PRICE RESPONSE FUNCTION WITH AND WITHOUT CHOICE SET
INFORMATION BY TWO-STAGED CONJOINT ANALYSIS
IN THE DENIM JEANS MARKET
by
YOUNGSIK KWAK, B.B.A., M.B.A.
A THESIS
IN
CLOTHING, TEXTILES, AND MERCHANDISING
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
MASTER OF SCIENCE
Approved
Accepted
D6€h of the Graduate S/bhool
August, 1997
ACKNOWLEDGEMENTS
Above all, I want to give my deepest appreciation to my loving father and mother,
Sunyoung Kwak and Sukja Chang. Their endless love and support in me led to the
completion of this study. And I cannot express my thank with any word to my elder
bother and his wife, Yoonsik Kwak and Nanhee Choi for their patient guidance and
encouragement during my study in Texas Tech University. I am also greatly indebted to
my loving two sisters, Hyunsuk Kwak and Hyimjeong Kwak for considerable supports
financially and mentally. My sincere thanks are extended to Professor Pil Hwa Yoo,
Professor Yong-June Kim, and Professor Sangman Han for their valuable teaching. I
appreciate the help of Drs. Eberspacher, Harp, and Dodd on my thesis.
u
TABLE OF CONTENTS
ACKNOWLEDGMENTS
ii
ABSTRACT
vi
LIST OF TABLES
vii
LIST OF HGURES
x
CHAPTER
I. INTRODUCTION
1
Statement of Purpose
4
Research Questions
5
Limitation of the Study
6
Defmition of Terms
6
II. REVIEW OF RELATED LITERATURE
8
The Criteria used in Evaluating Apparel
8
The Criteria Used in Evaluating Products
Variables Used in Evaluating Apparel
~8
-
-9
Pricing Research in Economics and Marketing
11
Price Response Function
17
Price Elasticity
18
Demand Fimction versus Price Response Fimction
18
Competitive Price Response Functions
19
Calibration of Price Response Functions
20
iii
TABLE OF CONTENTS
ACKNOWLEDGMENTS
ii
ABSTRACT
vi
LIST OF TABLES
~ vii
LIST OF HGURES
~ x
CHAPTER
I. INTRODUCTION
- -1
Statement of Purpose
-
4
Research Questions
5
Limitation of the Study
6
Definition of Terms
6
II. REVIEW OF RELATED LITERATURE
8
The Criteria used in Evaluating Apparel
8
The Criteria Used in Evaluating Products
8
Variables Used in Evaluating Apparel
9
Pricing Research in Economics and Marketing
11
Price Response Fimction
17
Price Elasticity
18
Demand Fimction versus Price Response Fimction
18
Competitive Price Response Functions -
19
Calibration of Price Response Functions—
iii
-
20
Application
24
Conjoint Analysis
25
Model
26
Conjoint analysis procedure
27
Application
28
Data gathering
29
Validity and Reliability
30
Multinomial Logit Model and Choice Set InformationModel
32
35
Summary of Literature Review
37
HI. METHOD
39
Selection of Sample
39
Research Instrument
41
Attributes and Levels Investigated
42
Collection of Data
45
Calibration of Price Response Fimction
45
Model Specification With Choice Set Information
—45
Price Response Function and Optimal Pricing
46
Revenue-Maximizing Price and Static Optimal Price
48
Model Specification Without Choice Set Information
50
Statistical Analysis of Data
—52
Research Question One
—52
iv
Research Question Two
-
52
Research (Question Three —
54
IV. RESULTS OF DATA ANALYSIS
55
Questionnaire Response Rate and Description of the Sample
55
Statistical Treatment of the Data
58
Research Question One
58
Research Question Two —
59
Research Question Three -
71
V. SUMMARY, CONCLUSIONS, AND FUTURE RESEARCH
Summary of the Study
Interpretation of Results
Research (Question One
-—73
74
-
-
75
—-
75
Research (Question Two
76
Research Question Three
79
Conclusions and Implications
—80
Suggestions for Future Study
81
REFERENCES
83
APPENDIX: QUESTIONNAIRE -
90
ABSTRACT
The primary purposes of this study were to develop a new methodology for
calibration of the continuous price response function and to compare differences in the
price response functions with and wdthout choice set information. The new methodology,
identified as the two-staged conjoint analysis, incorporated the logit transformation into
the conjoint analysis and added the choice set formation step in the conjoint questionnaire.
Two conjoint models were specified to compare the differences in the price
response functions with and without choice set information. These two models were
tested in a study of the denim jeans market. Participants included 103 students at a major
state-supported southern university.
In a conjoint model, the independent variables
consisted of brand, price, color, and style, while the dependent variables were equal to
each respondent's purchase intention data.
The two-staged conjoint questionnaire
consisted of three steps. Step one provided respondents with both a written and a visual
description of two randomly selected styles and colors of denim jeans.
In step two,
respondents were asked to choose the combination of attributes they intended to piu-chase.
In step three respondents were asked to rate each combination chosen at step two on a
scale of 1-100, with one as least likely to be purchased and 100 as most likely to be
purchased.
Three research questions were developed to accomplish the purposes of this study.
These questions were intended to; (1) examine the differences in the price response
function among brands; (2) compare the differences in the price response functions with
VI
and without choice set information; and (3) compare the optimal price of the price
response functions with and without choice set information for each brand. A variety of
statistical methods, including ANOVA, t-test, and regression analysis, were employed to
analyze the research questions. The results of ANOVA revealed no significant differences
in the purchase probabilities among the eight identified brands at $20, $40, $60, and $80.
However, results of the t-test and price elasticity showed that the price response functions
with and without choice set information had different price response structures. The
different shapes of the price response functions with and without choice set information
resulted in the differences in optimal price.
vu
LIST OF TABLES
2.1. Evaluated Cues in Investigating Apparel Quality
2.2. Criteria Types of Apparel Quality Evaluation
-10
12
2.3. Eckman et al's (1990) Summary of Intrinsic/Extrinsic Cues Used
by Researchers for Evaluating Apparel Quahty
13
2.4 Pricing Research In Economics and Marketing
14
2.5 Yoo's (1989) Pricing Classification
16
2.6 Economically Calibrated Price Response Function Based on Actual
Market Data
2.7 Comparison of Methods for Collecting Price Response Fimction Data
22
23
2.8 Examples of Segmentation and Market Structure Using Multinomial
Logit Model
—33
2.9 Examples of Choice Set Information Using Multinomial Logit Model
36
3.1 Brands and Price Ranges Observed in One Shopping Mall July, 1996
44
3.2 Product Profile of Selected Brands
-47
3.3 Dependent and Independent variables in Price Response Fimction
4.1 Demographic Characteristics of Sample
53
—56
4.2 Summary ANOVA for $20 Price Point
60
4.3 Summary ANOVA for $40 Price Point
61
4.4 Summary ANOVA for $60 Price Point
62
4.5 Summary ANOVA for $80 Price Point
4.6 The Results of T-Test at various Price Points
4.7 The Differences in Price Elasticity on the Price Response Function
viii
63
69
70
4.8 Estimated Price Response Function and the Optimal Price
—
72
5.1 The Coefficient Mean of the Conjoint Model
77
5.2 The Relative Inqjortance Among the Attributes
78
IX
LIST OF HGURES
2.1 Historical Development of Pricing Research in Marketing Between the U.S.
and Europe
15
2.2 Competitive Price Response Functions
21
2.3 Basic Components of the Pre-purchase Ahemative Evaluation Process
34
3.1 Flow of Calibration of Price Response Function and Optimal Price
40
3.2 Linkage Among the Mean Choice Probability for Price Response Function
49
4.1 Price Response Function With and Without Choice Set Information
65
CHAPTER I
INTRODUCTION
Today's retail environment is in a state of flux. Caught in a profit squeeze, retail
organizations are in a constant battle for market share. To succeed in a highly
competitive market dominated by strategic shoppers, marketers in the 1990's must focus
on the range of factors or attributes consumers use to make judgements or trade-offs in
determining their final purchase choice. In this context, it is important for manufacturers
and retailers to understand how price, promotion, and other marketing variables affect
product sales. Such information is the basic material for marketing mix decisions. How
consumers respond to price arid price changes and how manufacturers and retailers
determine a set of optimal prices can be used to identify and maximize market
opportunities.
Within the marketing mix, price is distinguished from other components by its
revenue producing characteristic. A 1996 (Dolan & Simon) study illustrated the
increased significance of price. When 186 managers (57 from the United States and 129
from Europe) representing a broad range of industries ranked 13 marketing issues, results
showed that managers rated pricing as the most pressing problem. Despite the
importance of the pricing decision, a number of researchers point out that price is still
mainly established by rule of thumb or by using the cost-plus approach (Bonoma,
Crittenden, & Dolan, 1985; Kucher & Hilleke, 1993). Pricing based on marginal costs
has the potential for missing a critical element in maximizing profits; the relative
perceived value of the product being priced. Therefore, managers are advised to set
prices for products according to customers' perceptions of product benefits and costs. It
means that the product should be seen through the customers' point of view and priced
accordingly.
The relationship between alternative prices and the resulting sales quantity is
called the price response function (PRE) (Simon, 1989). The price response function of a
product or a brand is a tool to understand the effect of price on sales for a product or a
brand. Researchers have recommended calibrating the price response function for a
product or a brand to find the optimal price for the product or the brand based on conjoint
analysis (Dolan & Simon, 1996; Kucher et al., 1993; Simon, 1989, 1992; Yoo, 1991).
Conjoint analysis has been recommended as an effective tool for determining the value of
a product and deriving price response functions and optimal prices (Dolan & Simon,
1996; Geurts & Whitiark, 1993; Simon, 1989, 1992; Weiner, 1994). This conjoint
measurement uses individual customer's preference, which also satisfies the condition of
setting the prices from the customers' viewpoint. However, one is still faced with the
problem of small data points in the price response function when following the guidelines
for calibrating the price response function recommended by Simon (1989) and Yoo
(1991). If using four price attribute levels, only four data points on the price response
function can be attained. Therefore, the price response function based on traditional
conjoint analysis cannot be regarded as a continuous function but rather a discrete
function.
Yoo and Ohta (1995) use the multinomial logit model to calibrate the continuous
price response function. However, under the assumption of Hedonic pricing, they do not
use price as an independent attribute to evaluate subject product. Because price has been
regarded as one of the most important attributes to evaluate products, Yoo and Ohta's
(1995) optimal pricing method can be falsified (Engel, Blackwell, & Miniard, 1995;
Jacoby, Olson, & Haddock, 1971; Szybillo & Jacoby, 1974; Wheatiy, Chiu, & Goldman.
1981).
The multinomial logit model has also been used to examine various market
phenomena such as a price cut effect on a market, market segmentation, choice set
change, brand switching, and so on (Bucklin, Gupta, & Han, 1995; Buckiln & Lattin,
1991; Hardie, Johnson, & Fader, 1993; Gupta, 1988). Previous studies have primarily
used non-durable products where researchers could obtain the purchase histories of the
subject consumers with scanner data. Using the purchase history, researchers were able
to estimate parameters suitable to explore the chosen phenomenon. However, obtaining
similar consumer information for durable products is difficult due to the relatively long
interpurchase time.
Several studies investigating individual consumer choice decision-making have
concluded that individuals have their own choice set for selecting a certain product
category (Han et al., 1995; Shocker, Ben-Akiva, Boccara, & Nedungadi, 1991; Siddarth,
Bucklin, & Morrison, 1995). However, the traditional conjoint analysis has only one
stage in which respondents are required to express their purchase intentions among the
prespecified combinations, although respondents may not consider one of the
prespecified combinations as the one they want to buy. That is, the one-stage conjoint
analysis can not consider a choice set (Shocker, Ben-Akiva, Boccara, & Nedungadi,
1991).
The current research employs a two-staged conjoint analysis. At the first stage,
using the profile with researcher specified multiattributes, respondents select the
combinations he/she intends to purchase, allowing for identification of each respondent's
choice set. Based on the selected choice set, the second stage obtains preference data
from each respondent by which the researcher can obtain the unique utility of subject
brands. Based on the utility for each respondent, the choice probability is calculated by
logit transformation (Green &'Krieger, 1997). Park (1994) and Urban and Hauser (1993)
described the method for transforming utility based on conjoint analysis into probability.
Using the choice probability, the researcher can calibrate the continuous price response
function for each brand.
In an effort to overcome the problem of purchase history for durable products, this
study purposed using a two-staged conjoint analysis. Instead of purchase history data
obtained through scanner data, purchase intention data obtained through conjoint analysis
were used to calibrate a price response function. Accordingly, the two-staged conjoint
analysis used in this study allowed for calibration of the continuous price response
function including choice set information.
Statement of Purpose
The primary purposes of this study were to develop a new methodology for
calibration of the continuous price response function and to compare the price response
functions with choice set information to the price response function without choice set
information. The purposes were achieved by completing three objectives: (1) to
empirically calibrate the price response function of the denim jeans market utilizing a
two-staged conjoint analysis with a multinomial logit formation; (2) to compare the
differences in the price response functions with and without choice set information; and
(3) to compare the optimal price for each brand with and without choice set information.
