239
Chapter 12
Applications of Data
Mining in Organizational
Behavior
Arash Shahin and Reza Salehzadeh
Introduction
Data mining has already been successfully applied in many domains. Research has
shown that data mining techniques have the most applications in the eld of market-
ing (e.g., Chang and Wand 2006; Lu et al. 2012; Sahi et al. 2012). In addition, many
studies have been conducted in accounting and nancial management (e.g., Lam
2003; Laitinen 2005; Gregoriou and Pascalau 2012); human resource management
activities (see the literature review performed by Strohmeier and Piazza 2013); opera-
tions research (Meisel and Mattfeld 2010; Corne et al. 2012); industrial manage-
ment (Braha 2001; Ni et al. 2007); and so on. However, one domain in which data
Contents
Introduction ......................................................................................................239
Association Rules Mining ..................................................................................240
Articial Neural Networks ................................................................................ 246
Clustering .........................................................................................................250
Decision Tree ....................................................................................................253
Conclusions ......................................................................................................255
References .........................................................................................................255
240Arash Shahin and Reza Salehzadeh
mining has rarely been applied is organizational behavior. Organizational behavior
is a eld of study that investigates the impact that individuals, groups, and structure
have on behavior within an organization, for the purpose of applying such knowl-
edge toward improving an organizations eectiveness (Robbins and Judge 2012).
Data used in data mining is usually from a previously prepared database but
data employed in the eld of organizational behavior is obtained from question-
naires. erefore, questionnaire data will have to be prepared for use in data min-
ing. Now the question is “Can we use the data obtained from questionnaire and
through self-reported for data mining too?” It seems that the application of data
mining in this eld can be eective for achieving better results. Questionnaires are
usually analyzed statistically using hypothesis testing, which are either conrmed
or rejected. Data mining techniques have the ability to seek knowledge (which is
previously unknown) from data, and thus gives the analyst insights for future deci-
sion making. On the other hand, organizational studies seek to explain, predict,
and control employees’ behaviors and for such purposes, data mining techniques
can be very helpful.
e objective of this chapter is to investigate the four dierent data mining
techniques utilizing data from questionnaires in the eld of organizational behav-
ior. e examples shown in each part will reveal the eective role of data mining in
organizational behavior studies.
Association Rules Mining
Association rules mining (ARM) was rst introduced by Agrawal et al. (1993) and
had received a great attention. Association analysis is a type of undirected data min-
ing that nds data patterns. Using association rules, dependencies and relationships
can be discovered among existing data in a database.
Association rules (AR) can be utilized in two ways for mining data from a question-
naire. e rst method involves investigating all possible relationships between ques-
tions under the variables of interest. For instance, consider a questionnaire for assessing
a variable of interest, “self leadership.” Using the association rules, the relationship
among self-leadership strategies can be investigated. Possible questions under this vari-
able will ask respondents about self-goal setting and self-cueing. AR will then be used
to assess relationships between the two questions (Shahin and Salehzadeh 2013).
e second method of utilizing AR in questionnaire data is to investigate rela-
tionships between demographic features and the variable of interest. For instance,
the relationship among demographics such as age, education, and gender and the
self-leadership strategies can be investigated, in order to nd if the ratio of age, edu-
cation, and gender type inuence the ratio of self-leadership strategies. For a better
understanding of the subject, consider the following example:
207 questionnaires including questions about the self-leadership strategies
(behavior-focused strategies, natural reward strategies, and constructive thought

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