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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)

Notes:

(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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Obama’s administration is taking a bold step in fighting overweight and moreover obesity: requiring chain restaurants and similar food establishments to post information on food calories for their items or dishes on menus and menu boards. The new directive published in November 2014 by the United States’ Food and Drug Administration (FDA) is mandated by the Affordable Care Act passed by Congress in 2010. The expectation is that restaurant customers will consider the nutritional values, particularly calories, of  food items on the menu if the information appears in front of them, inducing them to make more healthy choices. It is estimated that Americans consume a third of their calories dining out. But will consumers, who are not voluntarily concerned about healthy dietary, change their eating behaviour away-from-home just because the information is easily and promptly available?

The new requirements of the FDA apply to restaurant chains with 20 or more outlets, including fast food chains — likely a primary target of the new directive. Detail of total calorie content of food items should appear on print menus (e.g., at full-service restaurants) and menu boards positioned above counters for ordering (e.g., at fast-food restaurants). The rule covers meals served at a table or taken to a table by the customer to be consumed, take-away food like pizzas, and food collected at drive-through windows. Also included are sandwiches-made-to-order at a grocery store or delicatessen, coffee-shops, and even ice-cream parlours. (1)

  •  The FDA directive also refers in a separate section to food sold through vending machines by owners or operators of 20 or more machines.

Calorie content in a food item (actually kilocalorie) indicates the amount of energy it provides. Usually the energy intake of consumers from meals, snacks and refreshments is more than the body requires, and the surplus not “burned”   accumulates and adds to body weight. The rule maintains that additional information on components such as calories from total and saturated fat, sodium, carbohydrates, protein, and sugars should be made available on request in writing. Critics could argue that while a summary measure of energy is an important nutritional factor, other nutritional values as those mentioned by the FDA, and more (e.g., fat in grams, Vitamins A and C), also need to be transparent to consumers. Practically, loading menus, and foremost menu boards, with too many nutritional details may be problematic for both business owners and their customers. Therefore, there is logic in focusing on an indicator regarded of higher priority. Nonetheless, restaurants should offer a supplementary menu with greater nutritional values to customers who are interested. Again, the question is how many customers will request and use that extra information.

The food service industry overall reacted positively to the new rules. The National Restaurant Association in the US (representing 990,000 restaurant and food-service outlets) is satisfied with the way the FDA has addressed its major concerns. Contention remains over food sold in amusements parks and cinemas, and regarding fresh sandwiches and salads and ready-to-eat meals made by supermarkets for individual consumers (i.e., single-serving). In fact,  several restaurant chains have already been displaying nutritional information on menus voluntarily for several years to cater for more health-conscious customers and improve their retail-brand image (e.g., Starbucks, McDonalds, Subway). Some chains also provide detailed nutrition information and assistant tools for customers to plan their meals on the chains’ websites. It should further be noted that regulations for posting nutrition information in food-service establishments are in place at the level of local authorities in various cities and counties across the US. Business and regional administrative initiatives are not new in the US as well as in Canada and other countries. However, such measures will be obligatory in the US at a country-level within a year ahead.

Consumers are likely to have some general guidelines (a schema of rules) in memory that they can consult on what is more or less healthy to eat and how much to eat of different items (e.g., “high levels of calories, fat and salt in hamburgers and french fries”, “cream cakes are rich with calories and sugar”). When arriving to a restaurant or coffee-shop, the more conscious consumer may apply those guidelines to compose one’s meal with greater care for his or her health. Yet, the ability to extract accurate nutrition values of food items offered on the menu is likely to be rather limited — our memory is not accurate and retrieving information may also be biased by prior goals or hypotheses. Even if we consider only total calories, we would recall gross estimates or value ranges for general food categories. Consumers furthermore tend to take into account only the alternatives explicitly presented and attribute information available on them in a choice setting (a “context effect”). Information not provided (e.g., has to be retrieved from memory) is likely to be ignored. Customers anxious enough may pull out a mobile device and look up some more accurate nutritional information from an app or a website of the company or a third-party source. But for most consumers, it should appear, there is strong logic as well as justification to provide the nutrition information on specific food items easily accessible at the food outlet to allow them to consider it on-the-spot in their choices.

A probable cause of resistance from consumers to take into account the nutritional content of the food they are about to order is that this might spoil their pleasure of eating the meal.  People commonly prefer to concentrate on which items to order that will be more enjoyable for them on a given occasion. The negative nutritional consequences of the desired food could be considered as ‘cost’, just like monetary price and perhaps even worse, a notion consumers would like to avoid. There is also a prevailing belief that healthier food is less tasty. To make consumers more receptive they would have to be persuaded beforehand that this belief is false or that nutritional components have both positive and negative consequences to consider. Surely consumers have to account for constraints on their preferences; health advocates have to help and ease any barriers to embracing health constraints, or turn pre-conceived constraints into consumers’ own preferences.

