College of Business Administration

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2009/2010 No. 4

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William A. Orme

Timucin Ozcan, Daniel A. Sheinin

Completeness As A Product Positioning Strategy: A Framing Perspective

COMPLETENESS AS A

PRODUCT POSITIONING STRATEGY: A FRAMING PERSPECTIVE

Timucin Ozcan

Daniel A. Sheinin

October 2009

Timucin Ozcan is Assistant Professor of Marketing at the School of Business, Southern Illinois University - Edwardsville. Daniel A. Sheinin is Associate Professor of Marketing at the College of Business Administration, University of Rhode Island. The authors thank

Gabriel Biehal, Adam Brasel, Arch Woodside, Kunter Gunasti, Albert Della Bitta, and Sajeev Varki for reading earlier drafts of the paper.

Correspondence: tozcan@siue.edu

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Abstract

Positioning is a fundamental, yet under-researched, component of marketing

planning. We examine whether changing the positioning of a product with otherwise

identical features yields different choices and judgments. Specifically, we investigate

products positioned as “complete,” which contain all capabilities available in the

category. We conceptualize positioning as a perceptual frame, which influences

judgments of all features contained in the product. Based on this conceptualization, we

derive propositions about the implications of a complete positioning. We find complete-

positioned products are preferred, although the magnitude of their preference changes

under different levels of information load. We also examine price frames, interacting

first with positioning frames and then with price uncertainty. Similarly, we find the

magnitude of the preference for complete-positioned products changes under different

levels of these factors. We conclude by discussing implications and limitations of our

results, and future research.

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Positioning is “the act of designing the company’s offering and image to occupy a

distinctive place in the minds of the target market” (Kotler and Keller 2008 p. 268).

Marketers often try to differentiate by offering products positioned as “complete.” Such

products contain most or all capabilities available in the category, and include complete

(or comparable verbiage) as part of their positioning and naming. Examples abound

across many product and service categories, such as Colgate Total (complete 12 hour

protection), Centrum Multivitamin (from A to Zinc), Braun 360° Complete Shaver,

McAfee Total Protection, and Sprint Mobile’s Simply Everything Plan. This strategy has

increased in usage as categories have become more crowded with products and expanded

capabilities (Advertising Age, 2008).

Despite this increase in complete-positioned products, surprisingly little research

has focused on the effectiveness of the strategy. Research in positioning has focused on

examining differences between abstract versus attribute-specific (Pham and

Muthukrishnan 2002) and similar versus dissimilar to competition (Dubé and Schmitt

1999; Sujan and Bettman 1989; Carpenter and Nakamoto 1989). This work empirically

establishes positioning as important in understanding issues as diverse as how products

are categorized (Sujan and Bettman 1989) and how sensitive products are to revision

(Pham and Muthukrishnan 2002). Ries and Trout (1997; 2000) have provided ample

evidence as to the centrality of positioning in marketing decision-making (see also Kotler

and Keller 2008). Positioning not only helps to establish a product’s primary

differentiation, but can also influence judgments of other product beliefs. For example,

Tom’s of Maine is positioned as “all-natural” and uses the supporting slogan “Naturally,

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It Works.” This positioning should influence, or frame, the interpretation of other

products and features found in the Tom’s of Maine portfolio.

Only one study we have come across directly explores judgments about “all-in-

one” products (Chernev 2007). All-in-one (two-attribute) products did not perform as

well as a specialized product (single-attribute) on its specific attribute when the two

products were in the same choice set. This devaluation effect was eliminated when all-

in-one products were priced at a premium relative to the specialized alternatives.

Chernev’s novel work introduced the notion of all-in-one products, and shed light on the

influence of attribute quantity on choice. His finding that evaluative context shapes

feature beliefs is important, and underlies our work as well. Another recent paper

examines preference for technological convergence (Han, Chung, and Sohn 2009), which

represents the similar notion of electronic products containing more features that cross

categories as they evolve (e.g., cellphones containing mp3 players and digital cameras).

Therefore, the general question of consumer response to multi-feature appears topical and

under-researched.

It is thus important to more closely examine positioning in general, and complete-

positioned products. We extend previous work by investigating whether complete-

positioned products are assessed more favorably than other alternatives, and which

circumstances alter the magnitude of this potential preference. Unlike prior research, we

examine positioning itself – in other words, how different positionings may change

beliefs about otherwise identical feature sets. We propose that positioning operates as a

perceptual frame that sets the interpretive context for judging product features, and

explore whether a complete positioning may alter the devaluation effect found for all-in-

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one products (Chernev 2007). In three studies, we study the effects of complete

positioning on product judgments, as moderated by several factors. In Study 1, we

examine the influence of information load. In Study 2, we replicate (with a different

stimulus set) and extend Study 1 by additionally investigating price level. In Study 3, we

extend Study 2 by exploring the effects of price level and price uncertainty. After

presenting the results, we delineate conceptual and managerial implications, limitations,

and future research.

Conceptual Framework

Frames are interpretive contexts utilized to process relevant information.

Researchers have established the importance of frames in better understanding consumer

behavior (see Rajendran and Tellis 1994). Positively framed messages in marketing

communications are more persuasive than negatively framed messages when there is

little detailed processing, and the reverse occurs with more detailed processing

(Maheswaran and Meyers-Levy 1990). Interestingly, when level of processing is not

manipulated, positively-framed messages are analyzed more thoroughly than negatively-

framed ones (Roggeveen, Grewal, and Gotlieb 2006). Message framing also influences

judgments of comparative communications (Jain, Lindsey, Agrawal, and Maheswaran

2007), and moderates the relationship between price level and risk in new-product

judgment (Grewal, Gotlieb, and Marmorstein 1994).

Frames are also utilized in modeling buying behavior of biculturals. Language-

triggered frame-switching occurs among biculturals when cued by a particular language

(Luna, Ringberg, and Peracchio 2008). For example, when cued in Spanish, biculturals

shift their interpretive frame from when they are cued in English even when the direct

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meaning of the words and message were identical. Further, shifts in temporal frames are

examined. Here, consumers changed their judgments about a new product when it was

described as a future product versus a past product (Grant and Tybout 2008).

Similarly, positioning should act as a frame that influences the interpretation of

product features. A common finding in frame research is that different frames can

change the interpretation of otherwise identical information. For example, the exact same

new product was interpreted differently when it was framed as a future launch versus a

past one (Grant and Tybout 2008). Further support stems from Barsalou (1988, 1991,

1993). Frames strongly influence concept and category construction (1991) by

organizing and representing information in terms of relations not objective attribute lists

(1988). These relations can alter perceptions of the underlying features. Frames can

theoretically contain a lot of information, so finding a means of structuring the

information is important. According to Barsalou (1993), frames are constrained around

core attributes that are most diagnostic, or relevant, to understanding the underlying

concept. Similarly, positioning is frequently represented as the core dimension of a

product that is critical to understanding its purpose, differentiation, and selling rationale.

