Applying Importance-Performance Analysis to Information Systems: An Exploratory Case Study

By Ainin, Sulaiman; Hisham, Nur Haryati | Journal of Information, Information Technology, and Organizations, Annual 2008 | Go to article overview

Applying Importance-Performance Analysis to Information Systems: An Exploratory Case Study


Ainin, Sulaiman, Hisham, Nur Haryati, Journal of Information, Information Technology, and Organizations


Introduction

Information technology (IT) and information systems (IS) continue to be highly or significantly debated in today's corporate environments. As IT spending grows and becomes commoditized and as essential as electricity and running water, many organizations continue to wonder if their IT spending is justified (Farbey, Land, & Targett, 1992) and whether their IS functions are effective (Delone & McLean, 1992). IT and IS have evolved drastically from the heyday of mainframe computing to the environment that has reached out to the end-users. In the past, end-users interacted with systems via the system analyst or programmer who translated the user requirements into system input in order to generate the output required for the end-users' analysis and decisionmaking process. However, now end-users are more directly involved with the systems as they navigate themselves typically via an interactive user interface, thus assuming more responsibility for their own applications. Therefore, the ability to capture and measure end-user satisfaction serves as a tangible surrogate measure in determining the performance of the IS function and services, and of IS themselves (Ives, Olson, & Baroudi, 1983). Besides evaluating IS performance, it is also important to evaluate whether IS in an organization meet users' expectations. This paper aims to demonstrate the use of ImportancePerformance Analysis (IPA) in evaluating IS.

Research Framework

Importance-Performance Analysis

The Importance-Performance Analysis (IPA) framework was introduced by Martilla and James (1977) in marketing research in order to assist in understanding customer satisfaction as a function of both expectations concerning the significant attributes and judgments about their performance. Analyzed individually, importance and performance data may not be as meaningful as when both data sets are studied simultaneously (Graf, Hemmasi, & Nielsen, 1992). Hence, importance and performance data are plotted on a two dimensional grid with importance on the y-axis and performance on the x-axis. The data are then mapped into four quadrants (Bacon, 2003; Martilla & James, 1977) as depicted in Figure 1. In quadrant 1, importance is high but performance is low. This quadrant is labeled as "Concentrate Here", indicating the existing systems require urgent corrective action and thus should be given top priority. Items in quadrant 2 indicate high importance and high performance, which indicates that existing systems have strengths and should continue being maintained. This category is labeled as "Keep up good work." In contrast, the category of low importance and low performance items makes the third quadrant labeled as "Low Priority". While the systems with such a rating of the attributes do not pose a threat they may be candidates for discontinuation. Finally, quadrant 4 represents low importance and high performance, which suggests insignificant strengths and a possibility that the resources invested may better be diverted elsewhere.

The four quadrants matrix helps organizations to identify the areas for improvement and actions for minimizing the gap between importance and performance. Extensions of the importance-performance mapping include adding an upward sloping--a 45-degree line to highlight regions of differing priorities. This is also known as the iso-rating or iso-priority line, where importance equals performance. Any attribute below the line must be given priority, whereas an attribute above the line indicates otherwise (Bacon, 2003).

[FIGURE 1 OMITTED]

IPA has been used in different research and practical domains (Eskildsen & Kristensen, 2006). Slack (1994) used it to study operations strategy while Sampson and Showalter (1999) evaluated customers. Ford, Joseph, and Joseph (1999) used IPA in the area of marketing strategy. …

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