Incentive alignment in anchored MaxDiff yields superior predictive validity

Schramm, Joshua Benjamin; Lichters, Marcel Article — Published Version Incentive alignment in anchored MaxDiff yields superior predictive validity Marketing Letters Provided in Cooperation with: Springer Nature Suggested Citation: Schramm, Joshua Benjamin; Lichters, Marcel (2024) : Incentive alignment in anchored MaxDiff yields superior predictive validity, Marketing Letters, ISSN 1573-059X, Springer US, New York, NY, Vol. 36, Iss. 1, pp. 1-16, https://doi.org/10.1007/s11002-023-09714-2 This Version is available at: https://hdl.handle.net/10419/323376 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Marketing Letters (2025) 36:1–16 https://doi.org/10.1007/s11002-023-09714-2 1 3 Incentive alignment inanchored MaxDiff yields superior predictive validity JoshuaBenjaminSchramm1,2 · MarcelLichters2 Accepted: 26 December 2023 / Published online: 11 January 2024 © The Author(s) 2024 Abstract Maximum Difference Scaling (MaxDiff) is an essential method in marketing concerning forecasting consumer purchase decisions and general product demand. However, the usefulness of traditional MaxDiff studies suffers from two limitations. First, it measures relative preferences, which prevents predicting how many consumers would actually buy a product and impedes comparing results across respondents. Second, market researchers apply MaxDiff in hypothetical settings that might not reveal valid preferences due to hypothetical bias. The first limitation has been addressed by implementing anchored MaxDiff variants. In contrast, the latter limitation has only been targeted in other preference measurement procedures such as conjoint analysis by applying incentive alignment. By integrating anchored MaxDiff (i.e., direct vs. indirect anchoring) with incentive alignment (present vs. absent) in a 2 × 2 between-subjects preregistered online experiment (n = 448), the current study is the first to address both threats. The results show that incentive-aligning MaxDiff increases the predictive validity regarding consequential product choices—importantly—independently of the anchoring method. In contrast, hypothetical MaxDiff variants overestimate general product demand. The article concludes by showcasing how the managerial implications drawn from anchored MaxDiff differ depending on the four tested variants. In addition, we provide the first incentive-aligned MaxDiff benchmark dataset in the field. Keywords Best-worst scaling (BWS)· Incentive alignment· Market research methods· Anchored maximum difference scaling (MaxDiff)· Predictive validity· Preference measurement * Marcel Lichters [email protected] Joshua Benjamin Schramm [email protected]hemnitz.de 1 Faculty ofEconomics, Chemnitz University ofTechnology, Chemnitz, Saxony, Germany 2 Chair ofMarketing, Faculty ofEconomics andManagement, Otto von Guericke University Magdeburg, Magdeburg, Saxony-Anhalt, Germany 2 Marketing Letters (2025) 36:1–16 1 3 1 Introduction Companies continuously try to introduce successful products that fit consumers’ needs. Maximum Difference Scaling (hereafter MaxDiff, see Louviere etal., 2013) is a preference elicitation technique often applied in market research to assess consumers’ needs and design future products accordingly. Study participants hereby answer multiple MaxDiff tasks (typically comprising three or four alternatives) and indicate the best and the worst alternatives (Louviere etal., 2013). Typical MaxDiff use cases are testing different product flavors (e.g., Chrzan & Orme, 2019, p.4), prioritizing product attributes (e.g., Rausch etal., 2021), or testing advertising claims (e.g., Chapman & Rodden, 2023, p.195). MaxDiff was initially introduced as an alternative to ranking and rating scales (Finn & Louviere, 1992) to overcome issues such as that participants are not trading off between items when answering rating batteries or the cognitive burdens of ranking a high number of items (Louviere etal., 2013). Nowadays, however, MaxDiff is also mentioned in the same breath as conjoint analysis, which is often applied in business forecasting. In contrast to conjoint methods such as choice-based conjoint (hereafter CBC), MaxDiff is not used to predict the success of holistic product concepts, including product prices in market simulations. Instead, product attribute levels or consumer needs (e.g., toppings on a pizza, see Chapman & Rodden, 2023) constitute the alternatives. In the literature, MaxDiff is also known as best-worst scaling (hereafter BWS) case 1. Besides BWS case 1 (MaxDiff), there are two more BWS cases, namely case 2 (profile case) and case 3 (multi-profile case). In case 2, participants choose the best and worst attribute level from a profile that consists of multiple attributes (e.g., Flynn & Marley, 2014, p.183). Case 3 resembles the traditional CBC; however, participants must also choose the worst alternative besides the best alternative (e.g., Mühlbacher etal., 2016). The present research focuses exclusively on case 1 (i.e., MaxDiff), which has gained importance in market research practice in recent years (Sawtooth Software Inc. 2022b). However, this trend is not yet reflected in academic marketing research.1 One of the present article’s two goals is to initiate rethinking the common MaxDiff practices toward using holistic product concepts with corresponding prices as alternatives