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13:1 (2019) 50-72 | utm.my/intrest | ISSN: 1832-8505 |

APPLYING GROUNDED THEORY METHOD FOR MEASURING EMPLOYMENT

OPPORTUNITIES GENERATED BY A SHOPPING CENTRE DEVELOPMENT

Nurul Faiezah Ngadimin1*, Abdul Hamid Mar Iman2, Rohaya Abdul Jalil1

1Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia

81310 Skudai, Johor, Malaysia 2Faculty of Veterinary Medicine, Universiti Malaysia Kelantan

16100 Pengkalan Chepa, Kelantan, Malaysia

History:

Received: 29 January 2019

Received in revised form: 5 April 2019

Accepted: 25 April 2019

Available Online: 30 April 2019

Keywords:

Grounded theory method, shopping

centre, employment, employment

density

*Corresponding Author

[email protected]

ABSTRACT

A shopping centre development project brings about a positive change

to the community as well as generates a lot of business opportunities

which in turn creates more employment and job opportunities for the

locals. However, there is no numerical proof regarding employment

opportunities generated therefrom. In particular, there was neither

assessment nor measurement of employment opportunities generated

by a shopping centre development being developed. Therefore, this

paper attempts to measure the employment opportunities generated by

a proposed shopping centre development project by developing

physical parameters for the measurement on the basis of net-lettable

area (NLA). A grounded theory method (GTM) is adapted since there

is lack of the existing literature on the method or approach of

assessment. The GTM with an inductive approach as well as deductive

orientation was employed in measuring employment opportunities

utilizing data generated from the constant comparative method (CCM).

Thus, a CCM-GTM is adopted and six operational shopping centres in

Johor Bahru are selected as a comparable group, called CG6. The data

of CG6 were constantly compared for their commonalities and

variations, thus the trace out to the emerging pattern are found. This

study conceptualized the theory of employment generation and derived

the employment density equation from a set of employment quotients

(EQ). Besides, this paper presented an ideal figure of retail mix classes

existing within a shopping centre together with its percentages

distribution placement.

1.0 INTRODUCTION

A shopping centre is a place where retail shops

are located and grouped constituting diverse

qualities of products, fast food courts,

amusement centres, clubs, cinemas, playing

areas and relaxation spaces, commercial and

retail shops (Terblanche, 1999; Hariyono, 2002).

A shopping centre is also viewed as a group of

retail and other commercial establishments that

are developed and managed as a single property

(Guy, 1998), thus there are a few components in

shopping centre such as department store

(anchor store and draw tenant), stand-alone

stores (small shop or satellite tenant of high

street retailers), food court and entertainment

zone (cinema, entertainment, and sports centre).

A shopping centre houses a variety of business

INTERNATIONAL JOURNAL OF REAL ESTATE STUDIES

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INTREST 13(1)/2019, 50-72

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Applying Grounded Theory Method for Measuring Employment

Opportunities Generated by a Shopping Centre Development

International Journal of Real Estate Studies, Volume 13, Number 1, 2019 Page 51

tenants mix created based on the available floor

space or let-able space inside and outside the

building. It generates business and, in turn,

employment and job opportunities for the locals.

A survey conducted by Hammerson (2013)

shows that the principal benefit received by

residents resulting from a new shopping centre is

the employment; 85% of it is created through

retailers to people living within 20 miles of the

centre, while 37% of the retail employment

benefits individuals who were previously

unemployed and claiming Jobseeker’s

Allowance. A shopping centre development has

strong growth in employment largely in retail

trade and services (REMI Consulting, Inc,

2006), and it provides a variety of employment

opportunity for the middle and lower-class

people (Khaleeque et al., 2004). Therefore, the

shopping centre is welcomed into the

community as an employment generator and the

economic stimulator (Mitchell, 2006; Voyce,

2006; and Ryan, 2006).

