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Poja Shams what does it take to get  your attention

Poja Shams what does it take to get your attention



Decision making for fast-moving consumer goods involves a choice between numerous similar alternatives. Under such demanding circumstances, a decision is made for one product. The decision is ...

Decision making for fast-moving consumer goods involves a choice between numerous similar alternatives. Under such demanding circumstances, a decision is made for one product. The decision is dependent on the interaction between the environment and the mind of the consumer, both of which are filled with information that can influence the outcome. The aim of this dissertation is to explore how the mind and the environment guides attention towards considered and chosen products in consumer decision making at the point-of-purchase.



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    Poja Shams what does it take to get  your attention Poja Shams what does it take to get your attention Document Transcript

    • What Does it Take to Getyour Attention?The Influence of In-Store and Out-of-Store Factors on VisualAttention and Decision Making for Fast-Moving Consumer GoodsPoja ShamsDISSERTATION | Karlstad University Studies | 2013:5Business AdministrationFaculty of Arts and Social Sciences
    • DISSERTATION | Karlstad University Studies | 2013:5What Does it Take to Getyour Attention?The Influence of In-Store and Out-of-Store Factors on VisualAttention and Decision Making for Fast-Moving Consumer GoodsPoja Shams
    • Distribution:Karlstad UniversityFaculty of Arts and Social SciencesService Research CenterSE-651 88 Karlstad, Sweden+46 54 700 10 00©The authorISBN 978-91-7063-479-6Print: Universitetstryckeriet, Karlstad 2013ISSN 1403-8099Karlstad University Studies | 2013:5DISSERTATIONPoja ShamsWhat Does it Take to Get your Attention? - The Influence of In-Store and Out-of-StoreFactors on Visual Attention and Decision Making for Fast-Moving Consumer GoodsWWW.KAU.SE
    • “The eye sees only what the mindis prepared to comprehend”- Henri Bergson (1859–1941)
    • AbstractDecision making for fast-moving consumer goods involves a choice betweennumerous similar alternatives. Under such demanding circumstances, a decisionis made for one product. The decision is dependent on the interaction betweenthe environment and the mind of the consumer, both of which are filled withinformation that can influence the outcome. The aim of this dissertation is toexplore how the mind and the environment guide attention towards consideredand chosen products in consumer decision making at the point-of-purchase.Consumers are equipped with several effort reduction strategies to simplifycomplex decision making. The selection of strategies can be conscious orautomatic and driven by information in the environment or the mind of thedecision maker. The selected decision strategy reduces the set of options to onealternative in an iterative process of comparisons that are fast and rely onperceptual cues to quickly exclude irrelevant products. This thesis uses eye-tracking to explore this rapid processing that lacks conscious access or control.The purpose is to explore how product packaging and placement (as in-storefactors), and recognition, preferences, and choice task (as out-of-store factors)influence the decision-making process through visual attention.The results of the 10 experiments in the five papers that comprise this thesisshed new light on the role of visual attention in the interaction between theenvironment and the mind, and its influence on the consumer. It is said thatconsumers choose with their eyes, which means that unseen is unsold. Theresults of this thesis show that it is just as important to be comprehended as itis to be seen. In split-second decision making, the ability to recognize andcomprehend a product can significantly impact preferences. Comprehensionstretches beyond perception as consumers infer value from memory structuresthat influence attention. Hence, the eye truly sees what the mind is prepared tocomprehend.
    • AcknowledgementsDuring my time as a doctoral student, I have realized that I would be nothingwithout the people around me. I am defined by the people that have influencedme, as I admire good behavior that I do not possess. Consequently, I am notthe same person that I was when started this journey, and I have many peopleto thank for that. I have chosen to present my acknowledgements byassociating the words that I feel are strongly connected to the people who havehelped mold me into a better person and researcher.Sincerity: I have admired you and your good deeds throughout the years. Ihave learned many things from you, two of which have changed mefundamentally. I have learned the value of camaraderie and diligence, and theresults of these two important traits that in many ways define your personality.You are the most sincere person I know and what I have learned from you ispriceless. Anders Gustafsson, you have been a father figure to me and haveunconditionally taken care of me and given me opportunities in life. I willforever be grateful for what you have done for me. Polish your Abu-Atom,Anders, because this year we are going to catch the big one.Persistency: Throughout the years I have learned many things from you. Youhave enormous persistence in explaining and re-explaining things until Iunderstand. We have talked for countless hours and you always have time forme. Erik Wästlund, thank you for teaching me the French word for black (noir)and the levels of measurement (Nominal-Ordinal Interval Ratio) at the sametime. You are a unique person with whom I have had the honor to share manyexperiences, and I hope that there are many more to come.Courage and Empathy: The ability to truly care about a fellow man is rare ina world that encourages individualism. You are the person who has cared aboutmy well-being and showed me the path of empathy. You have the courage tospeak out when there is injustice and stand up for your colleagues. MartinLöfgren, you have given me strength to peruse my goals when everything seemshopeless. You gave me comfort when things have been stressful. You are agood friend and colleague who has always praised my performance withhonesty, and for that I trust and respect you. It has been an honor to have youin my life and I look forward to many more years of conferences, guitar playing,and singing.
    • Humility: The quality of being humble was unfamiliar to me until I got toknow Lars Witell. You have shown me what it truly means to be intellectuallyhumble. Lars, I want to thank you for your wisdom and for sharing yourapproach to research. You have made me a more humble man with bigger earsand a smaller mouth.Accountable: To my favorite writing companion and the most responsibleperson I know. You always perform above expectations, on time, and with asmile on your face. Tobias Otterbring, we are a match made in heaven and ithas been a great experience working with you.Percipience: There are few people I admire as much as Patrik Larsson. Myadmiration stems from your endless capability to help and understand. You area wonderful man with unique leadership skills that I have learned much from.Thank you for helping me through troublesome times.Support: Among the several people who have supported my journey indifferent ways are Thomas Wennstam, Henrietta Huzell, and Per Jonsson. Iwould like to thank all my colleagues at The Service Research Center (CTF) fortheir support and friendship. I also thank my brother Pasha Shams and mysister Kimiya Shams for your support.Financing: This research has been supported and financed by several institutesand organizations.The Swedish Research School of Management and IT (MIT): Thank you for providingdoctoral courses that have given me the theoretical foundation needed for thisjourney. Thank you also for financing my research throughout the years.Arizona State University (ASU): Thank you for providing doctoral courses thatexpanded my knowledge in consumer behavior and for providing anenvironment for me to conduct my experiments. Also, thank you Diane Davisfor helping me during my stay at ASU.Tobii Technology: Thank you for unconditionally providing eye-trackingequipment and support. Without your support this research would not havebeen possible.
    • The Packaging Arena (TPA): Thank you for your providing an environment andsupport for my experiments.Stiftelsen Gunnar Sundblads Forskningsfond: Thank you for financing my studies atArizona State University. Several of the papers in this thesis have beendependent on these studies.Strukturfonderna: Thank you for financing my research.KK-stiftelsen: Thank you for financing my research.Karlstad, February 2013Poja Shams
    • TABLE OF CONTENTS1 Introduction ...................................................................................................................... 91.1 The Research Domain .............................................................................................. 101.1.1 Decision-Making Journey................................................................................... 111.1.2 The Point-of-Purchase Domain.......................................................................... 121.1.3 Perspective on Information Processing and Attention ....................................... 141.2 Purpose..................................................................................................................... 151.3 Structure of Thesis .................................................................................................... 161.4 Conceptual Model ..................................................................................................... 161.5 Summary of Appended Papers................................................................................. 181.5.1 Paper I................................................................................................................ 181.5.2 Paper II............................................................................................................... 191.5.3 Paper III.............................................................................................................. 191.5.4 Paper IV ............................................................................................................. 201.5.5 Paper V .............................................................................................................. 212 Theoretical Framework.................................................................................................. 232.1 Decision-Making Framework..................................................................................... 232.2 Process Perspective of Decision Making.................................................................. 242.2.1 Staged Decision Making .................................................................................... 262.2.2 Decision-Making Strategies ............................................................................... 282.2.3 Attention in Decision making.............................................................................. 302.3 Influencing Factors.................................................................................................... 332.3.1 Recognition ........................................................................................................ 362.3.2 Preference.......................................................................................................... 392.3.3 Choice Task ....................................................................................................... 412.3.4 Product Packaging ............................................................................................. 432.3.5 Product Placement............................................................................................. 472.4 Summary of Theoretical Framework......................................................................... 503 Methodology................................................................................................................... 513.1 Measuring Vision....................................................................................................... 513.1.1 Eye tracking as a Process Trace Method .......................................................... 523.1.2 Eye tracking Laboratory Equipment and Setup ................................................. 523.1.3 Eye-tracking Measures ...................................................................................... 563.2 Measuring Decision................................................................................................... 623.2.1 Choice ................................................................................................................ 623.2.2 Self-reported Preference rating.......................................................................... 623.2.3 Self-reported Familiarity rating........................................................................... 633.3 Influencing Vision and Decision ................................................................................ 643.3.1 Recognition ........................................................................................................ 643.3.2 Choice Task ....................................................................................................... 643.3.3 Product Placement............................................................................................. 653.3.4 Packaging Attributes .......................................................................................... 664 Findings and Contributions within the Conceptual Model ........................................ 674.1 Paper I....................................................................................................................... 684.1.1 Findings.............................................................................................................. 694.1.2 Contributions ...................................................................................................... 694.2 Paper II...................................................................................................................... 70
    • 4.2.1 Findings.............................................................................................................. 714.2.2 Contributions ...................................................................................................... 714.3 Paper III..................................................................................................................... 724.3.1 Findings.............................................................................................................. 734.3.2 Contributions ...................................................................................................... 744.4 Paper IV: ................................................................................................................... 754.4.1 Findings.............................................................................................................. 764.4.2 Contributions ...................................................................................................... 764.5 Paper V ..................................................................................................................... 774.5.1 Findings.............................................................................................................. 784.5.2 Contributions ...................................................................................................... 795 General Contributions and Direction for Future Research........................................ 815.1 The Influence of Product Recognition....................................................................... 815.2 The Influence of the Choice Task ............................................................................. 825.3 The Influence of Product Packaging ......................................................................... 835.4 The Biased Decision-Maker...................................................................................... 855.5 Future Visual Attention Research in POP Decision Making ..................................... 86References............................................................................................................................ iAPPENDED PAPERSPaper I: Consumer Perception at Point-of-Purchase: Evaluating ProposedPackaging Designs in an Eye-tracking LabPaper II: Left isn’t always right: Placement of pictorial and textual packageelementsPaper III: The Verticality Heuristic: Why top shelf is not always top notch inproduct placementPaper IV: Familiar Packaging in a Crowded Shelf – The Influence of ProductRecognition and Visual Attention on Preference FormationPaper V: Using Heuristics to Revisit Consumer Choice Processes through theEyes of the Consumer
    • 91 IntroductionA new product launch is a challenge every consumer goods firm faces. Brandowners and product developers have many hurdles to overcome before aproduct is launched on the market. Product development in itself is often achallenge, as is convincing retailers to accept the brand. However, it is not untilthe product reaches the supermarket shelf that the effort is judged. The judgesare the consumers that will include or exclude the product in split-seconddecision making by employing simplification rules that quickly cut through theclutter of the environment with minor information processing and cognition.What does it take to be considered an option? This dissertation explores thisquestion by studying the influence of the environment and the mind of theconsumer on visual attention in point-of-purchase (POP) decision making.With individual stores now carrying up to 100,000 different items (Ball, 2004),the average consumer encounters a vast amount of products across all channelsof distribution. Yet the typical family gets 85 percent of their needs from only150 products (Roberts, 2003). Finding a product on the shelf has become achallenge, not just because of the number of options, but also because ofshared product attributes and copycat products. This has resulted in fiercecompetition for shelf space as retail selling space is a fixed resource (Ball, 2004).Hence, suppliers invest heavily in in-store marketing (Forster, 2002); in fact,suppliers have increased their in-store marketing by spending more on it ratherthan out-of-store promotions (Ball, 2005). This competition has changed therole of shelf space from stocking area to an essential and scarce strategicresource that needs to be controlled as efficiently as possible (Urban, 1998).In addition, fast-moving consumer goods (FMCGs) are increasingly competingfor the consumer’s attention, which has resulted in innovative methods for in-store communication. Despite the in-store activities that are designed to lureattention, in the end, it is the customer that makes a decision throurg theinteraction between the environment and the mind. The decision can beformed by experiences in-store and what has been experienced out-of-store.However, little research has been done on the influence of in-store factors andout-of-store factors on consumer attention and decision-making. Hence, in-store and out-of-store influence has become an increasingly important researchtopic, specifically in relation to visual attention throughout the POP decision-making process. This topic has been advocated by previous researchers for
    • 10many years (see e.g., Olshavsky, 1979; Payne, 1976; Russo, 2011). There havebeen technological and methodological hurdles to overcome before process-tracing methods could be used to study visual attention in the decision-makingprocess. However, some researchers have made great efforts to illuminateconsumers’ POP decision making despite these hurdles (Chandon, 2002;Chandon, Hutchinson, Bradlow, & Young, 2009; Pieters, Warlop, & Hartog,1997; Pieters & Warlop, 1999; Pieters, Warlop, & Wedel, 2002; Russo &Leclerc, 1994; Russo & Rosen, 1975; Van Der Lans, Pieters, & Wedel, 2008a;Van Der Lans, Pieters, & Wedel, 2008b). The main objective of the presentdissertation, as the following chapters outline, is to study the influence ofproduct packaging and placement (as in-store factors), and recognition,preferences, and choice task (as out-of-store factors) on consumer visualattention and decision making.1.1 The Research DomainThe types of decision making studied in the present thesis are characterized bynon-conscious and non-habitual POP consumer behavior. At first, these typesof decision making may seem a fraction of the decisions made in-store.However, planned brand-level purchasing is not the most common consumerbehavior as only 40 to 60 percent of the products that are bought are planned(Block & Morwitz, 1999; Inman, Winer, & Ferraro, 2009). Furthermore, amajority of FMCG buying decisions are made at POP (Bucklin & Lattin, 1991;Hoch & Deighton, 1989; Prone, 1993; Rundh, 2005; Underwood & Ozanne,1998; Van Der Lans et al., 2008b). A consumer is aided in planned purchasingwhen a shopping list is used, but not hindered from unplanned purchasing asdeviations from the shopping list are common and dependent on the levelinvolvement in the decision (Block & Morwitz, 1999). The decisions studiedhere require evaluation of options and represent the 20 percent of time spentin-store making decisions in front of the shelf (Sorensen, 2009). Instead oftrying to provide a universal model of decision making, a limited number ofinfluencing factors are studied in a controlled setting to understand thefundamental decision-making properties and their influence on attention,consideration, and choice. The present research has a descriptive approach todecision making with an emphasis on process tracing (Payne, 1976; Svenson,1979). The following sections will further illuminate the research domain andthe type of decision making studied.
    • 111.1.1 Decision-Making JourneyThere are many factors influencing consumer decision making for FMCGs inthe super market; as a whole, the in-store decision-making process cannot beexplored in one dissertation. However, in-store decision making can be brokendown into activities that can be isolated and studied collectively as a sub-process. FMCG decision making can be waived as a sequential process startingwith some need and then moving to information processing and ending withoutcome and, in some cases, confirmation or adjustment of choice (Brim,Glass, Lavin, & Goodman, 1962; Puccinelli et al., 2009). This process is muchlike any rudimentary problem-solving model (see e.g., Engel, Blackwell, &Miniard, 1995; McGuire, 1976). However, a multitude of decisions are madebefore the actual choice of a specific product. Some of these decisions areproduct-related, but many are not directly related to fulfilling the realized need.In the circumstance of, for example, running out of shampoo and realizing theneed to buy a new bottle, the need for shampoo starts at home and wouldeither be noted on a shopping list or memorized until the next shopping trip.Deciding whether to use a memory aid already greatly influences in-storebehavior (Block & Morwitz, 1999). At some point, the decision is made torealize the need of shampoo and presumably other products as well. Up untilentering the store of choice, there are several decisions made about where,when, and how to fulfill the needs. When entering the store, the needs will bereactivated through external or internal memory aids and the consumer willstart navigating to the store section containing the product category. Navigationis described as the bulk of in-store decision making, as, “…80 percent of shoppers’time is spent simply in moving from place to place in the store…” (Sorensen, 2009, p. 8).During navigation, the consumer is bombarded with information. Theseinfluencing factors span from store atmosphere to consumer-specific factors(Bitner, 1992; Baker, Grewal, & Parasuraman, 1994; Baker, Parasuraman,Grewal, & Voss, 2002; Donovan & Rossiter, 1994; Inman et al., 2009; Inman,McAlister, & Hoyer, 1990; Turley & Milliman, 2000). For example, if the storeof choice is playing music, consumers are expected to spend more time andmoney on the shopping trip, depending on music style and consumer age(Turley & Milliman, 2000). Furthermore, with consumers in a hurry, timepressure will influence the number of considered products (Pieters et al., 1997).
    • 121.1.2 The Point-of-Purchase DomainThe domain of this dissertation, namely POP decision making, is termed the“first moment of truth” by Procter & Gamble (Nelson & Ellison, 2005). Thissubpart of the greater decision-making process is defined as an opportunity forbrand owners to be, “…obtaining customers’ attention and communicating the benefits ofan offer.” (Löfgren, 2005, p. 113). The time to obtain attention and communicatebenefits is less than 40 seconds, as this is the time spent on buying a single item(Sorensen, 2009). In this limited timeframe, the consumer enters a decision-making process that is in itself influenced by a multitude of factors, excludingthose mentioned above. In the present thesis, there are some factors that arecontrolled for, some that are held constant, and some that are manipulated. Themanipulated and explored factors are described in depth in the comingchapters. However, three influencing factors, namely shelf elasticity, productprice, and purchase involvement, are described in this section to set thepremises of the present research.The shelf content and structure has been widely studied (Corstjens & Doyle,1981; Murray, Talukdar, & Gosavi, 2010) and is a strategic tool (Chen &Chaiken, 1999; Urban, 1998) since methods for shelf allocation influenceprofitability (Corstjens & Doyle, 1981; Lim, Rodrigues, & Zhang, 2004). Shelfspace is a restricted area that can be arranged according to many principles (e.g.,brand, color, size, price, profit). Space elasticity, defined as the relationshipbetween sales and the location of products on the shelf, has been modeled byprevious researchers (Curhan, 1972; Curhan, 1973; Dréze, Hoch, & Purk,1994). For the present thesis, space elasticity is held constant betweenmanipulations. Nevertheless, shelf space is included in this research, but fromthe perspective of consumer value association and value inferences. Hence, themain focus is on space quality (e.g., top-level versus floor-level) rather thanspace elasticity (Corstjens & Doyle, 1981).The well-studied factors of price elasticity and the price-quality relationship(Wilkinson, Mason, & Paksoy, 1982), have been excluded from the presentthesis since price is a debated topic that has been shown to have a modestinfluence on consideration and choice without other consumer-influencingfactors (Allenby & Ginter, 1995), and the relationship between price and qualityis product-specific and weak in general (Gerstner, 1985). Furthermore, thefactors addressed herein, such as product recognition, are non-price-related andprice comparisons could be confounding.
