I-HSUAN (SHAINE) CHIU

Transcription

1 I-HSUAN (SHAINE) CHIU S347 Pappajohn Business Building EDUCATION Ph.D. in Business Administration Marketing, Iowa City, IA 2017 (Expected) Master of Science in Statistics 2012 The Ohio State University, Columbus, OH Master of Business Administration 2007 National Taiwan University, Taipei, Taiwan Bachelor of Arts in Economics 2001 National Taiwan University, Taipei, Taiwan RESEARCH INTERESTS Configuration Data Analysis Product Bundles Data Fusion Psychology-Based Models WORKING PAPER Chiu, I-Hsuan (Shaine), Gary J. Russell and Thomas S. Gruca, Predicting Bundle Preference Using Configuration Data To be submitted Fall Target: Journal of Marketing Research WORK IN PROGRESS Combining Choice-Based Conjoint and Configuration Data to Estimate Bundle Preference with Gary J. Russell and Thomas S. Gruca Model development in progress. An Investigation of Consumer Decision Process under Choice-Based Conjoint and Configuration Studies with Gary J. Russell and Thomas S. Gruca Literature review in progress. Modeling Reference Point Effect and Regulatory Orientation with JaeHwan Kwon, Gary J. Russell, Dhananjay (DJ) Nayakankuppam and Jing (Alice) Wang Model development in progress. Page 1 of 6

2 DISSERTATION Models for Bundle Preference Estimation Using Configuration Choice Data (see appendix for abstract) Committee: Thomas S. Gruca (Co-Chair), Professor of Marketing, Gary J. Russell (Co-Chair), Professor of Marketing, Sang Hak Lee, Assistant Professor of Marketing, Ying Yang, Assistant Professor of Marketing, Jacob Oleson, Associate Professor of Biostatistics, PRESENTATIONS Predicting Bundle Preference Using Configuration Data, Mittelstaedt Doctoral Symposium, Lincoln, NE, April 2016 Predicting Bundle Preference Using Configuration Data,,, Iowa City, IA, December 2015 Predicting Bundle Preference Using Configuration Data, ISMS Marketing Science Conference, Baltimore, MD, June 2015 FELLOWSHIPS AND AWARDS External AMS Doctoral Consortium Fellow, Orlando, FL, 2016 Mittelstaedt Doctoral Symposium Fellow, University of Nebraska - Lincoln, 2016 Workshop on Quantitative Marketing and Structural Econometrics Fellow, Northwestern University, 2015 ISMS Doctoral Consortium Fellow, Baltimore, MD, 2015 Haring Symposium Fellow, Indiana University, 2015, 2016 Internal Graduate College Summer Fellowship,, 2016 Graduate College Post-Comprehensive Research Awards,, Fall 2015 Ponder Summer Fellowship,, Graduate Assistantship,, Fall Spring 2015, Spring 2016 TEACHING INTERESTS Marketing Analytics, Marketing Research, Marketing Models, Digital Marketing, Marketing Management, Marketing Strategy TEACHING EXPERIENCE Co-Instructor Introduction to Marketing Strategy (Responsible for weekly discussion sections) Teaching evaluation: median score 4.80/6.00 Spring 2016 Teaching evaluation: median score 4.11/6.00 Spring 2015 Teaching evaluation: median score 4.58/6.00 Fall 2014 Teaching evaluation: median score 4.33/6.00 Spring 2014 Teaching evaluation: median score 4.50/6.00 Fall 2013 Evaluation based on the question, Overall, this instructor is an effective teacher Page 2 of 6

3 Teaching Assistant Contemporary Topics in Marketing: Merchandising Spring 2016 Social Media Marketing Spring 2016 Marketing Research Fall 2012, Spring 2013 Retail Strategies Fall 2012, Spring 2013 PROFESSIONAL EXPERIENCE Department of International Business, National Taiwan University August July 2010 Administrative Assistant Taipei, Taiwan Managed graduate students admission process and processed graduate student funding allocation Migosoft Corp. April July 2009 Senior Marketing Specialist Taipei, Taiwan Managed client relationships for the following representative companies: Microsoft Taiwan, Acer, ASUS, Philips, LG, wwwins Isobar, and Ogilvy Identified client needs, communicated with programmers, and organized corporate resources to distribute consolidated digital marketing solutions Supervised data management process, including data formatting, cleansing, mapping, and final comprehensive analyses per client needs Analyzed online browsing behavior data of client campaign websites, and delivered timely and insightful reports Deloitte & Touche July May 2003 Auditor Taipei, Taiwan Conducted comprehensive financial audits and prepared tax documents SERVICE Profession Reviewer trainee International Journal of Research in Marketing Journal of Marketing Research Community Volunteer Museum of Contemporary Art, Taipei, Taiwan, 2010 Conservation Volunteers Australia, Brisbane, Australia, 2005 DOCTORAL COURSEWORK Marketing Seminar in Consumer Behavior I, II Dhananjay (DJ) Nayakankuppam Seminar in Marketing Models I, II Gary J. Russell Consumer Behavior Seminar Patricia M. West (at OSU) Ph.D. Seminar in Marketing Models Greg M. Allenby (at OSU) Statistics Survival Data Analysis Michael Jones Environmental and Spatial Statistics Dale Zimmerman Measurement Theory and Methods Ernest H. O Boyle Probability and Stochastic Processes I Ralph P. Russo Bayesian Analysis Joyee Ghosh Longitudinal Data Analysis Jacob Oleson Computer Intensive Statistics Luke Tierney Economics Econometrics N. Eugene Savin Microeconomics I Ayça Kaya Econometrics Stephen Cosslett (at OSU) Education Seminar in College Teaching Mitchell Kelly Page 3 of 6