The positioning frame we examine in this research is completeness.

Completeness is defined as “having all necessary parts, elements, or steps” (Merriam

Webster’s Collegiate Dictionary 1995). Complete-positioned products, therefore, should

include all important features that are currently offered in their particular categories. For

example, Colgate uses the positioning “Total” in its toothpaste line to clearly connote a

full feature set, and maximize its impact. Without this positioning, consumers would

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need to interpret the same full-feature set attribute-by-attribute to make a determination

of whether the product is in fact complete.

Pham and Muthukrishnan (2002) investigated two types of positioning – abstract

and attribute-specific. An abstract positioning is general, and summarizes the product’s

features (e.g., Ultimate Driving Machine; Best TV Picture). In contrast, an attribute-

specific positioning is specific, and details the product’s features via specific performance

claims (e.g., 0-60 in 6 seconds; brightest LCD screen). Per this conceptualization,

“complete” is an abstract positioning, and thus will be compared with other abstract

alternatives such as “effective.” For purposes of this paper, we conceptualize a complete-

positioning continuum anchored by complete on one end and non-complete on the other.

Effective would be an example of a non-complete, abstract positioning. Abstract

positionings are processed similarly and have similar implications for product judgments

(Pham and Muthukrishnan 2002).

In general, we expect a complete positioning to be preferred over a non-complete,

abstract one. Evidence stems from the library and information sciences literature in the

context of information quantity theory (Dutta-Bergman 2004; Eysenbach, Powell, Kuss,

and Sa 2002). These studies suggest the more complete the information, the better its

quality when all other variables such as relevance, recency, and accuracy are held

constant. Greater information completeness also increases argument strength, which in

turn influences persuasiveness and source credibility judgments (Dutta-Bergman 2004).

Additional support stems from work on zero-risk bias. Zero-risk bias means that

individuals favor small benefits which are definite to larger benefits that are indefinite

(see Baron 1994 and Gowda 1999 for a discussion). Given a full feature set, a complete

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positioning should be preferred to a non-complete positioning because the former would

eliminate the potential risk of missing a beneficial feature. This is in contrast to the

attribute devaluation effect found by Chernev (2007) for all-on-one products. However,

his finding occurred when all-in-one products were in the same choice set as single-

attribute products. Moreover, due to the choice set context, he did not compare an all-in-

one positioning with alternate positionings.

H1: For a full-featured product, a complete positioning will be preferred to a non-complete positioning.

We propose this effect will be moderated by information load. Information load

should influence the extent to which the positioning frame is diagnostic in forming

product judgments. According to the accessibility-diagnosticity framework (Feldman

and Lynch 1988), information is diagnostic if it is relevant to making a designated

judgment. When consumers evaluate a product, they search for and use only the

information they deem most diagnostic (Lynch, Marmorstein, and Weigold 1988; Lynch

and Srull 1982). The greater the diagnosticity of specific information, the stronger its

influence on judgments.

Researchers often manipulate information load through altering product-category

complexity and/or product choice-environment complexity (Malholtra 1982; Bettman,

Luce and Payne 1998). A more complex category represents high information load

versus a less complex category because the former is more difficult to understand

(Rogers 1995), contains many features, or requires a lot of steps to use (Burnham, Frels

and Mahajan 2003). Higher risk is associated with higher category complexity because

the difficulty in grasping product information results in uncertainty, increasing the

probability that an unfamiliar or unfavorable outcome may arise (Holak and Lehmann

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1990). In high-risk contexts, consumers seek means of reducing uncertainty (Cho and

Lee 2006). With higher category complexity, therefore, the positioning frame should be

diagnostic as a potential means of reducing uncertainty.

In a more complex decision environment, increased information load occurs via a

larger choice set, which leads to greater product confusion and longer choice delays

(Jacoby, Speller, and Kohn 1974; Lurie 2004; Malhotra 1982). 1

In a less complex decision environment, more complete cognitive resources are

available at information processing. Features should be evaluated more on their own

merit as opposed to within the context of a positioning frame even when product

complexity is high. Therefore, the influence of the positioning frame should be reduced

in magnitude, with the preference of the complete positioning concurrently lessened.

In this manner, cognitive

resources are strained, which leads to a greater use of heuristics (Scammon 1977).

Again, the positioning frame should be a highly diagnostic heuristic regardless of

whether or not it is complete. Use of the frame as a heuristic should inhibit in-depth

processing about each of the underlying product features. Thus, with high category and

decision complexity, positioning should be highly diagnostic. A complete positioning

should be strongly preferred due to its perceived lower risk, as it should reduce

uncertainty by providing an assurance that the product does indeed contain all of the key

category features.

1 Decision-environment complexity is orthogonal to product-category complexity. A more complex decision environment could co-occur with a less complex category. For example, the toothpaste category contains many different brands and a myriad of line extensions. Similarly, a less complex decision environment could co-occur with a more complex category. Here, companies considering utilizing a global logistics shipping-based partner large enough to offer customized programs have two primary alternatives in FedEx and UPS.

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Similarly, with low product complexity, decision risk is substantially reduced, and

therefore the chance of an unfavorable outcome diminishes regardless of decision

complexity. Again, the diagnosticity of the positioning frame should decline, with the

preference of the complete positioning concurrently again lowered.

H2a: For a full-featured product, complete positioning will be strongly preferred over a non-complete positioning when product category and decision environment complexity are high. H2b: For a full-featured product, the preference for complete positioning over a non-complete positioning will be reduced when product category complexity is high and decision environment complexity is low. H2c: For a full-featured product, the preference for complete positioning over a non-complete positioning will be reduced when product category complexity is low regardless of decision environment complexity.

We expect another moderator to be price level. Similar to positioning, price level

can act as a perceptual frame that alters judgments of otherwise identical beliefs (Grewal

and Lindsay-Mullikin 2006). For example, Vizio consistently underprices its HDTVs

versus Sony and Samsung even though its technical specs are often identical. Due to its

low-price leadership strategy, many consumers who are not knowledgeable about

HDTVs may believe Vizio’s features and picture quality are inferior to Sony and

Samsung even when they’re highly similar (e.g., an identical refresh rate may be

perceived as showing more motion blur in a Vizio).

