Posts Tagged ‘Information Acquisition’

During a shopping journey in a store where a consumer intends to buy multiple products, he or she is required to make a sequeqnce of choice decisions. Each decision is about to be made in a category with different product attributes, but beyond that there could also be differences in the settings of the choice situations, such as the size of the choice set, the structure of information display for product items, and information format. The transition between choice problems that differ in their characteristics should require shoppers to make some adjustments in preparation to reach a decision, each time in somewhat different settings. This is in fact true when filling a basket either in a physical store or on a website of an online store — shoppers have to shift between decision problems, and on the way they may need to replace or correct their choice strategy.

Researchers have been studying the paths that shoppers frequently follow, moving between sections of a store during their shopping trip. This type of research usually focuses on identifying and depicting the sequence in which store sections and product categories are visited, and the frequency in which category displays are stopped-by. However, the transitions from a choice decision in one category to another may also have  consequences for the decision process in any single category visited (e.g., as in adjusting for every new choice problem). Moreover, the sequence or order in which choice problems are resolved may have an effect on particular decisions.

  • Different techniques are applied for tracking the pathways of shoppers in brick-and-mortar stores (e.g., RFID, mobile-based GPS, video recording through surveillance cameras). Studies in supermarkets have shown what areas of a store shoppers approach first, and how they start by walking to the back of the store and then make incursions into each aisle (without leaving the aisle on the other end but returning to their point-of-entry). Hui, Bradlow and Fader reveal that as shoppers spend more time at the store, the checkout looms more attractive — the shoppers who feel a stronger time pressure become more likely to go through an aisle and approach a checkout counter. As perceived time pressure increases shoppers also tend to cut-off exploration and concentrate on visiting product displays from which they are most likely to purchase. (1)

Consumers have been described as adaptive decision-makers who adjust their decision strategies according to characteristics of the problem structure or context — for example, the amount of information available (given the number of alternatives or attributes), the type of information (e.g., scale, units), or the order in which information elements are displayed. In the outset, consumers may be guided by top-down goals — maximizing accuracy (relative to a maximum-utility ‘rational’ rule) and minimizing cognitive effort; a decision strategy (i.e., a rule like Equal-Weights or Lexicographic) can be selected in advance with respect to the accuracy-effort trade-off assessment of rules in a given choice situation, this according to Payne, Bettman and Johnson. However, they argue that this approach may not be sufficient on various occasions. When the characteristics of a choice problem are not familiar to the consumer, he or she will construct a strategy step-by-step as the structure and detail of information on alternatives is observed and learned. Even in cases the choice situation and context are familiar, the consumer may face unexpected changes or updates in information (e.g., inter-attribute relations) that may require her or him to modify the strategy. Hence, a consumer who started with a specific rule may replace it with another on-the-fly in response to data encountered, and often elements from different rules may be combined into an adaptive new choice strategy (as opposed to a ‘pure’ strategy)(2).

The construction of a decision strategy is therefore frequently the product of a delicate balance between top-down (goal driven) and bottom-up (data driven) processing. When in particular preferences also are not well-established by the consumer, preferences (e.g., importance weights of attributes) also are formed or constructed as one proceeds in the decision process. In such a case the preferences formed would be more contingent on the particular process followed and the strategy constructed thereby. Bettman, Luce and Payne extended the constructive choice model and added to the goals of maximizing accuracy and minimizing effort two more goals (directed by a perceptual framework): minimizing negative emotions (e.g., perceived losses, difficult trade-offs) and maximizing the ease of justifying decisions (to others or to oneself). (3)

However, the adaptation of consumers may not be complete, and thus a shopper may not fully “reset” or fit his decision strategy to features of the next choice problem, which may differ from features of the previous choice setting. Levav, Reinholtz and Lin investigated specifically the impact of one characteristic of decision problems on a decision process: the number of alternatives (4). They tested how many alternatives consumers would inspect more closely from each choice set, if the total number of alternatives available increases from the first to the last decision problem (e.g., 5, 10, 15 and so on until 50), versus a decrease in the number of alternatives available from the first to the last decision (e.g., 50, 45, 40 and so on until 5 — participants were allowed to sample songs to listen to before choosing a song for each track on a disc).

