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Posts Tagged ‘Information Processing’

Human thinking processes are rich and variable, whether in search, problem solving, learning, perceiving and recognizing stimuli, or decision-making. But people are subject to limitations on the complexity of their computations and especially the capacity of their ‘working’ (short-term) memory. As consumers, they frequently need to struggle with large amounts of information on numerous brands, products or services with varying characteristics, available from a variety of retailers and e-tailers, stretching the consumers’ cognitive abilities and patience. Wait no longer, a new class of increasingly intelligent decision aids is being put forward to consumers by the evolving field of Cognitive Computing. Computer-based ‘smart agents’ will get smarter, yet most importantly, they would be more human-like in their thinking.

Cognitive computing is set to upgrade human decision-making, consumers’ in particular. Following IBM, a leader in this field, cognitive computing is built on methods of Artificial Intelligence (AI) yet intends to take this field a leap forward by making it “feel” less artificial and more similar to human cognition. That is, a human-computer interaction will feel more natural and fluent if the thinking processes of the computer resemble more closely those of its human users (e.g., manager, service representative, consumer). Dr. John E. Kelly, SVP at IBM Research, provides the following definition in his white paper introducing the topic (“Computer, Cognition, and the Future of Knowing”): “Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans. Rather than been explicitly programmed, they learn and reason from interactions with us and from their experiences with their environment.” The paper seeks to rebuke claims of any intention behind cognitive computing to replace human thinking and decisions. The motivation, as suggested by Kelly, is to augment human ability to understand and act upon the complex systems of our society.

Understanding natural language has been for a long time a human cognitive competence that computers could not imitate. However, comprehension of natural language, in text or speech, is now considered one of the important abilities of cognitive computing systems. Another important ability concerns the recognition of visual images and objects embedded in them (e.g., face recognition receives particular attention). Furthermore, cognitive computing systems are able to process and analyse unstructured data which constitutes 80% of the world’s data, according to IBM. They can extract contextual meaning so as to make sense of the unstructured data (verbal and visual). This is a marked difference between the new computers’ cognitive systems and traditional information systems.

  • The Cognitive Computing Forum, which organises conferences in this area, lists a dozen characteristics integral to those systems. In addition to (a) natural language processing; and (b) vision-based sensing and image recognition, they are likely to include machine learning, neural networks, algorithms that learn and adapt, semantic understanding, reasoning and decision automation, sophisticated pattern recognition, and more (note that there is an overlap between some of the methodologies on this list). They also need to exhibit common sense.

The power of cognitive computing is derived from its combination between cognitive processes attributed to the human brain (e.g., learning, reasoning) and the enhanced computation (complexity, speed) and memory capabilities of advanced computer technologies. In terms of intelligence, it is acknowledged that cognitive processes of the human brain are superior to computers inasmuch as could be achieved through conventional programming. Yet, the actual performance of human cognition (‘rationality’) is bounded by memory and computation limitations. Hence, we can employ cognitive computing systems that are capable of handling much larger amounts of information than humans can, while using cognitive (‘neural’) processes similar to humans’. Kelly posits in IBM’s paper: “The true potential of the Cognitive Era will be realized by combining the data analytics and statistical reasoning of machines with uniquely human qualities, such as self-directed goals, common sense and ethical values.”  It is not sufficiently understood yet how cognitive processes physically occur in the human central nervous system. But, it is argued, there is growing knowledge and understanding of their operation or neural function to be sufficient for emulating at least some of them by computers. (This argument refers to the concept of different levels of analysis that may and should prevail simultaneously.)

The distinguished scholar Herbert A. Simon studied thinking processes from the perspective of information processing theory, which he championed. In the research he and his colleagues conducted, he traced and described in a formalised manner strategies and rules that people utilise to perform different cognitive tasks, especially solving problems (e.g., his comprehensive work with Allen Newell on Human Problem Solving, 1972). In his theory, any strategy or rule specified — from more elaborate optimizing algorithms to short-cut rules (heuristics) — is composed of elementary information processes (e.g., add, subtract, compare, substitute). On the other hand, strategies may be joined in higher-level compound information processes. Strategy specifications were subsequently translated into computer programmes for simulation and testing.

