Why Marshall
Leadership
Dean Geoffrey Garrett
Dean's Cabinet
Boards
Real-World Learning
Human Leadership
Tech Fluency
Global Opportunities
Diversity, Equity and Inclusion
Teaching + Innovation
Experiential Learning Center
Open Expression Statement
Programs
Undergraduate Programs
Admissions
Degrees
BS Business Administration (BUAD)
Business Emphases
BS Accounting (ACCT)
World Bachelor in Business (WBB)
BS Business of Cinematic Arts (BCA)
BS Artificial Intelligence for Business (BUAI)
Undergraduate Minors
Graduate Programs
MBA Programs
Full-Time MBA (FTMBA)
Executive MBA (EMBA)
Part-Time MBA (MBA.PM)
International MBA (IBEAR)
Online MBA (OMBA)
Specialized Masters
MS Business Administration (MSBUSAD)
MS Business Analytics (MSBA)
MS Entrepreneurship + Innovation (MSEI)
MS Finance (MSF)
MS Global Supply Chain Management (MSGSCM)
MS Marketing (MSMKT)
MS Social Entrepreneurship (MSSE)
Master of Business for Veterans (MBV)
Master of Management Studies (MMS)
Accounting Masters
Master of Accounting (MAcc)
Master of Business Taxation (MBT)
Master of Business Taxation for Working Professionals (MBT.WP)
PhD Program
Accounting
Data Sciences + Operations
Finance
Management + Organization
Marketing
Graduate Certificates
GC in Business Analytics
GC in Financial Analysis + Valuation
GC in Management Studies
GC in Marketing
GC in Optimization + Supply Chain Management
GC in Strategy + Management Consulting
GC in Sustainability + Business
GC in Technology Commercialization
GC in Library and Information Management – Online
Executive Education Redirect
Departments
Business Communication (BUCO)
Faculty
Data Sciences and Operations (DSO)
Finance + Business Economics (FBE)
Leventhal School of Accounting (ACCT)
Lloyd Greif Center for Entrepreneurial Studies (BAEP)
Management and Organization (MOR)
Marketing (MKT)
Institutes + Centers
Peter Arkley Institute for Risk Management
Brittingham Social Enterprise Lab
Center for Investment Studies
Initiative on Digital Competition
Randall R. Kendrick Global Supply Chain Institute
Center for Effective Organizations
Lloyd Greif Center for Entrepreneurial Studies
VanEck Digital Assets Initiative
Institute for Outlier Research in Business
Center for Global Innovation
Neely Center for Ethical Leadership and Decision Making
Trojan Network
Recruiting
Undergraduate
Graduate
Career Services
Giving + Support
Alumni Engagement + Resources
Student Organizations
Commencement
Sivaramakrishnan Siddarth is an expert in the development and use of response models to understand consumer and market behavior. His research has been published in the Journal of Marketing Research, Journal of Marketing, Management Science, and Marketing Science among others. Professor Siddarth currently serves on the editorial board of Marketing Letters. He is a recipient of the O’Dell outstanding paper award from the Journal of Marketing Research, the USC Mellon Award for excellence in mentoring graduate students, the Evan C. Thompson Faculty Teaching and Learning Innovation Award, and several Golden Apple teaching awards.
Areas of Expertise
RESEARCH + PUBLICATIONS
We analyze consumer adoption of hybrid cars using automobile transaction data from the Sacramento market during the first half of $2007$, a critical period in the lifecycle of hybrid technology. Modeling demand for durable goods such as automobiles is made more difficult by the absence of repeat purchase data and pooling information across similar consumers is one way to address this data scarcity. We propose a new multinomial spatial probit model that connects different consumers using multiple weighted networks, which are based on different similarity structures. Unlike in the traditional multinomial spatial probit, different subsets of the parameter vector, i.e., the preference and marketing coefficients can be correlated in their own unique ways.Parameter estimation is carried out via a novel Monte-Carlo Expectation-Maximization (MCEM) based approach, which enables the model to be used with a significantly greater number of consumers and choice alternatives than is possible with the standard model. The approach substitutes the computationally expensive E-step in the classical EM algorithm by an efficient Gibbs sampling-based evaluation and, additionally, implements the M-step using a fast back-fitting method that iteratively fits weighted regressions based on the associated similarity matrix for each coefficient subset. We establish the convergence properties of the proposed MCEM algorithm, present computational perspectives on the scalability of the proposed method, and provide a distributed computing-based implementation that yields parameter estimates and their standard errors. We apply the model to sales data for compact cars from the Sacramento market and find that the best fitting version of our model is one in which the intercepts are based on the geographical closeness between consumers and the slope coefficients on the similarity of their previously owned vehicles. We summarize the cross-price elasticity matrices to produce clout and vulnerability measures for each vehicle and to produce competitive maps of the product category. We show how the multiple network weights explain the changes in price sensitivity of consumers across geographic locations, captures the variation in brand preferences among consumers and together deliver more accurate estimates of a consumer's hybrid purchase probability. Finally, we demonstrate how an automobile manufacturer can leverage the estimated heterogeneous spatial contiguity effects to improve the effectiveness of targeted promotions that are designed to accelerate the consumer adoption of the Toyota hybrid.