Aim: effect of window display and landscaping on liking, mood and patronage intentions (go into shop, buy, recommend, revisit).

Method: online survey, 180 students (mostly female) at an American university. Imagine needing a new pair of jeans → read a description of a small clothes shop’s exterior. All had building details; they varied whether window display and/or landscaping were described (the IVs).

Results: window display + landscaping both positively influenced liking of the exterior and intention to shop there. Liking ↑ ⇒ pleasure & arousal ↑ ⇒ patronage intentions ↑.

Evaluate: self-report (may be inaccurate / misunderstood ⇒ validity ↓); hypothetical intentions, not real behaviour ⇒ low ecological validity; students, mostly female = limited sample.

Aim: do real-life layouts (grid/freeform/racetrack) work the same online? Four hypotheses (all based on physical-shop research):

• H1 grid most useful for planned purchases
• H2 freeform easier to use in general
• H3 racetrack more entertaining
• H4 more time shopping in freeform

Method: 120 participants (Greece + UK), lab experiment, one of 3 virtual layouts. Budget £20 (12 000 drachmas); whatever was ‘bought’ was really delivered. Mostly European brands (Coca-Cola, Pringles) + own-brand. Blank shopping list + info on what’s in store. Other features (background colour, images, speed) kept constant.

• Grid = hierarchical (category → sub-category → product)
• Freeform = reach any product directly (search button / item on page)
• Racetrack = forced paths; each page a choice of two ‘corridors’

Results: freeform = most useful for finding shopping-list items and most entertaining; grid = easiest to use, racetrack hardest; layout affected time — racetrack & freeform kept shoppers longer.

Conclusion: none of the 4 hypotheses supported ⇒ real-shop theories can’t simply be applied to online grocery retail. Customers prefer a hierarchical/tree structure (grid) and being able to reach any place directly (freeform).

Evaluate: lab = high control (↑validity) + standardised procedure (↑reliability) but lacks ecological validity.

Lighting, decoration, smell all shape atmosphere. North & Hargreaves (1998): student cafeteria — classical music ⇒ seen as more upmarket, people said they would spend more: 20.5% more than no music, 18.8% more than easy listening, 3.7% more than pop. But only intentions, not actual spending.

Why might music change spending?

• Like the music ⇒ stay longer ⇒ spend more
• Genre sets a mood: excite/arouse (impulse buys) or relax (spend freely)
• Priming: classical ⇒ associated with affluence & wealth ⇒ primes spending more

• Real restaurant. 1 week classical, 1 week pop, 1 week no music. Volume constant; two 76-minute CDs per condition of well-known classical or pop. Everything else same (lighting, temperature, menu).
• IV: background music (pop / classical / none). DVs: spend on starters, mains, desserts, coffees, bar drinks, wine, total food, total drink, total spend. Time from seating to paying also recorded (affects spend).
• Bills collected & analysed by an experimenter working as a waitress.
• Sample: 393 customers, Feb–Mar 2002; about equal males/females and about equal numbers per condition; opportunity sampling (whoever ate there).

Results: significant differences for starters, coffee, total food, total overall spend — each highest in the classical condition.

Conclusion: classical ⇒ significantly higher spending than pop or no music (in line with previous research).

Strengths: field ⇒ high ecological validity; highly controlled (all else constant, same volume & CDs) ⇒ confident change was due to music (validity ↑); large sample (~400) ⇒ lots of data.

Application: real nights, real restaurant, real bills ⇒ owners/managers can change music to raise profit.

Aim: can sounds not associated with eating still change perception of food?

Method (Study 1): 48 participants; each did 25 trials in each of 3 conditions — loud white noise / quiet white noise / no white noise (order counterbalanced). Seated, eyes closed, headphones, bite-sized foods. Soft & hard foods in salty (cheese, crisps) and sweet (flapjack, biscuit). Rated sweetness, saltiness, liking.

Results: sweetness & saltiness rated lower in loud than quiet. Soft vs hard foods not affected differently. Liking lowered by loud noise but not statistically significant.

Conclusion: taste reported as less intense in noise — for crunchy and soft food ⇒ not due to sounds associated with eating.

Aim: compare PAD (scent ⇒ mood ⇒ approach/avoid, via pleasure × arousal) with Lazarus’ cognitive theory (scent is perceived but doesn’t change mood).

Method: shopping mall in Canada. Control week = no odour; next week a light, pleasant citrus scent diffused in main corridor (citrus = effectively pleasing: Spangenberg et al., 1996). Sample of shoppers each week did a questionnaire about their shopping trip; not told the real purpose.

