Feeds:
Posts
Comments

Posts Tagged ‘Decision Process’

Dear Readers, In coming days the blog-site Consumer Gateway well be re-dressed. The format applied in the past decade (which I still like) has become outdated, and it also no longer supports new editing and design features. During the transition period disruptions may be caused to the look of some posts and pages; your patience and forgiveness will be much appreciated until I sort these out. I hope you find the new appearance pleasant to view and to read through. 


Brands can be imagined as signposts that help consumers navigate through their purchase decision processes. On many occasions brands simplify and shorten the decision process; a strong brand may show the consumer the route to an easier and safer choice decision. Over the years, symbolic (e.g., self-expression, self-image), social (e.g., status, relationship) and emotional meanings of brands gained more attention and emphasis in research and practice. However, we should not let those latter influences of brands overshadow or mitigate our recognition of the essential and useful role reserved for brands in organising and directing consumer decision-making.

An insightful approach to the function of brands in decision-making draws from the theory of information economics. Consumers are commonly met with imperfect and asymmetric information about products they intend to buy, and under these terms they have to make decisions. ‘Imperfect’ implies that the information is usually partial, and may also be inaccurate; ‘asymmetric’ information in particular means that the producers or suppliers know more about the products they sell (e.g., physical attributes, costs) than the consumers who buy from them.  The brand of a product can function in such settings as a signal of the credibility of the product’s origin. The signal could thus serve as a decision aid that helps consumers make a better or more gratifying choice. Theory and research of the past twenty years suggest that the brand as a signal may have impact not only on the outcome of the choice decision (e.g., its quality) but furthermore on the whole course of the decision process (e.g., consideration, evaluation, choice).

The perspective of information economics is relatively less familiar than other theoretical viewpoints. Reference is made here primarily to cognitive-driven theories of attitudes and information processing that receive greater coverage than information economics in the context of brands. Yet, the information economics viewpoint of the brand as a signal, led by Erdem, Swait and Louviere, can be employed beneficially side-by-side with Aaker’s model of brand assets or Keller’s concept of (differential) brand knowledge. These views offer complementing aspects with respect to the role and effects of brands in decision processes. The efficacy of the brand as a signal for credibility applies especially to strong brands. Moreover, each approach describes how consumer-based brand equity is built-up or materialised through decision processes, and also proposes how to model and measure it.

A more formal definition of the brand as a signal specifies the ability of a brand to act as a credible signal (e.g., trustworthy, believable) reflecting on positioning overall of the branded products. It implies that consumers’ perceptions of the branded product on multiple aspects, primarily perceived quality, would be stronger, more believable, or more reliable. Subsequently, we need to understand what can make the brand a more credible signal. Main drivers that contribute to brand credibility include consistency of the brand owner in delivering on its claims or promises (e.g., in advertising), which would make those claims more trustworthy; consistency in the performance of actions on marketing mix elements (e.g., pricing, product capabilities); clarity of messages (e.g., to support its positioning); and the scale of investment in the brand (e.g., offline and online advertising, website and mobile app, sponsorships).

Greater brand investment directly enhances brand credibility. But consistency in execution of marketing actions seems even more important by contributing directly, and strongly, to brand credibility as well as by supporting clarity, which is likely to further add to credibility of the brand. Consequent benefits of higher credibility to consumers are likely to be support for higher perceived quality, reduced perceived risk, and lower information costs (e.g., less search and validation of information). Perceiving less risk in buying the branded product can in addition free the consumer from looking for more information, and therefore reduce in turn the information costs even lower. [1, 2]

In a multi-attribute choice model, each product alternative is assigned values on a set of attributes according to a consumer’s perceptions or beliefs about those attributes. These perceptions may be ‘coloured’ by associations that the consumer holds with the product’s brand name (some associations would ascribe to physical or functional attributes of the product {or service} whereas others may relate to an intangible image of the brand). Utility weights are added for attributes, as applicable by the decision rule — these weights may differ between brands, for any attribute that may be judged, for example, as more compatible with, important for, or even unique to a specific brand. The brand hence may impact the choice decision from consideration of which brands to include in the choice set, through perceptions about the branded products, to utilisation of the information in the decision rule applied (e.g., by alternative or by attribute). (Note: Details about  random error components of perceptions and utilities are omitted here.)[2]

