Knowledge-based Configuration: From Research to Business Cases, FIRST EDITION (2014)

Part III. Advanced Topics

Chapter 14. Consumer Decision-Making and Configuration Systems

Monika Mandl^a, Alexander Felfernig^a and Erich Teppan^b, ^aGraz University of Technology, Graz, Austria, ^bAlpen Adria Universität Klagenfurt, Klagenfurt, Austria

Abstract

Configuring complex products and services can be challenging for users. Due to the complexity of the underlying decision tasks, decisions are often influenced by different types of decision biases. Such biases can move users toward unintended results and are often the major reason for suboptimal decisions. In this chapter, we provide an overview of different types of decision biases, which can be of relevance for tasks related to configuration processes.

Keywords

Knowledge-based Configuration; Consumer Decision Making; Decision Psychology

14.1 Introduction

A couple of theories that explain the existence of different types of decision biases in consumer decision making can be found in psychological literature. Consumers are influenced by the way in which the information is presented and, as a consequence, use different decision-making strategies (see, e.g., Asch, 1949; Bettman et al., 1991; Tversky and Kahneman, 1981). For example, the results of a study conducted by Asch illustrate the importance of an item’s position in a list. The experiment demonstrated that when presenting adjectives describing a person in sequence, the same words could result in very different ratings of that person depending on the order in which the words were presented. This effect is known as primacy effect which can also be expected in different orderings of product descriptions (Felfernig et al., 2007).

The results of the research of Mandel and Johnson (1998) indicate that web page design can have an impact on users’ perceived importance of product attributes and therefore on the final product choice—this is known as priming effect. In their study they confronted the participants with a user interface that supported the selection of furniture items. One version of the interface had a “money-related” background (coins were shown to the user), the other interface had a fleecy cloud background. When using the first version of the interface, users chose furniture items that were significantly less expensive.

If the user’s opinion is biased in the preference construction process,¹ this can lead to imprecise and erroneous preference information, and as a consequence, to less accurate product predictions. Another problem is that decision biases can lead to unscrupulous business practices since online retailers could exploit these biases to increase sales. This motivates the investigation of decision biases in the context of product configuration. An important research area is the development of user interfaces that either counteract these biases or exploit the effects in a way that improves user experience (Teppan and Felfernig, 2012).

The major contributions of this chapter are the following. We provide an overview of potential decision biases that can occur when interacting with a configuration system. With this overview we raise the awareness of the existence of decision biases and also encourage researchers from the areas of computer science and decision psychology to focus on related research questions.

In the remainder of this chapter we will summarize selected theories from decision psychology with respect to their potential impact on preference construction and product (configuration) choice processes—an overview of these theories is given in Table 14.1. First, we will present decision effects that are related to the inclusion of low-quality items/configurations (decoy items) to result sets (Section 14.2).² In Section 14.3 we discuss the impact of the ordering of items on item selection (primacy/recency effects). Finally, we investigate decision effects that are related to the presentation of defaults (Section 14.4; see also Tiihonen et al., 2014³). This chapter is concluded with Section 14.5.

Table 14.1

Selected decision psychological theories related to decision biases.

14.2 Decoy Effects

14.2.1 Overview

Decoy items can be, for example, (partial) configurations that are inferior to others in a given set. In this context, the inferiority or superiority of products, respectively, is measured by simply comparing the underlying properties.⁴ For example, in Table 14.3 computer RAM I dominates computer D in the dimensions price and capacity since it has both a lower price and a higher memory capacity. The inclusion of decoy products (configurations) can significantly influence the outcome of the decision process. This effect is known as the decoy effect (Huber et al., 1982; Simonson and Tversky, 1992; Teppan and Felfernig, 2009b) and is of special relevance in the result presentation phase within a configuration process. In the following subsections we will discuss different types of decoy effects (compromise, asymmetric dominance, and attraction) and explain how these effects can influence the outcome of a decision process. Figure 14.1 provides an overview of the different types of decoy effects discussed in this chapter. Note that the computers RAM I and RAM II appear on the Pareto line, which means that they have a comparable utility (computer RAM I has a higher capacity whereas RAM II has a lower price).

FIGURE 14.1 Different types of decoy items discussed in this chapter.

