Leo L. Pipino

Data quality assessment

How good is a companys data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

Process-Embedded Data Integrity

Despite the established theory and the history of the practical use of integrity rules, data quality problems, which should be solvable using data integrity rules, persist in organizations. One effective mechanism to solve this problem is to embed data integrity in a continuous data quality improvement process. The result is an iterative data quality improvement process as data integrity rules are defined, violations of these rules are measured and analyzed, and then the rules are redefined to reflect the dynamic and global context of business process changes. Using action research, we study a global manufacturing company that applied these ideas for improving data quality as it built a global data warehouse. This research merges data integrity theory with management theories about quality improvement using a data quality lens, and it demonstrates the usefulness of the combined theory for data quality improvement.

A study of the effect of communication channel and authority on group decision processes and outcomes

Abstract Experimental research on Group Decision Support Systems (GDSS) has generally focused on idea generation and choice tasks. The experiments have typically consisted of groups whose members share the same objectives and do not have a formally designated leader. This paper reports on the results of an experiment in which the groups worked on a mixed-motive task. A key feature of the study is that the participants did not have the same information (information asymmetry). The experimental study consisted of a 2×2 factorial design. The two factors were communication channel (face-to-face vs. computer mediated communication) and group leadership mode (group with a leader vs. groups without leader). The results indicate that there are differences in perceived as well as actual performance between groups meeting same place/same time (decision room) and groups meeting same time/different places (distributed communications environment). The presence or absence of a formal leader did not appear to have substantive effects.

Classifying the uncertainty arithmetic of individuals using competitive learning neural networks

Abstract The application of artificial neural network technology to a host of problems in pattern recognition has long been advocated. Several analyses comparing the performance of neural networks to the standard methods for achieving machine classification and machine learning, such as statistical pattern recognition and ID3, have been reported. Typically, supervised learning has been used and the specific learning algorithm has been back propagation. For many classification type problems, a priori categories are not available, that is, one does not know explicitly the number of categories existent nor the boundaries delineating these categories. Therefore, known targets with which to train the network are not available. A supervised learning approach is not appropriate under these circumstances; an unsupervised learning algorithm is required. In this article we report on the use of an unsupervised competitive learning algorithm as a classifier. The network was used to classify individuals into categories based on differences in the manner in which individuals manipulate the uncertainty associated with the chaining of rules. The experiment, from which the data to be classified were obtained, is described, results of the neural network approach are compared to classification using a distance measure and to classification using a standard clustering algorithm.

A comparison of the manipulation of certainty factors by individuals and expert system shells

The treatment of uncertainty in expert system shells is addressed, starting with a review of the modeling of uncertainty by expert system shells. An experiment to replicate earlier work investigating the manner in which individuals manipulate certainty factors in comparison to commercial shells is discussed. Comparisons are made among seven commercial shells, both personal-computer (PC)-based and mainframe-based, and individuals. A significant difference between individuals and shells themselves is indicated. Some implications for both expert system and decision-support-system methodologies are discussed.<<ETX>>

Cost/Benefit Analysis

This chapter contains sections titled: The Challenge, The Cost/Benefit Trade-off, A Case Example, Further Cost/Benefit Analysis Techniques, Concluding Remarks

The application of artificial neural networks to quality control charts

Reports on the development of artificial neural networks that function as alternatives to conventional quality control charts. Multilayered feedforward networks using a backpropagation learning algorithm were trained and tested. The results illustrate the feasibility of using artificial neural networks to detect out-of-tolerance conditions in a manufacturing process.<<ETX>>

The processing of numerical uncertainty associated with the components of if then rules: experiments with human subjects

The authors present the results of an experiment that was conducted to explore how individuals interpret and manipulate uncertainty in basic logical operations. Results indicate that the commonly used models are not as universally appropriate as has been assumed. These results have implications for the process of knowledge acquisition, the design of the system/user interface, and general issues of system development. Collecting information to construct an expert system, duplicating reasoning by using an expert system, and displaying results to decision makers require an understanding of how people actually reason with and use uncertain information.<<ETX>>

Determinants of adoption for open-source office applications: A plural investigation

ABSTRACT This article focuses on the determinants influencing organizational adoption of vendor-supported open-source software applications or open-source office applications (OSOAs). We employed a plural investigation with QUAL/QUAN sequence. Empirical analysis of the data obtained from 257 IT managers identified various technological, organizational, and environmental determinants that influence the OSOA adoption either positively or negatively. These findings offer valuable insights for IT managers and OSOA vendors which can guide them in formulating appropriate strategies for OSOA adoption.

Artificial Neural Networks as Alternatives to Statistical Quality Control Charts in Manufacturing Processes

The concept of quality has reentered the vocabulary of American business. The perception, whether founded in reality or not, that American products are inferior to their foreign counterparts, has contributed to the competitive disadvantage now faced by many American firms.