4 REFERENCES Gary J. Russell Henry B. Tippie Research Professor of Marketing S350 Pappajohn Business Building Phone: (319) Thomas S. Gruca Professor and Faculty Director, MBA Marketing Career Academy S356 Pappajohn Business Building Phone: (319) Dhananjay (DJ) Nayakankuppam Associate Professor and Director, PhD Program in Marketing W234 Pappajohn Business Building Phone: (319) Nancy J. Abram Lecturer of Marketing S379 Pappajohn Business Building Phone: (319) Page 4 of 6

5 APPENDIX DISSERTATION Models for Bundle Preference Estimation Using Configuration Data Proposal defended in May, 2016 Bundling is the practice of selling two or more separate products in a single package for a special price. Examples include desktop computer sets and digital single-lens reflex camera kits. Product bundles allow companies to identify different segments of consumers based on their reservation prices, and thus to maximize profits via segmented pricing. To successfully implement a segmented pricing strategy, understanding consumer preference for bundles is critical. One approach to collecting bundle preference data is build-your-own bundle or configuration analysis. In a configuration study, participants construct their ideal bundles by choosing several products from a given set of products. This procedure is attractive because it mimics the real decision process, is easy to implement, and is straight forward for participants to understand. This dissertation investigates the application of configuration data analysis, and examines how configuration data extends or complements choice-based conjoint analysis. Essay One: Predicting Bundle Preference Using Configuration Data In the first essay, we develop a modeling framework for bundle choices. We first develop an individuallevel choice model for bundles that is consistent with bundle characteristics. Next, we discuss and derive the preference heterogeneity distribution for bundles. Based on the individual-level model and the preference heterogeneity distribution, we show that the aggregate choice model for bundles takes the form of a multivariate logistic (MVL) model. To apply the MVL model to a bundle data collected via configuration procedure, we need to address the computational challenge posed by this procedure. That is, as the number of products to choose from increases, the total number of bundles grows geometrically. This is known as curse of dimensionality. Because MVL model is consistent with random utility theory, we adopt a sampling of alternatives procedure - an approach widely used in transportation literature - to deal with the curse of dimensionality issue. The proposed model is applied to bundle choice data from a hardware tool product category. Essay Two: Combining Choice-Based Conjoint and Configuration Data for Bundle Preference Estimation Choice-based conjoint (CBC) analysis is a well-established approach in the literature that can be used to study consumer preference for bundles. The major drawback of CBC analysis is that as the complexity of bundle design increases, it imposes more and more cognitive strains on participants, resulting in decision fatigue that negatively impacts the data quality. Configuration data, while its procedure is straightforward, does not provide enough variation on key variables that researchers are interested in. Thus combining these two data sources should improve bundle preference estimation and prediction. In this research, we examine and compare the performance of CBC and configuration data analysis. We then propose a model that takes the advantages of both data sources to improve bundle preference estimation. Essay Three: An Investigation of Consumer Decision Process under Choice-Based Conjoint and Configuration Studies Psychological theories have shown that people employ different strategies when making decisions. These strategies may be inconsistent with random utility theory. When the decision task is complex, consumers often use non-compensatory rules to simplify the decision process. For example, participants may adopt elimination-by-aspect strategy to make a choice from a given set in a CBC study when they do not have the cognitive resource to carefully examine each alternative. On the other hand, in a Page 5 of 6

6 configuration study participants may construct a product bundle that is good enough instead of one that is the best, because participants have limited resources to evaluate all the possible combinations. In this study, we examine the underlying psychological process that individuals use to reach a decision when participating in CBC and configuration studies. This research contributes to an understanding of individuals decision processes when participating in choice studies, and develops implications for preference estimation strategies. ADDITIONAL WORK IN PROGRESS Modeling Reference Dependence Effect and Regulatory Orientation When consumers evaluate a set of alternatives, they compare alternatives with reference points and make choice decisions based upon the position of alternatives relative to reference points. Specifically, consumers evaluate the losses and gains of alternatives, and react more strongly to losses. According to regulatory focus theory, people with different regulatory orientations will weigh losses and gains differently. Promotion-oriented consumers will weigh gain(loss) more(less) than prevention-oriented consumers given the same objective gain(less). The goal of this research is to develop a choice model that incorporates the two psychological processes: reference dependence effects from prospect theory and regulatory orientation effects from regulatory focus theory. Page 6 of 6