With a high price level, the strong preference for complete positioning under high

product category and decision environment complexity should be replicated. Price is

often also used as a heuristic in more complex decision environments (e.g., Lichtenstein

and Burton 1989; Carmon and Simonson 1998). Chernev (2007) finds all-in-one

products gain disproportionately versus specialized products in perceived performance

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and choice at higher prices. This finding, in conjunction with the well-established strong

correlation between higher prices and perceived higher quality, intimates that a higher

price for complete-positioned products should signal competence across all features. In

this manner, price should be used heuristically due the high decision complexity, and a

higher price level can help alleviate the risk associated with high category complexity.

The net effect would be to further strengthen the diagnosticity of the positioning frame on

product judgments. Therefore, a high price should exacerbate the strong preference

predicted above for complete-positioned products under high category and decision

complexity.

With higher product category and decision complexity and a lower price, the

positioning frame should still be used heuristically but the implication of its diagnosticity

should reduce the magnitude of preference for complete-positioned products. The lower

price should produce a perceived performance devaluation compared with a specialized

product on its lone feature (Chernev 2007), and signal an inability to achieve competence

across all features. Given the significance of the positioning frame’s impact on product

judgments, this signal should weaken the preference for complete-positioned products

relative to the higher price context.

When decision complexity is lower, then consumers should be less reliant on the

positioning frame heuristic as they can process the attribute information. Therefore, this

context should again reduce the diagnosticity of the positioning frame, and weaken the

preference for complete-positioned products regardless of price level.

Similarly, with lower product complexity, decision risk is substantially reduced,

and therefore the chance of an unfavorable outcome diminishes regardless of decision

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complexity. Again, the diagnosticity of the positioning frame should be reduced in

magnitude, with the preference of the complete positioning concurrently again lowered,

regardless of decision complexity and price level.

H3a: For a full-featured product, complete positioning will be strongly preferred over a non-complete positioning when product category and decision environment complexity are high, and price level is high. H3b: For a full-featured product, the preference for complete positioning over a non-complete positioning will be reduced when product category and decision environment complexity is high, and price level is low. H3c: For a full-featured product, the preference for complete positioning over a non-complete positioning will be reduced when product category complexity is high and decision environment complexity is low, regardless of price level. H3d: For a full-featured product, the preference for complete positioning over a non-complete positioning will be reduced when product category complexity is low, regardless of decision environment complexity and price level.

Finally, given the expected importance of price level, we investigate the

implication of price uncertainty on price level. This uncertainty occurs when consumers

lack a clear reference price in evaluating a potential purchase (Mazumbar and Jun 1993).

It may be caused by unfamiliarity with the product class (Rao and Sieben 1992),

inadequate pre-purchase search (Dickson and Sawyer 1990), or variable market prices

(Winer 1989). Price uncertainty is important in this context because it increases decision

risk (Mazumdar and Jun 1993). With higher decision risk, consumers seek greater

reassurance about product quality and performance (Sweeney, Soutar and Johnson 1999).

As we argue above, a higher price level should minimize this risk because it would signal

increased competence to deliver quality across all features (Chernev 2007). This is

consistent with the finding that price uncertainty raises reference prices by widening the

acceptable range of prices (Mazumdar and Jun 1993). Again, this should increase the

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diagnosticity of both the pricing and positioning frames, and enhance preference of

completeness. Therefore, with a high price level, complete-positioned products should be

preferred in a high price-uncertainty context than under low price uncertainty. In

contrast, although lower prices should exacerbate the risk inherent to a high price-

uncertainty context, they should be preferred to higher price level. These offsetting

effects should lead to no change in preference for complete-positioned products as a

function of price uncertainty.

H4: Under a low price level, judgments about complete-positioned products will be more positive than those under a high price level. H5a: Under a high price level, judgments about complete-positioned products will be more positive with high price uncertainty than low price uncertainty. H5b: Under a low price level, judgments about complete-positioned products will be unchanged regardless of price uncertainty.

Study 1

Overview

In Study 1, we test H1 – H2c.

Pretest

We conducted a pretest to understand perceptions about complete-positioned

products, and determine research stimuli. Participants (n=39) were asked several open-

ended and scaled questions about a complete positioning (these and all participants in the

three studies were upper-level undergraduates at a large New England university who

received extra course credit for their involvement). In response to the question “When

you see a product labeled as ‘complete,’ what do you think this means?”, 59% answered

“everything I need” while 64% responded “contained more features.” Most participants

thought computer protection software (73%), multivitamins (71%), laptops (68%) and

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cold medicine (65%) offered products with a complete positioning. Moreover, based on

a 7-point scale, computer protection software (M=3.73) and laptops (M=3.72) were

considered more complex than multivitamins (M=3.14; each p<.05) and cold medicine

(M=3.28; each p<.05). So, software and multivitamins were selected as stimuli for Study

1, while the other two were chosen for Study 2.

Design, Participants, and Manipulations

A 2 (positioning: complete or effective) x 2 (decision environment complexity:

high or low) x 2 (product category complexity: high or low) mixed design was employed.

Positioning and decision complexity were between-subjects, and category complexity

was within-subjects. Participants (n=72) were upper-level undergraduate students at a

large northeastern state university. They took the online survey in groups of about fifteen

in a computer lab monitored by one of the co-authors. For both categories, positioning

was manipulated by using two options for an identically full-featured product: complete

and effective. We chose effective as it was similarly abstract compared with complete.

Decision complexity was manipulated by presenting two different choice sets: eight

alternatives (high complexity) and two alternatives (low complexity). This is consistent

with previous research on information load that found consumers can optimally process a

maximum of six alternatives (Chernev 2003; Malhotra 1982). Category complexity was

manipulated per the pretest above, and was counterbalanced (no order effects for any

dependent measures).

------------------- Insert Appendix 1 about here -------------------

Procedure

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Participants were first exposed to a page of instructions, which indicated, “Please

take a look at these computer protection software (multivitamin) products. All the

software (multivitamin) products are from the same brand – Norton from Symantec Corp

(GNC). Please do not touch anything during the presentation, slides will pass

automatically. Once you see all the products, you will see a slide that will have a link for

the questionnaire. Click on that link and follow the instructions.” Then, they were

exposed to either two (low decision complexity) or eight (high decision complexity)

software (multivitamin) products for 20 seconds per product. The final product was full-

featured and contained the positioning manipulation (i.e., either complete or effective). It

contained seven features (for both categories, with information quantity kept the same).

Please see Appendix 1 for the four stimuli. The other products were attribute-positioned,

and contained features supporting that positioning. In the high decision complexity

condition, the final product contained one feature from each of the preceding seven ones.

In the low decision complexity condition, the final product contained the identical feature

set as in the high decision complexity condition. Note that the manipulated product in

terms of positioning always occurred at the end in all experimental conditions, thus

eliminating a potential order-effect bias. Moreover, participants were not notified prior to

product exposures that they would only be evaluating only the final product.