Compromise Effects. Compromise effects denote one specific archetype of decoy effects (see Table 14.2). Simonson and Tversky (1992) demonstrated that adding an extreme alternative to a choice set will result in people favoring the “middle” choice where attribute values are positioned between the values of the other alternatives. For example, the attractiveness of computer RAM I compared to computer RAM II can be increased by adding computer D to the set of alternatives (see Table 14.2). Since RAM I has a significantly lower price and only a marginally lower capacity compared to D, computer RAM I is established as a compromise between the alternatives RAM II and D.

Table 14.2

Compromise effect: RAM I is a compromise between RAM II and D.

Asymmetric Dominance Effects. The second archetype of decoy effect is called asymmetric dominance (depicted in Table 14.3). In this scenario, computer RAM I dominates computer D in both attributes (price and capacity), whereas computer RAM II dominates D in only one dimension (the price). Therefore, the addition of computer D to the set can increase the share of computer RAM I. The underlying effect is that users interpret RAM I of being the better choice since it outperforms—in contrast to RAM II—D in both dimensions.

Table 14.3

Asymmetric dominance effect: RAM I dominates D in both dimensions (capacity and price).

Attraction Effects. The third archetype of decoy effects is called attraction effect. In this scenario, decoy products are positioned between the target and the competitor product (see Table 14.4). In this context, computer RAM I appears to be only a little bit more expensive and simultaneously has a significantly higher capacity compared to computer D, and therefore the inclusion of computer D can increase the attractiveness of computer RAM I.

Table 14.4

Attraction effect: RAM I has a marginally higher price but a lower capacity.

14.2.2 Relevance of Decoy Effects in Product Configuration Scenarios

If decoy configurations are added to a result set, this can change the selection probability for configurations that were included in the original result set. The existence of decoy effects has been shown in a number of empirical studies in application domains such as financial services, e-tourism, and even software agents (see, e.g., Teppan and Felfernig, 2009a; Teppan et al., 2011, 2010). The major possibilities of exploiting decoy effects in product configuration scenarios are the following:

• Increased selection probability for target items: As already mentioned, adding additional inferior items to a result set can cause an increased share of target items (in our example denoted as computer RAM I). This scenario definitely has ethical aspects to be dealt with since companies can potentially try to apply decoy effects for selling products that are suboptimal for the customer.

• Increased decision confidence: Besides an increase of the share of the target product, decoy effects can be exploited for increasing the decision confidence of a user. In this context, decoy effects can be exploited for resolving cognitive dilemmas that occur when a user is unsure about which alternative to choose from a given set of nearly equivalent alternatives.

14.3 Serial Position Effects

14.3.1 Overview

In 1946 Solomon Asch conducted an experiment on formations of personality impression that aimed at analyzing the importance of an item’s list position (Asch, 1949). The results of this study showed that presenting adjectives describing a person in sequence, the same words could result in very different ratings of that person depending on the order in which the words were presented. A person described as intelligent, industrious, impulsive, critical, stubborn, envious was rated more positive by the participants than a person described as envious, stubborn, critical, impulsive, industrious, intelligent. This phenomenon is known as primacy effect and can be explained by a memory advantage that early items in a list have (Crowder, 1976; see Figure 14.2).

FIGURE 14.2 Serial position effect (Ebbinghaus et al., 1885). Items at the beginning and at the end of a list are more accurately recalled than those items positioned in the middle of a list.

Murphy et al. (2006) demonstrated serial position effects in an online environment where they manipulated the serial position of links (to the restaurant’s offerings) on the website of a popular restaurant. In this experiment, visitors tended to click the link on first position most frequently, but there was also an increased tendency to click on the links at the end of the list—this is known as recency effect. The results go along with the findings of Ebbinghaus et al. (1885), who first documented serial position effects, the relationship between recall probability of an item and its position in a list (see Figure 14.2).

Felfernig et al. (2007) investigated serial position effects in knowledge-based recommendation scenarios. They conducted a study where participants were asked to choose a tent out of a set of tents that he/she most likely would buy. The results of this study showed significant changes in the product selection behavior triggered by the ordering of the position of product attributes used to describe the tents (Felfernig et al., 2007). Finally, the existence of serial position effects due to different orderings of choice alternatives (options) is shown in Li and Epley (2009).