in MaxDiff (see Fig.1A, which illustrates the MaxDiff tasks of the reported study). The stimuli in a MaxDiff are list items (Flynn & Marley, 2014, p.181) consisting of only one holistic product/characteristic (e.g., different soft drink flavors). In each task, participants see different items and choose both the best and the worst-liked alternative (Louviere etal., 2013), therefore providing more information per task than in, for example, CBC. Importantly, MaxDiff does not permute combinations of levels of different attributes across these items, as is usually 1 A literature review (Web of Science, articles published between 2000 and 2023, search terms (Mühlbacher etal., 2016): “Best-Worst-Scaling” OR “MaxDiff” OR “Maximum Difference Scaling”) of the top marketing journals and Sawtooth Software Conference papers is presented in TableA1 in the Web Appendix. 3 1 3 Marketing Letters (2025) 36:1–16 seen in conjoint studies (Flynn & Marley, 2014, p.181). This has two advantages compared to CBC: First, it allows participants to evaluate all products of interest in a MaxDiff since a complete design (vs. fractional design) can be implemented. Fig. 1 Translated screenshots of MaxDiff variants in the present study 4 Marketing Letters (2025) 36:1–16 1 3 Second, the participants do not have to assess unrealistic product combinations (e.g., cheap products at premium prices). MaxDiff’s ultimate purpose is forecasting future purchases, making predictive validity a central tenet. Therefore, researchers constantly seek ways to improve the predictions drawn from MaxDiff studies (e.g., Chrzan & Peitz, 2019; Lagerkvist et al., 2012) since reducing prediction errors lowers the company’s cost (Hauser etal., 2014). Two drawbacks limit the usefulness of the traditional MaxDiff studies, and the second goal of the present article is to evaluate solutions for these issues. First, traditional MaxDiff studies measure relative instead of absolute preferences because participants cannot indicate that none of the items presented is an actual purchase option (Louviere etal., 2013). This is acceptable if managers are only interested in ranking the item list. However, if they want to know whether, for example, products are actually consideredas purchase option, traditional MaxDiff is not applicable for this type of question (Chrzan & Orme, 2019, p.86). Moreover, if the goal is to estimate purchase likelihood or to simulate a realistic market situation, not including a no-buy alternative lacks realism (Lichters etal., 2015) and ultimately hurts predictive validity. Second, researchers and practitioners have conducted MaxDiff studies exclusively in hypothetical settings where participants might be less motivated to reveal their true preferences since their choices do not bear economic consequences (Ding etal., 2005). To address the first issue, researchers developed, among others, the direct anchored (Lattery, 2010; Orme, 2009b) and the indirect anchored (Orme, 2009a; developed by Jordan Louviere) MaxDiff. Both anchoring approaches enable estimating an anchor in the utility space of list items, which brings the results of a MaxDiff to an absolute scale. This anchor can serve as a consumer’s no-buy threshold when the MaxDiff and anchor questions are adequately framed (see Fig.1B and C). To overcome the second issue, researchers introduced incentive alignment to preference elicitation methods other than MaxDiff. Extant studies in this field have mainly focused on CBC studies and have proved incentive alignment’s effectiveness in increasing predictive validity for consequential product-choice tasks (e.g., Ding et al., 2005). Although the implementation of anchored MaxDiff enables the estimation of the no-buy utility (i.e., the outside good’s utility) and, therefore, also makes consequential product choices a valid alternative in MaxDiff studies, an equivalent proposal for an incentive-aligned (anchored) MaxDiff study is still lacking (see Table A1 in the Web Appendix, hereafter WA). The present research addresses this gap and guides market researchers in deciding whether applying incentive alignment when conducting MaxDiff is worth considering and whether they should prefer a specific anchoring approach. Such an endeavor is necessary for multiple reasons. On the one hand, one can argue that in MaxDiff, any measures that seek to enhance participant motivation (i.e., incentive alignment) might have only a limited effect since MaxDiff tasks (compared to CBC tasks) are relatively simple per se (e.g., Lagerkvist etal., 2012), which could potentially weaken the incentive alignment’s overall effect. On the other hand, although anchored MaxDiff is already applied in commercial 5 1 3 Marketing Letters (2025) 36:1–16 software (i.e., Sawtooth Software; Lattery, 2010; Orme, 2009a), research on this topic is lacking in the top marketing journals. In the academic literature, traditional (unanchored) MaxDiff still dominates, which, as discussed above, does not enable the extraction of much information relevant to marketing questions and realistic market simulations. This paper, therefore, aims to increase awareness of the method’s advancements. Finally, this paper is the first to provide MaxDiff datasets with consequential product choices to evaluate predictive validity (the Open Science Framework, hereafter OSF, provides the complete data and R analysis scripts: https:// osf. io/ 5h4rk/). This unique data can be the basis for researchers to evaluate further questions, for example, the ability of different modeling approaches to foster predictive validity. Our results highlight that incentive-aligned MaxDiff bears superior predictive validity, while hypothetical MaxDiff studies overestimate the general product demand, which may have devastating downstream consequences for companies. With our research, we aim to initiate a rethink in market research toward a stronger emphasis on incentive-aligned preference measurement techniques. In particular, incentive-aligned anchored MaxDiff variants (when framed as product decisions) might constitute a fruitful alternative to more complex CBC studies. 