Results: scent ⇒ arousal ↑ but pleasure not ↑ ⇒ pleasure & arousal do not mediate the effect. Light pleasant scent directly improved perception of the shopping environment and product quality.

Conclusion: positive effect of scent on perception ✓, but supports the cognitive model, not PAD (direct effect on perception, no change in mood).

Aim: how does perceived crowding affect shopper satisfaction, and do emotions moderate this?

Method: 3 studies. 2 field experiments: students and non-students did a questionnaire about shopping across a range of retail environments. 1 lab experiment: students imagined going to a bookstore, watched a video clip with spatial and human crowding density manipulated, then a questionnaire (thoughts, feelings, perceptions).

Results: perceived crowding (spatial and human) lowers satisfaction; effect bigger for spatial crowding. Field studies: when pleasure & arousal are included as mediating factors, the crowding→satisfaction link drops but stays significant.

Conclusion: pleasure/arousal partly mediate the effect (PAD gets some support) but other factors matter too — e.g. expectation and tolerance.

Aim: how do spatial factors (signs, maps…) aid wayfinding in a mall?

Method: case study: spatial layout analysed + questionnaires to shoppers. Mall evaluated on architecture, graphics, verbal information:

• Architecture: central atrium at main entrance, shops lead off main corridor; almost symmetrical; 3 other entrances; 3 floors with circular corridors showing direction of travel; lifts & escalators link floors.
• Graphics: consistent style, almost all pictographs (not text) except ‘WC’ & ‘Exit’. Door numbers confusing: ground floor in 100s, but not every 2nd-floor shop in 200s / 3rd in 300s; not in order; some shops split later so new numbers out of order. ‘You Are Here’ map & directory just across from main entrance but parallel to direction of approach ⇒ hard to see. Directory confusing/illogical, perhaps organised in a bureaucratic hierarchy (biggest payers get best spaces) rather than for wayfinding.
• Verbal: information desk prominent in atrium; security guards helpful when approached.

Results: signage not seen as more important than building configuration, yet a significant relationship between rating the mall for wayfinding and its signage. Those finding it easy found signs significant; many didn’t notice signs, others found them insufficient. Most thought ‘You Are Here’ maps useful, but 47% didn’t think there were any (poorly placed). Slight link with door numbers; no architectural characteristic significant.

Conclusion: signage = most important for wayfinding. Building configuration, visual access, circular paths affect individuals differently ⇒ more research.

Aim: does store layout affect movement patterns, shopping duration and product interaction? Find distinctive patterns; link to shopper groups. No variables manipulated ⇒ non-experimental.

Method: 480+ shoppers approached → survey (age, gender, group size). CCTV (normally for security) tracked them using a coloured tag. On leaving: detailed interview (purpose of trip, shopping list use, satisfaction, money spent, habits e.g. frequency). CCTV gave areas visited, time per area, product interactions.

Results: behaviour strongly affected by product location; busiest = milk, fruit & veg, bread; least busy = non-food & baby. 4 movement patterns (below). No significant differences in who made which trip ⇒ individual preference.

Aim/prediction: items at the beginning and end of a menu category will be ordered more.

Study 1 (lab): 240 students randomly allocated to 4 menu designs differing only in order within each category (4 appetisers, 10 entrées, 6 soft drinks, 8 desserts). Names/descriptions copied from an Israeli pizza chain, in Hebrew, no prices. Menus: baseline (arbitrary order) · mirror (reverse of baseline) · inside-out baseline (reverse order within top half and bottom half separately ⇒ middle items become top/bottom and vice versa) · inside-out mirror. Each chose one item per category.

Result 1: significantly more likely to choose items at beginning or end than middle; advantage = 56%. No sig. difference between primacy and recency.

Study 2 (field): small coffee shop in Tel Aviv, 30 days; 60 items in 3 categories (coffee, soft drinks, desserts). Baseline menu alternated with inside-out version; staff recorded orders (orders without reference to a menu excluded). 459 orders baseline, 492 inside-out.

Result 2: mean advantage 55% (confirms Study 1). Bigger gain when moved from exact middle to extreme end (55%) than near-middle to near-end (51%). Hint of 59% advantage for being in the top half (no mirror condition used).

Application: put higher-profit items at beginning/end of category; could also nudge people to healthier options (salads at start/end).

Research methods: lab ⇒ high control (validity + reliability) but hypothetical choices ⇒ low ecological validity ⇒ followed by a field experiment that supported it ⇒ can generalise.

