Using Market Basket Analysis in Management Research

Market basket analysis (MBA), also known as association rule mining or affinity analysis, is a data-mining technique that originated in the field of marketing and more recently has been used effectively in other fields such as bioinformatics, nuclear science, and immunology. The goal of MBA is to identify relationships (i.e., association rules) between groups of products, items, or categories. We describe MBA and explain that it allows for inductive and deductive theorizing, can model contingency theories, does not rely on assumptions that are often untenable (as general linear model–based techniques do), allows for the use of data often considered “unusable” in management research, is uniquely suited to handle data considered “messy” and also data collected at different levels of analysis, and is practitioner-friendly. We explain how the adoption of MBA is likely to help bridge the micro–macro and science–practice divides and illustrate that its use can lead to important insights in substantive managemen...

[1]  Chuan-Fang Lee,et al.  The prescribing of Chinese herbal products in Taiwan: a cross‐sectional analysis of the national health insurance reimbursement database , 2008, Pharmacoepidemiology and drug safety.

[2]  P. Roth,et al.  Missing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques , 1999 .

[3]  D. Shepherd,et al.  Family Business, Identity Conflict, and an Expedited Entrepreneurial Process: A Process of Resolving Identity Conflict , 2009 .

[4]  Herman Aguinis,et al.  Customer-Centric Science: Reporting Significant Research Results With Rigor, Relevance, and Practical Impact in Mind , 2010 .

[5]  Layth C. Alwan,et al.  An Examination of Perceptions and Usage of Regression Diagnostics in Organization Studies , 1994 .

[6]  Zengyou He,et al.  Mining class outliers: concepts, algorithms and applications in CRM , 2004, Expert Syst. Appl..

[7]  Elizabeth E. Umphress,et al.  Unethical behavior in the name of the company: the moderating effect of organizational identification and positive reciprocity beliefs on unethical pro-organizational behavior. , 2010, The Journal of applied psychology.

[8]  Chester H. Ponikowski,et al.  OF THE NATIONAL COMPENSATION SURVEY , 2002 .

[9]  Hongxing He,et al.  Association Rule Discovery with Unbalanced Class Distributions , 2003, Australian Conference on Artificial Intelligence.

[10]  E. A. Locke.  The Case for Inductive Theory Building† , 2007 .

[11]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[12]  Aiko Hibino,et al.  Graphical Representation of Nuclear Incidents/Accidents by Associating Network in Nuclear Technical Communication , 2008 .

[13]  J. Martocchio.  Strategic reward and compensation plans. , 2011 .

[14]  Andreas Podelski,et al.  Master's Thesis in Computer Science , 2014 .

[15]  Edith Cohen,et al.  Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[16]  Yen-Liang Chen,et al.  Market basket analysis in a multiple store environment , 2005, Decis. Support Syst..

[17]  Patricia B. Cerrito,et al.  Choice of antibiotic in open heart surgery , 2007, Intell. Decis. Technol..

[18]  Sophia V. Marinova,et al.  A TRICKLE-DOWN MODEL OF ABUSIVE SUPERVISION , 2012 .

[19]  Shourya Roy,et al.  A Conversation-Mining System for Gathering Insights to Improve Agent Productivity , 2007, The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007).

[20]  Yen-Liang Chen,et al.  Context-based market basket analysis in a multiple-store environment , 2008, Decis. Support Syst..

[21]  Herman Aguinis,et al.  Meta-Analytic Choices and Judgment Calls: Implications for Theory Building and Testing, Obtained Effect Sizes, and Scholarly Impact , 2011 .

[22]  John Elder,et al.  Handbook of Statistical Analysis and Data Mining Applications , 2009 .

[23]  Herman Aguinis,et al.  What We Know and Don’t Know About Corporate Social Responsibility , 2012 .

[24]  D. Shepherd,et al.  Inductive Top-Down Theorizing: A Source of New Theories of Organization , 2011 .

[25]  James C. Beaty,et al.  Effect size and power in assessing moderating effects of categorical variables using multiple regression: a 30-year review. , 2005, The Journal of applied psychology.

[26]  Agathe Merceron,et al.  A Web-Based Tutoring Tool with Mining Facilities to Improve Learning and Teaching , 2003 .