2 Conceptual background 2.1 Anchored MaxDiff In MaxDiff studies, participants indicate the best and worst alternatives in multiple MaxDiff tasks (Fig. 1A presents a MaxDiff task from our study; Finn & Louviere, 1992). This helps establish a ranking among all items under research in participants’ utility space, a relative preference measure (Lagerkvist etal., 2012). Thus, the resulting individual-level utilities can neither be compared across participants (Lagerkvist etal., 2012) nor be used to predict choice shares in markets that include a no-buy alternative. To measure absolute preferences instead, researchers developed anchored MaxDiff. Two approaches are commonly referred to in the literature: the direct anchored (Lattery, 2010; Orme, 2009b) and the indirect anchored (Orme, 2009a)MaxDiff. What is lacking thus far is a rigorous assessment of the predictive validity of the two approaches, which would help researchers choose between them. We posit that the two approaches benefit market researchers most when the MaxDiff tasks are framed as purchase likelihood questions and when holistic products, including prices, are to be evaluated (see above). In this case, adhering to the direct approach, participants first answer all MaxDiff tasks, followed by an additional task indicating whether each product (or a subset) represents a purchase option (Fig.1B). In contrast, in the indirect anchored MaxDiff (Fig.1C), also known as the dual-response approach, participants answer whether all, none, or some of the items shown in each MaxDiff task represent a purchase option (Lagerkvist etal., 2012). 6 Marketing Letters (2025) 36:1–16 1 3 2.2 Incentive alignment Preference measurement techniques usually involve hypothetical decisions and do not consider that participants in such settings tend to overestimate their purchase likelihood and show less price sensitivity (e.g., Miller etal., 2011). To address this adequately, researchers in the domain of conjoint analysis have utilized incentive alignment (e.g., Ding etal., 2005;Sablotny-Wackershauser etal., 2024). By making each product choice in the survey potentially payoff-relevant, participants are sufficiently motivated to reveal their true preferences (Dong etal., 2010). In the CBC domain, researchers have introduced several mechanisms to incentive-align studies (for an overview, see Dong etal., 2010). Most frequently, participants receive one of their randomly drawn choice task decisions as a reward and pay the corresponding product price (Ding etal., 2005). This is possible because, in CBC, participants select the product with the highest purchase likelihood in each choice task, whereas, alternatively, they always have the option to indicate that none of the shown products is worth buying (i.e., the no-buy alternative). An equivalent mechanism for traditional MaxDiff would not have been feasible since participants cannot indicate that they do not want to receive a product they have marked as the best alternative for study disbursement. Here, it becomes clear that introducing anchored MaxDiff and reframing MaxDiff tasks as purchase likelihood questions about holistic products have paved the way for implementing incentive-aligned MaxDiff variants to increase predictive validity. This advancement ultimately enables a rigorous assessment of both described anchoring approaches with regard to incentive-aligned versions of anchored MaxDiff and consequential product choices as validation procedure. 2.3 Research goals Prior research suggests that the difference between hypothetical and incentive-aligned preference measurement methods and the difference between direct and indirect anchored MaxDiff lead to diverging forecasts of product choice as well as demand in market simulations. The question for market researchers remains: Which combination of the two principles provides the most realistic predictions? This study sets out to give an answer; first, by introducing incentive alignment to anchored MaxDiff and, second, by using a consequential validation procedure that allows assessing the relative merits of incentive alignment and anchoring in MaxDiff studies on product choices. 