Proceedings of Applied International Business Conference 2008
PERCEPTION ON MARKETING MIX: A STUDY OF 4Ps
Goi Chai Lee ψ
Curtin University of Technology, Malaysia
Abstract
The main objective of the study are to analyse whether Product, Place, Price and Promotion have
impact on marketing mix activities, as well as to identify which ‘P’ can be considered as the most
important among 4Ps. The study also tries to study the good fitness of marketing mix model, based on
suggestion by Kotler, Ang, Leong and Tan (1999). Based on analysis of 120 samples received, this
study has agreed with Kellerman, Gordon and Hekmat (1995). All parts of the marketing mix are
equally important. The result shows that mean for Product, Place, Price and Promotion are between
3.8333 and 3.9567. The study found that correlation between Product, Place, Price and Promotion are
positive, specifically between Weak Positive Correlation and Moderate Positive Correlation (0.25 –
0.50), as well as Moderate Positive Correlation and Strong Positive Correlation (0.50 – 0.75). Most of
variables under each P also show that the correlation is positive. Based on path coefficients, Product,
Place, Price and Promotion are significant at the 0.05 significance level. This study has proved that all
4Ps have statistically impact on marketing mix activities. Finally, the result also shows that GFI
(0.745), AGFI (0.697), NFI (0.594), RFI (0.552), IFI (0.772), TLI (0.741) and CFI (0.765) were less
than 0.90, which can be considered as not a good-fit model.
Keywords: Marketing Mix; 4Ps.
JEL Classification Codes: C32; F31; G10.
1. Introduction
Businesses need to make sure they are marketing the right product to the right person at the right price
in the right place and at the right time (The Chartered Institute of Marketing, 2004). The marketing mix
management paradigm has dominated marketing thought, research and practice (Grönroos, 1994), and
“as a creator of differentiation” (van Waterschoot, 2000) since it was introduced in 1940s. Borden
(1965) claims to be the first to have used the term “marketing mix” and that it was suggested to him by
Culliton’s (1948) description of a business executive as “mixer of ingredients”. An executive is “a
mixer of ingredients, who sometimes follows a recipe as he goes along, sometimes adapts a recipe to
the ingredients immediately available, and sometimes experiments with or invents ingredients no one
else has tried” (Culliton, 1948).
Borden’s original marketing mix had a set of 12 elements namely: product planning; pricing; branding;
channels of distribution; personal selling; advertising; promotions; packaging; display; servicing;
physical handling; and fact finding and analysis. Frey (1961) suggests that marketing variables should
be divided into two parts: the offering (product, packaging, brand, price and service) and the methods
and tools (distribution channels, personal selling, advertising, sales promotion and publicity). On the
other hand, Lazer and Kelly (1962) and Lazer, Culley and Staudt (1973) suggested three elements of
marketing mix: the goods and services mix, the distribution mix and the communication mix. However,
McCarthy (1964) regrouped Borden’s 12 elements to four elements or 4Ps, namely product, price,
promotion and place at a marketing manger’s command to satisfy the target market. Kent (1986) refers
to the 4Ps of the marketing mix as “the holy quadruple…of the marketing faith…written in tablets of
stone”. Marketing mix has been extremely influential in informing the development of both marketing
theory and practise (Möller, 2006).
The marketing mix concept has two important benefits. First, it is an important tool used to enable one
to see that the marketing manager’s job is, in a large part, a matter of trading off the benefits of one’s
competitive strengths in the marketing mix against the benefits of others. The second benefit of the
marketing mix is that it helps to reveal another dimension of the marketing manager’s job. All
ψ
Correspondence author. Goi Chai Lee. Department of Marketing & Management, School of
Business, Curtin University of Technology, CDT 250, 98009 Miri, Sarawak, Malaysia. Email:
[email protected]
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Proceedings of Applied International Business Conference 2008
managers have to allocate available resources among various demands, and the marketing manager will
in turn allocate these available resources among the various competitive devices of the marketing mix.
In doing so, this will help to instil the marketing philosophy in the organisation (Low and Tan, 1995).
2. Objectives
The main objective of the study are to analyse whether Product, Place, Price and Promotion have
impact on marketing mix activities, as well as to identify which ‘P’ can be considered as the most
important among 4Ps. The study also tries to study the good fitness of model.
3. Literature Review
Product
Kotler, Adam, Brown and Armstrong (2003, p. 253) defines product as “anything that can be offered to
a market for attention, acquisition, use or consumption that might satisfy a want or need. It includes
physical objects, services, persons, places, organisations or ideas”. Kotler, Adam, Brown and
Armstrong (2003, pp. 252 – 254) also suggested that a product should be viewed in three levels.
• Level 1: Core Product. Core product addresses the question ‘What is the buyer really buying?’
It consists of the problem-solving services or core benefits that consumers obtain when they
buy a product.
