In this section the data mining utilisation model to be adopted in this study is discussed. Figure 3.10 depicts a data mining model with utilisation or intention to utilise central in the model. Impacting on utilisation are the factors or reasons identified from previous research that have been found to play a role in utilisation decisions. Readiness to utilise is also built into the model identifying the factors and the influences that have been found to play a role in readiness to implement data mining technology. The final aspect of the model addresses the anticipated impact that the utilisation of data mining techniques will have.
Figure 3.10: Data mining utilisation research model
Utilisation/Intention to Utilise data mining
technology
Technological Organisational Human Resources
External
1. Improved Awareness & Knowledge about data mining
2. Integration of Information for Decision making
3. Better performance of AIS
4. Better decision making
Impact of Utilisation Readiness Factors Individual Characteristics Optimism Innovativeness Perceived usefulness Perceived ease to use
Gender Education Job Function AIS Experience Adopters Groups Adopters Intention to Adopt No Intention to Adopt Refine IT policies and management Influences/reasons in the decision to adopt/not adopt
The four main issues identified as influences/reasons for the utilisation of data mining technologies identified from the literature (technological, organisational, human resources and external factors) were developed as a combination of factors found to influence the adoption of ICT (Chau & Tam, 1997, Ang et al., 2001, Spanos et al., 2002, DAA technologies in organisation (Chang et al., 2003, Nemati & Barko, 2003, Hwang et al., 2004) and knowledge management (KM) technologies in the public sector environment (Syed-Ikhsan & Rowland, 2004a, 2004b). The reasons for not adopting a technology focus more on the first three factors than external issues. Previous literature suggests that reasons such as lack of expertise, lack of top management support were the significant reasons for not adopting data mining technologies (Wah & Abu Bakar, 2002). In this model these issues are important to consider if the Malaysian government is to manage and understand the best approach to implementing data mining tools within the Accounting Information Systems (AIS) in the public sector.
Data mining readiness is embraced in this model. Drivers such as optimism, innovativeness, perceived usefulness and perceived ease of use are addressed along with individual differences that may play a role such as gender, level of education, role within the organisation, years of experience in an AIS function, and whether or not the individual belongs to an adopter group. Optimism and innovativeness were the readiness drivers suggested by Parasuraman (2000) and used by Dahlan et al., (2002) while the two behavioural beliefs (perceived usefulness and perceived ease-to- use) were derived from the technology acceptance model (TAM). Strong readiness will increase the positive attitude toward an intention to use the technology (Davis, 1989, Legris et al., 2003, Riemenschneider et al., 2003, Amoako-Gyampah & Salam, 2004).
It argued in this model that these four variables are the primary determinants of data mining readiness amongst public sector staff and managers. Individual characteristics are expected to play a role in the individual attitude toward information technology, its adoption and reflect on readiness to adopt. Individual characteristics will be used to consider differences between groups (adopters,
intention to adopt, and no intention to adopt) and readiness to implement technology. Gender and level of education have been studied in previous work (Venkatesh & Morris, 2000, Kay, 2006, Agarwal & Prasad, 1999) and has been found to impact on attitudes toward technology. Identifying readiness between different individual’s job function and experience with technology with regard to their role in the accounting information system, that is, whether they manage documents, keep/enter records, produce statements, prepare budgets, or perform an audit function, may also extend the understanding about readiness among public sector staff and managers. Many studies found that experience is positively related to attitude toward technology (Davis, 1989, Agarwal & Prasad, 1999, Venkatesh & Morris, 2000).
Finally the potential impact of data mining utilisation is considered within the model. In terms of the public sector in Malaysia the impact of adopting data mining technologies is vital. The impact will be felt in improved decision making and performance within the Accounting Information System, additionally growth in the awareness of and knowledge of the benefits of adopting this technology would also be expected. Utilisation and leveraging technology has found to be as an enabler to the improvement of the organisation’s performance (Poston & Grabski, 2001). In the business environment for example, it has been shown to improve business performance in client service and client retention (Schlageter, 2005). Accounting firms and related organisations have argued that technological solutions permit result in increased productivity (Schlageter, 2005). The development of technologies will influence changes in accounting methods within the public sector. With the better use of technology, it will enable better performance of the AIS resulting in the production of more timely, accurate, complete and consistent information enhancing the process of decision making. Most users of accounting information require information that is current and continuous in nature (Sutton, 2000). Carrigan et al., (2003) argued that appropriate implementation of new technology and upgrading of core financial management systems would improve financial reporting capability helping managers to make better decisions by obtaining timely information, and enabling them to meet new accelerated reporting deadlines.
The Data Mining Utilisation (DMU) research model developed will identify the relationships between the variables reflecting organisational, technological, human resource and external issues with the adoption and implementation of data mining within the public sector in Malaysia. It explores the level of readiness and its differences in individual characteristics. The impact of data mining toward the performance of AIS and also decision making will be investigated. This model will also assist in exploring the level of awareness and knowledge amongst public servants toward technology itself. The level of awareness and readiness will indicate the intention to use within the department. Any further steps in refining Information Technology policies within the department might be taken from the findings.
3.5.1 Variables in the DMU research model
The variables in the research model have been grouped into the categories identified in the research model developed.
Influential factors and reasons in the utilisation of data mining. Four variables have been identified: (1) Technological (2) Organisational (3) Human resource, and (4) External factors. These four variables were used as they have been found to be influential in many studies on technology adoption and would be likely to play a role in the decision to utilise data mining technologies within the public sector. Specifically looking at reasons for utilisation the factors appear to be internally related rather than reflective of external factors.
Data mining readiness. Individual readiness variables are optimism, innovativeness, perceived usefulness and perceived easy to use. These four variables represent both readiness drivers and beliefs which have been widely used in technology readiness and adoptions.
Impact of utilisation. This will examine the impact of data mining in the AIS and in the decision making process. These variables include awareness and knowledge about data mining, impact on AIS performance and also integration and supporting for better decision making. Awareness and level of knowledge will be used in the model
as they provide insights into the awareness and knowledge of data mining within the public sector. The level of awareness and knowledge is expected to have a relationship with readiness and the intention to adopt data mining technology. While variables representing impact of utilisation on the performance of AIS and on decision making process used in measuring the impact of data mining on those two perspectives.
Individual differences. These are represented by demographics variables including gender, education, job function, work experience and a utilisation variable. Utilisation of data mining technologies is measured through a dichotomous measure of use versus non use7. These differences will be used to investigate their relation to their readiness toward accepting data mining.