Applied Conjoint Analysis
From Product and Service Design to Market and Pricing Strategies
- 2026
- Book
- Author
- Vithala R. Rao
- Book Series
- Springer Texts in Business and Economics
- Publisher
- Springer Nature Switzerland
About this book
This book provides different applications and methods of conjoint analysis in marketing. It gives an introduction into the basic ideas of conjoint analysis and describes the steps involved in designing a ratings-based conjoint study. This new revised second edition offers newer methods for estimating utility functions for products with multiple attributes such as Best-Worst Scaling, incorporating non-compensatory aspects, and auction methods. It features new chapters on advanced methods of analysis (e.g., machine-learning based and others) and conjoint analysis with other types of data such as eye tracking, visual design evaluations, search data and GPT, among others. While the focus of the book is on methods in marketing, these methods are also applicable for other business and social sciences.
This book is useful to academics, researchers, and scholars applied marketing science. This book is also suitable as a textbook for quantitative marketing coursework.
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Table of Contents
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Frontmatter
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1. Problem Setting
Vithala R. RaoThis chapter delves into the fundamentals of conjoint analysis, a method used to study consumer choice processes and determine trade-offs. It begins with an introduction to marketing decisions and the role of consumer choice, highlighting the importance of understanding how customers choose among competing alternatives. The chapter then provides a framework for understanding consumer choice, explaining how marketing decisions influence consumer perceptions and preferences, which in turn affect choices and market responses. The origins of conjoint analysis are explored, tracing its development from the 1920s to its seminal paper by Luce and Tukey in 1964. The chapter discusses the theory behind conjoint measurement, which involves decomposing total evaluations into component scores for each attribute level. Various types of conjoint methods are introduced, including traditional conjoint analysis, choice-based conjoint analysis, adaptive conjoint analysis, and self-explicated conjoint analysis. The chapter also covers the theoretical foundations of these methods, including measurement theory, random utility maximization, and the Fishbein-Rosenberg models of attitude formation. Practical applications of conjoint analysis in marketing are highlighted, such as new product design, target market selection, pricing, and competitive reactions. The chapter concludes with a discussion on the wide range of industries where conjoint analysis has been applied, including consumer goods, industrial products, financial services, and transportation.AI Generated
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AbstractSeveral interdependent decisions are involved in the formulation of a marketing strategy for a brand (of a product or service). These include not only decisions about the product’s characteristics but also its positioning, communication, distribution, and pricing to chosen sets of targeted customers. The decisions will need to be made in the wake of uncertain competitive reactions and a changing (and often unpredictable) environment. For a business to be successful, the decision process must include a clear understanding of how customers will choose among (and react to) various competing alternatives. It is well accepted in marketing that choice alternatives can be described as profiles on multiple attributes and that individuals consider various attributes while making a choice. While choosing, consumers typically make trade-offs among the attributes of a product or service. Conjoint analysis is a set of techniques ideally suited to studying customers’ choice processes and determining trade-offs. -
2. Some Consumer Behavior Paradigms
Vithala R. RaoThis chapter delves into the methodologies of conjoint analysis, focusing on the collection and modeling of consumer preferences and choices. It explores two primary data types: ratings and choices, and discusses the frameworks for understanding these data types. The chapter also introduces the concept of the attention process, measured through eye fixations and saccades, and its role in consumer decision-making. It highlights how eye-tracking data can be incorporated into conjoint studies to reveal simplification processes and predict consumer choices. The text provides a detailed overview of the processes involved in individuals making judgments on preferences and choices, supported by figures and illustrations. Additionally, it presents an overview of three conjoint analysis studies that utilize eye fixation data, offering insights into how these studies enhance the understanding of consumer behavior. The chapter concludes by emphasizing the importance of the background provided in understanding the design and analysis of rating data and choice data, which are discussed in subsequent chapters.AI Generated
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AbstractConjoint analysis involves collecting judgmental data (preferences or choices) from respondents toward hypothetical profiles or choice sets. There are two types of data: ratings and choices. We discuss the methods for designing profiles and choice sets in Chap. 5. -
3. Theory and Design of Conjoint Studies (Ratings Based Methods)