• Level 2 Actual Product. Marketers involved in product planning must next build an actual
product around the core product. It may have five characteristics: a quality level, features,
styling, a brand name and packaging.
• Level 3: Augmented Product. The product planner must build an augmented product around
the core and actual products by offering additional consumers and benefits. This may includes
installation, after sales service warranty, and delivery and credit.
Well acceptances of product by customer are based on product quality and design. Product quality
determines export levels (Piercy, 1981; Schneeweis, 1985; Szymanski et al., 1992, 1993). Pre- and
post-sale services are an important part of the product package and can contribute to enhance
performance (Czinkota and Johnston, 1981; Lalonde and Czinkota, 1981; Marr, 1987; Piercy, 1981).
Wide product lines also provide an opportunity for increased export sales (Campbell and Rao, 1988).
Broad product lines enhance profitability (Morrison and Travel, 1982; Robinson and Fornell, 1986) and
market share positions (Szymanski et al., 1992, 1993) in domestic and export markets (Shoham and
Kropp, 1998).
Place
Place strategy refers to how an organisation will distribute the product or service they are offering to
the end user. “A distribution system is a key external resource. Normally it takes years to build, and it
is not easily changed. It ranks it importance with key internal resources such as manufacturing,
research, engineering, and field sales personnel and facilities. It represents a significant corporate
commitment to large numbers of independent companies whose business is distribution and to the
particular markets they serve. It represents, as well, a commitment to a set of policies and practices that
constitute the basic fabric on which is woven an extensive set of long-term relationships” (Corey, 1991;
Kotler, Ang, Leong and Tan, 1999).
Placing products with end users involves marketing channels comprised of intermediaries such as
retailer (Wilkinson, 1996). At the outset it is clear that the distribution channel is of fundamental
importance to a treatment of physical distribution, because the channel is the arena within which
marketing and logistics culminate into consumer transactions (Bowersox, Smykay and La Londe, 1968;
Wilkinson, 1996)
Promotion
Promotion is a vital part of business and is an integral ingredient of the total marketing process. It helps
to make potential customers aware of the many choices available regarding products and services (Fam
and Merrilees, 1998). A successful product or service means nothing unless the benefit of such a
service can be communicated clearly to the target market (Learnmarketing.net, n.da). Perceptions on
promotion have provided a number of explanations as to why certain promotion tools are preferred
over others. The factors include media attributes, target audience reach capability, cost-effectiveness,
nature of business, demographic and nationality of the retailers (Fam and Merrilees, 1998). An
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Proceedings of Applied International Business Conference 2008
organisation’s promotional strategy can consist of sales promotion, advertising, sales force, public
relations and direct marketing (Kotler, Adam, Brown and Armstrong, 2003).
Price
Price is the only variable in the marketing mix that must be set in relation to the other three Ps (Low
and Tan, 1995). Pricing is one of the most important elements of the marketing mix, as it is the only
mix, which generates a turnover for the organisation. The remaining 3P’s are the variable cost for the
organisation. It costs to produce and design a product; it costs to distribute a product and costs to
promote it. Price must support these elements of the mix. Pricing is difficult and must reflect supply
and demand relationship. Pricing a product too high or too low could mean a loss of sales for the
organisation. Pricing should take into account on fixed and variable costs, competition, company
objectives, proposed positioning strategies, and target group and willingness to pay
(Learnmarketing.net, n.db).
Bilkey (1982, 1985) finds that higher prices lead to higher profitability. However, another study by
Cavusgil and Zou (1994) report that the relationship between price competitiveness and performance is
not significant. It may be that time horizons differed across studies. Higher prices may increase shortterm profitability, but, in the long-term, it may lead to lower sales and profits thus explaining the
conflicting findings (Shoham and Kropp, 1998).
According to Zikmund and D’Amico (1993), price may serve as a substitute for selling effort,
advertising, and product quality. Alternatively, price may be used to reinforce other activities in the
marketing mix programme, e.g. the usually inflated price charged for Kellogg’s and the resulting
association with product quality. In many cases price can provide an incentive to intermediaries and
company salespeople, the focus of promotional strategy, and a sign of value.
Previous Studies of Marketing Mix
The introductory marketing texts suggest that all parts of the marketing mix (4Ps) are equally
important, since a deficiency in any one can mean failure (Kellerman, Gordon and Hekmat, 1995).
Number of studies of industrial marketers and purchasers indicated that the marketing mix components
differ significantly in importance. Two surveys focused on determination of key marketing policies and
procedures common to successful manufacturing firms (Jackson, Burdick and Keith, 1985). Udell
(1964) determined that these key policies and procedures included those related to product efforts and
sales efforts. This followed in order by promotion, price, and place. In a replication of this survey,
Robicheaux (1976) found that key marketing policies had changed significantly. Pricing was
considered the most important marketing activity in Robicheaux’s (1976) survey, although it ranked
only sixth in Udell’s (1964) survey. Udell (1968) found that sales efforts were rated as most important,
followed by product efforts, pricing, and distribution. LaLonde (1977) found product related criteria to
be most important, followed by distribution, price, and promotion. Perreault and Russ (1976) found
that product quality was considered most important, followed by distribution service and price.