Accordingly, the study endeavored to:
1. Identify the criteria used in evaluating apparel which were used as the attributes in
the conjoint analysis;
2. Calibrate the price response function of the denim jeans market by the conjoint
analysis and logit transformation;
3. Compare the price response function including choice set information to the
price response function without choice set information;
4. Compare the optimal price for each brand with choice set information to the
optimal price for each brand without choice set information.
Research Ouestions
The following research questions were developed to guide the study:
1. Are there differences in the price response functions among brands in the denim
jeans market for college students enrolled at a major state supported university?
2. Are there differences in the price response functions with and without choice set
information for each subject brand in the denim jeans market for college students
enrolled at a major state supported southern university?
3. Are there differences in optimal prices with and without choice set information for
each subject brand in the denim jeans market for college students enrolled at a
major state supported southern university?
Limitations of the Studv
This study was limited to the case of a profit-maximizing firm that operates in a
static situation with no varying time effect in cost and competitor's response. Further, the
study was limited to the products and price ranges selected by the researcher. Therefore,
conclusions were limited to the sample selected, specific product categories, and price
range. Generalizations beyond this study must be made with caution.
Definitions of Terms
Choice Set: Brands/services seriously considered just prior to actual purchase
(Desarbo & Jedidi, 1995).
Conjoint Analysis: A technique which attempts to determine the relative
importance consumers attach to salient attributes and the utilities they attach to the level
of attributes (Malhotra, 1993).
Denim Jeans: Pants made of blue or indigo 100% denim worn by both males and
females (Kwon, 1993).
Heuristic Optimal Price: Optimal price calculated from profit function within the
levels in the price attribute of the conjoint analysis.
Mathematical Optimal Price: Mathematical solution derived from profit function
consisting of price response function and cost function.
National Brands: Manufacturers of a product who sell their brand lines to retail
stores across the country (Stone & Samples, 1990).
Price Response Function: The relationship between alternative prices and the
resulting sales quantity (Simon, 1989).
Price Response Function With Choice Set Information: Price response function
including brands and services seriously considered just prior to actual purchase (choice
set) for calibration of the price response function.
Price Response Function Without Choice Set Information: Price response
function excluding brands and services seriously considered just prior to actual purchase
(choice set) for calibration of the price response function.
CHAPTER n
REVIEW OF RELATED LFTERATURE
This study consisted of three tasks. Thefirsttask was to identify criteria used in
evaluating apparel which were used as attributes in a conjoint analysis. The second task
was to calibrate the price response function. The last task was to identify the optimal
price for each subject brand. To implement thefirsttask criteria used in apparel
evaluation were reviewed. To accomplish the second and third task the researcher
reviewed literature relating to the price response function, the conjoint analysis as a
measurement tool for the price response function, the reliability of the conjoint analysis,
the multinomial logit model, and choice set.
The Criteria Used in Evaluating Apparel
The Criteria Used in Evaluating Products
Purchase behavior requires that the customer be able to make judgments and
comparisons across products and services. Such judgments are associated with some or
all of the various items of information in which the product or service are identified,
evaluated, and integrated to form a composite judgment. These judgments are referred to
as information cues (Jacoby, Olson, & Haddrock, 1971).
Jacoby et al. (1971) classified cues as either intrinsic or extrinsic, suggesting that
the attribute of quality can be similariy divided. Intrinsic cues derive from the actual
physical product and cannot be changed without changing the product itself (e.g., color or
texture). Extrinsic cues are product-related but are not part of the physical product itself.
8
Price, brand name, and level of advertising are examples of extrinsic cues that appear to
serve as indicators of quality (Jacoby et al., 1971).
There is controversy among studies regarding the effect of extrinsic cues as a
determinant of quality. In several multi-cue experiments, some researchers reported that
extrinsic cues such as price had a small effect on perceptions of quality (Jacoby et al.,
1971; Szybillo & Jacoby, 1974), while other researchers reported the positive effect of
extrinsic cues (Wheatiy, Chiu, & Goldman, 1981). In the case of intrinsic cues, however,
studies revealed consistent results suggesting that intrinsic cues had a positive effect on
the process of product evaluation (Jacoby et al., 1971; Szybillo & Jacoby, 1974;
Wheatiy, Chiu, & Goldman, 1981).
Variables Used in Evaluating Apparel
Using the intrinsic versus extrinsic cues classification, numerous researchers
have investigated the importance of the impact of specific variables in evaluation of the
perception of quality in apparel. Table 2.1 provides a summary of the research
investigating the use of intrinsic and extrinsic cues in the evaluation of apparel quality.
Based on the results of these research studies, one can define and categorize intrinsic and
extrinsic cues which are statistically significant when evaluating the perception of apparel
quality. Examples of intrinsic cues include style, color, fabric content, ease of care, and
fit. Extrinsic cues include price, brand name, and store type.
Table 2.1. Evaluated Cues in Investigating Apparel Quality.
Reference
Products
Gardner (1971)
Men's dress shirts. Suit
Price, Brand
Davis (1985)
Skirt
Brand Labeling*
Dardis, Spivak,
and Shih (1985)
Men's dress shirts
Durability
i^pearance
Country of Origin,
Price, Brand
Hatch and
Roberts (1985)
Socks, Sweaters
Fiber Content
Price*, Care, Brand,
Seal of Content*,
Guarantee,*
Country of Origin
Stemquist and
Davis (1986)
Sweaters
Lee (1987)
Suits
Color*, Design*
Kim(1988)
Jeans, Suits, Coat
Fiber Content*,
Fit*, Color*
Norum and
Clark
(1989)
Women's blazers
Fiber Content*
Design*
Country of Origin,
Price*, Store Type*,
Store Location*
WaUand
Heslop
(1989)
Men's, Women's, and
Children's Clothing,
Men's and Women's
Footwear
Value, Styling,
Construction,
Ease of Care, Fit,
Durability, Color
Brand Name,
Country of Origin
Heisey
(1990)
Sweaters
Fiber Content*,
Care
Country of Origin,
Store Type*
Workman
(1990)
Jeans' Hangtag
Information
Easy of care*,
Price*
Fit*,Style*,Fabric
Lee& Lee
(1995)
Jeans Pants
Fiber Content*,
Care*, Design*
Intrinsic Cues
Extrinsic Cues
Price*, Vender Image*
Country of Origin
Note. * refers to significant variables.
10
Price*, Brand Name*
Price*, Brand Name*
In addition to Jacoby et al.'s (1971) classification, Eckman, Damhorst, & Kadolph
(1990) categorized the evaluation of apparel quality into four criteria types (Table 2.2).
Further, they summarized the intrinsic and extrinsic cues used by respondents for
evaluating apparel quality from 21 separate studies (Table 2.3). The most frequently
used variables in these studies were style, price, brand name, fabric and fiber content, fit
and color/design.
Pricing Research in Economics and Marketing
Price is an important topic in economics and marketing. Price is viewed and
determined differentiy depending upon the approach. Simon (1989) suggests a taxonomy
of pricing using economics and marketing classifications. He categorizes pricing into
three approaches: economics, qualitative/descriptive, and quantitative/methodologybased. The quantitative/methodology-based approach deals with measurement issues
associated with optimal price and price response. Because the purpose of the study was
to calibrate a price response function in an apparel product, the quantitative/methodology
-based approach was employed (Table 2.4).
Yoo (1989) reviewed the historical development of pricing research in marketing
in Europe and the United States (Figure 2.1). According to Yoo's (1989) classification,
pricing issues can be divided into three areas; normative, descriptive, and behavioral
(Table 2.5). Researchers utilizing the normative approach have dealt with the setting and
measurement issue of price.
11
Table 2.2. Criteria Types of Apparel Quality Evaluation.
Criteria Type
Examples
Aesthetic
Stylish, Fashionable, Attractive, Looks Good on Me, Pattern
Match
Performance
Launders Well, Holds Its Shape, Color Fast, Doesn't Shrink,
Durable
Usefulness
Fits Well in Wardrobe, Versatile, Usable
Extrinsic
Price, Designer Label, Brand Name, Store Where Purchased, Hang
Tags, Competition
Note. From Clothing and Textile Research Journal, 8(2) (p. 14). by M. Eckman, M. L.
Damhorst, & S. J. Kapolph (1990). Toward a model of the in-store purchase decision
process: Consumer use of criteria for evaluating women's apparel. Adapted.
12
Table 2.3. Eckman et al.'s (1990) Summary of Intrinsic\Extrinsic Cues Used by
Researchers for Evaluating Apparel Quality .
Extrinsic criteria
No. of citations
Intrinsic criteria
No. of citations
Price
9
Style
13
Brand name or label
9
Fabric & Fiber content
7
Country of origin
5
Color/Design
5
Store
4
Fit
5
Coordination with
wardrobe
2
Durability
4
Salesperson's evaluation
2
Comfort
3
Department in store
1
Safety
2
Warranty
1
Care
1
other
12
Total
52
35
Note. From Clothing and Textile Research Jpumal, 8(2) (p. 14). by M. Eckman. M. L.
Damhorst, & S. J. Kapolph (1990). Toward a model of the in-store purchase decision
process: Consumer use of criteria for evaluating women's apparel. Adapted.
13
Table 2.4. Pricing Research in Economics and Marketing.
Approach
Issues
Researchers
Economics
The Determination of Prices by
Supply and Demand, Economic
EflBciency of the Price System,
Problems of Market Equilibrium.
Coumot,
Chamberlin,
KreUe,
Stackelberg.
Qualitative/ Descriptive
Directional Recommendation for
Pricing.
Shapiro,
Monroe,
Oxenfelt
Quantitative/
Methodology-based
Pricing Oriented from Econometric
Analysis, Measurement Issues
(the Conjoint Measurement).
Rao, Nagie,
Simon, Kucher.
Note. From Price managemient (p. 8). by H. Simon, 1989, New York: North-Holland.
Adapted.
14
Microeconomics Pricing Theory
Qualitative
Econometric
Business-oriented
Approach
Approach
Pricing Research
Descriptive
Behavioral
Normative
Normative
Behavioral
Descriptive
Approach
Approach
Approach
Approach
Approach
Approach
Europe
U. S. A.
Figure 2.1. Historical development of pricing research in marketing between the U.S. and
Europe.
Note. From 'Tricing research in marketing: The integration of European and American
literature." (p. 171). by P. H. Yoo, 1989, Korean Marketing Review. 4(1). Adapted.
15
Table 2.5. Yoo's (1989) Pricing Classification.
Approaches
Topics
Normative
Defensive Pricing Strategy, Pricing a Product Line, Price
Determination Via Conjoint Analysis, Quantity Discount,
Price Bundling.
Behavioral
Price Awareness, Price Consciousness, Price as an Indicator
of Quality, Perceived Price, etc.
Descriptive
Decision Process of Price, Determinants for Price.
Note. From "Pricing research in marketing: The integration of European and American
literature." by P. H. Yoo, 1989, Korean Marketing Review, 4(1). Adapted.
16
Descriptive studies have explored the process of determining price, while behavioral
studies have examined the psychological issues associated with price. Based on Yoo's
(1989) classification scheme (Table 2.5), the current study utilized a normative approach
as a price determination via conjoint analysis. Additionally, because the process of price
determination often incorporates psychological aspects of pricing such as price as an
indicator of quality, the researcher explored various behavioral characteristics of price.
Conclusively, based upon Simon (1989) and Yoo's (1989) classifications, this study
utilized both the quantitative/methodology-based approach and the normative approach.
Price Response Function
In the quantitative/methodology-based approach pricing is dependent on external
and internal determinants. External determinants are those beyond the firm's control
(e.g., consumer characteristics, market stmcture, legal conditions), while internal
determinants are those under the firm's control (e.g., advertising, price, product,
distribution). These factors determine the number of product units that can be sold at
alternative prices (Yoo, 1991).
The relationship between alternative prices and the resulting sales quantity is
called the price response function (Simon, 1989). The dependent variable of the price
response function is sales volume or market share. Market share is an aggregation of
individual customer choice or preference. This individual customer choice or preference
can be explored by conjoint measurement. To supplement the price response function
two concepts were reviewed; price elasticity and the demand function of price.
17
Price Elasticitv
Samuelson and Nordhaus (1992) define price elasticity of demand (or price
elasticity) as the responsiveness of the quantity demanded of a good to changes in the
good's price, certeris paribus. The precise definition of price elasticity is the percentage
change in quantity demanded divided by the percentage change in price.
percentage change in quantity demanded
Price elasticity =
———
percentage change in price
Price elasticity, therefore, provides a measurement of the impact of price on sales. From
an economic perspective, the demand function may also be employed as a measurement
tool, because the demand curve also indicates the quantity as a function of price. Simon
(1989) and Yoo (1991), however, recommend the price response function as a tool to
measure impact of price on sales when utilizing the quantitative/methodology-based
approach.
