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Completeness As A Product Positioning Strategy: A Framing Perspective
Timucin Ozcan, Daniel A. Sheinin
200 9 /20 10 No. 4
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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

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 completepositioned 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-inone” 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 allin-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 completepositioned products. We extend previous work by investigating whether completepositioned 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 negativelyframed 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. Languagetriggered 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 attributespecific 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 completepositioning 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 singleattribute products. Moreover, due to the choice set context, he did not compare an all-inone positioning with alternate positionings.
H1: For a full-featured product, a complete positioning will be preferred to a noncomplete 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).
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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.
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.
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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 shippingbased 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 noncomplete 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 noncomplete 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 noncomplete 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 noncomplete 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 noncomplete 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 priceuncertainty 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 openended 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 fullfeatured 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
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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
χ 2
-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
(χ 2
=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 completepositioned 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, completepositioned 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
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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
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(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
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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 completepositioned 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 allin-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
23

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
24

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 completepositioned alternative, they may perceive the complete-positioned alternative as more or
25

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;
M
Low
=2.38, M
Medium
=3.91, M
High
=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 (χ 2
=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 (
χ 2
=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
(χ 2
=7.81; p<.01). Under a low price level, choice was
26

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-
27

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 completepositioned 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
28

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 completepositioned products had five to seven attributes. In this regard, he specifically investigated compensatory judgments made feasible by his choice-set and limitedattribute 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
29

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 lowlevel 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 allin-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
30

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 premiumpriced. 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.
31

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
32

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.
33

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
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.
D. Effective-Positioned Multivitamin
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.
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.

Appendix 2
Study 2 Sample Stimuli
A. Complete-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
B. Effective-Positioned Notebook – High
Price
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.
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
35

Table 1
Measures
Item
Category Complexity
This product require a lot of knowledge to use
This product is
Positioning
This product has all the features that I need.
This product is complete" in terms of features."
Purchase Intentions
At the price shown, I would consider buying this product.
The probability that I would consider buying the product is high/
My willingness to buy the product is high/
I would purchase this notebook
Product Evaluations
This product is
Scale Endpoints (7-point) Source
Unlikely/Likely Mukhejee and
Hoyer (2001)
Not complex at all/ Very
Complex
Disagree/Agree
Disagree/Agree
Mukhejee and
Hoyer (2001)
Sujan and Bettman
(1989)
Sujan and Bettman
(1989)
Disagree/Agree
Disagree/Agree
Disagree/Agree
Disagree/Agree
Good/Bad
Like/Dislike
Undesirable/Desirable
Unfavorable/Favorable
Dodds, Monroe and Grewal (1991)
Dodds, Monroe and Grewal (1991)
Dodds, Monroe and Grewal (1991)
Dodds, Monroe and Grewal (1991)
Mukhejee and
Hoyer (2001)
36

Table 2
Study 1 Results
Main Effect Results (H1)
Product category complexity
Choice
χ 2
,p
%
Complete
%
Effective
Purchase
Intentions
F,p
M
Complete
M
Effective
Beliefs
F,p
M
Complete
M
Effective
High
10.87**
74
1
35
28.50****
5.39
3.92
52.63****
6.00
4.37
Low n/s
61
39
11.28***
4.83
3.71
21.37***
5.46
4.20
Interaction Effect Results (H2a – H2c)
Product category complexity/Decision environment complexity
Choice
χ 2
,p
%
Complete
%
Effective
Purchase
Intentions
F,p
M
Complete
M
Effective
Beliefs
F,p
High/high High/low Low/high Low/low
16.15***
83
15
34.91****
5.62
3.60
45.40**** n/s
61
50
4.53*
5.17
4.23
14.15***
5.11*
56
17
24.74**
5.08
3.33
19.03** n/s
69
53 n/s
4.77
4.21 n/s
M
Complete
M
Effective
*p<.05
5.91
3.95
6.08
4.79
5.72
4.08
5.35
4.53
**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
37

