons. Readiness to implement data mining technology was

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‘H’ refers to each hypothesis tested.

Intention to use

Data Mining Readiness

Optimism Innovativeness Perceived usefulness Perceived ease to use Utilisation of data mining

technology Influence: Technological Organisational Human Resources External H1 Reasons: Technological Organisational Human Resource H2 1. Better performance of AIS 2. Better decision making Influence factors/Reasons Individual Differences Impact Knowledge about data mining No Differences: Gender (H4) Job Function (H6) Adopters Groups (H8) Differences: Education (H5) AIS Experience (H7) H10 H9 H3

discussed in terms of knowledge possessed regarding sata mining. Readiness was investigated in the context of optimism, innovativeness, perceived usefulness and perceptions of ease of use. Knowledge and the decision to utilize was recognized as influencing impact in terms of better AIS performance and improved decision making capability. Testing was also undertaken for individual differences – whether differences in gender, job function, adopter versus non adopter, education levels and experience utilizing the AIS.

Most of the respondents indicated their departments did not have any specific data mining software but did have a positive view in accepting it, and utilising it in the future. Knowledge and awareness about data mining plays a role in shaping perception and behaviour of officers and staff. The study found that limited knowledge is associated with lower expectations of the impact data mining could have on their accounting systems and the decision making process (H9). Departments using data mining technology would appear to have better accounting knowledge and be in a position to make better financial decisions. The study has found that the ability to utilise data mining would have an impact to the performance of accounting information systems (H10).