Demand Function versus Price Response Function
Both the demand function and price response function are a measurement of the
impact of price on sales. In determining the effect of price on sales, the classic economic
pricing model assumes that other marketing variables are held constant. This assumption
is evident in the usual treatment of the demand function as a relationship only between
quantity demanded and price. But this assumption does not consider the effect of other
marketing mix components, such as advertising, selling effort, and competitors' response
to the firm's marketing action, on price elasticity and similar interactions. In this regard,
the price response function has different characteristics from the demand function. The
18
price response function includes several factors which are not considered in the demand
function, such as external determinants and non-price marketing activities. Moreover,
sales of a product depend not only on its own price but also on the prices of other
products. In this regard, in the process of calibrating the price response function
researchers can consider competitive situations, develop several competitive price
response models, and use various marketing mix (Yoo, 1991). Therefore, employing the
price response function allows for the consideration of more variables than does the
demand function and, as a result, is a more useful tool for understanding particular
market situations.
Competitive Price Response Functions
A static price response function can be represented by a mathematical equation.
Simon (1989) categorized four types of competitive response models and mathematical
equations as follows (Sale quantity = dependent variable, price = independent variable):
• the linear model ( q = a - bp),
• the multiplicative model (q = ap^
a>0, b<0),
• the attraction/multinomial logit model (market share/probability of brand i = e' / J^c"
where i = ith brand among m brands, e = base of natural logarithm),
• the Gutenberg model ( q = a- bp - c 1 sin /2(c2(pi-pj))
a, b, c 1, c2=parameters).
Figure 2.2 illustrates the four models while Table 2.6 contains a selection of numerous
calibrations for each of the models. As shown in Table 2.6, numerous researchers have
calibrated the price response function using the models and actual market data.
19
Calibration of Price Response Functions
Calibration of the price response function requires price data and sale/market
share data. Simon (1989) classified the method for calibrating the price response
function into four categories; expert judgment, customer survey, price experiment, and
collection of actual market data. The customer survey method can further be divided into
two different types. Researchers can either directiy ask customers how they would react
to certain price levels, price changes, or price differentials, or one can ask customers
about their preferences and infer the information on price response from these preference
data; direct price response surveys and conjoint measurement. Table 2.7 provides an
evaluation of these methods on various criteria. Although Simon (1989) identifies the
reliability of conjoint analysis as uncertain, Reibstein, Bateson, and Boulding (1987)
reported that conjoint analysis was generally reliable with respect to reliability over the
stimulus set, the attributes set, and the data collection procedure. From Table 2.7, one
can infer that conjoint analysis is a useful method for estimating price response functions.
A number of factors must be taken into account in pricing decisions; the
objectives of the organization, customers' willingness to pay for the product, the costs of
producing and marketing the product, competition, changes in reservation prices, the
costs and the competition over time (Lilien, Kotler, & Moothy, 1992). From the static
situation approach to pricing, Simon (1989) and Yoo (1991) indicate that prices are
determined by the price response, the cost, and the objective function of a firm.
20
Linear
low
Attraction
Multiplicative
high
low
low
high
high
Gutenberg model
low
high price
Figure 2.2. Competitive price response functions.
Note. From Price management (p. 22).. by H. Simon, 1989, New York: North-Holland.
Adapted.
21
Table 2.6. Econometrically Calibrated Price Response Function Based on Actual Market
Data.
Methods
Research
Linear
Peles(1971)
Houston-Weiss (1974)
Prasad-Ring(1976)
Multiplicative
Wittink(1977)
Yon-Mount (1978)
Picconi-01son(1978)
Attraction/ Multinomial Logit model
Urban(1969)
Weiss (1969)
Bultez(1975)
Guadagni& Little (1983)*
Gutenberg
Simon (1982)
Albach(1973)
Kucher(1985)
* Multinomial logit model: Following publication of this article numerous studies using
the multinomial logit model reported price response in the market. The focus of these
articles was primarily non-durable goods (Bucklin & Lattin, 1991; Hardie, Johnson, &
Fader, 1993; Gupta, 1988).
Note. From Price management (p. 36). by H. Simon, 1989, New York: North-Holland.
Adapted.
22
Table 2.7. Comparison of Methods for Collecting Price Response Data.
Customer Survey
Criteria
Expert
Judgment
Price
Actual
Experiment Market Data
Direct Survey Conjoint Analysis
Validity
Medium
Very Low
Medium-high
Reliability
Mediumhigh
Uncertain
Uncertain
Costs
Very Low Low-medium
Medium
Mediumlow
High
Low
Mediumhigh
Depends on
Availability
High
&
Accessibility
Overall
Useful for Questionable
Evaluation New
Situations
Very Useful
Useful
Useful for
Established
Products
Note. From Price management (p. 36). by H. Simon, 1989, New York: North-Holland.
Adapted.
23
Application
A price response function is initially employed to explore the price response of a
market and to allow for calibration. At this point, it can be used to find the profitmaximizing price. In determining the profit-maximizing price, the firm must consider
manufacturing costs and sales quantities for altemative prices (Simon, 1989). The sales
quantities for altemative prices can be explored using the price response function.
Assume that sales are the function of price and costs are the function of sales (Simon,
1989; Yoo, 1991). Revenue is defined as price p times sale q. Accordingly, profit is
Profit = Revenue(p*^)"- Cost(c*^)
(2.1)
=pq(p) - C[q(p)]
where p = price,
q = quantity sold,
c = variable cost.
After calibrating a price response function using the price response data, one can
derive a mathematical equation for the price response function. Through application of
the equation with respect to price, one can find the revenue-maximization price point
(Yoo, 1991). Assume the price response function is linear in the simplest case.
Accordingly,
^ = a - b/7
(where a, b = coefficients)
qp = ap- bp^
Sqp/Sp = a - 2bpr = 0
=>pr = a/2b
(2.2)
24
(where pr = revenue-maximization price).
Therefore, the revenue-maximization price point, pr, is a/2b.
Further, if the marginal cost is known or can be assumed, the optimal static price.
P*, can be calculated. The optimal price refers to the profit-maximization price. If the
cost function is also linear, i.e.,
C = fixed cost + k (a-bp)
where k = marginal cost.
Accordingly, formula 1 can be expressed as follows;
Profit = (a-b/7)/7 - fixed cost - k (a-bp)
Setting marginal revenue equal to marginal cost, the optimal static price can be obtained.
a - 2bp* = -kb
P* = 1/2 (a/b + k).
(2.3)
Conjoint Analvsis
Green and Srinivasan (1978, 1990) defined conjoint analysis as any
decompositional method that estimates the structure of a consumer's preferences (i.e.,
estimates preference parameters such as part-worth, relative importance among attributes,
ideal points), given his/her overall evaluations of a set of alternatives that are prespecified
in terms of levels of different attributes. On the other hand, Malhotra (1993) defined
conjoint analysis as a technique which attempts to determine the relative importance
consumers attach to salient attributes and the utilities they attach to the level of these
attributes. Czepiel (1992) defined conjoint analysis from the manager's perspective.
25
According to his definition, conjoint analysis is a technique tiiat allows managers to
determine what elements in an offering are valued by customers.
Model
Conjoint analysis is one type of compensatory model. It is a multivariate
technique used to understand how respondents develop preferences for products or
services. Further, the conjoint measurement produces individual levels of information
for the subject product. That is, it produces the individual utility for the identified
attributes which enables the investigator to find the individual's overall utility for a given
product.
The basic conjoint analysis model is represented by the following formula
(Malhotra, 1993);
where U(x) = overall utility of an alternative,
aij = the part-worth contribution or utility associated with the jth level
(j,j=l,2,..., ki) of the ith attribute (i,i=l,2,...,m),
Xij = 1 if the jth level of the ith attribute is present: 0, otherwise,
ki = number of levels of attribute i,
m = number of attributes.
The basic conjoint equation is one variation of the regression model, where the partworth for the conjoint analysis is the same as the coefficient for the regression equation.
26
The importance of an attribute, Ij, is defined in terms of the range of the partworth, ajj, across the levels of that attribute (Malhotra, 1993):
Ij = {max(aij) - min(aij)}, for each i.
(2.5)
Conjoint Analvsis Procedure •
Czepiel (1992) summarizes the conjoint analysis procedure as follows:
Step 1: Identify relevant attributes of the product/service.
Step 2: Specify the levels of each attribute to include in the study.
Step 3: Develop conjoint questionnaire.
Step 4: Select respondents and administer conjoint questionnaire.
Step 5: Estimate utility of each level of each attribute for each respondent.
Step 6: (Optional) Develop preference segments by clustering or grouping
customers having similar attribute preferences.
Step 7: (Optional) Simulate choice share among altemative company and
competitor product/service offerings described by the attributes under
study.
Choice share can be obtained byfirstestimating utility, which will allow for simulating
customer's choices of various product offerings and competitive scenarios. For example,
assume that there are only two offerings. One is the firm's offering and the other is the
competitor's. If respondent 1 's utility for the firm's offerings is 0.2, and utility for the
competitive offering is 0.5, the prediction for this example is that respondent 1 will
choose the competitor's offering. By calculating the utility for each respondent in the
27
sample, the market share that the constituents in the market would obtain in the
simulated market scenario can be estimated (Czepiel, 1992).
Applications
Since the mid-1970's conjoint analysis has attracted considerable attention as a
method to realistically portray consumer's decisions as a trade-off among multiattribute
products or services (Wittink & Cattin, 1989). A recent survey reported applications in
the areas of new product, concept identification, competitive analysis, pricing, market
segmentation, advertising, and distribution (Wittink, Vriens, & Burhenne, 1994). Other
applications included an investigation into the relative importance among attributes in a
compensation package (Kwon, 1995) and evaluation of the service quality problem
(Desarbo, Huff, Rolandelli, & Choi, 1994).
Simon (1989) recommended a conjoint analysis as a calibrating tool for the price
response function. Kucher et al. (1993) further indicated that conjoint analysis is a more
sophisticated and valid pricing method than using the cost-plus approach, which uses the
perceived relative value of products. Finally, Wittink et al. (1994) reported that pricing is
the single most frequently identified purpose served by conjoint analyses in Europe,
whereas in the United States it was third in frequency.
Although conjoint analysis has gained widespread acceptance and use in many
industry settings, few researchers have applied it to the field of clothing and textiles. Oh
and Huh (1995) used conjoint analysis to determine optimal jeans product development
in South Korea. However, they failed to satisfy the conditions of mutual exclusiveness
between the levels of the various attributes; a condition recommended by Green and
28
Srinivasan (1990). Rather, Oh et al. (1995) used continuous levels in price; which could
have created bias in the results of the conjoint measurement. If they were to have
followed Green et al.'s (1990) suggestion, the levels of price should have been 30,000
won ($35), 50,000 won ($60), 70,000 won ($85) with the same interval between levels
instead of the less than 30,000 won ($35), between 30,000 won ($35) and 50,000 won
($60), and more than 70,000 won ($85) used in Oh & Hub's (1995) study. The levels
Oh et al. (1995) used did not differentiate the two turning price points at 30,000 won
($35) and 50,000 won ($60).
Virtually no research has been completed in the textiles and clothing field which
utilizes calibration of the price response function by conjoint analysis. In this regard,
application of this methodology will contribute significantly to the breadth of knowledge
in the textiles and clothing discipline.
Data Gathering
In order to conduct a conjoint analysis, information must be collected from a
sample of consumers. There are many different ways of collecting conjoint measurement
data, although two dominate; the full profile and Adaptive Conjoint Analysis (ACA).
Wittink et al. (1994) reported that 42% of all conjoint measurement studies were carried
out by using the ACA method and 24% used the full profile method, while 34% used
methods such as paired comparison and trade-off matrices.
29
Validitv and Reliabilitv
Malhotra (1982) examined structural reliability and stability of nonmetric
conjoint analysis under conditions of severe stmctures perturbation and substantial
variation in the number of stimulus profiles. The results indicated that conjoint analysis
is a fairly robust procedure for assessing an individual's preferences.
Reibstein et al. (1987) categorized data collection methods in the conjoint analysis
into three different data collection procedures; full profile, paired comparisons, and tradeoff matrices. They reported that all three data collection methods showed reliable and
stable results, although the degree of reliability differed depending upon the type of
reliability measured. Further Reibstein et al. (1987) classified the reliability of conjoint
analysis as the reliability over stimulus set, over time, over attribute set, and over data
collection procedure and reported them reliable. Jain, Malhotra, and Pinson (1980)
investigated the reliability over stimulus set between the trade-off versus the full profile
method by using a holdout sample procedure. The researchers concluded, in both cases,
that reliability was independent of the data collection method. Segal (1982) examined
the reliability over time between the trade-off versus the full profile method, computing
a correlation coefficient from the part worth across individuals within each attribute. He
concluded that there are significant differences between the methods, with the full profile
method performing better. A later study by Leigh, Mackey, and Summers (1984)
investigated the reliability over time between the paired comparisons versus the full
profile method. This study found no significant differences between methods.
According to Segal (1982), one can infer an overall preference for the multiple-factor
30
evaluation, or full profile method, among the data collection methods. Thus, conjoint
analysis is regarded as a reliable technique to investigate individual preference.
