17050942

Relationships between desired attributes,consequences and purchase frequency

Soonhong Min

Yonsei University, Seoul, South Korea

Jeffrey W. OverbyBelmont University, Nashville, Tennessee, USA, and

Kun Shin ImYonsei University, Seoul, South Korea

AbstractPurpose – Employing means-end theory, this paper seeks to examine the influence of specific types of product attributes upon desired consumptionconsequences and the mediating impact of desired consequences upon purchase frequency.Design/methodology/approach – The research employed means-end interviews to generate specific attribute and consequence measures. Thesemeasures were then administered in a survey instrument within the context of a fashion product. Partial least squares was used for testing themeasurement validity of the survey instrument and testing the structural model and related hypotheses.Findings – Style attributes significantly related to desired psychological and social consequences but did not significantly relate to functionalconsequences. Quality significantly related to functional consequences and social consequences but not psychological consequences. Price significantlyrelated to all consequences. Psychological consequences were the strongest predictor of purchase frequency followed by functional consequences.Finally, desired consequences played a mediating role between product attributes and purchase frequency, with no direct influence of attributes uponpurchase frequency.Research limitations/implications – The findings demonstrate the value of understanding the consumption consequences that consumers desire forproducts, especially after initial purchase. In doing so, the findings also provide some evidence that consequences may be better predictors ofbehavioral outcomes than product attributes.Practical implications – This study demonstrates that the consumer means-end value hierarchy can be used as a tool for understanding the meaningsthat consumers construct around products and services. Moreover, it indicates that marketers should consider customer value analysis as asegmentation tool.Originality/value – This paper represents one of the few to test the chain of cause-and-effect relationships of the means-end hierarchy within anintegrated framework. It is original in that it specifically tests the relationships between major attributes (i.e. style, quality, and price) and particularconsequence types (i.e. psychological, social, and functional).

Keywords Consequences, Attributes, Value, Mean-end, Purchase frequency, Product attributes, Consumer behaviour

Paper type Research paper

An executive summary for managers and executive

readers can be found at the end of this article.

Introduction

Shopping has become a central mechanism in society today –

both as a driver of economic success for nations and as a

leisure activity, social activity, and means of self-definition for

individuals (Mick et al., 1992; Miller, 1998; Jin et al., 2007;

Park and Park, 2009). Essentially, consumers shop and

consume in order to fulfill desired value (Kotler, 1972;

Gutman, 1982). From a strategic perspective, the delivery of

value to the customer has long been a central theme of

marketing and has been shown to link positively with

behavioral intentions, market share, and even corporate

profitability (e.g. Cronin et al., 2000; Ralston, 2003;

Pynnonen et al., 2011).Although the concept of value sounds simple, many

businesses have not been successful at determining what

shoppers value (Johnson, 1998; Komulainen, 2010). One

primary reason is that marketers have spent more time

conceptualizing a focus on customer value than developing

the tools needed to operationalize a focus on customer value

(Woodruff, 1997; Ulaga, 2001; Kumar et al., 2006). And

when marketers have operationalized customer value, it has

usually been only at the product attribute level (Holbrook,

1994; Woodruff and Gardial, 1996; Woodruff, 1997).Recently, however, researchers and practitioners have

turned to examining the actual determination of customer

value. Several researchers (e.g. Woodruff and Gardial, 1996;

Woodruff, 1997) have offered customer value analysis based

upon means-end theory as a way to examine customer value.

Means-end theory proposes that consumers desire product

attributes for the consequences (i.e. receipt of benefits and

avoidance of sacrifices) those attributes provide. In turn,

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing

29/6 (2012) 423–435

q Emerald Group Publishing Limited [ISSN 0736-3761]

[DOI 10.1108/07363761211259232]

423

consequences are desired because of the end-states (i.e.

personal values, goals, purposes) the consequences help theconsumer to fulfill (Gutman, 1982). Considering the means-

end perspective, there has been considerable research in the

marketing literature on the relation between the variouselements (attributes, consequences, and end-states) and

consumer behavior. However, there has not been as muchresearch that tests the chain of cause-and-effect relationships

of the means-end hierarchy within an integrated framework.Moreover, there has been limited research testing the

relationships between major attributes (i.e. style, quality,and price) and particular consequence types (i.e.

psychological, social, and functional). Given these researchgaps, this research specifically tests constructive relationships

between attributes and desired consequences and betweendesired consequences and consumer purchase behavior in one

integrated model.In terms of consumer behavior, value has been linked to a

variety of shopping behaviors and outcomes, including

intentions, loyalty, and satisfaction (Cronin et al., 2000;Sirdeshmukh et al., 2002; Olaru and Purchase, 2008). This

paper will specifically examine one particularly importantbehavior – purchase frequency. More often than not in the

literature, purchase frequency has been used as a predictorvariable and/or moderating variable rather than a dependent

variable (see Kim and Rossi, 1994; Taylor, 2001; Bhappu andSchultze, 2006). However, purchase frequency should be a

significant outcome in its own right. In fact, researchers (e.g.Anschuetz, 1997; Roy and Goswami, 2007) have argued that

Pareto’s rule applies to purchase frequency – 80 percent ofthe purchase of a product can be attributed to 20 percent of

the population. As such, frequent purchasers represent an

important target segment for marketers.Given this introduction, the two primary research questions

are within the context of means-end value: whether types ofdifferent attributes affect different desired consumption

consequences; and whether desired consumptionconsequences mediate the relationship between attributes

and behavioral outcomes (i.e. purchase frequency).

Conceptual development

Means-end chain value analysis

As far back as 1972, Kotler extolled the virtues of customervalue. He offered several corollaries, one of which states that

the essential activity of marketing is the creation and offeringof value. He went on to add that the marketer attempts to

receive value from the market by offering value to it. However,defining value can be an elusive exercise. Zeithaml (1988)

defines value as “the consumer’s overall assessment of theutility of a product based on perceptions of what is received

and what is given.” Holbrook (1994) defines value as “a

relativistic preference” characterizing a subject’s experience ofinteracting with some object. Finally, Woodruff and Gardial

(1996) define customer value as “the customer’s perception ofthe extent to which use of a product allows him/her to

accomplish some desired purpose or goal . . . it is the result oftradeoffs between the positive and negative consequences of

product use.”Value is thus based upon consumer perceptions and not

managerial judgments (Vargo and Lusch, 2008). Consumervalue perception judgments occur when the consumer

assimilates information about both benefits received and

what is given up. This concept is anchored in a conceptual

framework utilizing a means-end type of model originally

developed by Gutman (1982). Woodruff and Gardial (1996)use the means-end theory to demonstrate the essence of

customer value. Essentially, when purchasing and/or using aproduct or service, consumers desire certain attributes based

on the ability of those attributes to facilitate achieving desiredconsequences and the underlying value structures that are

satisfied as a result of those consequences (Woodruff, 1997).Means-end chains (MEC) link sequentially product

attributes (A) to desired consequences of product use (C),

and to individuals’ personal end-states (ES). For example, aconsumer might desire a certain brand name (A) in order to

attract attention (C) ultimately to fulfill a desire for status(ES) or self-esteem (ES). Attributes (A), which are directly

related to products and services (Olson and Reynolds, 1983),include product or service features that can be directly

perceived, such as styling and price and more abstract features

that are not directly measurable or perceived, such as quality.Consequences (C) represent the benefits and sacrifices that

result from consumption of a product or service (Verhallenand van Raaij, 1986; Sheth et al., 1991; Holbrook, 1994; Lai,

1995). End-states (ES) represent abstract goals (oftenreferred to as values) or valued states that people strive for.

Thus, the means-end chain (i.e. A-C-ES) sequentially links

product attributes to consumption consequences to end-states.

