Marketing Strategy Performance: Theory, Models, and Empirical Applications

Against the background of increasing pressure from the capital market and major corporate trends such as digitization, corporate social responsibility and corporate political activism, marketing managers are more and more than ever forced to demonstrate the performance and value relevance of their decisions for various stakeholders. Marketing scholars have responded to this development and produced numerous articles that relate marketing decisions (e.g., new product introductions, major price changes, investing in digital sales channels, partnering with social-media influencers) with the creation of market-based assets (e.g., customer satisfaction), product-market performance (e.g., market share), accounting performance (e.g., return on assets), and financial-market performance (e.g., stock returns). The course provides an overview of this literature, both from a conceptual/model-based perspective and from an empirical point of view. After having attended the course, students should be able to:

• Understand central concepts of marketing strategy performance research and be able to establish links between these concepts;
• Understand the basics of market response modeling and recognize the relevance of model specification for the validity of empirical estimation results;
• Understand, categorize, and criticize high-quality (“A+”) articles within the research field;
• Know key data analysis methods within the research field including their scope of application as well as their limitations and conduct first own analyses using standard software (R);
• Develop relevant and interesting research questions with a potential for a high-quality publication.

Date:

July 10-13, 2023

Location:

Fritz Thyssen Stiftung, Apostelnkloster 13-15, 50672 Köln

Language:

English

Lecturer:

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: June 11th, 2023

Lecturer:

Advanced Topics in Asset Pricing and Capital Market Research

Starting from a solid theoretical foundation, this course provides students with an understanding of important empirical methods and their application in asset pricing. It covers both the classical approaches based on Fama and MacBeth (1973) and Black, Jensen and Scholes (1972) – which are still widely used in current research – and GMM-based estimation methods. Furthermore, it shows how machine learning approaches can be meaningfully incorporated into modern asset pricing.

The course intends to enable students to plan and carry out empirical research in asset pricing on their own and prepares for an empirical PhD thesis in this area of finance.

Date:

03. – 06. Juli 2023

Location:

MLP Campus
Alte Heerstraße 40
69168 Wiesloch

Language:

English

Lecturers:

Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Registration Deadline: 4. Juni 2023

Choice-Based Optimization

Summary and study goals

Demand is an important quantity in many optimization problems such as revenue management and supply chain management. Demand usually depends on “supply” (price and availability of products, f. e.), which in turn is decided on in the optimization model. Hence, demand is endogenous to the optimization problem. Choice-based optimization (CBO) merges discrete choice models with math programs. Discrete choice models (DCM) have been applied by both practitioners and researchers for more than four decades in various fields. DCM describe the choice probabilities of individuals selecting an alternative from a set of available alternatives. CBO determines (i) the availability of the alternatives and/or (ii) the attributes of the alternatives, i.e., the decision variables determine the availability of alternatives and/or the shape of the attributes. We present CBO applications to location planning, supply chain management, assortment and revenue management.

Course Content

Students will learn how to develop and use predictive models (discrete choice models) in the software R and how to introduce such models in mathematical models for decision-making (i.e., mixed integer programs) to consider demand as an auxiliary variable. The models will be implemented in a modeling environment (GAMS). Case studies will be used for practicing purposes.

Date of Event

10. – 13. Juli 2023

Location

SlowDown Travemünde
Priwallpromenade 20
23570 Lübeck-Travemünde

Instructors

Registration

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or email us: prodok@vhbonline.org.

Registration Deadline: 7. Mai 2023

Meta-Analysis for Management Research

Abstract and Learning Objectives

In all disciplines within management research, numerous relationships (effects of some X on a specific Y) have been studied multiple times, and summarizing the existing empirical findings may result in an important scientific contribution. For example, one could determine the overall effect of a particular marketing instrument (price, advertising, etc.) on sales and whether the effect depends on market characteristics, study design, or other moderators. Meta-analysis encompasses a broad set of methods to conduct a systematic, quantitative review of the literature in order to derive empirical generalizations. As such, conducting a meta-analysis is an excellent project for a PhD student or other (junior) researcher having to review the literature on a particular topic.

This workshop will deal with methods for conducting a meta-analysis. The purpose is to train the participants to conduct and publish a high-quality scientific meta-analysis within the broad field of management research. The seminar will cover the entire meta-analysis research process, from problem formulation, literature search, coding of the effects, analysis, to reporting and publishing the findings.

The emphasis is on knowledge and skills needed to conduct a meta-analysis, not only on the statistical details. All steps of the meta-analysis process (including the statistical analyses) will be demonstrated and practiced in assignments during the workshop. In addition, all topics will be illustrated by means of actual meta-analysis examples. Participants will be informed about relevant literature (textbooks and journal articles) and software supporting meta-analysis projects. In particular, most analyses will be demonstrated using R; in particular the package metafor.

Date:

June 26-29, 2023

Location:

Teerhof58
Gästehaus der Universität
28199 Bremen

Directions: https://www.teerhof58.de/en/#c2081

Language:

English

Instructor:

Registration:

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or email us: prodok@vhbonline.org.

Registration Deadline:

May 28, 2023

Endogeneity in Applied Empirical Research

Many empirical research projects in business and economics that use non-experimental data struggle with the proper identification of causal effects of independent variables (e.g., price, management decisions) on dependent variables (e.g., demand, firm performance). The reason is that the identification of a causal effect hinges on the untestable assumption that the error term of a model is uncorrelated with the independent variables. If this assumption is not met, a model is plagued by endogeneity.

The topic of endogeneity has received considerable attention, and it is probably the most frequently encountered troublemaker in a review process at an academic journal.

This course therefore has the goal of making students familiar with the problem of endogeneity and potential remedies. This implies that it will cover the opportunities and problems associated with traditional approaches (e.g., Instrumental Variable estimation, Matching, Difference-in-Difference) as well as more recent developments (e.g., Gaussian Copulas; Machine Learning and Causal Inference; Synthetic Control Methods; Directed Acyclical Graphs (DAG)). The course will also cover how the data structure (e.g., panel data) can be utilized to address the problem.

Because the literature on endogeneity is often quite technical, this course aims at providing an easily accessible approach to this topic. Special emphasis will also be given to understanding when endogeneity indeed poses a real problem as compared to settings in which endogeneity is less likely to be a real threat to the validity of the findings.

After completing this course, students will be able to define and describe endogeneity problems in different empirical settings, they will have a better understanding of whether and when causal identification is possible how to implement techniques that address endogeneity, and they will be aware of the (dis)advantages of different methods.

Date:

7.9.2022 online and 14.-16.9.2022 face-to-face

Location:

Online & University of Tübingen

Language:

English

Lecturer:

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Participation fee:

Non-member: 650,00 Euro
VHB-member: 530,00 Euro

Registration deadline: August 7, 2022