In one of the decision contexts tested, most relevant here, the researchers simulated an online shopping trip: participants in the experiment were asked to choose in sequence from eight different product categories (e.g., body lotions, energy bars, notebooks, shampoo). For some of the participants the number of alternatives increased between categories (i.e., 5, 8, 13, 17, 20, 23, 26, 30) whereas for the others the number of alternatives in a choice set changed in a reverse order (product categories were also presented in two opposite sequences of alphabetical order). Participants could examine more closely each option in a choice set by mouse-hovering on a thumbnail photo of the product item to see its enlarged photo image, its price, and a short product description.

  • Note: In a physical store the equivalent would be picking a product package from a shelf, inspecting it from different angles, reading the label etc. Advanced 3-D graphic simulators let a user-shopper in a like fashion to virtually “pick” a product item from a shelf display image, rotate it, “zoom-in” to read more clearly its label, etc.

Levav and his colleagues found that the direction in which the size of the choice set changes matters, and that particularly a low or high number of options in the first decision problem induces consumers to examine more or less information on options through the shopping trip. If a shopper starts with a small choice-set, he or she is more strongly inclined to inspect every option or acquire more information on each option available. This tendency endures in the next choice problems as the number of options increases, though it may level-off at some point.

In the online shopping experiment, the “shoppers” in the increasing condition examine on average the description for each option more times than “shoppers” in the decreasing condition for smaller choice sets. The former gradually adjust downward the amount of information acquired on each option but the amount of information “gathered” overall does not decrease; for relatively small choice sets (up to 13 options) they would still examine more information on options than “shoppers” who started their journey with the largest choice set. A “shopper” who starts with a large choice set constrains himself from the beginning to inspect options less closely; even as the choice set may become more “manageable” in size, the average “shopper” does not intensify the examination of information on single options considerably, clearly not to the level as “shoppers” whose first decision is from the smallest choice set.

  • For choice sets larger than 17-20 options, where the task for “shoppers” in the increasing condition may become too time-and-effort consuming and “shoppers” in the decreasing condition may still feel too pressed, the level of information acquisition is more similar.

The researchers refer to this form of behaviour as “bounded adaptivity“; they explicate: “Our results indicate that people are actually “sticky adapters” whose strategies are adapted to new contexts — such as the initial choice set — but persist to a significant degree even in the face of changes in the decision environment” (p. 596). The authors suggest, based on results from one of their experiments, that an increasing condition, where consumers’ first choice decision is made from a small choice set, may activate in  consumer a ‘maximizing’ mind-set, searching deeper into information on alternatives (as opposed to a probable ‘satisficing’ mind-set of a consumer in a condition of decreasing size of choice set). Levav et al. note that while ‘maximizing’ has often been regarded in literature as a chronic trait of personality, they see the possibility that this mind-set can be triggered by a decision situation.

If decisions during the shopping trip are not made independently, since adaptation where necessary is not complete or “sticky”, studying in isolation the decision process a shopper goes through in front of a particular product display could be misleading. For instance, the shopper’s decision strategy may be influenced by a choice strategy used previously.  An “imperfect” or “sticky” adaptivity does not have to reflect a deficiency of the consumer-shopper. It may simply designate the sensible level of adaptivity needed in a given decision situation.

(1) Shoppers may not have to hurry to modify their strategy if the perceived change in conditions of the choice problem is small enough to allow them to act similar as before. Shoppers can often adjust their decision tactic gradually and slowly until they get to a situation when a more significant modification is required. (“Shoppers” in the decreasing condition above seem to be more “in fault” of remaining “sticky”.)