The theoretical model guiding this research does notspecifically examine a link between end-states and behavioral

outcomes (such as purchase frequency). This is because it isbelieved that end-states do not determine outcomes directly

but indirectly through their influence on desiredconsequences. In fact, end-states (often equated to personal

values) have been shown to be indirect and often weak

predictors of consumer behavior (Gutman, 1991; Lai, 1995;Sojka and Tansuhaj, 1995). Thus, we focus on desired

consequences and attributes as predictors of purchasefrequency.

Attributes

Products are known to represent a bundle of attributes, such

as packaging, labeling, brand name, and even sensory features(Muellera and Szolnokib, 2010). Keller (1999) defined

attributes as the descriptive features of a product or service,

dividing them into product-related attributes and non-product-related attributes. Product-related attributes are

explicit features of a product or service, such as price, color,or brand. Non-product related attributes are implicit, less-

observable features of a product or service, such as qualityand style. Attributes, whether explicit or implicit, have been

used to predict outcomes such as service quality andsatisfaction (Parasuraman et al., 1988; Driver and Johnston,

2001). Attributes are believed to be important to consumers

because they deliver certain desired benefits or consequences.

Consequences

A number of types of consequences have been identified inthe literature, including functional consequences, social

consequences, and psychological consequences (Green andPeloza, 2011).

Functional consequences (FC) result from the ability of aproduct or service to perform its utilitarian purposes and are

often evaluated based upon salient physical attributes.

Desired attributes, consequences and purchase frequency

Soonhong Min, Jeffrey W. Overby and Kun Shin Im


Volume 29 · Number 6 · 2012 · 423–435

424

Functional valuation is primarily founded on the concept of

utility from the field of economics. Sheth et al. (1991) defineutility as “the satisfaction derived from using a product or

service.” It is implied that satisfaction is derived from thephysical product performing its functions. In addition toproduct performance, utility can also result from the benefits

provided prior to, during, and after use of the product (Myersand Shocker, 1981; Woodruff, 1997).

Psychological consequences (PC) result from the ability of aproduct to satisfy important intrinsic goals. Such

consequences are similar to the idea of symbolic valuation,which is primarily founded on the concept of a product’scontribution to self-enhancement and self-symbolism (Auty

and Elliott, 1998). Levy (1959) was one of the earlyproponents of symbolic valuation. He acknowledged,

“people buy things not only for what they can do, but alsofor what they mean” (p. 118). McCracken (1990) similarlystates “consumer goods have a significance that goes beyond

their utilitarian character and commercial value” (p. 71).Essentially, consumers often desire products because those

products can help one to define oneself and can actually beused to improve one’s self-identity. Walker and Olson (1991)actually referred to psychosocial consequences when

explaining how consumers connect products with themselves.Social consequences (SC) result from the ability of a

product or service to portray an image to others. Unlikepsychological consequences, which represent a form of self-

symbolism, social consequences represent a form of socialsymbolism (Elliott, 1995). These consequences are oftenassociated with publicly consumed products and services. In

fact, Sheth et al. (1991) state that the consumption of almostany visibly identifiable product is likely to be at least partially,

if not primarily, influenced by social value. This value type isclosely related to the esteem value of Holbrook (1994).Holbrook claims that esteem value results from “the reactive

contemplation of one’s own status and prestige, as reflected inthe approbative opinion of others.”

Relationship between desired consequences and

attributes

Consumers place importance upon specific attributes becausethey expect those attributes to deliver certain desired goals or

consequences (Bagozzi and Dholakia, 1999). Such goal-basedrelationships have been addressed through various theories,including the theory of reasoned action (Ajzen and Fishbein,

1980) and means-end theory (Reynolds and Gutman, 1988).By choosing attributes because of the consequences they

deliver, consumers are able to connect products with the self(Walker and Olson, 1991). Attributes then serve as cues forthe consequences they deliver and, as such, attribute

importance is influenced by the importance of desiredconsequences (Zeithaml, 1988). We therefore propose that

product attributes will relate directly to desired consequences.In terms of more specific predictions, we believe the

relationships between attributes and desired consequences

will depend upon the functional or symbolic nature of theattribute. To tap both the functional and symbolic aspects of

attribute, we employ price, quality, and style as attributes thatpotentially drive the desire for consequences in the MEC.

First, style is largely a function of design and aesthetics. Asargued by Holbrook (1980), such attributes pertain topleasure derived (i.e. psychological consequence) from

seeing the product rather than the utility of the product.

Such attributes can also enable a consumer to gain attention

from others (i.e. social consequence) during consumption

(Creusen and Schoormans, 2005). Therefore, one would

expect style to relate both to psychological and social

consequences rather than functional consequences.Second, quality is based upon a consumer’s judgment of

excellence or superiority in a product and is generally

considered to be an intrinsic cue that is multidimensional and

often more difficult to evaluate (Parasuraman et al., 1988;

Varki and Colgate, 2001). Given its multidimensional nature,

quality exhibits both symbolic and utilitarian elements, and

one would expect it to relate both to functional consequences

and psychological consequences (largely for personal

achievement reasons).Finally, price is generally considered to be an extrinsic cue

(Varki and Colgate, 2001). Customers have been found to

evaluate quality based on the price sellers charge. In this case,

price as a product attribute serves a functional purpose and is

related directly to desired functional consequences. At times,

customers feel happy when they purchase what they want at a

bargain price and in doing so are also likely to feel a sense of

self-achievement. Other times, customers are willing to pay

premium price because high price tag represents aspiration

among peers. In summary, price as a product attribute relates

to all three types of desired consequences: functional,

psychological, and social consequences:

H1. Perceived attributes relate directly to desired

consequences.H1a. Style attributes relate directly to desired psychological

and social consequences but not desired functional

consequences.H1b. Quality attributes relate directly to desired functional

and psychological consequences but not desired social

consequences.H1c. Price attributes relate directly to desired functional,

psychological, and social consequences.

Consumption consequences and purchase frequency

Desired consumption consequences represent one of the

central mechanisms in means-end theory. Gutman (1982)

actually asserts that there is no direct relationship between

end-states and consumer choice behavior (i.e. product

attributes); instead, the two are connected through

consequences. The concept of consumption consequences is

not new, though the terminology may be somewhat new. For

example, benefits have been viewed as a key construct within

the marketing literature since Haley introduced benefit

segmentation in 1968 (see Wilkie and Pessemier, 1973;

Monroe, 1991; Kim et al., 2008; Boksberger and Melsen,

2011). The term “consequences” is broader than “benefits”

in that consequences include both benefits and sacrifices.A means-end approach to examining consumption

consequences is a more recent development. Although there

have been a number of means-end studies conducted in the

marketing literature, few have derived survey instruments for

attributes and consequences from means-end studies. Desired

consumption consequences are expected to directly determine

a number of consumers’ behavioral outcomes, including

information search (Michell and Prince, 1993), customer

loyalty (Park and Park, 2009), and postpurchase evaluations

(Weinstein, 2002).




Volume 29 · Number 6 · 2012 · 423–435

425

Because Batra and Homer (2004) demonstrated that

desired benefits (i.e. consequences) have a greater impacton actual behavioral outcomes than on attitudes, we decided

to focus on behavioral outcomes as direct effects of desired

consequences. One especially important behavioral outcomeis purchase frequency (Joo, 2006; Overby and Lee, 2006).