Although ACA is the most popular method for gathering data, there is
controversy among researchers regarding the validity of ACA . Green, Kriger, and
Agarwal (1991) reported several problems such as a difference in scaling between the
second and third steps of the ACA process. Further, Green et al. (1991) cited two papers
addressing the limitations of ACA methods; longer interview time and cross-validity of
ACA (Agarwal & Green, 1991; Johnson, 1991). Huber, Wittink, Fiedler, and Miller
(1993), however, demonstrated that ACA yields cross-validity greater than or equal to the
full profile conjoint analysis.
The full profile procedure utilizes stimuli described in terms of all critical
attributes, and the respondent is required to rank order all possible combinations of
attribute levels in the questionnaire. Kucher et al. (1993) strongly recommends selecting
the full profile method in commercial use cases only if one does not need more than nine
hypothetical products. Green et al. (1990) also points out that in the case of the full
profile method if one uses more than seven attributes, or more than 20 profiles,
respondents may have difficufty rating their preference. After comparing several data
gathering methods. Park (1994) suggested three guidelines for selecting the data
gathering method for conjoint analysis:
• Use the full profile method if one uses less than six attributes;
• Use the trade-off method if one uses seven or eight attributes;
• Use the ACA method if one uses more than nine attributes.
31
According to Park's (1994) recommendations, data for the current study was gathered
using the full profile method.
Multinomial Logit Model and Choice Set Formation
The multinomial logit model has been used primarily to examine customer choice
behavior, although it has also been used to examine various market phenomena such as
market share forecasting, store selection behavior, price cut effect on a market, market
segmentation, choice set change, and brand switching (An & Chai, 1993; Bucklin, Gupta,
& Han, 1995; Buckiln & Lattin, 1991; Gupta, 1988; Hardie, Johnson, & Fader, 1993).
The majority of these studies have utilized non-durables based upon scanner data. Table
2.8 provides an overview of the studies exploring choice behavior and competitive
market structure by the multinomial logit model.
Several additional studies regard the choice set issue as the most important
phenomena to be explored in consumer choice behavior (Engel, Blackwell, & Miniard,
1995; Han & Vanhonacher, 1995; Shocker, Ben-Akiva, Boccara, & Nedungadi, 1991;
Siddarth, Bucklin, & Morrison, 1995). Engel et al. (1995) suggest that the pre-purchase
altemative evaluation process is one necessary process in their consumer decisionmaking model. The pre-purchase alternative evaluation process consists of two basic
components; determining evaluative criteria and determining choice alternatives (Figure
2.3). Han and Vanhonacher (1995) and Siddarth et al. (1995) point out that individual
consumer choice decision-making has been followed by his/her own consideration set or
choice set formation for selecting a certain product category. Choice set is defined by
those brands/services seriously considered just prior to actual purchase (Desarbo &
32
Table 2.8. Examples of Segmentation and Market Stmcture Using Multinomial Logit
Model.
Researchers
Content
Products
Results
Grover & Srinivasan Identifying Market Stmcture
(^ ^^^)
and Market Segmentation
Based on Brand Choice
Probability with Promotion.
Coffee
Kamakura & Russell Segmentation Based on the
(1989)
Price Elasticity.
Anonymous Competitive
Food
Stmcture among
Segments,
Vulnerability and
Clout.
Bucklin & Gupta
(1992)
Brand Choice and Purchase
Incidence by Promotion.
Detergents
Brand Choice and
Purchase Incidence
by Promotion at
Segments Level, and
Identification of the
Profile Among
Segments.
Bucklin, Gupta, &
Han (1995)
Market Segmentation by
Market Response Regarding
Change of Marketing Mix.
Coffee
Brand-specific
Response at the
Segment Level.
33
Identification of the
Distribution of
Brands Among
Segments.
Determine
Determine
Evaluative
Choice
Criteria
Alternatives
Assess
Performance
of
Alternatives
Apply Decision Rule
Figure 2.3. Basic components of the pre-purchase altemative evaluation process.
Note. From Consumer Behavior (p.207). by J.F. Engel, R.D. Blackwell, and P.W.
Miniard, 1995, Fort Worth, TX: The Dryden Press. Adapted.
34
Jedidi, 1995). Therefore, the researcher should confirm whether the conjoint analysis can
overcome the choice set issue. Shocker et al. (1991) indicates that one of the drawbacks
of conjoint analysis is the lack of consideration for the choice set issue. Respondents are
required to express purchase intention among all prespecified combinations in the
conjoint questionnaire. However, occasionally one of the combinations is not within an
individual respondent's choice set. That is, the respondent does not consider a specific
combination as a product she/he wants to buy. Therefore, Shocker et al. (1991) points
out that conjoint analysis can not overcome the issue of choice set. Accordingly, the
researcher should develop an altemative; two-staged conjoint analysis. Table 2.9
presents examples where the multinomial logit model is used with the choice set.
Model
If individual / confronted with a choice from a set of alternatives, Ci, utility can
be expressed as follows, where altemative k is one of the alternatives Ci;
U.k = Vik + Eik,
(2.6)
Vik = Uik + pXik
(2.7)
where Vik = a deterministic component of i's utility,
8ik = a random component of i's utility,
Ujk = an intercept for brand k,
P = a vector of coefficients for variables X.
Both marketing variables and evaluative criteria are included in the Xik vector. The ek are
independently distributed random variables with a double exponential (Gumbel type II
extreme value) distribution.
35
Table 2.9. Examples of Choice Sets Formation Using Multinomial Logit Model.
Researchers
Content
Products
Results
Siddarth, Bucklin,
& Morrison
(1995)
Measurement of Changes
of Choice Sets by
Promotion.
Han &
Vanhonacker
(1995)
Using Price-Quality Tier, Detergents Identifying of Reference
Identifying the Changes of
Dependence and Loss
Choice Sets by Promotion.
Aversion in Choice Set
Formation and Brand
Selection Based on PriceQuality Tier.
36
Liquid
Promotion can Expand
Detergents Choice Sets, Providing
Excluded Brands a
Means to Gain Entry and
Long Term Sales
Benefits.
P(ek,< e) = exp[-exp(-e)]
- « < e <«,
(2.8)
Assume that individual / chooses the one with the highest utility among the alternatives.
Pik = P{Uik>Uij,JGCi}
(2.9)
Given assumptions (2.6)-(2.9), the conditional probability of choosing brand j can be
expressed by the multinomial logit model (k=l, 2,..., m) as follows;
m
pik = c\p{Vik) / ^ exp(V.m)
(2.10)
*=i
where Pjk = the probability of choosing brand k.
This expression is known as the multinomial logit (Ben-Akiva & Lerman, 1993;
Guadagni & Little, 1983).
Summarv of Literature Review
According to the review of literature, the following ideas were verified. First,
using the intrinsic versus extrinsic cues classification, numerous researchers have
investigated the importance of the impact of specific variables in evaluation of the
perception of quality in apparel. Significant intrinsic and extrinsic cues used in the
evaluation of apparel quality include style, price, brand name, fabric andfibercontent, fit
and color/design. Second, the price response function of a product or a brand is a tool to
understand the effect of price on sales for a product or a brand. The multinomial logit
model and conjoint analysis can be used to calibrate the price response function. Third,
choice set information is a necessary condition in the process of calibration of price
response function. Price response function with choice set information reflects consumer
37
purchasing behavior more faithfully than price response function without choice set
information.
38
CHAPTER m
METHOD
Two purposes were set forth to guide this study: one was to develop a new
methodology for calibration of the continuous price response function; the other was to
compare the differences in the price response functions with and without choice set
information. The first purpose was achieved by the new methodology of two-staged
conjoint analysis. Figure 3.1 illustrates the procedures to be followed for calibrating a
price response function and calculating the optimal price of a brand. The second purpose
of the study was achieved by applying the altemative path illustrated in Figure 3.1. The
same evaluative criteria and choice alternatives were applied to both price response
functions with and without choice set information. The methodology followed in the
study is discussed in the following sections: (a) selection of sample, (b) research
instrument, (c) collection of data, (d) calibration of price response function, and (e)
statistical analysis of the data.
Selection of Sample
The primary purposes of this study were to develop a new methodology for
calibration of the continuous price response function and to compare differences in the
price response functions with and without choice set information. Park (1994) and Urban
and Hauser (1993) recommend using at least 100 respondents for a relevant conjoint
analysis. Accordingly, in this study no criteria were employed to either admit or exclude
39
( start j
i
SPECIFYING CONJOINT MODEL
Determining evaluative chteha
PRFs WITH CHOICE SET INFO. PATH
CHOICE SET FORMATION
Detemiining purchasing intention
PRFs WITHOUT
CHOICE SET INFO. PATH
CONJOINT ANALYSIS
Numbering the combinations
Calculating coefficients for attnbutes levels
Calculating utility for each brand
1
MULTINOMIAL LOGIT FORMATION AND
CALIBRATING PRICE RESPONSE FUNCTION
T
COMPARISON BETWEEN
PRICE RESPONSE FUNCTION
Wrm AND WFTHOUT CHOICE SET INFO.
i
DISCUSSING
RESULTS AND IMPLICATIONS
1
Figure 3.1. Flow of calibration of price response function and optimal price.
40
subjects, and the convenience sample consisted of 103 voluntary respondents enrolled at
a major state supported southern university.
Research Instmment
Data were collected using a two part questionnaire developed by the researcher.
Section one was designed to collect preference data from each respondent using the full
profile method (Park, 1994). However, due to the large number of potential
combinations to be evaluated by respondents, a fractional factorial design was used
(Appendix).
Three steps were required to complete the first section. Step one provided
respondents with both a written and a visual description of two different randomly
selected styles and colors of denim jeans. In step two respondents were asked to choose
the combination of attributes they intended to purchase. Based upon the literature
review, these four attributes included: brand, style, price, and color. Respondents were
required to assess their purchase intentions for 32 combinations by marking "Yes" if
she/he would purchase a given combination and "No" if she/he would not purchase a
given combination (Appendix). This allowed for identification of each respondent's
choice set. Utilizing methodology recommended by Park (1994) and Urban and Hauser
(1993), instructions in step three required respondents to rate each combination marked
"Yes" on a scale of 1-100, with one as least likely to be purchased and 100 as most likely
to be purchased. This value served as the dependent variable for estimating the
parameters in the model. Green (1978) and Park (1994) recommend ordinal, interval, and
ratio scales to measure the preference among combinations.
41
Section two of the questionnaire was designed to elicit demographic information
from respondents. Age, gender, marital status, student classification, jean consumption,
jean ownership, and ethnic background served as demographic variables.
A pretest was conducted to test validity and interpretability of the instmment.
Participants consisted of four students at a major state supported southern university.
Students were randomly selected and represented both males and females of various ages.
Changes in section two of the questionnaire were made as necessitated by the
respondents including additional refinements in terms of rewording and reordering of
questions. No revisions were required in section one. The revised questionnaire used for
data collection appears in Appendix.
Attributes and Levels Investigated
The research process paralleled the typical procedures for developing a conjoint
task (Czepiel, 1993). Selection of product category served as the initial step in the
process. Because denim jeans are a frequently purchased product actively marketed by
manufacturers and retailers alike, this product category was selected for the current study.
After choosing the product category, the investigator selected the attributes for
use in the conjoint task. For the model's parsimony only four attributes were selected;
intrinsic cues included style and color, while extrinsic cues included price and brand
name. These four criteria have frequently been identified as significant cues for
evaluating apparel quality (Table 2.1). These four attributes served as independent
variables for the conjoint analysis model, while the dependent variable equaled each
respondent's purchase intention data numbered 1 to 100.
42
Brand and price levels for the study were determined based upon product
availability and brand characteristics at one shopping mall complex with several
department stores (Table 3.1). A total of 11 brands were available for consumer
purchase. From these 11 brands, eight national brands were selected for inclusion in the
study; Levi, Guess, Lee, Rocky Mountain, Pepe, Wrangler, Girbaud, and Dockers. Prices
ranged from $23 to $72. Based upon this information, price levels for the study were set
at $20, $40, $60, and $80.
Two basic styles were randomly selected by the researcher. The "Baggy Fit"
featured a straight leg and pleated front, while the "Easy Fit" was designed with a flat
front and narrow leg. Examples of both styles were presented to the respondents prior to
completion of the questionnaire.
Each manufacturer typically uses different terminology for various shades of blue
denim. Therefore, the researcher arbitrarily selected two distinctly different shades of
blue denim which were available to consumers simultaneously; a deep, unwashed denim
referred to as 'Dark Color' and a pre-washed denim referred to as 'Light Color'.
Accordingly, the conjoint analysis consisted of eight brands, four prices, two
designs, and two colors. Therefore, 128 combinations could be obtained from these
levels of attributes. By the fractional factorial design, 32 combinations were identified
(Appendix).
43
Table 3.1. Brands and Price Ranges Observed in One Shopping Mall July, 1996.