We may gain another insight into consumer food choices by considering the comparisons consumers utilise to make decisions. Simonson, Bettman, Kramer and Payne (2013) offer a new integrative perspective on the selection and effect of comparisons when making judgements and choice decisions — how consumers select the comparisons they rely upon vis-à-vis those they ignore, and what information is used in the process. They propose that the comparisons consumers seek have first to be perceived relevant and acceptable responses to the task (e.g., compatible with a goal); these comparisons fall within the task’s Latitude of Acceptance (LOA). They also need to be justifiable. Then, consumers will prefer to rely upon comparisons that are cognitively easier to perform (i.e., greater comparison fluency), given the information available on options. Importantly, even if bottom-up evidence suggests that certain comparisons require less effort to apply, these will be rejected unless they are instrumental for completing the task. Information factors that can facilitate the comparison between options may affect, however, which comparisons consumers perform among those included in the LOA. The following are factors suggested by the researchers that increase the probability that a comparison will be performed: attribute values that can be applied “as-is” and do not need additional calculation or transformation (i.e., “concreteness effect”); alignable input (i.e., values stated in the same units); information perceptually salient; and yet also information that can generate immediate, affective responses. (2)

Let us examine possible implications. Suppose that you visit a grill bar-restaurant of a large known chain. You have to choose the food composition of your meal, keeping with one or more of the following personal goals: (a) “not leave hungry” (satiated); (b) pleasure or enjoyment (taste/quality); (c) “eat healthy” (nutrition); (d) “spend as little as possible” (cost). Calorie values are stated on menu in a column next to price. If the primary goal is to keep a healthy diet you would most likely use calorie information to compare options. However, if “eat healthy” is not a valued goal for you, there is greater chance that calorie information will be ignored — even if values of calories are very easy to read-out, assess and compare. They may be perceived as distraction from considering and comparing, for instance, the ingredients of items that would determine your enjoyment from different food options. Consumers often have a combination of goals in mind, and thus if your goals are nutrition and price, there is an advantage to displaying numeric calorie and price values next to each other across items. It would be more difficult to weigh-in calories with information on ingredients that should predict enjoyment or satiation as your goals. Therefore, it can be important to display nutritional values in a format that facilitates comparison, and not provide too many values. Yet, if “eat healthy” is not one’s goal all those measures are unlikely to have much effect on choice.

  • Some would argue that a salient perceptual stimulus can trigger consumer response in the desired direction even unconsciously. That is a matter for debate — according to the viewpoint above strong perceptual or affective stimuli will not be influential if the consumer’s goal is driving him in another direction.
  • Given the growing awareness to health, justifying decisions based on calories to others may be received more favourably. Can this be enough to induce consumers to incorporate a nutrition comparison in their decision when it is not their personal goal?

A research study performed by the Economic Research Service (ERS) of the US Department of Agriculture (USDA) examined consumer response to display of nutrition information in food service establishments, comparing between fast-food and full-service chain restaurants. The researchers (Gregory, Rahkovsky, & Anekwe, 2014) show that consumers who see nutrition information have a greater tendency to use it during choice-making in full-service restaurants; overall, women are more sensitive to such information than men (especially using it in fast-food restaurants). Furthermore, they provide support that consumers who are already more conscious and care about a healthful diet are more likely to react positively to nutrition information in restaurants:

  • Consumers who inspect always or most of the time the nutrition labeling on food products purchased in a store (enforced in the US for more than twenty years) are more likely to see and then use the nutrition information presented in full-service restaurants (notably, 76% of those who inspect the store-food labeling regularly use the information seen in the restaurant versus 18% of those who rarely or never use the labeling on store-food).
  • Additionally, the researchers find that a Healthy Eating Index score (measuring habitude to using nutrition information and keeping a healthy diet) is positively correlated with intention to use nutrition information in fast-food or full-service restaurants (those who would often or sometimes use the information in full-service restaurants score 57-54 versus those who would use it rarely or never who score 50 on a scale of 1 to 100).

Gregory and his colleagues at USDA-ERS argue that following these findings, displaying nutrition information on menus at food-away-from-home establishments may not be enough to motivate consumers not already caring about healthful diet to read and use that information — “It may be too optimistic to expect that, after implementation of the nutrition disclosure law, consumers who have not previously used nutrition information or have shown little desire to use it in the future will adopt healthier diets.”

A research study in Canada involved an interesting comparison between two hospital cafeterias, a ‘control’ cafeteria that displays limited nutrition information on menu boards and an ‘intervention’ cafeteria that operates an enhanced programme displaying nutrition information in different formats plus educational materials (Vanderlee and Hammond, 2014). The research was based on interviews with cafeteria patrons. A significantly higher proportion of participants in the ‘intervention’ cafeteria reported noticing nutrition information (80%) than in the ‘control’ cafeteria (36%). However, among those noticing it, similar proportions (33% vs. 30%, respectively) stated that the information influenced their item choices. Hospital staff were more alert and responsive to the information than visitors to the hospital and patients. This research also indicates that customers who use more frequently nutrition labels on pre-packaged food products are also more likely to perceive themselves being influenced by such information.