From a strategic perspective, the ability to predict purchasefrequency is particularly significant, as frequent purchasers

have been shown to account for a much larger volume ofproduct and service sales than infrequent purchasers. In fact,

marketers often suggest targeting towards heavy users andfrequent purchasers rather than light users and infrequent

purchasers (Loudon and Della Bitta, 1993; Anschuetz, 1997).McDonald’s actively targets frequent users and Lands’ End

and LL Bean segment markets by usage patterns (Weinstein,2002). Roy and Goswami (2007) found a strong correlation

between psychographics and product/service groups withsimilar purchase frequencies. Given this discussion, the

following hypotheses are offered:

H2. Desired consequences directly influence consumerpurchase frequency.

Mediating relationships

Traditionally, attributes have been shown to play a mediatingrole between end-states and consumer behavior. For example,

the attribute-mediation approach suggests that valuesdetermine the importance of product attributes which in

turn influence produce evaluation and purchase (e.g.Gutman, 1982; Lindberg et al., 1989). However, others

question whether attributes and end-states are the strongestpredictor of behavior (Dabholkar, 1994; Liang and Wang,

2004). More recent research has argued that the attribute-

mediation approach may not be as strong of a predictor whensymbolic meaning and intangible attributes are involved

(Allen and Ng, 1999). Allen and Ng (1999) find that whenconsumers focus on the symbolic meaning of a product or

service, that meaning will have a direct influence uponconsumer behavior rather than product attributes. For

symbolic products, consumers focus more on the Gestaltmeaning (i.e. consequences) of the product rather than

tangible attributes when making purchase decisions(McCracken, 1986; Keaveney and Hunt, 1992).

Consumers have been shown to shift from attribute levelevaluations to higher-level evaluations when recalling pre-

purchase and post-purchase thoughts (Gardial et al., 1994).Myers and Shocker (1981) even suggest that benefit-based

models of preference and choice are preferable to attribute-based modes because benefits are closer to preference and

choice. Woodruff and Gardial (1996) argue that consumers

often use consequence-level factors when making decisions,and that a consequence-level view of the customer offers

opportunities for competitive advantage for businesspractitioners. Although there is significant evidence to

support the influence of desired consequences versusattributes, none of these studies have examined the

mediating relationship between attributes and consequenceson purchase frequency.

Given the more symbolic nature of consumptionconsequences vis-a-vis attributes, one would expect desired

consequences to mediate the relationship between productattributes and consumer purchase behavior for symbolic

products (such as fashion products). For example, Auty and

Elliott (1998) assert that it is often more important to fulfill

abstract symbolic needs (i.e. consequences) of consumers

because functional needs (and attributes) are dependent uponthe symbolic ones. Similarly, Liang and Wang (2004) argue

that benefits are more powerful purchase motivators.Asserting that attributes are not sufficient for improving

customer satisfaction, they found that perceptions offunctional and symbolic benefits were positively and

significantly related to customer satisfaction judgments.Corfman (1991) determined that many consumers find it

easier to focus on consumption consequences rather thanproduct attributes when making consumption decisions.

When comparing products, subjects tended to use higher

levels of comparison (value and utility) rather than productfeatures and function in order to make a choice.

Similarly, one would expect consequences to mediatebetween attributes and purchase frequency after the initial

purchase. Parasuraman (1997) asserts that the valuedimensions used by customers actually change in

abstraction, magnitude, and importance as experience grows(e.g. purchase frequency). Similarly, Mathwick (1999) found

that as consumers gain experience, they often shift fromattribute-based value judgments to abstract consequence-

based judgments. Given that this research examines purchase

during the consumer feedback process (i.e. frequency ofpurchase), the following hypothesis is offered:

H3. Desired consequences mediate the relationshipbetween product attributes and consumer purchase

frequency.

Methods

Empirical development of questionnaire

Although some studies have developed value dimensionmeasures from theory (e.g. Lapierre, 2000; Liang and Wang,

2004), we believe it is important to begin with qualitativemeans-end chains in order to develop value measures

grounded in a consumer perspective. Thus, the measuresemployed for this study were developed from qualitative

grand-tour laddering interviews with twelve women.Woodruff and Gardial (1996) adapted the grand-tour

laddering format for customer value research. Strengths of

this format include: it allows consumers to recall purchaseand consumption experiences in their own words while at the

same time avoiding many of the distractions associated withconcurrent verbalizations; and memory has been shown to be

predictive of future behaviors. Gardial et al. (1994) provide amore detailed discussion of the strengths and weaknesses of

this method. Laddering was conducted within the interviewswhen specific attributes or consequences were elicited.

Laddering allows the interviewer to measure the means-end

association between attributes, consequences and end-statesthat consumer hold towards products and services (Peter and

Olson, 2008). The interviews were recorded and transcribed.The interviews addressed the purchase of a handbag

because such a product was expected to produce multipletypes of desired consumption consequences, as fashion items

are often used to both fulfill functional and social needs andshape self and identity (Woodruffe-Burton, 1998). The

resulting interview transcripts were initially coded by twojudges according to means-end theory: attributes, desired

consumption consequences, and desired end-states. The




Volume 29 · Number 6 · 2012 · 423–435

426

judges then coded for specific types of attributes (e.g. quality,style, price) and consequences (e.g. functional, social,psychological). Based upon these codes, we developed asurvey questionnaire to specifically measure consumers’desired attributes and consequences. The attributes anddesired consequences were developed into items utilizing therespondents’ exact words and using a five-point agree/disagreeresponse scale. Each attribute and consequence began with“When trying to decide on a purse or handbag to purchase, Iwant one that . . . ” The questionnaire concluded withdemographic items, including age, marital status, education,income, and occupation. Respondents were also given threepossible categories to report how often they purchase ahandbag: “every 1-6 months,” “once a year,” “every 2 ormore years.”

Sample and data collection

The questionnaire was administered to a convenience sampleof 122 female members of a service organization in a mid-sized US city. Of 122 completed questionnaires, 120 wereusable. The 120 women ranged in age from 19 to 49 with amean of 22 (standard deviation ¼ 4.7). The distribution ofeducation was: less than four years of high school (2 percent),high school graduate (3 percent), technical or trade school (1percent), college (93 percent), and graduate school (1percent). The distribution for total income was: less than$15,000 (9 percent), $15,001-30,000 (62 percent), $30,001-50,000 (6 percent), $ 50,001-75,000 (11 percent), 75,001-100,000 (6 percent), and more than $100,000 (6 percent).Finally, the distribution for handbag purchases was: every 1-6months (26 percent), once a year (32 percent), every 2 ormore years (43 percent).

Data analysis and results

PLS method was used for testing the measurement validity ofthe survey instrument and testing the structural model. PLSwas chosen because of its ability to perform both principalcomponent and path analysis simultaneously (Barclay et al.,1995).

Test of measurement models

The validity and reliability of measurement items were testedprior to the structural model test. The adequacy of themeasurement model was determined by examining individualitem reliability, composite reliability (i.e. internalconsistency), and convergent and discriminate validity ofconstructs (Barclay et al., 1995). Each measurement itemused in the questionnaire reflected its correspondingconstruct and had a high correlation among them.Therefore, all the measurement items were analyzed withreflective indicators. The item loadings for the consequencesmeasures are listed in Table I. The items used to measure thethree attribute types – styling, quality, and price – were twoitems for each attribute type:. Styling (m ¼ 4.38, item correlation ¼ 0.486, p ¼ 0.000):

1) looks pretty; 2) is a classic style.. Price (m ¼ 4.15, item correlation ¼ 0.548, p ¼ 0.000): 1)

good value for the money; 2) reasonably priced.. Quality (m ¼ 3.89, item correlation ¼ 0.204, p ¼ 0.026):

1) is well made; 2) is of high quality.