According to Tegner (2009), a single

shopping centre can offer employment for

approximately 2,000 people, while International

Finance Corporation (IFC) (2008) stated that a

shopping centre typically requires one worker

per 20 sqm (about 215 sqft) of space and, thus, a

medium-sized, 50,000 sqm shopping centre

employs approximately 2,500 full-time retail

staff, plus 50-70 workers in management and

support functions such as tenant relations,

administration, marketing, cleaning, security,

maintenance, gardening, and parking. A survey

conducted by Malaysian Shopping Malls

Association’s (PPK Malaysia) shows that there

are currently 671 malls with the net-lettable area

ranging from a minimum of 50,000 square feet

(sqft.) to 2.2 million square feet (sqft) and they

provide approximately 56,600 direct

employments (Eugene, 2018). Employment is

generally the most important means of obtaining

adequate economic resources which are essential

for economic well-being and full participation in

today’s society (Waddell and Burton, 2006).

Simply, the development of a shopping centre

results in many microeconomic variables

prospering the local standard of living as well as

providing a better quality of life for the

community.

Employment opportunities generated is one

of primarily benefits from the shopping centre

development (Ihuah and Benebo, 2014;

Hanachor, 2012; Carrero et al., 2009; Taylor,

2003); however, there was no numerical proof of

it. Because of the absence of past studies, there

was neither assessment nor measurement on

employment opportunities generated by a

shopping centre. Therefore, a grounded theory

method (GTM) is proposed to address this issue.

A GTM is used for several reasons as follows:

a) The theory exists namely employment

density ratio (EDR) or employment space

factor is too abstract to be tested and

demonstrably inadequate (Martin and

Turner, 1986; Carson et al., 2001;

Suddaby, 2006) for measuring

employment opportunities in the context

of this study.

b) There is no research has been conducted

specifically related to the approach or

method in measuring employment

generated by a proposed shopping centre,

therefore the GTM is appropriate method

for analysing the complex social

phenomenon (Carson et al., 2001),

particularly when there is little known

about the situation under-investigated

(Sarantakos, 2005; Martin and Taylor,

1986; Glaser and Strauss, 1967).

c) The GTM is employed in assessing the

employment opportunities that are

possibly generated by a proposed new

shopping centre development since it is

not possible to make a generalization on

these opportunities without going to the

ground and making observations on what

has actually taken place. This study is

interpretative (Carson et al., 2001). d) There is a need for inductive theory

development to explain the pattern of

employment distribution; the types of

employment exist in a shopping centre by

its business mixes classification, its

distribution percentages placement and its

density ratio.

Theoretically, in every development process,

a feasibility study is essential at the early stage

of the project’s life cycle before entering into the




actual construction works (Huh et al., 2012). A

feasibility study involves identifying and

analyzing the strengths and weaknesses of a

project and at the same time, determining the

project’s opportunities and threats within the

construction industry. Therefore, in the context

of a shopping centre development project, the

benefits in terms of employment opportunities

generated are one of the essential elements that

should be listed in the feasibility study, and thus,

the Local Authority can ensure that the project

development is in line with the needs of a

community, is feasible and is worth pursuing.

The numerical analysis of employment

opportunities possibly generated by the

development of a shopping centre helps to

strengthen the evidence for its desirability

among the local community. Besides, it

contributes to the improvement of the feasibility

analysis report, and indirectly improves the

government’s policy in the planning permission

during the development process.

The analysis of employment opportunities

demonstrated in this paper is carried out on the

basis of the planned net-lettable area (NLA) of a

proposed new shopping centre. However,

indirect employment opportunities possibly

created out of the proposed development of this

shopping centre such as kiosks, cleaning,

security, maintenance, gardening, and parking

are excluded in the analysis.

The paper is organized as follows. Section 2

discusses the grounded theory in order to

identify the framework of the study. Section 3

presents the data and methodology. Section 4

analyses the outcome of the survey data. Section

5 briefly discusses the limitation of the study.

The conclusion of the study is presented in

Section 6.