14.3.2 Relevance of Serial Position Effects in Product Configuration Scenarios

Product configuration systems have been recognized as ideal tools to assist customers in configuring complex products according to their individual preferences (Blecker et al., 2004; Yang et al., 2005). In this context, serial position effects play an important role in the ordering choice alternatives; configuration systems must be aware of the fact that different rankings can trigger different selection behavior, and as well can increase or reduce a user’s decision-making effort. Murphy et al. (2006) suggest to place the most important item on the first position, and to place another important item on the last position of a list (the process should be continued with the order of importance). Felfernig et al. (2008) introduced an approach to calculate personalized item rankings, and to take into account primacy/recency effects in the presentation of result sets in the context of a recommendation scenario. They apply the concepts of Multi-Attribute Utility Theory (MAUT; Winterfeldt and Edwards, 1986) and derive the importance of interest dimensions from customer requirements (see also Felfernig et al., 2006, 2013).

A simple example of the concepts presented in Felfernig et al. (2008) is the following. Let us assume that economy and quality have been defined as interest dimensions for the computer product domain introduced in Section 14.2. In Tables 14.5 and 14.6 example scoring rules are defined, which describe the relationships between the computer attributes (price and capacity) and the corresponding interest dimensions (economy and quality). For example, Table 14.5 shows that an expensive computer has a low perceived value for interest dimension economy, and a high perceived value for interest dimension quality. Table 14.6 shows that a computer with a low capacity has a high valence in interest domain economy, and a low valence in interest dimension quality.

Table 14.5

Scoring rules for product attribute price.

Table 14.6

Scoring rules for product attribute capacity.

A personalized product ranking can be calculated on the basis of Formula 14.1. In this formula, contribution (r,i) defines the contribution of product to the interest dimension , and interest (i) shows the degree to which a specific customer is interested in dimension .

(14.1)

Let’s assume a concrete customer (customer A) with an equal interest in the dimension quality (importance of 0.5) and the dimension economy (importance of 0.5). Applying the scoring rules of Tables 14.5 and 14.6 to the computers of Table 14.4 results in the product (configuration) ranking shown in Table 14.7. This customer-specific ranking of products can now be used to identify an ordering of computers that takes into account primacy/recency effects; one can expect that on the basis of such an ordering, the probability of unreasonable choice can be reduced.

Table 14.7

Overall utilities of computers in Table 14.4.

14.4 Status Quo Effect

14.4.1 Overview

Research in human decision-making has revealed the fact that people have a strong tendency to keep the status quo when choosing among alternatives (see, e.g., Kahneman et al., 1991; Samuelson and Zeckhauser, 1988). This effect is known as status quo bias. A consequence of this is that proposed decisions (e.g., decisions proposed by experts or by the configurator application) that represent the status quo are accepted by the user. Defaults (see, e.g., Tiihonen et al., 2014⁵) can lead to such a status quo bias. The results of the research of Samuelson and Zeckhauser (1988) implied that an alternative was significantly more often chosen when it was designated as the status quo, and that the status quo effect increases with the number of alternatives. Kahneman et al. (1991) argue that thestatus quo bias can be explained by a notion of loss aversion, since the status quo serves as a neutral reference point, and users evaluate options in terms of gains and losses relative to the reference point. Since individuals tend to regard losses as more important than gains in decision-making under risk (i.e., alternatives with uncertain outcomes; Kahneman and Tversky, 1979) the possible disadvantages outweigh the advantages. Cosley et al. (2003) showed that presenting item ratings in collaborative filtering recommenders (Konstan et al., 1997) has an impact on the rating behavior of a user. For example, ratings were higher in situations where inflated predictions were presented to the user.

14.4.2 Relevance of the Status Quo Effect in Product Configuration Scenarios

Product configuration systems are increasingly being used by manufacturing companies to assist customers in specifying their requirements, and to find a product (configuration) that matches their preferences. A side effect of the high diversity of products offered by a configurator is that the complexity of the alternatives may outstrip a user’s capability to explore them and make a buying decision. Since humans have limited processing capacity, confronting consumers with too much information can lead to an information overload and therefore can result in decreased quality of decision performance (Jacoby et al., 1974). Huffman and Kahn (1998) state that “the key to customer satisfaction with the entire shopping interaction is to ensure that the customer is equipped to handle the variety.”