(2) Shoppers-consumers look for regularities in the environment in which they have to decide and act (i.e., arrangement of products, structure and format of information) that can save them time and effort in their decision process. Regularities are exhibited in the ways many stores are organised (e.g., repetitive features in display of products) that shoppers can gain from in decision efficiencies. Regularities are likely to reduce the level of ongoing adpativity shoppers may need to exercise.

(3) On some shopping trips, ordinary or periodic (e.g., at the supermarket), shoppers frequently do not have the time, patience or motivation to prepare and deliberate on their choice in every category candidate for purchase. They tend to rely more on routine and habit. Prior knowledge of the store (e.g., one’s regular neighbourhood store) is beneficial. Shoppers would want to adapt more quickly, perhaps less carefully or diligently, and they may be more susceptible to “sticky” adaptivity.

It can be difficult to influence when and how shoppers attend to various sections or displays for performing their decision in differing choice settings. But it is possible to identify what zones shoppers are more likely to visit in early stages of their shopping trip. If a store owner or manager wants to induce shoppers thereafter to search product selections at greater depth, he or she may arrange in those locations displays with a small number of options for a product type. It should be even easier to track movements and direct shoppers to planned sections in an online store website. On the other hand, the retailer may stage a display with some surprising or unexpected information features for disrupting the ordinary search, and induce shoppers to work-out their decision strategy more diligently, thus devoting more attention to the products. However, this tactic should be used more carefully and restrictively so as not to turn-away frustrated or agitated customers.

Displays in the store (physical or virtual) and information conveyed on product packaging (including graphic design) together influence the course of consecutive decision processes shoppers apply or construct.

Ron Ventura, Ph.D. (Marketing)


(1) Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior; Sam K. Hui, Eric T. Bradlow, & Peter S. Fader, 2009; Journal of Consumer Research, 36 (Oct.), pp. 478-493.

(2) The Adaptive Decision Maker; John W. Payne, James R. Bettman, & Eric J. Johnson, 1993; Cambridge University Press.

(3) Constructive Consumer Choice Processes; James R. Bettman, Mary Frances Luce, & John W. Payne, 1998; Journal of Consumer Research, 25 (Dec.), pp. 187-217.

(4) The Effect of Ordering Decisions by Choice Set Size on Consumer Search; Jonathan Levav, Nicholas Reinholtz, & Claire Lin, 2012; Journal of Consumer Research, 39 (Oct.), pp. 585-599.


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Models of consumer preferences and choice most often follow a rational paradigm of decision-making — the consumer weights the values of attributes of each alternative considered  to derive its total utility value, then compares those utilities and chooses the alternative achieving maximum utility. Assuming this well-defined and straightforward process, it is relatively easy to construct predictions of preference shares or market shares. Yet the decision rule hereby described  is not so trivial or easy for consumers to apply; it requires a relatively large amount of information and a non-negligible computation effort. In order to simplify the decision process, consumers may use any of a selection of short-cut rules or heuristics (alternative-based and attribute-based rules) that demand less information and less cognitive effort to make a choice. On many occasions the process may not even include the rational-optimizing weighted additive (WADD) rule. Hence, decision processes tend to be more diversified than normally assumed in marketing research.

When consumers apply rules other than the WADD rule, predicted preference shares based on a maximum utility criterion for choice may off-shoot consumer actual choices. Indeed, there are other factors that may be implicated in biasing predicted preference shares when measuring preferences in consumer surveys — factors such as product availability and the visual display of products in stores, accessibility of brand and product information in the real world, and time constraints. Preference shares may further deviate from actual market shares when quantity is a key factor (e.g., a consumer buys 1, 2 or 4 cups ofyoghurt on any single purchase occasion and when purchase frequency varies). However, I will focus in this post-article on the factor that resides within the consumers, that is, the decision rules they use. In fact, decision rules are elected by consumers in adaptation to situational and environmental conditions as noted above.