McDaniel and Hise (1984) found that chief executive officers judge two of the 4 Ps, pricing and
product to be somewhat more important than the other two – place (physical distribution) and
promotion. Kurtz and Boone (1987) found that on the average, business persons ranked the 4 Ps to be
of most importance in the following order: price, product, distribution, and promotion. Thus, it appears
from these studies that business executives do not really view the 4 Ps as being equally important, but
consider the price and product components to be the most important (Kellerman, Gordon and Hekmat,
1995).
4. Methodology
The Questionnaire Design
Primary data were collected using a self-administered questionnaire designed to serve the purpose of
the research objective. First part of the questionnaire is based on demographics and second part of the
questionnaire will be based on 5-points Likert Scale: (1) unimportant, (2) of little importance, (3)
moderately important, (4) important, and (5) very important. Four factors of the marketing mix were
included in this questionnaire: product, place, promotion and price.
The Sampling
To determine the number of the sampling units, literature suggests that ten to fifteen of participants in
the case of homogeneous group. The experience indicates that few new ideas are generated within a
homogeneous group once the size exceeds 30 well–chosen participants (Delbecq, Van de Ven and
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Proceedings of Applied International Business Conference 2008
Gustafson, 1975; Birdir and Pearson, 2000). The size of the samples is also determined according to
literature by Sekaran (2000). The size of samples would be 10 to 20 samples. Malhotra (1999) has
suggested that the minimum sampling size for problem solving research is 200 samples. Based on these
literatures, the suggestion of the size of the sampling unit is between 10 and 200. 500 hardcopies of
questionnaires were distributed. However, only 120 questionnaires were returned.
Data Collection
Number of journals and reports were selected to review the idea of marketing mix applied in the
current market. Self-administered questionnaire is designed to collect the data mainly from a person
graduated with business degree and business degree students around Malaysia. The reason to choose
this group is they have experiences as consumer and have some knowledge in marketing especially
marketing mix as what they learned during the study time.
Analysis Methods
The data collected were analysed using SPSS. The analysed data were then synthesised and presented
in tables. In the event of missing data or invalid answers, the questionnaire was considered void and not
used in the analysis. SPSS is also used to analyse collected data based on the coefficient correlation.
The coefficient of correlation describes the strength of the relationship between two sets of interval–
scaled or ratio–scaled variables. It can assume any value of –1.00 or +1.00 inclusive. The coefficient
correlation of –1.00 or +1.00 indicates perfect correlation (Mason, Lind and Marchal, 1999). The
coefficient of correlation was determined study whether there are positive relationship three factors of
Internet marketing activities. The following drawing summarises the strength and direction of the
coefficient of correlation (see Figure 1). AMOS also used to analyse the fitness of model.
Figure 1: The coefficient of correlation
Source: Mason, Lind and Marchal (1999)
5. Result and Analysis
55% (66 respondents) of the respondents are female compare to 45% are male (54 respondents). The
majority of the respondents are Chinese compare to other races like Malay, Indian and others. Majority
of these respondents are 20 to 21 years old (see Table 1).
Gender
Race
Age
Male
Female
Malay
Chinese
Indian
Others
<18
18 – 19
20 – 21
22 – 23
24 – 25
> 25
Table 1: Demographics
Frequency
54
66
12
95
1
12
0
10
82
23
2
3
Percentage
45
55
10
79.17
0.83
10
0
8.33
68.33
19.17
1.67
2.5
The study shows that all Ps can be considered equally important, which is between 3.8333 to 3.9567.
Based on study on variable of marketing mix, 11 variables were exceeding 4.000, which is between
important and very important. 5 variables (Quality: 4.6750, Design: 4.0750, Features: 4.1667, Services:
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Proceedings of Applied International Business Conference 2008
4.3667, Warranties: 4.1333) under Product factor; 1 factor (Locations: 4.2667) under Place factor, 2
variables (List Price: 4.2333 and Discounts: 4.1667) under Price factor, and 3 variables (Sales
Promotion: 4.2833, Advertising: 4.3250 and Public Relations: 4.0417) under Promotion factor (see
Table 2).