After being exposed to the stimuli products, participants then filled out the

dependent measures only about the final product. Thus, the task was memory-based, and

ensured that the decision complexity manipulation would be manifested in the processing

of the stimulus product. After they finished these measures, participants were exposed to

the alternatives in the second category, and again completed the dependent measures for

16

the final, full-featured product only in the choice set. Finally, participants were thanked

for their time and dismissed.

------------------- Insert Table 1 about here -------------------

Dependent Measures

Dependent variables were choice, purchase intentions, and product beliefs.

Choice is a common and highly diagnostic dependent variable in decision-making

contexts (see Chernev 2007). Choice was measured by a statement asking which of the

alternatives participants would select. Purchase intentions (see Table 1 for observed

variables and source) were measured by three items (this and all non-choice measures

used 7-point semantic differential scales with approximately 25% reverse-scored), and

beliefs were measured individually. Each full-featured product contained seven beliefs

(see Appendix 1), which represented complete capabilities.

Results

All constructs were reliable across both categories (.67<α<.93). A factor analysis

with Varimax rotation for purchase intentions and beliefs indicated a two-factor model

based on the criteria of factor loadings>.40 and eigenvalues>1. Therefore, we averaged

all items across each factor. Positioning completeness and category complexity (see

Table 1) were measured as manipulation checks, and were successfully manipulated. The

complete positioning was judged more complete than the effective alternative with

identical features (multivitamin F(1,71)=16.05, p<.001; software F(1,69)=133.9,

p<.0001), and software was judged more complex than multivitamins (p<.001). As we

used two categories, we measured category expertise (Mitchell and Dacin 1996), but it

was not a significant covariate with any dependent variable (all ps > .05).

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------------------ Insert Figure 1 about here ------------------

------------------- Insert Table 2 about here -------------------

Hypothesis Testing

For product choice, we compared the percentage of participants who selected the

final product positioned as complete versus positioned as effective, and analyzed the data

using linear regression. Positioning showed a main effect for both categories (see Table

2 for all χ2-values, F-values, p-values, and means for all reported results). Confirming

hypothesis H1, choice was higher for complete-positioned products in each category

(multivitamin p<.05, software p<.005). However, decision complexity moderated the

effect for software (χ2=8.37, p<.01) but not for multivitamin (p>.50). Confirming

Hypothesis H2a, under high category/decision complexity, the complete-positioned

product was strongly preferred (p<.0001) over the effective alternative. Confirming

hypothesis H2b, under high category/low decision complexity, preference for complete-

positioned products was reduced and in fact eliminated (p>.50). Confirming hypothesis

H2c, under low category complexity, once again preference for complete-positioned

products was reduced regardless of decision complexity (high p<.05, low n/s).

Purchase intentions and beliefs were each evaluated using 2 (positioning) x 2

(decision complexity) ANOVAs. Per hypothesis H1, the complete positioning displayed

higher purchase intentions (multivitamin p<.001, software p<.0001). As with choice, this

effect was moderated by decision complexity but only for software. Per Hypothesis H2a,

under high category/decision complexity, the complete positioning was strongly preferred

(p<.0001). Confirming hypothesis H2b, under high category/low decision complexity,

the magnitude of the preference for complete positioning was reduced (p<.05).

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Confirming hypothesis H2c, under low product complexity, the magnitude of the

preference for complete positioning was reduced regardless of decision complexity (high

p<.01, low n/s).

For beliefs, a 2 x 2 ANOVA revealed a main effect of positioning where complete

positioned products were again evaluated more positively for both categories

(multivitamin p<.0001, software p<.0001). The mean differences displayed the same

trend as with choice and purchase intentions. Under high category/decision complexity,

the complete positioning was strongly preferred (p<.0001). Under high category/low

decision complexity, the complete positioning was still preferred and, this time, just to a

slightly lesser extent (p<.001). Under low category complexity, the magnitude of the

preference for complete positioning was reduced regardless of decision complexity (high

p<.01, low n/s).

Additional Results

We also measured expected price as a dependent variable to test Chernev’s (2007)

finding that consumers would view all-in-one products as pricier than specialized

alternatives. Expected price data would also be useful for setting up price-based

extensions in Studies 2 and 3. It was a categorical variable measured with a single-item

measure: “Which one of these products is likely to be the most expensive?” (Chernev

2007). A regression indicated positioning again displayed a main effect, but this time

only for software (χ2=5.47, p<.05) not multivitamins (p>.05). For software, the

complete-positioned product was viewed as more expensive. 80.9% of the participants

picked the full-featured alternative as the most expensive in the choice set when it was

complete-positioned, while only 55.2% did so when it was effective-positioned.

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Discussion

In aggregate, the data in Study 1 support the hypotheses. There was a general

preference for complete versus non-complete positioning. Uniformly, complete-

positioned products displayed the strongest preference when category and decision

complexity were both high, and thus information load was maximized. Similarly,

complete-positioned products displayed the weakest preference, and in fact no

preference, when category and decision complexity were low. Here, information load

was minimized. We project this effect is due to participants using the positioning frame

as a heuristic to simplify the evaluation process, thus biasing them toward favoring

complete-positioned products under high information load. Under low information load,

they did not to utilize heuristics, and thus the positioning frame was less diagnostic in

forming product judgments.

Study 2

Overview

In Study 1, we demonstrate a strong preference for complete positioning when

category and decision complexity are high. The objective of Study 2 is to replicate Study

1 using a different stimulus set to enhance generalizability, and extend it by manipulating

price level. Therefore, we test H1 – H3d.

------------------- Insert Appendix 2 about here ---------------

Design, Participants, and Manipulations

A 2 (positioning: complete and effective) x 2 (decision complexity: high and low)

x 2 (price level: high and low) x 2 (category complexity: high and low) mixed design was

employed. Positioning, decision complexity, and price level were between-subject, and

20

category complexity was within-subject. The category order was counterbalanced (order

effect n/s). From the Study 1 pretest, notebook computers were higher in category

complexity, and cold medicine was lower. Positioning and decision complexity were

manipulated as in Study 1 (see Appendix 2 for stimuli with positioning manipulation).

On an initial slide, participants (n=133) were informed that the notebooks (cold

medicine) were offered by HP (Tylenol). The price level of the last (full-featured)

product was manipulated by moving up and down by two standard deviations from the

average market price at the time the study was conducted.

Procedure and Dependent Measures

Participants followed the same procedure and filled out similar measures as in

Study 1 except where noted. We used product evaluations (see Table 1) and beliefs as

dependent measures.