Different techniques have been developed since the 1950s for identifying and tracking the rules people utilise to reach a choice decision between alternatives (e.g., travel destinations, car brand and model). Not less important than identifying single rules is the task of mapping the sequence in which these are used in a complete decision process, and this is where the study of decision-making can become complex.

  • At the foundation of research in this field we find the “think-aloud” verbal protocol method for recording and mapping decision processes initiated by Simon and Newell. In this method, a consumer is given a choice problem and is requested to talk aloud  whatever thoughts come to his/her mind while (or right after) performing the task and reaching a decision (note: verbalising the thoughts aloud, not explaining them!) The content of protocols is later coded into procedures or mental operations that make up generic rules, and decision processes are mapped.
  • A related technique uses information display boards of brands and their associated product attributes. By tracking the sequences in which consumers retrieve information items (i.e., cells in a brand-attribute matrix) the researcher can infer what rules are likely to have been applied and map decision processes.  This approach is implemented in a software known as MouseLab for computer-aided data collection and analysis of the decision processes (now also available as an Internet-based application MouselabWEB). Researchers Payne, Bettmann and Johnson who developed the original software applied it for their ground-breaking work on the behaviour of the adaptive decision maker in selecting rules and constructing decision strategies.
  • Another interesting approach (Active Information Search [Brucks]) that allows decision makers to pose their own queries for product information (e.g., physical attributes, usage situations, price) may be seen as a semi-structured level between the protocol approach (least structured) and the approach of MouseLab (most structured).

More recently researchers in the area of decision-making have shown increased interest in the methodology of eye tracking for investigating search patterns for information. By measuring eye movements with specialised optical equipment, researchers capture locations where the eye fixates in a stimulus display (i.e., takes-in information) in-between rapid movements (saccades). This approach is already adopted often in studies of advertising effectiveness and shopper behaviour in retail scenes.

Researchers Reisen, Hoffrage, and Mast took the challenge of evaluating different techniques for tracking consumer decision processes (2008*). Primarily, they developed a multi-method framework approach called InterActive Process Tracing (IAPT). The investigative procedure has three stages:

  1. Active Information Search (selecting relevant attributes);
  2. Seeking specific attribute values of product alternatives (information acquisition) and choosing a preferred alternative;
  3. Verbal reporting of decision processes (i.e., retrospective but with assistance from a moderator in formalising descriptions of the decision rules applied).

Firstly, the IAPT approach allows to integrate three methods, one at each stage, and compare their contributions to our understanding of decision processes and predicting choices. Secondly, the researchers provide new methodological insights by comparing two alternative methods for the second stage: the more veteran method of MouseLab and the new method of eye tracking for recording search patterns.

  • In the second stage of information acquisition and choice, participants were shown several choice sets. Each choice set comprised a different selection of four alternative mobile phones (brands and their models) randomly drawn from a pool of phones available in the market. The form in which information on alternatives in a choice set is displayed is contingent on the method used for registering information acquisition: by mouse clicks or by eye fixations.

Let us consider first some key findings and insights obtained in the first study of Reisen and his colleagues, using MouseLab in the second stage for recording information acquisition:

  • Two major types of strategies were identified: additive strategies (alternative-based) with equal or varied weights assigned to attributes or elimination (by-attribute) simplifying strategies — the elimination strategies were used more frequently (30 out of 31 respondents) than additive strategies (23 out of 31) though most participants (71%) combined strategies of both types.
  • The difference in predictive ability of choices between additive (optimizing) strategies and elimination (simplifying) strategies is less dramatic as many might expect (55%-57% versus 47%-51%, respectively). Notably, predictive ability when using rules as described by participants in their individual protocols was much higher (73%) than in case of applying rules pre-defined according to literature as done for the comparison of strategies above.
  • When using an elimination strategy, logic suggests that one would not seek more information on an alternative after it had been eliminated. Interestingly, however, acquisition of specific attribute values was traced for an alternative that was supposedly already eliminated by respondents using an elimination-by-attribute type of strategy (67%). This  makes sense, according to the researchers, if consumers initially acquired information as they explored the alternatives available for choice and only in a second phase committed to a decision strategy (i.e., the strategy eventually described in the verbal protocol).

