INVESTIGATING OF PRODUCT ATTRIBUTES AND THEIR AFFECT
ON OVERALL SATISFACTION
Prof. Dr. SUBHASH C. LONIAL
Fatih University İ.İ.B.F. Management Department
Tel.: 9 0212 889 0810/2026
Fax: 9 0212 889 1142
e-mail: slonial@fatih.edu.tr
Yrd. Doç. Dr. SELIM ZAIM
Fatih University İ.İ.B.F. Management Department
Tel.: 9 0212 889 0810/2019
Fax: 9 0212 889 1142
e-mail: szaim@fatih.edu.tr
Key Words:
Customer satisfaction, product performance, attribute, factor analysis, symmetric, asymmetric
Abstract:
The purpose of this study is to determine which attributes are important for choosing
household personal shampoo. Using our results, we develop two impact measures of customer
satisfaction.
In the first approach, the relationship between positive- or negative-attribute product
performance and overall satisfaction is assumed to be linear and symmetric. In this case,
positive or negative product attributes should impact overall satisfaction in a direct manner. In
the second approach, the relationship between positive- or negative-attribute product
performance and overall satisfaction are considered as nonlinear and asymmetric. This
implies negative product attributes should have a relatively greater impact on overall
satisfaction than positive performance.
Our survey then summarizes the important attributes for consumers when purchasing
shampoo.
Introduction:
Product quality is an essential component to consider when attempting to enhance
customer satisfaction. Increasing customer satisfaction, by increasing customer value, is a key
issue for every company (1). Customer value is the customer’s perception of the attributes
they want in the product or service. Products or services create value for customers not by
delivering the products or services themselves, but by delivering consequences in their use
situations. Automobiles are a good example. Suppose a manufacturer advertises antilock
brakes on its cars. Antilock brakes as a product attribute are not inherently good or bad, but
they allow the customer to stop safely, which is a good consequence (2). Customer
satisfaction is the customer’s positive or negative feeling about the value that was received.
Thus if using a product or service yields the desired consequences, then the consumer
perceives high customer value. High customer value leads to customer satisfaction (3).
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Customer satisfaction is an inevitable outcome of the product purchase and
consumption experience resulting from a comparison of what was expected and what is
received. Expectations are a very complex concept and have been the subject of considerable
theoretical discussion as well as empirical verification. Such work has focused on the
following areas:
!
Conceptual definition of expectations;
!
Predictive versus normative expectations;
!
Expectations as the norm for comparison;
!
Expectations hierarchy;
!
Aspects that indirectly have an influence on expectations;
!
Absolute contra versus level of expectations;
!
Time for measuring expectations;
Several empirical studies have highlighted the effect of expectations on customer
satisfaction. Not surprisingly, expectations are an important determinant customer
satisfaction.
Our study focuses on product attributes to evaluate to overall satisfaction. In an
attribute-level approach, overall satisfaction is a function of attribute-level evaluations. These
evaluations are based on product performance and consumer experience. Relative to a global
evaluation approach, the multi-attribute model has two key advantages. First, it is consonant
with consumers’ memory. For example, Gardial found when making post-purchase
evaluations and describing consumption outcomes, consumers are almost twice as likely to
use specific attributes than the overall product. Second, an attribute-level analysis provides
specificity and diagnostic usefulness by enabling us to ask specific questions about the
determinants of satisfaction. For example, is non-confirmation for certain attributes more
critical in determining overall satisfaction than other attributes are? Previous models of the
determinants of customer satisfaction can be extended to the attribute-level to increase their
specificity and usefulness (4).
The purpose of this study is to determine which product attributes are important for
customer preferences and expectations. We use multi-attribute methodology to study
consumer satisfaction levels. We suggest two approaches.
In the first, the relationship between positive- or negative-attribute-level performance
and overall satisfaction is assumed to be linear and symmetric. Here any positive or negative
attribute-level product performance may impact overall satisfaction equally.