Aim: does the name/description of a dish change how likely it is bought?

Method: 4 focus groups, 48 self-selecting participants (mixed gender/age/income) replying to a letter. Shown 5 menus describing the same dish differently (e.g. consommé julienne · clear soup with julienne of vegetables · spring vegetables in fresh clear soup · ‘delicious flavourful…freshest, most tasty’ · organic naturally grown…crystal clear). Rated appeal 1 (most) – 5 (very unappealing); wrote reasons; group discussion. Then a postal survey (~1800 sent to local NZ homes, 200 usable replies; mixed) to check validity of focus-group findings.

Content analysis: discussions transcribed; software counted words/groupings ⇒ turns qualitative → quantitative data.

Results (focus groups): no differences by age/gender/income. 42% rated menu 1 (French) ‘very unappealing’; menu 3 (seasonal) = highest appeal; menu 5 (organic) ‘very appealing’ 27% but ‘very unappealing’ 22%. Word groups: fresh–interesting; trends, organic, season / pure, natural, products (importance of organic & fresh trends); mystique–occasion (the occasion affects the feel wanted); produce, actually, expect, understand, clear (clear language that really describes the dish).

Survey: occasion affects choice — menu 4 (elaborate) most selected overall; mother-in-law dinner: menu 5 joint top; business meeting: menu 3 joint top. Chicken-dish reasons: M1 ‘feels romantic / sophisticated’ · M2 ‘sounds delicious’ · M3 ‘not too fussy, tells me what I get’ · M4 ‘mouth-watering / sounds tender’ · M5 ‘organic / I like to know what I’m eating’.

Conclusion: words on a menu do influence choice; most preferred items that are clear, tasty, fresh and natural; but big individual differences ⇒ further research.

• Diners need personal space; invasion ⇒ discomfort & avoidance.
• Individual differences: men & older people need more space than women & younger people; bigger groups need more space per person (Knowles & Bassett, 1976); varies by culture and situation; and spatially — personal space is not a round bubble: narrower at the sides, broader in front/behind (Hayduk, 1981).
• Restaurant balance: fit more tables vs a pleasant place. If people feel invaded ⇒ uncomfortable, stay less (spend less), less likely to return/recommend.
• Gap: earlier work looked at chairs/spacing around a table (Sommer, 1969); Robson looked at space between tables.

Aim: how do diners perceive specific table distances in particular dining experiences, and how do these perceptions affect attitudes/preferences ⇒ give the industry guidance on table distances.

• IVs: table spacing (6, 12, 24 inches = 15, 30, 60 cm) × scenario (business / friend / romantic) ⇒ 9 conditions; participants randomly allocated to one. DVs: emotional & behavioural responses on Likert-type scales. Same images, distances, statements used for all (control).
• Sample: just over 1000 USA participants; balance of gender and urban/suburban/rural; range of age & ethnicity; volunteer sampling.
• Part 1: gender, age, ethnicity, residence, restaurant use frequency, worked in restaurant industry? Part 2: imagine one scenario, view images of 2 tables 6/12/24 in apart; rate 32 statements (1 = strongly disagree … 7 = strongly agree) on emotional, intentional & anticipated behavioural responses. Emotional items from the Stress Arousal Check List (SACL) (separates stress and arousal). Behavioural items: perceived control, physical & sensory privacy, goal blocking, general comfort; also perceived crowding.

• Significant differences on almost all responses across 6/12/24 in. Close spacing ⇒ less private, more crowded, less positive experience, more dissatisfied with table. 6-inch = more worried about being overheard / disturbing others. Arousal didn’t vary significantly but stress was higher at 6-inch; control & comfort markedly lower.
• Scenario: romantic ⇒ highest stress, least control, most discomfort at 6-inch. Business ⇒ no effect except comfort. Friend ⇒ moderate stress/discomfort.
• Gender: women more stress, less control, more discomfort; men more arousal. Frequent diners = more comfortable at all spacings. Some age/location/ethnicity differences but inconsistent, no significant interactions.
• Conclusion: strongly negative to 6-inch tables; 70% would have asked to be re-seated. The common 12 inches was also less desirable, especially for romantic. ⇒ tight seating ⇒ shorter stay, less spend, less return, less recommendation.

Strengths: large sample (1000+), balanced gender/location/age/ethnicity ⇒ representative ⇒ generalise; Likert = quantitative data easy to compare. Weaknesses: all USA ⇒ culture limits generalisation (Lomranz, 1976: personal-space comfort differs across cultures); Likert gives no detail & relies on subjective interpretation of scales ⇒ qualitative data (open questions/interviews) could add depth.