Vithala R. RaoThis chapter delves into the theory and design of conjoint studies, with a particular emphasis on ratings-based methods. It begins by outlining the basic principles of designing a marketing research study and the conceptual model of conjoint analysis, which postulates that the utility of a multi-attributed item can be decomposed into specific contributions of each attribute and possibly their interactions. The chapter reviews the standard or traditional approach in which a subset of full profiles of choice alternatives are rated by a respondent and the data are analyzed for each individual using regression analysis. It also presents and compares an array of alternative parameter estimation approaches, providing examples to illustrate the different approaches. The chapter covers the issues of stimulus presentation for data collection, reliability, and validity of data. It discusses the steps involved in designing a conjoint study, including problem definition, selection of attributes and levels, design of profiles and survey administration, analysis, and use of results. The chapter also explores different types of attributes and partworth functions, including categorical and quantitative attributes, and the methods for selecting attributes and levels. It delves into the construction of stimulus sets using experimental design procedures, such as full factorial designs, fractional factorial designs, orthogonal main effects plans, and incomplete block designs. The chapter also discusses various data collection methods, including the full profile approach, trade-off matrix method, paired comparison methods, self-explication methods, adaptive methods, and hybrid methods. It covers the issues of stimulus presentation, reliability, and validity of conjoint methods. The chapter concludes by summarizing the principal steps involved in the design of a conjoint study and the methods for constructing stimulus sets and collecting data. It also discusses the issues of reliability and validity of conjoint methods and the steps involved in the design of studies for collecting data. The next chapter will cover the remaining steps, focusing on analysis methods and models for estimating the partworth functions and using the results.AI Generated
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AbstractThe basic principles of designing a marketing research study will apply to any study that uses conjoint analysis. Differences arise in the conceptual foundations. The conceptual model of conjoint analysis is quite straightforward; it postulates that the utility of a multi-attributed item can be decomposed into specific contributions of each attribute and possibly their interactions. The approach is easy to implement if the number of attributes is small. But, problems arise in most practical problems because of the large number of possible hypothetical alternatives for a given problem. In general, only a subset of possible alternatives is chosen for the study. Experimental design methods exist for selecting such subsets. -
4. Analysis and Utilization of Conjoint Data (Ratings Based Methods)
Vithala R. RaoThis chapter delves into the analysis and utilization of conjoint data, focusing on ratings-based methods. It begins by discussing the data collection procedure and its impact on the type of analytical method used. The chapter then explores various analysis methods, including ordinary least squares regression for interval scales and special methods like monotone regression for ordinal data. It emphasizes the importance of analyzing data at the individual level and discusses aggregation techniques for large samples. The chapter also covers the use of prior information in analysis and introduces newer hybrid conjoint models that enable the estimation of individual-level partworths. Additionally, it discusses the application of latent class methods for uncovering segments and the use of choice simulation for predicting market share and other managerial decisions. The chapter concludes with a summary of the various analytical methods and their applications in conjoint studies.AI Generated
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AbstractWe saw in the previous chapter various methods for designing and collecting data in conjoint studies. The data collection procedure used almost invariably dictates the type of analytical method used in conjoint analysis. In addition, analysis methods depend on two major factors: the nature of the scale used for the dependent variable (preference) and the desired level of data aggregation. -
5. Choice Based Conjoint Studies: Design and Analysis
Vithala R. RaoThis chapter delves into the intricacies of choice-based conjoint studies, a method used to predict consumer choices and understand preferences. It begins by explaining the choice process and the role of random utility theory in analyzing stated choice data. The chapter then outlines various strategies for designing choice sets, including manual and computer-aided methods, and discusses the advantages and disadvantages of each. It also covers different analysis methods for choice-based conjoint data, such as the multinomial logit model, multinomial probit model, and heteroscedastic logit model. The text provides practical examples and case studies to illustrate these concepts, making it a valuable resource for professionals in market research and data analysis. By the end of the chapter, readers will have a comprehensive understanding of how to design and analyze choice-based conjoint studies, enabling them to make informed decisions based on consumer preferences.AI Generated