The second approach assumes the relationship between positive- or negative-level
product performance and overall satisfaction are nonlinear and asymmetric. Thus negative
attribute-level performance has more effect than positive level performance on overall
satisfaction (5).
Survey Instrument:
For this investigation, a survey questionnaire was designed. The survey questionnaire
was divided into two sections, entitled “Importance of Attribute” and “Performance of
Attribute”. In the first section, respondents were given a list of attributes which they were
asked to score which are important for choosing a shampoo on a scale of 1-5 where
1=unimportant and 5=very important. Respondents were also given a second five-point scale
to score shampoo performance level.
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We use a total of 17 variables in the survey. Survey results identify which attributes
are important. In addition, attribute importance represents a possible avenue of improvement
factors. The list of performance factors measures the actual performance of shampoo.
Gap Method (The First Approach)
One method for determining the relative importance of service or product attributes is
to measure customer expectations or ideals and calculate the gap between the expected and
actual performance. Gaps can be calculated for each attribute separately and the attribute with
the largest gap can be studied. This approach has limitations. One is that attribute interaction
is ignored. In addition, some product attributes with large gaps may be unimportant to the
customer (6).
According to first hypothesis, either negative- or positive-attribute-level performance
has equal effect on overall consumer satisfaction. Thus each individual attribute is considered
to have the same as other attributes. For example, suppose that shampoo price is perceived as
not being important by customer. However suppose actual performance level of price is
reported as being very expensive. According to first hypotheses, this attribute’s negative
performance does not affect overall satisfaction any more than if a positive attribute-level
performance had been recorded (7).
As explained earlier, respondent were given two lists of attributes (importance of
attributes and performance of attributes) and were asked to rate the importance of each
attribute as well as the actual performance level for that attribute.
Our study focuses on the following:
!
Identify shampoo attributes perceived as important or unimportant by the consumer.
!
Employ data-reduction techniques to improve interpretation.
!
Measure customer satisfaction via rating scales.
!
Base new product design on filling gaps in perceptual space.
Identify the important attributes:
There are several reasons to use multi-attribute models to analyze customer
satisfaction. First, consumers are more likely to render evaluations of their post-purchase
satisfaction at the attribute rather than at the product level. Second, an attribute-based
approach enables researchers to study commonly observed events, such as consumers
experiencing mixed feelings toward a product or service. A consumer may be satisfied with
one attribute but dissatisfied with another for the same product. Although such events are not
easy to model in an overall-satisfaction approach, the attribute-level approach provides simple
and useful approach. For example in a restaurant, a customer may be highly satisfied with the
food but highly dissatisfied with the service. The multi-attribute model provides a procedure
to study such situations.
The initial and most critical step of this study is the identification of what customers
want and expect from a shampoo. In this step, customer demands, expectations and
complaints are determined. Important data includes current customer expectations that are
important as well as potential expectations that would interest customers. Several methods can
be used to establish customers’ requirements, including customer panels, focus group
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discussions, structured or unstructured customer interviews, self-completion questionnaires,
in-depth customer observation, customers’ complaint and compliment databases, customers’
service inquiries database, and front-line staff feedback.
At any one time it is unlikely that an organization can satisfy all of its customers’
requirements. Therefore it is necessary to prioritize the needs that are to be met within a
planning cycle. Using a structured questionnaire, 240 customers were asked to rate the
importance of shampoo characteristics and to compare the performance of other shampoos
with their “ideal shampoo”. In this way it is possible to see which quality characteristics are
more important for meeting or exceeding customers’ expectations.
The Rate of Importance is a rating of customer demands on a scale of 1 to 5. On this
scale 5 denotes most important and 1 denotes relatively low importance. The customers
assigned these ratings. Mean, and standard deviation of the attributes is depicted in Table 1.