A wider-angle view will account for additional phases or processes surrounding the framework of choice model described above, for instance: (1) The search for information upon which perceptions are formed or updated and the costs that may be incurred in gathering the information; (2) Learning about products by using a form of hypothesis testing to evaluate and screen information; (3) Mental processes engaged during learning and decision-making (e.g., encoding, search and retrieval from memory, preference formation). When a brand helps to organise the information, it is employed as a basis or reference for testing a hypothesis, or affects the meaning given to attribute information, it exercises, and possibly enhances, its brand equity in the minds of consumers.[2]


  • Swait, Erdem, Louviere, and Dubelaar proposed a measure (metric) of consumer-based brand equity, constructed from the perspective of information economics which regards the brand as a signal for higher quality and reputation. They called their measure the “Equalization Price“. Deriving the EP estimate for a brand is based on a comparison between two settings: (1) A hypothetical market where there is no differentiation between brand alternatives, and total utility for all alternatives is the same (for simplicity, it can be set to 0 for all brands); (2) A simulated market (choice set scenario) where brand alternatives exhibit different total utilities. Their approach is rather different from many others in its reference to a ‘hypothetical alternative’ and to the total utility of an alternative instead of a brand-specific component.
  • The Equalization Price denotes the level to which the price for a brand-product alternative can be raised until its total utility for a consumer in the simulated market (choice scenario) becomes equal to the ‘common’ utility {0} (i.e., the price at which the utilities are equivalent). Weaker brands could be assigned a negative EP. The researchers applied their brand equity estimates to analyse the potential of brands to extend from the ‘mother’ category into a ‘new’ category (e.g., Levi’s extending from jeans to athletic shoes). (Technical note: The EP estimates are derived from a probabilistic multinomial choice model based on a choice experiment — the ‘total utility’ refers to the deterministic portion of utility). [3]

Let us look next in greater resolution at differences in the chain of effects of brand credibility between stages of the decision process. The contribution of brand credibility in reducing perceived risk is more crucial in the early stage of considering which brands are eligible at all to be chosen from. Brands associated with too much risk will be eliminated in this stage of constructing the consideration set, and they will be excluded from any further operations. The savings that can be gained in information costs will also be important at this stage. In other words, “perceived risk and information costs saved play a screening role in the choice process”. On the other hand, enhancing perceived quality, in virtue of greater brand credibility, has greater impact when evaluating alternatives prior to making the choice decision. Therefore, brand credibility can increase the probability of the branded product of both being considered for buying and of being eventually chosen, but there is a difference in how the outcomes are achieved between those decision stages. [1]

It has also been found that this distinction in impact of perceived risk and perceived quality between stages will be more pronounced in product categories characterised by greater uncertainty and higher sensitivity to uncertainty. At the brand level, inconsistency in executing marketing mix elements (e.g., pricing, distribution) is likely to increase consumer uncertainty regarding the brand claims, and thereof hurts the credibility of the brand (see the effect via clarity noted earlier). Erdem and Swait discuss managerial implications of the role of brand credibility for customer relationship management (e.g., cognitive and affective impacts of credibility) and brand extensions. They also review other research in which they substantiated the contributions of specific aspects of brand credibility over choice stages and product categories (e.g., overall and distinct effects of trustworthiness by consistently delivering on brand claims and expertise in execution of elements of the marketing mix, such as technological competence in product development and design).

The Internet opens before the consumers an ocean rich with information at their fingertips on personal computers and mobile devices, in a plethora of commercial and non-commercial websites and mobile applications. So it would seem that a great part of the problems confronted by the field of information economics have been resolved for consumers. Yet, searching and gathering relevant information for a purchase decision in many product categories still takes time and requires cognitive effort, and sometimes also psychic effort or emotional stress.

Different costs may be more significant these days than were in the pre-Internet age but they cannot be discarded. For example, with so many sources of information available and easily accessible, it takes more time to review just several of them, and it is increasingly necessary to cross-check information found on various websites or apps (e.g., direct competitors, online shopping platforms, trade and professional portals). In reality, consumers normally access and review only a small portion of information available in a domain (e.g., how many and how often consumers open a window to read technical specifications).

Furthermore, even if information is less imperfect, there are still issues concerning asymmetric information because a greater part of information on products and services is controlled and provided by interested commercial businesses. In addition, biases and diversions could be luring in online information sources that consumers may not suspect, because they are not directly associated with the companies and brands originally providing the product or service of interest (e.g., search engines, online shopping platforms, social media — younger consumers increasingly stay in the confinements of “closed gardens” of social network platforms and do not explore the Internet enough).

