Handbook of Market Research
- 2022
- Book
- Editors
- Prof. Dr. Christian Homburg
- Prof. Dr. Martin Klarmann
- Dr. Arnd Vomberg
- Publisher
- Springer International Publishing
About this book
In this handbook, internationally renowned scholars outline the current state-of-the-art of quantitative and qualitative market research. They discuss focal approaches to market research and guide students and practitioners in their real-life applications. Aspects covered include topics on data-related issues, methods, and applications. Data-related topics comprise chapters on experimental design, survey research methods, international market research, panel data fusion, and endogeneity. Method-oriented chapters look at a wide variety of data analysis methods relevant for market research, including chapters on regression, structural equation modeling (SEM), conjoint analysis, and text analysis. Application chapters focus on specific topics relevant for market research such as customer satisfaction, customer retention modeling, return on marketing, and return on price promotions. Each chapter is written by an expert in the field. The presentation of the material seeks to improve the intuitive and technical understanding of the methods covered.
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Table of Contents
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Methods
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Frontmatter
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Cluster Analysis in Marketing Research
Thomas Reutterer, Daniel DanCluster analysis in marketing research involves detecting groupings in data sets to summarize information and identify patterns. The chapter covers the historical evolution of clustering methods, from their origins in biology and psychology to their application in marketing. It discusses 'classical' marketing problems such as market segmentation and competitive market structure analysis. The chapter also explores distance-based clustering techniques, proximity measures, and algorithms, with practical examples using real-world shopping basket data. By illustrating the application of hierarchical and non-hierarchical clustering methods, the chapter offers insights into data compression and pattern discovery in marketing research.AI Generated
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AbstractCluster analysis is an exploratory tool for compressing data into a smaller number of groups or representing points. The latter aims at sufficiently summarizing the underlying data structure and as such can serve the analyst for further consideration instead of dealing with the complete data set. Because of this data compression property, cluster analysis remains to be an essential part of the marketing analyst’s toolbox in today’s data rich business environment. This chapter gives an overview of the various approaches and methods for cluster analysis and links them with the most relevant marketing research contexts. We also provide pointers to the specific packages and functions for performing cluster analysis using theRecosystem for statistical computing. A substantial part of this chapter is devoted to the illustration of applying different clustering procedures to a reference data set of shopping basket data. We briefly outline the general approach of the considered techniques, provide a walk-through for the correspondingRcode required to perform the analyses, and offer some interpretation of the results. -
Finite Mixture Models
Sonja GenslerFinite Mixture Models are a model-based segmentation approach that assumes an underlying statistical model for the population. This chapter compares them to traditional clustering methods, highlighting advantages such as better model fit, reduced experiment-wise error, and flexibility in handling variables measured at different scales. An illustrative example demonstrates the basic idea of Finite Mixture Models and their application in identifying consumer segments. The chapter also discusses the determination of the number of segments using statistical criteria and the assignment of consumers to segments probabilistically. Additionally, it explores the integration of Finite Mixture Models with multivariate methods of analysis, such as regression analysis and structural equation modeling, to reduce systematic biases and improve parameter estimation. The chapter concludes by emphasizing the practical relevance and widespread adoption of Finite Mixture Models in marketing research.AI Generated
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AbstractFinite Mixture models are a state-of-the-art technique of segmentation. Next to segmenting consumers or objects based on multiple different variables, Finite Mixture models can be used in conjunction with multivariate methods of analysis. Unlike approaches combining multivariate methods of analysis and cluster analysis, which require a two-step approach, the parameters are then directly estimated at the segment level. This also allows for inferential statistical analysis. This book chapter explains the basic idea of Finite Mixture models and describes some popular applications of Finite Mixture models in market research. -
Analysis of Variance
Jan R. LandwehrThe chapter begins with an introduction to analysis of variance (ANOVA), explaining its use in comparing mean differences between groups. It distinguishes between between-subjects and within-subjects designs, detailing the statistical advantages and disadvantages of each. The chapter then delves into specific ANOVA models, including one-way and two-way ANOVA, and discusses the analysis of covariance (ANCOVA) and multivariate analysis of variance (MANOVA). Throughout, it uses an exemplary dataset to demonstrate these techniques, ensuring a practical understanding of the theoretical concepts. The chapter concludes with a brief introduction to ANCOVA and MANOVA, emphasizing their applications in more complex datasets.AI Generated