    • 13Involvement, defined as, “A persons perceived relevance of the object based on inherentneeds, values, and interests” (Zaichkowsky, 1985, p. 342), is an important researchfactor, particularly as low involvement has been shown to decrease theinfluence of in-store factors (Hoyer, 1984; Olshavsky & Granbois, 1979),specifically, inter-brand comparisons (Baker, Hutchinson, Moore, &Nedungadi, 1986). Outcome-relevant involvement influences informationprocessing while higher involvement increases information search (Chaiken,1980; Fazio, 1990; Howard & Sheth, 1969; Pieters & Wedel, 2004; Rayner,Rotello, Stewart, Keir, & Duffy, 2001), thus increasing the probability ofinteraction with in-store influence factors. In the present thesis, lowinvolvement is parallel with habitual purchase behavior as consumers buyingproducts habitually are less malleable in their decision making (Hoyer, 1984;Leaong, 1993). To exemplify this, POP behavior of habitual buyers of the sameshampoo brand is about locating the product. Hence, any comparison betweenproducts on the shelf is done to exclude distractors. However, if the buyerrecently discovered that he or she has dandruff, the shampoo has to fulfill thisneed. In this situation, information processing has a heightened decision-making role as the buyer needs to consider options in detail. When makingnon-habitual decisions, consumers become more malleable and open for POPinformation, such as product packaging or displays.To summarize, involvement level is important as non-habitual decision makingencompasses evaluations made between options. For habitual decisions, visualattention is reduced to measuring visual search; that is, the act of locating atarget among distractors. To control for involvement, forced-choice tasks,which have proved effective in inducing involved decision making, wereimplemented to engage participants and increase information processing(Bialkova & Van Trijp, 2011; Chandon et al., 2009; Pieters & Wedel, 2007;Rayner et al., 2001; Wedel & Pieters, 2000). However, involvement level is notcomparable to high-involvement decision making, such as automotive choice,but rather is slightly heightened to induce both heuristic and analyticinformation processing via the complexity of the choice (Chaiken, Liberman, &Eagly, 1989; Chen & Chaiken, 1999; Payne, Bettman, & Johnson, 1992; Payne,Bettman, Coupey, & Johnson, 1992; Plous, 1993) and the involvement in thechoice, respectively (Chaiken, 1980).
    • 141.1.3 Perspective on Information Processing and AttentionIt is important to point out that the emphasis of this dissertation is notspecifically on information processing (e.g., top-down and/or bottom-upprocessing), but rather on how the processed information influences POPdecision making. It is assumed that both the mind and and the environmentinfluences any given decision situation (Desimone & Duncan, 1995; Pieters &Wedel, 2004; Pieters & Wedel, 2007; Wedel & Pieters, 2008b; Wolfe, 2000) andthat this is generally non-conscious (Bargh & Ferguson, 2000; Dijksterhuis,Smith, Van Baaren, & Wigboldus, 2005; Fitzsimons et al., 2002). Therefore, theemphasis is on the factors that influence information processing, henceforthdenoted as in-store (factors that influence consumers through visual attention)and out-of-store (factors that influence consumers through memory activation).To clarify the perspective of information processing, the “sculpture andconstruction” metaphor by Janiszewski (2008) is used. The metaphor iscentered on creation of meaning (a sculpture) through the interaction betweenenvironment (the piece of marble) and the individual mind (the sculptor). Themain perspective of this metaphor is that:“People are not machines that produce a consistent output given a constant input. The outputchanges depending on the meanings the person needs to generate, even if the environment isconstant. Thus, in the sculpture and construction metaphors, it is the variability in meaning,and the events that encourage a person to strive for each of the varied meanings, that is thefocus of inquiry.” (Janiszewski, 2008, p. 285).Hence, the sculptor can be told to create a sculpture and, depending on thefocus of the inquiry, the sculpture can differ, even with the same piece ofmarble. Thus, it is in the interaction between the environment and the mind ofthe consumer that meaning is created; by controlling inquires, the meaning canbe studied. Janiszewski (2008) formulates this as:“[t]o the extent we can understand the task a consumer is trying to accomplish, we should beable to understand the meanings that are acceptable or relevant to that task….The relativismof meaning should be predicted by the goals the perceiver is trying to accomplish during hisinteraction with the environment.” (p. 283).In addition, the role of attention is central to this perspective:
    • 15“Attention is a collection tool in the strategic behavior and attention is a chiseling tool inintuitive behavior.” (Janiszewski, 2008, p. 287).In the present thesis, the focus is on visual attention as the “chiseling tool” as itshed light on rapid information processing that lacks conscious access orcontrol (Wedel & Pieters, 2008b). This metaphorical perspective is fundamentalin the assessment of information processing. Throughout the present studies,choice tasks are used with different levels of semantic interpretation. Some arespecific, such as ‘buy dandruff shampoo’, and some are vague, such as ‘buy ashampoo’. Depending on the specificity of the tasks, the focus of the inquirychanges and guides the creation of meaning, thus revealing the semanticallyimportant factors that influence the decision-making process for that specificindividual and choice.1.2 PurposeThe purpose of the present dissertation is to explore the influence of in-storeand out-of-store factors on visual attention and consumer POP decisionmaking. Hence, the contextual framework is consumer POP decision makingfor FMCGs, while the methodological framework is primarily on visualattention. Furthermore, the theoretical framework is centered on consumerdecision-making and the explored influencing factors. To accomplish thispurpose, it was particularly important to understand consumers’ general visualbehavior in POP decision making. Hence, the starting point is to explore howconsumers generally observe the product shelf. This was underpinning for thecoming studies that reveal how five specific in-store and out-of-store factorsinfluence the consumer at POP. In general, the purpose of the thesis is studiedin the appended papers to answere the following questions: How does a consumer visually observe the product shelf when makingconsumer goods decisions? How does product packaging influence visual attention? How does product shelf placement influence visual attention in task-oriented decision making? How do product recognition and visual attention influence productpreference in decision making? How do product recognition and decision task influence the decision-making process?
    • 16The remainder of the present dissertation is dedicated to answering thesequestions and exploring the implications and theoretical contributions of theappended papers.1.3 Structure of ThesisThe structure of this dissertation consists of five chapters. The commonalitiesbetween the chapters are the four components that form the conceptual modelin Figure 1. The components decision making, visual attention, and theinfluencing factors are reviewed in the theoretical framework in chapter 2.Chapter 3 sets the foundation of the framework used in the appended papers tomeasure and manipulate the components in the conceptual model. In chapter 4,each appended paper is discussed in relation to the theoretical framework andconceptual model. Chapter 5 concludes the dissertation with a summary of themain results and implications for managers and future research, and is followedby references and the appended papers.1.4 Conceptual ModelThe conceptual model in Figure 1 consists of four main components, of whichout-of-store and in-store factors are, individually and conjointly, studied inrelation to their consumer decision-making influence through visual attention.Of visual attention’s three functions, the main function is to measure theinfluence of in-store and out-of-store factors on decision making. For example,in Paper II, the influence of the horizontal positioning of pictorial and textualelements on detection through visual attention was tested. Pictorial and textualelements are then treated as in-store factors that can only influence consumerdecision making through visual attention. Moreover, Paper III explores bothout-of-store and in-store influencing factors on consumer decision making. Inthis case, product packaging and vertical product placement are treated as in-store factors and consumer recognition and choice task as out-of-store factors.The second function of visual attention, which was examined in depth in PaperIV, is to measure its influence on decision making, independent of otherinfluencing factors. The third function is to explain the decision-makingprocess through visual attention. This is explored in Paper V. However, it isimportant to keep in mind that the appended papers have cross-related researchpurposes, such as the influence of product recognition on decision making.
    • 17Hence, the purpose of visual attention in the appended papers is generallydescribed above to clarify its multifaceted role. The last component of theconceptual model is decision making, which is treated as a process starting withdecision task and ending with a choice. Hence, the whole decision-makingprocess, from the initial observation to the final choice, is studied to explore theinfluence of in-store and out-of-store factors.Figure 1: The four components of the conceptual model, of which out-of-store and in-store components comprises five influencing factors.In summary, this conceptual model is primarily focused on decision making as aprocess that is studied through visual attention and influenced by theinteraction between the environment and the mind of the consumer. Theconceptual model consists of three decision-making constructs, namelyattention, signifying the act of attending information in the environment;perception, representing the interaction between the mind and theenvironment; and comprehension, which is the act of creating meaning fromthe attended and perceived information. The following section introduces theappended papers and contextualizes the model that supports the presentdissertation. The summary is followed by the theoretical framework in whichthe conceptual model is broken down into its components.
    • 181.5 Summary of Appended PapersThe appended papers have the same general purpose, which is to explore theinfluence of in-store and out-of-store factors on consumer visual attention anddecision making. The papers test the components of the conceptual modelfrom two perspectives, which involves the general visual attention ofconsumers, and individual factors and their influence on visual attention indecision making. In-store and out-of-store factors are emphasized as thesefactors conjointly and individually influence consumer decision making andvisual attention. This is further described in this section which introduces andsummarizes the appended papers.1.5.1 Paper I: Consumer Perception at Point-of-Purchase: EvaluatingProposed Packaging Designs in an Eye-tracking LabThis paper was coauthored with Dr. Erik Wästlund, who was the main author,Dr. Martin Löfgren, Dr. Lars Witell, and Dr. Anders Gustafsson. The paper hasbeen presented in the 1st Nordic Retail & Wholesale Conference (NRWC 2008) inNorrtälje, Sweden. This paper has been published in Journal of Business and RetailManagement Research (Wästlund et al., 2010).Paper I is exploratory and constitutes the first step toward understanding thegeneral properties of visual attention in POP decision making. The paper isdivided among three unrelated experiments that evaluate eye tracking as amethod for studying consumer POP decision making. The purpose of the firstexperiment was to determine general consumer visual behavior that must betaken into account when studying digitally displayed products. The secondexperiment was designed to explore the properties of visual attention inproduct findability; that is, the likelihood of a package being found based onpackaging attributes. The third experiment was conducted to evaluate theinfluence of minute differences in packaging design on visual attention anddecision making.In this opening paper, both general visual attention and individual factors werestudied to get a general understanding of how visual attention measures can beemployed for future experiments. This paper contributed greatly to thesubsequent papers by generating new ideas that were tested in the resultingexperiments. Second, the methodological knowledge accumulated in this paperwas used to improve procedural and statistical hurdles for the forthcoming
    • 19papers. Further contributions of this opening paper are discussed in theforthcoming paper summaries.1.5.2 Paper II: Left isn’t always right: Placement of pictorial and textualpackage elementsPaper II was coauthored with fellow doctoral student Tobias Otterbring, whowas the main author of the paper, Dr. Erik Wästlund, and Dr. AndersGustafsson. This paper is currently in press to be published in British FoodJournal.Paper II studies the horizontal positioning of textual and pictorial elements andtheir influence on visual attention and detection. What makes this study uniqueis that previous research has mostly focused on recall or preference ofpackaging elements; by contrast, this paper focuses on packaging elementdetection through visual attention. The addition of visual attentioncomplements traditional packaging element evaluation and allows for a precisemeasurement of packaging attribute positioning and its influence on detectionand recall. This paper derives from experiment 2 in Paper I, which testedtextual packaging elements and the detection of target products based onpackaging attributes.The results of this paper show that textual elements should be placed on theleft-hand side and pictorial elements on the right-hand side to receive the mostvisual attention.1.5.3 Paper III: The Verticality Heuristic: Why top shelf is not always topnotch in product placementPaper III is a working paper co-authored with Dr. Erik Wästlund and Dr.Anders Gustafsson. The paper has been presented at the 2nd Nordic Retail &Wholesale Conference (NRWC 2010) in Gothenburg, Sweden. The paper has alsobeen presented at The 12th International Research Symposium on Service Excellence inManagement (QUIS 12) in Ithaca, New York.Paper I found that consumers generally direct vision to the top shelf sectionbefore the bottom. This is intriguing and prompted questions about theinfluence of vertical product positioning that were the starting point for Paper
    • 20III and several experiments stretching over two continents. Verticality, ingeneral, is believed to influence decision making through cognitive processing,resulting in inferred value at different vertical positions. In this paper, verticalityis treated as a mental shortcut used by consumers to direct attention to theposition believed to contain products with a specific value (premium orbudget). The purpose of Paper III, which was to examine the influence ofvertical shelf position and product packaging on consumer visual attention,consideration, and choice, was thus two-fold: (1) to understand the influence ofvertical shelf position, and (2) to understand the influence of productpackaging. To further extend the contributions, participants were introduced tounfamiliar products. Familiarity as a concept was introduced to explore hownaïve decision making influences the use of verticality as an indication of value.The results show that consumers use verticality in the initial part of the searchprocess. However, verticality loses its influence during evaluation of products.Furthermore, the influence of product packaging on consideration and choice isindependent of product recognition. Thus, product packaging is the mainindicator of value based on cue-level familiarity such as color, glossiness, andother packaging attributes. The results of Paper III indicate that productrecognition has small influence on consideration and choice when consumerssearch for products with high or low value. Nonetheless, participants had aspecific task to find different quality levels; therefore, it is not known howfamiliarity would influence consideration and choice if a preference choice taskwere given. The questions resulting from Paper III initiated Paper IV, whichexplores the influence of product recognition and visual attention onpreference formation.1.5.4 Paper IV: Familiar Packaging in a Crowded Shelf – The Influenceof Product Recognition and Visual Attention on Preference FormationPoja Shams is the single author of this working paper. A previous version ofthis paper has been presented at The Scandinavian Workshop on Applied EyeTracking (SWAET 2012) in Stockholm.Complex consumer decision making is characterized by simplification strategiesto decrease cognitive load. One such strategy, product recognition, is a well-studied factor that has a positive influence on preference formation.Furthermore, visual attention influences preference formation as the consumer
    • 21moves further into decision making. The gist of preference formation throughvisual attention is that the more consumers look at a product, the more theyprefer the product. The purpose of Paper IV was to explore the relationshipbetween product recognition and visual attention. Results show that bothrecognition and visual attention contribute to preference formation.Furthermore, higher familiarity moderates the influence of visual attention onpreference construction. However, the influence of product recognition isgreater than that of visual attention on preference formation because itconstructs a consideration set the preferences of which are then assessedthrough visual attention.The result of Paper IV indicates how product recognition is used as asimplification strategy in decision making. However, the present paper does notempirically explore the decision-making process; rather, it is theoreticallyassumed. To understand the influence of product recognition in the decision-making process, then this assumption needed to be tested, which initiated PaperV and the investigation of product recognition.1.5.5 Paper V: Using Heuristics to Revisit Consumer Choice Processesthrough the Eyes of the ConsumerPaper V is coauthored with Dr. Erik Wästlund and Dr. Lars Witell. Thisworking paper has been presented at the Eye Tracking Research & ApplicationsSymposium (ETRA 2012) in Santa Barbara, California.Past research in decision making has divided it into stages that are understoodas sub-processes of a consumer choice process and consist of iterations ofelimination and consideration. Russo and Leclerc (1994) identified three stagesof the consumer choice process: (1) orientation, (2) evaluation, and (3)verification. They also identified that visual attention varies over the differentstages. In the initial stage, simplification strategies, such as product recognition,are employed to gather information to eliminate inappropriate alternatives,which results in a set of products that a consumer will consider in theevaluation stage. Furthermore, when consumers are given specific decisiontasks, memory structures are activated; the task influences visual attentiontoward the products with the features related to the task. However, dependingon whether consumers are given a choice or a consideration task, the decisioncan be more or less time-consuming. It is not established which stage accounts
    • 22for the added time and the role of product recognition in relation to thedecision task. The purpose of Paper V was to test the three-stage model, assuggested by Russo and Leclerc (1994). Furthermore, three experimentsexamined choice and consideration task, product recognition and thereinfluence on the three stages of the decision-making.The results of the first experiment generally confirmed the staged decision-making model with some differences in the number of attended productsduring each stage and the time spent in the evaluation and verification stages.When examining the influence of product recognition in the secondexperiment, the results showed that decisions for familiar brands take longerthan for unfamiliar brands and that product recognition mostly influencesevaluation and verification. In the third experiment, the results showed thatproduct recognition influence is dependent on choice task, since its influenceon staged decision-making does not apply when a consideration task is given toconsumers.
    • 232 Theoretical FrameworkThis section provides an overview of the relevant literature in decision makingand visual attention. The conceptual model (Figure 1) sets the foundation ofthe theoretical framework; each section is introduced with the componentsincluded in the model. Decision making is the first component, as it sets thepositioning of the framework.2.1 Decision-Making FrameworkThe disciplines of marketing and psychology have developed decision-makingtheories for over 60 years (Ranyard, Crozier, & Svenson, 1997), with a gradualshift from normative theory (Edwards, 1954; Morgenstern & Von Neumann,1944) to behavioral decision theory (Simon, 1955), which recognizes thelimitations of the human mind. The framework of bounded rationality definesthe restrictions of cognition as the processing that can be done at one momentof time, and sets the foundation of sequential processing in complex decisionmaking. This framework shifted the theoretical focus from decision outcomesto the process of decision making (Payne, 1976; Svenson, 1979). Many theorieshave developed within the framework of behavioral decision theory (Payne etal., 1992). However, the present section is not focused on one specific decision-making theory. Several theories, such as image theory (Beach & Mitchell, 1987);differentiation and consolidation theory (Svenson, 1992); Constructive Choiceframework (Bettman et al., 1998); The Adaptive Decision Maker (Payne et al.,1993); and feature-integration theory (Treisman & Gelade, 1980), amongothers, have jointly shaped the present theoretical framework.There are some commonalities between theories emphasized in the presentframework. First, the decision maker constructs preferences for one optionthrough several stages in a process. Second, in complex decisions, the decisionmaker first simplifies the decision into a reduced set of alternatives usingstrategies that demand less cognitive effort. Third, the simplification of thedecision is mostly a non-conscious and automatic process. Fourth, the reducedset of alternatives is evaluated to reach a final decision. Fifth, evaluation ofalternative can be non-conscious or conscious demand more cognitive effort byinvolving working memory. The coming section further establishes the presenttheoretical framework.