2.0 THE GROUNDED THEORY

METHOD (GTM)

2.1 Background

The GTM, developed by two sociologists,

Barney Glaser and Anselm Strauss in the early

1960s, is an inductive methodology that allows a

researcher to develop a conceptual account on

some phenomena of interest through empirical

observations of the real-world data with a

purpose of offering a logically consistent set of

data collection and analysis procedure aimed at

developing a theory (Martin & Turner, 1986;

Charmaz, 2003; 2006; Evans, 2013). The

emphasis in the GTM is theory development

(Strauss & Corbin, 1994). However, Punch

(1998) argued that GTM is not a theory at all; it

is a method, an approach, and a strategy, hence

it is best defined as a research strategy whose

purpose is to generate theory from data.

‘Grounded’ means that the theory is generated

on the basis of ground data; the theory will,

therefore, be grounded in the data. ‘Theory’ here

means that the objective of collecting and

analyzing the data is to conceptually explain a

new phenomenon of interest.

The GTM allows for the identification of

patterns in data that can help to drive a theory

that is empirically valid (Glaser and Strauss,

1967; Martin and Turner, 1986). This is so

because the theory-building process is so

intimately tied to the evidence that it is very

likely that the resulting theory will be consistent

with the empirical observation (Fernandez,

2004). By and large, it can be said that GTM

starts with some inductive logics (inductive

methodology) with which the theory is

systematically generated from a data collection

process.

A few studies have stated that in the GTM,

nothing happens in a vacuum (Glaser, 1978;

Glaser and Straus, 1967); everything is

integrated; it is an extensive and systematic

general methodology (independent of research

paradigm) where actions and concepts can be

interrelated with other actions and concepts.

This can be seen from a researcher’s ‘top-down’

work process - from theory to hypotheses to data

to add to or to contradict the theory (Creswell

and Plano Clark, 2007). Therefore, it can be seen

that in principle GTM is a deductive method.

GTM allows the researcher to develop a

theoretical account of the general features of the

topic while simultaneously grounding the

account in empirical observation of data. In

theory, the deduction begins with a general and

ends with a specific one, with arguments based

on law, rules and other widely accepted

principles are best expressed deductively

(Trochim, 2006).




2.2 The GTM in Measuring Employment

Opportunities

This paper applied GTM with mixed mode-

designs, which combines a qualitative and

quantitative data in measuring employment

opportunities generated form a proposed

shopping centre development. Although the

GTM tends to rely on qualitative data,

quantitative data may add more precision and

clearer understanding of the phenomenon of

interest as well as leads to unique insights

(Bhattacherjee, 2012). The jointly use of both

qualitative and quantitative data pertaining to the

employment generated by a shopping centre

significantly lead to the development of the

parameter of employment opportunities

measurement.

In this paper, a GTM with a systematic

design is used. In systematic design, the study

starts with the most specific information which

collected and summarized and moves to the

most abstract characteristics which were able to

be found through analyzing the data. However,

this study starts with the deductive approach, by

applying the existing theory of employment

density ratio (EDR) as a basis of measurement.

Then, a ground survey on several

comparable groups is conducted to collect all the

relevant data for measuring the employment

opportunity generated by a shopping centre. As

per LaRossa (2005) and Bowen (2005), it is

important to review prior research before

entering the field; one needs to know what

research exists and how others have treated a

particular topic so one can determine what

additional research is needed. Prior studies

provide a foundation, background, and context

of new research; it established a bridge between

the (proposed) research project and the extant

knowledge base. According to Suddaby (2006),

a common misperception in GTM is that

researcher enters the field without any

knowledge of prior research. Therefore, it is

suggested that the study should be aware of the

existing literature and the influence that might

have on their observations (Suddaby, 2006).

Generally, the employment generated or

created from a single property is measured on

the basis of employment density ratio (EDR),

measured as:

a) Square feet or square meter of required

floor area per person (Hamid, 2006;

Deloitte, 2010).

b) Average floor space per full-time

equivalent (FTE) member of staff

(Dancer, 2015).

c) Average number of employees per unit of

floor space provided (Fleming, 2014).