Price Ranges'
Maximum
Brands
Minimum
Levi's
$30
$42
Guess
$42
$68
Lee
$23
$30
Gap
$29
$58
Calvin Klein
$45
$48
Girbaud
$49
$72
Wrangler
$25
$33
Pepe
$49
$49
Arizona
$22
$30
Dockers
$32
$48
Rocky Mountain
$66
$68
* Unit: U.S. dollar
44
Collection of Data
Administration of the final questionnaire occurred in August, 1996. The
researcher randomly selected two undergraduate courses at a major state supported
southern university. Upon receiving instmctor permission students enrolled in two
business related courses. Restaurant, Hotel, & Instimtional Management in tiie College of
Human Sciences and Information Systems in the College of Business Administration,
were asked to voluntarily participate in the study. Of the 104 smdents initially contacted,
103 undergraduate smdents agreed to participate. Of the 103 completed questionnaires,
100% were usable resulting in a 100% response rate.
In order to ensure consistency and clarity an assistant was utilized to present
verbal instmctions for completion of the instmment. Physical examples of the denim
jeans styles and colors selected for the smdy were introduced prior to completion of the
questionnaire.
Calibration of Price Response Function
Model Specification With Choice Set Information
By the definition of conjoint analysis and the selected attributes, the conjoint
analysis model was specified. The Least Squares method was employed to estimate beta
coefficients of the model where the dependent variable equaled each respondent's
purchase intention data numbered 1 to 100, and the independent variables were brand
name, price, style, and color.
Vcik = Ucik + Pcpikpricck + pcsikstylck + pccikcolork
where Vck = utility assigned to brand k by consumer i with choice set
45
(3.1)
information,
Ucik = the intrinsic utility/value of brand k for consumer i with choice set
information (brand-specific intercept),
pricck = the net available price of brand k for consumer i,
stylck = the available style of brand k for consumer i, easy fit is 1;
otherwise 0,
colork = the available color of brand k for consumer i, dark color is 1;
otherwise 0,
Pcpik, Pccik, Pcsik = the parameters to be estimated for consumer i with
choice set information.
The current product profile of each brand investigated is shown in Table 3.2.
Using this profile the utility of each brand was calculated and probability was determined
based on the utility by the logit transformation. For example, based upon the model and
the current profile, Levi's utility function for consumer i was
VciLevis — UciLevis "*" Pcpiljevis4U + PcsiLevis I "^ PcciLevis
I.
The probability of consumer i choosing the Levi brand, according to formula 10, was
PiLevis = exp(UciLevis)/{eXp(UciLevis)+exp(UciGuess)+exp(UciLec) + exp(UciGap)+
eXp(UciGirt)aud) + exp(UciWrangler)+exp(UciCK)+eXp(UciArizona)}.
Price Response Function and Optimal Pricing
Because each brand had unique attributes on offering, it was expected that these
differences in attributes would be reflected as differences in the price response. In
determining the effect of price on utility, it is assumed that other variables were held
46
Table 3.2. Product Profile of Selected Brands.
Brand
Price
Fit*
Color*
Levi's
$40
Easy Fit
Light Color
Guess
$58
Easy Fit
Dark Color
Lee
$28
Easy Fit
Dark Color
Pepe
$49
Baggy Fit
Dark Color
Rocky Mountain
$68
Baggy Fit
Dark Color
Girbaud
$68
Easy Fit
Dark Color
Wrangler
$30
Easy Fit
Light Color
Dockers
$40
Baggy Fit
Light Color
Note. * Fit and color are assigned by the researcher arbitrarily.
47
constant. Further, various prices were applied to each brand's utility function thus
providing various choice probabilities for each brand corresponding to applied price
points. Based upon the continuous linkage among the various mean choice probabilities,
the continuous price response function for each brand was obtained.
Price response function served as a linkage among the mean choice probabilities
corresponding to applied price points. Because the sample size of this study was 103,
whenever applying a given price point on the utility function for each brand, the
researcher could obtain 103 choice probabilities. Therefore, to calibrate an aggregate
price response function, the mean choice probability for each price point was calculated
and linked with other mean choice probabilities corresponding to applied price points.
These procedures are shown in Figure 3.2.
After calibrating the price response function, an approximated function formation,
in this case the regression equation, was applied. With this equation and given the cost,
the optimal price for each brand was calculated. This study assumed that the cost of
each brand was fixed at 30% of the current profile of each brand. This fixed cost method
was originally employed in the process of calibration of price response function by Yoo
and Ohta (1995).
Revenue-Maximizing Price and Static Optimal Price
Once the price response function for each brand was determined, the revenuemaximizing price pr was derived. In addition to the revenue-maximizing price, given
that marginal cost remained constant at 30% of the current profile of each brand, the
optimal static price P* was calculated. Based upon the price response function data,
48
Quantity
•
Distribution of Choice Probability
at a Given Price Point
Price Response Function
Price
Figure 3.2. Linkage among the mean choice probability for price response function.
49
the mathematical price response function was derived by regression analysis. To find the
revenue-maximizing price pr for each brand, each mathematical price response function
was multiplied by price and derived with respect to price. For example, Levi's price
response function was
q = 37.91 - 0.2718;?
where unit of price is the U.S. dollar,
^p = 37.91 p-0.2718 p ^
(3.2)
Formula (3.2) was derived with respect top.
6qp/6p = Zl.91-0.5566 p=Q
=> pr = 68.159.
Optimal static price, according to formula 3,
p* = 1/2 (a/b + k)=/7r + k/2 = 68.159 + 6 = 74.159
These results implied that to maximize profit, according to the optimal pricing based on
the above equation, the price of Levi jeans should be increased from $40 to $74.
Model Specification Without Choice Set Information
The conjoint analysis model without choice set information was specified, which
was the same formation as the price response function with choice set information. The
difference in the conjoint analysis model with and without choice set information
stemmed from the use of a different dependent variable.
Respondents were required to assess their purchase intention for 32 combinations
by marking the box "No" if she/he would not purchase a given combination. This
allowed for identification of combinations which were not included in a respondent's
choice set. A score of zero was assigned to all purchase intention scores marked "No."
The dependent variable of the conjoint analysis model without choice set information
50
equaled each respondent's purchase intention data numbered 0 to 100, while the
dependent variable of the conjoint analysis model with choice set information equaled
each respondent's purchase intention data numbered 1 to 100. The Least Squares method
was employed to estimate beta coefficients for the model with dependent variables equal
to each respondent's purchase intention data numbered 0 to 100, and independent
variables of brand name, price, style, and color.
Vik = Uik + Ppikpricck + PsikStylCk + pcikcolork
(3.3)
where Vik = utility assigned to brand k by consumer i,
Uik = the intrinsic utility/value of brand k for consumer i
(brand-specific coefficient),
pricck = the net available price of brand k for consumer i,
stylck = the available style of brand k for consumer i, easy fit is 1;
otherwise 0,
colork = the available color of brand k for consumer i, dark color is I;
otherwise 0,
Ppik, Pcik, Psik = the parameters to be estimated for consumer i.
The calibration process from utility to probability for a price response function without
choice set information was the same as the calibration of the price response function with
choice set information. Using the profile shown in Table 3.2, the utility of each brand
was calculated. Probability were determined based on the utility by the logit
transformation. Various prices were then applied to each brand's utility function, which
allowed for determination of the choice probabilities for respondents corresponding to
applied price points. Based upon the continuous linkage among the various mean choice
51
probabilities, a price response function for each brand was obtained. Table 3.3
summarizes the dependent and independent variables used in the price response functions
with and without choice set information.
Statistical Analvsis of Data
Research Question One
Research question one sought to compare differences in the price response
functions among brands in the denim jeans market for college students enrolled at a state
supported southem university. Using ANOVA the differences in mean choice
probabilities at four price points for the price response functions among brands were
tested. Price levels selected for the study were $20, $40, $60, and $80.
Research Ouestion Two
Research question two sought to compare differences in the price response
functions with and without choice set information for each subject brand in the denim
jeans market for college students enrolled in a major state supported southem university.
Using the t-test, the differences in mean choice probabilities for the price response
functions with and without choice set information for each subject brand were tested.
52
Table 3.3. Dependent and Independent Variables in Price Response Function.
Cases
Independent Variables
Dependent Variable
Price Response Function
With
Choice Set Information
Brand, Price, Style, Color
Purchase hitention Data
(minimum 1, maximum 100)
Price Response Function
Without
Choice Set Information
Brand, Price, Style, Color
Purchase hitention Data
(minimum 0, maximum 100)
53
Research Ouestion Three
Research question three was designed to compare differences in optimal price
with and without choice set information for each subject brand in the denim jeans market
for college students enrolled in a major state supported southem university. After the
price response function was derived by regression analysis utilizing the mean choice
probability data, optimal price points were calculated for both price response functions
with and without choice set information.
54
CHAPTER IV
RESULTS OF DATA ANALYSIS
The purposes of this research were to develop a new methodology for calibration
of the continuous price response function and to compare differences in the price
response functions with and without choice set information. Three research objectives
were developed to achieve these purposes: (1) to empirically calibrate the price response
function of the denim jeans market utilizing a two-staged conjoint analysis with a logit
transformation; (2) to compare the differences in the price response functions with and
without choice set information; and (3) to compare the optimal price for each subject
brand with and without choice set information. The results of the study are reported in
the following sections: (a) questionnaire response rate and description of the sample, (b)
statistical treatment of the data, and (c) analysis of research questions.
Ouestionnaire Response Rate and Description of the Sample
Data were obtained by means of a hand distributed questionnaire. A total of 103
students enrolled at a major state supported southern university were asked to participate.
All completed questionnaires were usable yielding a total response rate of 100%.
Table 4.1 provides an overview of the demographic characteristics of the sample.
The sample consisted of both male (72.8%) and female (27.2%) participants, ranging in
age from 21 to 40 years with a mean age of 23.1 years (standard deviation (SD) of 3.83).
In addition the majority of the sample were single (82.5%). Over half of the respondents
were seniors (51.4%), 37.9% juniors and 10.7% sophomores. The majority of
55
Table 4.1. Demographic Characteristics of Sample.
Characteristics
Frequency
Percent
Gender
Male
Female
75
28
72.8
27.2
Age
21-25
26-30
31-35
36-40
81
17
4
1
78.7
16.5
3.9
0.9
Marital Status
Single
Married
85
18
82.5
17.5
Student Classification
Senior
Junior
Sophomore
53
39
11
51.4
37.9
10.7
Ethnic Group
White/Non-Hispanic
Hispanic
Asian/Pacific Islander
Black/Non-Hispanic
American Indian/Native American
81
11
5
4
2
78.6
10.7
4.9
3.9
1.9
Jean Consumption
7 times per week
6 times per week
5 times per week
4 times per week
3 times per week
2 times per week
1 times per week
22
13
25
20
11
7
5
21.4
12.6
24.3
19.4
10.7
6.8
4.9
56
Table 4.1. Continued.
Characteristics
Frequency
Percent
Denim Jean Ownership (pairs)
Jeans Pants
16 or above
11-15
6-10
Below 5
1
6
50
46
0.9
5.8
48.6
44.7
Denim Jean Ownership (pairs)
Jeans Shorts
11-15
6-10
Below 5
3
9
91
2.9
8.8
88.3
57
participants were White/Non-Hispanic (78.6%), 10.7% Hispanic, 4.9% Asian/Pacific
Islander, 3.9% Black/Non-Hispanic, and 1.9% American Indian/Native American.
Jean consumption per week ranged from 0 to 7 wearings, with a mean frequency
of 4.7 (SD = 1.74). Neariy a quarter of the sample (24.3%) wore denim jeans 5 times per
week, 21.4% 7 times per week, and 19.4% 4 times per week. The mean number of denim
jeans pants owned by respondents 6.5 (SD = 4.2), while the mean number of denim jeans
shorts was 3.1 (SD =2.7).
Statistical Treatment of the Data
A variety of statistical methods were used for the study, including ANOVA to test
Research Question One, t-test and price elasticity to examine Research Question Two and
comparison of optimal price to price response function to explore Research Question
Three. All data analysis was conducted using the Statistical Analysis System (SAS)
package. A probability level of 0.05 or less was considered statistically significant.
Research Ouestion One
Research Question One sought to compare differences in the price response
functions among brands in the denim jeans market for college students enrolled at a
major state supported southern university. Data addressing this question were analyzed
using a two step process: (1) calculation of respondent's choice probabilities of four price
points on the price response function for eight brands; and (2) application of ANOVA,
testing the null hypothesis (Ho: pi = p2 = p3 =p4 = p5 =p6 = p7 = p8) against the
altemative hypothesis (HI: pi ^A [i2 ^ [13 ^ [i4 :^ \i5 ^ [i6 ^ \il ^ \iS), io determine
58
whether differences existed in mean choice probabilities, at four points, for the price
response function among brands.
Results of the ANOVA for the four price points both with and without choice set
information are presented in summary tables 4.2, 4.3, 4.4, and 4.5. No significant
differences in mean choice probabilities were found at the 0.05 level of significance for
any of the four price points either with or without choice set information.