Addressing brand equity from the perspective of information economics highlights a crucial value a brand can offer, brand credibility, with a very practical function in purchase decision-making. There is somewhat an illusion in believing that consumers are far less challenged today by constraints and costs of obtaining and using information for making choice decisions. If only for that reason, brands are promised to continue to play a vital facilitating role in the decision process. Moreover, when consumers can rely on credibility of a brand as a signal, this continues to reinforce the brand equity.

Ron Ventura, Ph.D. (Marketing)

Feel Well. Keep Good Health.

 

References:

[1] The Information-Economics Perspective on Brand Equity; Tülin Erdem and Joffre Swait, 2016; Foundations and Trends in Marketing, 10 (1), pp. 1-59 (DOI: 10.1561/1700000041)

[2] Brand Equity, Consumer Learning and Choice; Tülin Erdem, Joffre Swait, Susan Broniarczyk, Dipankar Chakravarti, Jean-Noël Kapferer, Michael Keane, John Roberts, Jan-Benedict E.M. Steenkamp, & Florian Zettelmeyer, 1999; Marketing Letters, 10 (3), pp. 301-318

[3] The Equalization Price: A Measure of Consumer-Perceived Brand Equity; Joffre Swait, Tülin Erdem, Jordan Louviere, & Chris Dubelaar, 1993; International Journal of Research in Marketing, 10, pp. 23-45

 

 

 

 

 

 

 

 

 

 

 

 

Read Full Post »

A classic view regarding decision-making holds that attention serves foremost to acquire the information most relevant and important for choosing between alternatives. Thereby the role of attention is largely a passive one. However, an alternative view that is gaining traction in recent years, especially due to the help of eye tracking research, argues that attention plays a more active role in decision processes, influencing the construction of decisions.

This is a key message delivered by Orquin and Mueller Loose (2013) in their review on the role of attention in decision-making, as can be learnt from tracking of eye movements and subsequent fixations [1]. The approach taken by the researchers, however, is less usual: They do not constrain themselves concretely to the domain of decision-making; instead, they start their review and analysis of evidence from theories or models of tasks similar or related to decision-making (e.g., perception, information processing, visual search, working memory, top-down and bottom-up processes, problem solving).  Then they try to project how the functions of attention in such tasks may project to or be expressed in decision processes.

Furthermore, Orquin and Mueller Loose examine the extent to which the evidence coincides with four alternative theories and associated models of decision-making (i.e., whether empirical evidence substantiates or refutes assumptions or conclusions in each theory). They review evidence from previous research on similar or related tasks that could also be traced specifically in decision tasks, based on eye tracking in decision-making research, and evaluate this evidence in the context of the alternative decision-making theories.

The theories and related models considered are: (1) rational models; (2) bounded rationality models; (3) evidence accumulation models (e.g., the attention drift diffusion model [aDDM] posits that a decision-maker accumulates evidence in favour of the alternative being fixated upon at a given time); and (4) parallel constraint satisfaction models (a type of dual process, neural network model based on the conception of System 1’s fast and intuitive thinking [first stage] and System 2’s slow and deliberate thinking [second stage]). Rational models as well as bounded rationality models more explicitly contend that the role of attention is simply to capture the information needed for making a decision. ‘Strong’ rational models hold that all relevant, available information about choice alternatives would be attended to and taken into account, whereas ‘relaxed’ rational models allow for the possibility of nonattendance to some of the information (e.g., attributes or object [product] features). Bounded rationality models suggest that information is acquired just as required by the decision rules applied. The two other categories of models are more flexible in regard to how information is acquired and used, and its effect on the decision process and outcome. However, the authors argue that all four theories are found to be in deficit to a smaller or larger degree in their consideration of the role and function of attention in decision processes, having at least some of their assumptions being rejected by the evidence evaluated.

Selected insights drawn from the review of Orquin and Mueller Loose are enlisted here only briefly to shed light on the significance of attention in consumer decision-making.

A crucial question in decision-making is how information enters the decision process and is being utilised in reaching a choice decision: information may be acquired through attention guided by a top-down (goal-driven) process, yet information may also be captured by a bottom-up (stimulus-based) attentional process. The entanglement of both types of processes when making a decision is a prime aspect in this domain and has multiple implications. A more efficient selection process may be driven by greater experience with a task (e.g., more important information cues have a higher probability of being fixated on) and increased expertise in comprehension of visualisations (e.g., more fixations to relevant areas, and inversely fewer fixations to irrelevant areas, requiring shorter fixation durations, and longer saccades [‘jumps’ between more distant elements of information in a scene]). The interaction between bottom-up and top-down processing can amplify attention capture and improve the visual acuity of objects perceived. Bottom-up attention in particular is likely to be influenced by the saliency of a visual stimulus; however, it may not take effect when the task demands on attention are high, wherein priority is given to top-down directives for attention. Decision-making research has shown that visually salient alternatives or attributes are more likely to capture attention and furthermore affect the decision in their favour.