This means that EDR is used as a measure of

building use intensity (BUI) and it is an

indicator of how much space each person

occupies within the workplace. Therefore, EDR

is applied as a basis for measuring the

employment opportunities generated and then

the approach inductively generates theory from

the empirical set of data collection.

Theoretically, the GTM in this context of study

adopts inductive as well as deductive orientation

in investigating an issue. The application of

EDR as a basis is a deductive approach and the

theory generated from the data collection is an

inductive approach.

Table 1 shows the business components of a

shopping centre. As per Deloitte (2010) and

Dancer (2015), the EDR of business components

in shopping centre vary substantially in terms of

types of business (Table 2). The term

“components in a shopping centre” refers to the

retail mixes or retail business classification.

Table 1: Retail Mixes Classification

Mustel group (2010) Reikli (2012) ULI & ICSC (2008) Carter & Allen (2012)

Comparison: Children’s Wear Clothing and Accessories Family Apparel

Apparel Fashion Wear Clothing and Accessories Women’s Apparel

Footwear, Luggage & Fashion

Accessories Shoes Shoes Men’s Apparel

Cosmetics, Health, Bath &Beauty Health – Beauty Drug, Personal Service Jewellery

Jewellery, Watches & Accessories Pet and Pet Food Other Retail Card and Gifts

Electronics & Appliances Sportswear Hobby, Special Interest Fast Food



International Journal of Real Estate Studies, Volume 13, Number 1, 2019 4

Multimedia, Books & Music Books, Gifts Gifts, Hobby, Special Interest Home Furnishings

Home Furnishings & Accessories Jewelry-Accessories Jewelry Women’s shoes

Home Improvements Hypermarket Food Men’s & Boy’s Shoes

Toys, Hobbies,& Pets Food Court and Other

Food Food Service, Food Fast Food

Sporting & Recreational Goods Supermarket Food Specialty Food

Convenience: Furniture Home Furnishings

Grocery & Specialty Food Home Appliances Home Appliances – Music

Pharmacy Showroom Automotive

Alcohol & Tobacco Hardware Store Building Materials – Garden

Services Home Furnishings Home Furnishings

Food & Beverage: Services Personal Service, Other

Retail, Financial, Offices

Casual & Sit Down Dining/

Restaurant Entertainment Entertainment, Community

Entertainment & Leisure

Table 2: Employment Density Guide (Deloitte, 2010; Dancer, 2015)

Use Class Sub-category Sub-sector 2010 Density (sqm.) 2015 Density (sqm.)

A1

Retail (NIA)

High Street 19 15 - 20

Food store 17 15 - 20

Retail Warehouse 90 90

A2 Finance & Professional Services (NIA) 16 16

A3 Restaurants & Cafes (NIA) 18 15 - 20

D2

Fitness Centres

Budget

65

100

Mid-Market 65

Family

Cinema (GIA) 90 200*

Visitor & Cultural Attractions (GIA) 36 30 – 300

Amusement & Entertainment Centres (GIA) 70 70

*The cinema industry has been going through major restructuring in recent years, such that it has diversified

the range of facilities and improved the technology. Much of the cinema ticketing has now moved online,

reducing the need for cashiers and ticket sales staff within the cinema itself, replacing them with self-service

collection machine. Thus, within the greater range of facilities, employment densities within this market

segment tend to be higher.