Table 2: Marketing mix
Mean
Std. Deviation
PRODUCT
3.9567
.46377
Variety
3.6000
.88308
Quality
4.6750
.55250
Design
4.0750
.70009
Features
4.1667
.75963
Brand Name
3.7083
1.00750
Packaging
3.7167
.90918
Sizes
3.3250
.92729
Services
4.3667
.79846
Warranties
4.1333
.90687
Returns
3.8000
.93125
PLACE
3.8986
.56810
Channels
3.8917
.82804
Coverage
3.9917
.77238
Assortments
3.6583
.77238
Locations
4.2667
.77496
Inventory
3.7167
.80108
Transport
3.8667
.84945
PRICE
3.8333
.64108
List Price
4.2333
.80683
Discounts
4.1667
.83347
Allowances
3.6917
.86768
Payment Period
3.5750
.94968
Credit Terms
3.5000
.97877
PROMOTION
3.9467
.62321
Sales Promotion
4.2833
.73546
Advertising
4.3250
.75773
Sales Force
3.6333
.96086
Public Relations
4.0417
.86380
Direct Marketing
3.4500
1.02777
The study found that correlation between Product, Place, Price and Promotion is positive, specifically
between Weak Positive Correlation and Moderate Positive Correlation (0.25 – 0.50), as well as
Moderate Positive Correlation and Strong Positive Correlation. The study also shows that all
correlations are significant at the 0.01 level (see Table 3 for summary and Appendix for detail).
Table 3: Correlations
PRODUCT PLACE
Pearson Correlation
PRODUCT
1
.597**
Sig. (2-tailed)
.
.000
Pearson Correlation
PLACE
.597**
1
Sig. (2-tailed)
.000
.
Pearson Correlation
PRICE
.497**
.396**
Sig. (2-tailed)
.000
.000
PROMOTION
Pearson Correlation
.464**
.451**
Sig. (2-tailed)
.000
.000
** Correlation is significant at the 0.01 level (2-tailed).
156
PRICE
PROMOTION
.497**
.464**
.000
.000
.396**
.451**
.000
.000
1
.393**
.
.000
.393**
1
.000
.
Proceedings of Applied International Business Conference 2008
The path coefficient in Table 4 and model as shown in Figure 2 found that Product, Place, Price and
Promotion are significant at the 0.05 significance level. This study has proved that all 4Ps have
statistically impact on marketing mix activities.
Unstandardised
.69
e10
.26
e9
.38
e8
1
1
Quality
1
1
Features
.88
e6
.62
1
Packaging
.60
1
Sizes
.42
1
e3
e1
1
1
e16
.35
e14
.39
e13
.36
.57
e21
.56
1.71
1.00
Channels
1
1
e19
.22
e26
.39
e25
.28
e24
.47
e23
.73
e22
Inventory
.35
.30
Quality
.21
e8
Design
.23
e7
Features
.13
.21
.27
1
Discounts
1
Allowances
1
Payment Period
1.30
MARKETING MIX
1.00
List Price
1.00
PLACE
e29
1
.45
.63
.60
Brand Name .24
e5
Packaging
.29
e4
Sizes
.34
e3
Services
.39
e2
Waranties
.31
e1
Returns
e16
Channels
.40
Coverage
.40
e14
Assortments .34
e13
Locations
.43
e12
Inventory
.39
e11
Transport
e21
List Price
.18
PRICE
e20
Discounts
.46
e19
Allowances .76
.86
e18
e17
Sales Promotion
1
.32
.32
.61
Advertising
Sales Force
.39
1
Public Relations
1
Direct Marketing
.36
.49
.54
.58
e27
.75
PRODUCT
.63
.56
e28
.63
.58
.66
.62
.69
.63
Payment Period.65
.63
PLACE
.34
.43
.68
.87
.81
.79
MARKETING MIX
.64
e29
.12
.41
PRICE
.65
Credit Terms
.33
1.00
1
.32
.39
.46
.48
.86
e15
.84
1
Credit Terms
1
.15
.48
1
Transport
1
.36
.30
.27
.32
.32
Locations
1
e18
.33
e17
Coverage
Assortments
1
e20
.41
e9
e28
1
e11
Variety
e6
Waranties
Returns
1
e12
.44
1
PRODUCT
.35
e15
.36
1.00
e10
e27
.29
.26
Services
.49
e2
.59
.18
.22
.25
.23
Brand Name
1
e5
e4
.14
.11
.16
.18
Design
.44
e7
Standardised
.10
Variety
e30
e26
1
e25
Advertising
.58
.57
.84
.70
e24
Sales Force .37
e23
Public Relations.31
e22
Direct Marketing
PROMOTION
.43
Sales Promotion.32
.61
e30
.43
PROMOTION
.55
Figure 2: Marketing Mix Model
PRODUCT
PLACE
PRICE
PROMOTION
Variety
Quality
Design
Features
Brand
Packaging
Sizes
Warranties
Services
Returns
Channels
Coverage
Assortments
Locations
Inventory
Transport
List price
Discount
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Å
Table 4: Regression Weights
Estimate
MARKETING MIX
1.711
MARKETING MIX
1.296
MARKETING MIX
.836
MARKETING MIX
.864
PRODUCT
.144
PRODUCT
.109
PRODUCT
.162
PRODUCT
.184
PRODUCT
.181
PRODUCT
.223
PRODUCT
.252
PRODUCT
.286
PRODUCT
.234
PRODUCT
.262
PLACE
.350
PLACE
.298
PLACE
.297
PLACE
.274
PLACE
.321
PLACE
.322
PRICE
.212
PRICE
.272
157
S.E.