    • 242.2 Process Perspective of Decision MakingThe normative decision theory, developed from economics, assumes that thedecision maker is rational. Consumer norms can be explained as expectedbehavior for achieving the highest payoff. However, normative theory assumesthat decisions are based on unlimited information that can easily be utilizedwith full knowledge about options (Plous, 1993). Since consumers havebounded rationality (Simon, 1955), the assumptions of normative decisiontheory is a major issue (see e.g., Tversky & Kahneman, 1986; Payne et al.,1992). Bounded rationality, the starting point of behavioral decision theory, isdefined by Simon (1955) as consumers seeking satisfaction, not maximization.Hence, in complex decisions, consumers do not search for all availablealternatives, but choose the alternative that exceeds a certain level of payoff.The shift from normative decision theory paved the way for the processapproach of decision-making research (Payne, 1976; Svenson, 1979). Thesequential nature of the process approach divides decision making into stagesthat extend over time (Ranyard et al., 1997). In the process perspective, thedecision maker is seen as adaptive; thus, payoff is not constant (Payne et al.,1990; Payne et al., 1993; Payne, Bettman, Johnson, & Luce, 1995; Payne et al.,1988; Payne et al., 1992; Senter & Wedell, 1999). The adaptive decision makerhas a variety of strategies that can be employed in any given situation dependingon cost and benefit (Payne et al., 1993). The payoff is dynamically influenced byprocessed information, such as the environment (Bettman, Johnson, Luce, &Payne, 1993; Huber & Klein, 1991), and the mind of decision maker (Bettman& Park, 1980; Coupey, Irwin, & Payne, 1998; Marewski, Gaissmaier, Schooler,Goldstein, & Gigerenzer, 2010). Some decision strategies can involve multipleoptions at the same time; other strategies compare attributes across optionsserially. The strategies can be quick and demand little cognitive effort, or theycan be slow and place a greater demand on the decision maker’s workingmemory (Bettman, Luce, & Payne, 1998).It is important to emphasize that the process perspective applies to complexdecision making (Svenson, 1996), which can result from consumers’ boundedcognitive capacity and amplified by such factors as missing information, timepressure, level of experience, and number of alternatives (Payne et al., 1992).The most recognized driver of complexity is number of available alternatives,which increases choice complexity (Payne et al., 1992). In the context of POPdecision making, past research has shown that consumers favor an increase inassortment size (Broniarczyk, Hoyer, & McAlister, 1998; Chernev, 2008; Kahn
    • 25& Lehmann, 1991), which, nonetheless, increases the cognitive cost of decisionmaking (Chernev, 2006) and the complexity of the choice (Iyengar & Lepper,2000). One consequence of increased complexity is the need for simplification(Chaiken et al., 1989; Chen & Chaiken, 1999). For example, Payne (1976)argued that when there are many alternatives, the decision process is dividedinto stages that screen for acceptable alternatives that can be evaluated in moredetail. The initial stage is described as fast, crude, holistic, and parallel, while thesubsequent stage is deliberate, attentive, detailed, and sequential. The conceptof two processing modes is defined in the Dual Process Theory as two distinctsystems, namely System 1, which is automatic/heuristic/intuitive/associative,and System 2, which is controlled/systematic/analytic/rule-based (see e.g.,Evans, 2008, for review). System 1 is believed to involve implicit cognitiveprocessing, while System 2 involves more explicit cognitive processing (Evans,2008). Furthermore, System 1 is a supportive system that feeds a continuousstream of content to working memory for analytical processing in System 2.However, analytical reasoning in System 2 can intervene and adjust theautomatic judgment in System 1 (Evans, 2008). Thus, memory structures thatare seemingly conscious in nature, such as recognition, can automatically adjustjudgment.In the decision-making process approach, information processing is given aheightened importance as the source of predictability, which involves assessingand reaching a decision based on acquired and processed information. Therehave generally been two approaches to studying decision making (Ranyard etal., 1997), the structural approach, which builds on input and outcome withstatistical models explaining the strategies used by the decision maker (e.g.,Kahneman & Tversky, 1979), and the process-tracing approach, which differsfrom early decision-making research by focusing on the process prior todecision (e.g., Billings & Marcus, 1983; Payne, Braunstein, & Carroll, 1978;Svenson, 1979). Process tracing illuminates the mind of the consumer as thedecision can be explored in relation to exogenous and endogenous factorsthroughout the whole decision-making process. Process tracing has beenadvocated by researchers in the past as the decision outcome is not sufficientfor understanding human decision making (Lohse & Johnson, 1996; Olshavsky,1979; Payne, 1976; Russo, 1978; Svenson, 1979). Specifically, the use of eyemovements in complex decision making has been advocated (Bettman et al.,1998; Russo, 2011; Shah & Oppenheimer, 2008). In fact, visual attention iscentral to decision making (Russo, 2011; Russo, 1978) as all information
    • 26acquisition and processing activities are coordinated by the visual system(Sheliga, Riggio, & Rizzolatti, 1994). Hence, visual attention gives insight intothe processed information and its influence on the decision.2.2.1 Staged Decision MakingIndependent of context, an individual decision is not isolated temporally.Decisions span over time and are divided into stages that form a sequentialprocess (Brim et al., 1962; Simon, 1960). However, the sequential perspectivehas been criticized because it cannot be generalized for all decision contexts. Inaddition, some evaluations can be simultaneous with information collecting(Witte, 1972). Consequently, a more realistic view is that decision making isnon-sequential or, rather, parallel, meaning that the relationships betweendecision-making stages are iterative, and dynamic (Mintzberg, Raisinghani, &Théorêt, 1976). However, it is important to note that non-sequential decisionmaking is not analogous with considering large amounts of informationsimultaneously. Because of limited cognitive capacity, the amount ofinformation that can be considered simultaneously is about seven units (Miller,1956). Decision making has been studied by numerous researchers, resulting inmodels that divide the decision-making process into stages based on differentcriteria (Edwards & Fasolo, 2001; Payne et al., 1992; Reisen, Hoffrage, & Mast,2008; Russo & Leclerc, 1994; Senter & Wedell, 1999; Wedell & Senter, 1997).Historically, researchers have suggested that consumer decision making isstructured in a predetermined manner; however, there are no models that canaccurately predict consumer choice. Research has shown that consumer choiceis either a single-stage process or a multiple-stage process involving screening,evaluation, and choice (Elrod, Johnson, & White, 2004; Shao, Lye, & Rundle-Thiele, 2008; Russo & Leclerc, 1994). Single-stage models are based on acompensatory decision strategy, which means that every attribute of an objectaffects the utility of that object independent of its other attributes (Elrod et al.,2004). Moreover, these models suggest that consumers choose from a set ofalternatives by screening and choosing the first acceptable alternative (Andrews& Srinivasan, 1995). However, there is a common understanding within thefield that single-stage choice models do not explain the choice process incomplex environments with a large number of products and attributes(Andrews & Srinivasan, 1995; Gensch, 1987). On the other hand, multi-stageconsumer decision models are based on the idea that consumers first screen
    • 27and evaluate the alternatives and then enter the choice stage with clear decisionboundaries (Bettman et al., 1998; Olshavsky, 1979; Payne, 1976; Shao et al.,2008). Stages are defined as multiple behaviors with distinct parts that includescreening, consideration, elimination of alternatives and choice (Bettman et al.,1998; Fader & Hardie, 1996; Miller, 1993; Olshavsky, 1979; Payne, 1976; Wedell& Senter, 1997; Shao et al., 2008).The concept of a multi-staged decision-making process in the context of POPdecision making was introduced by Russo and Leclerc (1994) and broke withprevious research favoring two stage models. In their study, Russo and Leclercidentified three stages by using process tracing to map the visual attention ofconsumers during FMCG decision making. They used direct observation of eyemovements recorded through a one-way mirror and verbal protocols to drawboundaries between decision-making stages. The study aimed to identify anempirical foundation for a consumer choice process model. Based on their data,they suggested that consumer choice is a gradual discriminating among thealternatives. During evaluation, one alternative emerges as superior. Continuedevaluations based on acquisitions, comparisons, and eliminations bolster theidentified alternative until it is announced as the choice. The three-stageconsumer choice model includes (1) orientation, (2) evaluation, and (3)verification, where the third stage is itself divided into sub-stages (3A and 3B)that address verification before and after actual consumer choice. Inorientation, the decision maker gathers an overview of the product display. Thisstage is characterized by relatively short mean observation times and a lowproportion of single eliminations (looking at a product once). The second stage,evaluation, incorporates consideration and elimination, which is a set ofcomparisons that are considered more seriously. Selective visual attentionensures that non-selected information is suppressed and relevant information iselicited (Howard & Sheth, 1969). Thus, by minimizing options, fewer productsneed to be considered and decision making is simplified. The evaluation stageincludes the majority of observations (54 percent) made by consumers andsubsequently takes the longest time (63 percent of total time) to complete.Finally the third stage, verification, acts as a confirmation of the decision thatwas made. Russo and Leclerc suggested that the choice process is optimizedglobally, one of the key characteristics of which is that operations such aselimination of alternatives occur throughout the choice process and are notlimited to a specific stage.
    • 28Independent of the decision-making process being two-stage or multi-stage, theconsumer first screens the original set of products and then considers a reducedset of alternatives. For example, Russo and Leclerc (1994) identified theevaluation stage in the multi-stage model to be, “…devoted to deliberate, effortfulevaluation, especially of the alternatives that are considered more seriously” (p. 278). Insteadof considering all products on a shelf, a choice is made from options within aconsideration set (Bettman, 1979; Howard & Sheth, 1969; Huber & Klein,1991; Johnson & Payne, 1985). Even though it is difficult to directly observethis process (Shocker, Ben-Akiva, Boccara, & Nedungadi, 1991), there isempirical support that consumers use consideration sets (Hauser & Wernerfelt,1990; Payne & Ragsdale, 1978; Russo & Leclerc, 1994; Russo & Rosen, 1975).Some researchers have found that the screening stage may be used to gatherrelevant information to eliminate inappropriate alternatives (Andrews &Srinivasan, 1995; Glaholt & Reingold, 2009; Wedell & Senter, 1997). Hence, theinitial screening stage of decision-making results in a consideration set(Lapersonne, Laurent, & Le Goff, 1995; Payne, 1976), which is a subset of theavailable products (Howard, 1977). The consideration set is evaluated andfurther reduced to what some authors have termed the choice set that is furtherreduced to one alternative that is chosen (Lapersonne et al., 1995; Roberts,1989; Shocker et al., 1991). The construction of a consideration set is dynamic,hence changing during information processing (Chakravarti, Janiszewski, Mick,& Hoyer, 2003; Mitra, 1995; Nedungadi, 1990; Shocker et al., 1991). Thedynamic nature of consideration sets is influenced by the choice task that bringscertain cues to the mind (Nedungadi, 1990), hence increasing the probability ofbrands associated with those cues being included. Similarly, the decision-making strategies are dynamic and change depending on context and the stagesof decision-making process (Bettman, 1979; Gensch, 1987; Nedungadi, 1990;Payne et al., 1988; Wright & Barbour, 1977).2.2.2 Decision-Making StrategiesDecision making demands different amounts of cognitive effort and can beanalytic, which is more demanding, and simple, which is less demanding. Bothanalytic and simple strategies can be used in any given decision situation (Payneet al., 1995); however, simple strategies are used at the early decision-makingstages, while more cognitively demanding strategies are used in the later stages(Payne, 1976). In a simple strategy, consumers make fewer information searchtrade-offs (Jacoby et al., 1994), and quickly cut through the clutter to a
    • 29manageable consideration set. The strategies used to simplify the decision areknown as heuristics (Newell, Shaw, & Simon, 1958; Simon & Newell, 1971).The field of heuristic research is represented by two perspectives, namelyheuristics as bias (Kahneman & Tversky, 1996; Tversky & Kahneman, 1974)and heuristics as fast-and-frugal decision rules (Gigerenzer & Goldstein, 1996).Independent of perspectives, it is clear that heuristics are used to simplifydecision making (Newell et al., 1958; Simon & Newell, 1971). Heuristics rely onone or several basic rules that are unconsciously employed to reduce effort,such as (1) examining fewer cues, (2) reducing the difficulty associated withretrieving and storing cue values, (3) simplifying the weighting principles forcues, (4) integrating less information, and (5) examining fewer alternatives (Shah& Oppenheimer, 2008). Furthermore, heuristics are employed as thecomplexity of the decision increases (Chaiken et al., 1989; Chen & Chaiken,1999; Payne et al., 1992; Payne et al., 1992; Plous, 1993). Even though fewertrade-offs are made in simple heuristics, they can be more accurate thandemanding procedures (Gigerenzer, 2008; Plous, 1993). Simple heuristicsemploy informational structures that enable the decision maker to be accuratewith a small processing effort (Gigerenzer & Brighton, 2009).Goldstein and Gigerenzer (1999) argue that consumers are guided by differentheuristics during the retail choice process. A heuristic, they argue, is not aninferior mental shortcut performed by the lazy mind, but rather an effectiveselection strategy. It has been suggested that, depending on the context,individuals utilize different strategies from an “adaptive tool box” of heuristicsand core cognitive capabilities (Gigerenzer & Gaissmaier, 2011). Following thisline of research, a number of choice heuristics have been identified (see, e.g.,Gigerenzer & Gaissmaier, 2011; Gigerenzer & Goldstein, 2011). In terms ofconsumer choice, the fast-and-frugal decision rules are more likely to be used inrepeat-purchase, low-cost categories as seen in the supermarket, rather than inhigh-involvement transactions, such as automotive choices (Hauser, 2011).Heuristics can generally be divided between non-compensatory andcompensatory strategies. In non-compensatory strategies, trade-offs are notmade between cue-level information, and the focus is on meeting the cutofflevel for a specific criterion. Non-compensatory heuristics are based on therelationships between a list of cues used for the decision strategy. If the value ofone cue outweighs any possible succeeding cue or combination of cues, thenthe strategy is non-compensatory (Marewski et al., 2010). However, if the
    • 30succeeding cue outweighs the previous cue, then the strategy is compensatory.Hence, compensatory heuristics require extensive information processing tomake trade-offs on multiple cues for the criterion. Consequently, non-compensatory heuristics ignore cue-level criterion comparison, thus eliminatingproducts that don’t meet the requirements of the criterion. In some non-compensatory strategies, the decision maker first examines alternatives basedon attributes with highest importance. If alternatives do not match theattributes, they are eliminated and the set of alternatives decreases for the nextiteration. This process relies on the importance of cues, which is based onpreference (Hogarth & Karelaia, 2005). It is believed that the influence ofheuristics is reflected in the decision-making process (Shah & Oppenheimer,2008), specifically via visual attention (Russo, 2011).2.2.3 Attention in Decision makingVisual attention is composed of two separate systems, perception and attention.Perception is a non-conscious information collection tool that constantly feedsthe brain with neural impulses from sensory input. Visual attention is aconscious process of directing cognitive processing or working memory towardsensory input. To visually attain information for processing, there must be analignment between attention and perception (Posner, 1980). This has previouslybeen described as orienting, which is the “aligning of attention with a source of sensoryinput or internal semantic structure stored in memory” (Posner, 1980, p. 4). Thealignment of attention and visual perception can be voluntary or involuntary,meaning that the decision maker voluntarily searches the environment (e.g.,visual search) or involuntarily becomes conscious of the environment (e.g.,saliency effect) (Bettman et al., 1998). Visual search, the process of finding atarget among distractors (Wolfe, 1994; Wolfe, 1998), is an attention-guidingmechanism that relies on semantic memory structures (top-down information)and perceptual features (bottom-up information) (Treisman & Gelade, 1980;Wolfe & Horowitz, 2004).The two attention guiding mechanisms constitute the framework that has muchin common with the dual-process theory. The two component framework alsodivides visual search into two stages (Treisman & Gelade, 1980). In the firststage, the perceptual features are processed pre-attentively; in subsequentstages, the features are integrated to identify the product attentively. Thus, theframework is composed of a pre-attentive, parallel, bottom-up process, and an
    • 31attentive, serial, top-down process (Itti & Koch, 2001; Treisman & Gelade,1980). The bottom-up process is believed to be a fast-and-crude creation of atopographic map of the saliency of perceptual features. The top-down processis believed to be a task-dependent, slow, and analytical adjusting of thetopographical map to highlight its informative parts. In the two-state model,bottom-up processing is related to “where” information is located, and top-down processing to “what” the information is (Itti & Koch, 2001; Sagi &Julesz, 1985). When looking past trivial search tasks (find a red triangle amonggreen circles) that are common in visual attention research, inspection ofalternatives within a set of distractors is done serially (Treisman & Gelade,1980; Wolfe, 1994).There is strong support for the two-component visual search framework;however, there is some disagreement about the role of top-down and bottom-up processing and its influence on visual attention. Some research pointstoward top-down processing as predominant in influencing visual attention(Desimone & Duncan, 1995; Treisman & Gelade, 1980; Van Der Lans et al.,2008a), while other research points toward bottom-up structures (Itti & Koch,2000; Theeuwes, 2010). Nonetheless, there is a common understanding thatboth top-down and bottom-up information is processed during visual search(Desimone & Duncan, 1995; Lee, 2002; Wolfe, 1994; Wolfe, 2000; Yantis,2000)The saliency of a package is an important factor for visual search, as opposed tomerely standing out when a customer is looking at a shelf in general. However,saliency has previously been described as task-dependent because certaininformation can be salient for a specific task and not salient for other tasks(Van Der Lans et al., 2008b). According to previous research, visual search isguided by the sum of the bottom-up and top-down information that togetherconstructs a master saliency map (Treisman & Sato, 1990) during brand choice(Van Der Lans et al., 2008a). The master saliency map is based on theperceptual features (bottom-up saliency) that are suppressed or enhanceddepending on their relevance to the search task (top-down saliency). Inopposition to the dominance of top-down driven visual attention, it is arguedthat the visual attention is completely bottom-up-driven at first and that it isonly later that top-down information can bias visual attention (e.g., Connor,Egeth, & Yantis, 2004; Theeuwes, 2010). Pre-attentive processing is assumedfor bottom-up information to be dominant in the initial discrimination of
    • 32alternatives. In this case, the pre-attentive processing does not include any top-down information other than spatial information (Theeuwes, 2010).It is appealing to believe that visual search fully illuminates the attention andcognition of the consumer. However, it is important to understand that bothvision and attention are used in decision making and both these componentshave limitations. The volume of information collected from the visual system(10⁸ bits per second) exceeds the cognitive capacity of any consumer (Itti &Koch, 2001; Itti & Koch, 2000); hence, visual attention is bounded by theprinciples of behavioral decision making. The issue of “what has” and “whathas not” been processed and attained in the field of vision has been debated byprevious researchers. In addition to directing the eyes, consumers direct themind toward informative and meaningful parts of the gathered information.The fundamental issue of measuring vision is that only the attention of the eyes(overt attention) is measured; however, the attention of the mind (covertattention) is similarly important for decision outcome. The coupling of overtand covert attention is essential as the fundamental assumption is thatconsumers process through the visual system, hence giving insight into rapidinformation processing (Wedel & Pieters, 2008b). Covert attention can freelymove within the field of vision (Strasburger, Rentschler, & Jüttner, 2011), and adisassociation between overt and covert attention would constitutemethodological issues.Based on the previous research, it is reasonable to assume tight couplingbetween overt and covert attention in the context of POP decision making as(1) it is a complex decision task with many alternatives, and (2) the covertattention directs overt attention. Full dissociation between covert and overtattention, that is, the consumer using peripheral vision in decision making,would reduce the reliability of visual measurements. However, there is aconsensus within the field that disassociation is dependent on task complexity(Pieters & Wedel, 2008). In complex tasks, such as POP decision making, thereis a tight coupling between covert and overt attention (Findlay, 2005; Rayner &Castelhano, 2008) that has metaphorically been compared to a rubber band(Henderson, 1992; Pieters & Wedel, 2008). In most decision-making situations,covert and overt attention are linked such that it is possible to get insight intocovert attention through eye movements, hence insight into the mind ofconsumer in the decision-making process (Findlay, 2005; Findlay & Gilchrist,2003; Glaholt & Reingold, 2011; Pieters, Wedel, & Jie Zhang, 2007). However,
    • 33it is important to point out that covert attention moves to the extremities of thefield of vision and leads the way for overt attention (Hoffman, 1998). Thus,consumers use the whole field of vision in directing the eyes to the nextinteresting area (Deubel & Schneider, 1996; Hoffman, 1998; Kowler, Anderson,Dosher, & Blaser, 1995). Even so, the limits of the perceptual span withinperipheral vision is not clear (Rayner, 2009). Previous research has defined it assomewhere between 1.5 to 30 degrees from the center of the visual field(Rayner, 2009; Strasburger et al., 2011). However, peripheral vision is moreuseful for gist recognition then central vision (Larson & Loschky, 2009). Forexample, a person can understand the first gaze at a scene by gist recognition ifthey are looking at a living room or forest using peripheral vision. Furthermore,there is evidence showing that the gist of a scene is available to guide visualrecognition and selection (Chun & Jiang, 1998). Henceforth, covert and overtattention will be referred to as visual attention as both components togetherform the underlying information acquisition needed for processing the decisionenvironment.With this theoretical background on decision making, the focus of thisdissertation will shift to a review of the research on the factors that influencethe decision-making process.2.3 Influencing FactorsTo understand consumer behavior and decision making from the processperspective, it is fundamental to understand the factors influencing the process.Generally, for POP decision making these factors can be divided between in-store and out-of-store influences. The adaptive decision maker is known to beinfluenced by processed information from the environment (Bettman et al.,1993; Huber & Klein, 1991) and the mind (Bettman & Park, 1980; Coupey etal., 1998; Marewski et al., 2010). This is also reflected in the concept ofinformation processing and the two-component framework of visual search(Itti & Koch, 2001). Hence, out-of-store influencing factors are parallel withtop-down information processing that influences the consumer at POP throughsome level of memory activation (Bettman, 1979; Chandon et al., 2009; Drézeet al., 1994; Van Der Lans et al., 2008a). In contrast, in-store influencing factorsare parallel with bottom-up information processing that influences consumersonly by attending the information at POP (Bettman, 1979; Chandon et al.,2009; Dréze et al., 1994; Van Der Lans et al., 2008a).