3 Empirical study 3.1 Method andmaterial In a preregistered online experiment, 16 Sony PlayStation 5 (hereafter PS5) video games served as the focal products (see WAB for stimuli), as video games fulfill 7 1 3 Marketing Letters (2025) 36:1–16 the precondition of being holistic products (with fixed prices).2 Moreover, Sony also offers PS5 bundles. Determining the best games for bundles is essential and makes MaxDiff a valid and interesting method for these research questions. We chose the 16 PS5 games based on sales in Germany (e.g., GamesWirtschaft) and download numbers in the PS5 store for 2021. Furthermore, we included games released in 2022 (e.g., Gran Turismo) and genres that were underrepresented in our sample (e.g., simulation games such as Overcooked!). We determined prices based on market prices minus 5% to offer attractive products within the study. We randomly allocated participants to one of four MaxDiff conditions in a 2 (incentive-aligned: yes vs. no) × 2 (type of anchoring: direct vs. indirect) betweensubjects design. Each MaxDiff variant comprised 16 tasks with four alternatives, and each participant saw each video game four times. In each MaxDiff task, participants indicated which video game they were most likely to purchase and which they were least likely to purchase (see Fig.1A). We implemented both anchoring approaches in the same way as described in Section2.1. To assess predictive validity, each participant responded to the same four consequential validation tasks (see WAB; excluded from utilities’ estimation). The first two tasks offered 7 and 11 games, respectively, plus a no-buy alternative. The third validation task was a dual-response choice (a forced decision with subsequent nobuy question) offering eight games. Finally, the fourth task was an incentive-aligned ranking task comprising six games (Lusk et al., 2008), followed by asking up to which rank participants would opt for a buy or if they would buy none of the games. We incorporated a payout mechanism as follows: Besides receiving a fixed payment of €3.50, each participant had a 1-in-40 chance of winning a PS5 game and cash (the difference between the video game’s price and €55). More precisely, participants in the incentive-aligned groups were instructed that a randomly drawn MaxDiff or validation task could become payoff-relevant if a participant was drawn as a winner. In the hypothetical conditions, a randomly drawn validation task served as study disbursement. To ensure an understanding of the payoff mechanism, participants needed to answer one of a maximum of three consecutive probing questions correctly. Participants received their chosen game plus an amount of cash (see above) if validation task one, two, or three was randomly drawn. In the ranking task, the probability depended on the assigned rank. It was calculated following the formula J+1−rj ∑ J j= 1j × 100 , where J represents the number of alternatives and rj represents the assigned rank of the alternativej (Lusk etal., 2008, p.488). 3.2 Participants An independent German market research institute helped with recruiting participants for the online experiment. All participants needed to fulfill the following 2 https:// aspre dicted. org/ SLH_ 95Q; the preregistration also provides information on sample size planning and screening. 8 Marketing Letters (2025) 36:1–16 1 3 criteria: (I) interest in both video games and the PS5, (II) at least 18years old, and (III) playing video games at least occasionally. We also included participants who already own some of the games (regardless of the video console platform). We screened out 41 participants due to their response behavior (e.g., attention checks, see preregistration). In the net sample of n = 448 participants, (I) 118 replied to the direct anchored hypothetical, (II) 112 to direct anchored incentive-aligned, and 109 to the indirect anchored hypothetical or indirect anchored incentive-aligned MaxDiff (III and IV) respectively. The sample’s characteristics are 42% females, 57% males, and one diverse participant; Mage = 39.25, SDage = 14.25, and 67% with a monthly income above €1,000. No significant differences emerged between the groups.3 3.3 Results 3.3.1 Predictive validity We applied hierarchical Bayes multinomial logit analysis, making use of a single multivariate normal distribution (Allenby & Ginter, 1995) to estimate individual part-worth utilities (see TableC2 in WA) in Sawtooth Software Lighthouse Studio (Sawtooth Software Inc. 2022a).4 In Sawtooth Software, the best and worst choices are stacked together for an estimation in a single run (Chrzan & Orme, 2019, p.22). Therefore, the worst choice’s design matrix is negated (Chrzan & Orme, 2019, p.22). We applied the multinomial logit rule to predict product choice probabilities for the validation tasks. Based on these results, we calculated the hit rate (i.e., correctly predicted choices) and the mean hit probability (MHP, i.e., the predicted probability of theactual choice). We also applied a fourfold out-of-sample cross-validation within each MaxDiff condition for the first three validation tasks. In this cross-vali- dation procedure, we calculated the difference between the actual and the predicted choice share (i.e., mean absolute error). Lastly, for the product rankingtask (validation task 4), we calculated the mean rank of the predicted choice and the Spearman correlation between the assigned and predicted ranks. Table1 presents the main results (the OSF presents results for each validation task separately, as well as a comparison between unanchored and anchored MaxDiff for validation tasks 3 and 4). Each condition predicts better than chance for all validation tasks (binomial test p’s < 0.001). Incentive-aligned (vs. hypothetical) MaxDiff predicts participants’ product choices better, which is also true when examining out- of-sample prediction. 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