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AbstractExperiments are becoming increasingly important in marketing research. Suppose a company has to decide which of three potential new brand logos should be used in the future. An experiment in which three groups of participants rate their liking of one of the logos would provide the necessary information to make this decision. The statistical challenge is to determine which (if any) of the three logos is liked significantly more than the others. The adequate statistical technique to assess the statistical significance of such mean differences between groups of participants is called analysis of variance (ANOVA). The present chapter provides an introduction to the key statistical principles of ANOVA and compares this method to the closely related t-test, which can alternatively be used if exactly two means need to be compared. Moreover, it provides introductions to the key variants of ANOVA that have been developed for use when participants are exposed to more than one experimental condition (repeated-measures ANOVA), when more than one dependent variable is measured (multivariate ANOVA), or when a continuous control variable is considered (analysis of covariance). This chapter is intended to provide an applied introduction to ANOVA and its variants. Therefore, it is accompanied by an exemplary dataset and self-explanatory command scripts for the statistical software packages R and SPSS, which can be found in the Web-Appendix. -
Regression Analysis
Bernd Skiera, Jochen Reiner, Sönke AlbersLinear regression analysis is a crucial statistical method for examining the linear relationship between a dependent variable and one or more independent variables. This chapter provides a comprehensive guide to conducting regression analysis, starting with an introduction to the method and its applications in marketing. It covers the statistical explanation of the method, including the objective function and estimation of regression coefficients. The chapter also delves into the goodness of fit, significance testing, standardization of coefficients, and interpretation of results. Additionally, it addresses common issues such as multicollinearity, autocorrelation, heteroscedasticity, and outliers. The chapter concludes with a discussion of the assumptions of linear regression and provides practical tips for conducting the analysis. With its detailed approach and practical examples, this chapter is a valuable resource for professionals seeking to deepen their understanding of linear regression analysis.AI Generated
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AbstractLinear regression analysis is one of the most important statistical methods. It examines the linear relationship between a metric-scaled dependent variable (also called endogenous, explained, response, or predicted variable) and one or more metric-scaled independent variables (also called exogenous, explanatory, control, or predictor variable). We illustrate how regression analysis work and how it supports marketing decisions, e.g., the derivation of an optimal marketing mix. We also outline how to use linear regression analysis to estimate nonlinear functions such as a multiplicative sales response function. Furthermore, we show how to use the results of a regression to calculate elasticities and to identify outliers and discuss in details the problems that occur in case of autocorrelation, multicollinearity and heteroscedasticity. We use a numerical example to illustrate in detail all calculations and use this numerical example to outline the problems that occur in case of endogeneity. -
Logistic Regression and Discriminant Analysis
Sebastian Tillmanns, Manfred KrafftThe chapter 'Logistic Regression and Discriminant Analysis' delves into the application of logistic regression and discriminant analysis for predicting and explaining dichotomous outcomes. It begins by introducing the need for these methods when traditional OLS regression is inadequate for binary dependent variables. The chapter then outlines the foundations and assumptions of discriminant analysis, including its objectives, model considerations, and the steps involved in its application. It also discusses the estimation of discriminant functions and the assessment of their performance. The chapter then transitions to logistic regression, explaining its foundations, assumptions, and procedure. It highlights the differences and similarities between logistic regression and discriminant analysis, emphasizing the advantages of logistic regression in handling categorical data and outliers. The chapter concludes with an applied example demonstrating the use of both methods to explain factors driving the use of sales contests in companies, and a comparison of the two approaches.AI Generated
