The Fox-Base Approach

Chapter II introduced online cocitation counts retrieval using Dialog Classic and citation index files. Certainly Dialog Classic is an attractive alternative in that the user is using the readily available bibliographic databases and retrieval software. The majority of ACA research has used ISI databases and Dialog Classic to retrieve cocitation counts. However, this approach has well-known technical limitations as discussed earlier. They include the issue of Multiple Authorship, Name-Homographs, and Synonyms. This chapter introduces an alternative approach to retrieving a cocitation count from the custom databases through the system we have designed and implemented. Custom database and retrieval systems need time and investment to develop, but they can manage most of the technical limitations discussed. The book presents two other alternative approaches that can be used to retrieve cocitation counts in lieu of using ISI citation index files and Dialog Classic. This chapter introduces the fox-base approach in developing custom databases and the cocitation matrix generation system. The first part is concerned with the design of databases. The second part describes the cocitation retrieval system. We also discuss how our system can eliminate or minimize the technical limitations of the Thomson ISI database and Dialog Classic Software system.

The Changing Structure of Decision Support Systems Research

This chapter extends an earlier benchmark study (Sean B. Eom, 1995) which examined the intellectual structure, major themes, and reference disciplines of decision support systems (DSS) over the last two decades (1960-1990). Factor analysis of an author cocitation matrix over the period of 1990 through 1999 extracted 10 factors, representing 6 major areas of DSS research: group support systems, DSS design, model management, implementation, and multiple criteria decision support systems and five contributing disciplines: cognitive science, computer supported cooperative work, multiple criteria decision making, organizational science, and social psychology. We have highlighted several notable trends and developments in the DSS research areas over the 1990s.

ACA Analysis with the 15.0.0 Verison of SPSS for Windows

This chapter briefly introduces the use of SPSS version 15.0 to conduct ACA analysis. The SPSS accepts datafiles in many different formats including spreadsheets, database files, tab-delimited, and other types of ASCII text files. Assuming that cocitation frequency counts are stored in a spreadsheet file in Excel, we demonstrate each step of ACA analysis to produce outputs using factor, cluster, and multi-dimensional scaling analyses.

Multidimensional Scaling

This chapter discusses multidimensional scaling (MDS) procedures. MDS is a class of multivariate statistical techniques/procedures to produce two or three dimensional pictures of data (geometric configuration of points) using proximities among any kind of objects as input. Three SAS procedures (MDS, PLOT, and G3D) are necessary to convert the author cocitation frequency matrix to two or three dimensional pictures of data. The distance matrix produced earlier by using xmacro.sas and distnew.sas programs should be converted to a coordinate matrix, to produce twodimensional plots, and annotated three-dimensional scatter diagrams. This chapter also discusses how to label data points on a plot. The annotate facility in the SAS system produces figures with the name of the author on each data point. The PROC MDS procedure includes many of the features of the ALSCAL procedure.

The Distance and Cluster Procedure

This chapter describes the distance and cluster procedure of the SAS system. SAS version 9 introduced the proc distance procedure. All previous versions of SAS used two programs (xmacro.sas and distnew.sas) to process a transposed cocitation matrix (input) to produce a distance matrix (output). Cluster analysis is a data reduction technique for grouping various entities (individuals, variables, objects) into clusters so that the entities in the same cluster have more similarity to each other with respect to some predetermined selection criteria. The first section of this chapter explains the creation of a distance matrix, which is the input to the cluster procedure. The second part of this chapter focuses on the PROC CLUSTER statement which sets out the CLUSTER procedure steps. This chapter also includes the discussions of interpreting results of cluster analysis.

Principal Component Analysis Using the Factor Procedure

This chapter describes the factor procedure. The first section of the chapter begins with the definition of factor analysis. This is the statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables (factor). ACA uses principal component analysis to group authors into several catagories with similar lines of research. We also present many different approaches of preparing datasets including manual data inputs, in-file statement, and permanent datasets. We discuss each of the key SAS statements including DATA, INPUT, CARDS, PROC, and RUN. In addition, we examine several options statements to specify the followings: method for extracting factors; number of factors, rotation method, and displaying output options.

The Intellectual Structure of Decision Support Systems Research (1969-1989)

This is the capstone chapter that shows how the concepts, tools, and techniques discussed in each of the previous chapters can be applied in conducting author cocitation analysis using a real data in the DSS area. The step-by-step procedures are shown in detail from the preparation of data file in Excel format and importing the file to the SAS system for multivariate statistical analysis. This chapter also guides the readers through the process of analyzing the results of principal components analysis, cluster analysis, and multi-dimensional scaling. The chapter also shows how to apply different criteria to select the optimal number of factor solutions, cluster solutions, and evaluating the acceptability of multi-dimensional scaling outputs. This chapter reports part of the intellectual structure of the DSS field by means of an empirical assessment of the DSS literature over the period 1969 through 1989. Three multivariate data analysis tools (factor analysis, multidimensional scaling, and cluster analysis) are applied to an author cocitation frequency matrix derived from a large database file of comprehensive DSS literature over the same period. Four informal clusters of decision support systems (DSS) research subspecialties and a reference discipline were uncovered. Four of these represent DSS research subspecialties—foundations, model/data management, user-interface/individual differences, and group support systems. One other conceptual grouping defines a reference discipline of DS—organizational science.

Building Databases and the Cocitation Counts Generation System Using Microsoft Excel Program in Visual Basics

This chapter shows another alternative approach of building citation database and retrieval system using the spreadsheet program, Microsoft Excel. McIntyre built a custom database based on the Clothing and Textile Research Journal (CTRJ), covering from 1990 to 2006 as part of his master’s thesis. The database includes all the author citations, citations sorted by article, and the top cited author’s cocitation frequencies.

Overview of Author Cocitation Analysis Procedures

The previous two chapters examined the two alternative approaches of retrieving cocitation counts using custom databases and cocitation frequency counts extraction systems. The cocitaion frequency counts are the inputs to the SAS or SPSS systems for multivariate statistical analysis. The primary purpose of this chapter is to overview several important steps in author cocitation analysis. ACA consists of the six major steps beginning with the selection of author sets for further analysis, then collection of cocitation frequency counts, statistical analysis of the cocitation frequency counts, and the validation and interpretation of statistical outputs.

Diagonal Values in ACA

Diagonal values in the cocitation frequency counts matrix are a fundamental issue in ACA study. Diagonal values are the co-citation frequency counts between the author himself/herself excluding self-citation. Retrieving exact values of diagonal values in the co-citation matrix requires a manual and time consuming procedure. For that reasons, ACA researchers suggested many different approaches to create, not retrieving the real values, the diagonal cells in the cocitation matrix. They include the mean cocitation count, missing values, zeroes, highest off-diagonal counts, adjusted off-diagonal values, and the number of times cocited with himself/herself. The majority of ACA researchers seem to prefer to use either the adjusted value approach by adding three highest off-diagonal values and divided by two or the missing value approach. This chapter empirically examines the impact of these different approaches on the ACA outcomes. Based on the results of this study, if the pure cocitation counts are not used, the next best alternatives are as follows. They are the missing value approaches, mean cocitation value approach, and the highest off-diagonal value approaches in the order of the highest total variance explained.