Results

An exploratory factor analysis indicated a two-factor model for both stimuli based

on the criteria detailed in Study 1. For notebooks, four of the product belief items loaded

with the product evaluations items but three of them loaded as an orthogonal factor. We

analyzed and averaged only these latter three items. All aggregate measures were reliable

across both categories (.70<α<.91). Confirming the manipulation, notebooks (M=3.59)

were perceived as more complex than cold medicine (M=3.07; t(132)=26.37; p<.001).

Also confirming the manipulation, the complete positioning was viewed as more

“complete” for both notebooks [F (1,131) =19.65; p<.001] and cold medicine

[F(1,129)=14.81; p<.001].

------------------- Insert Table 3 about here -------------------

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Testing Hypotheses H1-H2c

To test hypotheses H1-H2c, purchase intentions and beliefs were each evaluated

using 2 (positioning) x 2 (decision complexity) ANOVAs (see Table 3 for all main effect

and interaction effect F-values, p-values, and means). Confirming hypothesis H1, for

both categories and all dependent variables, complete positioning displayed more positive

product evaluations (notebook p<.0001, cold medicine p<.01) and beliefs (notebook

p<.0001, cold medicine p<.0001). For notebooks, this effect was moderated by decision

complexity for both dependent variables (product evaluations F(1,131)=8.82, p<.005;

beliefs F (1,131)=13.99, p<.001), while there was no interaction for either dependent

measure with cold medicine (each p>.20). Confirming hypothesis H2a, under high

category/decision complexity, product evaluations (p<.0001) and beliefs (p<.0001) were

much greater for the complete versus effective positioning. Confirming hypothesis H2b,

the magnitude of the preference for complete positioning decreased under high

category/low decision complexity for product evaluations (p<.05) and slightly decreased

for beliefs (p<.001). Confirming hypothesis H2c, the magnitude of the preference for

complete positioning decreased under low category complexity regardless of decision

complexity, and in fact there was no difference between complete and effective (n/s for

each level of decision complexity and each dependent measure).

Testing hypotheses H3a-H3d

To test hypotheses H3a-H3d, we ran 2 x 2 x 2 ANOVAs on the two dependent

measures (See Table 3). The 3-way interaction with price level is directionally

significant for both product evaluations [F(1,131)=3.04, p<.09] and beliefs [F

22

(1,131)=3.48, p<.07]. Conversely, for cold medicine (the less complex category), no

interaction effects are present with positioning or price level (each p>.30).

------------------- Insert Figure 2 about here -------------------

Confirming hypothesis H3a (see Figure 2 for H3a-H3d graphically displayed),

with high category complexity/decision complexity/price level, complete positioning was

strongly preferred to effective in product evaluations (p<.0001) and beliefs (p<.0001).

Confirming hypothesis H3b, with high category/decision complexity and low price level,

preference for complete positioning is reduced for product evaluations (p<.01) and beliefs

(n/s). Confirming hypothesis H3c, with high category/low decision complexity,

preference for complete positioning was again reduced regardless of price level (high

product evaluations and beliefs n/s; and low product evaluations n/s and beliefs p<.05).

Finally, confirming H3d, with low category complexity, preference for complete

positioning was reduced regardless of decision complexity and price level (high/high

product evaluations n/s and beliefs p<.01; high/low product evaluations p<.05 and beliefs

n/s; low/high product evaluations n/s and beliefs p<.05; and low/low product evaluations

n/s and beliefs p<.05).

Discussion

In Study 2, we replicate and extend key findings from Study 1 with a different

stimulus set and dependent variable to enhance generalizability. We replicate an overall

preference for a complete positioning, and a very strong preference for it with higher

product and decision complexity. Further, we find a strong preference for complete

positioning with higher product and decision complexity, and higher price. The

significance of price extends Chernev’s (2007) finding that all-in-one products carry

23

higher expected prices. We find that this effect is in fact moderated by product and

decision complexity.

Study 3

Overview

As we have already replicated and extended the information-load effects

delineated in H1 and H2a-H2c in Studies 1 and 2 with different stimuli and dependent

variables, we turn our focus in Study 3 away from information-load effects and to

extending the price-level effects reported in the additional results section of Study 1 and

the hypothesis-testing section of Study 2 (H3a-H3d). In Study 3, we test H4-H5b, and

examine the effects of price levels and price uncertainty on preferences of complete-

positioned products. Once again, we use a different stimulus set, in fact a disparate

procedure, to enhance generalization. In this study, we do not manipulate product

positioning per se as in Studies 1 and 2. In contrast, we examine the effects of price level

X price uncertainty interactions on the choice and perceptions about complete-positioned

products. We continue the investigation of price-level framing effects begun in Study 2.

This serves to more closely proximate Chernev (2007), who did not manipulate

positioning (e.g., all-in-one positioning versus alternative positionings) but examined all-

in-one products in terms of choice and perceptual effects based on different choice-set

scenarios.

Design, Participants and Manipulations

A 3 (price level: high, medium, and low) x 2 (price uncertainty: high and low)

between-subjects design was employed. We used a medium price level to attempt to

capture boundary conditions of the proposed high price-level effects. In other words, we

24

wanted to investigate whether preferences about complete-positioned products under

medium price-levels would mirror those under low price-levels, thus indicating an

approximate price-level boundary condition for when the expected preference shifts

under high prices would occur. We measured the extent to which participants (n=214)

chose a complete-positioned product, and their beliefs about it. This is a scenario-based

experiment, which is commonly utilized in decision-making research (e.g., Kahneman

and Tversky 2000). In this approach, independent variables are manipulated by changing

details in the scenario, and participants’ beliefs are revealed through scenario responses.

The scenario utilized was making arrangements for a one-week vacation to an overseas

location. A second pretest (n=23) was conducted to understand beliefs about vacation

packages. The most important expenditures were accommodation, food, and

entertainment. Participants filled out their expected expenditures in these categories for a

one-week duration. The extent to which a destination was known was important as well.

To avoid confounding the effects of price uncertainty, vacation location and thus location

uncertainty were not revealed in the scenarios.

------------------- Insert Appendix 3 about here -------------------

Procedure and Dependent Measures

Participants took the survey in groups of about 15 in a computer lab monitored by

one of the authors, were randomly placed into an experimental condition, and received

extra-credit in a marketing course for their time. Participants first read through a brief

cover story stating that they would asked some questions about consumption decisions,

and that there were no right or wrong responses. After that, they read through the

scenario (see Appendix 3 for sample scenarios) in a self-paced time. Then, they had the

25

choice task where they were asked to select either the all-inclusive (complete) vacation or

the component (accommodation, food, and entertainment separately) vacation.

Following the choice task, they filled out the following scaled measures for the complete

vacation: purchase intentions and perceived expensiveness (as a manipulation check).

Finally, basic participant descriptive data was obtained.