The main objective of Simon was to gain better understanding of human thinking and the cognitive processes involved therein. He proclaimed that computer thinking is programmed in order to simulate human thinking, as part of an investigation aimed at understanding the latter (1). Thus, Simon did not explicitly aim to overcome the limitations of the human brain but rather simulate how the brain may work-out around those limitations to perform various tasks. His approach, followed by other researchers, was based on recording how people perform given tasks, and testing for efficacy of the process models through computer simulations. This course of research is different from the goals of novel cognitive computing.

  • We may identify multiple levels in research on cognition: an information processing level (‘mental’), a neural-functional level, and a neurophysiological level (i.e., how elements of thought emerge and take form in the brain). Moreover, researchers aim to obtain a comprehensive picture of brain structures and areas responsible for sensory, cognitive, emotional and motor phenomena, and how they inter-relate. Progress is made by incorporating methods and approaches of the neurosciences side-by-side with those of cognitive psychology and experimental psychology to establish coherent and valid links between those levels.

Simon created explicit programmes of the steps required to solve particular types of problems, though he aimed at developing also more generalised programmes that would be able to handle broader categories of problems (e.g., the General Problem Solver embodying the Means-End heuristic) and other cognitive tasks (e.g., pattern detection, rule induction) that may also be applied in problem solving. Yet, cognitive computing seeks to reach beyond explicit programming and construct guidelines for far more generalised processes that can learn and adapt to data, and handle broader families of tasks and contexts. If necessary, computers would generate their own instructions or rules for performing a task. In problem solving, computers are taught not merely how to solve a problem but how to look for a solution.

While cognitive computing can employ greater memory and computation resources than naturally available to humans, it is not truly attempted to create a fully rational system. The computer cognitive system should retain some properties of bounded rationality if only to maintain resemblance to the original human cognitive system. First, forming and selecting heuristics is an integral property of human intelligence. Second, cognitive computing systems try to exhibit common sense, which may not be entirely rational (i.e., based on good instincts and experience), and introduce effects of emotions and ethical or moral values that may alter or interfere with rational cognitive processes. Third, cognitive computing systems are allowed to err:

  • As Kelly explains in IBM’s paper, cognitive systems are probabilistic, meaning that they have the power to adapt and interpret the complexity and unpredictability of unstructured data, yet they do not “know” the answer and therefore may make mistakes in assigning the correct meaning to data and queries (e.g., IBM’s Watson misjudged a clue in the quiz game Jeopardy against two human contestants — nonetheless “he” won the competition). To reflect this characteristic, “the cognitive system assigns a confidence level to each potential insight or answer”.

Applications of cognitive computing are gradually growing in number (e.g., experimental projects with the cooperation and support of IBM on Watson). They may not be targeted directly for use by consumers at this stage, but consumers are seen as the end-beneficiaries. The users could first be professionals and service agents who help consumers in different areas. For example, applied systems in development and trial would:

  1. help medical doctors in identifying (cancer) diagnoses and advising their patients on treatment options (it is projected that such a system will “take part” in doctor-patient consultations);
  2. perform sophisticated analyses of financial markets and their instruments in real-time to guide financial advisers with investment recommendations to their clients;
  3. assist account managers or service representatives to locate and extract relevant information from a company’s knowledge base to advise a customer in a short time (CRM/customer support).

The health-advisory platform WellCafé by Welltok provides an example of application aimed at consumers: The platform guides consumers on healthy behaviours recommended for them whereby the new assistant Concierge lets them converse in natural language to get help on resources and programmes personally relevant to them as well as various health-related topics (e.g., dining options). (2)

Consider domains such as cars, tourism (vacation resorts), or real-estate (second-hand apartments and houses). Consumers may encounter tremendous information in these domains on numerous options and many attributes to consider (for cars there may also be technical detail more difficult to digest). A cognitive system has to help the consumer in studying the market environment (e.g., organising the information from sources such as company websites and professional and peer reviews [social media], detecting patterns in structured and unstructured data, screening and sorting) and learning vis-à-vis the consumer’s preferences and habits in order to prioritize and construct personally fitting recommendations. Additionally, it is noteworthy that in any of these domains visual information (e.g., photographs) could be most relevant and valuable to consumers in their decision process — visual appeal of car models, mountain or seaside holiday resorts, and apartments cannot be discarded. Cognitive computing assistants may raise very high consumer expectations.