In the second study, Reisen, Hoffrage, and Mast tested two methods for tracking search and information acquisition in the second stage: MouseLab and eye tracking, the more recent entry into this field of research. The information acquisition of each participant in this study was measured using both techniques sequentially (i.e., first series of choice tasks with MouseLab, and the second series with eye tracking [or vice versa]; half of the choice sets overlapped between series of choice tasks).  The following are noteworthy findings and insights:

  • Frequencies of usage of additive and elimination strategies were similar in relative terms to those found in the first study.
  • Predictive ability in choice tasks applying MouseLab was a little higher (69%) than in choice tasks with eye tracking (63%), but not statistically significant. More importantly, in repeated choice tasks (i.e., the same choice set), when respondents remained consistent in their choice under both conditions, predictive ability of the choice of that same alternative was considerably higher (78%) than in inconsistent tasks (40%).
  • Respondents took more time overall to acquire information by mouse-click (MouseLab: 37 seconds) than by just eye gazing at the display (Eye Tracking: 20 seconds).
  • However, eye gazing appears as more time-efficient from a respondent’s perspective: when eye tracking respondents accessed items in cells about 42 times on average compared with 22 cell-accesses while employing MouseLab. It is noted that the number of different cells accessed was similar between conditions (15-17 cells). Put differently, eye tracking allows for a higher rate of re-acquiring or double-checking information items rather than inspecting more of the information available on alternatives.
  • In this study again it was observed that “regardless of the condition [MouseLab or Eye Tracking], participants accessed about 50% more information than prescribed by their strategy.” Yet, it is also reported that considering the information that is needed by a strategy, the greater part of it (82%) was accessed as expected. That is, the excessive information acquisition does not come necessarily at the expense of required information.
  • Note: Findings from the second study suggest enlightening clues as to how respondents tackle choice tasks in choice-based conjoint studies. In particular, it points at the selective manner in which respondents consider information on alternatives included in a choice task, looking at only some of the attribute values shown, and yet they may re-access those same values several times until making their choice. Apparently this process may take less than 30 seconds to complete.

Following their assessment of findings in the second study, Reisen and his colleagues seem unconvinced that using eye tracking in IAPT is advantageous over MouseLab. With eye tracking respondents can finish each a choice task more quickly and yet access more information. However, they tend to repeatedly access the same information for a purpose the researchers describe as “validating a tentative choice.” Further difficulties in using the eye tracking methodology for tracing decision processes are suspected inaccuracies in capturing information acquisition (i.e., a respondent accidentally fixates on a cell while thinking; when respondents voluntarily mouse-click cells, the search paths seem more systematic) and failed calibration of equipment that leads to loss of unusable data.   The researchers conclude (p. 655):

It appears that this methodology improves neither the exactness of the description of the cognitive processes nor the quality of the results concerning the information search. Although this method allows for a more natural way of searching for information, it does not provide more informative data than does Mouselab.

Eye tracking can teach us a lot about how consumers look at and attend to different portions of ads such as bodies of pictures, text and brand logos, their  appreciation of package designs, or how shoppers inspect product displays on supermarket shelves. It can be helpful also in studying the decision processes consumers follow, but it is not more appropriate and accurate than former methods known for a similar purpose like MouseLab. More importantly, each of the four methods considered for IAPT specialises in capturing different aspects of the decision process (e.g., characterising patterns of information acquisitions vis-a-vis identifying the decision rule applied). A primary lesson to be taken from this research is that using multiple complementary methods with different scopes of specialisations can contribute considerably to obtaining a better mapping of decision processes and building models with higher predictive ability.

Ron Ventura, Ph.D. (Marketing)


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

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