    • 34Product recognition and preference formation are two examples that illustratethe intricacy of drawing boundaries between in-store and out-of-storeinfluencing factors. Preferences can be formed through retrieval (Smith, 1989;Smith, 1994) and construction (Lichtenstein & Slovic, 2006; Slovic, 1995;Slovic, Finucane, Peters, & MacGregor, 2002). Preference retrieval caninfluence decisions depending on previous experiences. In contrast, preferenceconstruction can influence decisions through the interaction between memorystructures and attention of the decision maker. Similar intricacy exists inproduct recognition, which is composed of familiarity and recollection(Hintzman, Caulton, & Levitin, 1998; Hintzman & Curran, 1994). With this inmind, the boundaries in the present thesis have been drawn based on theassumed influence of these factors. The assumption is that in complex decisionmaking with multitude of options, product recognition is primarily an out-of-store factor influencing the consumer as a simplification strategy (Gigerenzer &Goldstein, 1996; Simon, 1990). Similarly, preference formation is emphasized asan out-of-store influencing factor as it is perceived to be formed in the mind ofthe decision maker (Gregory, Lichtenstein, & Slovic, 1993; Payne et al., 1992)through choice task and memory activation (Payne et al., 1990; Payne et al.,1993; Payne et al., 1988). Hence, as product recognition and preferenceformation are not purely bottom-up-driven and involve top-down memoryactivation, they are categorized as out-of-store influencing factors in POPdecision making.Since past researchers have struggled with quantifying information seeking(Newman & Staelin, 1972), it has been a challenge to study the influence of in-store and out-of-store factors. However, the general concept of combiningthese factors has been acknowledged for a long period (Newman & Staelin,1972). Nevertheless, it was not until recent research that the influence of in-store and out-of-store factors has been explored in terms of their impact onattention, consideration, and choice in the decision-making process (Chandonet al., 2009; Van Der Lans et al., 2008a; Van Der Lans et al., 2008b).Figure 2 presents three out-of-store factors relevant to the thesis. The first isproduct recognition which is a simplification strategy (Gigerenzer & Goldstein,1996; Goldstein & Gigerenzer, 2002; Simon, 1990). The second is the conceptof choice task and task-oriented information search. When consumers are givenspecific search or choice tasks, memory structures (Huffman & Houston, 1993;
    • 35Huffman & Kahn, 1998; Meyvis & Janiszewski, 2002) influence visual attentiontoward products with associated features (Van Der Lans et al., 2008a; Wedel &Pieters, 2008b). The third is the influence of consumer preferences on decisionmaking and the formation of preferences through retrieval (Smith, 1989; Smith,1994) and construction (Slovic, 1995).Figure 2: Out-of-store influencing factorsIn-store promotion has been an important research topic as it has a greatinfluence on consumer POP decision making. In-store marketing is central asthe majority of FMCG buying decisions made at POP. Furthermore, consumerdecision making is especially malleable at POP as the decisions are rapid(Hoyer, 1984) with few price comparisons (Dickson & Sawyer, 1990; Woodside& Waddle, 1975) and low involvement level (Underwood & Ozanne, 1998). Infact, it has been shown that two-thirds of products’ saliency results from in-store factors (Van Der Lans et al., 2008a). As seen in Figure 3, two in-storeinfluencing factors have been studied, namely product packaging and productplacement. Product packaging influences visual attention (Wedel & Pieters,2008a) and product assessment based on packaging cues (Underwood & Klein,2002; Zeithaml, 1988). Product placement influences value via such things asvertical or horizontal placement (Chandon, Hutchinson, Bradlow, & Young,2008; Dréze et al., 1994; Sigurdsson, Saevarsson, & Foxall, 2009; Van Nierop,Van Herpen, & Sloot, 2011).
    • 36Figure 3: In-store influencing factorsIn the coming sections, research on the included in-store and out-of-storefactors and their influence on consumer visual attention and decision making isreviewed.2.3.1 RecognitionRecognition is the most rudimentary consumer knowledge that includes all pastexperience with a product independent of exposure context. Recognition cangenerally be divided between familiarity and recollection (Hintzman et al., 1998;Hintzman & Curran, 1994). Familiarity is the ability to identify with smallcognitive effort an object that has previously been processed through the use ofimplicit memory. Implicit memory is non-intentional and non-consciousretrieval of previously acquired information. Conversely, recollection involves ahigher level of cognition as it requires detailed, explicit, conscious recall ofprocessed information (Chun & Jiang, 1998). An example is to imagine all theproducts in the soda category. A few products, such as Coca-Cola, MountainDew, and Dr. Pepper, would be easy to recall. However, if you were exposed tothe shelf, you would with high certainty be familiar with several other brandsthat you could not recall.It is well established that product recognition results in higher choiceprobability (Axelrod, 1968; Haley & Case, 1979) and higher preference (Coupeyet al., 1998; Harrison, 1977; Kara, Rojas-Méndez, Kucukemiroglu, & Harcar,2009; Underwood & Klein, 2002; Zajonc, 1968). Rao and Monroe (1988)showed that familiarity acts as a mediator to price-perceived quality effects.Furthermore, price-perceived quality effects are stronger for high- and low-familiar subjects compared to moderately familiar subjects and cues had astronger effect on perceived quality with higher product familiarity than withlower product familiarity. In a later paper, Rao and Monroe (1992) returned to
    • 37the influence of familiarity and found that high-knowledge consumers did notdiscount price as much as low-knowledge subjects, mostly because of theirunderstanding of the actual quality. In addition, there findings show that higherknowledge leads to higher willingness to pay because of the ability to judgequality. Familiarity has also been researched in relation to product features inZhou and Nakamoto (2007), who showed that product features andpreferences are moderated by familiarity and that products with unique featuresare perceived more favorably by experienced consumers. Bredahl (2004) alsoresearched familiarity and found that different cues are used depending on thelevel of familiarity toward the products. Furthermore, Underwood and Klein(2002) discovered that familiar private label and national brands were ratedboth better-tasting and healthier then less-familiar brands.The influence of recognition on decision making can be explained bymechanisms of processing fluency that are perceptual or conceptual (Tulving &Schacter, 1990). Processing fluency can be explained as the ease with which aproduct comes to the consumer’s mind (Winkielman, Schwarz, & Fazendeiro,2003; Winkielman, Schwarz, Fazendeiro, & Reber, 2003), resulting in higherpreference (Willems, Van der Linden, & Bastin, 2007). Perceptual fluency is theease with which attributes from the environment can be identified through low-level processing (Jacoby, Kelley, & Dywan, 1989), while conceptual fluencyinvolves higher-level processing through interpretation of appropriate mentalconcepts for the product (Whittlesea, 1993). Processing fluency is the result ofthe mere exposure effect (Bornstein, 1989; Seamon et al., 1995) formed whenconsumers are repeatedly exposed to a product to make it less threatening(Zajonc, 1968; Zajonc, 1980) and more familiar (Coupey et al., 1998; Harrison,1977; Kara et al., 2009). Hence, exposure results in enhanced processing fluencythat makes a brand more accessible in memory (Jacoby & Dallas, 1981) andmore approachable and preferred by the consumer (Bornstein, 1989; Seamon etal., 1995). Furthermore, the exposure effect has been shown to influencepreference formation and choice at fast-exposure durations (Seamon, Marsh, &Brody, 1984). Hence, only a fraction of a second is needed for a consumer tobe influenced and become more familiar with a product. In fact, the mostextensively researched effect of familiarity has been on preference formationand the positive attitude resulting from mere exposure effect (Willems et al.,2007).
    • 38In their paper on brand familiarity, Baker et al. (1986) concluded thatrecognition results in (1) enhanced perceptual identification, (2) increasedprobability of inclusion in the consideration set, (3) forms preferences, and (4)enhanced influence on choice behavior. These conclusions can be explained bythe use of decision strategies throughout the decision-making process.First, regarding perceptual fluency, there is a strong belief that recognition is thefirst simplification strategy used by the consumer in the construction ofconsideration sets (Gigerenzer & Goldstein, 1996; Goldstein & Gigerenzer,2002; Simon, 1990). It is argued that product recognition results in a faster andmore selective decision process which in turn results in better decisionoutcomes (Kuusela, Spence, & Kanto, 1998; Russo & Leclerc, 1994). Forexample, Pieters and Warlop (1997) identified that product recognition has aprobable causal effect on attention attraction and in preference formation.Furthermore, research on recognition and attention showed that repeatedexposure to a setting produced a form of learning that will direct attentiontoward a target (Chun & Jiang, 1998). This can be explained by the consumers’use of recognition as a heuristic in the decision-making process. Therecognition heuristic is a non-compensatory strategy which states that if adecision-maker presented with two options and one option is more recognizedthen the other, then the recognized option will have a higher value for thecriterion (Goldstein & Gigerenzer, 2002). The recognition heuristic was laterextended to more than two options, thus more comparable to the concept offamiliarity (Gigerenzer & Goldstein, 2011). When the recognition heuristic isapplied to more than two options, it has been shown to be part of theformation of consideration sets (Marewski et al., 2010). However, when theconsideration set is established, the alternatives within the set can be rankedbased on cues (Marewski et al., 2010). Furthermore, product recognition is notlimited to one perceptual attribute, such as brand name, since all attributes canbe recognized as cues, such as color and shape (Wolfe & Horowitz, 2004),indicating properties that are diagnostic and meaningful for the consumer (Alba& Hutchinson, 1987). If products are not recognized, then consumers mayinfer values for a missing attribute using heuristics based on other information(Bettman et al., 1998) depending on the accessibility of the information.Non-compensatory or compensatory strategies are used in the later stages ofthe decision-making process to discriminate between the alternatives on a cuelevel. One such example is the take-the-best heuristic, which is a binary model
    • 39based on the inferred cue value (Gigerenzer & Goldstein, 2011). The rule forthis heuristic is to search through the cues in order of their validity. Search isstopped when the first cue that discriminates between alternatives is found. Thecues are ordered unconditionally according to their validity. In a multi-alternative setting, such as the retail shelf, take-the-best heuristic is reformulatedto the deterministic-elimination-by-aspect heuristic (Marewski et al., 2010),which proposes that attributes can be ordered in relative importance; in a multi-stage process, alternatives are examined sequentially by attributes (Hogarth &Karelaia, 2005). Thus, the decision-maker first examines alternatives based onattributes with highest importance. If alternatives do not match the attributes,they are eliminated and the set of alternatives decreases for the next iteration.This heuristic relies on the importance of cues and their ordering, which is inturn based on preference (Hogarth & Karelaia, 2005).In conclusion, the recognition heuristic is believed to be the first used incomplex decision making (Gigerenzer & Goldstein, 1996; Simon, 1990) whenconstructing the consideration set (Oeusoonthornwattana & Shanks, 2010) andserves as the first step into other strategies. However, after the initial screeningof the alternatives, recognition becomes one among several factors evaluated bythe consumer (Oeusoonthornwattana & Shanks, 2010). Recognition influencesthe decision-making process, but it is not the only precursor of choice.Consumers employ recognition-based strategies in the initial shelf screening,with recognized products progressing further into the decision-making process.Furthermore, these products will receive more attention and increasedpreference as a result of mere exposure and processing fluency. This indicatesthat the relationship between exposure, fluency, and preference formation is anongoing process as preference is constructed every time a consumer interactswith a product. Since preferences are known to be formed throughconstruction and retrieval (Payne et al., 1990; Payne et al., 1993; Payne et al.,1988), comparative cue judgment depends on preference formation throughboth in-store and out-of-store factors as discussed in the following section.2.3.2 PreferenceOne important difference between normative and behavioral decision theory isthe concept of preference construction. In normative decision theory, theassumption is that preferences are constant and independent of other availableoptions. However, in behavioral decision theory, preferences are constructed
    • 40using information processing strategies and subject to influence (Bettman,1979; Bettman & Park, 1980; Payne et al., 1992; Slovic, 1995) because of limitedcognitive capacity (March, 1978).Consumer preferences are adaptive and influenced by in-store and out-of-storefactors such as decision task, environment, and experience (Payne et al., 1990;Payne et al., 1993; Payne et al., 1988). Nonetheless, consumer preferences arenot always constructed as they are dependent on experience levels (Kuusela etal., 1998; Nowlis & Simonson, 1997). There are two main perspectives of howpreferences are formed during the decision-making process. First, someresearch points toward consumers having a consistent set of preferences thatare based on cognition and previous experiences (Smith, 1989; Smith, 1994).Second, research has identified that preferences are not fixed, but ratherconstructed in the decision-making process through anchoring, prominentdimensions, elimination of elements, and addition of new attributes (Slovic,1995). Consumers use previous experiences that influence preferences in anygiven situation (Slovic et al., 2002). Nevertheless, research has shown throughseveral preference-reversal experiments (Cox & Epstein, 1989; Cox & Grether,1996) that in complex decision-making situations, preferences are constructedrather than retrieved (Gregory et al., 1993; Payne et al., 1992). Furthermore,preferences can also be constructed when consumers have insufficient orconflicting preferences or insufficient familiarity (Lichtenstein & Slovic, 2006).Thus, preferences can be formed through construction and retrieval (Shafir,Simonson, & Tversky, 1993; Weber & Johnson, 2006) depending on the levelof complexity (Gregory et al., 1993; Shafir et al., 1993; Slovic, 1995) andfamiliarity (Lichtenstein & Slovic, 2006).In visual attention research, the phenomenon of preference construction hasbeen described as “preferential looking” (Shimojo, Simion, Shimojo, &Christian, 2003). Preferential looking is closely linked to the mere exposureeffect and explained as an increase of visual attention leading to increase ofpreference toward the object (the more we look, the more we like). Similar topreference construction, preferential looking is influenced by the complexity ofthe decision. As the decision making gets more complex, visual attention has agreater influence in preference formation (Shimojo et al., 2003). Themechanisms of preferential looking were developed and tested in an empiricalresearch study by Simion and Shimojo (2007) that examined the relationshipbetween mere exposure, visual attention, and preference. Simion and Shimojo
    • 41(2007) proposed that mere exposure effects interact with preferential looking tocreate a positive feedback loop, a process that is defined as the gaze cascademodel; that is, the more we look, the more we like, and the more we like, themore we look. In fact, in some cases, increased attention on a product adds 13percentage points to the probability of consideration for choice (Chandon et al.,2008). Previous research has shown that there is a relationship between visualattention and preference formation. One such example is Pieters and Warlop(1999) who used eye tracking to measure the effect of time pressure and taskmotivation on decision making. The stimuli were color slides of consumergoods, stacked in two rows of three brands on each row with productcategories ranging from rice to canned soup. Pieters and Warlop showed thatvisual attention on the chosen alternative was longer (53 milliseconds) then forthe unchosen alternative. Preference construction is believed to be based onactivated goals as a function of the cues that are available for the decision(Kruglanski et al., 2002) as discussed in the next section.2.3.3 Choice TaskThe constructive view of preference formation has been researched fromseveral perspectives (Payne et al., 1992). There are two closely related concepts,namely concreteness and framing, which are fundamental to preferenceconstruction (Lichtenstein & Slovic, 2006). The first is the concept ofconcreteness principle which states that people accept and use information andformat of the information which they are given (Slovic, 1972). Hence, whengiven the premises of a decision (decision criterion), the consumer will seek thepreferred alternative within that structure which that will influence decisionprocess and choice (Sethuraman, Cole, & Jain, 1994). The framing effect hasbeen shown to influence decision outcome through changes in informationacquisition based on how the decision criterion is framed (Tversky &Kahneman, 1986). By framing a problem with different decision criterion, thedecision-making will be influenced through changes in information acquisition(Bettman, 1979). Tversky and Kahneman (1981) describe that the framingeffect as a visual illusion more than a computation error, because framinggoverns the perception and attention of the decision maker. The influence offraming is described as, “individuals will devote more effort to examining information theybelieve will help them attain whichever goals are more heavily weighted in that situation”(Bettman et al., 1998, p. 193). However, if no decision criterion is given, theconsumer will construct their own criterion (Bettman & Sujan, 1987). One
    • 42interesting aspect of goal choice is its influence on consumers with differentlevels of familiarity. Independent of familiarity for the products, consumers canmake intelligent and goal-consistent choices when given a decision criterion(Huffman & Houston, 1993). It is also noteworthy that the influence of theframing effect is highly dependent on the decision criterion’s wording(Schneider, 1992; Tversky & Kahneman, 1986) and mental associations(Barsalou, 1999). Thus, if the consumer is asked to “buy” a product from theshelf, the associations made are dependent on relevant consumer cues.However, if the consumer is asked to “buy the healthiest” product on the shelf,associations are made toward the cues that the consumer believes communicatehealthiness, as defined by the decision criterion.In visual search, choice tasks influence visual attention deployment (Desimone& Duncan, 1995; Van Der Lans et al., 2008a; Yantis & Johnson, 1990) as theconsumer must visually search for task-relevant information to find a targetwithin a set of distractors (Yantis, 2000). Therefore, choice tasks activatememory structures (Huffman & Houston, 1993; Huffman & Kahn, 1998;Meyvis & Janiszewski, 2002) that direct visual attention (Van Der Lans et al.,2008a; Wedel & Pieters, 2008b) to different meaningful elements (Nedungadi,1990; Nedungadi, Chattopadhyay, & Muthukrishnan, 2001). Productrecognition and product preference are activated when consumers are givenchoice tasks (Lee & Labroo, 2004; Payne et al., 1990; Payne et al., 1993; Payneet al., 1988). Previous research has shown that it is possible to alter consumers’motivational mindset by using different choice goals or choice tasks (Morales,Kahn, McAlister, & Broniarczyk, 2005; Quinlan & Humphreys, 1987; Treisman& Sato, 1990; Wolfe, 1994). The alteration of the motivation mindset also altersdecision-maker searching behavior, which can be observed through visualattention (Underwood & Foulsham, 2006). One such example is the study byBialkova and Trijp (2011) which tested the effects of nutritional labels onattention. Their results showed that a shopping goal directed attention towarddesign features that were meaningful in relation to the goal. The shopping goalwas also tested by Chandon et al. (2009) who examined a choice orconsideration goal condition. Their results showed that participants spent lesstime in the choice than in the consideration goal condition. The participantswere fast and efficient when choosing a product and more analytical inconsidering one. The difference in time between the two tasks can be related tothe different heuristic strategies used in the decision-making process. Tversky etal. (1988) suggested that choice invokes qualitative reasoning, while