TABLE 1. Rate of Importance
Variables
Price of shampoo
Brand of shampoo
Fragrance of shampoo
Vitamins
To be natural
Prevents eye burn
Prevents dandruff
Softens hair
Provides brightness
Avoids hair lose
Easy to foam
Easy to rinse
Packaging
Ergonomics
Provides volume
Avoids stickiness
Appropriate for hair
Mean
3,05
3,70
3,90
4,25
4,05
3,09
4,25
4,33
4,35
4,63
3,85
3,96
2,71
3,09
4,25
4,55
4,50
Standard Deviations
1,23
1,20
1,06
0,97
1,02
1,36
1,05
0,92
0,91
0,81
0,99
1,02
1,25
1,33
0,99
0,78
0,64
In this case considering mean value “Avoids stickiness” and “Appropriate for hair”
attributes should be given the highest priority. “Avoids hair lose” the next priority. According
to the list, “Packaging” had the lowest importance. However to test if all attributes are
important, we use factor analysis.
Exploratory Factor Analysis:
These items were factor analyzed to see if they were structurally related. Factor
analysis is a multivariate technique which links the six attributes in the factor1 and 4
attributes in the factor2 and two attributes in the factor3 in such a way that only the unique
contribution each of the twelve attributes is considered for each factor. Thus factor analysis
avoids potential problems of multicollinearity.
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Exploratory factor analysis with varimax rotation was performed on the importance of
attribute in order to extract the dimensions underlying the construct. The factor analysis of the
17 attributes yielded three factors explaining 62.8% of total variance. Only twelve of the
seventeen items loaded on these three factors and, based on the items loading on each factor,
the factors were labeled “Manageability factor” (Factor 1), “Maintenance factor” (Factor 2),
“Cleanliness factor” (Factor 3). These twelve items are shown as items in the Table 2.
Therefore rest of the attributes were not considered (8).
TABLE 2. Factor Analysis
Attributes
Provides brightness
Provides volume
Softens hair
Fragrance of shampoo
Avoids stickiness
Prevents dandruff
Naturalness
Vitamins
Appropriate for hair
Avoids hair lose
Easy to foam
Easy to rinse
1
0.800
0.758
0.657
0.572
0.567
0.548
Factor
2
3
0.851
0.747
0.703
0.632
0.817
0.803
The Cronbach’s alpha measure of reliability for the three factors were 0.80 for Factor
1, 0.79 for Factor 2, and 0.74 for Factor 3. All three values are above of the traditionally
acceptable value of 0.70 in research (9).
Measure Customer Perception Via Rating Scale:
The dominant conceptual model in the customer satisfaction area is the
disconfirmation of expectations model. Here customer satisfaction is evaluated response of
product purchase and consumption experience resulting from a comparison of what was
expected and what is received. This framework assumes that customers have specific product
expectations and, by meeting those expectations, the product can satisfy the customer. In this
part of the study, customer satisfaction level was measured without brand discrimination.
The performance level also used a rating of customer perceptions on a scale of 1 to 5.
On this scale, 5 denotes most important and 1 denotes relatively low importance. In the
Table 3, mean, and standard deviation is depicted which were considered important in using
exploratory factor analysis. Therefore only twelve attributes were investigated.
After determining importance attributes and their actual performance scores, next step
is to calculate the gap between importance and performance.
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TABLE 3. Shampoo Performance Level
Attributes
Fragrance of shampoo
Vitamins
Naturalness
Prevents dandruff
Softens hair
Provides brightness
Avoids hair lose
Easy to foam
Easy to rinse
Provides volume
Avoids stickiness
Appropriate for hair
Mean
3,90
4,25
4,05
4,25
4,33
4,35
4,63
3,85
3,96
4,25
4.55
4.50
Standard deviation
1,06
0,97
0,97
1,05
0,92
0,91
0,81
0,99
1,02
0,99
0.78
0.64
Gaps between performance and importance:
As stated earlier, this calculation measures the gap between perceived importance of
an attribute and its actual emphasis. Table 4 shows the differences between importance and
performance of attributes of the shampoo.
In this table, if the differences are positive, it means that customer satisfaction is less
than their expectation. In this situation, this variable may be under emphasized therefore they
should be improved. It can be seen from Table 4 that one attribute is over the upper limit. This
attribute is “Avoids hair loss”. Its gap was calculated as 0.77.