Cognitive computing aims to mimic human cognitive processes that would be performed by intelligent computers with enhanced resources on behalf of humans. The application of capabilities of such a system would facilitate consumers or the professionals and agents that help them with decisions and other tasks — saving them time and effort (sometimes frustration), providing them well-organised information with customised recommendations for action that users would feel they  have reached themselves. Time and experience will tell how comfortably people interact and engage with the human-like intelligent assistants and how productive they indeed find them, using the cognitive assistant as the most natural thing to do.

Ron Ventura, Ph.D. (Marketing)

Notes:

1.  “Thinking by Computers”, Herbert A. Simon, 1966/2008, reprinted in Economics, Bounded Rationality and the Cognitive Revolution, Massimo Egidi and Robin Marris (eds.)[pp. 55-75], Edward Elgar.

2. The examples given above are described in IBM’s white paper by Kelly and in: “Cognitive Computing: Real-World Applications for an Emerging Technology”, Judit Lamont (Ph.D.), 1 Sept. 2015, KMWorld.com

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Consumers often use price information as a cue to infer the quality of products — it is a familiar phenomenon based on the belief that price and quality are positively correlated. Consider for instance  laptop computers: consumers may rely on price to predict the quality of a laptop model for which there is lack of information about attributes that determine its quality, or rather because they have a difficulty to understand the technical features and try to infer the laptop’s expected quality based on its (list) price. Wine is another excellent example for a product whose quality consumers try to assess based on its price. The perceived price-quality relation is not always well-substantiated, which may lead to some costly mistakes. Reliance on price to judge quality is contingent on individual, contextual (e.g., product type) and situational factors.

Consumers may rely on price as an informational cue for different purposes: (a) to reduce the risk of buying a product of an unacceptable low quality; (b) avoid or mitigate effort of evaluating complex product information; (c) anticipate differences in quality between product brands and models (but sometimes also their symbolic meanings associated with prestige and luxury). Price-quality judgements involve two essential steps: estimating the strength of a relationship between price and quality in a focal product category, and applying this judgement to predict the quality of a particular product item (e.g., a new product model). Consumers may differ in their proficiency both to assess the relationship and applying it in various every-day situations.

The magnitude of price-quality correlations varies between product categories, and most consumers are aware of it. However, their calibration of the price-quality relationship for particular product types is often flawed and consumers over-estimate the correlations. Consumers tend to follow a general belief about price-quality relation without properly testing it as a hypothesis in the product category under consideration for purchase; alternately they bias their judgement by considering only evidence consistent with the prior belief (e.g., as the load of information to process is larger and harder to grapple with, and when information is organised in a format that highlights price-quality correlation [1]). Consumers also differ in the first place in their propensity to hold a price-quality belief (i.e., how strongly are consumers price-quality schematic). Capturing the actual reliance on price as a quality cue may also turn to be elusive because applying such a rule depends on the amount and nature of product information available.

In a research recently published (2013) Lalwani and Shavitt study how consumer propensity to perceive a price-quality relationship is governed or moderated by thinking styles and modes of self-construal exerted from consumers’ relations with others in their groups of membership. They distinguish between (1) independents (individualists) who prefer to form their opinions and set personal goals on their own, in hope those will be accepted by their in-group peers but not to be censored by the latter, and (2) interdependents (collectivists) who are inclined to form opinions and set goals that are subordinated to those of the in-group to which they belong. They refer to cultural self-construal by acknowledging that independence has been associated more closely with Western nations or Caucasian societies and interdependence with South and East Asian nations or societies. The distinction is primarily relevant to the construction of price-quality judgements by its correspondence with analytic vs. holistic styles of thinking, respectively. The authors additionally examine specific conditions that may enhance or inhibit the use of price to infer quality.