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AbstractQuestions like whether a customer is going to buy a product (purchase vs. non-purchase) or whether a borrower is creditworthy (pay off debt vs. credit default) are typical in business practice and research. From a statistical perspective, these questions are characterized by a dichotomous dependent variable. Traditional regression analyses are not suitable for analyzing these types of problems, because the results that such models produce are generally not dichotomous. Logistic regression and discriminant analysis are approaches using a number of factors to investigate the function of a nominally (e.g., dichotomous) scaled variable. This chapter covers the basic objectives, theoretical model considerations, and assumptions of discriminant analysis and logistic regression. Further, both approaches are applied in an example examining the drivers of sales contests in companies. The chapter ends with a brief comparison of discriminant analysis and logistic regression. -
Multilevel Modeling
Till Haumann, Roland Kassemeier, Jan WiesekeMultilevel modeling is crucial in marketing research for analyzing phenomena involving multiple levels, such as salespeople and customers. This chapter discusses the conceptual and statistical relevance of multilevel modeling, outlining different types of constructs and models. It provides a step-by-step guide to estimating a two-level regression model, assessing model fit, and handling variable centering. The chapter also covers cross-level interaction effects and sample size considerations, making it a comprehensive resource for researchers and practitioners seeking to understand and apply multilevel modeling in their work.AI Generated
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AbstractMany phenomena in marketing involve multiple levels of theory and analysis. Adopting a multilevel lens to marketing phenomena can often yield richer and more rigorous results. However, the consideration of multiple levels of theory and analysis often leads to the challenge to cope with nested data structures in which a lower level unit of analysis is nested within a higher level unit of analysis. Explicitly acknowledging such nested data structures is important as its analysis with single level analysis techniques may result in biased results and thus incorrect conclusions because nested data structures often violate assumptions of conventional single level analysis techniques. A methodological approach which explicitly accounts for multiple levels of analysis and thus the nested structure of data is referred to as multilevel modeling. This chapter attempts to help researchers and practitioners interested in investigating multilevel phenomena by providing an introduction to multilevel modeling. It therefore describes the theoretic fundamentals of multilevel modeling by outlining the conceptual and statistical relevance of multilevel modeling. Furthermore, it provides guidance how to build a multilevel regression model using a step-by-step approach. The chapter also discusses how to assess the fit of multilevel models, how to center variables at different levels of analysis, and how to determine the sample sizes to adequately estimate multilevel models. Moreover, it offers insights how the logic of multilevel regression analysis could be expanded to multilevel structural equation modeling, discusses different statistical software packages that can be employed to estimate multilevel models, and provides a detailed example of building and estimating a multilevel model. -
Panel Data Analysis: A Non-technical Introduction for Marketing Researchers
Arnd Vomberg, Simone WiesPanel data analysis is a critical tool for marketing researchers, offering insights into changes at the individual level and addressing potential biases in observational data. This chapter provides a comprehensive guide to analyzing panel data, from defining research questions to preparing and exploring data, and analyzing panel data models. It highlights the advantages of panel data over cross-sectional data, the challenges of nonindependent observations, and the use of panel data estimators. The chapter is structured around a real-life research example, using statistical software to illustrate each step of the process.AI Generated
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AbstractThe analysis of panel data is now part of the standard repertoire of marketers and marketing researchers. Compared to the analysis of cross-sectional data, panel data allow marketers to alleviate endogeneity concerns when linking an independent variable (e.g., price) to an outcome variable (e.g., sales volume). The more accurate estimates that result from panel data analysis help improve marketers’ decision-making in focal areas such as price setting and marketing budget allocation. Besides, panel data allow marketers to track customer behavior changes and distinguish real loyalty effects (i.e., same customer repeatedly buys a brand) from spurious effects (i.e., the same number of, but each time different set of, customers buys a brand). This chapter provides a nontechnical introduction to panel data analysis. Marketers will learn how to manage and analyze panel datasets in Stata. They will learn about the focal panel data estimators (pooled OLS, fixed effects, and random effects estimator), their underlying assumptions, advantages, and pitfalls. Besides, we introduce the between effects estimator, the combined approach, the Hausman-Taylor approach, and the first differences estimator as further techniques to analyze panel data. Finally, readers will receive an introduction to advanced topics such as dynamic panel models, panel data multilevel modeling, and using panel data to address measurement errors. -
Applied Time-Series Analysis in Marketing