Vanderlee and Hammond subsequently found lower estimated levels of calories, fat and sodium in the food consumed in the ‘intervention’ cafeteria than the ‘control’ cafeteria (using secondary information on nutrition content of food items). In particular, customers at the ‘intervention’ cafeteria who specifically reported being influenced by the information consumed less energy (calories).(3)

Actions to consider: Fast-food restaurants may place menus with extended nutrition information, beyond calories, on or next to the counter where customers stand for ordering. Full-service restaurants may place extended menus on tables, or at least a card inviting customers to request such a menu from the waiter. It may be advisable to add one more nutrition value next to calories as a standard (e.g., sugars because of the rise in diabetes and the health complications it may cause). Notwithstanding, full-service restaurants could be allowed to implement the rule during the day (e.g., for business lunch), but in the evening spare customers the pleasure of dining-out as entertainment without worries. Nonetheless, menus with nutrition information should always be available on request.

Nutrition information displayed on menus and menu-boards can indeed help consumers in restaurants, coffee-shops etc., to make more healthy food choices, but it is likely to help mostly those who are already health-conscious and in habit of caring about their healthful diet. Information clearly displayed has a good chance to be noticed; yet, educating and motivating consumers to apply it for a healthier diet should start at home, in school, and in the media. A classic saying applies here: You can lead a horse to the water but you cannot make it drink. Nutrition information may be a welcome aid for those who want to eat more healthy but it is less likely to make those who do not care about healthful diet beforehand to use the information in the expected manner.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Overview of FDA Labeling Requirements for Restaurants, Similar Food Retail Establishments and Vending Machines, The Federal Food and Drug Administration (US), November 2014 http://www.fda.gov/Food/IngredientsPackagingLabeling/LabelingNutrition/ucm248732.htm; Also see: “US Introduces Menu Labeling Standards for Chain Restaurants”, Reuters, 24 Nov. 2014. http://www.reuters.com/article/2014/11/25/usa-health-menus-idUSL2N0TE1KP20141125

(2) Comparison Selection: An Approach to the Study of Consumer Judgment and Choice; Itamar Simonson, James R. Bettman, Thomas Karamer, & John W. Payne, 2013; Journal of Consumer Psychology, 23 (1), pp. 137-149

(3) Does Nutrition Information on Menus Impact Food Choice: Comparisons Across Two Hopital Cafeterias; Lana Vanderlee and David Hammond, 2013; Public Health Nutrition, 10p, DOI: 10.1017/S136898001300164X. http://www.davidhammond.ca/Old%20Website/Publication%20new/2013%20Menu%20Labeling%20(Vanderlee%20&%20Hammond).pdf; Also see: “Nutrition Information Noticed in Restaurants If on Menu”; Roger Collier; Canadian Medical Association Journal, 3 Aug., 2013 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3735740/

 

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With click rates on online ad banners ranging between o.5% and 2% it is not difficult to understand why many in the marketing, advertising and media professions often question the efficacy of click-based models of advertising on the Internet. It is a problem for both advertisers of products and services and the website owners that publish ad banners on their pages.

For advertisers, exposure of consumers to their ads is not a sufficient or satisfying criterion but immediate action in response to the ad banner is very difficult to elicit; perhaps clicking-through should not be expected just because these objects are “clickable links”.  Should the effectiveness of ad banners be doubted because of low traffic it may generate, or is it that the criteria used are inappropriate?

For the owners of websites used as vehicles for advertising (e.g., newsmedia, portals, social media), it is a question of effectiveness in generating satisfactory revenue from those ads, conditioned on mouse clicks. When webpages receive high volumes of visits, even very low click rates may be sufficient to collect a handsome sum of money, but this cannot be generalised to most websites and pages. On the other hand, if a website is loaded with ads across the pages to generate more revenue, it may end up cluttering its own content and chasing away visitors.

Internet users who browse websites in search for information on a particular subject (e.g., photography, nature), and  read or watch related content on webpages, are very likely to see ad banners as no more than a distraction from their main task. Clicking on a banner that sends the users to another page means an interruption of the kind many would not welcome. There are exceptions, of course, when for example the ads are for products (e.g., cameras, hiking gear) related to the main topic of the website and thus provide access to additional information that can be of interest on relevant options (i.e., context in which ads appear matters). Ads may be perceived less disturbing to surfers who are engaged in exploration with no planned goal but for fun and entertainment; checking on advertised companies and products may be accepted as part of the exploration, although maybe not in every condition (e.g., when users are wary of non-trusted solicitations, busy interacting with friends in social networks, engaged in watching music videos and so on).

However, viewing an ad banner for a brand can leave an impression, and a trace in memory, in consumers’ minds that will have its effect at a later time, especially if a choice situation in the same product domain is looming soon after. Consumers may register in memory the exposure episode, with the brand name and additional information contained in the ad, for checking-up later without being required to click-through at the same moment. Importantly, this “registration” does not have to occur consciously to make an impact.

If a consumer-surfer is interested, he or she may attempt intentionally to remember the ad and look-up for the brand’s website when the time becomes available and convenient. When  working on a computer or a mobile device, one can easily type a note or set-up a reminder, especially if the website address also appears on the banner. But an ad banner can operate without waiting for a voluntary response or overt reaction from the consumer.  It depends to a large extent on the kind of impression made by the visual image of the ad banner on the consumer-surfer at an initial or quick glance. An image that is easier for the eye and mind to process, that feels pleasant to look at, its informational content will become more readily acceptable and persuasive. Visual processing fluency (1) at the perceptual level suggests that principal elements of the image can be identified with little effort and great accuracy — for instance, in a banner’s image, that may include the brand/company name, logo icon, and picture of a product. Visual fluency can be facilitated by the use of colours and recognizable shapes that are pleasing to watch, symmetry, clear contrast between figure and ground, etc. Its persuasive effect may not be strong enough to trigger a mouse-click yet increased fluency can make the ad’s content better remembered as well as better liked by the viewer for a longer time after exposure.

An ad banner can influence consumer attitude and response also through a process of priming. This type of effect in the particular domain of ad banners on the Internet has been studied by Mitchel and Valenzuela (2). The consumer is initially introduced to the ad in a seemingly casual and incidental way. However, information in the ad stimulus, “planted” as a trace in the consumer’s memory, would prime her or him, unconsciously, to use it during a future task, for example when recalling brands or choosing between alternative brands. Such exposure could work simply by evoking a positive attitude towards the brand in the priming ad. In another procedure, a joint presentation of a brand with a product attribute in the ad banner would prime the consumer to look for and give priority to that same combination when it appears in the information provided on a set of product alternatives to choose from.

according to this research, priming by an ad banner can affect the consideration of brands for purchase (tested with airlines) in three significant ways. First, a brand whose ad had been shown earlier was more likely to be considered for purchase (of air-tickets) than if an ad for another brand had been shown or no ad at all (control). Second, this effect is stronger for a lower quality brand than for a higher quality brand, that is, a stronger brand has less to gain from priming through its ad banner. Third, when consideration is based on recall from memory, priming has a stronger effect in leveraging the likelihood of consideration of a primed brand than if the brands have to be selected from a constrained list — this may be explained by the added impact of priming through prior exposure on memory (note: this difference is valid only for the lower-quality brand!). Advantages of priming are established also when making the final choice of a single brand to purchase from (subject again to the second and third qualifications above).

Mitchel and Valenzuela further reveal in their research an interesting effect of priming of established brands on a “new” unfamiliar brand (i.e., a fictional airliner). All participants were exposed to an ad banner for the unfamiliar brand before given any tasks and therefore the relevant priming effects arise from the lower-quality and higher-quality brands. It is shown that results for the unfamiliar brand were more favourable if at the beginning of the research the higher-quality brand had been primed rather than if the lower-quality brand or neither of them had been primed. The more positive image of a higher-quality brand seems to spill over to the unfamiliar brand by lifting the brand’s evaluation higher and increasing its likelihood of consideration and being finally chosen — an advantage that earlier priming of a familiar but lower-quality brand cannot provide to the unfamiliar brand.

We may learn from this research that ad banners can be utilised to create an advantage for a brand during consumers’ decision processes without their full awareness of it but it will not help any brand — it is more suitable for brands that are currently weaker — and not in every situation. The placement of the ad banner for this purpose has to be planned wisely, preferably in websites, and on particular webpages, where consumers are engaged in learning about a product domain or making the first steps of searching and screening products. Designing an ad banner that is clear, concise and pleasant to watch can only help to maximise impact.

Measuring the effectiveness of ad banners is undoubtedly faced with difficulties and barriers. There is greater tendency to refer to statistics of page views to assess also potential exposure  to ads placed on a page (“impressions”). However, overall “page impressions” are not detailed enough as they refer to the whole webpage; they cannot tell us to which sections or objects, particularly ad banners, a consumer-surfer attends, nor at what level information is processed. Capturing fixations on particular objects by Internet users requires an application of the methodology of eye-tracking. Latency of eye fixations can already provide an indirect indicator of the extent of processing information. However, that methodology cannot be practically and economically applied on a large-scale nor can it be applied on a regular basis.

A third-way approach that is based on tracking mouse movements over a webpage, and is able to detect objects on which a mouse hovers even without clicking on them, provides a sort of middle-ground solution. It is not as complete and accurate as eye-tracking but it can provide a substantive even if partial information on objects to which a consumer-surfer attends; it is based on the premise that our hand often follows our eyes (i.e., visuo-motor correlation) and we tend to point the mouse on a place or item we concentrate at a given moment. And, not least, it is a more feasible solution, technically and economically, to operate on a large data scale. At this time, it seems as a viable platform for developing extensions and improved measures of consumer attention, browsing behaviour, and response to stimuli.

  • The Internet company ClickTale, for example, offers a range of methods for analysis and visualisation of users’ behaviour with a mouse (e.g., “heat maps” based on frequency of mouse “landings” in different locations over a webpage and tracking the movements of a mouse on a webpage).

There are remaining limitations to behavioural data that do not allow us to assess more fully the extent to which ad banners are processed and how it may affect our attitudes, thoughts and feelings. Difficulties can be foreseen for example in measuring the implicit effects of visual fluency or priming on consumers in a “live” environment in real-time. The way to test and measure these effects is by conducting experiments while combining cognitive, attitudinal and behavioural data. The new age of touch screens presents yet a new set of challenges in measuring covert and overt responses.

To conclude, here are a few points that may be worth considering:

  1. The relatively small area of a standard ad banner can make it challenging to construct and design effective ads. First, it is recommended to graphically design an image that is visually fluent for the consumers-surfers, as much as it is in control of the designer  — the rest is in the eye and mind of the viewer. Second, include sufficient information in the banner, like a key claim or description of strengths, that the consumer can relate to and keep in mind, consciously or unconsciously, without having to click-through anywhere else. Third, include a web address the consumer can save and use anytime later.
  2. Think a few steps ahead, what consumers-viewers may do next, that is, how consumers may be influenced by the information and utilise it in a subsequent activity (e.g., shopping online). Thereby, plan the content, placement and timing of the ad banner with respect to events or types of behaviour it intends to affect.
  3. Animated ad banners quickly capture the attention of viewers by their motion. However, such ad banners that appear especially on sidebars attract attention involuntarily at the periphery of the visual field, that is, even if the reader tries to avoid it. Limit the period of time the animation works or let the user stop it lest she is likely to abandon the page altogether.
  4. Beyond the advantages of motion and sound of ad video clips, they can be activated on-site and viewed without requiring the consumer-surfer to leave anywhere else, an important benefit of time-saving and convenience. They should display a visually appealing opening screen and be kept at time-lengths of 30 seconds to two minutes to attract and engage viewers for a reasonable period of suspension from other tasks on the website.

References:

1. Cognitive and Affective Consequences of Visual Fluency: When Seeing Is Easy on the Mind; Piotr Winkielman, Norbert Schwarz, Rolf Reber, & Tedra Fazendeiro, 2003; in Persuasive Imagery: A Consumer Response Perspective, L. M. Scott and R. Batra (eds.)(pp. 75-91), Lawrence Erlbaum Associates.

2. How Banner Ads Affect Brand-Choice Without Click-Through; Andrew Mitchel and Ana Valenzuela, 2005; in Online Consumer Psychology: Understanding and Influencing Consumer Behavior in the Virtual World, C. P. Haugtvedt, K. A. Machleit, & R. F. Yalch (eds.)(pp. 125-142), Lawrence Erlbaum Associates.

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Competition in health-related industries (i.e., health care services, pharmaceutical, biotechnology) has been increasing continuously in the past two to three decades. The health business has also become more complex and multilayered with public and private institutions, individual doctors and patients, as players. Consequently, decision processes on medical treatment may become more complicated or variable, being more difficult to predict which treatment or medication will be administered to patients. For example:

  • For many medical conditions there are likely to exist a few alternative brands or versions of the same type of prescribed medication. Depending on the health systems in different countries, and on additional situational factors, it may be decided by a physician, a health care provider and/or insurer, or a pharmacist what particular brand of medication a patient would use. In some cases the patient may be allowed to choose between a more expensive brand and an economic brand (e.g., original and generic brands, subsidised and non-subsidised brands).
  •  There are plenty of over-the-counter (OTC) medications, formulae and devices that patients can buy at their own discretion, possibly with a recommendation of a physician or pharmacist.
  • Public and private medical centers and clinics offer various clinical tests and treatments (e.g., prostate screening, MRI scanning, [virtual] colonoscopy), often going above the heads of general/family physicians of the concerned patients.
  • In more complex or serious conditions, a patient may choose between having a surgery at a public hospital or at a private hospital, depending on the coverage of his or her health insurance.

In the late 1990s, professionals, executives and researchers in health-related areas have developed an interest in methods for measuring preferences that would allow them to better understand how decisions are made by their prospect customers, especially doctors and patients (“end-consumers”). This knowledge serves (a) to address more closely the preferences of patients or requirements of physicians, and (b) to channel planning, product development or marketing efforts more effectively. In particular, they have become interested in methods of conjoint analysis and choice-based conjoint that have already been prevalent in marketing research for measuring and analysing preferences. Conjoint methods are based on two key principles: (a) making trade-offs between decision criteria, and (b) decomposition of stated preferences with respect to whole product concepts (e.g., a medication) by means of statistical techniques into utility values for levels of each attribute or criterion describing the product (e.g., administering 2 vs. 4 times in 24 hours). The methods differ, some argue quite distinctly, in terms of the form in which preferences are expressed (i.e., ranking or rating versus choice) and in the statistical models applied (e.g., choice-based conjoint is often identified by its application of discrete choice modelling). An important benefit for pharmaceutical companies, for example, is gained in learning what characteristics of a medication (e.g., anti-depressant) contribute more to convincing physicians to prescribe it, versus factors like risks or side-effects that lead them to avoid a medication.

The product concepts presented are hypothetical in the sense that they are specified by using controlled experimental techniques and do not necessarily match existing products at the time of study. This property is essential for deriving utility values for the various levels of product attributes studied, and to allow prediction by simulation of shares of preference (“market shares”) for future products. The forecasting power of conjoint models is considered their major appeal from a managerial perspective. In addition, conjoint data can be used for segmenting patients and designing refined targeted marketing strategies.

Interest in application of conjoint methods in a health context has grown in the past decade. According to a review research of conjoint studies reported in 79 articles published between 2005 and 2008, the number of studies nearly doubled from 16 in 2005 to 29 in 2007. The researchers estimated that by the end of 2008 the number of published studies would reach 40. The most frequent areas of application have been cancer (15%) and respiratory disorder (12%)(1). However, applications of conjoint techniques can be found also for guiding policy making and the design of health plans in a broader context of health-care services provided to patients (e.g., by HMOs).

Most conjoint studies in health (71%) apply choice experiments and modelling, becoming the dominant approach (close to 80%) particularly in 2008. A typical study includes 5 or 6 attributes with 2 or 3 levels for each attribute. Most studies in a choice-based approach involve 7 to 8 scenarios (choice sets) but studies with 10-11 or 14-15 scenarios are also frequent (2). A choice scenario normally includes 3 to 5 concepts from which a respondent has to choose a single most prefered concept.

Interpretation of conjoint studies among medical doctors needs a special qualification to be distinguished from studies of patients or consumers. That is because the physicians make professional judgements about the most appropriate treatment option for their patients.  Therefore, it is less appropriate to relate to personal preferences in this context. It is more sensible and suitable to talk about decision criteria that physicians apply, their priorities (i.e., represented by importance weights), and requirements of physicians from pharmaceutical or other treatment alternatives available in the market.

Including monetary cost in conjoint studies on products and services in health-care may be subject to several complications and limitations. That may be the reason for the relatively low proportion of articles on conjoint studies in health that were found to include prices (40%)  (3). For instance, doctors do not take money out of their own pockets to pay for the medications they prescribe, so it is generally less relevant to include price in their studies. It may be sensible, however, to include cost in cases where doctors are allowed to purchase and hold a readily available  inventory of medications for their visiting patients in their private clinics (e.g., Switzerland). It may still be useful to examine how sensitive doctors are to the cost of medication that their patients will have to incur when prescribing them. However, this practice may be additionally complicated because the actual price patients pay for a specific medication is likely to change according to the coverage of their health plan or insurance. It is appropriate and recommended to include price in studies on OTC medications or health-related devices (e.g., for measuring blood pressure). Aspects of cost can be included in studies on health plans such as the percentage of discounts provided on medications and other types of clinical tests and treatments in the plan’s coverage.

An Example for a Conjoint Study on Health-Care Plans:

A choice-based conjoint study was conducted to help a health-care coverage provider assess the potential for a new modified heath plan it was considering to launch. Researchers Gates, McDaniel and Braunsberger (4)  designed a study with 11 attributes including provider names (the client and two competitors), network of physicians accessible, payment per doctor visit, prescription coverage, doctor quality, hospital choice, monthly premium, and additional attributes. Each respondent was introduced to 10 choice sets where in each set he or she had to choose one out of four plans. This setting was elected so that in subsequent simulations the researchers could more accurately test scenarios with existing plans of the three providers plus a new plan by the client-provider. The study was conducted among residents in a specific US region by mail. Yet beforehand a qualitative study (focus group discussions) and a telephone survey have been carried out to define, screen and refine the set of attributes to be included in the conjoint study. 506 health-care patients returned the mail questionnaire (71% response rate out of those in the phone survey who agreed to participate in the next phase).

The estimated (aggregate) utility function suggested to the researchers that the attributes could be divided into two classes of importance: primary criteria for choosing a health plan and secondary considerations. The primary criteria focused on access allowed to doctors in the region of residence and cost associated with the plan, representing the more immediate concerns to target consumers in the market in choosing a health-care plan by a HMO. It was mainly confirmed in the study that consumers are less concerned by narrowing the network of doctors they may visit, as long as they can keep their current family physician and are not forced to replace him or her with another on the list. Respondents appeared to rely less on reported quality ratings of doctors and hospitals. Vision tests and dental coverage were among the secondary considerations. Managers could thereby examine candidate modifications to their health plan and estimate their impact on market shares.

The conjoint methods offer professionals and managers in health-related organizations research tools for gaining valuable insights into patient preferences or criteria governing the clinical decisions of doctors on medications and other treatments. These methods can be particularly helpful in guiding the development of pharmaceutical products or instruments for performing clinical tests and treatments when issues of marketing and promoting them to decision makers come into play. As illustrated in the example, findings from conjoint studies can be useful in policy making on health-care services and designing attractive health plans to patients. This kind of research-based knowledge is acknowledged more widely as a key to success in the highly competitive environs of health-care.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1)  Conjoint Analysis Applications in Health – How Are Studies Being Designed and Reported? An Update of Current Practice in the Published Literature Between 2005 and 2008, D. Marshall, J.F.P. Bridges, B. Hauber, R. Cameron, L. Donnalley, K. Fyie, and F.R. Johnson, 2010, The Patient: Patient-Centered Outcomes Research, 3 (4), 249-256

(2) Ibid. 1.

(3) Ibid. 1.

(4) Modeling Consumer Health Plan Choice Behavior to Improve Customer Value and Health Plan Market Share, Roger Gates, Carl McDaniel, and Karin Braunsberger, 2000, Journal of Business Research, 48, pp. 247-257 (The research was executed by DSS Research to which Gates is affiliated).

Additional sources:

A special report on conducting conjoint studies in health was prpared in 2011 by a task force of the International Society for Pharmaeconomics and Outcomes Research. The authors provide methodological recommendations for guiding the planning, design, and analysis and reporting conjoint studies in health-related domains.

Conjoint Analysis Applications in Health – A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force, John F.P. Bridges, and A. Brett Hauber et al., 2011, Value in Health, 14, pp. 403-413

http://www.ispor.org/taskforces/documents/ISPOR-CA-in-Health-TF-Report-Checklist.pdf

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Measuring the value or equity of a retailer name from a customer’s point-of-view is usually challenging because of the diversity of products from various brands retailers offer on display and additional dimensions of performance that are specific to the retail store environment. For a long time retailers are not merely distributors that bring forward products to the consumers but offer products in their own names; furthermore, experiences of shoppers on-site of the store have a stronger influence on their purchase decisions. Thus, assigning customer values to retail names is often not a simple matter.

A similar problem with respect to the diversity of products may arise when trying to measure the value of a manufacturer corporate name to consumers, if the manufacturer uses its corporate name as an umbrella or even as a higher-level endorser for a wide range of products of different types. In this condition consumers may become confused as to which type of product they should refer and find it difficult to generalise their value judgements too broadly. Particularly, when trying to translate the subjective value to monetary terms, as often exercised with conjoint models of preference, one cannot plainly specify a price range that will be relevant to various types of products (e.g., TVs, stereo systems, washing machines, etc.) because the over-arching corporate name is too abstract. One has to conduct an evaluative study for each product category separately to obtain valid and relevant evaluations. The evaluation problem becomes several times more complex for a retail chain by accounting for the internal competition between manufacturers’ brands and a retailer’s own brand, and the other facets of the shopper experience in store (e.g., design and atmosphere, convenience, service).

  • As a case in example consider the branch of fashion retail. Castro, a leading Israeli homegrown fashion retail chain, has expanded greatly over the past 15 years (operating around 100 stores) and is a well-known and favourable name in many homes. According to a survey by Israeli business daily paper TheMarker with market research firm “Meida Shivuki” (27 Jan. 2012 (1)), Castro is the most familiar fashion retailer in Israel, remaining stable in this position six years in a row. Nearly half of adult Israelis have purchased an item or two in one of Castro’s stores in the past year. Fox, a low-cost retailer, is second, and Zara, a global Spanish-originated retailer, is in the third place (fourth year in a row). However, newly coming international retailers like H&M and Gap are tailing Castro, and more international brands like American Eagle or Banana Republic are expected to arrive soon. H&M climbed in awareness from 18.5% to 27% in six months, while Castro withdrew a little from 68% to 64%. Competition in the local fashion arena is becoming fierce, maintains TheMarker. In this setting, we may ask how well the value of Castro, from a consumer perspective, fares against rival international retail brands. One may also question what is the “attraction power” of Castro in terms of willingness-to-pay, and does it have to drag itself into a price war with the rivals to win?

In a conjoint analysis or choice study, brand is usually defined as one of the attributes describing a product, with several different brand names suggested as options (e.g., a choice set with four alternative products, each from another brand). This approach provides a single-numeric measure of value for each brand that some criticize as of too limited scope. Hence, further analyses on the subjective brand-equity values are advised, such as translating them also to monetary values of brand premiums by accounting also for consumers’ price sensitivity.

Moreover, in order to learn about the sources of brand values, we can analyse variation in brand values at the individual level vis-a-vis brand perceptions on several relevant dimensions of brand image (e.g., performance, reliability, or courteous service). Several techniques allow that, including with discrete choice modelling. It is worth mentioning in that context an unusual approach suggested more than 20 years ago of a brand-anchored model for evaluating the images of retailer brands (2). In that conjoint model, rather than including retailer names as options in a single brand attribute, retailer names are represented as options in each of several retail image attributes: For example, convenience of shopping is like at store of retailer “A”, “B”, or “C”.  This model does not offer overall values for each retailer, but it does suggest the relative values of a retailer name on each dimension of image. It’s like combining two-stage analyses proposed at the top of this paragraph in a single analysis. One conspicuous weakness of this approach is that respondents who do not know what would be the level of performance of an existing retailer on any of the dimensions will face difficulty in making reliable judgements of the retailer “portfolios” suggested to them or make a choice between them (the researchers have shown that consumers are likely to recall better the brands that score higher).

In the remaining of this post-article I suggest three alternative approaches for evaluating customer-based brand equity of retail chains in the framework of conjoint models, the first two apply a monetary currency whereas the third proposes distance as the currency of cost.

A Common Set of Products (“basket”) — In this approach we present to respondents-shoppers a well-defined set of products and ask them to suppose that they are going to shop for this set in stores of several optional retailers. Since retailers frequently offer on display a large variety of products, this set should serve as a common reference for comparison with regard to price levels. In some domains, such as food and grocery, we may be able to construct a “basket” of particular product items, including specific brands, because most stores hold the same product brands. Yet, in other domains like fashion this task could be more daunting because retailers choose to offer more differentiated clothing designs and specialise in bringing clothing items from different designer names. In the case of fashion we may have to describe in more general terms an outfit composed of several items but be specific enough about the quality and style of the items (e.g., think of dressing a mannequin with an outfit).

Applying this approach, therefore, is more domain-contingent. Our aim is to estimate the price premium that shoppers are willing to pay in order to purchase a set of products from a particular retailer. However, there is greater risk in this approach of confounding the value of a retailer with the values of products included in the set of reference.

Retailers’ Qwn Brands — Many retail chains in various domains offer products in selected categories carrying their own retail name as brand or their unique private labels available only at their chain-stores. Emphasizing the retailer’s own brand of products helps to better focus attention on the retailer on all aspects of shopping from its chain-stores. It may be seen as a special case of the first approach, only that here respondents-shoppers are advised that all product items included in the set are carrying the retailer’s name or private label. Thus, the differences in quality between retailers with respect to their own branded products can be taken into consideration by the respondents-shoppers.

This method represents a more round-up approach for assessing the monetary premium shoppers are willing to pay when buying at a particular retail chain on ground of both products identified with the retailer and the experience of shopping at its stores. Yet, it is applicable only if all retailers proposed have salient brands of their own for comparison.

Distance from a Retailer’s Nearest Store — Taking on a different perspective, this approach breaks with the common use of monetary price as the currency of cost. As implied in the first two methods described above, the monetary currency may introduce quite difficult complications in the context of retailer evaluation. Nonetheless, there are types of cost consumers are likely to incur while making purchase decisions such as time and psychic effort or stress. Particularly in the context of retail, Sorensen relates to time and angst in addition to money as the three currencies of cost shoppers may incur while looking for products they require or desire in-store (3). However, even before entering the store, another type of cost may be the distance the shopper has to make to reach his or her favourite store. Distance is often suggested also as a measure of loyalty: How far are you willing to go in order to find your favourite brand or to shop in a store of your favourite retailer?

According to this approach, a “cost” attribute would inform respondents-shoppers, for instance that “the nearest store of Retailer A is 500 meters away from you”. This type of conjoint application measures the retailer’s brand premium in terms of extra distance shoppers are willing to go to reach one of its stores (relative to a competing retail chain). It is possible that some consumers would want to go further actually to find lower prices or better value, but that perception could also be engrained in the retailer’s image. Indeed, the conjoint model alone may not tell us whether a retailer’s brand is preferred due to price/value perceptions or shopping experience aspects. On the other hand, it provides a measure of loyalty that may fit more smoothly in the context of choosing a retailer and poses no pre-conditions on the specific products each consumer may wish to buy at the store.

Each of these three approaches to measuring customer-based brand equity of retailers may be more appropriate, sensible, and easier to apply in some domains rather than others. The third approach appears for example the more suitable in the domain of fashion. However, if pricing issues arise, the  first or second approaches may be more practical albeit with some greater difficulty. That is where experience and good judgement of managers and researches comes in.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Hebrew readers may find the original article of TheMarker at http://www.themarker.com/consumer/1.1627373.

(2) “Reliability and Validity of the Brand-Anchored Conjoint for Measuring Retailer Images, Jordan J. Louviere and Richard D. Johnson, 1990, Journal of Retailing, 66 (4). pp. 359-382.

(3) “Inside the Mind of the Shopper (The Science of Retailing)”, Herb Sorensen, 2009, Pearson Education.

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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)

Reference:

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.

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