Generally, the determination of components

in a shopping centre is related to the concept of

tenant selection. As per Reikli (2012), tenant-

selection study practically seeks to answer two

questions; (1) what components or retail mix

should be included in a shopping centre? (2)

How much space should these components or

retail mix get? In tenant selection, Baker (1999)

analyzed the International Council of Shopping

Centre’s data and found that 45% of the floor

space was assigned to fashion-wear retailer

(including women and family wear, and shoe);

22.5% to home furnishing; 11.25% to home

appliances, books, sport equipment, personal

services and jewel; and 6.1% to food (including

fast food and specialty food). In recent year, the

appearance of shopping centre shows a great

progress; consequently, the patterns of tenant

mixes are growing and changing. Mouton (2014)

analyses the percentages distribution placement

of tenant mixes for the convenience shopping

centre, which generally quite similar to the

regional and community shopping centre. The

distributions percentage space are as follows:

27% of food store or food specialties

(hypermarket or supermarket), 16% of fashion

and accessories, 9% of services, 8% of leisure

and entertainment, 7% of shoes, 7% of health

and beauty, 6% of coffee and restaurant, 6% of

sport equipment and fashion, 6% of household,

5% of kids and toys, and 3% of multimedia and

electronics. According to ICSC Research and

CoStar Realty Information, Inc., (2017),

convenience shopping centre provides for the

sale of personal services and convenience goods

similar to those of a community and regional

shopping centre. They are usually anchored by

some other type of personal/convenience service




such as a supermarket, hypermarket or

department store. However, they are different in

term of size (sqft) and the merchandising;

regional shopping centre offers general

merchandise or fashion-oriented, while the

community and convenience shopping centre

offer general merchandise or convenience-

oriented.

Bruwer (1997) and Greenspand (1987)

argued that tenant mix is an art or 'puzzle'

performed by a management team of a shopping

centre and it is not a static condition. Yuo et al.

(2004) argued that tenant mix always changes

over time as do the customer preferences and

fashion trends. Consequently, the management

team has to adjust the tenant mix constantly to

keep with the market trends. Nevertheless, the

best strategy for tenant selection merely sticks to

the concept that "a good tenant selection should

include a variety of compatible or

complementary retail/service providers as well

as sufficient public facilities/services, and

constantly enhance the quality of shopping

centre environment as well as satisfy shopper’s

need" (Yuo et al., 2004).

Generally, in the early stages of the

development of a shopping centre, the developer

still has not yet determined the tenant selection,

so, there is no information on such data.

Therefore, with the objective in mind and based

on the existing literature, the authors have set

three research questions, as follow:

a) What are the components exist in a

shopping centre?

b) What is the percentage distribution of

placement of each retail mix classes from

the total of NLA?

c) What is the employment density of each

retail mix classes?

The steps for assessing the employment density

of a shopping centre development project are

depicted in Figure 1.

Although the employment density figures in

Table 1 and Table 2 can be used as a basis for

measuring employment opportunities discussed

in this paper, the real-world situation of

employment density of a shopping centre is

different in each country. Accordingly, we

might compute various possible densities of

employment, which will make the results for a

particular shopping centre of interest

questionable. Therefore, to achieve the objective

of measuring employment opportunities possibly

generated by a proposed development of

shopping centre, a ground survey should be

conducted employing a constant comparative

method (CCM). CCM is one of the strategies in

the GTM. It entails an iterative process of

concurrent data collection and analysis which

involves “systematic choice and study of several

comparison groups” (Cho & Lee, 2014), and it is

important in developing a theory that is

grounded in the data (Glaser and Strauss, 1967).

A ground survey must be conducted on several

comparable groups within a cluster of

operational shopping centre.

Figure 1: Step in Measuring Employment Density and Its

Variables

(Author’s concept)

Generally, the GTM offers systematic and

flexible guidelines for data collection and

analysis to construct theories which consist of

abstract conceptualizations of substantive

problems. In this paper, CCM and the logic of

theoretical sampling are applied to collect only

those data, in those categories, which will

provide meaningful comparisons and lead to the

sought-after theory. Theoretically, CCM is used

to develop concept from the data by coding and

analyzing at the same time (Taylor & Bogdan,

1998). It incorporates four stages, namely

comparing incidents applicable to each category,

integrating categories and their properties,

delimiting the theory, and finally writing the

theory (Glaser & Strauss, 1967). Throughout

the four stages of the CCM, a researcher

continually sorts through data collection,




analyzed and code the information, and

reinforces theory generation through the process

of theoretical sampling. Bryman and Bell (2007)

highlighted that the most important tools of

GTM are constant comparison and theoretical

sampling together with coding and theoretical

saturation. Figure 2 shows the process of GTM

followed in this study.