Research Ouestion Two
Research Question Two sought to compare differences in the price response
functions with and without choice set information for each subject brand in the denim
jeans market. Data addressing this question were analyzed using the following steps: (1)
calculation of the utility function of each brand through conjoint analysis: (2) application
of various prices to each brand's utility function to determine respondent's choice
probabilities, under the assumption that the prices of other brands be fixed; (3)
determination of a price response function for each brand from the mean choice
probabilities; and (4) application of the t-test to determine differences in mean choice
probabilities for the price response functions with and without choice set information for
each brand.
59
Table 4.2. Summary ANOVA for $20 Price Point.
Source
DF
Sum of Squares
Mean Square
F Value
Pr>F
0.04314365
0.00616338
0.03
0.9999
0.18237039
0.24
0.9755
With Choice Set InformationModel
Error
816
148.81423729
Total
823
148.85738094
Without Choice Set InformationModel
0.21230474
0.03032925
0.12677096
Error
816
103.44510050
Total
823
103.65740524
60
K
Table 4.3. Summary ANOVA for $40 Price Point.
Source
DF
Sum of Squares
Mean Square
F Value
Pr>F
0.30
0.9545
With Choice Set InformationModel
0.0096019
0.0013717
0.00459492
Error
816
3.74945085
Total
823
3.75905285
Without Choice Set InformationModel
0.01739671
0.00248524
0.00275666
Error
816
2.24943329
Total
823
2.26683000
61
1.90
0.5045
Table 4.4. Summary ANOVA for $60 Price Point.
Source
DF
Sum of Squares
Mean Square
F Value
Pr>F
0.00372790
0.00053256
0.12
0.9973
0.00461343
0.40
0.9014
With Choice Set InformationModel
Error
816
3.76455627
Total
823
3.76828417
Without Choice Set InformatipnModel
0.00714201
0.00102029
0.00253905
Error
816
2.07186491
Total
823
2.07900692
62
Table 4.5. Summary ANOVA for $80 Price Point.
Source
DF
Sum of Squares
Mean Square
F Value
Pr>F
0.00383624
0.00054803
0.01
0.9999
0.06452644
0.02
0.9999
With Choice Set Information-r
Model
Error
816
52.65357306
Total
823
52.65740930
Without Choice Set InformationModel
0.00728097
0.00104014
0.06805649
Error
816
55.53409708
Total
823
55.54137804
63
Figure 4.1 illustrates the price response functions with and without choice set
information for each of the eight brands. In each case as prices increased a decrease in
choice probability was revealed. For seven of the brands, this phenomenon was
evidenced between the price range of $20-$60. The range for the last brand was slightly
greater from $20 to $67.50. There after as price increased mean choice probability
increased slightly.
Utilizing the t-test, differences between the price response functions with and
without choice set information were examined for each brand (Table 4.6). Significant
differences in mean choice probabilities were found at the 0.05 level of significance at
several price points on the price response functions with and without choice set
information.
To further examine the differences in the price response functions with and
without choice set information, a comparison was made of price elasticities. Although
the results of the t-tests indicated differences in mean choice probability at a given price
point between the price response functions with and without choice set information, the ttests did not examine the differences between two price points on the price response
functions. Table 4.7 illustrates the differences in price elasticity on the price response
function. All brands appear to have lower price elasticities with choice set information
than without choice set information at the $20 price point, while higher price elasticity is
evidenced with choice set information from $25 to $45 than without choice set
information. Based upon the results and these of the t-tests, it can be considered that the
price response function
64
Levi's Prize Response Functbn
0.8
•H
0.7
XI 0.6
iH
•H
(0
XI 0.5
0
u 0.4
Ch
Q)
U
03
•H
0
0.2
CJ
0.1
20
25
30
35 40
45 50 55
prce ($)
-•—wih choi:e setiifc>inati)n
60
65 70
75
80
wihoutchoie setiifcmatbn
(a) Levi's Price Response Function
Guess" Proe Response Function
J
20
I
25
I
\
30
1
L
^Hi-4^-i-A-4-4-i^^^ $ I $ r r t
35 40
45 50 55
p i t e ($)
60
65 70
75
80
wih c h o h e setiifcimatin —•—wAoutchoie setiifcmatijn
(b) Guess' Price Response Function
Figure 4.1. Price response functions with and without choice set information.
65
Lee's P i t e Rei^x^nse Functbn
ttJAtiiiiiHI
0
20
25 30 35 40 45 50 55 60 65 70 75 80
prize ($)
wJSi choi:e s e t n f o m a t b n —•—wihoutchoire s e t n f o m a t b n
(c) Lee's Price Response Function
Pepe's Prize Response Function
A A m : t-M-U-*-M-M
20
25 30 35 40 45 50 55 60 65 70 75 80
prize ($)
wih choize sethfcinatiDn —•—wihoutchoize s e t n f o m a t b n
(d) Pepe's Price Response Function
Figure 4.1. Continued.
66
kA^^i«4»«
Gi±)aud's Prize Response Functbn
•H
n T
U ./
•H
0.6
«0
0.5
g|[
-
Xi
0 0.4
04
u
0)
03
V
-
u 02
xi 0.1
u
0
\
1
•H
O
N^
1
20
25
1
1
30
1
'
1 7 T ¥
35
40
W W •
45
50
•
•
P P T
55
60
p 1 1
65
70
1 1
75
1 1
80
prize ($)
with c h o i e s e t n f o m a t b n —»—wfiioutchoize s e t n f o m a t b n
(e) Girbaud's Price Response Function
W langifer's Prize Re^xDnse Functbn
0.7
0.6
-
XI
(0 0.5
XJ
-
iH
•H
o 0.4
u
-
03
-
u 02
-
x: 0.1
u
0
-
Ch
0)
•H
0
20
25
30
35 40
45 50 55
prize ($)
60
65 70
75
80
wlh choize setiifomatbn —•—wihoutchoize setiifomatbn
(f) Wrangler's Price Response Function
Figure 4.1. Continued.
67
R o c l ^ M oimtaii's Prize Response Functijn
iiiiiili^-l-4^-l-i
20
25 30 35 40 45 50 55 60 65 70 75
prize ($)
80
••—wih choize setiifomatbn —•—wiftioutchoize setiifomatbn
(g) Rocky Mountain's Price Response Function
Docker's Price Response Functbn
l^m i H I M M M t
20
25 30 35 40 45 50 55 60 65 70 75
prize ($)
-•—wih choize setiifomatiDn
80
wihoutchoize setiifomatbn
(h) Docker's Price Response Function
Figure 4.1. Continued.
68
Table 4.6. The Results of T-Test at Various Price Points.
Price
$20
$25
$30
Levi's
*
*
*
Guess
*
j_^g
*
Pepe
*
j
^
j
^
*
wv rjinp^lpr
^
Dockers
*
$40
*
*
^
$45
$50
$55
$60
^
^i
Hf
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Girbaud
$35
^
•
•
•
•
*
*
*
*
*
Note. * denotes that there is a significant difference in mean choice probability at a given
price with 95% confidence.
69
Table 4.7. The Differences in Price Elasticity on the Price Response Function.
Price
Brand
Choice Set
Information
$20
$25
$30
$35
$40
$45
Levi's
With
Without
-0.040
-0.053
-0.063
-0.055
0.000
0.000
-0.017
-0.015
-0.010
-0.010
-0.007
-0.007
Guess
With
Without
-0.040
-0.052
-0.064
-0.051
0.000
0.000
-0.016
-0.013
-0.009
-0.008
-0.006
-0.006
Lee
With
Without
-0.025
-0.049
-0.044
-0.066
0.000
0.000
-0.043
-0.027
-0.025
-0.017
-0.017
-0.012
Pepe
With
Without
-0.039
-0.051
-0.063
-0.048
0.000
0.000
-0.018
-0.012
-0.011
-0.008
-0.007
-0.006
Girbaud
With
Without
-0.039
-0.051
-0.062
-0.048
0.000
0.000
-0.018
-0.012
-0.010
-0.008
-0.007
-0.006
Wrangler
With
Without
-0.040
-0.052
-0.064
-0.050
0.000
0.000
-0.016
-0.012
-0.009
-0.008
-0.006
-0.006
Rocky
Mountain
With
Without
-0.039
-0.053
-0.063
-0.057
0.000
0.000
-0.019
-0.017
-0.011
-0.011
-0.008
-0.008
Dockers
With
Without
-0.040
-0.052
-0.063
-0.051
0.000
0.000
-0.017
-0.013
-0.010
-0.009
-0.007
-0.006
70
with choice set information has a different stmcture than the price response function
without choice set information.
Research Ouestion Three
Research Question Three was designed to compare differences in the optimal
price of the price response functions with and without choice set information for each
subject brand in the denim jeans market. The optimal price points were determined using
the following steps: (1) estimation of the price response function for each brand through
regression analysis; (2) determination of cost for each brand; and (3) calculation of the
profit-maximization price point within the levels in the price attribute.
Utilizing regression analysis parameter estimates for the price response functions
were attained. With an R~ spread from 0.922 to 0.967, all parameters were significant at
the 0.001 level. Under the assumption that the cost of each brand is fixed at 30% of its
current profile and based upon the estimated price response function, the optimal price
for each brand was attained (Table 4.8).
71
Table 4.8. Estimated Price Response Function and the Optimal Price.
Brands
Cases
Estimated Price Response Fimction
Costs
Optimal
Price
Levi's
WithCSI*
Without CSI
1.753-0.075p + 0.0O08p^ (R^ = 0.933)
2.259-0.095p + 0.001p^ pi^ = 0.966)
$12
$12
$25.0
$24.5
Guess
WithCSI
Without CSI
1.745-0.074p + 0.0007p^ (R^ = 0.931)
2.202-0.094p + 0.001p^ (R^ = 0.962)
$17.4
$17.4
$26.0
$25 5
Lee
WithCSI
Without CSI
1.819-0.074p + 0.0008p^ (R^ = 0.938)
2.315 - 0.094p + 0.0009p^ (R^ = 0.965)
$8 4
$8.4
$26.5
$25.5
Pepe
With CSI
1.773 - 0.075p + O.OOOSp^ (R^ = 0.937)
Without CSI 2.143 - 0.092p + 0.0009p^ (R^ = 0.961)
$14.7
$14.7
$25.0
$24.5
R.M.
WithCSI
1.764-0.074p + 0.0008p^ (R^ = 0.936)
Without CSI 2.157 - 0.093p + 0.0009p^ (R^ = 0.962)
$20.4
$20.4
$26.6
$26.5
Girbaud
With CSI
1.655 - 0.069p + 0.0007p^ (R^ = 0.922)
Without CSI 2.067 - 0.087p + 0.0009p^ (R^ = 0.952)
$20.4
$20.4
$26 5
$26.6
Wrangler With CSI
1.778 - 0.075p + O.OOOSp^ (R^ = 0.936)
Without CSI 2.275 - 0.096p + O.OOlOp^ (R^ = 0.967)
$9
$9
$25.3
$24.9
Dockers
$12
$12
$25.0
$22.5
WithCSI
1.756 - 0.075p+ 0.0008p^
Without CSI 2.188- 0.094p + 0.0009p^
(R^ = 0.934)
(R^ = 0.964)
Note. *CSI(Choice Set Information). All coefficients are significant at 99% level.
72
CHAPTER V
SUMMARY, CONCLUSIONS AND FUTURE RESEARCH
The price response function of a product or a brand is a tool to help understand
the effect of price on sales for that product or brand. Researchers have recommended
calibrating the price response function to find the optimal price for the product or brand
based on conjoint analysis (Dolan & Simon, 1996; Kucher et al., 1993; Simon, 1989,
1992; Yoo, 1991). However, utilizing the traditional conjoint analysis limits the results
due to the problem of small data points in the price response function and therefore must
be regarded as discrete rather than continuous.
Several studies investigating individual consumer choice decision-making have
concluded that individuals form their own choice set just prior to actual purchase (Han et
al., 1995; Shocker, Ben-Akiva, Boccara, & Nedungadi, 1991; Siddarth, Bucklin, &
Morrison, 1995). However, the traditional conjoint analysis can not consider a choice set
(Shocker, Ben-Akiva, Boccara, & Nedungadi, 1991). In this regard, the price response
function with choice set information reflects consumer purchasing behavior more
faithfully than does the price response function without choice set information.
However, virtually no research has been reported in the pricing field which utilizes the
calibration of the continuous price response function with choice set information by
conjoint analysis.
The primary purposes of this study were to develop a new methodology for the
calibration of a continuous price response function and to compare differences in the
price response functions with and without choice set information. This chapter contains
73
the summary of the study, interpretation of results, conclusions, implications, and
recommendations for future research.
Summarv of the Studv
A new methodology for calibration of a continuous price response function
including choice set information was developed to meet the purposes of the study. The
new methodology, identified as the two-staged conjoint analysis, incorporated the logit
transformation into the conjoint analysis and added the choice set formation step in the
conjoint questionnaire.