Method: train stations, convenience shops etc. A confederate (someone in the study who acts as if they aren’t) calmly approached a point between the 3rd and 4th person and said neutrally ‘Excuse me; I would like to get in here’, joined without waiting for a reply. If explicitly told to leave they left; otherwise stayed 1 minute. Observer nearby recorded physical, verbal and non-verbal reactions. 129 intrusions.

IVs: number of intruders (1 or 2) × buffers (0, 1, 2) = 6 conditions. A buffer = confederate passively standing between the intrusion point and the next ‘naive’ queuer ⇒ tests if responsibility to object is displaced to others.

• Queuers behind the intruder far more likely to object than those ahead. Two intruders ⇒ much greater reaction than one; buffers dampened the response.
• Verbal objections (polite → hostile; ‘excuse me, this is a queue’; ‘go to the back’) most common: 21.7% of queues. Non-verbal (disapproving looks, glares, gestures to move to the end): 14.7%. Physical action (tap shoulder, tug sleeve, push out): 10% — usually from the person immediately behind.
• Conclusion: queue = social system with shared beliefs ⇒ instils social order; the people ahead expect those who come after to wait their turn. Most believe it’s unacceptable to push in even though we’d love to ourselves. The anger comes from the injustice of violating the rules of the system (Cooley, 1902), not just the extra seconds. The queue doesn’t defend itself as a whole — it’s the job of those behind, especially the person directly behind. Defence is almost always normative (expectations of the group): ‘this is a queue’, ‘you can’t push in here’.

Aim/predictions: consumers use compensatory strategies on a site designed for comparison and non-compensatory when it isn’t available; the number of alternatives also affects the strategy.

Method: 24 participants read a document on 3 compensatory + 3 non-compensatory strategies, did a quiz to test understanding. Then used one of two websites with very different designs — CompareNet (comparison technology) or Jango (no comparison). Lists of alternatives found, refined etc.; spoke aloud (think-aloud) while deciding which item to buy. Then a questionnaire (strategy used, satisfied/frustrated, confidence). Experimenter recorded item selected, number of alternatives, time taken. Repeated for 4 items each.

Results: significantly more compensatory on CompareNet, more non-compensatory on Jango. < 30 alternatives ⇒ more compensatory; > 100 alternatives ⇒ more non-compensatory. No sig. difference between strategies in confidence, satisfaction, frustration, perceived or actual time. But participants more satisfied with CompareNet ⇒ comparison technology is more satisfying.

Field experiment: 1 week, 86 stores, same-size shelf label showing original price (99 cents) and either single-unit promo (75 cents) or multiple-unit (2 for $1.50). 13 common food items.

Results: multiple-unit pricing = 32% increase in sales vs single-unit control; higher for 12 of 13 products, significant for 9.

Limits: no self-report ⇒ can’t know why (maybe confusion); data only showed total sales ⇒ don’t know if more people bought or same people bought more.

Lab experiment (suggestive selling): shopping scenario, 120 undergraduates, 6 well-known products at one of 3 price levels (actual convenience-store price, 20% off, 40% off). Suggestive slogans: either no quantity anchor (‘Snickers bars – buy them for your freezer’) or an explicit anchor (‘…buy 18 for your freezer’). Not told whether the price was a discount; gave purchase quantity intentions.

Results: both suggestive selling and discount level ↑ intended quantity; suggestive selling ↑ it even without a discount; the quantity anchor ↑ it too, even with no discount.

Aim: relationship between heuristics and decision-making styles (not studied before).

Method: 320 participants (wide age range, ~equal M/F), computer task choosing between 5 egg cartons — with or without time pressure (40 s). Four cartons designed to target take-the-best (cheapest, only €2.99), recognition, an emotional response, or higher cognitive processing (extra info: GM-free, salmonella-free, guaranteed origin…); the 5th was a filler (barn eggs, grade B). After: questionnaire (reasons, buying behaviour, attitudes) + Scott & Bruce style test.

Results: many differences between the 2 experiments ⇒ cultural differences to investigate. Vienna: time pressure ⇒ significant shift from ‘cognitive’ to ‘take-the-best’; Madrid: not ⇒ limited evidence time pressure ↑ heuristics. Vienna: high spontaneous ⇒ more likely to pick ‘recognition’ (as expected). Unexpected: high rational ⇒ chose take-the-best more than cognitive, regardless of time pressure. Madrid: high rational less likely to choose take-the-best vs cognitive without time pressure; reversed with time pressure.

