Exploratory Factor Analysis Assignment 2013-2014(1)

Name: Tiezheng Yuan

Student No: 110562836

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MKT 3004 Analytical Techniques for Marketing

Assignment 1: Exploratory Factor Analysis

Name: Tiezheng Yuan

Student Number: 110562836

Degree Title: NN52 Marketing and Management

Word Count: 2489

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Table of Contents

Section number and title Page

1. Introduction 3

2. Theory 4

3. Application to Marketing 5

4. Method 7

5. Results 8

6. Marketing Implications 10

7. Summary /Conclusions 13

List of References 14

Appendices

Appendix 1 SPSS Output 15

List of Tables

Table 1 Rotated Factor Matrix for Importance of Store Attributes 8

List of Figures

Figure 1 Segmentation Targeting Positioning framework 10

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1. Introduction

The aim of this study is to apply exploratory factor analysis to the data collected

which represents student attitude to the importance of supermarket features.

Exploratory factor analysis is a technique that identifies the underlying dimensions of

metric correlated data. Besides that it also achieves data reduction so that original set

of variables are replaced by smaller set of factors. In addition, exploratory factor

analysis can also be used to confirm the dimensionality of existing scales. In this

study, applying factor analysis to the student scale will reveal the dimensions

underlying the importance of supermarket features. Therefore, improving the

understanding of student food shoppers and it will help in the effectiveness of

marketing grocery shoppers.

This study is structured as follows, section two dues with the theory of exploratory

factor analysis. Next, section three will touch on application to marketing of

exploratory factor analysis through published article. Section 4 will describe the

method used to conduct factor analysis. Section 5 will present the result of the

analysis. Section six will mention the marketing implications of results. Lastly,

section 7 will be summary and conclusion of the study.

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2. Theory

This section aims to explain the theory of factor analysis by describing the objectives

of factor analysis, the data requirement, the full equation of factor analysis, some key

assumptions of theory and basic features of model.

Exploratory factor analysis is a technique whose objectives are to identify underlying

dimensions of an original large set of matric correlated variables with the aim of

minimum information loss. The factors derived are uncorrelated and jointly explains

the total variance of the original variables in descending order of importance.

The two broad aims of factor analysis are to:

1. Identify the number of factors

2. Interpret the meaning of the factors.

In order to conduct factor analysis, the original data are required to be metric and

correlated.

The factor analysis full equation is:

xp = bp1f1 + bp2f2 + … + bpkfk + ep

The equation assumes that each of p original variables (x’s) are determined by a linear

combination of k non-observable common factors (f’s) and the influence of a non-

observable unique factor (e).

There are several assumptions that form the basis of the equation:

1. The x’s are standardised to have a mean of zero and a variance of unity

2. The common factors are standardised to have a mean of zero and a variance of

unity

3. The covariance between common factors are zero so that the correlations between

common factors are zero

4. The covariance between common and unique factors are zero so that they are not

correlated

5. The covariance between unique factors is zero so that pairs of unique factors are

not correlated

The basic feature of the model focuses on three sets of relationships:

1. How their values are determined

2. How their variances are determined

3. How their covariance are determined

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3. Application to Marketing

This section aims to explain and evaluate the practical application of factor analysis

through reviewing the study conducted by Gayatri and Chew (2013). It will explain

the aim of the study, provide description of data and measures, explain the result and

interpretation, mention the value of study and lastly critic the study.

The study conducted by Gayatri and Chew (2013) aim to investigate the service

quality on Muslim (Islamic follower) customer perception and behaviour.

The measure consists of a 42-item scale designed to measure service quality in the

context of Islamic culture. A seven-point Likert scale with anchors of 1 - strongly

disagree to 7 - strongly agree were used.

Six distinct factors were identified through the study. The interpretation of factors is

established through the strength of correlation between factor and the original scale

items. Factor 1 is most strongly associated, in descending order of importance, with

‘Respect for Muslim Customers’ (.790), ‘Religious tolerance’ (.770), ‘Accommodate

the needs of Muslim customer’ (.740) and ‘Understanding Islamic rules’ (.720). It is

therefore interpreted as Islamic values.

Factor 2 is most strongly associated, in descending order of importance, with ‘Display

a certificate of Halal/Haram’ (.840), ‘Holds a Halal certificate for product/service’

(.830), ‘Statement of Halal’ (.630), ‘Categorized product/service that are Halal/Haram

separately’ (.600), ‘Declare products/services according to Islamic rule of

Halal/Haram’ (.560) and ‘Follow Islamic rule of Halal/Haram’ (.540). It is therefore

interpreted as Halal/Haram.

