Clustering Marketing Professionals Based on Ethical Evaluations Marketing Research Help

Cluster analysis can be used to explain differences in ethical perceptions by using a large multi-item,multidimensional scale developed to measure how ethical different situations are. One such scale was developed by Reidenbachand Robin.This scale has 29 items that compose five dimensions that measure how a respondent judges a certain action. For illustration, a given respondent will read about a marketing researcher that has provided proprietary information of one of his clients to a second client. The respondent is then asked to complete the 29-itemethics scale.For example, the respondent marks the scale to indicate if this action is:

                               Just: _:_:_:_:_:_:_: Unjust
Traditionally acceptable: _:_:_:_:_:_:_: Unacceptable
Violates: _:_:_:_:_:_:_: Does not violate an unwritten contract

This scale could be administered to a sample of marketing professionals. By clustering respondents based on these29 items, two important questions should be investigated. First, how do the clusters differ with respect to the five ethical dimensions in this case, Justice, Relativist, Egoism, Utilitarianism, and Deontology? Second, what types of firms compose each cluster?Theclusterscouldbe described in terms of NorthAmerican IndustryClassification System (NAlCS) industrial category,fum size, and firm profitability.Answers to these two questions should provide insight into what types of fums use what dimensions to evaluate ethical situations. For instance, do large firms fall into a different cluster than small firms? Do more profitable firms perceived questionable situations more acceptable than less profitable fumes? Anempirical study conducted recently comparedTaiwaneseand U.s. perceptionsof corporate ethics. A self-administered questionnaire was used that consisted of five measures.One of measures, individual moraI values, was measured usingtheReidenbach andRobinscale.Results showed that in both national cultures, individual perceptions of corporate ethics appear to determine organizational commitment more than individual moral values.

Statistical Software 

We discuss the use of SPSS and SAS in detail in the subsequent sections. Here, we briefly describe the use of MINITAB. In MINITAB, cluster analysis can be accessed in the Stat> Multivariate>Cluster Observation function. Also available are Clustering of Variables and Cluster K-Means. Cluster analysis is not available in EXCEL.

SPSS and SAS Computerized Demonstration Movies  

We have developed computerized demonstration movies that give step-by-step instructions for running all the SPSS and SAS Learning Edition programs that are discussed in this chapter. These demonstrations can be downloaded from the Web site for this book. The instructions for running these demonstrations are given in Exhibit 14.2.

SPSS and SAS Screen Captures with Notes 

The step-by-step instructions for running the : arious SPSS and SAS Learning Edition programs discussed in this chapter are also illustrated in screen captures with appropriate notes. These screen captures can be downloaded from the Web site for this book

SPSS Windows 

In SPSS, the main program for hierarchical clustering of objects or cases is HIERARCHICAL CLUSTER. Different distance measures can be computed, and all the hierarchical clustering procedures discussed here are available. For nonhierarchical clustering, the K-MEANS CLUSTER program can be used. This program is particularly helpful for clustering a large number of cases. The TWOSTEP CLUSTER procedure is also available. To select these procedures using SPSS for Windows, click:

Analyze>Classify>Hierarchical Cluster …
Analyze>Classify>K-Means Cluster ….
Analyze>Classify>TwoStep Cluster ….

The following are the detailed steps for running hierarchical cluster analysis on attitudinal data (VI to VJ ofTable 20.1.

  1. Select ANALYZE from the SPSS menu bar.
  2. Click CLASSIFY and then HIERARCHICAL CLUSTER.
  3. Move “Fun [vII,” “Bad for Budget [v2).” “Eating Out [v3],” “Best Buys [v4I,” “Don’t Care [v5I,” and “Compare Prices [v6]” into the VARIABLES box.
  4. In the CLUSTER box check CASES (default option). In the DISPLAY box check STATISTICS and PLOTS (default options).
  5. Click on STATISTICS. In the pop-up window, check AGGLOMERATION SCHEI)ULE. In the CLUSTER MEMBERSHIP box check RANGE OF SOLUTIONS. Then, for MINIMUM NUMBER OF CLUSTERS enter 2, and for MAXIMUM NUMBER OF CLUSTERS enter 4. Click CONTINUE.
  6. Click on PLOTS. In the pop-up window, check DENDROGRAM. In the ICICLE box check ALL CLUSTERS (default). In the ORIENTATION box, check VERTICAL. Click CONTINUE.
  7. Click on METHOD. For CLUSTER METHOD select WARD’S METHOD. In the MEASURE box check INTERVAL and select SQUARED EUCLIDEAN DISTANCE. Click CONTINUE.
  8. Click OK.

The procedure for clustering of variables is the same as that for hierarchical clustering except that in step 4 in the CLUSTER box check VARIABLES. The following are the detailed steps for running nonhierarchical (K-Means) cluster analysis on attitudinal data (VI to VJ of Table 20.1

  1. Select ANALYZE from the SPSS menu bar.
  2. Click CLASSIFY and then K-MEANS CLUSTER.
  3. Move “Fun [vI],” “Bad for Budget [v2J,” “Eating Out [v3],” “Best Buys [v4],” “Don’t Care [v5],” and “Compare Prices [v6]” into the VARIABLES box.
  4. For NUMBER OF CLUSTERS select 3.
  5. Click on OPTIONS. In the pop-up window, in the STATISTICS box, check INITIAL CLUSTER CENTERS and CLUSTER INFORMATION FOR EACH CASE. Click CONTINUE.
  6. Click OK

The following are the detailed steps for running TwoStep cluster analysis on attitudinal data (VI to VJofTable20.1

  1. Select ANALYZE from the SPSS menu bar.
  2. Click CLASSIFY and then TWOSTEP CLUSTER.
  3. Move “Fun [vI],” “Bad for Budget [v2],” “Eating Out [v3],” “Best Buys [v4],” “don’t Care [v5I,” and “Compare Prices [v6]” into the CONTINUOUS VARIABLES box.
  4. For DISTANCE MEASURE select EUCLIDEAN.
  5. For NUMBER OF CLUSTERS select DETERMINE AUTOMATICALLY.
  6. For CLUSTERING CRITERION select AKAIKE’S INFORMATION CRITERION (AlC).
  7. Click OK

Posted on November 30, 2015 in Cluster Analysis

Share the Story

Back to Top