    • 43consideration requires more quantitative searching. It is well known that searchtasks influence consumers’ visual attention toward the inherent properties ofthe search task (Henderson, Weeks, & Hollingworth, 1999; Henderson, 2003).Van der Lans et al. (2008a) exemplified this phenomenon by using ketchup as asearch task. If the consumer is given the task to find ketchup, the cues (e.g., redcolor) will be more salient than other cues that are not relevant. The influenceof shopping goals in consumer evaluation, search, and shopping behavior is animportant future research area (Puccinelli et al., 2009).In the present dissertation, choice task or shopping goals are used to alter themotivational mindset of the consumer. Shopping goals are used to direct visualattention of the consumer towards perceptual features that are related to thetask, hence giving homogenous preparatory retrieval of cues. Nonetheless, theshopping goals used in the present thesis are not target (ketchup) or brand-specific (Heinz); rather, they are more conceptual (exclusive/healthy) andrelated to associations between conceptual and perceptual features. Productpackaging is an influencing factor that embodies both perceptual andconceptual features as discussed in the coming section.2.3.4 Product PackagingThere are several definitions of product packaging that span over differentresearch fields (Ampuero & Vila, 2006; Vila & Ampuero, 2007). Prendergastand Pitt (1996) divide the function of product packaging into logistic andmarketing functions. Even though both perspectives are important researchareas, the focus of the present thesis is on the marketing function, since a largebody of research has illustrated the importance of packaging design as acommunicative tool that influences consumer attention (Berkowitz, 1987;Bloch, 1995; Bloch, Brunel, & Arnold, 2003; Schmitt, Simonson, & Peters,1997). Packaging is the first point of interaction with the product in the retailenvironment; product perception is based on this interaction which in turninfluences the decision making (Bloch et al., 2003). Furthermore, mostconsumers make their purchase decisions solely by looking at the front of thepackage (Urbany, Dickson, & Kalapurakal, 1996). Product packaging has beenacknowledged as a silent salesperson (Pilditch, 1973) that not only capturesconsumers’ attention, but maintains it amid the visual clamor of competingproducts (Judd, Aalders, & Melis, 1989). Thus, product packaging can be usedas a powerful POP in-store influencing tool (Prone, 1993). Since a majority of
    • 44decisions are made in-store, brand owners can greatly influence decisionmaking through packaging (Bucklin & Lattin, 1991; Hoch & Deighton, 1989;Prone, 1993; Rundh, 2005; Van Der Lans et al., 2008b); this is especially truefor low-involvement consumer nondurables (Underwood & Ozanne, 1998).As a marketing tool, product packaging is used by the consumer as a surrogateindicator of value (Sullivan & Burger, 1987) based on the cues that togetherform the holistic impression of a product. The cue utilization theory labelattributes that signal cue properties of a product (Richardson, Dick, & Jain,1994) and divides them into intrinsic and extrinsic cues (Olson & Jacoby, 1972).Intrinsic cues involve the physical composition of the product; such as, flavor,texture, and sweetness (Zeithaml, 1988). Extrinsic cues are product-related, yetare not a part of the physical product itself. Information such as brand name,price, and logo are considered as conceptual extrinsic cues (Zeithaml, 1988).Furthermore, what the product conveys in its appearance, such as color,luminance, shape and size, are perceptual extrinsic cues; based on theclassifications by Zeithaml (1988) and Underwood and Klein (2002). Thesecues are combined together to communicate the diagnosticity of the product;that is, the likelihood of cues leading the consumer to a successful taskresolution (Dick, Chakravarti, & Biehal, 1990).Prior research on cue utilization has predominantly tested the relationshipbetween price and quality perception (Bredahl, 2004; Burnkrant, 1978; Dodds,Monroe, & Grewal, 1991; Jacoby, Olson, & Haddock, 1971; Mitra, 1995; Rao &Monroe, 1988; Rao & Sieben, 1992; Richardson et al., 1994; Rigaux-Bricmont,1982; Szybillo & Jacoby, 1974; Valenzi & Andrews, 1971; Warlop, Ratneshwar,& Van Osselaer, 2005). In a study testing the relationship between price andtaste quality, Valenzi and Andrews (1971) showed that taste quality ratings werepositively related to price information; that is, that price is a quality cue. Thiswas further tested by Jacoby et al. (1971) in an experiment with price, brandimage, and product tasting that found that when price is the only varied cue, ithas an effect on quality perception. However, price is not an indicator of qualityin a multi-cue setting. Similar results were found by Szybillo and Jacoby (1974)who showed that price had no effect on quality perception in a multi-cuesetting. Furthermore, Burnkrant (1978) discovered in a price and advertisementstudy that price is related to product quality perception if the product iscomplex. In a study of willingness to buy, Dodds et al. (1991) investigated howprice, brand name, and store information influenced perceptions of product
    • 45quality and value and showed that consumers’ quality perception was based onprice when it was the only available cue. Dodds et al. further showed thatproduct evaluation expands beyond price-perceived quality relationships. Infact, the relationship between price and quality is product-specific and weak ingeneral (Gerstner, 1985). On the other hand, product packaging cues have inseveral studies been shown to influence quality perception.Rigaux-Bricmont (1982) showed that packaging and brand name influencesproduct quality perception. In an effortful study with 1,564 participants usingproduct packaging ingredients, Richardson et al. (1994) discovered that in theassessment of product quality, consumers rely on packaging cues. Furthermore,in a study of individual packaging cues, Underwood and Klein (2002)discovered that manipulations on packaging cues have a strong influence onattitude towards the product. Bredahl (2004) added several more packaging andproduct-related cues. Some cues tested were brand name, price, cardboard tray,product label, package sleeve, and information leaflet. Bredahl also showed thatcues were used to assess health and expected eating quality. For both, brand,not price, was the predominant cue. In an experimental study, Warlop et al.(2005) investigated whether brand name, price, packaging shape, and packagingcolor facilitates quality difference awareness. Warlop et al. discovered thatbrand name has a greater effect on awareness and quality retrieval if the name isdifferentiated, as compared to similar or alphabetic names. However, even ifbrand names are similar but packaging shapes and colors are differentiated,memory-based quality judgments will increase based on packaging cues.Despite the use of deceptive price cues, distinctive brand cues improve theaccuracy of quality judgment independent of price manipulations. In summary,it is apparent that price is not a decisive cue in the judgment of quality;however, product packaging cues have an influence on quality perception anddecision outcome.Even though packaging cues have influence, it is important to point out thatthey only have an influence if they are meaningful for the consumer (Sullivan &Burger, 1987). For example, imagine the cues that are important for purchasinga tomato. Certainly the color is one such cue as it indicates ripeness of thetomato. However, this is dependent on how experienced the consumer is inbuying tomatoes. If the consumer never purchased a tomato before, then coloris not predictive for the decision outcome as the consumer is unable to use thiscue confidently. This concept was introduced by Cox (1967) and named the
    • 46predictive and the confidence value of cues. If the packaging design cues havehigh predictive and confidence value, then the salient beliefs are trigged and theconsumer can evaluate the product. This is the case when the designer isolatesand incorporates meaningful cues into the packaging design. However, whenthis fails, the product becomes invisible to the consumer (Dick et al., 1990).Previous research has shown that appealing and attractive packaging designinfluences memory and visual attention while unattractive designs activateuncertainty, disgust, and expected risk (Stoll, Baecke, & Kenning, 2008).Furthermore, it has been argued that the influence of product packaging onconsumer behavior should be studied by focusing on separate package elementsand how they relate to decision making (Underwood, Klein, & Burke, 2001). Inaddition, design element organization can facilitate information search andinfluence which packaging aspects receive attention (Janiszewski, 1998).Textual and pictorial cues have been recognized as important in influencingperception. Researchers often classify text and pictures into separate categories,such as verbal and visual (Rettie & Brewer, 2000), or informational and visualpackage cues (Silayoi & Speece, 2004; Silayoi & Speece, 2007). Pictorialelements are central for capturing and retaining consumer attention (Childers &Houston, 1984; Pieters & Wedel, 2004; Underwood et al., 2001), and textualelements have a large impact on consumers’ choices (Pieters & Wedel, 2004).For instance, Feiereisen et al. (2008) showed that words, more than pictures,generally enhance comprehension of new products. There is quite extensiveresearch regarding the processing of textual and pictorial information in thehuman brain. The functional asymmetry of the brain’s hemispheres has beenacknowledged for more than a century (Witelson & Pallie, 1973). Because ofthe cross-connection between the hemispheres and the visual fields,information from the left visual field is processed in the right hemisphere, andinformation from the right visual field is processed in the left hemisphere innormal subjects (Hansen, 1981; Jordan et al., 2003; Koivisto & Revonsuo,2003). Apart from some recent studies (Deng & Kahn, 2009; Pieters & Warlop,1999; Rettie & Brewer, 2000; Silayoi & Speece, 2004; Silayoi & Speece, 2007;Underwood et al., 2001; Underwood & Klein, 2002), there is a general lack ofresearch on the communicative aspects of textual and pictorial packageelements, specifically in relation to visual attention.In summary, product packaging is the most influential tool in capturing POPconsumer attention (Berkowitz, 1987; Bloch, 1995) and communicating value
    • 47(Page & Hen, 2002). The visual aesthetics of packaging design can influenceconsumer perception by differentiation from competitors, comprehension ofproperties, and formation of a relationship between consumer and productthrough the sensory experience (Bloch et al., 2003). The package influencesdecision making because it is a medium of attention, information, andaesthetics. Packaging that captures consumer attention facilitates quick in-storedecision making (Silayoi & Speece, 2004). Attracting consumer attention,however, can be difficult because of the large number of competing productsin-store, and the fact that most of these products are ignored (Inman et al.,2009), specifically by consumers who shop habitually (Underwood et al., 2001).Nonetheless, once the consumers’ attention has been caught, the features of thepackaging can serve to underline the uniqueness and originality of the product(Silayoi & Speece, 2007). Furthermore, perceptual packaging cues play a majorrole, especially during low-involvement shopping or rushed shopping situations(Silayoi & Speece, 2004). Few studies have investigated the communicativeeffects of product packaging, as well as the link between packaging andconsumer attention (Underwood et al., 2001). Nevertheless, even the slightestchange in attention can have a significant impact on brand memory (Wedel &Pieters, 2000), perception (Pieters & Warlop, 1999), and sales (Janiszewski,1998). Even though capturing attention is not always enough for brandselection (Janiszewski, 1993), visual attention is crucial for choice as explainedpreviously. Consumers choose with their eyes (Clement, 2007) in brandconsideration (Pieters & Warlop, 1999; Russo & Leclerc, 1994), which meansthat unseen products are unsold products. On the other hand, a majority of theseen products are also unsold; hence, communicating values is as important asbeing seen at POP.2.3.5 Product PlacementAs mentioned in the previous chapter, product packaging has influence onconsumer decision making. Nevertheless, the product is rarely exposedunaccompanied by competitors in the shelf display; thus, such factors ascategory structure (Nedungadi et al., 2001) and space elasticity (Chandon et al.,2009; Dréze et al., 1994) also influence consumer attention, perception, andchoice. For instance, Dréze et al. (1994) showed through an empirical study onshelf space management that number of facings and facing area influencesconsumer decision making. Furthermore, product value is also communicatedthrough the placement of the product (display orientation) in the shelf (Hansen,
    • 48Raut, & Swami, 2010; Murray et al., 2010). In the present thesis, the focus is onspace quality (e.g., top-level versus floor-level shelving), specifically the inferredvalue from vertical placement of products.Only a few research papers have tested the influence of product placement onconsumer decision making (Chandon et al., 2008; Chandon et al., 2009; Drézeet al., 1994; Hansen et al., 2010; Sigurdsson et al., 2009; Valenzuela & Raghubir,2010; Valenzuela & Raghubir, 2009; Van Nierop et al., 2011). In a study of 115items in eight product categories, Dréze et al. (1994) showed that sales almostdoubled when moving products from the worst (top- or bottom-level) to thebest (mid-level) vertical location. However, a horizontal movement did nothave a similar effect. Comparable results were found by Hansen et al. (2010),who showed that the effect of vertical positioning is double the size ofhorizontal positioning and that facing effect is less than position effect.Sigurdsson et al. (2009) and Van Nierop et al. (2011) also found similar results,with the latter showing that sustainable brands received more market sharewhen placed in the middle of the shelf and that eye level is the best verticalposition.One explanation for how product placement influences decision making is thatproducts at certain placements in the shelf can have a perceptual advantage(eye-level position grabs attention). Of the few studies testing the influence ofshelf verticality on visual attention, one by Chandon et al. (2008) explored POPbrand consideration using visual measurements. The study was done on twoproduct categories with 309 participants. The shopping goal was to find brandsthey would consider buying. Results showed that brands in the center of theshelf were seen by almost all consumers and the likelihood was low for noticinga brand on shelf extremities. However, this result could be related to the naturalresting position of the eyes (Dréze et al., 1994). In fact, previous research invisual attention has identified central fixation bias or center bias (Foulsham &Underwood, 2008; Parkhurst, Law, & Niebur, 2002; Tatler, 2007; Tseng, Carmi,Cameron, Munoz, & Itti, 2009) that can explain this effect. In an attempt toexplore center bias, Tatler (2007) tested 120 natural scenes in an empirical studywith 52 participants with free viewing and search task condition. Tatlerdiscovered that when participants view complex scenes, there is a strongertendency to look at the center of the image then at the extremities. Similarresults were found by Foulsham and Underwood (2008) in an empirical studywith 22 participants using eye tracking to predict fixation locations. Their
    • 49results indicated that participants were more likely to move from any positionof the shelf to the center region across any task or image. Furthermore,Chandon et al. (2008) showed that shelf verticality has a greater effect onattention then shelf facing and that eye-tracking experiments should be used toexplore product placement. In a later paper, Chandon et al. (2009) tested in-and out-of-store factors in relation to visual attention, consideration, andchoice. Their results showed that shelf verticality has a strong influence onattention: a product on the top shelf (compared to bottom) increased visualattention by 17 percent and choice by 20 percent. They believed their resultswere related to participants’ preferences for premium products in the chosencategories.A second explanation for how product placement influences decision making isthat products at certain placements can have a conceptual advantage (e.g., topshelf is exclusive), resulting in inferred value. Consumers use several decisionstrategies from which they infer value and the placement of products can beone such inference point (Buchanan, Simmons, & Bickart, 1999). Consumersmake many product evaluation inferences during the decision-making processand these inferential beliefs also have impact on the decision outcome (Huber& McCann, 1982). For instance, it has been observed that verticality is used toinfer value on objects or people. Schubert (2005) investigated the social conceptof power and found that it is embodied in vertical spatial positions. Meier andRobinson (2004) showed that objects that are high in visual space are perceivedas good, whereas objects that are low in visual space are perceived as bad.Hence, previous research indicates that value can be inferred from verticalobject positioning. This was further studied in the context of POP decisionmaking by Valenzuela and Raghubir (2009), who showed that when consumers’goals are consistent with purchasing the most popular items, items in the centerare more likely to be chosen. Furthermore, in a later study, Valenzuela andRaghubir (2010) showed that consumers make value inferences based on shelfverticality, as products on the bottom were priced lower then products on thetop shelf.
    • 502.4 Summary of Theoretical FrameworkIn summary, it is evident from past research that consumers are adaptivedecision makers who are bounded by the limitations of cognition and attention.These limitations force consumers to simplify complex decision making atdifferent stages of the decision-making process by employing appropriatestrategies. The selection of strategies can be conscious or automatic, stemmingfrom information that is processed in the environment or from the mind of thedecision maker. Iterations of comparisons based on the selected decisionstrategy reduce the set of alternatives to one option. Product recognition isbelieved to be the first strategy used in complex decision making whenconstructing the consideration set, and it also serves as the first step towardsother strategies. Recognition-based strategies are used in the initial shelfscreening, with recognized products progressing further into the decision-making process. The recognized products will receive greater attention andincreased preference as a result of mere exposure and processing fluency.However, preferences are formed through construction and retrieval, whichmeans that they depend on both in-store and out-of-store factors. Productpackaging is one such in-store factor that induces processing fluency as amedium of attention, information, and aesthetics. In fact, product packaging isbelieved to be the most influential tool for capturing POP consumer attentionand communicating value. Product value is also communicated through theplacement of the product on the shelf, as consumers can infer value fromdisplay orientation. The influence of out-of-store and in-store factors isreflected in the decision-making process and can be measured via visualattention and manipulated by choice tasks. The choice tasks change the focusof the inquiry and guide visual attention, thereby revealing the semanticallyimportant factors that influence the decision-making process. The followingsection describes the methods used to manipulate and measure the influence ofin-store and out-of-store factors on consumer decision making.
    • 513 MethodologyThis chapter provides detailed background on the equipment and proceduresthat constituted the two methods that were combined in all experiments toexplore the research questions. This chapter also covers the differentexperiments’ stimulus image manipulations and a justification for their design inrelation to the selection of eye tracking as a process-tracing method.3.1 Measuring VisionEye movement studies have their roots in reading research (Just & Carpenter,1976) and pre-date modern computers by 100 years. Initially, an observer usingmirrors registered the participant’s eye movements on paper. The tools thatwere developed in the end of 18th century were crude and often inaccurate.Techniques for tracking eye movements were intrusive, involving directmechanical contact with the eyes. Progress in photography led to a techniquebased on retaining the light reflected from the cornea on photographic plate.Early in the 20th century, the first non-intrusive eye-tracker was developed,using light reflections from the cornea (Holmqvist, Nyström, Andersson,Dewhurst, & Van de Weijer, 2011). Many advances in eye tracking wereeventually made by combining the corneal reflection and video-basedtechniques. Although less intrusive eye-trackers had been developed by the endof the 1940s, intrusiveness was still a problem. Eye movement researchflourished in the 1970s, with advances in both technology and methods to linkeye movements to cognitive decision-making processes. Prior to the 1970s,psychologists who studied eye movements attempted to avoid factors such asmemory and cognitive workload, preferring to focus on lower-level visualstimulus properties, such as contrast and target location. However, the focuschanged in the 1970s to higher-level cognition and eye movement.Because of recent technological development, modern eye-trackers are moreaccurate and less intrusive than their predecessors. Independent of thedevelopments, an important aspect of eye-tracking is to understand itsapplicability to research questions. The tool in itself only records theconsumer’s eye movements, but eye movements have no meaning if theycannot be translated into insights or actions. The developed research methodsplay a central role in the practical use of the tool. In the coming sections, theapplication of eye tracking to the present thesis is discussed.