Measurement validation

Individual item reliability was assessed by examining thefactor loading of each item on its construct. The normal

guideline of individual item reliability suggests that the factor

loading should be at least greater than 0.6. Accordingly, the

items with a factor loading value lower than 0.6 were

dropped. Table I shows that all of the retained items have a

factor loading value greater than 0.6, indicating adequate

individual item reliability. Composite reliability is internal

consistency as defined by Fornell and Larcker (1981) and was

examined using the composite scale reliability index (similar

to Cronbach’s alpha), which should exceed 0.7. As shown in

Table II, all measures met this criterion; thus they could be

considered reliable.In Table II, the diagonal values represent the square root of

the average variance extracted (AVE) which is a measure of

convergent validity. All diagonal values exceed 0.5, suggesting

that convergent validity was established. Convergent validity

was also demonstrated by items loaded highly (factor

loading . 0.6) on their associated constructs. To assess

discriminant validity of constructs, two criteria were

adopted: the square root of the AVE for each construct

should be greater than the off-diagonal correlations between

constructs; and each item within the construct should load

highly on the construct that it is intended to measure and the

cross-loadings must be lower than the intra-construct item

loadings (Barclay et al., 1995). As shown in Tables I and II, all

constructs exhibited sufficient discriminant validity. As a

result, the reliability and validity of the measurement items

were all found to be acceptable.

Hypothesis testing

Based on the adequate measurement model, the proposed

hypotheses were tested by assessing the structural model.

Assessment of the structural model involves estimating the

path coefficients (i.e. path analysis) and assessing the R2 value

of endogenous constructs. We used the bootstrap resampling

method of PLS to determine the significance of path

coefficients. Table III shows the result of the structural

model assessment. Over 14 percent of the variance in

purchase frequency was explained by the three consequence

types and control variables, indicating that our research model

was acceptable.We found partial support for H1. Seven of the nine paths

were statistically significant. As expected, three attribute types

significantly influenced each of three desired consequences,

except for two paths which were from quality (attribute) to

psychological consequences and from style (attribute) to

functional consequences. Specifically, we found full support

for H1a (the statistically significant paths from style to

psychological and social consequences) and H1c (the

significant paths from price to psychological, social, and

functional consequences). However, we found only partial

support for H1b as the path from quality to psychological

consequence was insignificant while the paths from quality to

social and functional consequences were significant.We found partial support for H2. Only two paths from

desired consequences to purchase frequency were statistically

significant: psychological consequences and functional

consequences were significant in their effect upon purchase

frequency. Interestingly, psychological consequences had a

positive effect upon purchase frequency and functional

consequences had a negative effect. the path from social

consequences to purchase frequency was not significant

(Figure 1).




Volume 29 · Number 6 · 2012 · 423–435

427

We found support for H3. As expected in the means-end

chain’s feedback mechanism, desired consequences

functioned as a mediator between attributes and purchase

frequency. There was no direct link between product

attributes and purchase frequency.

Discussion

In this paper, we tested a chain of causal relationships

between product attributes and desired consequences and

between desired consequences and the behavioral outcome of

purchase frequency. The overall findings are in accordance

with our hypotheses. First, there is support for H1: specific

product attributes relate to particular desired consequences.

Not all attributes significantly linked to all desired

consequences. In particular, style attributes significantly

related to desired psychological and social consequences but

did not significantly relate to functional consequences.

Therefore, H1a was supported. This is to be expected given

that functional consequences are primarily utilitarian rather

than symbolic.H1b was partially supported: perceived quality significantly

related to functional consequences and social consequences

but not psychological consequences. This finding is a bit

Table I Loadings and cross loadings of consequence measures

Scale item Psychological consequences Social consequences Functional consequences

Makes me feel good as a person 0.673 0.403 0.215

Makes me feel young 0.627 0.384 0.129

Makes me feel exciting 0.760 0.384 0.128

Makes me happy 0.755 0.471 0.138

Makes me feel like I am not like everybody else 0.728 0.189 0.004

Makes me fell unique 0.729 0.211 0.102

Makes me feel different 0.718 0.221 0.143

Gives me a sense of accomplishment 0.790 0.272 0.221

Makes me feel like I have succeeded 0.782 0.277 0.148

Others will like 0.317 0.806 0.044

People important to me will like 0.311 0.779 0.085

Is appealing to others around me 0.359 0.853 0.139

Is flattering to me 0.362 0.721 0.404

Allows me to keep my hands free for carrying other things 0.083 0.071 0.657

Makes it easier to carry packages 0.082 0.015 0.712

Is comfortable on my shoulder(s) 0.204 0.125 0.784

Allows me to move around easily 0.061 0.269 0.838

Can be used for a variety of occasions 0.110 0.226 0.771

Can be used everyday 0.067 0.149 0.721

Is convenient to use 0.182 0.229 0.777

Is easy to carry 0.204 0.282 0.817

Will last for a long time 0.183 0.258 0.853

Can handle a lot of wear 0.207 0.279 0.785

Makes it easy for me to quickly get to my car keys 0.097 0.160 0.724

Does not hurt my back 0.185 0.161 0.716

Does not strain or bother my neck 0.174 0.158 0.719

Table II Reliability and validity

Correlation and AVE of constructs

Constructs No. of items Composite reliability 1 2 3 4 5 6 7 8 9 10 11

Psychological consequences 9 0.91 0.731

Social consequences 4 0.87 0.437 0.791

Functional consequences 13 0.95 0.194 0.259 0.762

Styling attribute 2 0.81 0.407 0.480 0.392 0.828

Quality attribute 2 0.89 0.305 0.445 0.558 0.511 0.891

Price attribute 2 0.87 0.395 0.259 0.340 0.293 0.215 0.876

Purchase frequency 1 1.00 20.304 20.102 0.143 20.084 20.073 0.101 1.000

Education 1 1.00 20.117 20.086 20.024 0.014 0.069 0.061 0.020 1.000

Income 1 1.00 0.027 0.000 0.127 0.158 0.167 20.062 20.054 0.026 1.000

Age 1 1.00 20.160 20.144 0.142 0.005 0.119 0.015 0.139 0.056 20.018 1.000

Marital status 1 1.00 20.171 20.110 0.062 20.082 20.135 0.080 0.100 0.041 0.079 0.366 1.000




Volume 29 · Number 6 · 2012 · 423–435

428

Table III Test of hypothesized relationships

Hypothesis From To Path coefficient t-value p-value

H1 Style attribute PC 0.267 2.477 0.007 * * *

Quality attribute PC 0.105 0.908 0.183

Price attribute PC 0.294 3.775 0.000 * * *

R2 ¼ 0.257

H1 Style attribute SC 0.314 3.444 0.000 * * *

Quality attribute SC 0.261 2.341 0.010 * *

Price attribute SC 0.111 1.338 0.092 *

R2 ¼ 0.295

H1 Style attribute FC 0.092 0.893 0.187

Quality attribute FC 0.466 3.818 0.000 * * *

Price attribute FC 0.213 2.373 0.010 * *

R2 ¼ 0.369

H2 PC Purchase frequency 0.333 3.420 0.000 * * *

SS Purchase frequency 0.003 0.029 0.488

FC Purchase frequency 20.209 1.900 0.030 * *

Control Education Purchase frequency 0.106 0.147 0.442

Control Income Purchase frequency 0.071 0.720 0.236

Control Age Purchase frequency 20.048 0.511 0.305

Control Martial Purchase frequency 20.018 0.190 0.425

R2 ¼ 0.144

Notes: *p , 0.1; * *p , 0.05; * * *p , 0.01

Figure 1 Results of path analysis




Volume 29 · Number 6 · 2012 · 423–435

429

surprising. One would expect quality to relate to psychological

consequences because one would anticipate a consumer tohave an internal feeling of achievement or success when

purchasing a product that is well-made. Perhaps this feeling issubsumed within functional consequences or perhaps

functional consequences are lower level consequencesmediating the relationship between some attributes and

upper level consequences such as psychological ones. Inaddition, quality was found to relate to desired socialconsequences. We speculate that purchasing a good quality

handbag makes one feel that one is meeting certain socialnorms (such as fitting in or even conserving resources). This

argument is in line with Taguchi’s (1987) social loss functionthat posits that quality is the loss a defective product causes to

society in various forms such as repairs, returns, anddisposition.