.605
.318
.181
.202
.055
.042
.056
.060
.060
.065
.078
.093
.075
.086
.068
.061
.059
.056
.061
.064
.060
.062
C.R.
2.828
4.076
4.619
4.288
2.633
2.602
2.870
3.053
2.997
3.418
3.213
3.070
3.138
3.057
5.131
4.881
5.046
4.865
5.274
5.000
3.549
4.351
P
.005
***
***
***
.008
.009
.004
.002
.003
***
.001
.002
.002
.002
***
***
***
***
***
***
***
***
Proceedings of Applied International Business Conference 2008
Allowance
Payment period
Credit terms
Sales promotion
Advertising
Sales force
Public relations
Direct marketing
Å
Å
Å
Å
Å
Å
Å
Å
Estimate
.448
.631
.605
.320
.324
.605
.394
.430
PRICE
PRICE
PRICE
PROMOTION
PROMOTION
PROMOTION
PROMOTION
PROMOTION
S.E.
.065
.074
.072
.057
.057
.084
.067
.079
C.R.
6.882
8.588
8.377
5.625
5.678
7.172
5.876
5.451
P
***
***
***
***
***
***
***
***
Goodness-of-fit test determines if the model being tested should be accepted or rejected. As a test of
the measurement and path models, a mixture of fit-indices was employed to assess model fit. The ratio
of chi-square to degrees of freedom (x2=df) was computed, with ratios of less than 2.0 indicating a
good fit. However, since absolute indices can be adversely affected by sample size (Loehlin, 1992),
three other relative indices, GFI, AGFI, and TLI were computed to provide a more robust evaluation of
model fit (Tanaka, 1987; Tucker and Lewis, 1973). Kline (1998) recommends at least four tests, such
as chi-square; GFI, NFI or CFI; TLI; and SRMR. A good fit does not mean each particular part of the
model fits well. Many equivalent and alternative models may yield as good a fit that is fit indexes rule
out bad models but do not prove good models. A good fit also does not mean the exogenous variables
are causing the endogenous variables. For instance, one may get a good fit precisely because one’s
model accurately reflects that most of the exogenous variables have little to do with the endogenous
variables (Garson, 2006).
The chi-square value (CMIN) is 519.563, which is highly significant (p = <0.000). However, that this
does not mean the model is good. In fact it is the opposite, from the point of view of statistical
significance. We may say that the model is badness-of-fit.
Goodness-of-fit are based on fitting the model to sample moments, which means to compare the
observed covariance matrix to the one estimated on the assumption that the model being tested is true.
These measures thus use the conventional discrepancy function. The chi-square value should not be
significant if there is a good model fit, while a significant chi-square indicates lack of satisfactory
model fit. That is, chi-square is a badness of fit measure in that a finding of significance means the
given model’s covariance structure is significantly different from the observed covariance matrix. If
model chi-square < 0.05, the model is rejected. Hoelter’s critical N is the size the sample size must
reach for the researcher to accept the model by chi-square, at the 0.05 or 0.01 levels. This throws light
on the chi-square fit index’s sample size problem. Hoelter’s N should be greater than 200 (Garson,
2006). Carmines and McIver (1981) state that relative chi-square should be in the 2:1 or 3:1 range for
an acceptable model. Kline (1998) says 3 or less is acceptable. Some researchers allow values as high
as 5 to consider a model adequate fit, while others insist relative chi-square be 2 or less. Hoelter, at the
0.05 or 0.01 levels is 77 or 82, which is less than 200 as suggested by Garson (2006) and relative chisquare (CMIN/df) less than 5, which is 1.761. Thus, on the basis of the results obtained for Hoelter, at
the 0.05 or 0.01 levels and relative chi-square, we would say that the model is adequate fit.
For GFI and AGFI, coefficients closer to unity indicate a good fit, with acceptable levels of fit being
above 0.90 (Marsh, Balla and McDonald, 1988). AGFI can yield meaningless negative values. AGFI >
1.0 is associated with just-identified models and models with almost perfect fit. AGFI < 0 is associated
with models with extremely poor fit. The closer the RMR to 0 for a model being tested, the better the
model fit (Garson, 2006). Garson (2006) also agree that for CFI, IFI and TLI, coefficients closer to
unity indicate a good fit, with acceptable levels of fit being above 0.90. NFI, TLI, CFI and RFI are
varies from 0 to 1. NFI, TLI, CFI and RFI close to 1 indicate a very good fit. The fit indices of GFI and
AGFI were 0.745 and 0.697, respectively, suggesting that this model not provides a good fit. NFI
(0.594), RFI (0.552), IFI (0.772), TLI (0.741) and CFI (0.765) were less than 0.90, which can be
considered as not a good-fit model.