Analytic thinking orientates to process and evaluate a single piece of information at a time — for example, examine a value for a product item on a specific attribute. The ‘analytic’ consumer may compare between a few models on a specific attribute but ignore any other attributes. In a pictorial image, analytic thinking implies that the individual would look at each object in the image separately rather than inspecting a collection of elements in a scene. Holistic thinking, on the other hand, orientates to observe and evaluate relations between attributes and objects. It is much less focused on single items of information in favour of considering collections of them and how they relate to each other. In a pictorial image, holistic thinking means that an individual more easily identifies combinations of elements and conceives inter-relations between them in the whole scene. The argument put forward, and tested, by Lalwani and Shavitt posits that interdependents (collectivists) who are reliant on their social connections, and who are more considerate of the needs and goals of others in their in-groups before their own, are more predisposed to apply holistic thinking; independents (individualists) who tend to focus on their single-self’s needs and goals before others are more inclined to adopt an analytic style of thinking. Holistic thinking that endorses relational processing is clearly essential for making judgements about a price-quality relationship. The authors are particularly concerned with the boundary conditions under which the advantage of holistic thinking in making price-quality judgements has an impact.

Lalwani and Shavitt take notice that independent and interdependent modes of self-construal are not exclusive of each other, that is, they may be exhibited simultaneously in the same person or within a particular society. Therefore, following previous research, the authors apply two scales, one to measure independence and the other for interdependence as opposed to treating these modes as polar ends of the same continuum. They find that a stronger tendency to perceive a price-quality relationship (a global belief) is predicted by greater inclination for interdependent self-construal. No similar relation is found with independent self-construal. This confirms that only interdependent self-construal may support consumer tendency to rely on a price-quality relationship. [2]

Asians and Hispanic (in the US), representing interdependent self-construals, have been found to utilise price to infer the quality of a “new” target product item (alarm clock) whereas Caucasians (independents) showed no significant sensitivity to differences in price for the target product. It is emphasised that the Asians/Hispanics participants not just considered price-quality information available on “base” items but also practically used price in its evaluation of quality for the target item.

The difference in type of self-construal does not clarify sufficiently how this should lead to differences in approach to the perceived price-quality relationship. That is where the difference between holistic and analytic thinking takes its role. If we look only at the distinction between American nationals and Indian nationals, it would be relatively difficult to understand why the Indians have been found to exhibit a stronger tendency to rely on price as a quality cue. This difference is partially explained (mediated) once the researchers account for a difference in tendency to think holistically — the Indians also have a stronger tendency for that type of thinking that better supports processing of relations between price and quality.

Even more convincing are the results from a study in which an exercise with a pictorial image was conducted to encourage (prime) analytic versus holistic thinking by participants (American Asians/Hispanic vs. Caucasians). As expected, holistic thinking facilitated reliance on price when evaluating the quality of a “new” target product item (calculator) for both Asians/Hispanic and Caucasians. That is, they evaluated the higher priced target brand to be of higher quality than a lower priced brand. Nonetheless, the Asian/Hispanic who are more likely to be ‘interdependent’ differentiated even more strongly the quality between higher- and lower-priced target brands — revealing their advantage for relational processing. In contrast, when both Asians/Hispanic and Caucasians are primed to think analytically, none of them seems to use price as a quality cue. This highlights the power of holistic thinking for making price-quality judgements; vice versa, “imposing” analytic thinking on those who have a stronger tendency for holistic thinking seems to over-ride their advantage in predicting quality based on price.

Lalwani and Shavitt point-out that an advantage for relational processing in using price as a quality cue takes effect in kind of intermediate conditions: when there is a logical basis and supportive evidence (e.g., market conditions, product information available) for relying on price to infer quality, yet neither when conditions are poor/prohibitive nor when evidence of a price-quality relationship is just obvious and applying it is fairly easy. This is demonstrated in two cases: (a) an advantage for relational processing with regard to non-symbolic, functional or practical products (e.g., paper towels) vs. symbolic products that are better able to express one’s identity (e.g., watches, bicycle) — the latter product type induces a price-quality tendency in both ‘independents’ and ‘interdependents’; (b) an advantage for relational processing when information is provided on (non-price) attributes of moderate bandwidth (e.g., quality, durability, reliability), not for broad, generalised evaluations/attitudes (everybody uses price) and not narrow, specific features (nobody uses price). When conditions are sufficient but not too permissive, only those who have the advantage will discriminate products on perceived quality according to price.

The distinction between independent and interdependent self-contrual is somewhat circumstantial with respect to the utilisation of price as a quality cue. It does not immediately make sense why the two behavioural phenomena should be related. References to national and ethnic origins may also be too liberal generalisations that do not contribute enough to our understanding except for exposing the relationship. At the bottom of a distinction between modes of self-construal regarding price-quality judgement underlies the important distinction between holistic and analytic thinking. Lalwani and Shavitt effectively suggest that the extent to which people think in terms of relations between objects or their attributes corresponds with their attitude towards relations with other people, and hence the latter’s connection with the relationship between price and perceived quality. The distinction between thinking styles therefore seems to shed more light on conditions that induce or limit reliance on price as a quality cue.