Finally, H1c was fully supported: price was significantlyrelated to all desired consequences rather than only functionalconsequences as hypothesized. Apparently, price has the

ability to communicate psychological, social, and functionalmeaning for consumers. One would expect that getting a good

economic deal (or buying a desired product/service at a goodprice) gives a consumer an internal feeling of achievement or

success. Price also carries a utilitarian connotation in thatprice is determined by cost/benefit analysis. Finally, price

results in social consequences when the price conveys socialmessages such as exclusivity or status.

Second, the test results appear to offer partial support for

H2. Desired consequences (functional and psychological)seemed to directly influence purchase frequency for a

consumer product. Psychological consequences were thestrongest predictor of purchase frequency followed by

functional consequences. Though social consequences werealso hypothesized to directly influence purchase frequency,

they only had a positive but insignificant effect. What is moreintriguing from the findings are the strength and direction ofthe coefficients. As one might expect for a fashion item,

desired psychological consequences are likely to have apositive influence on purchase frequency. If one desires to feel

good about oneself through consumption, one would expectthat person to purchase more frequently. Conversely, if one is

more motivated by functional aspects of a shopping item suchas quality and durability, one would expect that person topurchase less frequently. As such, it appears that

psychological consequences capture most of the experientialand sensory aspects of product consumption whereas

functional consequences capture aspects inherent in theproduct itself. What is most surprising by the findings is the

apparent insignificance of desired social consequences uponpurchase frequency. One would expect that a shopper who

cares about what others think would be more likely topurchase frequently in order to stay “in fashion”. However,that was not the case. It might be possible that the

psychological consequences subsume similar motivations associal consequences. If so, then there may be support for

using only the hedonic and utilitarian distinctions found inother shopping literature (e.g. Babin et al., 1994; Childers

et al., 2001; Cottet et al., 2006).Finally, the test results appear to offer support for H3.

While desired consequences directly influenced purchasefrequency for a consumer product, product attributes did soonly indirectly. This provides additional empirical support to

the means-end hierarchy, as proposed by Gutman (1982).

Such findings are significant given that most satisfaction and

product/service quality measures employed today incorporate

product and service attributes but rarely include desiredconsequences (Spreng et al., 1996). Perhaps this finding

explains the disconnect between satisfaction and postpurchase

behaviors often identified in the literature (Churchill andSurprenant, 1982; Oliver, 1999). These findings also suggest

that the incorporation of consumption consequence measures

into satisfaction and product/service quality studies mightimprove their predictive capabilities. There is no doubt that

attributes are important to consumers. However, it isultimately the desired consequences that are expected to

result from these attributes that motivate consumers to

purchase. Given the fact that most marketers are interested inattracting and keeping frequent purchasers, these findings

indicate that marketers should be focusing on more abstract

measures of value rather than simple price/quality tradeoffmeasures.

In this research, not a single demographic variable had asignificant effect on consumer purchase behavior. This

finding reveals that demographic variables alone are not

sufficient predictors of consumer behavior, in this casepurchase frequency for a fashion product. This is not exactly a

surprise and may explain the increasing usage of

psychographics and behavioral segmentation variables bymarketers. However, this is not to say that demographic

variables are unimportant. In fact, Branca (2008) recentlyfound that demographic characteristics contribute to

consumer usage frequency of bank delivery channels

indirectly via cognitive and affective mediators.

Conclusions

Not only does this study reveal that the customer valuehierarchy allows marketers to better understand the meanings

that consumers construct around products and services (Auty

and Elliott, 1998; Woodruffe-Burton, 1998), but it alsodemonstrates that an understanding of consumers’ desired

consequences can help marketers understand and possibly

predict consumer purchase behavior. Marketers havetraditionally focused more of their efforts on consumer

desires for and evaluations of product attributes. In fact, mostcustomer satisfaction surveys measure satisfaction with

product attributes or features and not consequences

(Woodruff and Gardial, 1996). Woodruff and Gardial(1996) assert that consequences “provide a relatively more

stable strategic focus” than product attributes.This study also indicates that marketers should consider

customer value analysis as a segmentation tool. For example,

marketers may segment the market based on desiredconsequences: psychological, social, and functional. For

those consumers who are motivated by desired functional

consequences, marketers may manipulate not only price butalso quality. Similarly, marketers may manipulate not only

style but also quality and price for those consumers who are

motivated by social desired consequences. Obviously, thisfinding holds implications not only for product development,

but also for other elements of the marketing mix, especially

pricing and promotion.A number of limitations should be acknowledged. First, the

sample is not necessarily representative of all women in theUS – the women in the study were younger and better

educated than average. Second, the product used in the study,




Volume 29 · Number 6 · 2012 · 423–435

430

a handbag, is a fashion item. Although this fact may have

inflated the importance of fashion-oriented value dimensions,

the findings do provide evidence that value dimensions serve

as a good predictor of behavior. Future research shouldexamine more functional goods along with fashion items.

This research also raises additional research questions. One

new question is whether purchase frequency affects thenumber of consequences and attributes desired. One would

expect all desired consequences and attributes to be

significant for infrequent purchasers whereas frequentpurchasers may be more likely to rely on fewer

consequences and attributes. For example, researchers (Ou

et al., 2006) have found that frequent purchasers exhibit less

cognitive effort because they rely more on memory and recallfrom previous purchases such as prior pricing information.

Similarly, Parasuraman (1997) found that first-time

customers were more likely to focus on attributes when

evaluating products and services whereas short-term andlong-term customers were more likely to focus on

consequences and end-states.Future research should also address what other variables,

such as the consumption situation, might moderate the

relationship between desired consumption consequences and

purchase frequency. For example, Foxall and Greenley (2000)provide tentative evidence that the setting or situation can

influence the type of consequences invoked. However, more

evidence is needed. Finally, future research should examine

how desired consumption consequences relate to otherconsumer behaviors, such as product choice, usage, and

postpurchase evaluations. It is likely that the type of

consequence desired will significantly influence customersatisfaction (Woodruff, 1997).

These findings, conclusions, and future research issues

demonstrate the value of understanding the consumptionconsequences that consumers desire for products and

services. It is hoped that this research will spark additional

investigation into the subject, both among academics and

practitioners.

References

Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes andPredicting Social Behavior, Prentice Hall, Englewood Cliffs,NJ.

Allen, M.W. and Ng, S.H. (1999), “The direct and indirect

influences of human values on product ownership”, Journalof Economic Psychology, Vol. 20 No. 1, pp. 5-39.

Anschuetz, N. (1997), “Profiting from the 80-20 rule of

thumb”, Journal of Advertising Research, Vol. 37 No. 6,pp. 51-6.

Auty, S. and Elliott, R. (1998), “Fashion involvement, self-

monitoring and the meaning of brands”, Journal of Product& Brand Management, Vol. 7 No. 2, pp. 109-23.

Babin, B., Darden, W. and Griffin, M. (1994), “Work and/orfun: measuring hedonic and utilitarian shopping value”,

Journal of Consumer Research, Vol. 20, pp. 644-56.Bagozzi, R.P. and Dholakia, U.M. (1999), “Goal setting and

goal striving in consumer behavior”, Journal of Marketing,

Vol. 63, pp. 19-32.Barclay, D., Thompson, R. and Higgins, C. (1995),

“The partial least squares (PLS) approach to causal

modeling, personal computer adoption and use as an

illustration”, Technology Studies, Vol. 2 No. 2, pp. 285-324.