An interplay occurs between working memory and ‘instant’ attention: As the load of information fixated becomes larger, more elements are passed to working memory, and information is accessed from there for processing; however, as the strain on working memory increases, consumers turn to re-fixating information elements and consider them instantly or just-in-time (i.e., fixations are thus used as external memory space). This type of interplay has been identified in tasks of problem solving. Toggling between working memory and fixations or re-fixations in decision tasks can be traced, for instance, in alternative comparisons. Greater demands imposed by information complexity and decision difficulty (due to greater similarity between alternatives) may require greater effort (operations) in acquiring and processing information, yet the process may be shortened on the other hand through learning.

  • Another area with interesting implication is processing of visual objects: Previous research has shown that visual objects are not encoded as complete representations (e.g., naturalistic product images) and the binding of features is highly selective. Thereof, encoding of particular features during an object-stimulus fixation may be goal-driven, and a re-fixation may be employed to refer just-in-time to specific object [product] features as needed in a decision task, thus saving on working memory capacity.

Consumers have a tendency to develop a bias during a decision task towards a favoured alternative. This alternative would get more fixations, and there is also a greater likelihood for the last alternative fixated to be the one chosen (put differently, consumers are likely to re-affirm the choice of their favourite alternative by re-fixating it just before making the decision). A desired or favoured attribute can also benefit from a similar effect by receiving more frequent attention (i.e., fixations). The authors point, however, to a difficulty in confirming evidence accumulation models: whether greater likelihood of a more fixated alternative to be chosen is due to its higher utility or greater exposure to it. They suggest a ‘soft’ model version in support for a greater effect of extended mere exposure leading to choice of an alternative. They add that a down-stream effect of attention from perception onto choice through a bottom-up process may play a role of gatekeeping the alternatives entering a consideration set. It is noted that a down-stream effect, arising from a bottom-up process, is clearly distinguishable from a utility effect, since the former is stimulus-driven and the latter is goal-driven.

Consistent with bounded rationality theory, heuristics shape patterns of attention, directed by the information that a heuristic calls for (e.g., by alternative or by attribute). Yet, eye-tracking studies conducted to trace the progression of decision processes could not corroborate the patterns of heuristics used as proposed in the literature. More formally, studies failed to substantiate the assumption that heuristics in use can be inferred from the patterns of attention recorded. Transitions of consumers between alternative-wise and attribute-wise rules during a decision task make inferences especially difficult. Not only decision rules influence what information is attended to, but information cues met with during the decision process can modify the course of the decision strategy applied — consider the potential effect that salient stimuli captured unexpectedly in a bottom-up manner can have on the progression of the decision strategy.

In summary, regarding the decision-making theories, Orquin and Mueller Loose conclude: (a) firmer support for the relaxed rational model over the strong model (nonattendance is linked to down-stream effects); (b) a two-way relationship between decision rules and attention, where both top-down and bottom-up processes drive attention; (c) the chosen alternative has a higher likelihood of fixations during the decision task and also of being the last alternative fixated — they find confirmation for a choice bias but offer a different interpretation of the function of evidence accumulated; (d) an advantage of the favoured alternatives or most important attributes in receiving greater attention, and advantage of salient alternatives receiving more attention and being more likely to be chosen (concerning dual process parallel constraint satisfaction models).

Following the review, I offer a few final comments below:

Orquin and Mueller Loose contribute an important and interesting perspective in the projection of the role of [visual] attention from similar or related tasks onto decision-making and choice. Moreover, relevance is increased because elements of the similar tasks are embedded in decision-making tasks. Nevertheless, we still need more research within the domain because there could be aspects specific or unique to decision-making (e.g., objectives or goals, structure and context) that should be specified. Insofar as attention is concerned, this call is in alignment with the conclusions of the authors. Furthermore, such research has to reflect real-world situations and locations where consumers practically make decisions.


In retail stores, consider for example the research by Chandon, Hutchinson, Bradlow, and Young (2009) on the trade-off between visual lift (stimulus-based) and brand equity (memory-based); this research combined eye tracking with scanner purchase data [2]. However, it is worth looking also into an alternative approach of video tracking as used by Hui, Huang, Suher, and Inman (2013) in their investigation of the relations between planned and unplanned considerations and actual purchases (video tracking was applied in parallel with path tracking)[3].