Two conjoint models were specified to compare the difference in the price
response functions with and without choice set information. These two models were
tested in a study of the denim jeans market. Participants included 103 students at a major
state supported southern university. In a conjoint model, the independent variables
consisted of brand, price, color, and style, while the dependent variables were equal to
each respondent's purchase intention data. The two-staged conjoint questionnaire
consisted of three steps. Step one provided respondents with both a written and a visual
description of two randomly selected styles and colors of denim jeans. In step two,
respondents were asked to choose the combination of attributes they intended to
purchase. In step three respondents were asked to rate each combination chosen at step
two on a scale of 1-100, with one as least likely to be purchased and 100 as most likely to
be purchased.
Three research questions were developed to accomplish the purposes of this
study. These questions were intended to; (1) examine the differences in the price
74
response function among brands; (2) compare the differences in the price response
functions with and without choice set information; and (3) compare the optimal price of
the price response functions with and without choice set information for each brand. A
variety of statistical methods, including ANOVA, t-test, and regression analysis, were
employed to analyze the research questions.
Interpretation of Results
Research Ouestion One
The first research question investigated whether differences existed in the price
response functions among brands in the denim jeans market for college students enrolled
at a major state supported southem university. The results of ANOVA revealed no
significant differences in the purchase probabilities among the eight brands at $20, $40,
$60, and $80.
The results can be explained by one of characteristics of model specification; a
strong effect on utility by the shared attributes among brands. The model used in this
study was based on the model specification of Guadagni and Little (1983), Han (1993),
and Bucklin, Gupta and Han (1995). According to their models, the deterministic
component of a respondent's utility for brandy is expressed as a linear function of the
attributes of brand/ These attributes can then be divided into two categories; attributes
unique to brandy and attributes common in all brands (Guadagni & Little, 1983).
In this study, the brand specific constant was used as the attribute unique to brand
j , while price, style, and color were used as the attributes common in brand;.
Accordingly, each respondent shared the same price, color and style coefficient for all
75
subject brands. Table 5.2 summarizes the difference in the relative importance among
the attributes used in conjoint analysis. With choice set information the relative
importance of price, style, and color is 74.8%, while brand is only 25.2%. Without
choice set information, the relative importance of attributes common to the brandy drops
slightly (67.1%), while the relative importance of the brand remains fairly low at 32.9%.
In conclusion, in the model used, the relative importance of attributes common to the
brands had a strong effects on utility, resulting in no differences in the choice
probabilities among brands.
Research Ouestion Two
Research Question Two investigated whether there were differences in the price
response functions with and without choice set information in the denim jeans market for
college students enrolled at a major state supported southem university. Results of the ttest and price elasticity showed that the price response functions with and without choice
set information had different price response stmctures (Tables 4.6 & 4.7).
The different shapes of the price response functions with and without choice set
information resulted from the different coefficients of the attributes between the two
conjoint models (Table 5.1). The different coefficients of the attributes resulted from the
difference in the variance of the levels in the attributes. Each respondent's choice set
information affected the variance of the levels in the attributes of the conjoint models.
The price response function for each brand included a price range where mean
choice probability increased with each incremental increase in price. This phenomena
76
Table 5.1. The Coefficient Mean of the Conjoint Model.
Attributes
Mean Beta Coefficient
With Choice Set
Information
Mean Beta Coefficient
Without Choice Set
Information
Attributes common to all brands
Color
-0.00489
Style
-0.00697
0.07880
Price
-0.30509
-0.27871
Wrangler
-0.02760
-0.13417
Rocky Mountain
-0.04440
-0.08024
Girbaud
0.05814
0.01741
0.06231
-0.0353
0.01008
-0.08602
-0.03111
0.03169
0.04972
0.08229
0*
0*
1.08358
Attribute unique to brand
Pepe
Lee
Guess
Levi's
Dockers
Note. * Dockers is a reference brand to estimate parameters for brand.
77
Table 5.2. The Relative Importance Among the Attributes.
Attributes
Relative Importance
With Choice Set
Information
Relative Importance
Without Choice Set
Information
Price
72.4%
41.9%
Style
1.5%
12.3%
Color
0.9%
12.8%
Brand
25.2%
32.9%
Total
100%
100%
Attributes common to all brands
Attribute unique to brand
78
can be explained by the signal theory of price, which suggests that if price is a reliable
signal for quality of apparel products in the U.S. market, the increase of the choice
probability can be interpreted in terms of the price-quality relationships (Gardner, 1971;
Gerstner, 1985; Lee, 1996). That is, if consumers regard price as the indicator of quality,
when price increases, the choice probability of a product can increase for a productspecific price range.
Research Question Three
Research Question Three sought to compare differences in the optimal price of
the price response functions with and without choice set information. In this study, the
optimal price with choice set information was different from the optimal price without
choice set information (Table 4.8). The different shapes of the price response functions
with and without choice set information resulted in the differences in optimal price.
In this study, the optimal price was sought within the levels in the price attribute
of the conjoint model. The profit-maximization price points as the mathematical solution
of the profit function could be found outside of the levels in price attribute. However, the
work conducted by Yoo and Park (1996) concluded that "the Heuristic Optimal Price
(HOP)" within the levels in the price attribute is more valid for determination of the
optimal price, because the price coefficient of the conjoint model is estimated by the
variance of levels in the price attribute. Therefore, the optimal price reported in this study
is the Heuristic Optimal Price within the levels of the price attribute, not the
mathematical optimal price for subject brand (Yoo & Park, 1996).
79
Conclusions and Implications
The purposes of the study were to suggest a new methodology for calibration of
the continuous price response function and compare differences in the price response
functions with and without choice set information. The research objectives were
intended to: (1) examine the differences in the price response function among brands; (2)
compare the differences in the price response functions with and without choice set
information; and (3) compare the optimal price of the price response functions with and
without choice set information for each brand. The results of ANOVA revealed no
significant differences in the purchase probabilities among the eight identified brands at
$20, $40, $60, and $80. However, results of the t-test and price elasticity showed that the
price response functions with and without choice set information had different price
response stmctures (Tables 4.6 & 4.7). The different shapes of the price response
functions with and without choice set information resulted in the differences in optimal
price (Table 4.8).
The results of this study have implications for market researchers, brand managers
and retailers as they seek to improve consumer satisfaction and increase practitioner
profits. Retailers of the 1990's are continually challenged to differentiate product
offerings in an intensely competitive market place and understanding consumer choice
decision making is fundamental to successful product positioning. The results of this
research provide a more effective tool for evaluating consumer purchase practices
thereby providing the basis for sound marketing mix decisions. Tested on one apparel
product category, calibration of the product response function with choice set information
clearly reflected consumer purchase behavior more faithfully than did price response
80
function without choice set information. Further, results suggested that optimal price
varies among consumer groups. This would suggest that retailers should consider
tailoring price to specific target markets through application of optimal price calculations.
Ultimately this would allow retailers greater opportunity to maximize profits and to more
effectively price goods to meet consumer expectations.
Suggestions for Future Studv
The purposes of the study were to develop a new methodology for calibration of
the continuous price response function and compare differences in the price response
functions with and without choice set information. Although the primary purposes were
successfully achieved, the following are suggestions for further research related to the
results:
1.
Develop a new model for calibration of the price response function to include
only the deterministic component with attributes unique to subject brand in the
utility function.
2.
Explore the price-quality relationship to interpret thefluctuationof choice
probability. Future research should explore the price response function,
explaining the price-quality relationship.
3.
Replicate the study using evaluative cues other than color and style. For
parsimony this study used only four attributes in the utility function. However, in
the case of the price response function with choice set information, color and style
did not play a major role in affecting the utility for each brand. Therefore, other
evaluative cues may elicit significantly different results.
81
4.
Conduct future research which consider competitive situations. This study
assumed that when the price of a brand changes in utility function, other brands
do not take any action in price and other marketing variables. Game theory can
provide a framework for analyzing the competitive market situation.
5.
Replicate the current study utilizing product categories where validity tests are
available. This study did not include a procedure to test the validity of the new
methodology with market share data. However, the real market data available in
the U.S. apparel market was based on the company level rather than brand level.
82
REFERENCES
Agarwal, M.K. & Green, P.E. (1991). Adaptive conjoint analysis versus self-explicated
models: Some empirical results. International Journal of Research in Marketing, 8
(June), 141-146.
An, K.H. & Chai, S.I. (1993), An Empirical study on store selection behavior exploration
by multinomial logit model. Korean Management Review, 22(2). 101-120.
Bearden, W. O., Netemeyer, R. G., & Mobley, M. F. (1993). Handbook of Marketing
Scales: Multi-Item Measures for Marketing and Consumer Behavior Research.
London: Sage.
Ben-Akiva, M. & Lerman, S. R. (1993). Discrete Choice Analvsis: Theory and
Application to Travel Demand. London: The MIT Press.
Blattberg, R., Buessing, T., Peacock, P., & Sen, S. (1978). Identifying the deal prone
segment. Journal of Marketing Research, 15(Aug.), 369-377.
Boulinger, M. S. (1993). Merchandise Buving (4th ed.), Needham, MA: Allyn and
Bacon.
Bucklin, R.E. & Gupta, S. (1992), Brand Choice, Purchase Incidence, and Segmentation:
An integrated Modeling Approach, Journal of Marketing Research, 29 (May),
210-215.
Bucklin, R. E., Gupta, S., & Han, S. (1995) A brand's eye view of response segmentation
in consumer choice behavior. Journal of Marketing Research, 32(Feb.), 66-74.
Bucklin, R. E. & Lattin, J.M. (1991). A two-state model of purchase incidence and brand
choice. Marketing Science, IQ(Winter), 24-40.
Chowdhary, U. (1989). Fashion information seeking by younger and older consumers.
Clothing and Textile and Research Journal, 8( 1), 49-55.
Clarke, K., & Belk, R. (1979). The effects of product involvement and task definition on
anticipated consumer effort. In W. L. Wilkie (Ed.), Advances in Consumer
Research, 6. 313-318, Ann Arbor: Association for Consumer Research.
Cohen, J. B., & Goldberg, M. E. (1970). The dissonance model in post-decision product
evaluation. Journal of Marketing Research, 7, 315-321.
Czepiel, J.A. (1992). Competitive Marketing Strategy. Englewood Cliffs, NJ: PrenticeHall.
83
Davis, L. L. (1987). Consumer use of label information in ratings of clothing quality and
clothing fashionability. Clothing and Textile Research Journal. 6( 1), 8-14.
Dardis, B., Spivak, S., & Shih, C. M. (1985). Price and quality differences for imported
and domestic men's dress shirts. Home Economics Research Journal. 13. 391399.
Desarbo, W. S., & Jedidi, K. (1995). The spatial representation of heterogeneous
consideration sets. Marketing Science. 14(3), 326-342.
Dobson, G., & Kalish, S. (1993). Heuristics for pricing and positioning a product-line
using conjoint and cost data. Management Science. 39(2). 160-175.
Dolan, R.J., & Simon, H. (1996), Power Pricing. New York, NY: Free Press.
Eckman, M., Damhorst, M.L., & Kapolph, S. J. (1990). Toward a model of the in-sore
purchase decision process: Consumer use of criteria for evaluating women's
apparel. Clothing and Textile Research Journal. 8(2). 13-22.
Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1995). Consumer Behavior (8th ed.).
New York, NY: Dryden Press.
Gardner, M. D. (1971). Is there a generalized price-quality relationship? Journal of
Marketing Research, 8, 241-243.
Gay, L. R. (1987). Educational Research: Competencies for Analvsis and Application (4'^
ed.). New York, NY: MacMillian Publishing Company.
Gerstner, E (1985). Do higher prices signal higher quality? Journal of Marketing
Research, 22, 209-215.
Geurts, M. D., & Whitiark, D. (1993). Forecasting market share. Journal of Business
Forecasting, 11(4). 17-22.
Green, P. E., & Krieger, A. M. (1991). Segmenting markets with conjoint analysis.
Journal of Marketing, 55(Oct.). 20-31.
Green, P. E., & Krieger, A. M. (1992). Modeling competitive pricing and market share:
Anatomy of a decision support system. European Journal of Operational
Research. 60(1). 31-44.
Green, P. E., & Krieger, A. M, (1997). Using conjoint analysis to view competitive
interaction through the customer's eyes. In G.S. Day and D.J. Reibstein (Eds.),
Wharton on Dynamic Competitive Strategy. New York, NY: John Wiley & Sons.
84
Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive Conjoint Analysis:
Some caveats and suggestions. Journal of Marketing Research. 28(Mav). 215222.
Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New development
with implications for research and practice. Journal of Marketing. 54. 3-19.
Grover, R. & Srinivasan, V. (1987), A Simultaneous Approach to Market Segmentation
and Market Stmcturing, Journal of Marketing Research. 24(Mav). 139-153.
Guadagni, P. M., & Littie, J.D.C. (1983), A logit model of brand choice calibrated on
scanner data. Marketing Science. 17(August). 319-350.
Gupta, S. (1988). Impact of sales promotions on when, what, and how much to buy,
Journal of Marketing Research. 25(Nov.). 342-355.