Batra, R. and Homer, M.P. (2004), “The situational impact

on brand image beliefs”, Journal of Consumer Psychology,

Vol. 14 No. 3, pp. 318-30.Bhappu, A. and Schultze, U. (2006), “The role of relational

and operational performance in business-to-business

customers’ adoption of self-service technology”, Journal of

Service Research, Vol. 8 No. 4, pp. 372-85.Boksberger, P. and Melsen, L. (2011), “Perceived value:

a critical examination of definitions, concepts and measures

for the service industry”, Journal of Services Marketing,

Vol. 25 No. 3, pp. 229-40.Branca, A.S. (2008), “Demographic influences on

behaviour”, International Journal of Bank Marketing,

Vol. 26 No. 4, pp. 237-59.Childers, T., Carr, C., Peckc, J. and Carson, S. (2001),

“Hedonic and utilitarian motivations for online retail

shopping behavior”, Journal of Retailing, Vol. 77 No. 4,

pp. 511-35.Churchill, G.A. and Surprenant, C. (1982), “An investigation

into the determinant of customer satisfaction”, Journal of

Marketing Research, Vol. 19 No. 4, pp. 491-504.Corfman, K. (1991), “Comparability and comparison levels

used in choices among consumer products”, Journal of

Marketing Research, Vol. 28 No. 3, pp. 368-74.Cottet, P., Lichtle, M.C. and Plichon, V. (2006), “The role of

value in services: a study in a retail environment”, Journal of

Consumer Marketing, Vol. 23 No. 4, pp. 219-27.Creusen, M. and Schoormans, J. (2005), “The different roles

of product appearance in consumer choice”, Journal of

Product Innovation Management, Vol. 22, pp. 63-81.Cronin, J.J. Jr, Brady, M.K. and Hult, G.T.M. (2000),

“Assessing the effects of quality, value, and customer

satisfaction on consumer behavioral intentions in service

environments”, Journal of Retailing, Vol. 76 No. 2,

pp. 193-218.Dabholkar, P.A. (1994), “Incorporating choice into an

attitudinal framework: analyzing models of mental

comparison processes”, Journal of Consumer Research,

Vol. 21 No. 1, pp. 100-18.Driver, C. and Johnston, R. (2001), “Understanding service

customers: the value of hard and soft attributes”, Journal of

Service Research, Vol. 4 No. 2, pp. 130-9.Elliott, R. (1995), “Existential consumption and irrational

desire”, European Journal of Marketing, Vol. 31 Nos 3/4,

pp. 285-96.Fornell, C. and Larcker, D. (1981), “Evaluating structural

equation models with unobservable variables and

measurement error”, Journal of Marketing Research, Vol. 18

No. 1, pp. 39-50.Foxall, G.R. and Greenley, G.E. (2000), “Predicting and

explaining responses to consumer environments: an

empirical test and theoretical extension of the behavioural

perspective model”, The Service Industries Journal, Vol. 20

No. 2, pp. 39-63.Gardial, S.F., Clemons, D.S., Woodruff, R.B., Schumann,

D.W. and Burns, M.J. (1994), “Comparing consumers’

recall of prepurchase and postpurchase product evaluation

experiences”, Journal of Consumer Research, Vol. 20 No. 4,

pp. 548-60.Green, T. and Peloza, J. (2011), “How does corporate social

responsibility create value for consumers?”, Journal of

Consumer Marketing, Vol. 28 No. 1, pp. 48-56.




Volume 29 · Number 6 · 2012 · 423–435

431

Gutman, J. (1982), “A means-end chain model based on

consumer categorization processes”, Journal of Marketing,

Vol. 46 No. 2, pp. 60-72.Gutman, J. (1991), “Exploring the nature of linkages

consequences and values”, Journal of Business Research,

Vol. 22, pp. 143-8.Holbrook, M.B. (1980), “Some preliminary notes on research

in consumer esthetics”, in Olson, J.C. (Ed.), Advances inConsumer Research, Vol. 7, Association for Consumer

Research, Ann Arbor, MI, pp. 104-8.Holbrook, M.B. (1994), “The nature of customer value:

an axiology of services in the consumption experience”,

in Rust, R. and Oliver, R.L. (Eds), Service Quality: NewDirections in Theory and Practice, Sage, Newbury Park, CA,pp. 21-71.

Jin, B., Lee, Y.K. and Kwon, S.H. (2007), “Dimensions of

experiential value: is it the same across retail channels?”,Journal of Global Academy of Marketing Science, Vol. 17

No. 4, pp. 223-45.Johnson, M.D. (1998), Customer Orientation and Market

Action, Prentice Hall, Upper Saddle River, NJ.Joo, Y.J. (2006), “Detecting structural change in NBD

model”, Journal of Global Academy of Marketing Science,Vol. 16 No. 1, pp. 13-26.

Keaveney, S.M. and Hunt, K.A. (1992), “Conceptualization

and operationalization of retail store image: a case of rivalmiddle-level theories”, Journal of the Academy of MarketingScience, Vol. 20 No. 2, pp. 165-75.

Keller, K.L. (1999), “Managing brands for the long run:brand reinforcement and revitalization strategies”,

California Management Review, Vol. 41 No. 3, pp. 102-25.Kim, B.-D. and Rossi, P. (1994), “Purchase frequency,

sample selection, and price sensitivity: the heavy-user bias”,

Marketing Letters, Vol. 5 No. 1, pp. 57-67.Kim, D., Ferrin, D. and Rao, H. (2008), “A trust-based

consumer decision-making model in electronic commerce:

the role of trust, perceived risk, and their antecedents”,

Decision Support Systems, Vol. 44, pp. 544-64.Komulainen, H. (2010), “Customer perceived value of

emerging technology-intensive business service”, academic

dissertation, University of Oulu, Oulu, 11 June.Kotler, P. (1972), “A generic concept of marketing”, Journal

of Marketing, Vol. 36 No. 2, pp. 46-54.Kumar, V., Lemon, K.N. and Parasuraman, A. (2006),

“Managing customers for value - an overview and researchagenda”, Journal of Service Research, Vol. 9 No. 2, pp. 87-94.

Lai, A.W. (1995), “Consumer values, product benefits and

customer value: a consumption behavior approach”,in Kardes, F.R. and Sujan, M. (Eds), Advances inConsumer Research, Vol. 22, Association for Consumer

Research, Provo, UT, pp. 381-7.Lapierre, J. (2000), “Customer perceived value in industrial

contexts”, Journal of Business & Industrial Marketing, Vol. 15

Nos 2/3, pp. 122-40.Levy, S.J. (1959), “Symbols for sale”, Harvard Business

Review, Vol. 37 No. 4, pp. 117-24.Liang, C.-J. and Wang, W.H. (2004), “Attributes, benefits,

customer satisfaction and behavioral loyalty: an integrative

research of financial services industry in Taiwan”, Journal ofServices Research, Vol. 4 No. 1, pp. 57-91.

Lindberg, E., Garling, T. and Montgomery, H. (1989),

“Differential predictability of preferences and choices”,

Journal of Behavioral Decision Making, Vol. 2, pp. 205-19.

Loudon, D.L. and Della Bitta, A.J. (1993), ConsumerBehavior, McGraw-Hill, New York, NY.

McCracken, G. (1986), “Culture and consumption:a theoretical account of the structure and movement of

the cultural meaning of consumer goods”, The Journal ofConsumer Research, Vol. 13 No. 1, pp. 71-84.

