Re-examining information systems user performance: Using data mining to identify properties of IS that lead to highest levels of user performance

As competitive pressures increase, managers try to realize every bit of productivity from people, business processes and new information technologies (IT). This leads one to ask, how can managers configure information systems to achieve higher levels of performance from end users? In this regard, managers continually seek advice on how to meet the promises and expectations of continued increases in productivity through the use of IT. However, results from research on how to achieve higher performance through the use of IT in organizations has been mixed. Consequently, it has been difficult for IS researchers to give managers any advice on investing in specific aspects of IS that would lead to the highest performance possible. We focus on this question in this research. We use a data mining approach to tease out information about specific characteristics of IS that managers can manipulate to achieve desired outcomes with regards to individual performance. Our findings offer both researchers and managers significant new knowledge that can make a difference to IT user performance research theory and the practice of user performance management. Further, our research method offers a novel approach to linking theory and practice in IS research, a problem that is of great concern to many IS researchers. The approach is generalized and can be implemented by academic or industry researchers who are interested in generating hypotheses from data for the purpose of theoretical or applied research.     

An approach for incorporating quality-based cost–benefit analysis in data warehouse design

Data are considered to be important organizational assets because of their assumed value, including their potential to improve the organizational decision-making processes. Such potential value, however, comes with various costs, including those of acquiring, storing, securing and maintaining the given assets at appropriate quality levels. Clearly, if these costs outweigh the value that results from using the data, it would be counterproductive to acquire, store, secure and maintain the data. Thus cost–benefit assessment is particularly important in data warehouse (DW) development; yet very few techniques are available for determining the value that the organization will derive from storing a particular data table and hence determining which data set should be loaded in the DW. This research seeks to address the issue of identifying the set of data with the potential for producing the greatest net value for the organization by offering a model that can be used to perform a cost–benefit analysis on the decision support views that the warehouse can support and by providing techniques for estimating the parameters necessary for this model.     

Building ontology based knowledge maps to assist business process re-engineering

Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.     

Using regression splines to assess the impact of information technology investments on productivity in the health care industry

Abstract. This paper explores the impact of information technology (IT) investments on productivity using a new technique, multivariate adaptive regression splines (MARS). We believe that it provides additional insights on the nature of the impact of IT investments on productivity. The results from our study are compared with findings from a previous study that has also used the same data set. While the results of a previous study indicate that IT investments have a positive but uniform impact on productivity, our study suggests that the impact of IT on productivity is not uniform but is contingent on other complementary factors. Our findings describe that the complementary relationship exists between IT and non‐IT related investments. Thus, improved organizational productivity cannot be expected from investment in IT alone but only together with non‐IT investments. Our findings also point out that further investment may not necessarily bring on higher organizational productivity.     

Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework

We explore the theoretical foundations on how firm and IT characteristics explain the market value variations in e-commerce initiatives by examining the announcements of 946 e-commerce initiatives in the public media. Our approach combines the Event study methodology and Decision tree induction to examine the main and interaction effects of IT and firm characteristics on Cumulative Abnormal Returns (CAR). In particular, we generate complex interaction models that can guide e-commerce investment decisions so managers can know, for example, which combination of IT and firm characteristics are more likely to be viewed positively by investors. The selected study variables as well as explanation of the proposed framework are informed by innovation, resource-based view, transaction cost economics and complementarity theories. We have inductively developed a set of propositions that can be deductively tested to assess the validity of our proposed theoretical framework. Hence our study provides an initial roadmap for theory development on e-commerce and CAR.     

The communicative power of knowledge visualizations in mobilizing information and communication technology research

ABSTRACT

This editorial explores knowledge visualization, a field of study that investigates the power of visual formats to represent knowledge, as a strategy to enhance knowledge mobilization of results from ICT4D research. We highlight the fact that there are evidence-based guidelines for creating and crafting visualizations in academic writing. We also provide some visualization examples that highlight general knowledge visualization criteria such as anchor and extend, familiarity, clarity and consistency, include text, prudent simplicity and aesthetics. Although visualization is not the central theme of any of the papers in this issue, the papers offer a variety of visualization techniques as appropriate to the knowledge domain.     

Ontology-based data mining model management for self-service knowledge discovery

Data mining (DM) models are knowledge-intensive information products that enable knowledge creation and discovery. As large volume of data is generated with high velocity from a variety of sources, there is a pressing need to place DM model selection and self-service knowledge discovery in the hands of the business users. However, existing knowledge discovery and data mining (KDDM) approaches do not sufficiently address key elements of data mining model management (DMMM) such as model sharing, selection and reuse. Furthermore, they are mainly from a knowledge engineer’s perspective, while the business requirements from business users are often lost. To bridge these semantic gaps, we propose an ontology-based DMMM approach for self-service model selection and knowledge discovery. We develop a DM³ ontology to translate the business requirements into model selection criteria and measurements, provide a detailed deployment architecture for its integration within an organization’s KDDM application, and use the example of a student loan company to demonstrate the utility of the DM^3.     

How Internet Security Breaches Harm Market Value

Decision-tree induction is an effective technique for examining the factors influencing abnormal stock market returns when security breaches are announced in the public media. In this article, the authors extend a previous study, specifically identifying new relationships between abnormal returns and firm and attack characteristics and subject them to traditional statistical testing. They relate their results to the confidential, integrity, and availability dimensions of information security and discuss the findings' technical and managerial implications. The Web extra for this article presents detailed event data.