Factor 3 is most strongly associated, in descending order of importance, with ‘Place

for saying prayer’ (.920), ‘Purifying facilities’ (.910), ‘Provide Sajadah/Mukena

praying tools’ (.890), ‘Provide direction of Mecca’ (.890), ‘Maintain sanctity of place

for praying’ (.890), ‘Provide proper praying facilities’ (.850) and ‘Important of

Islamic religious activities’ (.790). It is therefore interpreted as Attention to Islamic

religious activity.

Factor 4 is most strongly associated, in descending order of importance, with ‘No

fraudulent in business dealing’ (.800), ‘No uncertainty in business transaction’ (.790),

‘Honest in business dealings’ (.780), ‘No tamper with measurement scales’ (.750),

‘Not only engage in profiteering’ (.680), ‘Deliver service according to promise’ (.650)

and ‘No support for gambling’ (.610). It is therefore interpreted as Honesty.

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Factor 5 is most strongly associated, in descending order of importance, with ‘Modest

outfit’ (.830), ‘Uniform that promotes modesty for its staff’ (.780) and ‘Follow

Islamic dress code’ (.660). It is therefore interpreted as Modesty.

Factor 6 is most strongly associated, in descending order of importance, with

‘Humane treatment for its customers’ (.780), ‘Humane touch during transaction’

(.780), ‘Service using humane standard’ (.720), ‘Treat employees with humaneness’

(.720), ‘Show trust when serving customer’ (.580), ‘ Develop trustworthy standard’

(.530) and ‘Give high trust to its customers during service transaction’ (.530). It is

therefore interpreted as Humaneness and trustworthiness.

The result of the study contributes to the extant literature by developing a service

quality measure that is relevant to the Islamic context. Besides that, through the study,

the researchers discovered that consumers with Muslim background are heavily

influenced by the factors identified which have been omitted in the previous

literatures of service quality measurement. In addition, the measurement model used

in the study is useful for future researchers who are interested in exploring other

markets and market segments in other Muslim countries. Lastly, through this study,

several practical recommendations were given to practitioners. For example,

restaurant owners were recommended to cover the windows during the fasting month

of Ramadan as high religious awareness improves customer’s perception of service

quality and potentially long-term loyalty.

There are some limitations of the study conducted by Gayatri and Chew (2013).

Firstly, the use of convenience sampling may reduce the quality of representation

compared to probability sampling techniques (Cassady, 1945). The haphazard

selection of subjects may introduce bias as researchers were given considerable

leeway to exercise their judgement concerning selection of respondents (Zikmund and

Babin, 2010). Therefore, objective statistical inferences are difficult to make when

non-probability sampling is used (Ngulube, 2005). Secondly, the study was conducted

in Indonesia, where there is a separation of state and religion and the majority of the

population is Sunni Muslim (Seibel and Agung, 2006). Thus, the applicability of the

Islamic service quality measure may be questionable in countries where Islam is the

state religion or in countries where majority of population are Shi’a Muslims.

The study is extended by conducting confirmatory factor analysis to evaluate the

model. Reliability was also conducted.

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4. Method

This section aims to explain and justify the method used by providing explanation of

data and measures, confirmation of data are metric and correlated and provide

explanation of method.

The data consist of 14-item scale designed to measure the importance of supermarket

features (1 = Not at all important, 5 = Very important).

The use of ordinal scale, with explicit scores and with equal intervals of unity

between descriptors, means that the scale is assumed to have interval measurement

properties and is thus metric.

In order to confirm that the data are correlated, Bartlett’s Test for Sphericity is

conducted based on the following hypotheses:

H0: None of the variables are correlated

H1: The Variables are correlated

Confirmation that the test variable are inter-correlated is indicated by a KMO index of

.697, categorised by Kaiser (1974) as ‘Middling’ , while Bartlett’s Test of Sphericity

results in the rejection of the null hypothesis at the five percent significance level

(χ2(91) = 1848.233, Sig = .000).