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AbstractOne of the major objectives in conjoint analysis has been to predict the choices made by a sample of individuals for a new item, which is described in terms of a set of attributes used in a conjoint study. Ratings-based conjoint studies involve the conversion of an individual’s stated utility for an item (or rating) to predict the probability of choice of an alternative under various conditions (e.g., when other alternatives are available). As described in Chap. 4, such a prediction is made using preference data (ratings or rankings) collected on a set of hypothetical choice alternatives. A parallel stream of research pursues the path of choice experiments in which an individual makes a choice among a set of choice alternatives (a choice set), each of which is typically described by a set of attributes; several choice sets are presented to each individual. These choice data, across all the choice sets and all individuals, are then analyzed using a choice model (usually a multinomial logit model and sometimes a multinomial probit model) to obtain a function that relates the attribute levels to probability of choice. This approach has come to be known as choice-based conjoint analysis and has its roots in discrete choice analysis; these methods are also called “stated” choice methods (or stated choice experimental methods) because they represent the intended choices of respondents among hypothetical choice possibilities. This chapter describes these methods. -
6. Methods for a Large Number of Attributes
Vithala R. RaoThis chapter delves into the challenges of handling a large number of attributes in conjoint analysis, a common issue in market research. It explores various methods to address this problem, including fractional factorial designs, partial profile conjoint analysis, and self-explicated methods. The text provides a detailed comparison of these methods, highlighting their advantages and disadvantages in terms of theoretical basis, incentive compatibility, ease of implementation, and partworth estimation. Additionally, it discusses the practical applications of these methods and their potential to improve the accuracy and reliability of conjoint analysis. The chapter concludes with a summary of the most promising methods for tackling the problem of large numbers of attributes, offering valuable insights for professionals in the field.AI Generated
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AbstractIn the previous chapters we discussed various conjoint analysis methods for ratings-based ad choice-based studies. One problem that nags applied researchers is how to deal with the issue of large numbers of attributes (and levels) to be included that arise in any practical problem. This problem may arise particularly for technologically complex products which usually have a large number of attributes. Over the years, researchers have come up with different methods to deal with this problem. While we have mentioned tangentially some of the applicable methods, this chapter will pull together various methods developed. In the next section (Sect. 6.2), we will describe the main problem when a conjoint study has to deal with a large number of attributes and then present an overview of the methods available in the literature. In Sect. 6.3, we will describe each method in some detail (data collection approach and analysis method) along with an application. Section 6.4 compares the methods on a set of relevant criteria. Finally, we will offer several directions for future research on the issue of a large number of attributes in any conjoint study and conjecture possible newer developments. Some newer data collection methods that use auctions also deal with the large number of attributes problem. -
7. Advanced Methods of Analysis (Machine-Learning Based and Other)
Vithala R. RaoThis chapter delves into the latest advancements in machine learning and artificial intelligence methods for conjoint analysis, highlighting their relevance in marketing research. It begins with a classification of newer methods, distinguishing between machine learning techniques like Support Vector Machines (SVM) and artificial intelligence approaches, including natural language processing (NLP) and generative AI. The chapter explores the application of SVM methods in conjoint data analysis, demonstrating their robustness and accuracy in handling noisy and high-dimensional data. It also examines the role of large language models (LLMs), particularly ChatGPT, in generating human-like survey responses and conducting conjoint analysis efficiently. The use of ChatGPT in survey responses is discussed, including its ability to mimic human decision-making processes and the potential limitations, such as sensitivity to prompt phrasing and biases in training data. Additionally, the chapter covers methods for integrating textual data with standard conjoint responses, using models like Latent Dirichlet Allocation (LDA) and Grade of Membership Model (GoM). The chapter concludes with a futuristic view of these emerging methods, emphasizing the need for researchers to stay informed about ongoing developments in AI and machine learning technologies.AI Generated
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AbstractPrevious chapters described analysis methods for different kinds of data collected in conjoint studies. These studies typically utilize small samples (e.g., less than 1000 respondents). Situations arise when researchers need to develop or test new analysis methods required for large sample sizes. Fortunately, several developments have been made in methodologies that can be applied to conjoint analysis. This is an emerging area. -
8. Noncompensatory Models for Conjoint Analysis