Manipulations

Price level was manipulated by changing the price of the complete-positioned

alternative, while the price uncertainty factor was manipulated by altering the range of

prices (e.g., Mazumdar and Jun 1993) among the three vacation components

accommodations, food, and entertainment. Appropriate prices were obtained from the

second pretest and online travel web-sites. Price levels were manipulated by determining

the lowest and highest acceptable prices for each expenditure, and utilizing these prices in

the high price uncertainty condition. The averages of these prices were used in the low

price uncertainty condition by giving a trivial difference between two price levels (see

Mazumdar and Jun 1993 for an identical procedure).

The price of the complete-positioned alternative was determined based the lowest,

medium, and highest prices of the component ranges. For example, the low price of the

complete-positioned alternative was calculated by adding $350 (accommodation) + $200

(food) + $150 (entertainment). With this manipulation, a potential confound is perceived

expensiveness of the complete-positioned alternative in different price uncertainty

conditions. Since participants likely use the manipulated range of prices as their

reference price (see Janiszewski and Lichtenstein 1999) to evaluate the complete-

positioned alternative, they may perceive the complete-positioned alternative as more or

26

less expensive in different price uncertainty conditions. Therefore, we measured

perceived expensiveness of the complete-positioned alternative as a potential covariate.

Dependent Measures

Choice and purchase intentions were measured identically as in the previous

studies.

Results

Purchase intentions were reliable (α=.91) and loaded onto one factor. We

measured perceived expensiveness by asking: “The $X all-inclusive price is expensive.”

Our rationale was to use the measure as a manipulation check and a potential covariate.

The data indicated price level was successfully manipulated as perceived expensiveness

increased as the price of the complete alternative increased [F(2,212)=75.63; p<.001;

MLow=2.38, MMedium=3.91, MHigh=5.37]. Yet, perceived expensiveness was not a

potential confound as the complete-positioned alternative was not viewed as more

expensive in different price uncertainty conditions (p>.05).

---------------------- Insert Table 4 about here ----------------------

--------------------- Insert Figure 3 about here ----------------------

Choice was investigated using regression. Price of the complete-positioned

alternative showed a main effect (χ2 =35.06; p<.0001), where the complete alternative

was preferred when its price was lower, while price uncertainty was not significant

(p>.05). This confirms hypothesis H4. However, the main effect was conditioned by a

price level X price uncertainty interaction (χ2 =3.72; p<.05). As Figure 3 indicates, under

a high price level, choice was higher for the complete-positioned product under high

price uncertainty versus low (χ2 =7.81; p<.01). Under a low price level, choice was

27

unchanged for complete-positioned products regardless of price uncertainty (p>.30). This

effect was replicated at a medium price level (p>.40). These findings confirm hypotheses

H5a-H5b.

The 3 x 2 ANOVA on purchase intentions of the complete-positioned alternative

showed identical results. Price level had a main effect [F(2,209)=20.89; p<.0001], where

intentions increased as price level decreased, and price uncertainty was not significant.

The price-level effect was again conditioned by an interaction with price uncertainty

[F(2,209)=3.19; p<.05]. Under the high price level, purchase intentions were higher with

high uncertainty versus low uncertainty [F(1,83)=8.95; p<.01]. In contrast, under the low

price level, purchase intentions were unchanged between high and low price uncertainty

(p>.90). This effect was again replicated at a medium price level (p>.90).

Discussion

In Study 3, we replicate the importance of price level in understanding response to

complete-positioned products. Here, under high price levels, complete-positioned

products were preferred under high versus low price uncertainty. Under low (and

medium) price levels, this preference is eliminated.

General Discussion

Conceptual Implications

In this research, we examined the role of positioning and price-level frames on

judgments about complete-positioned products. In Study 1, we find judgments about

complete-positioned products were more positive than effective-positioned products.

However, judgments were much more positive for complete-positioned products under

high information load, as operationalized by high product-category and decision-

28

environment complexity, than lower information-load contexts. In Study 2, we replicated

these two findings. In addition, we found judgments were much more positive about

complete-positioned products under high information load and high price level than

lower information-load and price-level contexts. The significance of price level was

explored further in Study 3. There, we found a preference for a high-priced complete-

positioned product when price uncertainty was higher versus lower. Low-priced and

medium-priced complete-positioned products do not exhibit preference as a function of

price uncertainty.

Overall, these results extend the research in positioning. Other work in

positioning has examined differences in categorization between one and three distinct

features (Sujan and Bettman 1989), and differences in judgment revision between

attribute-specific versus abstract positionings (Pham and Muthukrishnan (2002). Our

work looked at the direct effects of positioning on product judgments by specifically

contrasting a complete positioning with other alternatives. We find different positionings

do in fact alter judgments about identical features, conditioned by factors that alter the

magnitude of these judgments. In this manner, positioning appears to act as a perceptual

frame in which consumers judge product features, similar to other applications of frame

theory (cf Barsalou 1993; Maheswaran and Meyers-Levy 1990; Luna, Ringberg, and

Peracchio 2008; Grant and Tybout 2008).

Our findings also extend Chernev’s (2007) important work on all-in-one products.

Like his work, we affirm that identical features can be assessed differently as a function

of the evaluation context. However, unlike his work, we do not find evidence of attribute

devaluation of all-in-one products. In fact, we find the opposite effect – a complete

29

positioning leads to more positive product judgments, including more positive beliefs.

This suggests an attribute enhancement effect. This finding is consistent with evidence

from library science suggesting completeness intimates comprehensiveness, information

quality and persuasiveness (Dutta-Bergman 2004; Eysenbach, Powell, Kuss, and Sa

2002).

There are several reasons for this difference between our results and those of

Chernev (2007). Chernev (2007) changed the composition of the choice set in which the

all-in-one product was evaluated between two and three alternatives. In contrast, we

shifted the interpretive context of product features by introducing different positionings

and price levels. His all-in-one products had two attributes, where our complete-

positioned products had five to seven attributes. In this regard, he specifically

investigated compensatory judgments made feasible by his choice-set and limited-

attribute contexts. Our procedure was quite different in that we established products with

identical feature sets, but different positionings, in contexts varying in information load,

price level and price uncertainty. The full-featured product, with the positioning

manipulation, was evaluated separately not in a multi-product choice set. This allowed

us to directly compare the effects of a complete and effective positioning on product

judgments, as conditioned by price factors. Therefore, positioning per se was assessed,

as opposed to Chernev (2007) who investigated the implications of different choice sets

on judgments of all-in-one products.