A possibility to support users in the specification of their requirements is to provide defaults (Falkner et al., 2011; Tiihonen and Felfernig, 2010). Defaults in configuration systems can be defined as preselected options used to express personalized feature recommendations. For example, if the user wants to play computer games, the recommended computer should have a high performance. Thus defaults are a means to help the user identify meaningful alternatives that are compatible with their current preferences. A major risk of defaults is that they could be exploited for misleading users and making them choose options that are not needed to fulfill their requirements. Ritov and Baron (1992) suggest counteracting the status quo bias by presenting the options in such a way that keeping as well as changing the status quo needs user input. They argue that “when both keeping and changing the status quo require action, people will be less inclined to err by favoring the status quo when it is worse.”

14.5 Conclusion

In this chapter we have presented a selected set of decision-psychological phenomena that have impacts on the development of decision support systems such as product configurators. A number of related empirical studies clearly show the importance of taking into account such theories when implementing software systems. For more information on aspects of consumer decision-making in online purchasing scenarios refer to Häubl and Trifts (2000) and Mandl et al. (2010).

References

1. Asch S. Forming impressions of personality. Journal of Abnormal and Social Psychology. 1949;41(3):258–290.

2. Bettman JR, Johnson EJ, Payne JW. Consumer decision making. In: Robertson TS, Kassarjian HH, eds. Handbook of Consumer Behavior. NJ: Prentice Hall; 1991:50–84. (Chapter 2).

3. Bettman J, Luce M, Payne J. Constructive consumer choice processes. Journal of Consumer Research. 1998;25(3):187–217.

4. Blecker T, Abdelkafi N, Kreuter G, Friedrich G. Product configuration systems: state of the art, conceptualization and extensions. In: Proceedings of the Eight Maghrebian Conference on Software Engineering and Artificial Intelligence (MCSEAI), Sousse, Tunisia. 2004:25–36.

5. Cosley D, Lam S, Albert I, Konstan J, Riedl J. Is seeing believing? How recommender system interfaces affect users opinions. In: CHI 2003 Conference on Human Factors in Computing Systems. Ft. Lauderdale, FL: ACM, NY; 2003;585–592.

6. Crowder R. Principles of learning and memory. In: The Experimental Psychology Series. Hillsdale, NJ: Lawrence Erlbaum Associates; 1976.

7. Ebbinghaus H, Ruger HA, Clara EB. Memory: a contribution to experimental psychology. In: The Experimental Psychology Series. Teachers College, Columbia University, NY 1885.

8. Falkner A, Felfernig A, Haag A. Recommendation technologies for configurable products. AI Magazine. 2011;32(3):99–108.

9. Felfernig A, Friedrich G, Jannach D, Zanker M. An integrated environment for the development of knowledge-based recommender applications. International Journal of Electronic Commerce (IJEC). 2006;11(2):11–34.

10. Felfernig A, Friedrich G, Gula B, et al. Persuasive recommendation: exploring serial position effects in knowledge-based recommender systems. In: De Kort Y, IJsselsteijn W, Midden C, Eggen B, Fogg BJ, eds. Second International Conference of Persuasive Technology (Persuasive 2007). Palo Alto, CA: Springer; 2007;283–294. Lecture Notes in Computer Science vol. 4744.

11. Felfernig A, Gula B, Leitner G, Maier M, Melcher R, Teppan E. Persuasion in knowledge-based recommendation. In: Oinas-Kukkonen H, Hasle PFV, Harjumaa M, Segerståhl K, Øhrstrøm P, eds. Persuasive Technology, Third International Conference (PERSUASIVE 2008). Oulu, Finland: Springer; 2008;71–82. Lecture Notes in Computer Science vol. 5033.

12. Felfernig A, Schippel S, Leitner G, et al. Automated repair of scoring rules in constraint-based recommender systems. AI Communications. 2013;26(2):15–27.

13. Häubl G, Trifts V. Consumer decision making in online shopping environments: the effects of interactive decision aids. Marketing Science. 2000;19(1):4–21.