    • 523.1.1 Eye tracking as a Process Trace MethodResearchers have advocated process-tracing methods in studying decision-making mechanisms (Olshavsky, 1979; Payne, 1976), such as effort reductionand simplification strategies based on heuristics (Shah & Oppenheimer, 2008).Specifically, visual attention measurements are highlighted as a promisingmethod for studying decision making (Glaholt & Reingold, 2011; Russo, 2011).Eye-movement measurement boosts the understanding of consumer POPdecision making as every retail environment decision requires visualinformation acquisition. Following the sequence of eye movements from thefirst interaction to choice illuminates the mind of the consumer and the factorsthat are influential in this process (Russo, 2011; Wedel & Pieters, 2008b). Theuse of eye tracking in consumers decision making research has made it possibleto study the decision-making process (Clement, 2007; Russo & Leclerc, 1994),the effects of time pressure during brand choice (Pieters & Warlop, 1999), thesaliency of a package in terms of its top-down and bottom-up qualities(Chandon et al., 2008), and how placement and the number of facings affectattention (Chandon et al., 2009). Furthermore, rigorous tests have shown thateye fixations are a valid measure of attention and that equipment does notdisturb ongoing cognitive processing (Russo, 1978). It has also been shown thateye tracking is a valid tool, even when there is a possibility for social desirabilitybias (Russo, 1978). Eye tracking also has high predictive validity whenmeasuring information processing that is generated from cognition (Rosbergen& Pieters, 1997), which is similar to the information processing explored in thecurrent thesis. Eye tracking has been advocated by Russo for well over 30 yearsbecause of its advantages in understanding consumer behavior and decisionmaking (Russo, 2011; Russo, 1978).3.1.2 Eye tracking Laboratory Equipment and SetupThere are several techniques for measuring eye movements, but the mostcommon procedure is through video-based, combined-pupil/corneal reflectionsystems (Duchowski, 2007; Holmqvist et al., 2011). This method has theadvantage of point-of-regard measurement, which allows for a higher degree offreedom for head movements when multiple ocular features are measured.Corneal reflection is created by infra-red light and is measured relative to thelocation of the pupil center. These ocular features are thus constant to each
    • 53other, independent of head movement. The distance between the cornealreflection and the pupil remains constant during head movements; however, thedistance between these points shifts during eye movement. The output of avideo-based corneal reflection device is x and y coordinates from the positionof the eyes relative to the digital or physical stimulus. Measurements with acorneal reflection device have an accuracy of 0.3 to 2.0 degrees depending onthe eye-tracking system, the setup of the equipment, and environmentalconditions (Holmqvist et al., 2011). The different systems on the market rangefrom 25 Hz to 2,000 Hz in camera sampling frequency.Eye trackersIn the process of writing the present thesis, there have been many advances ineye-tracking technology. There are two types of equipment used in theexperiment, namely the Tobii X50 and Tobii X120, produced by TobiiTechnology. The eye trackers are similar in function and setup; however, theX120 is the most recent generation of static remote eye-trackers developed andprovided by Tobii Technology. Both the X50 and X120 are binocular, video-based, combined-pupil/corneal reflection systems; however, the samplingfrequency differs between the units. The X50 has a 50 Hz and the X120 has a120 Hz sampling frequency of the camera that records the corneal reflectionand pupil. The X50 is used in the experiments for Paper I and the X120 insucceeding papers. The eye-tracking units have a freedom of head movementwithin a 12x19x12 inch (30x22x30 cm) head box at a 25.6 inch (65 cm) distancefrom the eye tracker.Eye-tracking SetupsThroughout the experiments, three different eye-tracking laboratories havebeen used. The setup of the labs has been almost identical with some minutedifferences in projection quality and projected screen size. Table 1 describes theequipment used in the three different lab setups. In general, Figure 4 illustratesthe setup of the three labs used for this thesis. The labs were designed with twoseparate rooms. One room was used as a waiting room and the other dedicatedfor eye-tracking recordings. Eye-tracking experiments were performed on full-scale product shelf projections resembling real product shelves in size andproportion. To be able to perform tests on full-scale shelves, a remote eye-tracker was mounted on a specially developed foot-stand. For the purpose ofshelf-testing, the quality of the projected shelf is important and was measuredby the resolution of the projected picture in relation to the projected distance.
    • 54Higher resolution gives a smaller individual pixel size at a specific distance. Forexample, the projection distance at one of the eye-tracking laboratories is 122inches (310 cm), from lens to screen, with a resolution at 720P. This setup givesa pixel size of approximately 0.0071 inch (1.8 mm). This is sufficient toillustrate shelves that are three sections wide and five sections tall with greatpicture clarity and detail.Eye-tracking experiments can be done on both digital and real shelf mockups.The disadvantage of using real shelf mockups is the aspect of moving realproducts and switching between product categories. Changing productpositions and switching between product categories becomes time- consumingas real products need to be moved around. Experimental flow is also a problemas the respondent will need to wait between each shelf mockup. Digital shelfmockups have the advantage of flexibility. Changes to the shelf mockup aredone quickly and switching between shelf stimuli takes only milliseconds.Another aspect of this method is the parallel face angle between the products atdifferent vertical shelf levels. If the face angle is the same on all the products onthe shelf, the products on the lower level will be seen from the same aspect asthe products in the middle and the top. The advantage is that none of the faceswill be seen more or less due to the angle. However, the disadvantage is that thepicture will be unrealistic for subjects that notice the visual glitch.
    • 55Figure 4: General setup of the three eye-tracking laboratories.Lab Eye-trackerProjector Screen Size Distances AppliedinPackagingMedia Lab –ThePackagingArenaTobiiX50TobiiX120Projection design F30Resolution:1280x720Aspect ratio:16:9Luminance: 3,000 lmWidth: 101” (256 cm)Height: 63” (160 cm)Diagonal: 119” (302cm)DPS: 106”(270 cm)DES: 77”(195 cm)Paper IPaper IIIPaper VMobile LabSystem –Arizona StateUniversityTobiiX120Toshiba TDP EW25Resolution:1280x800Aspect ratio:16:9Luminance: 3,000 lmWidth: 95” (240 cm)Height: 53” (135 cm)Diagonal: 108” (276cm)DPS: 110”(280 cm)DES: 84”(214 cm)Paper IIIPaper IVPaper VCTFConsumerLab – KarlstadUniversityTobiiX120Toshiba TDP EW25Resolution:1280x800Aspect ratio:16:9Luminance: 3,000 lmWidth: 95” (240 cm)Height: 53” (135 cm)Diagonal: 108” (276cm)DPS: 110”(280 cm)DES: 84”(214 cm)Paper IITable 1: Description of equipment used in the three eye-tracking laboratoriesEye-tracking recordings produce a vast amount of data as the movements ofthe eyes are recorded at high frequency. The data in turn can be used toproduce a great number of measures for analysis and interpretation of POPconsumer behavior. The following section provides an overview of themeasures used in the present thesis.ProjectorFloor StandEye-trackerDistance Participant-Screen (DPS)Distance Eye-tracker Screen (DES)
    • 563.1.3 Eye-tracking MeasuresThe eye-tracking data can generally be divided between (1) eye movementstoward or (2) eye position at a specific area of interest (AOI). An AOI is astimulus surface area that has established boundaries, such as two differentproducts in a product shelf or two different shelf levels. The boundaries of theAOIs are set by the researcher and stem from research inquires and theexperiment design. Eye movements and positions are aggregated on AOIs overtime. The AOIs are then statistically analyzed in compressions between eachother and/or in relation to other variables. The eye movements and positiondata can be categorized in more than 120 different measures that have beendefined through many years of research (see Holmqvist et al., 2011 for anoverview of measure definitions).Eye movements are often related to fast eye motion from one AOI to the nextAOI. These rapid movements are expressed as saccades (Duchowski, 2007;Holmqvist et al., 2011). During the saccadic movement that lasts between 30–80 milliseconds (ms), the visual input is stopped until the next onset of the eyeson an AOI (Holmqvist et al., 2011; Rayner, 1998). Saccadic eye movements arenot the focus of the present dissertation as information gathered from saccadiceye movements is not applicable, in the present context, and often thesemeasures are not validated. Furthermore, the eye-tracking equipment used inthe present dissertation does not allow for analysis of saccadic eye movementswith high reliability. Hence, the applied measures are related to eye positiondata such as aggregation of observations on an AOI and time until an AOI isobserved.Eye position measures are defined when the eyes rest for a short moment andvisual information is gathered; this is known as a fixation. The definition of afixation is that the eyes remain somewhat still over a period of time that lastsbetween 200–300 ms depending on the decision task (Duchowski, 2007;Holmqvist et al., 2011; Rayner, 2009). In all experiments for the presentdissertation, 200 ms is used as the defining fixation limit. Hence, fixationsbelow 200 ms are not included in the fixation-based measures. Several time-and count-based measures can be defined from fixations and aggregation offixation. The following section describes application and purpose of the eye-tracking measures used in the present dissertation. Table 2 shows aclassification for the different mesures used in the papers.
    • 57Measure Name Definition Function AppliedinAlternativeNameProportion ofParticipants NotedPercent ofparticipants thatnoted AOIExplore spread ofattention and possiblebiasesPaper I NotingFixation Count Number of fixationwithin an AOIGeneral viewing patternSemantic importance ofshelf structurePaper I Number ofFixationsTime to FirstFixationOrder of visual impactbetween differentAOIs expressed intimeSaliency of AOIsdepending on searchtask and locationPaper IPaper IIPaper IIINoting,AttentionObservation Count Number transition intoan AOIDistribution ofobservations duringstages of decisionmakingPaper V TotalObservationCount, MeanObservationCountObservationDurationAggregation offixations durationswithin an AOISemantic importance ingeneral and relation tosearch taskPreference constructionand attentionDistribution of attentionover stages of decisionmakingPaper IPaper IIIPaper IVPaper VTotalObservationDuration,MeanObservationDuration,ConsiderationTime to FirstFixationOrder of visual impactbetween differentAOIs expressed intimeSaliency of AOIsdepending on searchtask and locationPaper IPaper IIPaper IIINoting,AttentionObservation Count Number transition intoan AOIDistribution ofobservations duringstages of decisionmakingPaper V TotalObservationCount, MeanObservationCountTable 2: Table of eye-tracking measures used with definition and function in thesis.Proportion of Participants NotedThis is one of the most basic measurements of eye movement, which is used tofind the proportion of participants that have looked at a certain AOI. This is astraightforward measure that is expressed in percentage of participants thatnoted different AOIs. The measure is interpreted as reflecting the attention-grabbing properties of an AOI (Holmqvist et al., 2011). If the AOI is related toa successful task resolution, then a low proportion of participants noting is anindicator for redesigning or reallocating the AOI (Poole & Ball, 2005). In thepresent thesis, this measure is used to identify the general spread of attentionon the product shelf. The spread of attention is expressed as the proportion ofparticipants that have observed the horizontal and vertical positions of the shelf
    • 58independent of shelf content. This measure was used in Paper I to understandparticipants’ visual behavior at POP and to identify possible attention biases.Fixation CountFixation count is the number of fixations (dwells over 200 ms) within a specificAOI. The fixations are aggregated over time, either throughout the wholestimulus exposure or at specific time segments. As illustrated in Figure 5, eachcircle represents a fixation within four different AOIs. There are a total of eightfixations in Figure 5. However, the fixation count differs between the differentAOIs. For example, AOI A1 has three fixations while AOI B2 has one fixation.Number of fixations is one of the most used and most rudimentary measures(Holmqvist et al., 2011). However, the interpretation of fixation count is two-fold as it is positively correlated with semantic importance (Henderson et al.,1999) and negatively correlated with search efficiency (Goldberg & Kotval,1999). Hence, if an AOI is meaningful to the search task, then an increase offixation count is expected. On the other hand, if the AOI is confusing ordifficult to interpret, the fixation count will also increase on the AOI. Thedefinition of fixation count as a measure of semantic importance is moreapplicable in the context of POP decision making, as participants wouldexclude confusing products as they are not semantically important. Fixationcount was used in Paper I as a measure of general gaze behavior.Time to First FixationTime-to-first-fixation (TTFF) is the time it takes from stimulus onset until aspecific AOI receives its first fixation. This measure shows the order of visualimpact between different AOIs, expressed in time. For example, the AOI B1 inFigure 5 has a TTFF at 0 ms as this is the entry point in the metric of AOIs.However, AOI A1 has a TTFF at 700 ms resulting from sum of successivefixations in B1. Furthermore, AOI A2 will have a TTFF at 1,100 ms resultingfrom the sum of fixation time in B1 and A1. Only two fixations are made in A1before the transition to A2; hence, the added time from fixations in A1 is 400ms. Shorter TTFF reflects increased efficiency in locating an AOI (Holmqvistet al., 2011), which can be related to the visual saliency of the AOI. Typicalvisual impact measurements include the TTFF and proportion of participantsnoted.TTFF has been applied as a measure in Papers I–III for different purposes inrelation to the research question. In Paper I, TTFF was applied to explore
    • 59general viewing patterns toward the vertical and horizontal parts of the shelfindependent of product shelf content or structure. Furthermore, TTFF wasalso used to understand the influence of minute differences in packaging ondetection time. In Paper II, TTFF was used to measure the saliency of pictorialand textual elements at different horizontal locations on the packaging. TTFFhad a decisive role in Paper III as search tasks were combined with shelfmanipulations to understand how shelf verticality and product packaginginfluence the direction of attention.Observation CountObservation count is the number of occurrences a specific AOI has beenobserved over time. Observation count is different from fixation count as theremay be many fixations within one AOI, but few observations on the same AOI.This is illustrated in Figure 5, as AOI A1 has three fixations and twoobservations. Similarly, AOI A2 has two fixations and two observations.Hence, every transition to an AOI adds one unit of observation to that AOI.Similar to fixation count, observation count is believed to be related to semanticimportance (Henderson et al., 1999). Furthermore, observation count is one oftwo critical measures in the three-stage decision-making model by Russo andLeclerc (1994). Observation count was applied in Paper V as the three-stagedecision-making model was replicated with modern eye-tracking equipment.Observation DurationObservation duration is the main measure used in this dissertation and includedin several experiments. This measure is defined as the aggregation of fixationduration within an AOI for each observation count. In Figure 5, AOI B1 hasobservation duration of 700 ms based on two fixations and one observation.However, AOI A1 has two observations with the first observation having 400ms observation duration and the second have 200 ms, as the first observationconsists of two fixations and the second one fixation. Observation duration canin turn be expressed as mean observation duration or total observationduration. Mean observation duration is the average duration over the numberof observations; hence, AOI A1 has a mean observation duration of 300 ms.Total observation duration is the aggregation of all durations within an AOI;hence AOI A1 has a total observation duration of 600 ms. Similar to fixationcount and observation count, observation duration reflects the semanticimportance of an AOI in relation to a search task (Henderson et al., 1999;Holmqvist et al., 2011).
    • 60Observation duration is of great interest as it is a good indicator ofconsideration and choice in the context of POP decision making. A positiverelationship exists between observations on a product and consideration of thatproduct (Janiszewski, 1998; Pieters & Warlop, 1999; Russo & Leclerc, 1994;Wedel & Pieters, 2008b). For example, it has been shown that observations ona product add 13 percentage points to the probability of consideration(Chandon et al., 2008). The relationship between observation duration,consideration, and choice can be understood through the nature of stageddecision making. As participants include some options into a consideration set,the excluded options will receive fewer observations. Furthermore, theevaluation of options within a consideration set initiates an iterative searchprocess that aggregates further visual attention on the evaluated products(Russo & Leclerc, 1994). At the end of decision making, only a few options areconsidered and these options have travelled the furthest in the evaluationprocess, hence aggregating the most observations throughout the process.Observation duration, as total and mean, has been used in Papers I and III–V.In Paper I, it was used to understand the general distribution of observationtime on multiple shelves. It was also used to measure the semantic importanceof packaging elements in relation to their design features and placement. InPaper III, observation duration measured whether participants draw valueinferences from shelf verticality or from product packaging. In Paper IV,observation duration measured the influence of visual attention in relation toproduct recognition in preferences constructed during product choice. Similarto observation count, observation duration is a critical measure in the differentstages of the three-stage decision-making model by Russo and Leclerc (1994).Hence, in Paper V, observation duration is included as a measure to replicateand extend their findings.
    • 611 2ABFigure 5: Illustration of fixation sequence on four AOIs.Note: Small circle represents a fixation at 200 ms andlarge circle 500 ms.In conclusion, several years of rigorous analysis of the different measuresdetailed a covariance between the measures used in this dissertation. Forexample, increase in observation count often results in an increase in fixationcount as alternatives are evaluated and compared. Furthermore, they bothrepresent semantic importance or interest in relation to choice task. In addition,the increase of fixation and observation count in turn increases the totalobservation duration on an AOI. Hence, instead of exploring the variance withnumerous measures that point toward the same direction, the focus is set onfew measures. Holmqvist et al. (2011) wrote that, “the number of methods andmeasures for eye-tracking grows much faster than the validation of their interpretations” (p.468). For this exact reason, this dissertation is geared toward using fewmeasures and focusing instead on their theoretical contributions.
    • 623.2 Measuring DecisionIndependent of decision context, environment, or task, the endpoint of thedecision-making process is some form of choice. In the present thesis, choice ismeasured via several methods. Some methods explicitly indicate the chosenproduct from a set of alternatives. Other methods are used for indicatingfamiliarity and preference level for the chosen alternative among a set ofalternatives. In the coming section, these methods are described.3.2.1 ChoiceIn the initial pilot experiments for Paper I, the discovery was made that thechoice of the participant needed to be separated from the screening andevaluation process due to a procedural issue with eye-tracking recordings. Theissue was that the choice of the participant had to be registered at the end ofthe recording without disturbing the process data that had been recorded. Aseye-tracking equipment records all eye movements from stimulus onset tooffset, the participant’s choice through verbal cues, laser pointer, or mouse-clicking records artificial process data related to the choice procedure. To solvethis issue, an external human-computer-interaction device (large red button)was given to the participants to initiate the recording and define the point ofchoice that ended it. To initiate stimulus onset and eye-tracking recording, theparticipant used the button, which was placed comfortably in front of them.Participants were instructed to push the button again when the choice wasmade. After pushing the button a second time, they received an identical shelf-image and were asked to indicate their decision by left-clicking the mousecursor on the chosen product. The separation between the process data imageand choice data image was not noticeable by the human eye. In conclusion, theseparation of decision process and choice was made to remove any eyemovements that were caused by looking at the mouse cursor while indicatingthe chosen option. This procedural method was used for all experiments withdifferent types of choice cues (button and verbal cues) and choice indicationmethods (mouse cursor, laser pointer, and verbal indication).3.2.2 Self-reported Preference ratingThe purpose of Paper IV required preference measurements, as the relationshipbetween product recognition, visual attention, and preference formation was
    • 63explored. Three approaches were used to measure participants preference forthe products on the shelf. The first measure was the likelihood of buying, whichwas based on rating three products from the entire set of options on a printedshelf image in the questionnaire. The products would then be ordered inlikelihood of buying, with 1 representing most likely to buy, 2 and 3representing the second- and third-best options. The second measure ofpreference was choice, which was directly extracted from the eye-tracking partof the experiment. The third and most applicable measure was through a seven-point scale for the entire set of options from a printed product images in thequestionnaire. The participants were asked to rate how likely they were to buyproducts on a scale of 1 to 7 where 1 represented the lowest and 7 the highestlikelihood of buying that product. The likelihood-of-buying measure was usedas an indicator of preference, independent of whether the preference wasformed through construction or retrieved from memory. From a heuristicdecision-making standpoint, the assumption was that the likelihood of choicewould reflect preference formation on a cue-level comparison. Hence, productsthat fulfilled the value of a multitude of cues would have a higher “likelihood ofchoice” rating.3.2.3 Self-reported Familiarity ratingFor different reasons, attention measures in familiarity, preference, and choiceresearch are often related to duration of stimulus exposure (Chun & Jiang,1998; Hamid, 1973; Harrison & Zajonc, 1970; Janiszewski, 1993; Marcus &Hakmiller, 1975; Monahan, Murphy, & Zajonc, 2000; Reber, Winkielman, &Schwarz, 1998; Russo & Rosen, 1975; Seamon et al., 1984; Zajonc, 1968). PaperIV did not allow for any exposure experiments as visual attention in itself wastested as an influencing factor in preference formation. Hence, familiarity couldnot be manipulated trough exposure time or frequency. Instead, familiaritymeasure was self-reported similar to the method used by Coupey et al. (1998).For each product on the shelf, the participants were asked to indicate whetherthey frequently bought the product and recognized it from their regularshopping trips. The two questions produced three levels of familiarity: (1) HighFamiliarity – products that were frequently bought; (2) Moderate Familiarity –products that were recognized, but not bought; and (3) Low Familiarity –products that were not recognized. Products that were rated as frequentlybought, but not recognized were excluded.