Wanxin Wang, Gokhan YildirimThe chapter delves into the application of time-series analysis in marketing, focusing on how historical data can be leveraged to understand and predict future trends. It begins by introducing the basics of time-series analysis, including the treatment and diagnostics of univariate models. Traditional models like ARIMA are discussed, along with more advanced models such as Vector Autoregressive (VAR) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. The chapter emphasizes the importance of these models in capturing the dynamics and variations in marketing data, enabling marketers to make informed decisions. Additionally, it covers how to interpret model results, such as impulse response functions and forecast error variance decomposition, to understand the impact of marketing actions on performance. The chapter concludes by highlighting the power of these models in uncovering hidden linkages between factors and their practical applications in marketing, such as evaluating return on marketing investments and optimizing resource allocation.AI Generated
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AbstractTime-series models constitute a core component of marketing research and are applied to solve a wide spectrum of marketing problems. This chapter covers traditional and modern time-series models with applications in extant marketing research. We first introduce basic concepts and diagnostics including stationarity test (the augmented Dicky-Fuller test of unit roots), and autocorrelation plots via autocorrelation function (ACF) and partial autocorrelation function (PACF). We then discuss single-equation time-series models such as autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models with and without exogenous variables. Multiple-equation dynamic systems including vector autoregressive (VAR) models together with generalized impulse response functions (GIRFs) and generalized forecast error variance decomposition (GFEVD) are then discussed in detail. Other relevant models such as generalized autoregressive conditional heteroskedasticity (GARCH) models are covered. Finally, a case study accompanied by data and R codes is provided to demonstrate detailed estimation steps of key models covered in this chapter. -
Modeling Marketing Dynamics Using Vector Autoregressive (VAR) Models
Shuba SrinivasanThe chapter delves into the use of Vector Autoregressive (VAR) models to analyze marketing dynamics, offering a step-by-step approach to modeling time-dependent relationships among marketing actions and performance metrics. It covers unit root and cointegration testing, model specification, policy simulation, and the application of VAR models in various marketing contexts. The chapter also discusses the importance of VAR models in distinguishing between short-term and long-term marketing effects, making it a valuable resource for both practitioners and researchers in the field.AI Generated
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AbstractTime-series data include repeated measures of marketing activities and performance that are typically equally spaced in time. In the context of such data, Vector Autoregressive (VAR) models are uniquely suited to capture the time dependence of both a criterion variable (e.g., sales performance) and predictor variables (e.g., marketing actions, online consumer behavior metrics), as well as how they relate to each other over time. The objective of this chapter is to provide a foundation in VAR models and to enable the readers to apply them in their own research domain of interest. To this end, the chapter will discuss both the underlying perspectives and differences among alternative VAR models, and the practical issues with testing, model choice, estimation, and interpretation that are common in empirical research in marketing.From a marketing strategy perspective, both managers and academic researchers pay attention to whether a performance change is temporary (short-term) or lasting (long-term). Establishing the distinction between short-term and long-term marketing effectiveness is central to the understanding of marketing strategy and its implications, which this chapter aims to do. The interaction among appropriate marketing phenomena, modeling philosophy, and contemporary substantive topics sets this work apart from previous treatments on the broader topic of econometrics and time-series analysis in marketing (e.g., Dekimpe and Hanssens, Persistence modeling for assessing marketing strategy performance. In: Lehmann D, Moorman C (eds) Cool tools in marketing strategy research. Marketing Science Institute, Cambridge, MA, 2004; Hanssens et al., Market response models: Econometric and time series analysis. Springer Science and Business Media, 2001; Pauwels, Found Trends Market 11(4):215–301, 2018). -
Structural Equation Modeling
Hans Baumgartner, Bert WeijtersStructural Equation Modeling (SEM) has evolved from an advanced statistical method to a standard tool for data analysis in both academic and industry research. This chapter explores the history and applications of SEM, highlighting its ability to handle measurement error, model complex relationships, and test hypotheses across different populations. Practical considerations for model estimation and testing are discussed, along with guidelines for ensuring robust and reliable results. The chapter also introduces extensions and innovations in SEM, such as the inclusion of formative indicators and the analysis of non-normal data, making it a comprehensive resource for researchers seeking to apply SEM in their work.AI Generated