However, the preference for complete over effective positioning changed in

magnitude depending on context. With higher product and decision complexity, a

complete positioning was most strongly preferred. In that context, it appeared to be used

30

to reduce the uncertainty associated with the product risk (Holak and Lehmann 1990) and

used heuristically to reduce the information load linked with decision complexity

(Scammon 1977). This result is interesting in the context of the Han, Chung, and Sohn

(2009) finding that convergence products are preferred to the dedicated alternatives at

low levels of technological performance, with the preference reversed at high levels of

technological performance. Perhaps the latter situation presents a clear decision at a low-

level of risk. In other words, consumers may use an attribute-based heuristic, eliminating

any alternative, including a converged or complete-positioned product, that lacks the

high-technology feature.

When a higher price was associated with higher product and decision complexity,

a complete positioning again was most strongly preferred. Here, it appeared the high

price connoted higher quality and thus better competency in performance across all

features. In this manner, similar to positioning, price appeared to act as a perceptual

frame that altered judgments of the product’s underlying features (e.g., Grewal and

Lindsay-Mullikin 2006). This extends Chernev (2007), who also found evidence that all-

in-one products were judged as more expensive than specialized alternatives, and a high

price-level mitigated the attribute devaluation disadvantage of all-in-one products.

Therefore, both Chernev (2007) and us conclude that a high price level can boost

judgments about all-in-one or complete-positioned products.

Finally, our findings that complete-positioned products are preferred under a high

price level with high versus low price uncertainty validates the premise that high

uncertainty increases decision risk (Sweeney, Soutar and Johnson 1999) and widens the

acceptable range of prices (Mazumdar and Jun 1993). As before, high prices apparently

31

reduce risk by signaling a threshold quality level across all features. In turn, this

counterbalances the more negative perception caused by the high price that is apparent

under low price uncertainty. These results thus extend Chernev (2007) by finding high

price uncertainty can moderate preference for high-priced complete-positioned products

by leading to a reversal versus the low price-uncertainty context.

Managerial Implications

Most importantly, these data give managers confidence that a complete

positioning will lead to favorable judgments. For example, when Colgate Total was

launched in 1997 (the first time a complete positioning was used in the toothpaste

category), it took the long-lasting market leadership from Crest in less than a year with a

25.1% share to Crest’s 24.6% (Advertising Age 9/28/98). However, for managers of

more complex products, such as in the technology sector, a complete positioning can be

especially potent when information load is high (e.g., many category competitors) and

when price levels are high. In fact, our results suggest managers of complex products

that are completely positioned may want to price more aggressively to signal competence

across all their products’ features. Apple’s iPhone is a good example of this

phenomenon. It was positioned as complete, from a feature and functionality

perspective, and had all of the components of a cellphone, mp3 player, and internet

browser. Apple priced the iPhone at $599 at launch, much higher than competition. Even

when Apple dropped its price by $200 six months post-launch, it was still premium-

priced. Although clearly some of the price premium was accountable by Apple’s strong

brand equity, another part of it likely reflected its desire to communicate a high

performance level of its features.

32

While a complete positioning can potentially boost sales, it may have some

drawbacks managers should be aware of before contemplating using it. Selling products

with too many features may cause feature fatigue, dissatisfaction, and low repurchase

consideration post-purchase (Thompson, Hamilton and Rust 2005). Managers of

completely-positioned products should incorporate this in their planning by offering

extended product trials, making sure the complete-feature package remains user-friendly,

and focusing on usage experience as well. In addition, there may be pressure on

marketers of a complete-positioned product to make sure it performs equally and

competently across all of its features. One underperforming feature could lead to buyer

inferences that all other features are sub-par as well.

Limitations and Future Research

A clear limitation is the use of convenience samples. However, pretests were

conducted to make the stimuli were germane to the participant population. Another

limitation is that although we hypothesized participants used heuristics in certain

circumstances, we could not verify this empirically. Future work should measure

response latencies to better understand the extent to which heuristics are actually being

utilized in these different processing contexts. A third limitation is we did not present a

competitive context. A potentially attractive positioning, such as completeness, may

become less impactful if it is not differentiated. For example, Crest finally launched a

toothpaste line called Pro Health positioned on complete benefits in response to Colgate

Total, and re-took its historic market share leadership for the first time since 1997 in the

first quarter of 2007 (Advertising Age 5/15/2007). Finally, we did not examine the

relative diagnosticity of a positioning versus pricing frame. Future work should

33

investigate under which circumstances a pricing frame, or other type of frame, may

reduce the diagnosticity of a positioning frame. For example, price-sensitive customers

may disregard the positioning frame in favor of solely price-based judgments unless the

frame alters product judgment above a threshold level of attractiveness. This may be

especially likely under periods of economic recession.

Appendix 1 Study 1 Sample Stimuli

A. Complete-Positioned Software B. Effective-Positioned Software

Complete Security

• Provides real-time anti-spyware protection against viruses. spyware, adware and Trojan horses.

• Aggressive pop-up blocking capabilities.• The embedded Trojan-wall increases your

protection against password theft.• Provides automatic email forwarding, e-mail/

Chat/IM blocking.• Clears complete history of your Internet actions.• Safeguards you against online identity and fraud.• Provides a strong firewall to controls the network

traffic.

C. Complete-Positioned Multivitamin D. Effective-Positioned Multivitamin

Effective Support• Contains gingko for continuing dynamism.• Has calcium for bone and tooth protection.• Supports healthy brain and heart functions

and memory retention.• Features digestive enzymes that facilitate

normal digestion. • Has lutein and bilberry for eye and skin.• Includes B6, B12 and folic acid for heart.• Includes antioxidants for immune system.

Efficient Security

• Provides real-time anti-spyware protection against viruses. spyware, adware and Trojan horses.

• Aggressive pop-up blocking capabilities.• The embedded Trojan-wall increases your

protection against password theft.• Provides automatic email forwarding, e-mail/

Chat/IM blocking.• Clears complete history of your Internet actions.• Safeguards you against online identity and fraud.• Provides a strong firewall to controls the network

traffic.

Complete Support• Contains gingko for continuing dynamism.• Has calcium for bone and tooth protection.• Supports healthy brain and heart functions

and memory retention.• Features digestive enzymes that facilitate

normal digestion. • Has lutein and bilberry for eye and skin.• Includes B6, B12 and folic acid for heart.• Includes antioxidants for immune system.