Parsimony measures are used in goodness-of-fit measures. The higher parsimony measure represents
the better fit. For RMR and RMSEA, evidence of good fit is considered to be values less than 0.05;
values from 0.05 to 0.10 are indicative of moderate fit and values greater than 0.10 are taken to be
evidence of a poorly fitting model (Browne and Cudeck, 1993). The closer model is to the saturated
model, the more PNFI and PCFI is penalised. There is no commonly agreed-upon cut-off value for an
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Proceedings of Applied International Business Conference 2008
acceptable model (Garson, 2006). As shown in Table 5, the value of RMR (0.069) and the value of
RMSEA (0.080), which is between 0.05 and 0.10. Thus, it provides evidence of moderate good fit.
Finally, PNFI and PCFI were 0.539 and 0.694. The closer model is to the saturated model (<0.001), the
more PNFI and PCFI is penalised (Garson, 2006). The result shows that GFI (0.745), RMSEA (0.080),
AGFI (0.697) and NFI (0.594), which does not achieve the recommended values.
Table 5: Testing of Model Fit
Default
Saturated
Model
Model
Discrepancy
519.563
0.000
Degree of freedom
295
0
P=Value
<0.001
Number of Parameters
56
351
Discrepancy/df
1.761
Root mean Square residual
0.069
0.000
Goodness of fit index
0.745
1.000
Adjusted goodness of fit index
0.697
Parsimony goodness of fit index
0.626
Normed Fit Index
0.594
1.000
Relative fit index
0.552
Incremental fit index
0.772
1.000
Tucker-Lewis index
0.741
Competitive fit index
0.765
1.000
Parsimony ratio
0.908
0.000
Parsimony adjustment to the Normed Fit Index
0.539
0.000
Parsimony adjustment to the Competitive fit
0.694
0.000
index
Noncentrality parameter
224.563
0.000
Lower boundary of a two-sided 90%
165.000
0.000
confidence interval for the population NCP
Upper boundary of a two-sided 90%
291.980
0.000
confidence interval for the population NCP
Minimum discrepancy function F
4.366
0.000
Estimated population discrepancy
1.887
0.000
Lower boundary of a two-sided 90%
1.387
0.000
confidence interval for the population F0
2.454
0.000
Upper boundary of a two-sided 90%
confidence interval for the population F0
Root Mean Square Error of Approximation
.080
Lower boundary of a two-sided 90%
.069
confidence interval for the population RMSEA
Upper boundary of a two-sided 90%
.091
confidence interval for the population RMSEA
P for test of close fit
0.000
Akaike Information Criterion
631.563
702.000
Browne-Cudeck Criterion
664.432
908.022
Bayes Information Criterion
787.662
1680.410
Consistent AIC
843.662
2031.410
Expected cross validation index
5.307
5.899
Lower boundary of a two-sided 90%
4.807
5.899
confidence interval for the population ECVI
Upper boundary of a two-sided 90%
5.874
5.899
confidence interval for the population ECVI
MECVI
5.583
7.630
Hoelter .05
77
Fit Measure
Hoelter .01
82
159
Independence
Model
1278.783
325
<0.001
26
3.935
0.182
0.376
0.326
0.348
0.000
0.000
0.000
0.000
0.000
1.000
0.000
0.000
Macro
CMIN
DF
P
NPAR
CMIN/DF
RMR
GFI
AGFI
PGFI
NFI
RFI
IFI
TLI
CFI
PRATIO
PNFI
PCFI
953.783
847.606
NCP
LO 90
1067.503
HI 90
10.746
8.015
7.123
FMIN
F0
LO 90
8.971
HI 90
.157
.148
RMSEA
LO 90
.166
HI 90
0.000
1330.783
1346.044
1403.257
1429.257
11.183
10.291
PCLOSE
AIC
BCC
BIC
CAIC
ECVI
LO 90
12.139
HI 90
11.311
35
MECVI
Hoelter
.05
Hoelter
.01
37
Proceedings of Applied International Business Conference 2008
6. Conclusion
This study has agreed with Kellerman, Gordon and Hekmat (1995). All parts of the marketing mix are
equally important, since a deficiency in any one can mean failure. The result shows that mean for
Product, Place, Price and Promotion are between 3.8333 and 3.9567. The study of each variable for
each mix also shows that the mean is between important and very important.