Factor analysis was applied to a 14-item scale designed to measure the importance of

supermarket features (1 = Not at all important, 5 = Very important). The analysis

using SPSS 21.0 (2008) employed principal components analysis with Varimax

rotation and the extraction criterion was to derive factors with eigenvalues greater

than unity. Goodness of fit was evaluated using total variance explained and

communalities. The minimum acceptable value for communalities was set at 0.5 (Hair

et al., 2006: p 149). Following Hair et al. (2006: p128) the cut-off point for the

inclusion of factor loadings consistent with a sample size of 731 was set as .30. The

analysis resulted in a solution of 5 factors. Factor scores were saved for subsequent

analysis (See Appendix 1).

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5. Results

This section aims to present the results in a logical structure. Firstly, table of results

will be presented. Secondly, it will define the measures of goodness of fit and report

and evaluate the goodness of fit of the results. Lastly, it will explain method of

interpretation and interpret the results.

Table 1 Rotated Factor Matrix for Importance of Store Attributes

Store Feature Factor Number h2

1 2 3 4 5

Convenient location .181 .156 .114 .069 -.769 .665

Parking facilities -.013 .123 .132 .161 .781 .668

Pleasant atmosphere .008 -.010 .594 .402 -.187 .550

Well-known brands .010 .173 .769 .020 .099 .632

Own label products .601 .044 .132 -.255 .211 .490

High quality products .047 .066 .734 .100 .037 .557

Value for money .767 .078 .098 .084 -.148 .634

Low prices .833 -.067 -.097 .077 -.123 .729

Special offers .757 .054 -.017 .095 -.090 .594

Friendly, helpful staff .103 -.026 .201 .805 -.114 .714

Check-out speed .021 .555 .043 .531 .074 .597

Methods of payment .077 .824 .162 .024 -.013 .711

Cash-back facilities .003 .846 .055 .006 -.027 .719

Other facilities -.020 .088 .087 .560 .291 .414

Eigenvalue 2.271 1.799 1.630 1.535 1.440

Variance % 16.219 12.849 11.640 10.962 10.286

Cumulative variance % 16.219 29.068 40/709 51.670 61.956

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Goodness of fit is evaluated from total variance explained and communalities. Total

variance explained is combined contribution to total variance of the set of all 5

derived factors. Total variance explained is 62%. This is regarded as acceptable for

social science data. Communality is the proportion of the variance of a specific

variable explained by all the derived factors. The communalities are generally

respectable apart from Own label products and Other facilities. In summary 4

communalities were strong, 4 were respectable, 4 were acceptable and 2 were weak.

In summary, goodness of fit was regarded as acceptable.

The interpretation of factors is established through the strength of correlations

between each factor and the original scale items. Factor 1 is most strongly associated,

in descending order of importance, with ‘Low prices’ (.833), ‘Value for money’

(.767) and ‘Special offers’ (.757). It is therefore interpreted as Price and Value.

Factor 2 is most strongly associated, in descending order of importance, with ‘Cash-

back facilities’ (.846) and ‘Methods of payment’ (.824). It is therefore interpreted as

Transaction methods.

Factor 3 is most strongly associated, in descending order of importance, with ‘Well-

known brands’ (.769) and ‘High quality products’ (.734). It is therefore interpreted as

Quality and Branding.

Factor 4 is most strongly associated with ‘Friendly, helpful staff’ (.805). It is therefore

interpreted as Employee attitude.

Factor 5 is most strongly associated, in descending order of importance, with ‘Parking

facilities’ (.781) and ‘Inconvenient location’ (.769). The negative coefficient for

‘Convenient location’ (-.769) in Table 1 is interpreted as Factor 5 is positively

associated with ‘Inconvenient location’. Therefore, Factor 5 is interpreted as

Accessibility.

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6. Marketing Implications of Results

This section aims to apply strategic and tactical marketing theory to the results

through the use of Segmentation, Targeting and Positioning (STP) framework.

However, this section will only briefly mention the strategic (STP) theory. It will

focus more on tactical (7ps) aspect of marketing theory. The 7ps are Product, Price,

Place, Promotion, People, Process and Physical evidence.

Figure 1 Segmentation Targeting Positioning framework

Strategic (STP)

Demographic segmentation (age) was used in the study as the research was conducted

at Newcastle University with full-time undergraduates (18-25 years old).

Differentiated targeting approach was adopted to specifically target the undergraduate

segment. Product and Brand positioning strategy would be specifically designed

based on undergraduate’s characteristics.