Vithala R. RaoThis chapter delves into the world of noncompensatory models within the realm of conjoint analysis, offering a fresh perspective on how consumers make choices. It begins by contrasting noncompensatory models with the more traditional compensatory models, highlighting the limitations of the latter in capturing the complexity of consumer decision-making processes. The text then explores various noncompensatory rules, such as lexicographic, elimination by aspects (EBA), conjunctive, and disjunctive rules, illustrating each with practical examples. The chapter also discusses the stages of the choice process, including the consideration and choice stages, and how noncompensatory models can be applied in these contexts. Empirical studies are presented to compare the effectiveness of noncompensatory models against compensatory models, providing valuable insights into their real-world applications. The chapter concludes with a summary of the key findings and their implications for market research and consumer behavior analysis.AI Generated
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AbstractWe have seen several utility functions for product alternatives (in the ratings and choice-based methods). These utility functions discussed so far are specified in terms of product attributes of the profiles can be labeled as “compensatory”; this means that the utility change in an increase in one attribute needs to be compensated by an appropriate change in other attributes to maintain the same level of utility. The actual magnitude of change in the other attribute(s) will depend on the values of the attribute coefficients of the utility function. As we have seen in the previous chapters, these utility functions are fundamental to infer the attribute weights and further analyses such as market simulations. -
9. Applications for Product and Service Design and Product Line Decisions
Vithala R. RaoThis chapter delves into the practical applications of conjoint analysis for product and service design, focusing on maximizing market share and profit potential. It begins by outlining the general problem of product and product portfolio design, emphasizing the importance of defining competitive sets, identifying key product attributes, and modeling consumer decision processes. The chapter then presents a unified framework for product design, utilizing choice simulators to optimize product characteristics and pricing strategies. Several real-world applications are discussed, including the design of a truck engine, a single-lens reflex (SLR) camera, a university course, microfinance products, dental benefit plans, a hotel, an electronic toll collection system, and a pharmaceutical product. Each application illustrates how conjoint analysis helps in determining the optimal configuration of product attributes to meet consumer preferences and market demands. The chapter also explores the complexities of product line decisions, highlighting the need to consider both demand and cost interdependencies among products in a line. It concludes with a discussion on combining stated preference (SP) data and revealed preference (RP) data to improve the accuracy of market predictions and product design strategies. The chapter underscores the importance of conjoint analysis in guiding firms toward optimal product and service design decisions, ultimately enhancing their competitive positioning and profitability.AI Generated
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AbstractThe methodology of conjoint analysis has been most frequently used to tackle the difficult marketing problem of product/service design and product line selection. The typical conjoint approach for these problems is to implement a conjoint study (as per the details discussed in Chaps. 2 and 3) and to use the results to estimate attribute partworths preferably at the individual respondent level. These partworths are then used to determine the values of attributes (or design characteristics of a product or service) so as to optimize an objective function for a firm. This process requires the knowledge of the competitive set in which the new product(s) or product lines will compete and product costs (as a function of the product attributes). Usually the firm’s objective is to maximize the long-run profit potential for the new product(s) or product lines based on stable market shares of the new product(s). If cost information is not available, the objective of long run sales is used. -
10. Applications for Product Positioning and Market Segmentation
Vithala R. RaoThis chapter delves into the strategic applications of conjoint analysis for product positioning and market segmentation, focusing on pharmaceuticals and consumer goods. It begins by distinguishing between product design and product positioning, emphasizing the importance of communicating product benefits effectively. The chapter then explores various methods of market segmentation, including a priori and post hoc segmentation, and highlights the advantages of behavior-based segmentation using conjoint results. Several case studies are presented, including the positioning of an antidepressant drug, market segmentation for cameras and food processors, and the segmentation of buyers of an antifungal medication. The chapter also discusses a simulation study comparing nine different segmentation methods based on ratings data, concluding that no single method is universally preferable. Additionally, it touches on the application of conjoint analysis in the online gaming industry. The chapter concludes with a summary of the key findings and insights from the discussed applications.AI Generated
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AbstractAs we discussed in the previous chapter, there is a subtle difference between product design and product positioning. While product design deals with decisions on the “optimal” characteristics of a product, product positioning deals with issues of how best to communicate the corresponding benefits (or attributes) to the target consumers (for more details, see Kotler and Keller (2012) and Kaul and Rao (1995)). Naturally the benefits of a product arise from its characteristics and the way consumers interpret them. In applications of conjoint analysis to product positioning, an analyst describes the possible benefits and their levels in the same way as one would in the case of product design; then the problem of determining the best positioning is identical to that of product design. In some cases, the analyst may include both product benefits and characteristics. -
