Online decision aids: the role of decision-making styles and decision-making stages

PurposeDecision aids (DAs) in online retail stores ease consumers' information processing. However, online consumers do not use all decision aids in purchase decision-making. While the literature has documented the effects of individual decision aids or two decision aids at a time, no study has compared the efficacy of multiple decision aids simultaneously. Also, very few studies have looked at the use of decision aids for consumers with maximizing and satisficing tendencies. Hence, this study aims to understand the preferences of maximizers and satisficers towards online decision aids during the choice-making process.Design/methodology/approachThis is an observational study with 60 individuals who were asked to purchase either a search-based or an experience-based product online. Participants' browsing actions and verbalizations during online shopping, were recorded and analysed using NVivo, and later the use of decision aids was mapped along their choice process.FindingsConsumer's preference of decision aids varies across the two stages of the choice process (that is, consideration set formation and evaluation & choice). In their choice formation, maximizers use different decision aids in both stages, that is, filter tool and in-website search tool for search products, and collaborative filtering-based recommender systems and eWOM for experience products. Satisficers used more decision aids as compared to maximizers across the two stages for both product types.Originality/valueThis study is an exploratory attempt to understand how consumers use multiple decision aids present on e-commerce websites.

Revealed Preference with Limited Consideration

We derive revealed preference tests for models where individuals use consideration sets to simplify their consumption problem. Our basic test provides necessary and sufficient conditions for consistency of observed choices with the existence of consideration set restrictions. The same conditions can also be derived from a model in which the consideration set formation is endogenous and based on subjective prices. By imposing restrictions on these subjective prices, we obtain additional refined revealed preference tests. We illustrate and compare the performance of our tests by means of a dataset on household consumption choices. (JEL D11, D12, M31)

Should Optimal Designers Worry About Consideration?

Consideration set formation using noncompensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via noncompensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power and design profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to suboptimal design decisions when the population uses consideration behavior; convergence of compensatory models to noncompensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in noncompensatory screening is more valuable than heterogeneity in compensatory tradeoffs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply more profitable design decisions; different model forms can provide “descriptive” rather than “predictive” information that is useful for design.

Should Optimal Designers Worry About Consideration?

Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to this data, and then optimize design decisions using the estimated models. Model predictive power, design “error”, and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide “descriptive” rather than “predictive” information that is useful for design.