14. Huber J, Payne W, Puto C. Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. Journal of Consumer Research. 1982;9(1):90–98.

15. Huffman C, Kahn B. Variety for sale: mass customization or mass confusion. Journal of Retailing. 1998;74(4):491–513.

16. Jacoby J, Speller D, Kohn C. Brand choice behavior as a function of information load. Journal of Marketing Research. 1974;11(1):63–69.

17. Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica. 1979;47(2):263–291.

18. Kahneman D, Knetsch JL, Thaler RH. Anomalies: the endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives. 1991;5(1):193–206.

19. Konstan J, Miller B, Maltz D, Herlocker J, Gordon L, Riedl J. Grouplens: applying collaborative filtering to usenet news full text. Communications of the ACM. 1997;40(3):77–87.

20. Li Y, Epley N. When the best appears to be saved for last: serial position effects on choice. Journal of Behavioral Decision Making. 2009;22(4):378–389.

21. Mandel N, Johnson E. Constructing Preferences Online: Can Web Pages Change What You Want? Marketing Department. Philadelphia, PA: The Wharton School, University of Pennsylvania; 1998.

22. Mandl M, Felfernig A, Teppan E, Schubert M. Consumer decision making in knowledge-based recommendation. Journal of Intelligent Information Systems (JIIS). 2010;37(1):1–22.

23. Mandl M, Felfernig A, Tiihonen J, Isak K. Status quo bias in configuration systems. In: 24th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2011). Syracuse, NY: Springer; 2011;105–114. Lecture Notes in Computer Science vol. 6703.

24. Murphy J, Hofacker CF, Mizerski R. Primacy and recency effects on clicking behavior. Journal of Computer-Mediated Communication. 2006;11(2):522–535.

25. Ritov I, Baron J. Status-quo and omission biases. Journal of Risk and Uncertainty. 1992;5(1):49–61.

26. Samuelson W, Zeckhauser R. Status quo bias in decision making. Journal of Risk and Uncertainty. 1988;1(1):7–59.

27. Simonson I, Tversky A. Choice in context: tradeoff contrast and extremeness aversion. Journal of Marketing Research. 1992;29(3):281–295.

28. Teppan, E., Felfernig, A., 2009a. The asymmetric dominance effect and its role in e-tourism recommender applications. In: Ninth Internationale Tagung Wirtschaftsinformatik (WI’2009) – Business Services: Konzepte, Technologien, Anwendungen (In German), vol. 2, Vienna, Austria, pp. 791–800 (in German: Der Asymmetrische Dominanzeffekt und seine Bedeutung für E-Tourismus-Plattformen).

29. Teppan EC, Felfernig A. Calculating decoy items in utility-based recommendation. In: 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2009), Tainan, Taiwan. 2009b:183–192. Lecture Notes in Computer Science vol. 5579.

30. Teppan E, Felfernig A. Minimization of product utility estimation errors in recommender result set evaluations. Web Intelligence and Agent Systems. 2012;10(4):385–395.

31. Teppan E, Friedrich G, Felfernig A. Impacts of decoy effects on the decision making ability. In: 12th IEEE Conference on E-Commerce and Enterprise Computing (CEC2010). Shanghai, China: IEEE; 2010;112–119.

32. Teppan E, Felfernig A, Isak K. Decoy effects in financial service E-sales systems. In: RecSys’11 Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’11), Chicago, IL. 2011:1–8.

33. Tiihonen J, Felfernig A. Towards recommending configurable offerings. International Journal of Mass Customization. 2010;3(4):389–406.

34. Tiihonen J, Felfernig A, Mandl M. Personalized configuration. In: Felfernig A, Hotz L, Bagley C, Tiihonen J, eds. Knowledge-based Configuration – From Research to Business Cases. Waltham, MA: Morgan Kaufmann Publishers; 2014:167–179. (Chapter 13).

35. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211(4481):453–458.

36. Winterfeldt D, Edwards W. Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press; 1986.

37. Yang Y, Zhang X, Liu F, Xie Q. An internet-based product customization system for CIM. Robotics and Computer-Integrated Manufacturing. 2005;21(2):109–118.