• 180 participants (118 female), aged 16–80, Sweden; opportunity sampling — passers-by asked to join a ‘quality control test’ of teas & jams (pairs). Each tasted/smelled then said which of each pair they preferred. For each participant either the jam or the tea condition was manipulated. Half told they’d get their chosen tea/jam as a thank-you gift (incentive). Order, manipulation type and which jams/teas were randomly allocated.
• Manipulation: Experimenter 1 asked questions, took notes, ran recorder; Experimenter 2 ran the test. Participant tasted two jars/teas and rated liking (1–10); while rating, Exp 2 secretly turned the jar upside-down so the other sample was now on top (each ‘jar’ was really two jars attached bottom to bottom, with screw lids and a paper label hiding the join). Participant asked to taste preferred option again, defend choice, rate difficulty and confidence. Then asked if anything odd, debriefed, asked again if suspected.
• 3 detection criteria: concurrent (noticed something unusual immediately after tasting/smelling) · retrospective (said they noticed the trick after the experiment, before/after debrief) · sensory-change (didn’t say they noticed, but commented on the change in taste/smell). Concurrent detectors finished the taste study but their results were excluded.

• Detected concurrently: jam 14.4%, tea 13.8%. Retrospectively: jam 6.2%, tea 6.9%. Sensory-change: jam 12.4%, tea 11.5%. Total detected: 33.3% jam, 32% tea ⇒ no more than a third.
• Those offered the tea as a gift were less likely to detect (opposite to prediction); no incentive differences for jam. In two-thirds of detected trials, participants didn’t react at the time. Even very different pairs (cinnamon-apple vs grapefruit; mango vs Pernod, strong aniseed) were detected concurrently in < half and ≤ one-fifth of trials.
• For non-manipulated trials and non-detected manipulated trials, ease of distinguishing samples scored high (7.3 jam, 8.0 tea) and confidence was high ⇒ they could easily tell the two apart but still didn’t notice the switch ⇒ real choice blindness, not just hard-to-tell samples.
• Conclusion: in most trials people were blind to the mismatch between intended and actual choice, believing the taste/smell matched their first choice — even with real-world consequences.
• Eval + opportunity sampling = quick, easy, convenient; reasonable spread (16–80, men + women); pilot testing to pick easily distinguishable jams/teas ⇒ validity ↑. Eval − no control over who the sample is ⇒ may not be representative.
• Application: low-stakes here, but could matter in high-stakes cases — e.g. a crime witness who wrongly picks a suspect from photos, then defends it at trial.

Aim: is memory for an advert hindered by a later (or earlier) similar advert for another brand or the same brand? Role of information processing objective (the reason for looking: decide on purchase vs just rate interest). 3 experiments reported; first 2 here.

Exp 1 (retroactive): shown 12 adverts (3 targets near the beginning). Objective manipulated: decide how likely to buy vs rate interest. The other adverts were similar products by different brands, different products by the same brand, or a range of products & brands. Then a questionnaire on magazine/TV viewing as a distractor task (stops conscious rehearsal), then a surprise recall test (given brand name + product number, 2 minutes to recall info).

Results 1: recalled on average twice as much when rating purchase intention (closer attention). Recall significantly lower after similar-product or same-brand adverts ⇒ retroactive interference. Objective mediated it: purchase-intent adverts were less affected.

Exp 2 (proactive): same design but targets shown later in the series. Purchase-intent group recalled more; similar-product adverts remembered less (proactive interference); but the objective had no significant mediating effect on similar-product interference here.

Method — 3 techniques: ① observations at a Christmas gift-wrapping stall ② 20 in-depth interviews (‘Do you prefer to receive wrapped gifts?’, ‘In what instance do you wrap / not wrap gifts?’) ③ 6 workshops: in pairs, wrap two gifts — one for someone close, one for an acquaintance — while talking about gift wrapping.

Results: most preferred to receive wrapped gifts; clear expectations of what a gift looks like. Interviewees liked the ‘reveal’ and saw wrapping as a sign of extra thought. All 24 workshop gifts looked like gifts (traditional paper, decorative bags, ribbons, bows) ⇒ social expectations. For an acquaintance → ‘playing it safe’; for a close friend/family → tailored colours/patterns to the person. Wrapping is traditional — ‘what you do’, done without much thought.

Conclusion: gifts should be wrapped — makes the exchange easier because giver and receiver fall into their roles without confusion.