Tactical (7ps)

Product

Supermarkets could focus on ensuring high quality of products as quality is one

important dimension identified. Retailers could communicate clearly with its suppliers

regarding quality standards. Besides that, supermarkets could encourage suppliers to

adopt a quantitative (statistic software) approach to quality control. Customer

Strategic • Segmentation

Startegic • Targeting and Positioning

Tactical • Marketing Mix (7ps)

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feedback on quality could be conducted by supermarkets as a way to monitor quality

and satisfaction. In addition, internal (employee) feedback on quality could also be

encouraged. Maintaining high quality standard could be integrated into the

organisation culture.

Price

Price and value is the most important dimension identified by student shoppers

concerning supermarket features. Supermarkets could adopt a cost-plus pricing

strategy when targeting students. Besides that, supermarkets could adopt lean

production techniques such as Just-in-time (JIT) stock management approach.

Retailers only hold stocks that it need and therefore reducing storage cost. The cost

saved could be passed on to customers thus lowering product price to attract student

shoppers.

Place

Accessibility is one dimension identified by student shoppers. There could be parking

facilities nearby supermarkets. If the supermarket is located at an inconvenient

location, the availability of parking facilities gives students the opportunities to use

their cars.

Promotion

Promotional efforts could focus on price and brand as these two dimensions are

identified important to student shoppers. Retailers could step up promotional efforts

when there are special discounts. In addition, quality brand names could also be

included in promotions. Retailers could use both above and below the line promotion

methods. For example advertise through internet, radio and magazines.

People

Staff attitude is one important factor identified by student consumers. Organisations

could place more importance in its human resource management. It needs to recruit

people with the right values and attitudes. Adequate training could be provided to

equip employees with the necessary skills to provide quality service. Occasionally,

there could be refresher or upgrader courses to keep up with raising customer demand.

Employees could also be rewarded for showing consistent good attitude. Customer

feedback could be encouraged to reflect on staff attitude.

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Process

Retailers could introduce convenient transaction methods to improve customer

shopping process. It could introduce cash-back service at the tiles. Besides that,

retailers could provide variety of payment methods. For example customers could pay

by credit card, direct debit or cash.

Physical Evidence

Employees could wear a smiley badge as evidence to show commitment towards

friendly and helpful service. Posters showing commitment towards quality assurance

could be displayed.

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7. Summary

This section will provide a brief summary of research aim, research method, key

results and value of results. It will also evaluate the study and provide suggestions for

future research.

The aim of this study is to apply exploratory factor analysis and identify the

underlying dimensions concerning students’ attitude to the importance of supermarket

features. Factor analysis was applied to a 14-item scale designed to measure the

importance of supermarket features (1 = Not at all important, 5 = Very important).

Five factors were identified, namely, Price and Value, Transaction methods, Quality

and Branding, Employee attitude and Accessibility. The results generated contribute

to the understanding of student segment concerning supermarket features. It also

enables marketers to develop more specific marketing strategies targeting student

segment.

The study can be further improved by using probability sampling technique such as

stratified sampling to provide a better representation of the population. The use of

quota sampling (non-probability) technique in the study may not provide an unbiased

representation of population (Peterson and O’Dell, 1950). As a result, objective

statistical inferences are difficult to make when non-probability sampling is used

(Ngulube, 2005).

In future research, the study could be conducted using mixed method approach. For

example, focus group could be conducted before factor analysis. Such a way, the data

gathered is triangulated and therefore improve the credibility and validity of result

(Homburg et al., 2012). In addition, more information could be gathered using mixed

method approach. Participants might be willing to provide more information in a

focus group compared to face-to-face survey as they feel more secure answering

questions in a group (Powell and Single, 1996). Therefore enable the researcher to

gather more information.

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List of References

Cassady Jr, R. (1945) 'Statistical sampling techniques and marketing research',

Journal of Marketing, 9(4), pp. 317-341.

Gayatri, Gita and Chew, Janet (2013) ‘How do Muslim consumers perceive service

quality?’, Asia Pacific Journal of Marketing and Logistics, 25(3), pp. 472-490.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006)

Multivariate Data Analysis. Upper Saddle River, New Jersey: Pearson Education, Inc.

Homburg, C., Klarmann, M., Reimann, M. and Schilke, O. (2012) 'What drives key

informant accuracy?', Journal of Marketing Research (JMR), 49(4), pp. 594-608.

Kaiser, H. F. (1974) ‘An index of factorial simplicity’, Psychometrika, 39(1), pp. 31-

36.