[27]  M. Hitt,et al.  Construct measurement in strategic management research: illusion or reality? , 2005 .

[28]  Andrew Stranieri,et al.  Discovering Interesting Association Rules from Legal Databases , 2002 .

[29]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[30]  S. Robinson,et al.  THE IMP ACT OF COMMUNITY VIOLENCE AND AN ORGANIZATION ' S PROCEDURAL JUSTICE CLIMATE ON WORKPLACE AGGRESSION , 2003 .

[31]  Wayne F. Cascio,et al.  Research in industrial and organizational psychology from 1963 to 2007: changes, choices, and trends. , 2008, The Journal of applied psychology.

[32]  Herman Aguinis,et al.  THE BEST AND THE REST: REVISITING THE NORM OF NORMALITY OF INDIVIDUAL PERFORMANCE , 2012 .

[33]  T. Davenport,et al.  Competing on talent analytics. , 2010, Harvard business review.

[34]  Edward E. Lawler,et al.  Generating Knowledge That Drives Change , 2012 .

[35]  Susan Chiu,et al.  Data Mining and Market Intelligence for Optimal Marketing Returns , 2008 .

[36]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[37]  N. Foss.  Invited Editorial: Why Micro-Foundations for Resource-Based Theory Are Needed and What They May Look Like , 2011 .

[38]  Holger Schiele,et al.  SUPPLIER INNOVATIVENESS AND SUPPLIER PRICING: THE ROLE OF PREFERRED CUSTOMER STATUS , 2010 .

[39]  Robert E. Ployhart,et al.  Longitudinal Research: The Theory, Design, and Analysis of Change , 2010 .

[40]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[41]  So Young Sohn,et al.  Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce , 2009, Expert Syst. Appl..

[42]  Yehuda Lindell,et al.  A Statistical Theory for Quantitative Association Rules , 1999, KDD '99.

[43]  T. Mitchell,et al.  Building Better Theory: Time and The Specification of When Things Happen , 2001 .

[44]  J. Colquitt,et al.  TRENDS IN THEORY BUILDING AND THEORY TESTING: A FIVE-DECADE STUDY OF THE ACADEMY OF MANAGEMENT JOURNAL , 2007 .

[45]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[46]  M. Steinbach,et al.  Global teleconnections of climate to terrestrial carbon flux , 2003 .

[47]  Ruixin Yang,et al.  Improved associated conditions in rapid intensifications of tropical cyclones , 2007 .

[48]  Shichao Zhang,et al.  Association Rule Mining: Models and Algorithms , 2002 .

[49]  Qingfeng Chen,et al.  Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle , 2006, BMC Bioinformatics.

[50]  Rebecca P. Ang,et al.  An introduction to association rule mining: An application in counseling and help-seeking behavior of adolescents , 2007, Behavior research methods.

[51]  Sandra L. Robinson,et al.  Dysfunctional Workplace Behavior , 2008 .

[52]  R. Dodhia.  A Review of Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.) , 2005 .

[53]  H. P. Bowen.  Testing Moderating Hypotheses in Limited Dependent Variable and Other Nonlinear Models , 2012 .

[54]  Gary J. Russell,et al.  Multiple-Category Decision-Making: Review and Synthesis , 1999 .

[55]  Steve Pan,et al.  Finding Ideal Menu Items Assortments: An Empirical Application of Market Basket Analysis , 2010 .

[56]  R. Coff,et al.  Invited Editorial: Drilling for Micro-Foundations of Human Capital–Based Competitive Advantages , 2011 .

[57]  Sergio A. Alvarez,et al.  Chi-squared computation for association rules: preliminary results , 2003 .

[58]  Luca Cagliero,et al.  CAS-Mine: providing personalized services in context-aware applications by means of generalized rules , 2010, Knowledge and Information Systems.

[59]  M. Croon,et al.  Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model. , 2007, Psychological methods.

[60]  B. Boyd,et al.  Walking New Avenues in Management Research Methods and Theories: Bridging Micro and Macro Domains , 2011, Journal of Management.

[61]  Gary J. Russell,et al.  Analysis of cross category dependence in market basket selection , 2000 .

[62]  Paul W. Thurston,et al.  A Monte Carlo Study of Missing Item Methods , 2000 .

[63]  Soichi Koike,et al.  Association analysis of food allergens , 2009, Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology.