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Choosing reading books can be a serious undertaking. Even the choice of a novel or a detective book may not be taken lightly by readers. There are different ways in which consumers may get into choosing a book; some search and selection patterns in the decision process carried out by consumers can be observed in bookstores. It is possible to infer from observations, with some limitations, styles of shopping for books, involving certain tactics or rules utilised in the process. Book fairs especially offer an interesting and vibrant venue for book shopping with options not regularly available at stores. Such events may also provide an opportunity to detect new or distinctive patterns and styles of shopping that arise from the dynamic happening and busy environment.

The open-air Hebrew Book Fair has been taking place in a main central square in Tel-Aviv for over forty years in every June. Originally the book fair was held for a week but in recent years it has been extended by three more days due to its high popularity. It must immediately be noted that the book fair is an event reserved for publishers. It is a kind of ‘direct-sales’ event in which publishers meet face-to-face with readers to present their book collections to them for purchase on special discounts (the main bookstore chains run their own parallel competitive events with discounts in-store or near their stores). Visitors at the book fair can find Hebrew-native books and books translated to Hebrew from English and other languages; topical categories cover, for instance, prose, poetry  and novels; detective and thrillers; history, science, and other areas of knowledge; and last but not least children & youth books. Such an enormous selection of books is not available ordinarily at bookstores in the country. The larger publishing houses may occupy ten or more counters in-line.

The visitor traffic at the event, as in this year, suggests that print books are still highly desired by people. Nevertheless, to attract even more visitors, particularly families with children, the organisers added in the past few years food and drink stands and a sitting area with tables in the square’s centre. It may help to increase the convenience to visitors and festivity of the event though it could sacrifice a bit the respectability of this literary event. However, it may be a matter of necessity or priority to make the event more popular and vibrant so as to bring larger reader audiences back to books.

As suggested above, this book fair is a busy event with tens of thousands of books of numerous titles on display from different publishers and across a wide range of topics. It retains also a long tradition wherein Israeli authors attend to sign their books for visitors-buyers. Some book counters may become crowded with shoppers during certain hours through the afternoon and evening (i.e., after work and school hours) which can make it harder to access books and check them out more deeply. Hence it may require shoppers to apply tactics for choosing books of their interest and taste a little differently than they would while shopping in a bookstore. Yet visitors find their ways to browse books, sometimes more loosely, sometimes more meticulously; it seems to happen overall in an orderly manner, each visitor getting his or her place at a book counter or desk.

Visitors can be seen walking along counters of a given publisher, staying at a counter for a while to observe its books, then moving along. After selecting a few books from separate but adjacent counters of the same publisher, the visitor often returns to a previous counter to pay. However, visitors-buyers are also offered the option to keep books already selected behind the counter (a combination of convenience and security for both sellers and customers).

Three forms of browsing candidate books of interest can be primarily noticed: Firstly, eye-scanning the front covers of books from top. Secondly, lifting a book, turning it over and reading its back cover — an abstract, short review recommendations, or a brief biography of the author(s). A visitor may examine a few books from a counter this way, but being able to do so comfortably may truly depend on how many people are already at the counter. Hence, visitors who cannot find a free spot at a counter are often seen looking over a counter-top quickly, moving to the next counter, then coming back if perhaps there was a book that had caught their attention previously to check on the book more closely. But visitors generally do not have to wait too long to find a free spot at a counter. Thirdly, one gets to open a book and sample-read sections from its pages, or looking at photographs, charts or maps inside the book. Instances of reading inside books were observed much less frequently.

Examining a book’s content more deeply to form a better founded impression or opinion of it is more difficult and hence is less likely than would be seen at bookstores. Yet, if time and space at the counter allow, it is possible to find a visitor examining a book more meticulously. It appears to be particularly relevant and appropriate for ‘knowledge books’ such as in history, sciences and technology, the social sciences, economics and business. For example, a visitor in his ~70s was leaning over an open book on the history of WW2 by Max Hastings, appearing concentrated in reading and observing maps and photographs (‘Inferno/All Hell Let Loose’, translated). He seemed interested overall in history of the two world wars of the 20th century, judging from other books he browsed; after nearly ten minutes he handed three chosen books to keep, and continued searching [A].

  • Please be advised that the age estimates of visitors are based on observation alone in best judgement of the author.

Comparing books on a given topic can be an even more difficult task to perform at a counter. It is hardly practical to hold two books open simultaneously for comparison, but visitors may examine books sequentially in attempt to evaluate and choose which one is more suitable to their objectives. For instance, a visitor (male, ~60) looked into a book — its introduction, inner pages, and content — on the history of the state of Israel (by Michael Bar-Zohar), but he apparently did not find what he was looking for as he asked the seller if there were books on the period preceding the establishment of the state. The seller brought him two books (concerning the Arab-Israeli conflict): he opened one of them, went through its pages, and put it aside, then browsed at greater length pages in the other book and looked at photographs. Eventually he chose the first book on the state of Israel, after looking into it again, and the third book (total time 15 minutes, [B]).

The search and examination of books sometimes involves moments of deliberation. In some cases, as above [B], the visitor may ask for advice from a seller. Alternately, as in another case observed, a seller who noticed a visitor (female, 30-35) hesitating, offered her help with recommendations. The visitor-shopper was already holding two books and the seller brought her more books the latter thought may suit the shopper accordingly in prose or novels by Israeli authors. They continued talking about the books as the shopper browsed loosely inside some of the books or read from the back cover [C].

Deliberation can take some additional forms. For example, a female visitor (~45) was considering the purchase of a book on equity investments. She was checking in particular a book purporting to be adapted and designated for women. The visitor went through some book pages, being unsure it was a good choice, and seemed recoiled upon noticing the book was from 2011 (i.e., ‘Is it still valid and relevant?’). But eventually, following a short exchange with the (female) seller, the visitor-shopper decided to take it anyway [D]. A visitor (male, 25-30) at another publisher has shown an intriguing shopping process with deliberation to the last moment: He was already holding a book when moving to another counter to look over books of prose, selected one of them, then browsed some science and knowledge books (e.g., by an Israeli scholar, lecturer and prolific writer on sciences and philosophy, Haim Shapira), but collected none. Subsequently the shopper moved to a more remote counter where he picked-up instantly a book, came back to the previous counter of science and knowledge books to purchase three books. However, after he had already paid and the books were put in a bag by the seller and handed over to him, he took out one of the books and picked-up instead a different book in front of him on biblical philosophy (by Shapira, 10 minutes, [E]).

Shopping patterns can range from exploratory, looking for opportunities with little idea pre-conceived in mind, to being pre-minded, that is, having a goal to find a particular book. Moreover, visitors-shoppers may mix styles at different levels of search, examination and choice while shopping from the same publishing house. Mixed tactics could be seen above in the shopping of visitors [E] and [C]. Following are two more examples of this kind: (1) A young visitor (female, ~17-18) was browsing prose or fiction books, going through pages and reading inside some of the books or reading from the back covers of others, then passed to looking from top at books in adjacent counters of the publisher (a more haphazard quick scan), finally returning to the first counter to buy [F]; (2) A visitor (male, ~45, at a counter of books on history and politics) took a cursory look over a biography of one of Israel’s prominent leaders of the past, kept searching and shortly after found a book on the history of Sephardic Jews (‘Marranos’, Yirmiyahu Yovel) and looked into the book more dedicately; the visitor, who seemed overall interested in Israeli and Jewish history, picked up a book at the last moment by an Israeli historian on the commanders of the Nazi concentration camps (‘Soldiers of Evil’) and purchased it with the book on Marranos [G].

  • In a curious brief episode, demonstrating an apparent pre-determined choice of book, a visitor in his mid-40s approached a counter, stood pausing or looking over the books, then instantly extended his hand to pick-up three copies of a book on the Bitcoin, which he purchased; one of the sellers seemed so impressed that she asked to take a photo of him holding the books with her mobile phone to which he smilingly agreed [H].

The main publishing houses presenting at the book fair offered deals of ‘3 for 100’, that is, three books for 100 shekels (~$28 in June). One publisher even offered five books for 150 shekels. These deal offers were displayed on signage boards above counters. A fourth book could be purchased for 50% of its list price, but this offer was not displayed. Visitors-shoppers who had already selected three books enquired whether there would be a discount for additional books, and were replied with the 50% offer. For instance, visitor [A] so enquired before continuing his search. Another visitor (male, ~30) who was holding four books by Ken Follett seemed unable to make up his mind which three to buy, posed the question about a fourth book discount, deliberated a little longer while shuffling the books in his hand, and finally passed all four to the seller to purchase [I]. In some cases, however, it was the seller who initiated the offer of discount on a fourth book in hope to increase the sale. Visitor [C], for example, accepted an offer as such and bought four books, probably in appreciation of, and perhaps feeling obliged to reciprocate, the advice she received from the seller. Conversely, another visitor (~30), who selected three books in history and politics on his own refused the offer by the seller when submitting his books to purchase [J].

Visitors were induced by these deals to buy more books from any single publisher. A single book could usually be bought with a 20% discount but this offer was not made public, proposed by a seller only on request of the visitor. This policy makes it simply unworthy economically for visitors to cherry-pick the books they most require or desire from different publishers (consider that many of the books cost 80-120 shekels each!). The greater problem, however, is that it may drive consumers to buy books they do not care for or do not have time to read soon. Henceforth, visitors could end up buying a pack of books, collected from several publishers, for the whole year to read. It puts quantity before quality in buying books. The ones standing to suffer from this policy are of course the book retailers who will likely see fewer shoppers at their stores in the coming months. From a publisher’s viewpoint, they may see it as only a reprisal to similar deals offered at bookstores throughout the year.

Visitors-shoppers at the book fair appear to use composite decision strategies for choosing books at the counters of a publisher: a different type of rule or method may be fitted to choose among different books (e.g., picking-up a book planned ahead to purchase, using book titles or author names as memory cues for books they have considered recently, examining inside books with greater scrutiny to evaluate them). Furthermore, the book shoppers are searching for informational cues, starting from the front cover of a book, going to the back cover, then getting inside the book. They could be extending the search for cues about a book as they feel is needed (e.g., cut the search short if sufficient information has been retrieved) or are stimulated to learn more about the book (e.g., intrigued by information on the back cover to look inside).

The difference in shopping for books at the book fair compared with bookstores seems to be not so much in the types of rules or tactics used as in the extent and frequency they are used. Book shoppers may feel at greater ease to search for a book at a store with a print of a book review cut from a newspaper (as observed in a store) than they would in the book fair (surely the same applies if one seeks guidance from his or her smartphone). One may also feel more comfortable and free to browse inside a book at a bookstore, at a quiet corner to stand or perhaps on a couch or sofa to sit and read, than at the book fair. Yet, visitors of the book fair seemed to adapt quite well to the conditions at the counters; they appear to use rules or methods similar to those that can be seen at bookstores, only adjusting them to search and choose more efficiently, particularly by restricting deeper examinations to situations where a book demands it.

  • Additional research methods can aid in identifying and verifying more accurately the book images and information viewed by visitors and the decision rules they use. Those methods include particularly eye-tracking and a real-time protocol of the shopping decision process (‘think aloud’). But executions of such methods may be inconveniently intrusive and interfere with the natural course of the shopping trip for visitors. Another method to consider with less intervention is an interview with a visitor-shopper after concluding a shopping episode.

Gaining greater insight into shopping for books and understanding the decision processes visitors-shoppers follow at a book fair can help in devising new designs of book displays (e.g., better organise books by topics or themes, easier-to-find) and improved practices to accommodate the visitors at the event. The organisers and publishing houses may also come up with a new co-operative scheme that would allow visitors to accomplish more effectively their objective in selecting and buying the books that interest them most or they desire to read.

Ron Ventura, Ph.D. (Marketing)

 

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The strength, impact and value of a brand are embodied, fairly concisely, in the concept of ‘brand equity’. However, there are different views on how to express and measure brand equity, whether from a consumer (customer) perspective or a firm perspective. Metrics based on a consumer viewpoint (measured in surveys) raise particular concern as to what actual effects they have in the marketplace. Datta, Ailawadi and van Heerde (2017) have answered to the challenge and investigated how well Consumer-Based metrics of Brand Equity (CBBE) align with Sales-Based estimates of Brand Equity (SBBE). The CBBE metrics were adopted from the model of Brand Asset Valuator (Y&R) whereas SBBE estimates were derived from modelling market data of actual purchases. They also examined the association of CBBE with behavioural response to marketing mix actions [1].

In essence, brand equity expresses an incremental value of a product (or service) that can be attributed to its brand name above and beyond physical (or functional) attributes. Alternately,  brand equity is conceived as the added value of a branded product compared with an identical version of that product if it were unbranded. David Aaker defined four main groups of assets linked to a brand that add to its value: awareness, perceived quality, loyalty, and associations beyond perceived quality. On the grounds of this conceptualization, Aaker subsequently proposed the Brand Equity Ten measures, grouped into five categories: brand loyalty, awareness, perceived quality / leadership, association / differentiation, and market behaviour. Kevin Keller broadened the scope of brand equity wherein greater and more positive knowledge of customers (consumers) about a brand would lead them to respond more favourably to marketing activities of the brand (e.g., pricing, advertising).

The impact of a brand may occur at three levels: customer market, product market and financial market. In accordance, academics have followed three distinct perspectives for measuring brand equity: (a) customer-based — an attraction of consumers to the “non-objective” part of the product offering (e.g., ‘mindset’  as in beliefs and attitudes, brand-specific ‘intercept’ in a choice model); (b) company-based — additional value accrued to the firm from a product because of a brand name versus an equivalent product but non-branded (e.g., discounted cash flow); financial-based — brand’s worth is the price it brings or could bring in the financial market (e.g., materialised via mergers and acquisitions, stock prices)[2]. This classification is not universal:  for example, discounted cash flows are sometimes described as ‘financial’; estimates of brand value derived from a choice-based conjoint model constitute a more implicit reflection of the consumers’ viewpoint. Furthermore, models based on stated-choice (conjoint) or purchase (market share) data may vary greatly in the effects they include whether in interaction with each competing brand or independent from the brand ‘main effect’ (e.g., product attributes, price, other marketing mix variables).

A class of attitudinal (‘mindset’) models of brand equity may encompass a number of aspects and layers: awareness –> perceptions and attitudes about product attributes and functional benefits (+ overall perceived quality), ‘soft’ image associations (e.g., emotions, personality, social benefits) –> attachment or affinity –> loyalty (commitment). Two noteworthy academic studies have built upon the conceptualizations of Aaker and Keller in constructing and testing consumer-based measures:

  • Yoo and Donthu (2001) constructed a three-dimension model of brand equity comprising brand loyalty, brand awareness / associations (combined), and perceived quality (strength of associations was adopted from Keller’s descriptors of brand image). The multidimensional scale (MBE) was tested and validated across multiple product categories and cultural communities [3].
  • Netemeyer and colleagues (2004) demonstrated across products and brands that perceived quality, perceived value (for the cost), and uniqueness of a given brand potentially contribute to willingness to pay a price premium for the brand which in turn acts as a direct antecedent of brand purchase behaviour [4]. Price premium, an aspect of brand loyalty, is a common metric used for assessing brand equity.

Datta, Ailawadi and van Heerde distinguish between two measurement approaches: the consumer-based brand equity (CBBE) approach measures what consumers think and feel about the brand, while the sales-based brand equity (SBBE) approach is based on choice or share of the brand in the marketplace.

The CBBE approach in their research is applied through data on metrics from the Brand Asset Valuator model developed originally by Young and Roubicam (Y&R) advertising agency (the brand research activity is now defined as a separate entity, BAV Group; both Y&R and BAV Group are part of WPP media group). The BAV model includes four dimensions: Relevance to the consumers (e.g., fits in their lifestyles); Esteem of the brand (i.e., how much consumers like the brand and hold it in high regard); Knowledge of the brand (i.e., consumers are aware of and understand what the brand stands for); and  Differentiation from the competition (e.g., uniqueness of the brand)[5].

The SBBE approach is operationalised through modelling of purchase data (weekly scanner data from IRI). The researchers derive estimates of brand value in a market share attraction model (with over 400 brands from 25 categories, though just 290 brands for which BAV data could be obtained were included in subsequent CBBE-SBBE analyses) over a span of ten years (2002-2011). Notably, brand-specific intercepts were estimated for each year; an annual level is sufficient and realistic to account for the pace of change in brand equity over time. The model allowed for variation between brands in the sensitivity to their marketing mix actions (regular prices, promotional prices, advertising spending, distribution {on-shelf availability} and promotional display in stores) — these measures are not taken as part of SBBE values but indicate nonetheless expected manifestation of higher brand equity (impact); after being converted into elasticities, they play a key role in examining the relation of CBBE to behavioural outcomes in the marketplace.


  • Datta et al. seem to include in a SBBE approach estimates derived from (a) actual brand choices and sales data as well as (b) self-reported choices in conjoint studies and surveys. But subjective responses and behavioural responses are not quite equivalent bases. The authors may have aimed reasonably to distinguish ‘choice-based’ measures of brand equity from ‘attitudinal’ measures, but it still does not justify to mix between brands and products consumers say they would choose and those they actually choose to purchase. Conjoint-based estimates are more closely consumer-based.
  • Take for instance a research by Ferjani, Jedidi and Jagpal (2009) who offer a different angle on levels of valuation of brand equity. They derived brand values through a choice-based conjoint model (Hierarchical Bayes estimation at the individual level), regarded as consumer-level valuation. Vis-à-vis the researchers constructed a measure of brand equity from a firm perspective based on expected profits (rather than discounted cash flows), presented as firm-level valuation. Nonetheless, in order to estimate sales volume they ‘imported’ predicted market shares from the conjoint study, thus linking the two levels [6].

 

Not all dimensions of BAV (CBBE) are the same in relation to SBBE: Three of the dimensions of BAV — relevance, esteem, and knowledge — are positively correlated with SBBE (0.35, 0.39, & 0.53), while differentiation is negatively although weakly correlated with SBBE (-0.14). The researchers reasoned in advance that differentiation could have a more nuanced and versatile market effect (a hypothesis confirmed) because differentiation could mean the brand is attractive to only some segments and not others, or that uniqueness may appeal to only some of the consumers (e.g., more open to novelty and distinction).

Datta et al. show that correlations of relevance (0.55) and esteem (0.56) with market shares of the brands are even higher, and the correlation of differentiation with market shares is less negative (-0.08), than their correlations with SBBE (correlations of knowledge are about the same). The SBBE values capture a portion of brand attraction to consumers. Market shares on the other hand factor in additional marketing efforts that dimensions of BAV seem to account for.

Some interesting brand cases can be detected in a mapping of brands in two categories (for 2011): beer and laundry detergents. For example, among beers, Corona is positioned on SBBE much higher than expected given its overall BAV score, which places the brand among those better valued on a consumer basis (only one brand is considerably higher — Budweiser). However, with respect to market share the position of Corona is much less flattering and quite as expected relative to its consumer-based BAV score, even a little lower. This could suggest that too much power is credited to the name and other symbols of Corona, while the backing from marketing efforts to support and sustain it is lacking (i.e., the market share of Corona is vulnerable).  As another example, in the category of laundry detergents, Tide (P&G) is truly at the top on both BAV (CBBE) and market share. Yet, the position of Tide on SBBE relative to BAV score is not exceptional or impressive, being lower than predicted for its consumer-based brand equity. The success of the brand and consumer appreciation for it may not be adequately attributed specifically to the brand in the marketplace but apparently more to other marketing activities in its name (i.e., marketing efforts do not help to enhance the brand).

The degree of correlation between CBBE and SBBE may be moderated by characteristics of product category. Following the salient difference cited above between dimensions of BAV in relation to SBBE, the researchers identify two separate factors of BAV: relevant stature (relevance + esteem + knowledge) and (energized) differentiation [7].

In more concentrated product categories (i.e., the four largest brands by market share hold a greater total share of the category), the positive effect of brand stature on SBBE is reduced. Relevance, esteem and knowledge may serve as particularly useful cues by consumers in fragmented markets, where it is more necessary for them to sort and screen among many smaller brands, thus to simplify the choice decision process. When concentration is greater, reliance on such cues is less required. On the other hand, when the category is more concentrated, controlled by a few big brands, it should be easier for consumers to compare between them and find aspects on which each brand is unique or superior. Indeed, Datta and colleagues find that in categories with increased concentration, differentiation has a stronger positive effect on SBBE.

For products characterised by greater social or symbolic value (e.g., more visible to others when used, shared with others), higher brand stature contributes to higher SBBE in the market. The researchers could not confirm, however, that differentiation manifests in higher SBBE for products of higher social value. The advantage of using brands better recognized and respected by others appears to be primarily associated with facets such as relevance and esteem of the brand.

Brand experience with hedonic products (e.g., leisure, entertainment, treats) builds on enjoyment, pleasure and additional positive emotions the brand succeeds in evoking in consumers. Sensory attributes of the product (look, sound, scent, taste, touch) and holistic image are vital in creating a desirable experience. Contrary to expectation of Datta and colleagues, however, it was not found that stature translates to higher SBBE for brands of hedonic products (even to the contrary). This is not so good news for experiential brands in these categories that rely on enhancing relevance and appeal to consumers, who also understand the brands and connect with them, to create sales-based brand equity in the marketplace. The authors suggest in their article that being personally enjoyable (inward-looking) may overshadow the importance of broad appeal and status (outward-looking) for SBBE. Nevertheless, fortunately enough, differentiation does matter for highlighting benefits of the experience of hedonic products, contributing to a raised sales-based brand equity (SBBE).

Datta, Ailawadi and van Heerde proceeded to examine how strongly CBBE corresponds with behavioural responses in the marketplace (elasticities) as manifestation of the anticipated impact of brand equity.

Results indicated that when relevant stature of a brand is higher consumers respond favourably even more strongly to price discounts or deals  (i.e.,  elasticity of response to promotional prices is further more negative or inverse). Yet, the expectation that consumers would be less sensitive (adverse) to increased regular prices by brands of greater stature was not substantiated (i.e., expected positive effect: less negative elasticity). (Differentiation was not found to have a positive effect on response to regular prices either, and could be counter-conducive for price promotions.)

An important implication of brand equity should be that consumers are more willing to pay higher regular prices for a brand of higher stature (i.e., a larger price premium) relative to competing brands, and more forgiving when such a brand sees it necessary to update and raise its regular price. The brand may benefit from being more personally relevant to the consumer, better understood and more highly appreciated. A brand more clearly differentiated from competitors with respect to its advantages could also benefit from a protected status. All these properties are presumed to enhance attachment to a brand, and subsequently lead to greater loyalty, making consumers more ready to stick with the brand even as it becomes more expensive. This research disproves such expectations. Better responsiveness to price promotions can help to increase sales and revenue, but it testifies to the heightened level of competition in many categories (e.g., FMCG or packaged goods) and propensity of consumers to be more opportunistic rather than to the strength of the brands. This result, actually a warning signal, cannot be brushed away easily.

  • Towards the end of the article, the researchers suggest as explanation that they ignored possible differences in response to increases and decreases in regular prices (i.e., asymmetric elasticity). Even so, increases in regular prices by stronger brands are more likely to happen than price decreases, and the latter already are more realistically accounted for in response to promotional prices.

Relevant stature is positively related to responsiveness to feature or promotional display (i.e., consumers are more inclined to purchase from a higher stature brand when in an advantaged display). Consumers also are more strongly receptive to larger volume of advertising by brands of higher stature and better differentiation in their eyes (this analysis could not refer to actual advertising messages and hence perhaps the weaker positive effects). Another interesting finding indicates that sensitivity to degree of distribution (on-shelf availability) is inversely associated with stature — the higher the brand stature from consumer viewpoint, larger distribution is less attractive to the consumers. As the researchers suggest, consumers are more willing to look harder and farther (e.g., in other stores) for those brands regarded more important for them to have. So here is a positive evidence for the impact of stronger brands or higher brand equity.

The research gives rise to some methodological questions on measurement of brand equity that remain open for further deliberation:

  1. Should the measure of brand equity in choice models rely only on a brand-specific intercept (expressing intrinsic assets or value of the brand) or should it include also a reflection of the impact of brand equity as in response to marketing mix activities?
  2. Are attitudinal measures of brand equity (CBBE) too gross and not sensitive enough to capture the incremental value added by the brand or is the measure of brand equity based only on a brand-intercept term in a model of actual purchase data too specific and narrow?  (unless it accounts for some of the impact of brand equity)
  3. How should measures of brand equity based on stated-choice (conjoint) data and actual purchase data be classified with respect to a consumer perspective? (both pertain really to consumers: either their cognition or overt behaviour).

Datta, Ailawadi and van Heerde throw light in their extensive research on the relation of consumer-based equity (CBBE) to behavioural outcomes, manifested in brand equity based on actual purchases (SBBE) and in effects on response to marketing mix actions as an impact of brand equity. Attention should be awarded to positive implications of this research for practice but nonetheless also to the warning alerts it may signal.

Ron Ventura, Ph.D. (Marketing)

Notes:

[1] How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response?; Hannes Datta, Kusum L. Ailawadi, & Harald J. van Heerde, 2017; Journal of Marketing, 81 (May), pp. 1-20. (DOI: 10.1509/jm.15.0340)

[2] Brands and Branding: Research Findings and Future Priorities; Kevin L. Keller and Donald R. Lehmann, 2006; Marketing Science, 25 (6), pp. 740-759. (DOI: 10.1287/mksc.1050.0153)

[3] Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale; Boonghee Yoo and Naveen Donthu, 2001; Journal of Business Research, 52, pp. 1-14.

[4]  Developing and Validating Measures of Facets of Customer-Based Brand Equity; Richard G. Netemeyer, Balaji Krishnan, Chris Pullig, Guangping Wang,  Mahmet Yageci, Dwane Dean, Joe Ricks, & Ferdinand Wirth, 2004; Journal of Business Research, 57, pp. 209-224.

[5] The authors name this dimension ‘energised differentiation’ in reference to an article in which researchers Mizik and Jacobson identified a fifth pillar of energy, and suggest that differentiation and energy have since been merged. However, this change is not mentioned or revealed on the website of BAV Group.

[6] A Conjoint Approach for Consumer- and Firm-Level Brand Valuation; Madiha Ferjani, Kamel Jedidi, & Sharan Jagpal, 2009; Journal of Marketing Research, 46 (December), pp. 846-862.

[7] These two factors (principal components) extracted by Datta et al. are different from two higher dimensions defined by BAV Group (stature = esteem and knowledge, strength = relevance and differentiation). However, the distinction made by the researchers as corroborated by their data is more meaningful  and relevant in the context of this study.

 

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Fifteen years have passed since a Nobel Prize in economics was awarded to Daniel Kahneman to this time (Fall 2017) when another leading researcher in behavioural economics, Richard Thaler, wins this honourable prize. Thaler and Kahneman are no strangers — they have collaborated in research in this field from its early days in the late 1970s. Moreover, Kahneman together with the late Amos Tversky helped Thaler in his first steps in this field, or more generally in meeting economics with psychology. Key elements of Thaler’s theory of Mental Accounting are based on the value function in Kanheman and Tversky’s Prospect theory.

In recent years Thaler is better known for the approach he devised of choice architecture and the tools of nudging, as co-author of the book “Nudge: Improving Decisions About Health, Wealth and Happiness” with Cass Sunstein (2008-9). However, at the core of the contribution of Thaler is the theory of mental accounting where he helped to lay the foundations of behavioural economics. The applied tools of nudging are not appropriately appreciated without understanding the concepts of mental accounting and other phenomena he studied with colleagues which describe deviations in judgement and behaviour from the rational economic model.

Thaler, originally an economist, was unhappy with predictions of consumer choice arising from microeconomics — the principles of economic theory were not contested as a normative theory (e.g., regarding optimization) but claims by economists that the theory is able to describe actual consumer behaviour and predict it were put into question. Furthermore, Thaler and others early on argued that deviations from rational judgement and choice behaviour are predictable.  In his ‘maverick’ paper “Toward a Positive Theory of Consumer Choice” from 1980, Thaler described and explained deviations and anomalies in consumer choice that stand in disagreement with the economic theory. He referred to concepts such as framing of gains and losses, the endowment effect, sunk costs, search for information on prices, regret, and self-control (1).

The theory of mental accounting developed by Thaler thereafter is already an integrated framework that describes how consumers perform value judgements and make choice decisions of products and services to purchase while recognising psychological effects in making economic decisions (2).  The theory is built around three prominent concepts (described here only briefly):

Dividing a budget into categories of expenses: Consumers metaphorically (but sometimes physically) allocate the money of their budget into buckets or envelopes according to type or purpose of expenses. It means that they do not transfer money freely between categories (e.g., food, entertainment). This concept contradicts the economic principle of fungibility, thus suggesting that one dollar is not valued the same in every category. A further implication is that each category has a sub-budget allotted to it, and if expenses in the category during a period surpass its limit, a consumer will prefer to give up on the next purchase and refrain from adding money from another category. Hence, for instance,  Dan and Edna will not go out for dinner at a trendy restaurant if that requires taking money planned for buying shoes for their child. However, managing the budget according to the total limit of income in each month is more often unsatisfactory, and some purchases can still be made on credit without hurting other purchases in the same month. On the other hand, it can readily be seen how consumers get into trouble when they try to spread too many expenses across future periods with their credit cards, and lose track of the category limits for their different expenses.

Segregating gains and integrating losses: In the model of a value function by Kahneman and Tversky, value is defined upon gains and losses as one departs from a reference point (a “status quo” state). Thaler explicated in turn how properties of the gain-loss value function would be implemented in practical evaluations of outcomes. The two general “rules”, as demonstrated most clearly in “pure” cases, say: (a) if there are two or more gains, consumers prefer to segregate them (e.g., if Chris makes gains on two different shares on a given day, he will prefer to see them separately); (b) if there are two or more losses, consumers prefer to integrate them (e.g., Sarah is informed of a price for an inter-city train trip but then told there is a surcharge for travelling in the morning — she will prefer to consider the total cost for her requested journey). Thaler additionally proposed what consumers would prefer doing in more complicated cases of “mixed” gains and losses, whether to segregate between the gain and loss (e.g., if the loss is much greater than the gain) or integrate them (e.g., if the gain is larger than the loss so that one remains with a net gain).

Adding-up acquisition value with transaction value to evaluate product offers: A product or service offer generally exhibits in it benefits and costs to the consumer (e.g., the example of a train ticket above overlooked the benefit of the travel to Sarah). But value may arise from the offering or deal itself beyond the product per se. Thaler recognised that consumers may look at two sources of value, and composing or adding them together would yield the overall worth of a product purchase offer: (1) Acquisition utility is the value of a difference between the [monetary] value equivalent of a product to the consumer and its actual price; (2) Transaction utility is the value of a difference between the actual price and a reference price. In the calculus of value, hides the play of gains and losses. This value concept was quite quickly adopted by consumer and marketing researchers in academia and implemented in means-end models that depict chains of value underlying the purchase decision process of consumers (mostly in the mid-1980s to mid-1990s). Thaler’s approach to ‘analysing’ value is getting more widely acknowledged and applied also in practice, as expressions of value as such in consumer response to offerings can be found in so many domains of marketing and retailing.

A reference price may receive different representations, for instance: the price last paid; price recalled from a previous period; average or median price in the same product class; a ‘normal’ or list price; a ‘fair’ or ‘just’ price (which is not so easy to specify). The transaction value may vary quite a lot depending on the form of reference price a consumer uses, ceteris paribus, and hence affect how the transaction value is represented (i.e., as a gain or a loss and its magnitude). Yet, it also suggests that marketers may hint to consumers a price to be used as a reference price (e.g., an advertised price anchor) and thus influence consumers’ value judgements.

We often observe and think of discounts as a difference between an actual price (‘only this week’) and a higher normal price — in this case we may construe the acquisition value and transaction value as two ways to perceive gain on the actual price concurrently. But the model of Thaler is more general because it recognizes a range of prices that may be employed as a reference by consumers. In addition, a list price may be suspected to be set higher to invoke in purpose the perception of a gain vis-à-vis the actual discounted price which in practice is more regular than the list price. A list price or an advertised price may also serve primarily as a cue for the quality of the product (and perhaps also influence the equivalent value of the product for less knowledgeable consumers), while an actual selling price provides a transaction value or utility. In the era of e-commerce, consumers also appear to use the price quoted on a retailer’s online store as a reference; then they may visit one of its brick-and-mortar stores, where they hope to obtain their desired product faster, and complain if they discover that the price for the same product in-store is much higher. Where customers are increasingly grudging over delivery fees and speed, a viable solution to secure customers is to offer a scheme of ‘click-and-collect at a store near you’. Moreover, when more consumers shop with a smartphone in their hands, the use of competitors’ prices or even the same retailer’s online prices as references is likely to be even more frequent and ubiquitous.


  • The next example may help further to illustrate the potentially compound task of evaluating offerings: Jonathan arrives to the agency of a car dealer where he intends to buy his next new car of favour, but there he finds out that the price on offer for that model is $1,500 higher than a price he saw two months earlier in ads. The sales representative claims prices by the carmaker have risen lately. However, when proposing a digital display system (e.g., entertainment, navigation, technical car info) as an add-on to the car, the seller proposes also to give Jonathan a discount of $150 on its original price tag.
  • Jonathan appreciates this offer and is inclined to segregate this saving apart from the additional pay for the car itself (i.e., ‘silver-lining’). The transaction value may be expanded to include two components (separating the evaluations of the car offer and add-on offer completely is less sensible because the add-on system is still contingent on the car).

Richard Thaler contributed to the revelation, understanding and assessment of implications of additional cognitive and behavioural phenomena that do not stand in line with rationality in the economic sense. At least some of those phenomena have direct implications in the context of mental accounting.

One of the greater acknowledged phenomena by now is the endowment effect. It is the recognition that people value an object (product item) already in their possession more than when having the option of acquiring the same object. In other words, the monetary compensation David would be willing to accept to give up on a good he holds is higher than the amount he would agree to pay to acquire it —  people principally have a difficulty to give up on something they own or endowed with (no matter how they originally obtained it). This effect has been most famously demonstrated with mugs, but to generalise it was also tested with other items like pens. This effect may well squeeze into consumers’ considerations when trying to sell much more expensive properties like their car or apartment, beyond an aim to make a financial gain. In his latest book on behavioural economics, ‘Misbehaving’, Thaler provides a friendly explanation with graphic illustration as to why fewer transactions of exchange occur between individuals who obtain a mug and those who do not, due to the endowment effect vis-à-vis a prediction by economic theory (3).

Another important issue of interest to Thaler is fairness, such as when it is fair or acceptable to charge a higher price from consumers for an object in shortage or hard to obtain (e.g., shovels for clearing snow on the morning after a snow storm). Notably, the perception of “fairness” may be moderated depending on whether the rise in price is framed as a reduction in gain (e.g., a discount of $2o0 from list price being cancelled for a car in short supply) or an actual loss (e.g., an explicit increase of $200 above the list price) — the change in actual price is more likely to be perceived as acceptable in the former case than the latter (4). He further investigated fairness games (e.g., Dictator, Punishment and Ultimatum). Additional noteworthy topics he studied are susceptibility to sunk cost and self-control.

  • More topics studied by Thaler can be traced by browsing his long list of papers over the years since the 1970s, and perhaps more leisurely through his illuminating book: “Misbehaving: The Making of Behavioural Economics” (2015-16).

The tactics of nudging, as part of choice architecture, are based on lessons from the anomalies and biases in consumers’ procedures of judgement and decision-making studied by Thaler himself and others in behavioural economics. Thaler and Sunstein looked for ways to guide or lead consumers to make better choices for their own good — health, wealth and happiness — without attempting to reform or alter their rooted modes of thinking and behaviour, which most probably would be doomed to failure. Their clever idea was to work within the boundaries of human behaviour to modify it just enough and in a predictable way to put consumers on a better track to a choice decision. Nudging could mean diverting a consumer from his or her routine way of making a decision to arrive to a different, expectedly better, choice outcome. It often likely involves taking a consumer out of his or her ‘comfort zone’. Critically important, however, Thaler and Sunstein conditioned in their book ‘Nudge’ that: “To count as a mere nudge, the intervention must be easy and cheap to avoid. Nudges are not mandates“. Accordingly, nudging techniques should not impose on consumers the choice of any designated or recommended options (5).

Six categories of nudging techniques are proposed: (1) defaults; (2) expect errors; (3) give feedback; (4) understanding “mappings”; (5) structure complex choices; and (6) incentives. In any of these techniques, the intention is to allow policy makers to direct consumers to choices that improve the state of consumers. Yet, the approach they advocate of ‘libertarian paternalism’ is not received without contention —  while libertarian, that is without coercing a choice, a question remains what gives an agency or policy maker the wisdom and right to determine which options should be better off for consumers (e.g., health plans, saving and investment programmes). Thaler and Sunstein discuss the implementation of nudging mostly in the context of public policy (i.e., by government agencies) but these techniques are applicable just as well to plans and policies of private agencies or companies (e.g., banks, telecom service providers, retailers in their physical and online stores). Nevertheless, public agencies and even more so business companies should devise and apply any measures of nudging to help consumers to choose the better-off and fitting plans for them; it is not for manipulating the consumers or taking advantage of their human errors and biases in judgement and decision-making.

Richard Thaler reviews and explains in his book “Misbehaving” the phenomena and issues he has studied in behavioural economics through the story of his rich research career — it is an interesting, lucid and compelling story. He tells in a candid way about the stages he has gone through in his career. Most conspicuously, this story also reflects the obstacles and resistance that faced behavioural economists for at least 25-30 years.

Congratulations to Professor Richard Thaler, and to the field of behavioural economics to which he contributed wholesomely, in theory and in its application.    

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Toward a Positive Theory of Consumer Choice; Richard H. Thaler, 1980/2000; in Choices, Values and Frames (eds. Daniel Kahneman and Amos Tversky)[Ch. 15: pp. 269-287], Cambridge University Press. (Originally published in Journal of Economic Behaviour and Organization.)

(2) Mental Accounting and Consumer Choice; Richard H. Thaler, 1985; Marketing Science, 4 (3), pp. 199-214.

(3) Misbehaving: The Making of Behavioural Economics; Richard H. Thaler, 2016; UK: Penguin Books (paperback).

(4) Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias; Daniel Kahneman, Jack L. Knetsch, & Richard H. Thaler, 1991/2000; in Choices, Values and Frames (eds. Daniel Kahneman and Amos Tversky)[Ch. 8: pp. 159-170], Cambridge University Press. (Originally published in Journal of Economic Perspectives).

(5) Nudge: Improving Decisions About Health, Wealth, and Happiness; Richard H. Thaler and Cass R. Sunstein, 2009; UK: Penguin Books (updated edition).

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A new film this year, “Sully”, tells the story of US Airways Flight 1549 that landed safely onto the water surface of the Hudson River on 15 January 2009 following a drastic damage to the plane’s two engines. This article is specifically about the decision process of the captain Chesley (Sully) Sullenberger with the backing of his co-pilot (first officer) Jeff Skiles; the film helps to highlight some instructive and interesting aspects of human judgement and decision-making in an acute crisis situation. Furthermore, the film shows how those cognitive processes contrast with computer algorithms and simulations and why the ‘human factor’ must not be ignored.

There were altogether 155 people on board of the Airbus A320 aircraft in its flight 1549 from New-York to North Carolina: 150 passengers and five crew members. The story unfolds whilst following Sully in the aftermath of the incident during the investigation of the US National Transportation Safety Board (NTSB) which he was facing together with Skiles. The film (directed by Clint Eastwood, featuring Tom Hanks as Sully and Aaron Ackhart as Skiles, 2016) is based on Sullenberger’s autobiographic book “Highest Duty: My Search for What Really Matters” (2009). Additional resources such as interviews and documentaries were also used in preparation of this article.

  • The film is excellent, recommended for its way of delivering the drama of the story during and after the flight, and for the acting of the leading actors. A caution to those who have not seen the film: the article includes some ‘spoilers’. On the other hand, facts of this flight and the investigation that followed were essentially known before the film.

This article is not explicitly about consumers, although the passengers, as customers, were obviously directly affected by the conduct of the pilots as it saved their lives. The focus, as presented above, is on the decision process of the captain Sullenberger. We may expect that such an extraordinary positive outcome of the flight, rescued from a dangerous circumstance, would have a favourable impact on the image of the airline US Airways that employs such talented flight crew members. But improving corporate image or customer service and relationships were not the relevant considerations during the flight, just saving lives.

Incident Schedule: Less than 2 minutes after take-off (at ~15:27) a flock of birds (Canada geese) clashed into both engines of the aircraft. It is vital to realise that from that moment, the flight lasted less than four minutes! The captain took control of the plane from his co-pilot immediately after impact with the birds, and then had between 30 seconds to one minute to make a decision where to land.  Next, just 151 seconds passed from impact with the birds and until the plane was approaching right above the Hudson river for landing on the water. Finally, impact with water occurred 208 seconds after impact with the birds (at ~15:30).

Using Heuristics: The investigators of NTSB told Sully (Hanks) about flight calculations performed in their computer simulations, and argued that according to the simulation results it had not been inevitable to land on the Hudson river, a highly risky type of crash-land. In response, Sully said that it had been impossible for himself and Skiles to perform all those detailed calculations during the four minutes of the flight after the impact of the birds with the aircraft’s engines; he was relying instead on what he saw with his eyes in front of him — the course of the plane and the terrain below them as the plane was gliding with no engine power.

The visual guidance Sully describes as using to navigate the plane resembles a type of ‘gaze heuristic’ identified by professor Gerd Gigerenzer (1). In the example given by Gigerenzer, a player who tries to catch a ball flying in the air does not have time to calculate the trajectory of the ball, considering its initial position, speed and angle of projection. Moreover, the player should also take into account wind, air resistance and ball spin. The ball would be on the ground by the time the player makes the necessary estimations and computation. An alternative intuitive strategy (heuristic) is to ‘fix gaze on the ball, start running, and adjust one’s speed so that the angle of gaze remains constant’. The situation of the aircraft flight is of course different, more complex and perilous, but a similar logic seems to hold: navigating the plane in air safely towards the terrain surface (land or water) when there is no time for any advanced computation (the pilot’s gaze would have to be fixed on the terrain beneath towards a prospect landing ‘runway’). Winter winds in New-York City on that frozen day have probably made the landing task even more complicated.  But in those few minutes available to Sully, he found this type of ‘gaze’ or eyesight guiding rule the most practical and helpful.

Relying on Senses: Sullenberger made extensive use of his senses (visual, auditory, olfactory) to collect every information he could get from his surrounding environment. To start with, the pilots could see the birds coming in front of them right before some of them were clashing into the engines — this evidence was crucial to identifying instantly the cause of the problem though they still needed some time to assess the extent of damage. In an interview to CBS’s programme 60 Minutes (with Katie Couric, February 2009), Sully says that he saw the smoke coming out from both engines, smelled the burned flesh of the birds, and subsequently heard a hushing noise from the engines (i.e., made by the remaining blades). He could also feel the trembling of the broken engines. This multi-modal sensory information contributed to convincing him that the engines were lost (i.e., unable to produce thrust) in addition to failure to restart them. Sully also utilised all that time information from the various meters or clocks in the cockpit dashboard in front of him (while Skiles was reading to him from the manuals). The captain was thus attentive to multiple visual stimuli (including and beyond using a visual guidance heuristic) in his decision process, from early judgement to action on his decision to land onto the water of the Hudson river.

Computer algorithms can ‘pick-up’ and process all the technical information of the aircraft displayed to the pilots in the cockpit. The algorithms may also apply in the computations additional measurements (e.g., climate conditions) and perhaps data from sensors installed in the aircraft. But the computer algorithms cannot ‘experience’ the flight event like the pilots. Sully could ‘feel the aircraft’, almost simultaneously and rapidly perceive the sensory stimuli he received in the cockpit, within and outside the cabin, and respond to them (e.g., make judgement). Information available to him seconds after impact with the birds gave him indications about the condition of the engines that algorithms as used in the simulations could not receive. That point was made clear in the dispute that emerged between Sully and the investigating committee with regard to the condition of one of the engines. The investigators claimed that early tests and simulations suggested one of the engines was still functioning and could allow the pilots to bring the plane to land in one of the nearby airports (returning to La Guardia or reverting to Teterboro in New-Jersey). Sully (Hanks) disagreed and argued that his indications were clear that the second engine referred to was badly damaged and non-functional — both engines had no thrust. Sully was proven right — the committee eventually updated that missing parts of the disputed engine were found and showed that the engine was indeed non-functional, disproving the early tests.

Timing and the Human Factor: The captain Sullenberger had furthermore a strong argument with the investigating committee of NTSB about their simulations in attempt to re-construct or replicate the sequence of events during the flight. The committee argued that pilots in a flight simulator ‘virtually’ made a successful landing in both La Guardia and Teterboro airports when the simulator computer was given the data of the flight. Sully (Hanks) found a problem with those live but virtual simulations. The flight simulation was flawed because it made the assumption the pilots could immediately know where it was possible to land, and they were instructed to do so. Sully and Skiles indeed knew immediately the cause of damage but still needed time to assess the extent of damage before Sully could decide how to react. Therefore, they could not actually turn the plane towards one of those airports right after bird impact as the simulating pilots did. The committee ignored the human factor, as argued by Sully, that had required him up to one minute to realise the extent of damage and his decision options.

The conversation of Sully with air controllers demonstrates his assessments step-by-step in real-time that he could not make it to La Guardia or alternatively to Teterboro — both were effectively considered — before concluding that the aircraft may find itself in the water of the Hudson. Then the captain directed the plane straight above the river in approach to crash-landing. One may also note how brief were his response statements to the air controller.  Sully was confident that landing on the Hudson was “the only viable alternative”. He told so in his interview to CBS. In the film, Sully (Hanks) told Skiles (Ackhart) during a recuperating break outside the committee hall that he had no question left in his mind that they have done the right thing.

Given the strong resistance of Sully, the committee ordered additional flight simulations where the pilots were “held” waiting for 35 seconds to account for the time needed to assess the damage before attempting to land anywhere. Following this minimum delay the simulating pilots failed to land safely neither at La Guardia nor at Teterboro. It was evident that those missing seconds were critical to arriving in time to land in those airports. Worse than that, the committee had to admit (as shown in the film) that the pilots made multiple attempts (17) in their simulations before ‘landing’ successfully in those airports. The human factor of evaluation before making a sound decision in this kind of emergency situation must not be ignored.

Delving a little deeper into the event helps to realise how difficult the situation was.  The pilots were trying to execute a three-part checklist of instructions. They were not told, however, that those instructions were made to match a situation of loss of both engines at a much higher altitude than they were at just after completing take-off. The NTSB’s report (AAR-10-03) finds that the dual engine failure at a low altitude was critical — it allowed the pilots too little time to fulfill the existing three-part checklist. In an interview to Newsweek in 2015, Sullenberger said on that challenge: “We were given a three-page checklist to go through, and we only made it through the first page, so I had to intuitively know what to do.”  The NTSB committee further accepts in its report that landing at La Guardia could succeed only if started right after the bird strike, but as explained earlier, that was unrealistic; importantly, they note the realisation made by Sullenberger that an attempt to land at La Guardia “would have been an irrevocable choice, eliminating all other options”.

The NTSB also commends Sullenberger in its report for operating the Auxiliary Power Unit (APU). The captain asked Skiles to try operating the APU after their failed attempt to restart the engines. Sully decided to take this action before they could reach the article on the APU in the checklist. The operation of the APU was most beneficial according to NTSB to allow electricity on board.

Notwithstanding the judgement and decision-making capabilities of Sully, his decision to land on waters of the Hudson river could have ended-up miserably without his experience and skills as a pilot to execute it rightly. He has had 30 years of experience as a commercial pilot in civil aviation since 1980 (with US Airways and its predecessors), and before that had served in the US Air Force in the 1970s as a pilot of military jets (Phantom F-4). The danger in landing on water is that the plane would swindle and not reach in parallel to the water surface, thus one of the wings might hit water, break-up and cause the whole plane to capsize and break-up into the water (as happened in a flight in 1996). That Sully succeeded to safely “ditch” on water surface is not obvious.

The performance of Sullenberger from decision-making to execution seems extraordinary. His judgement and decision capacity in these flight conditions may be exceptional; it is unclear if other pilots could perform as well as he has done. Human judgement is not infallible; it may be subject to biases and errors and succumb to information overload. It is not too difficult to think of examples of people making bad judgements and decisions (e.g., in finance, health etc.). Yet Sully has demonstrated that high capacity of human judgement and sound decision-making exists, and we can be optimistic about that.

It is hard, and not straightforward, to extend conclusions from flying airplanes to other areas of activity. In one aspect, however, there can be some helpful lessons to learn from this episode in thinking more deeply and critically about the replacement of human judgement and decision-making with computer algorithms, machine learning and robotics. Such algorithms work best in familiar and repeated events or situations. But in new and less familiar situations and in less ordinary and more dynamic conditions humans are able to perform more promptly and appropriately. Computer algorithms can often be very helpful but they are not always and necessarily superior to human thinking.

This kind of discussion is needed, for example, in respect to self-driving cars. It is a very active field in industry these days, connecting automakers with technology companies for installing autonomous computer driving systems in cars. Google is planning on creating ‘driverless’ cars without a steering wheel or pedals; their logic is that humans should not be involved anymore in driving: “Requiring a licensed driver be able to take over from the computer actually increases the likelihood of an accident because people aren’t that reliable” (2). This claim is excessive and questionable. We have to carefully distinguish between computer aid to humans and replacing human judgement and decision-making with computer algorithms.

Chesley (Sully) Sullenberger has allowed himself as the flight captain to be guided by his experience, intuition and common sense to land the plane safely and save the lives of all passengers and crew on board. He was wholly focused on “solving this problem” as he told CBS, the task of landing the plane without casualties. He recruited his best personal resources and skills to this task, and in his success he might give everyone hope and strength in belief in human capacity.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “Gut Feelings: The Intelligence of the Unconscious”, Gerd Gigerenzer, 2007, Allen Lane (Pinguin Books).

(2) “Some Assembly Required”, Erin Griffith, Fortune (Europe Edition), 1 July 2016.

 

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From a consumer viewpoint, choice situations should be presented in a clear and comprehensible manner that facilitates consumers’ correct understanding of what is at stake and helps them to choose an alternative that fits most closely their needs or preferences. But policy makers may go farther and design choices to direct the decision-making consumers to a desirable or recommended alternative in their judgement.

It is very likely for Humans (unlike economic persons, or Econs) to be influenced in their decisions by the way a choice problem is presented; even if unintentional — it is almost unavoidable. Sometimes, however, an intervention to influence a decision-maker is done intentionally. Choice architecture relates to how choice problems are presented: the way the problem is organised and structured, and how alternatives are described, including tools or techniques that may be used to guide a decision-maker to a particular choice alternative. Richard Thaler and Cass Sunstein have called such tools ‘nudges’, and the designer of the choice problem is referred to as a ‘choice architect’. In their book, “Nudge: Improving Decisions About Health, Wealth and Happiness” (2009), the researchers were very specific, nonetheless, about the kinds of nudging they support and advocate (1). A nudge may be likened to a light push of a consumer out of his or her ‘comfort zone’ towards a particular choice alternative (e.g., action, product), but it should be harmless and left optional to consumers whether to accept or reject.

Thaler and Sunstein argue that in some cases more action is needed to ‘nudge’ consumers in a right direction. That is because consumers, as Humans, often do not consider carefully enough the choice situation and alternatives, they tend to err, and may not do what would actually be in their own best interest. It may be added that consumers’ preferences may not be well-established, and when these are unstable it could make it furthermore difficult for consumers to find an alternative that fits their preferences more closely. Hence, the authors recommend acting in a careful corrective manner that guides consumers towards an alternative that a policy maker assesses will serve them better (e.g., health-care, savings). Yet they insist that any intervention of nudging should not be imposed on the consumer. They call their approach ‘libertarian paternalism’ — a policy maker may tell consumers what alternative would be right for them but the consumer is eventually left with the freedom of choice how to act. They state that:

To count as a mere nudge, the intervention must be easy and cheap to avoid. Nudges are not mandates. Putting the fruit at eye level counts as a nudge. Banning junk food does not.

Thaler and Sunstein suggest six key principles, or types, of nudges: (a) Defaults; (b) Expect error (i.e., nudges designed to accommodate human error); (c) Give feedback (nudges reliant on social influence may be included here); (d) Understanding ‘mappings’ (i.e., a match between a choice made and its welfare outcome, such as consumption experience); (e) Structure complex choices; (f) Incentives. The authors discuss and propose how to use those tools in dealing with choice issues such as complexity and a status quo bias (inertia) (e.g., applied to student loans, retirement pensions and savings, medication plans).