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AbstractThis chapter presents an overview of the process of structural equation modeling, involving the steps of model specification, model estimation, overall fit evaluation, model respecification, and local fit assessment (including interpreting the parameters of the model). Various extensions of the core structural equation model are described to enable more general representations of measurement and latent variable models as well as applications of the model to heterogeneous populations. An empirical example is provided to illustrate the process of structural equation modeling and to demonstrate some of the complexities that may arise in practical applications. -
Partial Least Squares Structural Equation Modeling
Marko Sarstedt, Christian M. Ringle, Joseph F. HairThe chapter begins by introducing the historical context of Partial Least Squares Structural Equation Modeling (PLS-SEM), tracing its origins to the work of Swedish econometrician Herman Wold. PLS-SEM is presented as an alternative to covariance-based SEM, which is more restrictive in its data distribution and sample size assumptions. The chapter explains the fundamentals of measurement and structural model specification in PLS-SEM, including reflective and formative measurement models. It also delves into the evaluation of PLS-SEM results, providing guidelines for assessing the quality of reflective and formative measurement models, and the structural model. A key feature of PLS-SEM is its focus on predictive power, making it particularly useful for exploratory research settings. The chapter concludes with a practical application of PLS-SEM using SmartPLS software, demonstrating its use in a corporate reputation model. Throughout, the chapter emphasizes the advantages of PLS-SEM in terms of its flexibility, ability to handle complex models, and suitability for prediction-oriented research.AI Generated
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AbstractPartial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method. -
Automated Text Analysis
Ashlee HumphreysThe chapter begins by introducing automated text analysis, tracing its origins back to the 1960s and highlighting its relevance in today's digital age. It discusses the foundations of text analysis, comparing traditional content analysis with modern, computer-assisted methods. Key approaches such as dictionary-based and classification methods are explored, with examples of their applications in sentiment analysis, word-of-mouth communication, and market structure analysis. The chapter also covers the measurement of organizational attention through text analysis and addresses potential issues in working with textual data. A detailed example of a text analysis study on consumer response to a product launch is provided, illustrating the practical application of the methods discussed.AI Generated
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AbstractThe amount of text available for analysis by marketing researchers has grown exponentially in the last two decades. Consumer reviews, message board forums, and social media feeds are just a few sources of data about consumer thought, interaction, and culture. However, written language is filled with complex meaning, ambiguity, and nuance. How can marketing researchers possibly transform this rich linguistic representation into quantifiable data for statistical analysis and modeling? This chapter provides an introduction to text analysis, covering approaches that range from top-down deductive methods to bottom-up inductive methods for text mining. After covering some foundational aspects of text analysis, applications to marketing research such as sentiment analysis, topic modeling, and studying organizational communication are summarized and explored, including a case study of word-of-mouth response to a product launch. -
Image Analytics in Marketing
Daria Dzyabura, Siham El Kihal, Renana PeresImage analytics has become increasingly important in marketing due to the rise of social media and digital platforms. This chapter delves into the significance of images in marketing communications, product design, and consumer research. It discusses the challenges and opportunities of using image analytics, including the selection of appropriate data sources and methods. The chapter also provides a decision matrix to help researchers match their research questions with the right data and methods. Additionally, it offers a hands-on tutorial to assist in implementing image analytics techniques. The chapter highlights the potential of image analytics to revolutionize marketing research and practice by providing deeper insights into consumer behavior and brand perceptions.AI Generated
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AbstractRecent technical advances and the rise of digital platforms enhanced consumers’ abilities to take and share images and led to a tremendous increase in the importance of visual communication. The abundance of visual data, together with the development of image processing tools and advanced modeling techniques, provides unique opportunities for marketing researchers, in both academia and practice, to study the relationship between consumers and firms in depth and to generate insights which can be generalized across a variety of people and contexts.However, with the opportunity come challenges. Specifically, researchers interested in using image analytics for marketing are faced with a triple challenge: (1) To which type of research questions can image analytics add insights that cannot be obtained otherwise? (2) Which visual data should be used to answer the research questions, and (3) which method is the right one?In this chapter, the authors provide a guidance on how to formulate a worthy research question, select the appropriate data source, and apply the right method of analysis. They first identify five relevant areas in marketing that would benefit greatly from image analytics. They then discuss different types of visual data and explain their merits and drawbacks. Finally, they describe methodological approaches to analyzing visual data and discuss issues such as feature extraction, model training, evaluation, and validation as well as application to a marketing problem. -