• Package = first thing a consumer sees ⇒ must stand out; helps decisions when there’s no other info (new products). Affects perception (taste, quality, price expectation) and even actual experience of the product. Key features: colour and shape.
• Colour (Bloch, 1995) sways taste: Hine (1995) added 15% more yellow to the green of 7-Up ⇒ people tasted more lemony though the drink was unchanged.
• Shape: angular ⇒ energy, toughness, strength; rounded ⇒ approachability, friendliness, harmony (Berlyne, 1976). Shape also affects perceived quality and price expectation (Chitturi et al., 2019).

4 hypotheses: H1 angular ⇒ taste more intense · H2 highly saturated colour ⇒ taste more intense · H3 shape–colour congruency (angular + highly saturated, or rounded + lowly saturated) ⇒ more positive attitude than incongruency · H4 all effects greater for people sensitive to design.

151 participants, customers of a large German supermarket; equal split men/women; 15–81 yrs; approached at the entrance ⇒ opportunity sampling; each in one condition only. Asked to do a ‘taste test’ for a new yoghurt brand. Pre-testing (different participants rated 3 angular + 3 rounded shapes, and 2 pairs of lemon-green saturations) chose the pairs with the biggest rating difference ⇒ 4 lemon yoghurt package variants. Each saw a 20-second 3D clip of one package on a laptop, then tasted a sample (same sample for all), then a computer questionnaire with 7-point Likert-type scales: taste experience, product evaluation, price expectation, + sensitivity to design.

• Potency (‘strikes me as potent’ = powerful, intense, strong): angular shape sig. more potent; colour no overall effect except for high design sensitivity (highly saturated more potent).
• Taste intensity: neither shape nor colour sig. overall — except high design sensitivity where angular ⇒ more intense.
• Attitude: colour no effect, but angular ⇒ more positive attitude than rounded.
• Price expectation: angular ⇒ sig. higher; lowly saturated colour ⇒ marginally higher than highly saturated. Angular ⇒ higher price because seen as more potent.
• Conclusion: shape & colour affect potency ⇒ higher price expectation; angular pack ⇒ intense taste (cross-modal correspondence, Schifferstein & Spence, 2008), esp. for design-sensitive people. Colour didn’t act as expected (variation maybe too subtle). Practical: low-saturation colour ⇒ higher price expectation than high saturation — goes against the common sale strategy of bright colours (could signal ‘cheap/low quality’).
• Eval + field ⇒ high ecological validity; participants didn’t know the aim ⇒ less demand characteristics; balanced gender, wide age; standardised (same instructions/clips/sample/questions) ⇒ reliability; Likert ⇒ quantitative, comparable.
• Eval − all from one area of Germany ⇒ culture; no open questions ⇒ no qualitative detail (thoughts/feelings/reasons).
• Application: designers can use shape/colour info to guide packaging decisions — packaging is a main component of the purchase decision.

Study 1A: screen with a planogram (3 brands, each appearing 3 times) of vitamin supplements / meal-replacement bars, shown on a 3 × 3 matrix. Discreet infrared camera recorded eye gaze (eye fixations). Participants reviewed each product ‘as if on a shop shelf’, pressed enter when ready (stimulus then disappeared) and ticked the matching box. Result: centre brands got more fixations, longer looking (esp. near decision time = central gaze cascade) and were chosen more often — unrelated to other inferences about the product; purely due to increased visual attention.

Study 1B: same but array shown to the left or right of the screen centre (centre of array ≠ centre of visual field; no repositioning allowed). Result: same ⇒ being central in the array (shelf position) is what matters, not the visual field.

Study 2: more realistic — pick from 3 fictitious energy-drink brands. A real-shop study also showed centre items chosen more. Interesting: central brand got more attention and was chosen more but wasn’t rated more positively ⇒ more research needed on why.

Aim: effect of DTR on money contributions; does NFCC affect DTR success?

Study 1 (field): European supermarket stand, special offer on Christmas candy. DTR: ‘The price is now 100 eurocents (2-sec pause), that’s one euro. It’s a bargain!’ Control (reframe only): ‘The price is one euro. It’s a bargain!’ Adding boxes to trolley = complied. 147 customers listened in full; 54% bought; 65% DTR vs 44% control.

Study 2 (field): male confederate (fictitious student interest group), Dutch university campus, asked students to join for 3 euros (‘…300 eurocents (pause) that’s three euros. That’s a really small investment.’). Half DTR, half not; then a 20-item NFCC scale. Overall 22% joined: 30% DTR vs 13% control. High NFCC: 43% DTR vs 17% control; low NFCC: 16% vs 9% (not significant). DTR stronger for high NFCC.