Ngulube, P. (2005) 'Research procedures used by Master of Information Studies

students at the University of Natal in the period 1982–2002 with special reference to

their sampling techniques and survey response rates: A methodological discourse',

The International Information & Library Review, 37(2), pp. 127–143.

Peterson, P.G. and O'Dell, W.F. (1950) 'Selecting sampling methods in commercial

research', Journal of Marketing, 15(2), pp. 182-189.

Powell, R.A. and Single, H.M. (1996) 'Focus Groups', International Journal for

Quality in Health Care, 8(55), pp. 499-504.

Seibel, H.D. and Agung, W.D. (2006), Islamic microfinance in Indonesia, University

of Cologne Development Research Centre, Cologne.

SPSS (2008), SPSS for Windows (Version 21.0), Chicago, IL, USA: SPSS Inc.

Zikmund, W.G. and Babin, B.J. (2010) Exploring Market Research (10

th edition).

London: Cengage Learning.

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Appendix 1 SPSS Output

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.

.697

Bartlett's Test of

Sphericity

Approx. Chi-Square 1848.233

df 91

Sig. .000

Communalities

Initial Extraction

Convenient Location 1.000 .665

Car-Parking Facilities 1.000 .668

Pleasant Shopping

Atmosphere

1.000 .550

Wide range of well known

brands

1.000 .632

Wide range of own-label

products

1.000 .490

High Quality Products 1.000 .557

Value for money 1.000 .634

Low Prices 1.000 .729

Special Offers 1.000 .594

Friendly, Helpful Staff 1.000 .714

Speed of Check-Out 1.000 .597

Method of Payment 1.000 .711

Cash-Back Facilities 1.000 .719

Other Facilities 1.000 .414

Extraction Method: Principal Component Analysis.

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Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.754 19.669 19.669 2.754 19.669 19.669 2.271 16.219 16.219

2 2.219 15.853 35.522 2.219 15.853 35.522 1.799 12.849 29.068

3 1.370 9.787 45.310 1.370 9.787 45.310 1.630 11.640 40.709

4 1.312 9.368 54.678 1.312 9.368 54.678 1.535 10.962 51.670

5 1.019 7.278 61.956 1.019 7.278 61.956 1.440 10.286 61.956

6 .858 6.125 68.081

7 .807 5.763 73.844

8 .677 4.836 78.681

9 .621 4.436 83.117

10 .597 4.267 87.384

11 .516 3.686 91.070

12 .447 3.191 94.261

13 .439 3.137 97.398

14 .364 2.602 100.000


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Component Matrixa

Component

1 2 3 4 5

Convenient Location .339 -.302 .003 -.677 .027

Car-Parking Facilities .114 .417 -.025 .693 -.034

Pleasant Shopping

Atmosphere

.489 .239 .444 -.209 .118


brands

.468 .343 .187 .041 .508


products

.282 -.399 -.113 .404 .275

High Quality Products .460 .270 .295 .009 .430

Value for money .561 -.553 .013 .112 .007

Low Prices .423 -.707 .042 .185 -.117

Special Offers .486 -.568 -.014 .170 -.080

Friendly, Helpful Staff .514 .176 .459 -.105 -.444

Speed of Check-Out .539 .357 -.199 -.016 -.373

Method of Payment .544 .260 -.577 -.097 .069

Cash-Back Facilities .455 .277 -.646 -.134 .008

Other Facilities .297 .340 .188 .227 -.351


a. 5 components extracted.

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Rotated Component Matrixa

Component

1 2 3 4 5

Convenient Location .181 .156 .114 .069 -.769

Car-Parking Facilities -.013 .123 .132 .161 .781

Pleasant Shopping

Atmosphere

.008 -.010 .594 .402 -.187


brands

.010 .173 .769 .020 .099


products

.601 .044 .132 -.255 .211

High Quality Products .047 .066 .734 .100 .037

Value for money .767 .078 .098 .084 -.148

Low Prices .833 -.067 -.097 .077 -.123

Special Offers .757 .054 -.017 .095 -.090

Friendly, Helpful Staff .103 -.026 .201 .805 -.114

Speed of Check-Out .021 .555 .043 .531 .074

Method of Payment .077 .824 .162 .024 -.013

Cash-Back Facilities .003 .846 .055 .006 -.027

Other Facilities -.020 .088 .087 .560 .291


Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 5 iterations.

Exploratory Factor Analysis Assignment 2013-2014(1)

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