Social Network Analysis
Hans Risselada, Jeroen van den OchtendThis chapter delves into the application of social network analysis in marketing, highlighting the importance of understanding social influence and network structures among consumers. It discusses fundamental concepts and metrics, such as degree centrality and betweenness centrality, and provides practical examples of how to analyze network data. The chapter also explores the relevance of social influence in different consumer decisions and offers insights into how marketers can leverage this information to develop effective marketing strategies. Additionally, it covers various data collection methods and sampling techniques for network analysis, making it a valuable resource for both marketing practitioners and researchers.AI Generated
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AbstractThe increased awareness about the presence of social effects in consumer networks has inspired marketers to better understand and address the needs of their consumers through network analyses. In this chapter we consider network analyses as a set of techniques which allows researchers to analyze how the social structure of relationships around consumers affects their attitudes and behavior, and vice versa, how attitudes and behavior may affect the social structure. We focus on the types of network analyses that are currently most prominent within the field of marketing. We provide basic network theory and notation with references to key publications in the field. We also provide suggestions for software (packages) and useful functions including code snippets to support researchers and practitioners in setting up their first social network analyses. At the end of the chapter we discuss several more advanced network analysis methods and list several resources that might be useful to the interested reader. -
Bayesian Models
Thomas OtterBayesian models have gained prominence in marketing due to their ability to manage short panel data with many observational units and incorporate prior knowledge about heterogeneous characteristics. These models are particularly advantageous when dealing with limited dependent variables and small samples, offering coherent inference even in data-scarce situations. The chapter discusses the practical aspects of Bayesian modeling, including the use of computational algorithms and software like Stan and the R package bayesm. It also highlights the importance of Bayesian models in decision-making processes, facilitating the accurate incorporation of uncertainty in managerial decisions. The chapter concludes by exploring the applications of Bayesian models in hierarchical structures and the use of data augmentation techniques for inference.AI Generated
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AbstractBayesian models have become a mainstay in the tool set for marketing research in academia and industry practice. In this chapter, I discuss the advantages the Bayesian approach offers to researchers in marketing, the essential building blocks of a Bayesian model, Bayesian model comparison, and useful algorithmic approaches to fully Bayesian estimation. I show how to achieve feasible Bayesian inference to support marketing decisions under uncertainty using the Gibbs sampler, the Metropolis Hastings algorithm, and point to more recent developments – specifically the no-U-turn implementation of Hamiltonian Monte Carlo sampling available inStan. The emphasis is on the development of an appreciation of Bayesian inference techniques supported by references to implementations in the open source softwareR, and not on the discussion of individual models. The goal is to encourage researchers to formulate new, more complete, and useful prior structures that can be updated with data for better marketing decision support. -
Choice-Based Conjoint Analysis
Felix Eggers, Henrik Sattler, Thorsten Teichert, Franziska VölcknerThe chapter introduces choice-based conjoint analysis as a method to understand consumer preferences for ebook readers. It discusses the importance of identifying relevant attributes and levels, and the advantages of using choice-based conjoint over traditional rating-based methods. The chapter also covers the experimental design of conjoint studies, including factorial and choice designs, and the estimation of partworth utilities. Additionally, it explores advanced topics such as interaction effects, adaptive conjoint analysis, and incentive alignment mechanisms. The chapter concludes with a discussion on the practical applications of conjoint analysis in new product development, pricing, branding, and market simulations.AI Generated