In conclusion, it can be said that the GTM

begins with the deductive approach, which is an

adoption of the theory of EDR as a basis of

measurement, and then from the ground data on

a systematic research, a theory is generated (as

per Figure 3).

Figure 2: The Process of GTM


Figure 3: Inductive Approach with Deductive Orientation


a) What are the components existing in a shopping centre?

b) What is the percentages distribution of placement of each retail mix classes from the

total NLA?

c) What is the employment density of each retail mix classes?

Ind

ucti

ve a

pp

roach

Research questions

Theory of EDR is applied as a basis. Then the approach inductively generates theory

from the empirical set of data collection through ground survey.

A few selected operational shopping centres

Theoretical sampling

Data collection

Constant comparison

Theoretical sensitivity

Deductive approach

Coding

Saturated categories Sorting, writing/ theorizing, cross referencing with literature

Conceptualize / theorize Parameters of measurement

Open coding

Themes, sub-categories, and core categories

Axial coding

Emerging as a

central category

Selective coding

All categories unite around one/ more central

category; a systematic discovery of substantive

relations between the other categories

Decision to collect secondary data/documentation from Management Team of

comparable groups (answering research question ‘a’ & ‘b’) and verbal conversation/interview survey on retailers/workers of every comparable group

(answering research question ‘c’).

Theory Employment quotients (EQ)

& employment density (ED) figures

Theory of EDR

Retail mix class and its percentage distribution of

placement Constant

Comparison

Data

INDUCTIVE DEDUCTIVE




2.3 Development of Employment Density

Parameters

The Business Dictionary (2019) defines a

parameter as a measurable and constant or

variable characteristic, dimension, property, or

value selected from a set of data (or population)

considered to be essential to understand a

situation (or to solve a problem). Yee and

Bradford (1999) argued that a density parameter

is a building density, which is the square footage

of building per number of employees. In the

context of employment density, a parameter

refers to the EDR which is measured in square

feet or square meter of required floor area per

person.

From the ground survey of comparable

groups, the data of employment density (ED) for

every retail mix class of each comparable group

is obtained and calculated. This comes with

what is called employment quotient (EQ). EQ

gave a value of fraction or ratio. It is expresses

as:

EQ = NW/TNA (Equation 1)

where:

EQ = employment quotient

NW = total number of workers

TNA = total net-lettable area of floor space

by retail mixes class

For example, if TNA = 400 sqft and NW = 4

workers, EQ = 0.01. The effect of EQ is such

that any increase in the total NLA by retail mix

has multiple impacts on the number of workers.

This means, for each square foot of NLA, 0.01

number of worker will indirectly be created.

Theoretically, EQ differs by retail mix class.

Therefore, the measurement of employment

density of a shopping centre comes with a set of

EQ based on retail mix class, then; those

employment quotients will form an equation of

employment density. Simply, the employment

density equation (EDE) is derived from a set of

figures of employment quotients.

The equation above considers a perfect

situation, but in the complex real-world, no one

believes that we can capture precisely the

complexities of economic and business

behavior. Therefore, in developing EDE, a

general (linear) equation has to be developed as

follows:

𝑦 = 𝑎𝑥 + 𝑏

where:

“x” is an independent variable (predictor

variable), and “y” is a dependent variable

(criterion variable). “a” and “b” are the

coefficients, which are often real numbers. The

coefficient may be considered as parameters of

EDE. To yield a meaningful equation for non-

zero values of b, the coefficients are required not

to be all zeros.