The study found that correlation between Product, Place, Price and Promotion are positive, specifically
between Weak Positive Correlation and Moderate Positive Correlation (0.25 – 0.50), as well as
Moderate Positive Correlation and Strong Positive Correlation. The summary of all variables of 4Ps are
under few categories: Weak Negative Correlation and No Correlation; No Correlation and Weak
Positive Correlation; Weak Positive Correlation and Moderate Positive Correlation; Moderate Positive
Correlation and Strong Positive Correlation; and Strong Positive Correlation and Perfect Positive
Correlation
Based on path coefficients, Product, Place, Price and Promotion are significant at the 0.05 significance
level. This study has proved that all 4Ps have statistically impact on marketing mix activities. Finally,
the result also shows that GFI (0.745), AGFI (0.697), NFI (0.594), RFI (0.552), IFI (0.772), TLI
(0.741) and CFI (0.765) were less than 0.90, which can be considered as not a good-fit model.
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Appendix
RODUCVa Qu De Fe BN Pa Si Se Wa Re LACECh Co As Lo In TrPRICELP Di Al PP CTOMOTI SP Ad SF PR DM
PROD PC 1450*459*582*567*464*563*615*565*639*
610*597*410*402*442*404*489*397*497*335*304*290*402*446* .464*344*349*381*336*263*
Sig
.000000000000000000000000000000 000 000000000 000000 000 000000001001000000 .000 000 000 000000 004
VarietyPC.450* 1179226*238*255*30088031088219*365*205*217*340*182*242*365*199*132137112146 85* .199*047 208*073220*163
Sig.000 .050013009005 58338737338016 000 025017000 047008 000 029150136224111043 .029 613 022 426016 075
QualityPC.459*179 1281*491*066066306*158272*
216*180*106151112 145189*068 206*285*173070087 48 .071 166 034 090059 038
Sig.000050 .002000474473001085003018 050 249099224 113039 460 024002058450345 08 .439 069 715 327519 682
DesignPC.582*226*281* 1514*127337*299*206*236*78 128 116048157 040113 073 182*088180*163086 41 .229*236*080 191*134 175
Sig.000013002 .000168000001024010052 162 209604088 662219 425 047340049075349 24 .012 010 383 037145 056
Featur PC.567*238*491*514* 1152 30352*051 99*
226*280*189*260*227*181*203*139 313*265*274*206*192*203* .299*231*095 280*169 269*
Sig.000009000000 .098 58000582029013 002 038004013 048027 130 001003002024035026 .001 011 303 002066 003
Brand PC.464*255*066127152 1450*228*061025063 420*314*256*238*305*344*327*226*095128204*203*75 .454*271*235*444*323*323*
Sig.000005474168098 .000012509790496 000 000005009 001000 000 013303162026026056 .000 003 010 000000 000
Packa PC.563*130066337*130450* 1240*295*79 51 372*283*272*280*323*281*157 282*171018187*297*312* .463*347*330*400*358*237*
Sig.000158473000158000 .008001051 00 000 002003002 000002 086 002062841041001001 .000 000 000 000000 009
Sizes PC.615*088306*299*352*228*240* 1235*328*
290*401*375*297*215*264*283*269*284*246*245*126225*90* .164 061 076 201*151 083
Sig.000338001001000012008 .010000001 000 000001018 004002 003 002007007172014038 .073 508 412 028100 366
Servic PC.565*031158206*051061295*235* 1651*
416*361*289*264*218*275*321*172 321*140109140351*376* .266*208*371*188*185*053
Sig.000737085024582509001010 .000000 000 001004017 002000 061 000127234126000000 .003 023 000 040043 564
WarranPC.639*088272*236*199*025 79328*651* 1539*323*176254*282*212*284*176 380*175193*224*369*379* .149 144 205*086122 016
Sig.000338003010029790051000000 .000 000 054005002 020002 055 000055035014000000 .103 115 024 353186 860
ReturnPC.610*219*216*178226*063 51290*416*539* 1 353*135150301*226*363*327*327*286*238*141198*313* .181*194*212*086136 060
Sig.000016018052013496 00001000000 . 000 141103001 013000 000 000002009123030000 .048 034 020 348139 517
PLACEPC.597*365*180*128280*420*372*401*361*323*
353* 1 759*700*696*666*721*715*396*342*190*246*348*298* .451*190*370*403*340*297*
Sig.000000050162002000000000000000000
. 000000000 000000 000 000000038007000001 .000 038 000 000000 001
Chann PC.410*205*106116189*314*283*375*289*76 35 759* 1603*454*360*384*421*232*189*039164272*61 .344*134 324*288*253*226*
Sig.000025249209038000002000001054 41 000