Study 3 (lab): random assignment to DTR / reframe-only / disrupt-only video — actor says Student Advocacy Council wants a tuition rise of 7500 pennies (pause) $75 — ‘a really small investment’. Then NFCC scale + ambiguity/attitude/compliance scales.

Results 3: low NFCC ⇒ DTR didn’t change perceived ambiguity; high NFCC ⇒ disruption raised ambiguity and reframing reduced it. High NFCC ⇒ more favourable attitudes in DTR than reframe-only, and reframe-only more than disrupt-only. Low NFCC ⇒ DTR no better than reframe-only but better than disrupt-only. Same on behavioural measures.

Conclusion: DTR effectiveness increases as NFCC increases; disruption motivates consumers to accept a reframed message that gives closure by reducing ambiguity.

Aim: factors influencing shoppers’ choice of shops (grocery, chemist, lifestyle, books & music, clothes…) — in India, where little retail-behaviour research existed (most was Western).

Method: shoppers given a structured questionnaire as they left the shop — open questions + Likert-scale statements on shop choice and reasons. Asked for up to three reasons (only 40% gave three; most gave one or two).

Results: main two factors = convenience and merchandise (70% gave these as first reason); then ambience, patronage (visited before), service. Convenience most important for groceries, chemists, lifestyle; merchandise for durables, books & music, clothes & accessories. Age weak effect: proximity & merchandise primary; 30–40 and 41–50 higher preference; merchandising also important for 25–29 & 30–40; ambience for 25–29 & 30–40. Gender: men more by proximity, women by merchandise; men see shopping as a chore to finish fast, women spend more time/effort. Proximity valued, but shoppers travel further for good value merchandise and (less so) good ambience.

Eval: open Qs = qualitative detail; Likert = quantitative, easy to compare, covers all areas quickly; many shop types ⇒ ↑ validity; asking reasons for the trip they were just making ⇒ more valid than hypothetical lab questionnaires.

Aim: does dissonance occur choosing between organic and non-organic food — i.e. low-involvement shopping (most dissonance work is on big purchases like cars)?

Method: 100 participants, individual virtual shopping spree ~20 min. Phase 1: 50 common grocery items one at a time (25 organic, 25 non-organic) — rated how often they buy it (1 never – 7 buy very often; used instead of ‘desirability’). Pairs (one organic, one not) scoring similarly (≤ 2 points apart) re-shown ⇒ choose which goes into basket (dissonance since previously rated alike). Phase 2: re-rate all items (reminded which was chosen/rejected) + select reasons for choice (price, environment, health…). Social-responsibility questionnaire.

Results: chosen item rating rose significantly (2.62 → 2.9) ⇒ dissonance reduced by raising chosen item’s rating. Expected lowering of rejected item — not found. Total reduction tended that way but not significant; no difference organic vs non-organic. Reasons: non-organic chosen ⇒ price, then physical appearance (‘more attractive’ or ‘looked familiar’?); organic chosen ⇒ environmental concerns, animal welfare. Participants scored higher than average on social responsibility yet didn’t choose organic more ⇒ attitude–behaviour discrepancy; ability to rationalise explains why people keep deciding against their values.

Method: mock newspaper in 3 media: website on laptop, printed paper, PDF on tablet; 25 real static adverts in each. 72 participants read at own pace on one medium while eye tracking + EEG (scalp electrodes record cortical activity; here to measure frustration). Then 1 hour distraction task, then recognition test: 50 adverts (25 seen, 25 unseen) — say if seen.

Results: fixation time on adverts differed significantly: most on tablet, least on website. Recognition: most accurate for tablet, least for website. EEG: less frustrated on tablet & paper, more frustrated on websites (internet ads perceived as irritating; ads accepted as part of newspaper on paper/tablet). Paper vs tablet not sig. different, but website sig. different from tablet on all measures.

Conclusion: the medium matters — marketing should consider it.

• No single advertising approach always wins. Depends on the product (luxury vs budget, practical vs fun) and the consumer’s personality (Fox, 1984) — here self-monitoring.
• High self-monitors adjust their behaviour to fit each situation (Snyder & Monson, 1975). Low self-monitors aren’t changed by the situation — guided by their own attitudes/feelings, so private attitudes match behaviour (Snyder & Swann, 1976).
• ‘Soft-sell’ = product image central (e.g. coffee ad with a handsome well-dressed man). ‘Hard-sell’ = product quality central (taste/blend info). Both work (Fox, 1984).