    • 643.3 Influencing Vision and DecisionThroughout the experiments, the main objective was to understand participantsdecision making through influencing endogenous or exogenous variables.Hence, exploring such factors as vertical product placement or productrecognition entailed several manipulations on shelf structure, productcategories, and individual products. Furthermore, to increase the validity of themeasures in relation to the purpose of the experiments, several types of searchtasks were used. The following section describes these manipulations and theirrole in influencing participants’ visual attention and decision making.3.3.1 RecognitionProduct familiarity has been manipulated in several experiments to understandthe influence of product recognition on the decision-making process whenparticipants are totally or partly unfamiliar with the products in the shelf. InPaper III for example, participants were exposed to an unfamiliar product shelfwith foreign products. The purpose of the manipulation was two folded. First,to control for product recognition as an influencing factor during decisionmaking, hence increasing the probability of influence from other factors thatparticipants use to infer value on products. Second, to study the influence ofproduct recognition throughout the decision-making process. In Paper V, bothfamiliar and unfamiliar products were included in the product shelf. Byincluding totally unfamiliar and somewhat familiar products in the shelf, theinfluence of product recognition could be controlled for and measuredthroughout the decision-making process.3.3.2 Choice TaskUsing different consideration and choice tasks has been one of the mostimportant influencing factors in this dissertation. Eye tracking is particularlysensitive to decision task as it can fundamentally change the distribution ofattention. In the present thesis, search tasks have been used to induce bias inthe decision-making process. The bias in itself constitutes validity as the samedecision criterion is used for an experimental condition. The decision criteriaare always related to the purpose of the papers and experiments. This sectionwill not cover all applied choice tasks, as these are explained in detail in thepapers. However, a classification is found in Table 3 for tasks used in different
    • 65experiments and papers. The great advantage of using a search task is thatvisual attention, consideration, and choice can be paired with informationprocessing that stems from the task and influenced by in-store or out-of-storefactors. Hence, participants can evaluate the given search task based onpreconceived knowledge or they can gather information from the shelf thatcommunicates the semantic importance of the search task. Whichever is used,visual measurements will show when participants instinctively or selectivelymove their eyes toward products and shelf sections. Generally, there were twotypes of tasks used, namely consideration and choice tasks. Furthermore, insome experiments, the task was to look at the product shelf without making anydecisions.Choice Task Applied inIndicate target package when identified. Paper IExperiment 2Which is the most budget product on the shelf?Which is the most premium product on the shelf?Paper IIIExperiment 1Experiment 2Please choose a product that you would like tobuy, as if you were about to do so in the store.Paper IVExperiment 1Paper VExperiment 1Experiment 2Experiment 3Buy the healthiest product on the shelf. Paper VExperiment 3Table 3: Search and choice task used in the thesis3.3.3 Product PlacementManipulations of shelf layout positioning were done mainly in Paper III, whichmeasured the effect of vertical product placement. One version of the shelfstructure was congruent and the other incongruent with regard to the actualproduct placement in the supermarket. The congruent version had the premiumproducts on the top and budget products on the bottom. The incongruentversion had the budget products on the top and the premium products on thebottom. By switching the placement of the top and bottom of the shelf, theinfluence on attention, consideration, and choice of verticality inferred valuewas measured. There was only one target area and this was the shelf sectionthat coincided with search task. Accordingly, if a participant was asked tosearch for a premium product that was placed at the bottom of the shelf, thenthis position was the target position. By manipulating the shelf structure in
    • 66combination with value specific search tasks, the influence on visual attention,consideration and choice could be pinpointed in relation to product recognitionand packaging.3.3.4 Packaging AttributesThe influence of packaging attributes on attention has been studied in Papers Iand II from two different perspectives. Paper I explored information use indifferent designee cues and whether efficiency in locating target products couldbe influenced by manipulations to packaging attributed. Furthermore, Paper Iexplored whether minute differences in packaging could influence attention andidentification. The first part of Paper I was designed as a response timeexperiment, whereby the participants were shown a series of pictures, each ofwhich contained a set of four packages. Each set included three identicalinstances of the package containing the product intended for each specificapplication and one package containing the product intended for a differentapplication, which was the target package. The participants were asked toindicate by clicking with a mouse which of the packages was the target package;that is, the product intended for a different application. The target packagescame in four varieties: the original and three new designs. The manipulations onthe three new designs were on the position and color of one informative part ofthe packaging related to the application of the product. The second part ofPaper I addressed whether subtly different surface materials are detected.Participants were asked to view a number of displays, each of which containedthree packages, and indicate whether they perceived any differences betweenthem. The displays were arranged such that they contained one majordifference and a number of minor differences. The deviations were on thecardboard material. In Paper II, the main purpose was to test the influence oftextual and pictorial design element positioning on visual attention toward theseelement types. For this paper, a potato chip package was manipulated withthree different design features (one pictorial and two textual), which werelocated on the top right or top left side of the package, thus creating sixconditions. The pictorial element was a green clover symbol, and the textualelements contained the linguistic information “Win 100,000” and “New”. Thelocations (top left corner, top right corner) were chosen based on previousresearch showing that fixations are primarily performed toward a stimulus’ topleft area and because a stimulus’ right side location is seen as a heavy position,which can make certain food products (such as snacks) more preferable.
    • 674 Findings and Contributions within the Conceptual ModelThis chapter summarizes the research findings of the five appended papers andtheir individual contributions in the present dissertation. The papers’ maincontributions are divided between components of the model. As previouslyexplained, the conceptual model consists of four individual components withthe appended paper contributing to the different component. Together, thefindings from the appended papers form the foundation of the conceptualmodel depicted in Figure 6. The following is a description of the individualcontributions of the appended papers and their position within the conceptualmodel. Furthermore, the major results and contributions of each paper aredescribed. The purpose of this chapter is to set the background for the generalcontribution of the thesis as a whole.Figure 6: The complete conceptual model with individual influencing factors included.
    • 684.1 Paper I: Consumer Perception at Point of Purchase: EvaluatingProposed Packaging Designs in an Eye-tracking LabThis paper was the starting point in exploring the role of visual attention indecision making. For this paper, the relationship between in-store influencingfactors, visual attention, and decision making was primarily examined, as seen inFigure 7. In addition, for experiment 2 a choice task was given to theparticipants. The approach of this paper was somewhat of a ‘fishing trip’ asHolmqvist et al. (2011) would call it. Despite the empirical focus, the result ofthis paper set the foundation for the succeeding papers. The general purpose ofthis paper was to evaluate eye-tracking methodology in package and shelftesting. This was done in three unrelated experiments.Experiment 1: Determine whether there are any specific viewing patterns, interms of viewing order, that must be taken into account when studying digitallydisplayed products.Experiment 2: Evaluate the findability of a package; that is, the likelihood of apackage being found when looked for.Experiment 3: Evaluate the influence of small, perceived, and non-perceivedvariations in the packaging design on attention.Experiment 1 Experiment 2 Experiment 3Figure 7: Position of Paper I within the conceptual model.
    • 694.1.1 FindingsConsumers have general viewing patterns independent of product category.Consumers start looking at the center of the image; more participants seeproducts in the center. Subsequent to the central position, they look at the topsection, then move left or right, and finally move to the bottom section.Furthermore, they have more fixations on the left and right side of the shelffollowed by the top and bottom section. Consumers’ evaluations of packagingelements contribute to target product findability. In addition, the design ofpackaging elements proved to be decisive in identification speed. Consumerscan identify small deviations in packaging design and their attention is directedtoward the deviating packaging faster.4.1.2 ContributionsThere are some visual attention biases that have been discovered in earlierresearch. One such bias is central fixation bias or “central bias” (Foulsham &Underwood, 2008; Parkhurst, Law, & Niebur, 2002; Tatler, 2007; Tseng, Carmi,Cameron, Munoz, & Itti, 2009). Chandon et al. (2009) noted that the centerpositions receive more, but shorter fixations, which they interpret as ‘steppingstone’ fixations; that is, fixations during eye movements between locations thatexceed possible saccade length. Similar to previous findings, this paper pointstoward a bias of attention at the center of the shelf. The central bias effect canhave its foundation in the natural resting place of the eyes, as pointed out byDréze et al. (1994). Independent of its origin, the central bias was controlled forin the following papers.Other than the central bias, verticality bias was one of the main contributionsof this paper. The tendency of verticality bias was discovered as consumersgenerally start looking at the center of product shelf and then move to the topvertical position of the shelf. This effect has also been noticed by previousresearchers as products on the top shelves attract more attention than productson the middle or bottom shelves (Chandon et al., 2008; Chandon et al., 2009).Previous research has qualified these findings as being artifacts of brand effects(Chandon et al., 2008) and preferences for premium products (Chandon et al.,2009). This was fascinating as the general viewing pattern can either be relatedto inferred value from vertical position or the product packaging. This questionwas further studied in Paper III.
    • 70Previous research has described the decisive role of packaging in consumerdecision making (Berkowitz, 1987; Bloch, 1995; Bloch et al., 2003; Schmitt etal., 1997). Furthermore, it has been argued that the influence of productpackaging on consumer behavior should be studied by focusing on separatepackage elements and how they relate to decision making (Underwood et al.,2001). In addition, element organization in the packaging design can facilitateinformation search and influence which aspects receive attention (Janiszewski,1998). For example, Bialkova and Trijp (2011) showed that attention is directedtoward design features with semantic importance. Similar findings werediscovered in this paper as, the placement and design of information onproduct packaging proved to be important in relation to findability.Furthermore, the design of the element proved to be decisive in the speed ofidentification of target product. However, these results sparked new questionsas the information on the packaging can be textual and pictorial in nature(Silayoi & Speece, 2004; Silayoi & Speece, 2007) and depending on if theinformation is textual or pictorial it can influence consumers’ attention(Childers & Houston, 1984; Pieters & Wedel, 2004; Underwood et al., 2001) orchoice (Pieters & Wedel, 2004). This was the starting point of Paper II whichfocused on how textual and pictorial design elements influence consumers’attention.4.2 Paper II: Left isn’t always right: Placement of pictorial and textualpackage elementsThis paper focused on placement of pictorial and textual attributes on productpackaging and their influence on visual attention and detection. Packagingelements are in-store POP influencing factors as they form the holisticimpression of a product. Furthermore, in this paper, packaging elements wereexplored as factors that influence visual attention. Hence, the position of thispaper within the conceptual model was the relationship between in-storeinfluencing factors and visual attention, as seen in Figure 8.The purpose of this paper was to investigate how the positioning of textual andpictorial design elements on a package affects visual attention toward theseelement types.
    • 71Figure 8: Position of Paper II within the conceptualmodel4.2.1 FindingsThe results of this study show that package element positioning influencesdetection time. Textual elements are detected fastest when they are located onthe left side of a package, whereas pictorial elements are detected fastest whenthey are located on the right side. Further, this proves the importance of properelement organization as “wrong” location may leave a salient package elementunseen.4.2.2 ContributionsThe paper has both methodological and theoretical contributions. Previousresearch has focused on recall (Rettie & Brewer, 2000) or preference (Silayoi &Speece, 2007) to measure the influence of pictorial or textual elements. Thefocus of the present study was whether respondents actually visually attendedthe different elements on a package, and how long it took them to detect suchelements. Detection time for certain element types can be viewed as a new andcomplementary way of evaluating the position of package elements.Nevertheless, the main contribution is theoretical and contradicts previousfindings (Rettie & Brewer, 2000) by showing that, to facilitate detection,important textual elements should be positioned on the left side, whileunappealing but necessary textual information should be located on the rightside. However, selling or attention-grabbing pictorial elements should be placedon the right side of the package to minimize detection time. Furthermore, tofacilitate and maximize recall, the element organization should be the reverse.These findings offer some support to Silayoi and Speece’s (2007) findings that
    • 72packages were preferred when there was textual information to the left andpictorial information to the right.4.3 Paper III: The Verticality Heuristic: Why top shelf is not always topnotch in product placementThe general purpose of this paper was to examine the influence of shelfverticality and product packaging on consumers’ attention, consideration, andchoice. As both these factors are known to influence consumer decisionmaking, the focus was to explore the point at which they were used in thedecision-making process. Hence, this paper involved all components of theconceptual model. Product packaging is an in-store factor that influencesconsumers as they examine the shelf. Similarly, vertical product placement is anin-store factor, as consumers can infer value based on space quality. Choicetasks were used to activate top-down structures related product placement. Inaddition, product recognition was introduced as an out-of-store influencingfactor to test the influence of verticality on decision making when consumerproduct knowledge is controlled for. Furthermore, this was examined throughvisual attention throughout the decision-making process, as seen in Figure 9.This paper consisted of two separate experiments to test the influence productplacement with and without product recognition.Experiment 1: The purpose of this experiment was to examine to what extentthe participants’ attention, consideration, and choice were influenced by shelfverticality and product packaging when participants were instructed to buy apremium or budget product.Experiment 2: The purpose of this experiment was to investigate influence ofproduct packaging and shelf verticality on attention, consideration, and choicewhen product recognition was unavailable.
    • 73Experiment 1 Experiment 2Figure 9: Position of Paper III within the conceptual model.4.3.1 FindingsThe main finding of the present paper was that consumers tended to movetheir eyes to the shelf level they predicted would contain their target products,independent of the recognition of those products on the shelf. Furthermore, allresults from the two experiments confirmed that verticality was employed as aheuristic at the beginning of decision making. However, the expectation wasthat shelf verticality would influence consumer consideration and choice, butthis was not confirmed as consumers mainly considered and chose the targetproducts, independent of vertical placement. Consumers’ choices were in linewith their consideration and a majority of decisions were made on the targetproducts. Furthermore, it was predicted that when consumers are unfamiliarwith the products, shelf verticality would influence consumer consideration andchoice. This expectation was also not in line with the findings as consumersspent more time considering and a majority of the choices were on the targetproducts independent of product category familiarity. The findings of thispaper showed that consumers use verticality as a heuristic, but ultimately valueis inferred from product packaging independent of familiarity.
    • 744.3.2 ContributionsThe main contribution of this paper is that consumers use verticality to makeexpected value inferences at different shelf levels when decision making isinitiated. The inferences on shelf verticality resembled the criterion for heuristicdecision making (Shah & Oppenheimer, 2008) as the search effort was reducedin relation to different levels of value. One major contribution is that shelfverticality was used as a heuristic to infer value, which suggests that theverticality heuristic is a simplification strategy.One further contribution is that consumers do not make value inferences basedon shelf verticality when considering and choosing products. This findingcontradicts two previous empirical studies (Chandon et al., 2009; Valenzuela &Raghubir, 2010). Valenzuela and Raghubir (2010) maintained that consumersinfer value based on shelf verticality when pricing items or positioning productson a shelf based on price. Furthermore, they shows that inferences made onshelf verticality were part of a controlled and conscious process. Contrary toValenzuela and Raghubir (2010), no shelf verticality influence was foundbeyond the initial direction of visual attention, independent of productrecognition. In their empirical study, Chandon et al. (2009) found thatconsumers attended products on the top shelf and these results were attributedto the influence of verticality as they believed that the consumers preferredpremium products, and consequently, were looking at the top shelf level. Insupport of these speculations, the findings show that consumers used theverticality heuristic, which directs attention toward the expected placement ofthe search goal. Contrary to these speculations, consideration and choice weredriven by product packaging; the verticality heuristic influenced only the initialpart of the search process, not consumer consideration and choice. Hence, ifthe premium products were placed on the top shelf section in the study byChandon et al. (2009), the assumption is correct.The final contribution of this paper is that the influence of product packaging isstronger than shelf verticality, even when consumers were totally unfamiliarwith the product category. This last finding was interesting as the expectationwas that consumers would infer values for a missing attribute using heuristicsbased on other information (Bettman et al., 1998). The expectation was thatverticality would extend from initial attention to consideration and choice ofproducts. However, familiarity is one among several factors that can influenceattention, consideration, and choice. Another factor is preference for products,
    • 75which has been recognized as an influence independent of familiarity (Zajonc,1980). Previous studies showed that preference can be predicted from eyemovements (Pieters et al., 1997) and that familiarity increases attention (Pieterset al., 2002). However, the relationship between familiarity, attention, andpreference, and their influence on consideration and choice, has not beenexplored yet. In fact, Russo (2011) pointed out the importance of this: “Weaccept that preference often drives perception in that people look more at what they prefer . . .However, might the opposite hold, namely that inducing people to look at a stimulus longer ormore often increases their preference for it?” (p. 58). This initiated Paper IV and theexploration of the relationship between product recognition and visualattention in preference formation.4.4 Paper IV: Familiar Packaging in a Crowded Shelf – The influence ofProduct Recognition and Visual Attention on Preference FormationThe focus of this paper was to understand the influence of product recognitionand visual attention on product preference formation in decision making.Recognition is an out-of-store influencing factor, as it is related to the productknowledge that the consumer brings to decision making based on previousexperiences. Similarly, preferences are treated as an out-of-store factor. Visualattention reflects the consideration of alternatives throughout the decision-making process. Hence, the position of this paper within the conceptual modelinvolves the relationship between out-of-store influencing and visual attentionin the decision-making process, as seen in Figure 10. The purpose of this studywas to explore the influence of product recognition, visual attention andpreference formation during the consumer decision-making process.Figure 10: Position of Paper IV within theconceptual model.