35

Appendix 2 Study 2 Sample Stimuli A. Complete-Positioned Notebook – High

Price B. Effective-Positioned Notebook – High

Price Complete Notebook Solutions

($2,279)• Features the Intel® Core™ Duo T2500 processor

(2.0GHz) and 1GB of PC2-4200 DDR2 memory. • NVIDIA® GeForce™ Go 7600 GT graphics and 256MB of

GDDR3 video memory and 80 GB hard drive (5400 rpm). • Connectable to all peripherals including TVs and

cameras. Bluetooth™ technology and 802.11abg support.• Features an HD-DVD ROM to enjoy and create DVDs and

CDs to share your files. • Built-in Web camera with microphone. Supports

ExpressCard™/54 for transfers of video and large files. • Sleek design with three different skin color options.• Spill resistant keyboard and corrosion protected

electronics. Indoor/outdoor viewable displays. C. Complete-Positioned Cold Medicine –

High Price D. Effective-Positioned Cold Medicine –

High Price

Complete Relief ($12.29)

• 30 mg pseudoephedrine for nasal decongestion

• 220 mg naproxen sodium for relief of muscle and body aches

• 500 mg acetaminophen for headache relief

• 100 mg calcium carbonate to relieve acid indigestion

• 10 mg dextromethorphan to suppress coughs

Effective Relief ($12.29)

• 30 mg pseudoephedrine for nasal decongestion

• 220 mg naproxen sodium for relief of muscle and body aches

• 500 mg acetaminophen for headache relief

• 100 mg calcium carbonate to relieve acid indigestion

• 10 mg dextromethorphan to suppress coughs

Effective Notebook Solutions ($2,279)

• Features the Intel® Core™ Duo T2500 processor (2.0GHz) and 1GB of PC2-4200 DDR2 memory.

• NVIDIA® GeForce™ Go 7600 GT graphics and 256MB of GDDR3 video memory and 80 GB hard drive (5400 rpm).

• Connectable to all peripherals including TVs and cameras. Bluetooth™ technology and 802.11abg support.

• Features an HD-DVD ROM to enjoy and create DVDs and CDs to share your files.

• Built-in Web camera with microphone. Supports ExpressCard™/54 for transfers of video and large files.

• Sleek design with three different skin color options.• Spill resistant keyboard and corrosion protected

electronics. Indoor/outdoor viewable displays.

36

Table 1 Measures Item Scale Endpoints (7-point) Source Category Complexity This product require a lot of knowledge to use

Unlikely/Likely Mukhejee and Hoyer (2001)

This product is Not complex at all/ Very Complex

Mukhejee and Hoyer (2001)

Positioning This product has all the features that I need.

Disagree/Agree Sujan and Bettman (1989)

This product is complete" in terms of features."

Disagree/Agree Sujan and Bettman (1989)

Purchase Intentions At the price shown, I would consider buying this product.

Disagree/Agree Dodds, Monroe and Grewal (1991)

The probability that I would consider buying the product is high/

Disagree/Agree Dodds, Monroe and Grewal (1991)

My willingness to buy the product is high/

Disagree/Agree Dodds, Monroe and Grewal (1991)

I would purchase this notebook Disagree/Agree Dodds, Monroe and Grewal (1991)

Product Evaluations This product is Good/Bad

Like/Dislike Undesirable/Desirable Unfavorable/Favorable

Mukhejee and Hoyer (2001)

37

Table 2 Study 1 Results

Main Effect Results (H1) Product category complexity

High Low Choice χ2,p %Complete %Effective

10.87**

741 35

n/s

61 39

Purchase Intentions F,p MComplete MEffective

28.50****

5.39 3.92

11.28***

4.83 3.71

Beliefs F,p MComplete MEffective

52.63****

6.00 4.37

21.37***

5.46 4.20

Interaction Effect Results (H2a – H2c)

Product category complexity/Decision environment complexity High/high High/low Low/high Low/low Choice χ2,p %Complete %Effective

16.15***

83 15

n/s

61 50

5.11*

56 17

n/s

69 53

Purchase Intentions F,p MComplete MEffective

34.91****

5.62 3.60

4.53*

5.17 4.23

24.74**

5.08 3.33

n/s

4.77 4.21

Beliefs F,p MComplete MEffective

45.40****

5.91 3.95

14.15***

6.08 4.79

19.03**

5.72 4.08

n/s

5.35 4.53

*p<.05 **p<.01 ***p<.001 ****p<.0001 1 = %s represented those who chose either the effective-positioned product or the complete-positioned product instead of one of the specialized alternatives

38

Table 3 Study 2 Results

Main Effect Results (H1) Product category complexity

High Low Product Evaluations F,p MComplete MEffective

49.95****

5.67 4.52

7.47*

5.36 4.85

Beliefs F,p MComplete MEffective

44.10****

5.17 4.27

21.81****

4.95 4.19

Interaction Effect Results (H2a – H2c)

Product category complexity/Decision environment complexity High/high High/low Low/high Low/low Product Evaluations F,p MComplete MEffective

50.03****

5.79 4.17

8.82**

5.55 4.86

n/s

5.58 5.17

n/s

5.15 4.85

Beliefs F,p MComplete MEffective

33.16****

5.20 4.15

13.99***

5.14 4.39

n/s

5.19 4.99

n/s

4.70 4.89

39

Interaction Effect Results (H3a – H3d)

Product category complexity/Decision environment complexity/Price level

High/high/high

High/high/low

High/low/high

High/low/ Low

Product Evaluations F,p MComplete MEffective

54.60****

6.11 3.98

9.63**

5.38 4.29

n/s

5.91 5.55

n/s

5.06 4.56

Low/high/high

Low/high/low

Low/low/high

Low/low/ Low

Product Evaluations F,p MComplete MEffective

n/s

5.16 4.70

6.32*

5.92 5.02

n/s

5.25 4.89

n/s

4.99 4.81

High/high/high

High/high/low

High/low/high

High/low/ Low

Beliefs F,p MComplete MEffective

20.71****

5.36 4.06

10.11**

4.97 4.20

n/s

5.30 4.98

7.56*

4.90 4.12

Low/high/high

Low/high/low

Low/low/high

Low/low/ Low

Beliefs F,p MComplete MEffective

7.72**

5.08 4.04

n/s

5.25 4.54

5.32*

4.97 4.31

4.32*

4.47 3.87

*p<.05 **p<.01 *** p<.005 **** p<.0001

40

Table 4 Study 3 Results

Interaction Effect Results (H4a – H4b) Price Level/Price Uncertainty

Choice χ2 ,p %Complete %Complete

7.80**

High/High

45%

High/Low 16%

n/s

Medium/High

67%

Medium/Low 77%

n/s

Low/High

70% Low/Low

80% Purchase Intentions F,p MComplete

MComplete

8.95**

High/High 4.42

High/Low

3.27

n/s

Medium/High 5.03

Medium/Low

5.04

n/s

Low/High 5.43

Low/Low

5.42 *p<.05 **p<.01 *** p<.001 **** p<.0001

41

Figure 1 Study 1 Choice Results

42

Figure 2 Study 2 Product Evaluations Results

Figure 3

43

Study 3 Choice Results

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