.000000 000000 000 011039676074003080 .000 146 000 001005 013
CoveraPC.402*217*151048260*256*272*297*264*254*50 700*603* 1432*285*363*318*194*165041084236*61 .261*063 306*279*265*037
Sig.000017099604004005003001004005 03 000 000 .000 002000 000 034072654362010079 .004 492 001 002003 692
Assort PC.442*340*112157227*238*280*215*218*282*
301*696*454*432* 1 350*399*353*325*156194*243*293*272* .276*083 220*237*072 333*
Sig.000000224088013009002018017002001 000 000000 . 000000 000 000089034008001003 .002 367 016 009435 000
LocatioPC.404*182*145040181*305*323*264*275*212*
226*666*360*285*350* 1434*425*205*330*061111132 22 .426*205*266*415*372*249*
Sig.000047113662048001000004002020013 000 000002000 .000 000 025000510228149 85 .000 024 003 000000 006
InventoPC.489*242*189*113203*344*281*283*321*284*
363*721*384*363*399*434* 1 487*343*363*210*163227*279* .340*180*250*279*248*248*
Sig.000008039219027000002002000002000 000 000000000 000 . 000 000000021075013002 .000 049 006 002006 006
TranspPC.397*365*068073139327*57269*172 76327*715*421*318*353*425*487* 1 382*254*257*274*315*273* .279*142 212*228*237*175
Sig.000000460425130000086003061055000 000 000000000 000000 . 000005005002000003 .002 123 020 012009 056
PRICEPC.497*199*206*182*313*226*282*284*321*380*
327*396*232*194*325*205*343*382* 1540*637*768*832*798* .393*372*251*306*152 327*
Sig.000029024047001013002002000000000 000 011034000 025000 000
.000000000000000 .000 000 006 001097 000
List Pr PC.335*132285*088265*095 71246*140 75286*342*189*165156 330*363*254*540* 1304*212*262*245* .236*270*067 220*119 166
Sig.000150002340003303062007127055002 000 039072089 000000 005 000 .001020004007 .010 003 465 016197 070
DiscouPC.304*137173180*274*128018245*109 93*
238*190*039041194*061210*257*637*304* 1409*345*288* .092 183*047 024068 147
Sig.001136058049002162841007234035009 038 676654034 510021 005 000001 .000000001 .319 046 613 791460 109
Allowa PC.290*112070163206*204*87*26140224*41 246*164084243*111163 274*768*212*409* 1594*529* .296*230*128 256*152 270*
Sig.001224450075024026041 72126014 23 007 074362008 228075 002 000020000 .000000 .001 011 163 005098 003
PaymePC.402*146087086192*203*297*225*351*369*98*348*272*236*293*132227*315*832*262*345*594* 1719* .399*294*322*288*206*318*
Period Sig.000111345349035026001014000000030 000 003010001 149013 000 000004000000 .000 .000 001 000 001024 000
Credit PC.446*185*148141203*175312*90*376*379*
313*298*161161272*122279*273*798*245*288*529*719* 1 .366*350*300*295*124 259*
Sig.000043108124026056001038000000000 001 080079003 185002 003 000007001000000 . .000 000 001 001176 004
PROMPC.464*199*071229*299*454*463*64266*49 81*451*344*261*276*426*340*279*393*236*092296*399*366*
1 653*635*840*707*717*
Sig.000029439012001000000073003 03048 000 000004002 000000 002 000010319001000000
. 000 000 000000 000
Sales PC.344*047166236*231*271*347*061208*44 94*190*134063083 205*180*142 372*270*183*230*294*350* .653* 1 316*505*259*341*
Promo Sig.000613069010011003000508023 15034 038 146492367 024049 123 000003046011001000 .000
. 000 000004 000
Advert PC.349*208*034080095235*330*076371*205*
212*370*324*306*220*266*250*212*251*067047128322*300* .635*316* 1 477*326*242*
Sig.000022715383303010000412000024020 000 000001016 003006 020 006465613163000001 .000 000
. 000000 008
Sales PC.381*073090191*280*444*400*201*188*086086 403*288*279*237*415*279*228*306*220*024256*288*295* .840*505*477* 1525*458*
Sig.000426327037002000000028040353348 000 001002009 000002 012 001016791005001001 .000 000 000
.000 000
Public PC.336*220*059134169323*358*51185*22 36 340*253*265*072 372*248*237*152119068152206*24 .707*259*326*525* 1 386*
RelatioSig.000016519145066000000 00043 86 39 000 005003435 000006 009 097197460098024 76 .000 004 000 000 . 000
Direct PC.263*163038175269*323*237*083053016060 297*226*037333*249*248*175 327*166147270*318*259* .717*341*242*458*386* 1
MarketSig.004075682056003000009366564860517 001 013692000 006006 056 000070109003000004 .000 000 008 000000 .
**.
Correlation is significant at the 0.01 level (2-tailed).
*.Correlation is significant at the 0.05 level (2-tailed).
1