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AbstractConjoint analysis is one of the most popular methods to measure preferences of individuals or groups. It determines, for instance, the degree how much consumers like or value specific products, which then leads to a purchase decision. In particular, the method discovers the utilities that (product) attributes add to the overall utility of a product (or stimuli). Conjoint analysis has emerged from the traditional rating- or ranking-based method in marketing to a general experimental method to study individual’s discrete choice behavior with the choice-based conjoint variant. It is therefore not limited to classical applications in marketing, such as new product development, pricing, branding, or market simulations, but can be applied to study research questions from related disciplines, for instance, how marketing managers choose their ad campaign, how managers select internationalization options, why consumers engage in or react to social media, etc. This chapter describes comprehensively the “state-of-the-art” of conjoint analysis and choice-based conjoint experiments and related estimation procedures. -
Exploiting Data from Field Experiments
Martin Artz, Hannes DoeringThe chapter introduces the concept of field experiments and their importance in business research, highlighting the challenges in establishing causality. It discusses three key methodologies for analyzing field experiment data: difference-in-differences, regression discontinuity designs, and instrumental variables. Each method is explained with its core area of application, critical assumptions, and examples of real-world applications. The chapter also provides guidance on implementing these methods using standard software packages like STATA, R, and SPSS. It concludes by emphasizing the growing relevance of field experiments in the era of big data and their potential to provide more reliable causal insights compared to purely correlational data.AI Generated
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AbstractThis chapter gives an introduction on how to exploit data from field experiments and aims to provide an intuitive understanding for managers and researchers alike. We outline the relevance and hurdles in identifying causal effects compared to observing purely correlational associations in studies which take place in the real world. We further provide a framework to classify different kinds of field experiments, such as quasi field experiments and natural field experiments. The core of this chapter focuses on giving an understanding of three standard econometric methods to exploit data from field experiments: difference-in-differences, regression discontinuity, and instrumental variables. For each method, we provide an intuitive understanding of the core features and its critical assumptions. We complement those explanations with an in-depth look at one practical application of each method in a field experiment setting and with a variety of practical examples from recently published research. Lastly, we provide a brief overview on how to implement each method in standard software packages such as STATA, R, and SPSS. -
Mediation Analysis in Experimental Research
Nicole Koschate-Fischer, Elisabeth SchwilleMediation analysis in experimental research is a crucial method for understanding the causal mechanisms behind the effects of marketing stimuli on consumer behavior. This chapter introduces the concept of regression-based mediation analysis, which involves investigating how an independent variable (X) influences a dependent variable (Y) through one or more mediating variables (M). The chapter begins by explaining the basic conceptual and statistical principles of mediation analysis, using a simple mediator model as an example. It then delves into more complex models, including multiple mediator models, where multiple mediating variables are considered, and conditional process models, which include a moderating variable. The chapter also discusses the importance of strengthening causal inference through design, additional evidence, and statistical methods. Throughout, the chapter provides practical insights and examples to illustrate the application of mediation analysis in experimental research.AI Generated
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AbstractThis chapter introduces the conceptual and statistical basics of mediation analysis in the context of experimental research. Adopting the respective terminology, mediation analysis can be referred to as an array of quantitative methods developed to investigate the causal mechanism(s) through which an independent variable influences a dependent variable. The chapter takes a regression-based approach to mediation analysis and focuses on mediation models likely to be tested in experiments (i.e., the single mediator model, parallel and serial multiple mediator models, and conditional process models). Yet, the scope of mediation analysis beyond an experimental setting will also be touched upon. Furthermore, the chapter addresses the question how to strengthen causal inference in mediation analysis through design, the collection of additional evidence, and statistical methods. It closes with a discussion of common topics of relevance when implementing mediation analysis such as sample size and power, mean centering in conditional process analysis, coding of categorical independent variables, advantages and disadvantages of a regression-based approach to mediation analysis, and software options to perform mediation analysis.
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- Title
- Handbook of Market Research
- Editors
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Prof. Dr. Christian Homburg
Prof. Dr. Martin Klarmann
Dr. Arnd Vomberg
- Copyright Year
- 2022
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-319-57413-4
- Print ISBN
- 978-3-319-57411-0
- DOI
- https://doi.org/10.1007/978-3-319-57413-4
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