Yet, establishing a connection between self-construal. particularly represented by national or ethnic (sociocultural) origins, and reliance on price as a quality cue, can be most productive and helpful for segmentation — it facilitates the identification of and access to relevant segments for marketing initiatives associated with the price-perceived quality relationship. The implications may be in devising advertising messages or premium product offering that target consumers with expected greater tendency to make price-quality inferences.  Consequently those consumers would likely be more favourable towards and receptive of higher-priced products/brands. This research further contributes to previous knowledge in the field by suggesting conditions under which most consumers or only selective segments would be evoked to make price-quality judgements. Marketers may consider the breadth of attributes described (broader dimensions vs. features) in addition to the structure of information presented to consumers [e.g., rank-order products by quality vs. random order, [3]).

Source:

You Get What You Pay For? Self-Construal Influences Price-Quality Judgements; Ashok K. Lalwani and Sharon Shavitt, 2013; Journal of Consumer Research, 40 (August), pp. 255-267, DOI:
10.1086/670034

Notes:

[1] A Selective Hypothesis Testing Perspective on Price-Quality Inference and Inference-Based Choice; Maria L. Cronley, Steven S. Posavac, Tracy Meyer, Frank R. Kardes, & James J. Kellaris, 2005; Journal of Consumer Psychology, 15 (2), pp. 159-169

[2]  Statistical Note: The validity of the results of multiple regression analysis performed is contingent on the two scales of individualism-independence and collectivism-interdependence not being negatively correlated. Such evidence is not reported. Turning to the source (Oyserman, 1993) reveals, as logically expected, that some of the statements are in contradiction between the pair of scales. In this case, the version of scales adopted by the authors suggests less conflict and the correlation between them is near zero. On the one hand, it is a little surprising that not even a low negative correlation was found to indicate the contrast between these constructs. On the other hand, a strong negative correlation between the scales could mean that only the stronger predictor, ‘interdependence’, won over the other confounded predictor and thus came out as the single significant predictor.

[3] Ibid. 1.

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It is an ever lasting quest of advertisers to find the content, format and style that will draw more consumer attention to their ads, and subsequently elicit a positive response to the ads and their target brands. Consumers would have to focus on the ad long enough to capture some critical elements (e.g., visual or textual, informational and affective) so as to grasp a key message from the ad. With a print ad, often just a few seconds should be enough but on some ads it may take a minute or two to properly comprehend the ad and make sensible inferences. For video clip ads, on TV or the Internet, the consumer may ponder on the ad for no longer than its duration (e.g., 20-40 seconds), yet sometimes he or she may elaborate or relate to the ad for a few more minutes afterwards (e.g., particularly for humourous ads with a punch). It is a puzzle never really and fully solved, among other reasons because there is no single “secret solution” to this puzzle, and even the best solution for the same brand and audience can change over time and across situations.

There is a growing propensity among advertising professionals to claim that marketers should not expect consumers to think too much on an ad, that an ad should include minimum product information and instead concentrate more on gaining a pleasant emotional reaction. The problem of low involvement when consumers encounter ads, particularly during commercial breaks on TV, is a topic widely and extensively researched. Yet advertisers should not use this challenge as an excuse to produce simplistic ads of little informative value. There are enough occasions where it is suitable or even desirable to create more intriguing and thought-provoking ads. Ads that emphasise graphic elements in their design can be either gross and superficial or imaginative and clever. Advertisers should not shy from turning consumers to utilise the central route of processing product-relevant information contained in their ads (1). But then ads may induce consumers to think a little further, beyond a typical “central”, analytical processing of an ad to decode its message; these are cases where thinking may be accompanied by positive emotions like enjoyment and amusement. When catching the clever punch in a humourous ad, the consumer is entertained by both feelings of fun and the gratification that “I got it”.