For tracing decision processes more generally, refer for example to a review and experiment with eye tracking (choice bias) by Glaholt and Reingold (2011)[4], but consider nonetheless the more critical view presented by Reisen, Hoffrage and Mast (2008) following their comparison of multiple methods of interactive process tracing (IAPT)[5]. Reisen and his colleagues were less convinced that tracking eye movements was superior to tracking mouse movements (MouseLab-Web) for identifying decision strategies while consumers are acquiring information (they warn of superfluous eye re-fixations and random meaningless fixations that occur while people are contemplating the options in their minds).


 

It should be noted that a large part of the research in this field, using eye-tracking measurement, is applied with concentrated displays of information on alternatives and their attributes. The most frequent and familiar format is information matrices (or boards), although in reality we may also encounter other graphic formats such as networks, trees, layered wheels, and more art-creative diagram illustrations. Truly, concentrated displays can be found in shelf displays in physical stores and also in screen displays online and in mobile apps (e.g., retailers’ online stores, manufacturers’ websites, comparison websites). However, on many occasions of decision tasks (e.g., durables, more expensive products), consumers acquire information through multiple sessions while constructing their decisions. That is, the decision process extends over time. In each session consumers may keep some information elements or cues for later processing and integration, or they may execute an interim stage in their decision strategy. If information is eventually integrated, consumers may utilise aides like paper notes and electronic spreadsheets, but they do not necessarily do so.

Orquin and Mueller Loose refer to effects arising from spatial dispersion of information elements in a visual display as relevant to eye tracking (i.e., distance length of saccades), but these studies do not account for temporal dispersion of information. Studies may need to bridge data from multiple sessions to accomplish a more comprehensive representation of some decision processes. Yet, smartphones today can help in closing somewhat the gap since they permit shoppers to acquire information in-store while checking more information from other sources on their smartphones — mobile devices of eye tracking may be used to capture this link.

Finally, eye tracking provides researchers with evidence about attention to stimuli and information cues, but it cannot tell them directly about other dimensions such as meaning of the information and valence. The importance of information to consumers can be implied from measures such as the frequency and duration of fixations, but other methods are needed to reveal additional dimensions, especially from the conscious perspective of consumers (vis-à-vis unconscious biometric techniques such as coding of facial expressions). An explicit method (Visual Impression Metrics) can be used, for example, to elicit statements by consumers as to what areas and objects in a visual display that they freely observe they like or dislike (or are neutral about); if applied in combination with eye tracking, it would enable to signify the valence of areas and objects consumers attend to (unconsciously) in a single session with no further probing.

The review of Orquin and Mueller Loose opens our eyes to the versatile ways in which [visual] attention may function during decision tasks: top-down and bottom-up processes working in tandem, toggling between fixations and memory, a two-way relation between decision strategies and visual attention, choice bias, and more. But foremost, we may learn from this review the dynamics of the role of attention during consumer decision-making.

Ron Ventura, Ph.D. (Marketing)

References: 

[1] Attention and Choice: A Review of Eye Movements in Decision Making; Jacob L. Orquin and Simone Mueller Loos, 2013; Acta Psychologica, 144, pp. 190-206

[2] Does In-Store Marketing Work? Effects of the Number and Position of Shelf Facings on Brand Attention and Evaluation at the Point of Purchase; Pierre Chandon, J. Wesley Hutchinson, Eric T. Bradlow, & Scott H. Young, 2009; Journal of Marketing, 73 (November), pp. 1-17

[3] Deconstructing the “First Moment of Truth”: Understanding Unplanned Consideration and Purchase Conversion Using In-Store Video Tracking; Sam K. Hui, Yanliu Huang, Jacob Suher, & J. Jeffrey Inman, 2013; Journal of Marketing Research, 50 (August), pp. 445-462.

[4] Eye Movement Monitoring as a Process Tracing Methodology in Decision Making Research; Mackenzie G. Glaholt and Eyal M. Reingold, 2011; Journal of Neuroscience, Psychology and Economics, 4 (2), pp. 125-146

[5] Identifying Decision Strategies in a Consumer Choice Situation; Nils Reisen, Ulrich Hoffrage, and Fred W. Mast, 2008; Judgment and Decision Making, 3 (8), pp. 641-658

Read Full Post »