McCracken, G. (1990), “Culture and consumer behavior: an

anthropological perspective”, Journal of the Market ResearchSociety, Vol. 32 No. 1, pp. 3-11.

Mathwick, C. (1999), “How customer perceptions of valuechange as relationships develop”, in Brown, S.P. and

Sudharshan, D. (Eds), 1999 AMA Educators’ Proceedings:Enhancing Knowledge Development in Marketing, Vol. 10,

American Marketing Association, Chicago, IL, pp. 136-7.Michell, V.-W. and Prince, G.S. (1993), “Retailing to

experienced and inexperienced consumers”, InternationalJournal of Retail & Distribution Management, Vol. 21 No. 5,pp. 10-21.

Mick, D.G., Demoss, M. and Faber, R.J. (1992),

“A projective study of motivations and meanings of self-gifts: implications for retail management”, Journal ofRetailing, Vol. 68 No. 2, pp. 122-44.

Miller, D. (1998), A Theory of Shopping, Polity, Cambridge.Monroe, K.B. (1991), Pricing – Making Profitable Decisions,

McGraw-Hill, New York, NY.Muellera, S. and Szolnokib, G. (2010), “The relative

influence of packaging, labeling, branding and sensory

attributes on liking and purchase intent: consumers differ in

their responsiveness”, Food Quality and Preference, Vol. 21No. 7, pp. 774-83.

Myers, J.H. and Shocker, A.D. (1981), “The nature of

product-related attributes”, Research in Marketing, Vol. 5,pp. 211-36.

Olaru, D. and Purchase, S. (2008), “From customer value to

repurchase intentions and recommendations”, Journal ofBusiness & Industrial Marketing, Vol. 23 No. 8, pp. 554-65.

Oliver, R.L. (1999), “Whence consumer loyalty?”, Journal ofMarketing, Vol. 63, pp. 33-44.

Olson, J.C. and Reynolds, T.J. (1983), “Understanding

consumers’ cognitive structures: implications for

advertising strategy”, in Percy, L. and Woodside, A.

(Eds), Advertising and Consumer Psychology, LexingtonBooks, Lexington, MD, pp. 77-90.

Ou, W.M., Abratt, R. and Dion, P. (2006), “The influence of

retailer reputation on store patronage”, Journal of Retailing& Consumer Services, Vol. 13 No. 3, pp. 221-30.

Overby, J.W. and Lee, E.J. (2006), “The effects of utilitarian

and hedonic online shopping value on consumer preferenceand intentions”, Journal of Business Research, Vol. 59

Nos 10/11, pp. 1160-6.Parasuraman, A. (1997), “Reflections of gaining competitive

advantage through customer value”, Journal of the Academyof Marketing Science, Vol. 25 No. 2, pp. 154-61.

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988),“SERVQUAL: a multiple-item scale for measuring

customer perceptions of service quality”, Journal ofRetailing, Vol. 64 No. 1, pp. 12-40.

Park, K.W. and Park, J.Y. (2009), “Shopping value, shopping

goal and WOM – focused on electronic-goods buyers”,

Journal of Global Academy of Marketing Science, Vol. 19

No. 2, pp. 73-84.Peter, J.P. and Olson, J.C. (2008), Consumer Behavior and

Marketing Strategy, McGraw-Hill Irwin, New York, NY.




Volume 29 · Number 6 · 2012 · 423–435

432

Pynnonen, M., Ritala, P. and Hallikas, J. (2011), “The new

meaning of customer value: a systemic perspective”, Journal

of Business Strategy, Vol. 32 No. 1, pp. 51-7.Ralston, R.W. (2003), “The effects of customer service,

branding, and price on the perceived value of local

telephone service”, Journal of Business Research, Vol. 56

No. 3, pp. 201-13.Reynolds, T.J. and Gutman, J. (1988), “Laddering theory,

method, analysis, and interpretation”, Journal of Advertising

Research, Vol. 28, pp. 11-31.Roy, S. and Goswami, P. (2007), “Psychographics and its

effect on purchase frequency – a study of the college-goers

of Kolkata, India”, Decision, Vol. 34 No. 1, pp. 63-94.Sheth, J.N., Newman, B.I. and Gross, B.L. (1991),

Consumption Values and Market Choices - Theory and

Applications, South-Western Publishing, Cincinnati, OH.Sirdeshmukh, D., Singh, J. and Sabol, B. (2002), “Consumer

trust, value, and loyalty in relational exchanges”, Journal of

Marketing, Vol. 66 No. 1, pp. 15-37.Sojka, J.Z. and Tansuhaj, P.S. (1995), “Cross-cultural

consumer research: a twenty-year review”, in Kardes, F.R.

and Sujan, M. (Eds), Advances in Consumer Research,

Vol. 22, Association for Consumer Research, Provo, UT,

pp. 461-74.Spreng, R., MacKenzie, S. and Olshavsky, R. (1996),

“A reexamination of the determinants of consumer

satisfaction”, Journal of Marketing, Vol. 60 No. 3, pp. 15-32.Taguchi, G. (1987), “The evaluation of quality”, paper

presented at the 40th Annual Quality Congress

Transactions, American Society for Quality Control,

Milwaukee, WI.Taylor, G.A. (2001), “Coupon response in services”, Journal

of Retailing, Vol. 77 No. 1, pp. 139-51.Ulaga, W. (2001), “Customer value in business markets: an

agenda for inquiry”, Industrial Marketing Management,

Vol. 30 No. 4, pp. 315-9.Vargo, S.L. and Lusch, R.F. (2008), “Service-dominant logic:

continuing the evolution”, Journal of the Academy of

Marketing Science, Vol. 36 No. 1, pp. 1-10.Varki, S. and Colgate, M. (2001), “The role of price

perceptions in an integrated model of behavioral

intentions”, Journal of Service Research, Vol. 3 No. 3,

pp. 232-40.Verhallen, T.M.M. and van Raaij, W.F. (1986), “How

consumers trade off behavioural costs and benefits”,

European Journal of Marketing, Vol. 3 No. 4, pp. 19-34.Walker, B.A. and Olson, J.C. (1991), “Means-end chains:

connecting products with self”, Journal of Business Research,

Vol. 22, pp. 111-8.Weinstein, M. (2002), “Customer retention: a usage

segmentation and customer value approach”, Journal of

Targeting, Measurement and Analysis for Marketing, Vol. 10

No. 3, pp. 259-68.Wilkie, W. and Pessemier, E. (1973), “Issues in marketing’s

use of multi-attribute attitude models”, Journal of Marketing

Research, Vol. 10 No. 4, pp. 428-41.Woodruff, R.B. (1997), “Customer value: the next source for

competitive advantage”, Journal of the Academy of Marketing

Science, Vol. 25 No. 2, pp. 139-53.Woodruff, R.B. and Gardial, S.F. (1996), Know Your

Customer: New Approaches to Customer Value and

Satisfaction, Blackwell, Cambridge, MA.

Woodruffe-Burton, H. (1998), “Private desires, public

display: consumption, postmodernism and fashion’s ‘newman’”, International Journal of Retail & DistributionManagement, Vol. 26 No. 8, pp. 301-10.

Zeithaml, V.A. (1988), “Consumer perceptions of price,

quality, and value: a means-end model and synthesis ofevidence”, Journal of Marketing, Vol. 52 No. 3, pp. 2-22.

About the authors

Dr Soonhong Min is an Associate Professor at YonseiUniversity in Seoul, Korea. He has published in numerous

marketing journals, including Journal of Retailing, Journal ofthe Academy of Marketing Science, Journal of Business Research,Industrial Marketing Management, and Journal of Business andIndustrial Marketing, among others.