On one hand, a print ad may include an impressive photographic image, complete with detail and colours at high-resolution (e.g., visualise a photo-scenery in National Geographic quality) that make them imagine themselves “jump-into the scene”. This approach may be suitable, for example, in the area of travel and tourism when advertising a vacation resort. Perception of highly vivid images is likely to interfere with voluntary mental imagery by consumers-viewers, based on their own ideas and experiences; but the picture-image can inspire the viewer to “experience” the scene-imagery as proposed by the advertiser (2). On the other hand, an ad may mask or omit in its composition certain visual elements, letting the consumer-viewer complete the image (e.g., following rules of Gestalt), and thereby arrive via this additional contemplation more independently to the main message of the ad. Such ads are engaging consumers by stimulating them to work-out the whole ad-scene; it has some risk, but when the viewer makes the extra effort to get the message, it is a rewarding experience.

More sophisticated and artful methods for creating intriguing ads use visual rhetorical figures such as rhymes (schemes) and metaphors (tropes). Visual figures, however, are still less frequent than verbal figures. Meaningful visual metaphors are particularly more difficult to construct (e.g., a package of tablets against a feeling of nausea is placed instead of the buckle in a car seatbelt). McQuarrie and Mick have shown that ads with visual figures are perceived more artful and clever than respective control “regular” ads, evoking more elaboration by being more vivid, interesting and provoking to viewers. They also induce greater pleasure in seeing the ad, implying a more positive attitude towards the ad. Moreover, these effects are stronger for ads that include a metaphor or pun than a scheme. The problem is that these ads are generally more difficult to comprehend, hence the risk in using this creative approach. The balance between pleasure and difficulty is very important — a visual metaphor, for instance, can create pleasure when it is intriguing at first sight and is interesting to resolve, yet it should not be too difficult to comprehend, confusing or ambiguous, lest it may cause frustration and fail to persuade (3). The visual figure intrigues viewers to “think into it” to imply its meaning (“implicature”); when the figure is too difficult to interpret, viewers are likely to imply more original but irrelevant meanings (4). Hence, the designer should keep in mind that while a visual rhetoric figure like a metaphor has to present a challenge, it must not be too sophisticated to allow the viewers to resolve it successfully.

Another perspective on the effort consumers have to invest in processing advertising information observes the difference between presenting product information as a list of attributes or conveyed in a “story”. Nielsen and Escalas suggest that making the information in the ad more difficult to process can have inverse effects on brand preferences or attitudes depending on how information is conveyed, having a negative effect when consumers process a list of attributes in an analytic mode versus a positive effect when consumers read a “brand story” in a narrative mode. Preference fluency defines the ease at which consumers are able to construct their preference for a brand. When consumers encounter a difficulty in reading or interpreting information relating to a brand, thus lowering preference fluency, they are more likely to conclude that something is wrong with that option and decline it. The researchers argue and demonstrate that while this consequence holds in the case of analytic processing, a different process happens when engaged in a narrative mode: the decreased fluency induces the consumers-viewers to get more immersed into the story, possibly by developing their own imagery around the base-story in search of meaning (a phenomenon known as “narrative transportation”), leading to stronger preference or a more positive brand evaluation (5).

In a series of three experiments, Nielsen and Escalas reveal some interesting differences between the two modes of processing information in ads. They show that making the information more difficult to perceive (e.g., using small vs. large font) in a list of attributes results in lower brand evaluation (consistent with previous research) but in a storyboard the result is a higher brand evaluation, as hypothesised. However, an instruction to participants to be critical and skeptical about the ad, directing them to analytic processing of a storyboard that should have involved narrative processing, a small font indeed produces a negative effect on their brand evaluations. The researchers also substantiate in two experiments (in two different product categories) the role of narrative transportation: when displaying a story, greater processing (reading) difficulty has a positive effect on brand evaluation but that is obtained by first evoking narrative transportation, and then narrative transportation positively effects the brand evaluation. This research thereof demonstrates how driving consumers to invest more cognitive effort in comprehending a story can benefit the target brand in the advertising.

There is also a basis for criticism of the research of Nielsen and Escalas. I wish to point out two weaknesses.