• Study 1: do high and low self-monitors respond differently to image-focused vs quality-focused adverts?
• Study 2: does the advert type affect how much they’re willing to pay?
• Study 3: do these differences extend to the likelihood of trying/consuming a product?

• Study 1 (lab): American undergraduates (male + female) did the Self-Monitoring Scale; 50 participants — half low, half high. 3 sets of 2 magazine adverts for whisky, cigarettes, coffee: same picture, slogan varied (image vs quality). Looked at all 3 sets; order counterbalanced; then a 12-item questionnaire comparing the two adverts in each set.
• Study 2: 40 participants, same adverts; groups of 3–4, randomly assigned image- or quality-oriented adverts; questionnaire after each advert — critical item: ‘How much would you be willing to pay?’ (range of prices).
• Study 3 (field-like): 40 participants; an experimenter (blind to self-monitoring scores) phoned, claimed to be a market researcher asking about trying a new shampoo. Image message: lab tests ⇒ average on cleaning but above average on how it makes hair look. Quality message: average on looks, above average on cleaning. Then rated willingness (definitely not … definitely yes) + percentage (0–100%).

• Study 1: high SM more positive about the image advert; low SM more favourable to quality adverts — for all 3 products.
• Study 2: high SM would pay more for image-advertised products; low SM would pay more for quality-advertised. Strongest for whisky, less for coffee, least for cigarettes.
• Study 3: high SM more likely to try shampoo if it would leave hair looking good; low SM if it would leave hair very clean.
• Conclusion: S1 = different preferences; S2 = preference changes the price they’d pay; S3 = also changes willingness to use the product.
• Eval + lab (S1, S2) ⇒ high control ⇒ validity; same images, only the slogan changed ⇒ confident response is due to the slogan; reliable (replicable procedure); counterbalancing removes order effects; S3 = field experiment (thought they spoke to a genuine market researcher) ⇒ ecological validity.
• Eval − S1 & 2 lack ecological validity — participants asked to view & comment on adverts, not real life.
• Application: examined real-world factors — how much they’d pay and whether they’d try it (what matters in sales) ⇒ campaigns for high and low self-monitors to maximise sales.

Aim: effect of product placement on children’s subsequent choices; does age matter?

Sample: 105 children, UK state schools, two age groups: 6–7 yrs (‘limited processors’ — struggle with storage/retrieval cues even when prompted) and 11–12 yrs (‘cued processors’ — can use cognitive strategies if prompted). Randomly allocated to experimental or control.

Method: Experimental: clip from Home Alone — family eating pizza, drinking milk and Pepsi; Pepsi also named in dialogue. Control: similar-length clip where Kevin eats macaroni cheese and drinks milk (no Pepsi). Individually asked if they’d like a drink from a table with 2 cans Pepsi + 2 cans Coca-Cola (implicit preference test). Choice recorded while describing what they remembered about the film; if Pepsi not mentioned, a series of prompts to recall the brand (number of prompts recorded); asked if seen the film before and how often. Control asked what was drunk in the clip (to check for mistaken Pepsi identification).

• Experimental group more likely to choose Pepsi: ratio 62:38 vs control 42:58.
• No sig. age difference in ability to recall Pepsi, but younger needed more prompts (mean 4.43 vs 2.06). The more times they’d seen the film, the fewer prompts needed.
• Reminders: children who had seen the film before were sig. more likely to choose Pepsi if in the experimental group (saw Pepsi in clip that day) — effect even greater with previous viewing. Prior exposure to the brand only influenced choice for those reminded that day.
• Conclusion: product placement is effective.

Aim: investigate brand awareness in children; a game technique to measure logo recognition.

Method: 22 brand logos on cards, no clues to the product: 10 children’s brands (Disney, Disney Channel, McDonald’s, Cheerios…), 7 adult brands (Ford, Apple, Kodak…), 5 cigarette brands. Children matched each logo to one of 12 product categories pictured on a game board (cigarettes, television, cereal…). 229 children aged 3–6, USA.

Results: good recognition of children’s brands: 91% (Disney Channel) to 25% (Cheerios). All cigarette brands well recognised; one by over half. Other adult products 16%–54%, car logos most. Recognition increased with age for children’s, cigarette and adult brands; no effect of race or gender; children of smoking parents more likely to recognise cigarette logos.

Conclusion: high recognition for products aimed at children and adults. Unsurprising for McDonald’s/Disney/cars — but surprising and worrying that children recognise cigarette brands (not allowed to advertise on US TV for decades) ⇒ shows the power of ‘environmental advertising’ (billboards, sponsorship displays, T-shirts).