Upper limit is calculated as 0.50. Next attribute is “Provides volume”. Its gap was
calculated as 0.49. It is very close to the upper limit. Following improvement attributes are
“Prevents dandruff”, and “Vitamins”. Avoids hair loss, “Provides volume” and “Prevents
dandruff” belong to Manageability factor (Factor 1) and “Vitamins” is in the Maintenance
factor (Factor 2).
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If the difference is negative then it implies that the attribute is developed more than
customer’s requirement and need. Therefore there is no need for more improvement in that
area. These variables may currently be significantly over-emphasized in performance
measurement systems. These resources could be employed on the other variable that needs to
be improved. Table 4 shows that two attributes were improved more than customer’s
requirements. These attributes are “Fragrance of the shampoo”, and “Easy to foam”.
Fragrance is in the Manageability factor (Factor 1) and Easy to foam is in the Cleanliness
factor (Factor3). In Table 5, the rank of all variables is given in descending order.
TABLE 5. Importance – Performance Gaps for Demand Factors
Variables
Provides volume
Avoids stickiness
Appropriate for hair
Avoid hair lose
Vitamins
Prevents dandruff
Provides brightness
Easy to rinse
Naturalness
Softens hair
Easy to foam
Fragrance of shampoo
Importance –
Performance
0.77
0.49
0.43
0.43
0.41
0.4
0.4
0.4
0.28
0.04
-0.04
-0.12
Mean
(Importance)
4.63
4.25
4.25
4.26
4.5
4.54
4.05
4.35
4.33
3.95
3.91
3.85
Mean
(Performance)
3.86
3.76
3.82
3.83
4.09
4.14
3.65
3.95
4.05
3.91
3.95
3.97
Linear Regression Model (The Second Approach)
In the second approach, the relationship between positive- or negative-attribute-level
performance and overall satisfaction is assumed to be nonlinear and asymmetric. This
approach suggests that negative performance on an attribute have a greater impact on overall
satisfaction than positive performance has on that same attribute, and overall satisfaction
displays diminishing sensitivity to attribute level performance. Similarly, in a given set of
attributes, negative performance on a single attribute could outweigh positive performance on
many other attributes combined. Oliver finds that attribute-level satisfaction and
dissatisfaction significantly affect overall satisfaction with a product and a service and
attribute dissatisfaction has a larger weight than attribute satisfaction for the product and
service. However, no theoretical motivation for the observed disparity between the impact of
attribute satisfaction and dissatisfaction is provided. The theoretical logic is developed along
two lines of reasoning. One is based on prospect theory and the other is rooted in cognitive
research that examines the memorability of positive versus negative event (10).
Prospect theory assumes people’s judgments display reference dependence (carriers of
value are gains and losses from a reference point) and loss aversion (the function is steeper in
the negative than in the positive domain). In addition, evaluation display diminishing
sensitivity (marginal values of both gains and losses decrease with their size). The two key
properties of the value function are loss aversion and diminishing sensitivity (11).
The loss aversion built into prospect theory suggests that losses loom larger than
gains. Psychologically, one unit loss is weighted more than an equal amount of gain. In a
satisfaction context, negative outcomes on attribute performance should carry more weight in
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the overall satisfaction judgment than equal amounts of positive outcomes on attribute
performance. For example, if the car’s mileage were to decrease by 10 miles per gallon, it
would have a greater impact on the overall satisfaction judgment than if the car’s mileage
were to increase by 10 miles per gallon. Thus, negative performance on an attribute will loom
larger than positive performance on the same attribute. In addition, with prospect theory,
overall satisfaction also should display diminishing sensitivity toward attribute performance.
That is high (low) levels of performance, positive (negative) performance on an attribute
should not affect satisfaction as dramatically as it does at lower levels of performance.
Overall satisfaction is rooted in cognitive research that examines the consumer
memory of positive versus negative events. Memory accessibility is a function of stimulus
prominence, among other things. Evidence shows that negative information is more
perceptually salient than positively valence information, is given more weight than positive
information, and elicits a stronger physiological response than positive information.