11. Applications for Pricing Decisions
Vithala R. RaoThis chapter delves into the application of conjoint analysis for pricing decisions, focusing on determining optimal prices for new products and understanding consumer preferences. It explores the use of economic theories and methods to estimate price elasticities, reservation prices, and competitive reaction elasticities. The text provides practical examples, such as the Alpha-catering firm's bidding strategy and the National Academies Press's pricing of digital content. It also discusses the separation of informational and allocative effects of price, offering insights into how these distinct roles of price impact consumer choices. Additionally, the chapter covers the estimation of willingness to pay for attribute changes and the application of conjoint methods to multipart pricing. The conclusion highlights the importance of considering nonlinear price effects in certain contexts, such as bundling products with disparate prices. Overall, the chapter provides a comprehensive guide for professionals seeking to optimize pricing strategies using conjoint analysis.AI Generated
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AbstractOne significant application of conjoint analysis is in helping the manager with pricing decisions. The determination of the optimal price for a new product (or brand) is a typical application. One way to determine the best price is to estimate the market obtainable from the new product at different feasible prices for the new product profile. We described the use of conjoint simulators in Chap. 3. Additional information on cost functions can be integrated into the estimates of market share to yield estimates of profit from the new product at various prices. The price at which the computed profit is highest can be deemed to be the best price for the new product. This approach can also yield a generic estimate of price elasticity for the product category as a whole. -
12. Applications to a Miscellany of Marketing Problems
Vithala R. RaoThis chapter delves into the diverse applications of conjoint analysis in marketing, showcasing its versatility in addressing a wide array of problems. It begins with an overview of conjoint analysis and its traditional applications in product design, market segmentation, and pricing. The chapter then explores how conjoint simulators can be used to evaluate competitive strategies, with a focus on the concept of Nash equilibrium. A detailed case study on cellular phone market strategies illustrates the practical application of these methods. The text also covers other marketing problems such as store location decisions, sales quota setting, and resource allocation, demonstrating how conjoint analysis can be tailored to different scenarios. Additionally, it discusses the measurement of brand equity and customer satisfaction, highlighting the role of conjoint methods in these areas. The chapter concludes with a summary of the key applications and their implications for marketing strategy, emphasizing the importance of conjoint analysis in modern marketing research.AI Generated
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AbstractWe have seen applications of conjoint analysis to marketing problems such as product design, market segmentation, product positioning, and pricing. We have also seen that conjoint simulators have been quite helpful in dealing with these questions. In this process, we have tangentially dealt with the design of appropriate competitive strategies. The objective of this chapter is to present an overview of several other applications to demonstrate the versatility of the methodology of conjoint analysis for general research in marketing. -
13. Recent Developments and Future Outlook
Vithala R. RaoThis chapter delves into the recent developments and future outlook of conjoint analysis, a vital tool in marketing research. It begins by exploring experimental designs that combine mixture and mixture-amount, particularly useful in service contexts. The chapter then introduces innovative methods such as Barter Conjoint and Conjoint Poker, which enhance data collection and predictive validity. Additionally, it discusses Best-Worst Scaling (BWS) and its comparison with established conjoint methods. The text also covers methods for measuring peer influence and incorporating non-compensatory processes, as well as techniques for combining preference and choice data. Applications in self-designed products and bundle choice problems are also highlighted. The chapter concludes with an assessment of these developments and identifies future research directions, emphasizing the ongoing relevance and potential of conjoint analysis in understanding consumer behavior and making informed marketing decisions.AI Generated
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AbstractThe previous chapters described several approaches employed for determining partworths of attributes and tradeoffs among them. The chapters dealt with various methods for both of ratings-based and choice-based conjoint methods. In addition, we described several applications of conjoint methodology to different marketing problems such as product design, product positioning, pricing, market segmentation, and several miscellaneous problems. During the last thirty plus years since these methods were introduced to marketing research, researchers have tackled various problems that are encountered in applying these methods in practice. As Hauser and Rao have noted, conjoint analysis is alive and well. In fact there have been several developments in the last 5–10 years that place this methodology as one of the most vibrant techniques in marketing research. -