Dr Jeffrey W. Overby is Associate Professor of Marketing

and Director of the Center for International Business atBelmont University in Nashville, USA. His academic research

interests are in the areas of international marketing,international business strategy, and cross-cultural consumerbehavior. He has published in a number of leading journals,

including Journal of the Academy of Marketing Science, Journalof Business Research, The CASE Journal, InternationalMarketing Review, Industrial Marketing Management, TheService Industries Journal, The Journal of ConsumerSatisfaction, Dissatisfaction and Complaining Behavior,International Journal of Service Industry Management, andInternational Journal of Management. Jeffrey W. Overby is the

corresponding author and can be contacted at:[email protected]

Kun Shin Im is Associate Professor of Information Systemsat Yonsei University. His research interests include IT impactson consumer behavior and organizational structure, IT

investments evaluation, IT adoption, and IT trainingeffectiveness. He has published several studies in these areas

in MIS Quarterly, Information Systems Research, Journal of theAIS, Journal of Information Technology Management, Journal ofOrganizational and End User Computing, and other journals.

Executive summary and implications formanagers and executives

This summary has been provided to allow managers and executivesa rapid appreciation of the content of this article. Those with aparticular interest in the topic covered may then read the article intoto to take advantage of the more comprehensive description of theresearch undertaken and its results to get the full benefits of thematerial present.

As various scholars have noted, shopping makes a significantcontribution to today’s world. The activity drives economicperformance, while individual consumers participate for

leisure, social engagement and self-identity purposes.Obtaining a desired level of value is thought to motivate

much shopping and consumption behavior. Providing value toconsumers has been the focus of much research attention. It is

evident that firms able to deliver on this promise can positiveinfluence consumer purchase intentions and increase bothmarket share and profitability.

Conclusive understanding of what determines customervalue has so far remained elusive. The only aspect that is

widely accepted is that value is derived from consumer




Volume 29 · Number 6 · 2012 · 423–435

433

perceptions rather than managerial judgment. Different

studies have acknowledged the relevance of productattributes in value creation. This had led to someconsideration of “means-end” theory which posits that

consumers seek product attributes that lead to desiredconsequences.

Attributes can be divided into tangible and intangible

categories. Those which can be directly perceived are labeledas “product-related” and include aspects like packaging,price, color and brand. In contrast, quality and style are

examples of more abstract features that do not directly relateto the product or service in question. Analysts have found thatconsumers rely on both attribute types in their anticipation ofdesired outcomes like quality and satisfaction.

Extant literature indicates that consequences of purchaseand consumption behavior typically fall into different types,

the key ones being:. Functional consequences (FC). This essentially refers to the

physical performance of the product. Satisfaction will

result if the product or service carries out the functions forwhich it is designed. Some authors claim that productutility can occur before and after performance, as well as

during.. Psychological consequences (PC). Objectives linked with

self-enhancement and self-identity are examples of this

type. Products can take on symbolic value and peopleoften select things because of what they might signify aswell as what functional purpose they serve.

. Social consequences (SC). How a product or service canconvey an image to others is the issue here. Products orservices which are consumed publically are especially

relevant. Some theorists argue that most products that are‘visibly identifiable’ will be chosen at least in part for thesocial value offered. This value is considered similar to

esteem, which results from the opinion of relevant others.

It is proposed that desired consequences or outcomes willprompt consumers to prioritize certain attributes over others.

The importance of attributes is therefore governed by theirability to connect products to the self. To some extent, thedegree to which attributes are functional or symbolic will

shape relations between attributes and desired consequences.A consideration of style, quality and price illustrates how

different attributes are likely to be associated with different

outcome types:. Style is based on design and aesthetics and reflects visual

rather than functional qualities of a product. Because style

creates pleasure and can secure attention from others, itarguably relates to psychological and social consequences.

. Quality is determined by consumer judgment and canrefer to both symbolic and utilitarian product dimensions.An association with both psychological and functionalconsequences is therefore assumed.

. Price often serves as a quality indicator. In addition, a highprice can indicate status and prestige. Another proposal is

that people might experience a sense of self-achievement ifthey purchase an item at a bargain price. There are thussound reasons for believing that price relates to all threeconsequence forms.

Although studies are limited, it is likely that desiredconsumption outcomes directly shape various consumer

behaviors such as information search, post purchaseassessments, and customer loyalty. Evidence indicates that

actual behaviors rather than attitudes may be more influenced

by desired outcomes. Given this possibility, desired

consequences could directly determine subsequent purchase

frequency. Being able to predict buying frequency would

hugely assist marketers as a considerable percentage of

product and services sales are made by regular purchasers.Contrasting evidence exists with regard to the relationship

between product attributes, consequences and behavioral

outcomes. Previous assumptions that attributes play a

mediating role have been challenged recently, especially

when intangible attributes are present. The argument here is

that symbolic meaning influences consumer behavior more in

these circumstances. Other researchers have argued that

consequences mediate the relationship between attributes and

purchase behavior. They posit that consumption decisions are

likelier to be driven by consequences than by product

attributes. Studies offer some support, suggesting that this

tendency increases as they gain more purchasing experience.In the current study, Min et al. aim to gain additional

insight of the key factors. Female workers aged between 19

and 49 in a US service organization participated in the study

and a final sample of 120 was obtained. Subjects were

interviewed about the purchase of a handbag. This product

was chosen as it able to meet both functional and hedonic

needs, and therefore could be expected to generate different

forms of consumption consequences.Hypothesis testing revealed:

. a direct link between style attributes and desired

psychological and social consequences but not direct

functional consequences;. price attributes directly relate to all three desired

consumption consequences; and. the association between product attributes and consumer

purchase frequency is mediated by desired consequences.

Other predictions were only partially upheld. For instance, it

was deemed surprising that perceived quality did not

significantly relate to psychological consequences. The

authors thus speculate that the expected sense of self-

achievement at purchasing a “well-made” product could be

incorporated with the functional consequences. The

relationship between quality and social consequences was

not anticipated either. A feasible explanation is that

consumers who buy a good quality handbag could believe

they are complying with certain social norms.That the positive association between social consequences

and purchase frequency was insignificant was likewise

unexpected. It is suggested by Min et al. that frequent

purchasing might be the norm for people who feel a need to

impress socially. Given this finding, they purport that such

motivations could merge into the psychological consequences

category.The impact on purchase frequency by desired

consequences was found to be direct. Product attributes, on

the other hand, have only limited and indirect influence. Since

desired consequences of attributes seem to drive purchase

behavior, the authors recommend that marketers should place

a greater emphasis on “more abstract measures of value”.Another notable finding was the negligible impact on

purchase behavior of all demographic variables. Although

other research has indicated the continuing importance of

such variables, it would appear unwise to regard them as sole

predictors of consumer purchase behavior.




Volume 29 · Number 6 · 2012 · 423–435

434

On this evidence, marketers should segment customers onthe basis of desired consequences rather than attributes. Theyshould accordingly focus on the attributes which most directlyrelate to each specific consequence. For instance, an emphasison price and quality would be appropriate when functionalconsequences are prioritized.

Additional studies using more representative samples andfunctional goods are advised. Another option is to investigateshopping experience. Earlier evidence reveals that individuals

may rely on fewer attributes and consequences as their

shopping experience and frequency rises. Other variables like

the consumption situation and consumer behaviors could also

determine which consequence type is preferred.

(A precis of the article “Relationships between desired attributes,

consequences and purchase frequency”. Supplied by Marketing

Consultants for Emerald.)




Volume 29 · Number 6 · 2012 · 423–435

435

To purchase reprints of this article please e-mail: [email protected]

Or visit our web site for further details: www.emeraldinsight.com/reprints

17050942

Documents

Transcript of 17050942