    • 764.4.1 FindingsThe findings of this paper show that both familiarity and visual attentionpredict preference formation. The effect of attention was moderated by thelevel of familiarity such that the more familiar the product was, the higher theinfluence of visual attention on preference formation. However, the influenceof familiarity was greater than the influence of visual attention.4.4.2 ContributionsPrevious research has shown that the recognition heuristic is used at thebeginning of the search to reduce the alternatives for evaluation in aconsideration set (Gigerenzer & Goldstein, 2011; Marewski et al., 2010), aprocess that results in recognized products continuing further into the decision-making process. As comparisons are made within the consideration set, otherheuristics are employed (Gigerenzer & Goldstein, 1996; Oeusoonthornwattana& Shanks, 2010; Simon, 1990) to further reduce options through cue-levelcomparisons (Hogarth & Karelaia, 2005). Consequently, recognized productswill receive more visual attention. This study confirmed that recognition has aninfluence on visual attention and that highly familiar products aggregate morevisual attention than moderately or unfamiliar products. Previous researchestablished that visual attention is involved in the construction of preferencesduring the decision-making process (Shimojo et al., 2003; Simion & Shimojo,2007). The combined effect of recognition and visual attention was the maintheoretical contribution of this research study. The results indicated that theinfluence of visual attention on preference formation increased as a function ofrecognition. Recognition was the main driver of preference formation;nonetheless, visual attention increased this effect for the recognized product.Hence, it is not until consumers are familiar with a product that visual attentioncontributes to POP preference construction; the more consumers recognize aproduct, the more they look, and the more they look, the more they prefer theproduct.One limitation of the present paper is the theoretical assumption that therecognition heuristic is used in the screening stage (Gigerenzer & Goldstein,2011; Marewski et al., 2010) to construct a set of products that a consumer willconsider in the evaluation stage of the decision-making process (Bettman, 1979;Gensch, 1987; Howard & Sheth, 1969; Huber & Klein, 1991; Lussier &Olshavsky, 1979; Wright & Barbour, 1977). However, if the recognition
    • 77heuristic were used in the screening stage, then the proportion of familiarproducts would be greater in the evaluation stage. In this study, this relationshipwas assumed through aggregation of attention on familiar products. Tounderstand the role of recognition in decision making, this assumption neededtesting. This was the starting point of Paper V and the exploration of the threestage decision-making model by Russo and Leclerc (1994).4.5 Paper V: Using Heuristics to Revisit Consumer Choice Processesthrough the Eyes of the ConsumerThis paper centered on the reliability of the three-stage decision-making modelby Russo and Leclerc (1994), which was tested in a series of experiments withmodern eye-tracking equipment. Furthermore, the present paper explored therole of product recognition, choice, and consideration task in relation to thedistribution of visual attention across the model. Hence, the paper primarilyinvolved the conceptual model’s decision-making component in relation to therecognition and choice task factors, as seen in Figure 11. Even though thegeneral purpose was to test the decision-making model by Russo and Leclerc(1994), the paper consisted of three separate experiments to test and addfurther insight into the model.Experiment 1: The purpose is to replicate the original experiment by Russoand Leclerc (1994) in order to better understand the different stages and theirconsumer-choice characteristics.Experiment 2: The purpose is to investigate the influence of productrecognition on visual attention across the stages of the decision-making model.Experiment 3: The purpose is to test the influence of choice task and productrecognition on visual attention across the stages of the decision-making model.
    • 78Experiment 1 Experiment 2 & Experiment 3Figure 11: Position of Paper V within the conceptual model.4.5.1 FindingsThis paper has shown through three sequential experiments that consumerchoice follows a pattern whereby mean observation time increases throughoutthe decision-making process. This is in contrast to the original experimentsconducted by Russo and Leclerc (1994), whereby the mean observation timedecreases in the verification stage. Russo and Leclerc suggested that since theevaluation stage consists of inferences, it should have longer observation timesthan both orientation and verification. Instead, in the present study verificationconsists of observations that are approximately 20 percent longer than those inthe evaluation stage. Looking at the activities in the choice process, there aremore comparisons that continue throughout evaluation into verification; assuch, verification includes a more complex task than previously suggested.Furthermore, the findings show that a choice among familiar brands takeslonger than a choice among unfamiliar brands, suggesting that familiar productsare evaluated in more detail. Finally, in the preference-based choice task,familiar products also receive more attention than unfamiliar products.However, in the specific qualities consideration task, participants spent aproportionally equal amount of attention on familiar and unfamiliar products.The findings also show the importance of familiarity; even though the inclusionof a specific task shifted the balance, most choices were among familiarproducts.
    • 794.5.2 ContributionsAt a general level, our empirical investigation made three major contributions.First, the replication of Russo and Leclerc’s 1994 experiment providedempirical support for the three-stage model of consumer nondurables, althoughsome noteworthy exceptions were identified. The omission of the verbalprotocols makes the choice process almost 50 percent shorter, although withover 40 percent more observations. This shows that the present eye-trackingtechnology provides much more detail about the choice process and that theomission of the verbal protocols reduces the choice process length. In addition,mean observation times are equally long in the evaluation and verificationstages and shorter in the orientation stage, contradicting the findings of Russoand Leclerc (1994). Furthermore, the increased number of eliminations andcomparisons and the decreasing number of acquisitions for unfamiliar productsthroughout the choice process is consistent with the findings of Marewski et al.(2010) who showed the recognition heuristic lead to the initial consideration setformation.Second, two experiments have shown that the general pattern with increasingmean observation times throughout the consumer choice process is only validunder certain conditions. Changing the task and degree of familiarity with thebrands influences both the total time and the mean observation times in theconsumer choice process’s individual stages. The mean observation time forpreference and consideration choice followed the general pattern of theconsumer choice process in the orientation and evaluation phase. However, forthe consideration choice, the mean observation time was reduced duringverification. Furthermore, familiarity influenced both the mean and totalobservation time, such that the total time of the evaluation stage was longer forfamiliar products then unfamiliar products. The mean observation time wasshorter for unfamiliar products in the evaluation and verification stages.Additionally, when moving from a preference-based choice to a considerationbased choice, the difference in total evaluation time disappears. Finally,participants in the preference-based choice task chose more familiar products,whereas participants in the specific qualities consideration task chose moreunfamiliar products.Third, the alterations in mean observation times can be explained byconsumers’ use of heuristic decision-making. Product recognition, employed asa heuristic in the decision-making process, has a great influence on the
    • 80distribution of attention. The present paper shows that a choice among familiarbrands takes longer than a choice among unfamiliar brands. This suggests thatfamiliar products receive a more thorough evaluation and that such a choice isverified more promptly, disconfirming the suggestion that familiarity leads to ashorter choice processes as proposed by Kuusela et al. (1998). Russo andLeclerc (1994) disregarded the fact that familiarity has a positive effect on themean observation time in the verification stage. It seems that what wasregarded as a false alarm was actually an important result that can help us tobetter understand the role of product recognition in consumer choice.However, the speed of the choice process is not an effect of familiarity per se,but rather of the complexity of the decision heuristic. Hence, while consumersmake choices among familiar products, the choices are based on a heuristicwith the objective of finding the best solution, such as take-the-best(Gigerenzer & Goldstein, 1996) or tallying (Dawes, 1979). However, whilechoosing among unfamiliar products, the choices are based on a good-enoughheuristic (Simon, 1955; Todd, 1999).
    • 815 General Contributions and Direction for Future ResearchThroughout the present studies, it has become increasingly evident that visualattention plays a key role in decision making. Consumer visual attention hasshown how decisions are influenced by in-store and out-of-store factors, whichhas resulted in a number of contributions within POP decision makingresearch. This chapter summarizes the main contribution of the present thesisand sets the basis for future research.5.1 The Influence of Product RecognitionBy studying consumer visual attention, it was possible to explore the influenceof product recognition as a heuristic in construction of consideration sets, andin preference formation. It is now apparent that product recognition has amajor role in decision making. Previous researchers have shown that productrecognition results in higher choice probability and increased preference.Nevertheless, this thesis has pinpointed the mechanisms that cause productrecognition to be influential.The present findings were postulated by William H. Baker and colleagues(1986), who proposed that recognition results in: (1) enhanced perceptualidentification, (2) increased probability of consideration set inclusion, (3)preference formation, and (4) choice behavior influence. Looking at the firstproposition, it is evident from the findings that recognition enhances perceptualidentification in the initial stages of decision making. As a heuristic, recognitionis used by the consumer to simplify complex decision tasks. This in turninfluences the second proposition made by Baker et al. (1986); namely anincreased probability of consideration set inclusion. As the recognition heuristicis employed, alternatives are reduced for evaluation, a mechanism that results inrecognized products continuing further into the decision-making process. Ascomparisons are made within the consideration set, other heuristics areemployed to further reduce options through cue-level comparisons. Thissequence sets the foundation for preference formation, which is the thirdproposition of Baker et al. (1986). Preferences are formed either as a resultingeffect of recognition or increased attention. Recognition is known to influencepreferences as consumers have a favourable attitude toward recognizedproducts. Furthermore, recognized products will receive more visual attentionbecause they are included in the consideration set and evaluated further. The
    • 82aggregation of visual attention on recognized products during evaluationcontributes to the formation of preferences by the mechanisms of the gazecascade model. Hence, the more consumers recognise a product, the more theylook, and the more they look, the more they prefer the product. Subsequently,recognition forms preferences, which are then the foundation of the fourth andlast proposition by Baker et al. (1986), namely, its influences on choicebehavior.This chain of events, from the initial influence of recognition to choice, is welldocumented and tested from several perspectives. Three of the five papersinvolve product recognition as an influencing factor and it is empirically provenand firmly believed that the increased visual attention in turn forms preferencesthat induce choice behavior. However, recognition stretches beyond the visualsensory input and is also used by consumers to infer value before visual sensoryinput is available. In this thesis, the verticality heuristic is introduced as asimplification strategy that is faster and more frugal than product recognition.The verticality heuristic is, in a sense, similar to the recognition heuristic as it isused to simplify the decision. However, there is one major difference asinferences from verticality influence the consumer attention before productsare inspected. Hence, when verticality is semantically important for the decisionoutcome, it is employed as a simplification strategy to direct attention towardsvertical extremities of the product shelf. It is important to point out thatverticality only influences the initial visual attention, and that product packagingis much more influential on consumer decision outcome, even if the consumeris entirely unfamiliar with the product on the shelf. In sum, product recognitioninfluences the initial direction of attention, construction of consideration sets,formation of preferences, and choice. However, in this thesis, it was discoveredthat this influence is dependent on the decision task.5.2 The Influence of the Choice TaskThe task can either be conceptual (for example, find a premium product) orperceptual (for example, find the green ketchup bottle). It is believed thatconceptual tasks rely on top-down processing and perceptual tasks rely onbottom-up processing. The decision tasks used in the present thesis areprimarily conceptual. The findings of all of the studies in the thesis show thatdecision tasks influence visual attention towards relevant information; however,their influence depends on the specificity of the task. Previous research has
    • 83suggested that choice task invokes qualitative reasoning that is less time-consuming, whereas consideration requires more quantitative searching, whichis more time-consuming. The results of this thesis show that consideration-task-related specific properties of the options, such as “choose the healthiestproduct,” influence the implementation of simplification strategies andinformation search. While the recognition heuristic plays a major role inpreference-based choice tasks, such as “choose the product you would like tobuy,” consideration tasks force the consumer to evaluate products in detail anduse more cognitively demanding strategies, which reduces the influence of fast-and-frugal simplification strategies, such as the recognition heuristic.5.3 The Influence of Product PackagingTo be seen is not analogous to being sold in POP decision making. Asexplained previously, visual attention constructs preferences for products.However, product recognition generates attention-based preferences.Recognized products aggregate more visual attention, as they travel further intothe decision-making process; increased attention results in increased preference.Nonetheless, capturing attention is not always enough for brand selection andthe slightest change can have an impact on brand memory, perception, andsales. As explained previously, visual attention is crucial for choice; unseenproducts are unsold products. On the other hand, a majority of the seenproducts are also unsold; hence, communicating values is as important as beingseen at POP. From this perspective, product packaging has both a perceptualand conceptual function. Perceptual attributes guide visual search and attentionand communicate the product’s holistic conceptual impression as surrogatevalue indicator. Processing perceptual and conceptual features relies on theconcepts of “top-down” and “bottom-up” information processing. Few studieshave investigated the communicative effects of product packaging, as well asthe link between packaging and consumer attention. Several studies hereincontribute to this field.First, it is important to point out that consumer decision making is primarilydriven by product packaging and the values acquired from its perceptualfeatures. The findings showed that consumers, independent of decision strategyor product recognition, make correct choices in relation to the conceptualchoice task. Furthermore, the results show that some perceptual features thatare confounding in relation to the conceptual task have no influence on the
    • 84decision maker (one such example is cylindrical shape found in experiment 2for Paper III). The results indicate that brand recognition is one among severalfactors influencing a product’s conceptual impression and that some perceptualfactors are meaningless in relation to the conceptual task. It seems that theconceptual impression is the resulting effect of individual perceptual featuresthat are activated by the conceptual choice task. Hence, when asked to findhealthy products, some perceptual attributes are given precedence over others;this is strongly dependent on the task. The conclusion is that the holisticscheme of the product is not as important as the individual perceptual attributesin relation to the conceptual choice task. This conclusion can be supportedfrom several different perspectives. In cue utilization research, it is pointed outthat packaging perceptual features can only influence decision making if theyare meaningful for the consumer. In visual attention research, perceptualfeature saliency has been described as task-dependent because certain featurescan be salient for a specific task and not for other tasks. In a task-driven visualsearch, attention is guided by the sum of the bottom-up and top-downinformation that together constructs a master saliency map during brandchoice. If a given brand or package is not salient, it will not be considered as analternative at POP. Heuristic research believes that decision making can involvealternatives or attributes. However, when heuristics are employed by consumersto simplify decision making; non-compensatory heuristics are part of the initialscreening process, limiting attention to small parts of the available information.Heuristics are believed to be involved in guiding cue search. Hence, theconceptual task is used as a simplification criterion by guiding informationsearch to meaningful perceptual features. In conclusion, it is firmly believedthat product packaging is not dependent on its holistic impression during visualsearch, as is argued in some previous research (Orth & Malkewitz, 2008). Thefocus should be on specific perceptual features that influence the attention,consideration, and choice of the decision maker (Underwood et al., 2001).Furthermore, the results of the present thesis show that the placement of theperceptual features is equally important as the features themselves. Hence,element organization facilitates information search and influences whichaspects of the packaging receive attention.One further conclusion is that the influence of product recognition is notnecessarily related to the holistic design of the product packaging. In visualsearch, recognition of individual perceptual features can influence the initialscreening of alternatives. For instance, one perceptual feature can constitute the
    • 85criterion used for fast-and-frugal heuristics, such as the recognition heuristic.However, if the perceptual feature is unavailable or unattended, the productswill not continue further in the decision-making process. Thus, the perceptualfeatures of the product packaging need to (1) be salient if they are important incommunicating the primary values of the product, and (2) be recognized asrelevant to the primary values of the product. This would generate the bestfoundation for the product to continue further into the decision-makingprocess. In short, products that are both eye-catching and recognized will havegreater likelihood of receiving attention, considered as an option, and chosen.Recognition, however, is not about creating “me-too” packaging that copiescompetitor designs, but rather is about comprehending packaging attributes andbeing able to attach these attributes to the values sought during the searchprocess. Further, recognition is about communication of values in split-seconddecision making and getting through with perceptual features that compose theholistic expression of the product.5.4 The Biased Decision-MakerIt is well established that the decision maker is influenced by the mind and theenvironment through top-down and bottom-up information processing. It is anaccepted and well-established decision-making phenomenon; however, there issome disagreement about the role of top-down and bottom-up processing andits influence on visual attention. Both these mechanisms can bias visualattention deployment. Bottom-up bias stems from perceptual packagin featuresthat deviate from the surrounding products (e.g., saliency and pop-out effect).Top-down bias shifts attention through decision tasks, activating memoriesfrom product recognition and preference that direct visual attention towardtask-relevant information. The disagreement is about the temporal influence oftop-down and bottom-up processing on visual attention. It is argued that visualattention is bottom-up-driven at first and that it is only later that top-downinformation can bias visual attention. It is believed that bottom-up processing isrelated to where information is located and top-down processing to what theinformation is. Furthermore, it is believed that preattentive processing does notinclude any top-down information other than spatial information (Theeuwes,2010). The competition between these two attention-biasing components isbelieved to be task-dependent (Desimone & Duncan, 1995). The presentfindings show that consumers visually attend information that is solelyestablished by the conceptual task. This indicates that consumers preattentively
    • 86process top-down information that directs the deployment of visual attentiontoward the expected placement of a target, rather than the actual placement ofthe target. This indicates that that the conceptual task (premium or budget) inthe present research was related to spatial information, giving further evidencethat the value inferred from verticality is preattentive, involved in directingvisual attention as a top-down factor, and a simplification strategy.In addition to the previously mentioned top-down biases, there are viewingstrategies, such as the central fixation, or center bias, that influence visualattention toward the central location of an image. It is believed that center biasis related to previous knowledge associated with spatial information (e.g.,photos, television, and advertisements) (Parkhurst et al., 2002). This results inconsumers having a stronger tendency to look at the center of the image then atthe extremities and being more likely to move from any position of the shelf tothe center region across any task or image. In the present thesis, it wasdiscovered that when consumers are not given conceptual tasks related tospatial information, the center position is attended faster, more frequently, andmore time is spent in the center. Furthermore, there are vertical bias tendenciesthat direct consumers’ visual attention toward the top shelf section independentof content or choice task. The present findings show that consumers generallystart looking at the center of a product shelf and then move to the top verticalposition.5.5 Future Visual Attention Research in POP Decision MakingThe main area for future research is to test the ecological validity of the resultsproduced in the lab environment. This is a limitation in all the appended papersas none of the findings has been tested in the retail environment. A suggestionfor future research would be to investigate whether the findings are replicablein a retail environment, with real product shelves. In a recent eye-trackingstudy, differences in TTFF between stimuli presented in physical and virtualshopping environments were found (Tonkin, Duchowski, Kahue, Schiffgens, &Rischner, 2011). Physically presented stimuli (cereal boxes) received fixationsfaster than their corresponding virtual images. Since studies of real-world eyemovements are still at their very beginning (Holmqvist et al., 2011), it would beinteresting to explore customers’ visual attention and decision making in morerealistic settings, both to find out if the results presented here are generalizableand to see whether the eye-tracking methodology is valid.
    • 87With the development of head-mounted eye tracking, consumer eyemovements can be recorded in the natural environment. This is a moreintrusive method for tracking eye movements, but the method brings newopportunities in measuring consumer behavior in the retail environment. Thesubject can move freely with no restrictions. The advantage of using a head-mounted unit is that the subject’s eye movements can be followed with all thefactors that affect the decision-making process. The disadvantage of head-mounted eye-tracking is the difficult to quantify data. The future of head-mounted eye-tracking is dependent on the ability to quantify the data based onindividual AOIs or specific objects in the field of view.In the future, eye-tracking systems will be integrated into shelf structures. Thisinteresting technological development is ongoing (Cho, Yap, Lee, Lee, & Kim,2012), and long-range eye-tracking systems will enable nonintrusive eyemovement measurements in the retail environment, which will open up newopportunities for researchers and managers to understand consumer behaviorat POP.
    • 88
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    • What Does it Take to Get your Attention?Decision making for fast-moving consumer goods involves a choice betweennumerous similar alternatives. Under such demanding circumstances, a decisionis made for one product. The decision is dependent on the interaction betweenthe environment and the mind of the consumer, both of which are filled withinformation that can influence the outcome. The aim of this dissertation is toexplore how the mind and the environment guide attention towards consideredand chosen products in consumer decision making at the point-of-purchase.The results of the 10 experiments in the five papers that comprise this thesisshed new light on the role of visual attention in the interaction between theenvironment and the mind, and its influence on the consumer. It is said thatconsumers choose with their eyes, which means that unseen is unsold. Theresults of this thesis show that it is just as important to be comprehended asit is to be seen. In split-second decision making, the ability to recognize andcomprehend a product can significantly impact preferences. Comprehensionstretches beyond perception as consumers infer value from memory structuresthat influence attention. Hence, the eye truly sees what the mind is prepared tocomprehend.DISSERTATION | Karlstad University Studies | 2013:5ISSN 1403-8099ISBN 978-91-7063-479-6