14. Beyond Conjoint Analysis: Advances in Preference Measurement
Vithala R. RaoThis chapter delves into the evolving landscape of preference measurement, moving beyond the traditional confines of conjoint analysis. It explores how preference measurement is being applied to a broader range of problems, including those involving consumers, policy makers, and health care professionals. The text discusses new forms of data collection that engage respondents and improve the quality of preference measurement data. It also highlights the incorporation of behavioral context effects, non-compensatory processes, and dynamic effects into preference models. The chapter emphasizes the importance of considering the objectives and context of preference measurement studies throughout each step of the process, from design to model estimation and action. It concludes by outlining future research directions and the potential for integrating statistical and optimization methods to enhance the field of preference measurement.AI Generated
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AbstractWe identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework. We summarize cutting edge research and identify fruitful directions for future investigations pertaining to the framework’s three components and to their integration. -
15. An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research
James Agarwal, Wayne S. DeSarbo, Naresh K. Malhotra, Vithala R. RaoConjoint analysis, a celebrated research tool in marketing and consumer research, has seen remarkable developments over the past decades. This interdisciplinary review provides an organizing framework for the vast literature, integrating insights from various disciplines such as choice behavior and statistical theory. The article critically discusses several advanced issues and developments, including the move from nonmetric to metric orientation, the growing popularity of hierarchical Bayesian and latent class models, and the adaptability of conjoint models to online choice tasks and social influences. It also identifies directions for future research, encouraging scholars to explore new problems and applications of consumer preference measurement, develop new forms of data collection, and devise new estimation procedures. The review covers key areas such as behavioral and theoretical underpinnings, researcher issues for research design, respondent issues for data collection, researcher issues for data analysis, and managerial issues concerning implementation. It highlights the dynamic nature of conjoint analysis and its potential for understanding consumer preferences in various marketing problems, including estimating product demand, designing new product lines, and calibrating price sensitivity and elasticity. The article concludes by setting a comprehensive research agenda for future development in conjoint analysis methodology.AI Generated
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AbstractThis review article provides reflections on the state of the art of research in conjoint analysis—where we came from, where we are, and some directions as to where we might go. We review key articles, mostly contemporary published since 2000, but some classic, drawn from the major marketing as well as various interdisciplinary academic journals to highlight important areas related to conjoint analysis research and identify more recent developments in this area. We develop an organizing framework that attempts to integrate various threads of research in conjoint methods and models. Our goal is to (a) emphasize the major developments in recent years, (b) evaluate these developments, and (c) identify several potential directions for future research. -
16. Sawtooth Software’s Influence on the Conjoint Analysis Industry
Vithala R. RaoThis chapter delves into the significant influence of Sawtooth Software on the conjoint analysis industry, tracing its history from the pioneering work of Rich Johnson to its current standing as a leader in market research tools. The text covers the development of various conjoint analysis methods, including Adaptive Conjoint Analysis (ACA), Choice-Based Conjoint (CBC), and Best-Worst Scaling (MaxDiff), and their applications across different industries. It highlights the innovative software solutions developed by Sawtooth Software, which have made conjoint analysis more accessible and efficient. The chapter also explores the impact of these tools on market research practices, providing real-world examples of their use in industries such as healthcare, automotive, and consumer goods. Additionally, it discusses the future of conjoint analysis, including the potential of AI and machine learning to enhance data collection and modeling. The conclusion emphasizes the continued relevance and growth of conjoint analysis in the face of big data and AI advancements, underscoring its value in guiding strategic decision-making.AI Generated
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AbstractWe are grateful to Vithala Rao for his invitation to contribute a chapter to this important book on conjoint analysis. Vithala has encouraged us to cover the history of Sawtooth Software and its influence on the conjoint analysis industry, some details about the multiple flavors of conjoint analysis and market simulator tools it has commercialized, along with examples illustrating their impact across multiple fields and industries. -
Backmatter
- Title
- Applied Conjoint Analysis
- Author
-
Vithala R. Rao
- Copyright Year
- 2026
- Publisher
- Springer Nature Switzerland
- Electronic ISBN
- 978-3-032-00894-7
- Print ISBN
- 978-3-032-00893-0
- DOI
- https://doi.org/10.1007/978-3-032-00894-7
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