Academic journal article Issues in Informing Science & Information Technology

Introducing Students to Business Intelligence: Acceptance and Perceptions of OLAP Software

Academic journal article Issues in Informing Science & Information Technology

Introducing Students to Business Intelligence: Acceptance and Perceptions of OLAP Software

Article excerpt

Introduction

In a fast-changing discipline such as information systems, it is important for academics to keep up with major current trends, and to give students both a strong practical and theoretical insight into areas regarded as important by industry and society. Since 2004 business intelligence has been a "top three" key information systems (IS) management issue and application development area both locally and internationally (e.g. Hart, Berkowitz, Ryan & Waspe, 2004; Luftman & McLean, 2004). On-line analytical processing (OLAP) is a major component of business intelligence that is mature and more accessible to a wider range of people in industry than, say, data mining. Second year students majoring in information systems at the University of Cape Town are given some lectures on business intelligence areas such as data warehousing, OLAP and data mining in their Database Systems course. Since 2004 they have also been given a practical project where they play the role of product, brand or country managers, analyse international sales data stored in multi-dimensional OLAP cubes using a desktop version of the Cognos Powerplay product (www.cognos.com), and report on their findings. Sometimes students have also had to construct the multi-dimensional cube.

In 2006 the course organizers decided to make use of some of the business intelligence facilities offered by the US-based Teradata Student Network (TSN) (Wixom, 2004; www.teradatastudentnetwork.com), instead of just continuing with the previous OLAP tool. The authors' country has until recently had only one national telecommunications provider, resulting in typically low available Internet bandwidth by international standards, due to its exorbitant costs. Initial investigations with the TSN showed that the benefits of having free access to an Internet-based OLAP product and datasets were offset by very slow student access and by certain limitations in the version of the product offered.

The project for 2006 was therefore finally designed to cover analysis of a sales data cube with a desktop version of Powerplay 7 (PP) as well as analysis of a different sales dataset with a web-based version (some functionality excluded) of MicroStrategy Web (MS) (www.microstrategy.com). Students could use PowerPlay at the university or their place of residence, and could access MicroStrategy over the Internet from anywhere once they had registered. As brand manager of certain retail products, they were required to write a report on subsets of each of the two OLAP datasets, compare their perceptions of the two OLAP software products, and comment on their experiences while doing the project. The main aims of the project were to give them practical exposure to using two top-ranked OLAP tools, and to a real-world business situation, as well as to improve their general analytical and writing skills. Students were in addition later requested to complete a Likert-scale questionnaire on their attitude to various questions about each of the OLAP tools. This instrument was based on past technology acceptance research, and provided useful validated material with which course organizers and graduate students could evaluate student reaction to the products, and extend past research. It also enabled comparison of student perceptions of the products with those previously obtained from industry users. In addition, the qualitative and quantitative information gathered provided useful feedback for arranging future student projects.

The paper commences with a few comments on what is commonly referred to as business intelligence, and on OLAP as one of its main components. It then switches to an overview of adoption and acceptance of new technologies, as viewed through the Davis (1989) technology acceptance model (TAM) and its successors. Research objectives are specified in conjunction with an adapted TAM2 model, and the research design is described. Results of both quantitative and qualitative analysis are then given, followed by discussion and conclusions. …

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