Similar physiological operations should occur for customer satisfaction because
satisfaction is linked to memory-based processing. To the extent that attributes with negative
performance will be more perceptually salient than attributes with positive performance,
attributes with negative performance should have a greater impact on the cumulative
satisfaction judgment. Thus, within a given set of attributes, the relative impact of each
attribute will be asymmetric. Consequently, when combined, attributes with negative
performance should have a greater impact on overall satisfaction than their corresponding
attributes with positive performance combined.
In a given set of attributes, each additional instance of positive performance on an
attribute will have a smaller impact than the other attributes. Conversely, each additional
instance of negative performance should have a correspondingly smaller negative impact on
overall satisfaction. Therefore overall satisfaction will display diminishing sensitivity to
additional instance of negative or positive performance. In other words, each additional
instance of positive or negative performance should have a smaller impact on overall
satisfaction.
An impact analysis was conducted to understand how different attributes of the
shampoo affect the overall position of the product in term of overall satisfaction. Impact
analysis combines the importance of an attribute with the product’s performance on that
attribute.
The impact of each attribute is defined as follows:
Impact = [importance] x [performance] "
Here importance reflects the degree to which an attribute is related to overall
satisfaction; The stronger the relationship that the more important an attribute is deemed to be.
The performance component measures the number of positive and negative occurrences for
each attribute.
The impact, by utilizing the importance and performance for each attribute shows how
each attribute effects overall satisfaction. Those attributes which have high importance and a
high number of negative occurrences (performance) may affect overall satisfaction adversely.
Thus, the impact index is a summary measure to capture the importance and performance of
each attribute. Hence this concept can be represented as follows:
Impact = [(factor loading) x (regression coefficient)] x [% in satisfaction category]
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The impact is calculated by multiplying the proportion of positive or negative occurrences by
the weighted regression coefficient for each attribute. The proportion of respondents who fall
into satisfied, or dissatisfied categories are showed in Table 6. Factor loading was already
given in Table 2.
TABLE 6. Consumer Satisfaction Measures
Attributes
Provides brightness
Provides volume
Softens hair
Fragrance of shampoo
Avoids stickiness
Prevents dandruff
Naturalness
Vitamins
Appropriate for hair
Avoids hair lose
Easy to foam
Easy to rinse
Dissatisfied %
5.9
9.9
5.4
4.5
3.9
8.9
14.5
6.4
2.5
9.9
5.9
6.0
Satisfied %
72.4
67
77.7
76.1
82.3
68
61
69.8
78.6
68.8
76.7
72.4
After measuring the overall satisfaction, regression coefficients were determined using
a dummy variable regression.
In this study, the overall satisfaction was considered as a dependent variable. The
dependent variable is defined using a 7-point satisfaction scale where 1=completely
dissatisfied, 7=completely satisfied. The independent variables were based on 12 attributes
were evaluated by consumers.
Overall satisfaction = intercept + regression coefficient x (satisfaction level when
expectations are met) + regression coefficient x (satisfaction level when expectations are
exceeded) + regression coefficient x (satisfaction level when expectations are not met).
Using this equation, all regression coefficients for the binary variables were
calculated. This analysis is similar to a conventional regression analysis. However, it
incorporates the added dimension of the asymmetry discussed earlier. More specifically, in
this analysis separate estimates of the relationship of an attribute with overall satisfaction can
be computed for those respondents satisfied by the product which they bought (those whose
expectations are exceeded) versus those dissatisfied (those whose expectations are not met).