Let’s look at some examples of how choice architecture may influence consumer choice:

A default may be set-up to determine what happens if a consumer makes no active choice (e.g., ‘too difficult to choose’, ‘too many options’) or to induce the consumer to take a certain action. Defaults can change the significance of opt-in and opt-out choice methods. A basic opt-in could ask a consumer to tick a box if she agrees to participate in a given programme. Now consider a slight change by pre-ticking the box as default — if the consumer does not like to join, she can uncheck the box (opt-out). A more explicit default and opt-out combination could state up-start (e.g., in a heading) that the consumer is automatically enrolled in the programme and if she declines she should send an e-mail to the organiser. If inclusion in a programme is the default, and consumers have to opt-out of the programme, many more will end-up enrolled than if they had to actively approve their participation. Yet the effect may vary depending on the ease of opting-out (just unchecking the box vs. sending a separate e-mail). Defaults of this type may be used for benign purposes such as subscription to a e-newsletter versus sensitive purposes like organ donation (2).

  • A default option is particularly attractive when the ‘alternative’ action is actually choosing from a long list of other alternatives (e.g., mutual and equity funds for investment).

Making a sequence of choice decisions is a recurring purchase activity. As a simple example, suppose you have to construct a list of items that you want to purchase (e.g., songs to compile, books to order) by choosing one item from each of a series of choice sets.  Presenting choice-sets in an increasing order of choice-set size is likely to encourage the chooser to enter a maximising mind-set — starting with a small set, it is easier to examine more closely all options in the set before choosing, and while the set size increases the chooser will continue trying to examine options more exhaustively. When starting with a large choice-set and decreasing the size thereon, the opposite happens where the chooser enters a simplifying or satisficing mind-set. Thus, over choice-sets, the chooser in an increasing order condition is likely to perform a deeper search and examine overall more options. As described by Levav, Reinholtz and Lin, consumers are “sticky adapters” (3). When constructing an investment portfolio, for instance, a financial policy maker may nudge investors to examine more of the funds, bonds and equities available by dividing them into classes to be presented as choice-sets in an increasing order of size (up to a reasonable limit).

Multiple aspects of choice design or architecture arise in the context of mass customization. Taking the case of price, a question arises whether to specify the cost of each level of a customized attribute (actually the price premium for upgraded levels vs. a baseline level) or the total price of the final product designed. A proponent opinion argues that providing detailed price information for levels of quality attributes allows consumers to consider the monetary implications of choosing an upgraded level on each attribute. It is not as difficult as trying to extract the marginal cost of a level chosen on each quality attribute from the total price. Including prices for levels of quality attributes leads consumers to choose more frequently intermediate attribute levels (compared with a by-alternative choice-set)(4). A counter opinion posits that carefully weighing price information on each attribute is not so easy (consumers report higher subjective difficulty), actually causing consumers to be too cautious and configure products that are less expensive but also of lower quality. Hence, providing a total price for the outcome product could be sufficient and more useful for the customers (5). It is hard to give any conclusive design suggestion in this case.

In a last example, the form in which calorie information is provided on restaurant menus matters no less than posting it. As a recent research by Parker and Lehmann shows, it is practically possible to be over-doing it (6). Consistent with other studies, the researchers find that when posting calorie figures next to food dishes, consumers choose from the calorie-posted menu items with lower calorie content on average than from a similar traditional menu but with no calorie figures. Separating low-calorie items from their original categories of food type (e.g., salads, burgers) into a new group, as some restaurants do, may eliminate, however, the advantage of calorie-posting. While the logic of a separate group is that it would make the group more conspicuous and easier for diners to attend to it, it could make it easier for them instead to exclude those items from consideration. Nevertheless, some qualification is needed as the title given to the group also matters.

Parker and Lehmann show that organising the low-calorie items in a separate group explicitly titled as such (e.g., “Low Calories”, “Under 600 Calories”) attenuates the posting effect, thus eliminating the advantage of inducing consumers to order lower-calorie items. The title is important because it is easier this way for consumers to screen out this category from consideration (e.g., as unappealing on face of it). It is demonstrated that giving a positive name unrelated to calories (e.g., “Eddie’s Favourites”, “Fresh and Fit”) would generate less rejection and make it no more likely to be screened out as a group than other categories. In a menu that is just calorie-posted, consumers are more likely to trade-off the calories with other information on a food item such as its composition and price. But if the consumers are helped to screen the low-calorie group as a measure of simplifying their decision process in an early stage, it means they would also ignore their calorie details.

  • An additional explanation can be suggested for disregarding the low-calorie items when grouped together: If those items are mixed in categories of other items similar to them in type of food, each item would stand-out as ‘low calorie’ and be perceived as different and more important. If the low-calorie items are aggregated on the other hand in a set-aside group, they are more likely to be perceived as of diminished importance or appeal collectively and be ignored together. (cf. [7]). Therefore, creating a separate group of varied items pulled out from all the other groups sends a wrong message to consumers and may nudge them in the wrong direction.

Both public and private policy makers can use nudging. But there are some limitations deserving attention especially with regard to private (business) policy makers. Companies sometimes act out of belief that in order to recruit customers they should present complex alternative plans (e.g., mobile telecoms, insurance, bank loans), which includes obscuring vital details and making comparisons between alternatives very difficult. They see nudging tools that are meant to reduce complexity of consumer choice as playing against their interest (e.g., if choice is complex it will be easier for the company to capture [trap-in] the customer). That counters the intention of Thaler and Sunstein, and they stand against this kind of practice.

In the case of helping customers to see more clearly the relation, and match, between their patterns of service usage and the cost they are required to pay, Thaler and Sunstein propose a nudge scheme called RECAP — Record, Evaluate, and Compare Alternative Prices. The scheme entails publishing in readily accessible channels (e.g., websites) full details of their service and price plans as well as provide existing customers periodic reports that show how their level of usage on each component of service contributes to total cost. These measures that increase transparency would help customers understand what they pay for, monitor and control their costs, and reconsider from time to time their current service plan vis-à-vis alternative plans of the same provider and those of competitors. The problem is that service providers are usually reluctant to hand over such detailed information from their own good will. Public regulators may have to require companies to create a RECAP scheme, or perhaps nudge them to do so.

In the lighter scenario, companies prefer to avoid nudging techniques that work in the benefit of consumers because of concern it would hurt their own interests. In the worse scenario, companies misinterpret nudging and use tools that actively manipulate consumers to choose not in their benefit (e.g., highlight a more expensive product the consumer does not really need). Thaler and Sunstein are critical of either public or private (business) policy makers who conceive and apply nudges in their own self-interest. They tend to dedicate more effort, however, to counter objections to government intervention in consumers’ affairs and popular suspicions of malpractice by branches of the government (i.e., these issues seem to be of major concern in the United States that may not be fully understood in other countries). Of course it is important not turn a blind eye to harmful usage of nudges by public as well as private choice architects.

There are many opportunities in cleverly using nudging tools to guide and assist consumers. Yet there can be a thin line between interventions of imposed choice and free choice or between obtrusive and libertarian paternalism. Designing and implementing nudging tools can therefore be a delicate craft, advisably a matter primarily for expert choice architects.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “Nudge: Improving Decisions About Health, Wealth and Happiness”; Richard H. Thaler and Cass R. Sunstein, 2009; Penguin Books (updated edition).

(2) Ibid 1, and: “Beyond Nudges: Tools of Choice Architecture”; Eric J. Johnson and others, 2012; Marketing Letters, 23, pp. 487-504.

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

(4) “Contingent Response to Self-Customized Procedures: Implications for Decision Satisfaction and Choice”; Ana Valenzuela, Ravi Dahr, & Florian Zettelmeyer, 2009; Journal of Marketing Research, 46 (December), pp. 754-763.

(5) “Marketing Mass-Customized Products: Striking a Balance Between Utility and Complexity”; Benedict G.C. Dellaert and Stefan Stremersch, 2005; Journal of Marketing Research, 42 (May), pp. 219-227.

(6) “How and When Grouping Low-Calorie Options Reduces the Benefits of Providing Dish-Specific Calorie Information”; Jeffrey R. Parker and Donald R. Lehmann, 2014; Journal of Consumer Research, 41 (June), pp. 213-235.

(7) Johnson et al. (see #2).

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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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