  • First, the authors focus on factors that influence the ease or difficulty of perceiving the ad (i.e., its perceptual fluency), viewing the ad image and reading text. They do not treat in their experiments semantic aspects of the ad, that is how well attributes are described or how clearly a story is told, its meaningfulness and associations it elicits in consumers (i.e., conceptual fluency). Is the presentation of text in small font the true motivation to increase effort by narrative transportation?  The research is lacking in that respect.
  • Second, the storyboard composed of a sequence of image-frames with captions and the single image of an ad with a list of product attributes do not match as parallels of the same ad format (video vs. print ad, respectively). The storyboard is not the natural way in which consumers view video-audio ads and process their “story”. Alternatively, an attribute-based style should have been contrasted with other configurations that convey a story but are compatible with the print format; for example, providing the same attribute information in a rich paragraph told in the frame of a story or a combination of image and text-paragraph.

Different predication prevail with regard to the occurrence of mental imagery and the type of processing it follows. Nielsen and Escalas explain that their display of product attributes should give rise to analytic processing. However, it has been argued that a single product profile described by concrete words is more likely to be conceived in a holistic manner, possibly in the form of mental image. On the other hand, a comparative ad with two adjunct product profiles encourages an analytic by-attribute type of processing. Rich verbal descriptions with concrete words,  pictures, and explicit instructions to imagine or visualise are recognized as effective techniques for eliciting mental imagery. In many cases a combination between them is the most productive strategy (e.g., joining a picture with concrete words, instructions accompanied by concrete words) (6). It may be noted that techniques applied in the ad design that are capable of eliciting imagery fit with the expectation of imagery during narrative transportation.

The research in this field is interesting and offers many insights on the possibilities and opportunities for creating more clever, intriguing and imaginative advertising. It has to appeal not only to advertising professionals in its creativity and sophistication but also to the consumers, capturing and driving them willingly to invest the extra cognitive effort. Yet, due to the importance of striking a right balance between difficulty of comprehension and pleasure, and the greater effort required to design successful ads, advertisers and advertising professionals often remain unconvinced that pursuing this course is cost-effective. They need more convincing empirical evidence that producing advertising that makes consumers think harder — but not too hard — can deliver the desired reactions and rewards.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) In reference to the Elaboration Likelihood Model: “Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement”, Petty, R.E., Cacioppo, J.T., & Schumann, D., 1983, Journal of Consumer Research, 10 (Sept.), pp. 135-146.

(2) “Brain Areas Underlying Visual Mental Imagery and Visual Perception: an fMRI Study”, Ganis, G., Thompson, W.L., & Kosslyn, S.M., 2004, Cognitive Brain Research, 20, pp. 226-241; “The Role of Imagery Instructions in Facilitating Persuasion in a Consumer Context”, Mani, G. & MacInnis, D.J., 2003, in Persuasive Imagery: A Consumer Response Perspective, Scott, L.M. & Batra, R. (eds.)(pp. 175-187), NJ: Lawrence Erlbaum Associates.

(3) “Visual Rhetoric in Advertising: Text-Interpretive, Experimental, and Reader-Response Analyses”, McQuarrie, E.F. & Mick, D.G., 1999, Journal of Consumer Research, 26 (June), pp. 37-54; also see their other article “The Contribution of Semiotic and Rhetorical Perspectives to the Explanation of Visual Persuasion in Advertising” in Persuasive Imagery: A Consumer Response Perspective (ibid. 2)(pp. 192-221).

(4) “Thinking Into It: Consumer Interpretation of Complex Advertising Images”, Philips B.J., 1997, Journal of Advertising, 26 (2), pp. 77-87.

(5) “Easier Is Not Always Better: The Moderating Role of Processing Type on Preference Fluency”, Nielsen, J.P. & Escalas, J.E., 2010, Journal of Consumer psychology, 20, pp. 295-305. (Available on the website of eLab at Vanderbilt University: http://elab.vanderbilt.edu/research_papers.htm)

(6) “The Role of Imagery in Information Processing: Review and Extensions, MacInnis, D.J. & Price, L.L., 1987, Journal of Consumer Research, 15 (March), pp. 473-491; “The Role of Imagery Instructions in Facilitating Persuasion in a Consumer Context” (ibid. 2); “The Effects of Information Processing Mode on Consumers’ Response to Comparative Advertising”, Thompson, D.V. & Hamilton, R.W., 2006, Journal of Consumer Research, 32 (March), pp. 530-540. (For more background on decision processes consult also the work of Payne, Bettman and Johnson on the constructive approach).

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