Han, S. & Vanhonacker, W.R. (1995). Reference dependence and loss aversion in
choice set formation and brand selection. Working paper. Hong Kong University
of Science and Technology.
Hatch, K., & Roberts, J. A. (1985). Use of intrinsic and extrinsic cues to assess textile
product quality. Journal of Consumer Studies and Home Economics, 9, 341-357.
Hardie, B.G., Johnson, E.J., & Fader, P.S. (1993). Modeling loss aversion and reference
dependence effects on brand choice. Marketing Science, 12(Fall), 378-394.
Heisey, L. F. (1990). Perceived quality and predicted price: Use of the minimum
information environment in evaluating apparel. Clothing and Textiles Research
Journal, 8, 22-28.
Howard, J. A., & Sheth, J. N. (1969). The Theory of Buyer Behavior. New York, NY:
Radius Press.
Huber, J., Winntik, D.R., Fiedler, J.A., & Miler, R. (1993). The effectiveness of
altemative preference elicitation procedures in predicting choice. Journal of
Marketing Research, 30 (Febmary), 105-114.
Hwang, S., & Jung, C. (1993). An exploratory study on apparel distribution system and
its countermeasure in the view of market liberalization. Journal of the Korean
Society of Clothing and Textile, 17(2). 265-279.
Jacoby, J., Olson, C. J., & Haddock, A. R. (1971). Price, brand name, and product
composition characteristics as determinants of perceived quality. Journal of
Applied Psychology. 55. 570-579.
85
Jain, A. K., Malhotra, N. K., & Pinson, C. (1980). Stability and reliability of part-worth
utility in conjoint analysis: A longitudinal investigation. European Institute of
Business Administration. Working Paper 80/05.
Johnson, R.M. (1991), Comment on Adaptive Conjoint Analysis: Some caveats and
suggestions. Journal of Marketing Research, 28(Mav). 223-225.
Kamakura, W. A. & Russell, G.J. (1989), A probabilistic choice model for market
segmentation and elasticity stmcture. Journal of Marketing Research, 26
(November), 379-390.
Kim, M.Y. (1988), An study on clothing evaluation criteria with apparel product
category. Journal of the Korean Consumer Economic. 26(3). 1-12.
Kucher, E., & Hilleke, K. (1993). Value pricing through conjoint analysis: A practical
approach. European Management Journal. 11(3). 283-290.
Kwon, E. Y. (1993). The study on brand image in jeans casual wear. Unpublished
master's thesis. Sung Kyun Kwan University, Seoul, Korea
Kwon, H. (1995). The study on the employee segmentation and the product policy for the
internal customers: Focused on the compensation package for the advertising
agency employees. Unpublished doctoral dissertation. Sung Kyun Kwan
University, Seoul, Korea
Lee, D.H. (1996). The relationship between price and objective apparel quality: A
comparison between the United States market and Korean market. Unpublished
master's thesis. Texas Tech University, Lubbock, TX.
Lee, H. J. (1987). An study on the market segmentation by the life-style of consumers:
With women in labor market in Seoul. Unpublished master's thesis, Seoul
National University, Seoul, Korea
Lee, H. J. (1993). An experimental study on the fashion merchandising system: With
special reference to the life-style of consumers and the marketing strategy for the
fashion industry. Unpublished doctoral dissertation, Sook Myung Women's
University, Seoul, Korea.
Leigh, T. W., Mackey, D. B., & Summers, J. O. (1984). Reliability and validity of
conjoint analysis and self explicated weights: A comparison. Journal of Marketing
Research, 21. 456-462.
Lennon, J. S., & Fairhurst, E. A. (1994). Categorization of the quality concept. Home
Economics Research Journal. 22. 267-285.
86
Lilien, G. L., Kotler, P., & Moothy, K. S. (1992), Marketing Models. Englewood Cliffs,
NJ: Prentice-Hall.
Ludbner-Pupert, J., & Winakor, G. (1985). Male and female style preference and
perceived fashion risk. Home Economics Research Journal. 13(3). 256-266.
Malhotra, N. K. (1982). Stmctural reliability and stability of nonmetric conjoint analysis.
Journal of Marketing Research. 19. 199-207.
Malhotra, N. K. (1993). Marketing Research: An Applied Orientation. Englewood Cliffs,
NJ: Prentice-Hall.
Mansfield, E. (1988). Microeconomics: Theory and Applications (6th ed.). New York:
W.W. NORTON.
Morganosky, M. A. (1990). Store and brand type influences on the perception of apparel
quality: A congmity theory approach. Clothing and Textiles Research Journal. 9
(1), 45-49.
Nagle, T. (1984). Economic foundations for pricing. Journal of Business. 57(1). 2-25.
Nomm, S. P., & Clark, A. L. (1989). A comparison of quality and retail price of
domestically produced and imported blazers. Clothing and Textile Research
Journal, 7, 1-9.
Park, C. S. (1994). Conjoint analysis. In Yoo, P. H. (Ed.). Modern Marketing Science,
(pp. 121-185). Seoul: Bummunsa.
Park, Y.S. (1996). The study on the service pricing: Focused on the non-linear pricing for
the shipping industry. Unpublished doctoral dissertation. Sung Kyun Kwan
University, Seoul, Korea
Reibstein, D., Bateson, J. E. G., & Boulding, W. (1987). Conjoint analysis reliability:
Empirical findings (Report No. 87-101). Cambridge, MA: Marketing Science
Institute.
Samuelson, P. A., & Nordhaus, W. D. (1992). Economics (14th ed.). New York, NY:
McGraw-Hill.
Segal, M. N. (1982). Reliability of conjoint analysis: Contrasting data collection
procedure. Journal of Marketing Research, 19. 139-143.
Shocker, A.D., Ben-Akiva, M., Boccara, B., & Nedungadi, P. (1991). Consideration set
influences on consumer decision-making and choice issues, models, suggestions.
Marketing Letters. 2(3). 181-197.
87
Siddarth, S., Bucklin, R.E., & Morrison, D.G. (1995). Making the cut; Modeling and
analyzing choice set restriction in scanner data. Journal of Marketing Research,
32(Aug.), 225-266.
Simon, H. (1989). Price Management. New York, NY: North-Holland.
Stemquist, B., & Davis, B. (1985). Store status and country of origin as information
cues: consumer's perception of sweater price and quality. Home Economics
Research Journal, 15. 124-131.
Stone, E. & Samples, J.A. (1990). Fashion merchandising: An introduction (5*'' ed.). New
York, NY: McGraw-Hill.
Szybillo, J. G,. & Jacoby, J. (1974). Intrinsic versus extrinsic cues as determinants of
perceived product quality. Journal of Applied Psychology, 59. 74-78.
Tigert, D. J., Ring, L. R., & King, C.W. (1976). Fashion involvement and buying
behavior: A methodological study. In B. B. Anderson (Ed), Advances in
Consumer Research Vol. 3 , (pp. 46-52), Provo, UT: The Association for
Consumer Research.
Traylor, M. B., & Joseph, B. W. (1984). Measuring consumer involvement in
products. Psychology & Marketing, 1(2), 65-76.
Urban, G. L. & Hauser, J. R. (1993). Design and Marketing of New Product (2nd ed.).
Englewood Cliffs, NJ: Prentice-Hall.
Wall, M., & Heslop, A. L. (1989). Consumer attitudes towards the quality of domestic
and imported apparel and footwear. Journal of Consumer Studies and Home
Economics, 13, 337-358.
Weiner, J. (1994). Forecasting demand: Consumer electronics marketer uses a conjoint
approach to configure its new product and set the right price. Marketing
Research: A Magazine of Management & Applications, 6(3), 6-11.
Wheatley, J. J., Chiu, S.Y. J., & Goldman, A. (1981). Physical quality, price, and
perceptions of product quality: Implications for retailers. Journal of Retailing.
5L 100-115.
Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update.
Journal of Marketing. 53. 91 -96.
Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis
in Europe: Results and critical reflections. International Journal of Research in
Marketing. 11.41-52.
88
Workman, J. E. (1990). Effects of fiber content labeling on perception of apparel
characteristics. Clothing and Textiles Research Journal, 8 (3), 19-24.
Yoo, D. & Ohta, H.(1995), Optimal pricing and product-planning for new multiattribute
products based on conjoint analysis. International Journal of Production
Economics. 38, 245-253.
Yoo, P. H. (1989). Pricing research in marketing: The integration of European and
American literature. Korean Marketing Review, 4(1). 168-200.
Yoo, P. H. (1991). Pricing Policy. Seoul: Pakyoungsa.
Yoo, P. H., Dolan, R. J. & Rangan, V. K. (1987), Dynamic pricing strategy for new
consumer durables, Zeitschrift fur Betriebswirtschaft, 57(Oktober), 1024-1043.
Yoo, P.H. & Park, Y.S. (1996, November). The studv on the service pricing: Focused on
the non-linear pricing for the shipping industry. Paper presented at the Fall
Conference of Korean Marketing Society, Chunju, Korea.
89
APPENDDC
QUESTIONNAIRE
90
Questionnaire
This questionnaire is designed to examine your purchase intention of blue jeans.
Please answer each items as accurately as possible. Your identity will be kept
confidential. The questionnaire and the demographic information sheet will be coded and
kept in a secure place. All responses on the questionnaires will be reported as group data.
Thank you for your assistance in the study.
91
Section 1.
Step 1: You have been shown two pair of blue jeans which are further described below.
Use these descriptions to complete the following questionnaire.
Style
Baggy Fit
Easy Fit
features
straight leg and pleated front
narrow leg and flat front
color
Dark Color
Light Color
features
' Unwashed
Pre-washed
Easy Fit + Light Color
Baggy Fit + Dark Color
92
Step 2: Based upon the combination of attributes identified in each row, please indicate
your purchase intentions by marking the box Yes or No.
Step 3: For each of the items marked yes, please assign the number (1-100) which most
accurately reflects your purchase intention.
1 =least acceptable
No.
Brand
100=most acceptable
Price
Style
Color
Yes or No
Number
1 - 100
1
Levi's
$20
Easy Fit
light blue
2
Levi's
$40
Baggy Fit
dark blue
3
Levi's
$60
Easy Fit
light blue
4
Levi's
$80
Baggy Fit
dark blue
5
Guess
$20
Baggy Fit
dark blue
6
Guess
$40
Easy Fit
light blue
7
Guess
$60
Baggy Fit
dark blue
8
Guess
$80
Easy Fit
light blue
9
Lee
$20
Easy Fit
dark blue
10
Lee
$40
Baggy Fit
light blue
11
Lee
$60
Easy Fit
dark blue
12
Lee
$80
Baggy Fit
light blue
13
Pepe
$20
Baggy Fit
light blue
14
Pepe
$40
Easy Fit
dark blue
15
Pepe
$60
Baggy Fit
light blue
16
Pepe
$80
Easy Fit
dark blue
17
Girbaud
$20
Easy Fit
light blue
18
Girbaud
$40
Baggy Fit
dark blue
19
Girbaud
$60
Easy Fit
light blue
20
Girbaud
$80
Baggy Fit
dark blue
93
No.
Brand
Price
Style
Color
Yes or No
Number
1-100
21
Wrangler
$20
Baggy Fit
dark blue
22
Wrangler
$40
Easy Fit
light blue
23
Wrangler
$60
Baggy Fit
dark blue
24
Wrangler
$80
Easy Fit
light blue
25
Rocky Mountain
$20
Easy Fit
dark blue
26
Rocky Mountain
$40
Baggy Fit
light blue
27
Rocky Mountain
$60
Easy Fit
dark blue
28
Rocky Mountain
$80
Baggy Fit
light blue
29
Dockers
.$20
Baggy Fit
light blue
30
Dockers
$40
Easy Fit
dark blue
31
Dockers
$60
Baggy Fit
light blue
32
Dockers
$80
Easy Fit
dark blue
94
Section 2. Please give us some information about yourself.
1. Age:
2. Gender:
1. Female
2. Male
3. Marital status:
1. Married
2. Single
4. Classification:
1. Freshmen
2. Sophomore
3. Junior
4. Senior
5. How often do you wear jeans pants/jeans shorts each week?
(
days/week)
6. How many of following do you own?
Jeans pants (
pairs)
Jeans shorts (
pairs)
7. Race
1. White/Non Hispanic
2. Black/ Non Hispanic
3. Hispanic
4. Asian/Pacific Islander
5. American Indian/Native American
6. Other
Thank you.
95
PERMISSION TO COPY
In presenting this thesis in partial fulfillment of the requirements for a
master's degree at Texas Tech University or Texas Tech University Health Sciences
Center, I agree that the Library and my major department shall make it freely
available for research purposes. Permission to copy this thesis for scholarly
purposes may be granted by the Director of the Library or my major professor.
It is understood that any copying or publication of this thesis for financial gain
shall not be allowed without my further written permission and that any user
may be liable for copyright infringement.
Agree (Permission is granted.)
Student's Signature
Xy
/
Dafe
Disagree (Permission is not granted.)
Student's Signature
Date