In this model, the impact of each attribute on overall satisfaction is calculated individually
which is depicted in Table 7. For example the impact of “Provides volume” can be calculated
as follows:
Dissatisfaction impact = [(0.758)(0.29)] x [9.9%] = 0.021
Satisfaction impact = [(0.758)(0.604)] x [67%] = 0.306
According to the impact analysis, attributes with a relatively large dissatisfaction
impact should be eliminated. In this case, this occurs if the dissatisfaction value impact is
larger than the value of satisfaction impact for the same attribute. Therefore to determine
which attribute needs to be improved, one needs to rank them according to their
dissatisfaction impact value as a descending order. As it is depicted in the Figure 2,
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“Naturalness” has the highest value of the dissatisfaction impact. It implies this attribute
should be considered first for improvement. For that reason, the dissatisfaction impact of this
attribute must be eliminated. After that “Brightness”, “To give volume”, and “Fragrance of
the shampoo” occur respectively. Although all of the negative impact value is less than the
positive impact value of the same attribute, to improve the customer dissatisfaction impact of
these four attributes must be eliminated or decreased.
TABLE 7. Attribute Impact Results
Attributes
Provides brightness
Provides volume
Softens hair
Fragrance of shampoo
Avoids stickiness
Prevents dandruff
Naturalness
Vitamins
Appropriate for hair
Avoids hair lose
Easy to foam
Easy to rinse
Dissatisfaction impact Satisfaction impact
0.024
0.36
0.021
0.306
0.008
0.308
0.019
0.220
0.003
0.354
0.006
0.129
0.044
0.214
0.007
0.327
0.001
0.413
0.010
0.263
0.003
0.425
0.010
0.344
TABLE 8. Coefficient Table
Attributes
Provides brightness
Provides volume
Softens hair
Fragrance of shampoo
Avoids stickiness
Prevents dandruff
Naturalness
Vitamins
Appropriate for hair
Avoids hair lose
Easy to foam
Easy to rinse
Negative Coefficient
-0,509
-0,229
-0,226
-0,748
-0,146
-0,125
-0,358
-0,158
-0,08
-0,162
-0,06
-0,214
Positive Coefficient
0,618
0,604
0,605
0,507
0,760
0,348
0,413
0,629
0,749
0,607
0,679
0,592
CONCLUSION:
Customer satisfaction is a necessary step in loyalty formation and business success.
The most common method for measuring customer satisfaction is to assess the attribute-level
performance.
Attribute-level performance has been employed to measure customer satisfaction
rather than overall satisfaction. In an attribute-level approach, overall satisfaction is a function
of attribute level evaluations. Relative to the global evaluation in overall satisfaction, the
multiattribute model has important advantages.
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Figure 2.
In this study, 12 attributes of the shampoo were investigated under three factors. The
factors were named “Manageability factor” (factor 1), “Maintenance factor” (factor 2), and
“Cleanliness factor” (factor 3). Considering these three factors to measure the performance of
the shampoo two assumptions have been taken. The first one is the traditional method that the
relationship between attribute-level performance and overall satisfaction has been
conceptualized as linear and symmetric. In this approach, the following attributes which are
“Avoids hair loss”, “Provides volume”, “Avoids stickiness”, and “Appropriate for hair”
were determined to need improvement.
In the second approach, the relationship between attribute-level performance and
overall satisfaction is assumed to be nonlinear and asymmetric. According to the second
hypotheses, dissatisfaction impact makes more influence on the overall satisfaction than
satisfaction impact does. Therefore which attribute has large dissatisfaction impact then it
should be eliminated or decreased. In this case “Naturalness” has the largest dissatisfaction
impact on overall satisfaction. If only symmetric effects were considered, naturalness would
not be seem important. But asymmetric impact shows that it is the most important attribute for
improvement. After that “Provides brightness”, “Provides volume”, and “Fragrance of
shampoo” have the high dissatisfaction impact value. Other dissatisfaction impacts of
attributes might be ignored because of the low impact value.
If the symmetric and asymmetric analyses are compared with each other then
“Provides volume” seems the only common attribute among them.
As a result, searching the attribute-level performance of fast moving consumer goods
such as shampoo, dissatisfaction impact may not be as useful as in the service sector. In spite
of low dissatisfaction values, asymmetric impact analysis of attribute-level performance can
be investigated to catch customers’ voices.
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