Table 3
Study 2 Results
Main Effect Results (H1)
Product category complexity
High Low
Product
Evaluations
F,p 49.95**** 7.47*
5.67
4.52
5.36
4.85
M
Complete
M
Effective
Beliefs
F,p
M
Complete
M
Effective
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
M
Complete
M
Effective
Beliefs
F,p
50.03****
5.79
4.17
33.16****
8.82**
5.55
4.86
13.99*** n/s
5.58
5.17 n/s n/s
5.15
4.85 n/s
M
M
Complete
Effective
5.20
4.15
5.14
4.39
5.19
4.99
4.70
4.89
38

Product
Evaluations
F,p
M
Complete
M
Effective
Product
Evaluations
F,p
M
Complete
M
Effective
Beliefs
F,p
M
Complete
M
Effective
Interaction Effect Results (H3a – H3d)
Product category complexity/Decision environment complexity/Price level
High/high/ high
54.60****
6.11
3.98
Low/high/ high n/s
5.16
4.70
High/high/ high
20.71****
5.36
4.06
Low/high/ high
7.72**
5.08
4.04
High/high/ low
9.63**
5.38
4.29
Low/high/ low
6.32*
5.92
5.02
High/high/ low
10.11**
4.97
4.20
Low/high/ low n/s
5.25
4.54
High/low/ high n/s
5.91
5.55
Low/low/ high n/s
5.25
4.89
High/low/ high n/s
5.30
4.98
Low/low/ high
5.32*
4.97
4.31
High/low/
Low n/s
5.06
4.56
Low/low/
Low n/s
4.99
4.81
High/low/
Low
7.56*
4.90
4.12
Low/low/
Low
4.32*
4.47
3.87
Beliefs
F,p
M
Complete
M
Effective
*p<.05
**p<.01
*** p<.005
**** p<.0001
39

Table 4
Study 3 Results
Choice
χ 2
,p
%
Complete
Interaction Effect Results (H4a – H4b)
Price Level/Price Uncertainty
7.80** n/s
%
Complete
Purchase
High/High
45%
High/Low
16%
Medium/High
67%
Medium/Low
77% n/s
Low/High
70%
Low/Low
80%
Intentions
F,p
M
Complete
8.95**
High/High
4.42 n/s
Medium/High
5.03 n/s
Low/High
5.43
M
Complete
*p<.05
High/Low
3.27
Medium/Low
5.04
Low/Low
5.42
**p<.01
*** p<.001
**** p<.0001
40

Figure 1
Study 1 Choice Results
41

Figure 2
Study 2 Product Evaluations Results
Figure 3
42

Study 3 Choice Results
43

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48

Founded in 1892, the University of Rhode Island is one of eight land, urban, and sea grant universities in the United States. The 1,200-acre rural campus is less than ten miles from Narragansett Bay and highlights its traditions of natural resource, marine and urban related research. There are over
14,000 undergraduate and graduate students enrolled in seven degreegranting colleges representing 48 states and the District of Columbia.
More than 500 international students represent 59 different countries.
Eighteen percent of the freshman class graduated in the top ten percent of their high school classes. The teaching and research faculty numbers over 600 and the University offers 101 undergraduate programs and 86 advanced degree programs. URI students have received Rhodes,
Fulbright, Truman, Goldwater, and Udall scholarships. There are over 80,000 active alumnae.
The University of Rhode Island started to offer undergraduate business administration courses in 1923. In 1962, the MBA program was introduced and the PhD program began in the mid 1980s. The College of Business Administration is accredited by
The AACSB International - The Association to Advance Collegiate Schools of Business in
1969. The College of Business enrolls over 1400 undergraduate students and more than 300 graduate students.
Mission
Our responsibility is to provide strong academic programs that instill excellence, confidence and strong leadership skills in our graduates. Our aim is to (1) promote critical and independent thinking, (2) foster personal responsibility and
(3) develop students whose performance and commitment mark them as leaders contributing to the business community and society. The College will serve as a center for business scholarship, creative research and outreach activities to the citizens and institutions of the State of Rhode Island as well as the regional, national and international communities.
The creation of this working paper series has been funded by an endowment established by William A. Orme, URI
College of Business Administration,
Class of 1949 and former head of the
General Electric Foundation. This working paper series is intended to permit faculty members to obtain feedback on research activities before the research is submitted to academic and professional journals and professional associations for presentations.
An award is presented annually for the most outstanding paper submitted.
Ball e ntine Hall
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Univ. of Rhode Island
Kingston, Rhode Island