In the context of this study, “a” refers to the

parameter, which is EQ () for retail classes,

while “b” refers to the autonomous employment

(). “y” is employment density (ED) and “x” is

net-lettable area for retail mix class (NLAj).

Therefore, ED = f(NLAj; ; ), expresses in

Equation 2.

EDE = + NLAj (Equation 2)

Theoretically, an autonomous describes

things that function separately and

independently (Merriam-Webster, 2019), or

does not explicitly depend on the independent

variable. For example, autonomous employment

in property real estate refers to the employment

existed on a particular site which not results

from any property development. This type of

employment considered automatic and

necessary, whether the property being developed

or not; such as employment created from a

business of car parking, stalls and etc.

In this study, autonomous employment is the

number of employment even if NLA = 0 for all

j. It can be anything such as indirect on-site

employment or on-site seasonal employment as

a result of the proposed shopping centre. Thus,

autonomous employment () refers to the

employment derived from special event

entertainment (SEE) held in a shopping centre

(such as Hari Raya Aidilfitri, Chinese New

Year, Christmas, Deepavali, etc), for which

some common area in a shopping centre is let

out but closed on other days. Those events

created business opportunities for retailers or

tenants to promote their products through the

floor space provided for SEE utilization.




Autonomous employment () in this study

came from the measurement of three

abovementioned elements, as follows:

1) Auxiliary space is the extra or temporary

space used by a shopping centre

management for some specialized events as

mentioned before. Auxiliary space

categorized as non-lettable area (NoLA),

which expressed as follows:

NoLA = GFA − NLA

Most of literatures describe NoLA as a

common area; however, NoLA specifically

refers to the not commonly lettable area

including the common area. The

components of NoLA vary among shopping

centre (for example mezzanine, exhibition

space, common area, sidewalks, foyer, etc)

(depicted in Figure 4). During the SEE,

some space from NoLA will be let out for

rent; it usually refers to a concourse area

(CA). A concourse is often located where

many paths or hallways meet (Wikipedia,

2019; Merriam-Webster, 2019). CA is large

enough for many people to congregate there

and is suitable for use as the SEE space. CA

usually forms some percentage (determined

by the management team) of the total

NoLA;

CA = % from NoLA

Figure 4: General Concept of NoLA

2) Employment quotient (EQ) for SEE. For any

SEE, the shopping centre management

allocates CA that will be rented out and

determines the number of tenants (T) to be

placed in the CA. Usually; one tenant has an

average worker (W) of two people.

Therefore, EQ for SEE is measured by

dividing the total number of workers for

every tenant with the total square feet area

of CA, expressed as follow:

EQ for SEE = (T × W)/CA

3) SEE intensity (SEEi). SEEi is the frequency

of SEE held in a shopping centre (how many

times of SEE per years). This varies from

one shopping centre to another and, thus,

will need to be ascertained from the

management team.

With all the relevant elements, the measurement

of autonomous employment is computes as

follows:

= CA × EQ × SEEi (Equation 3)

where;

= autonomous employment

CA = concourse area (% from NoLA)

EQ = employment quotient for SEE

intensity (average space allocated for

each worker during these event)

SEEi = intensity (how many times) of

special event entertainment

Therefore, an employment density equation

(EDE) developed in this study is expressed as

per Equation 2 above.

3.0 DATA AND METHODOLOGY

Based on the famous dictum of Glaser (2002),

“all is data”, the GTM utilizes many forms of

data from observations, interviews, and

documents, all of which could contribute to

generating a substantive theory. In this study,

CCM-GTM design was employed utilizing

data/information from six operational shopping

centres (selected as a comparable group) in

Johor Bahru, called CG6. The selection was

based on the occupancy rate - only a shopping

“The wide, open area, open

space or hall where crowds

gather”

(Wikipedia, 2019; Merriam-

Webster, 2019)

NoLA

Common area Concourse area (CA)

“Belonging to or shared by two or

more individuals or by all members

of a group”

(Wikipedia, 2019)

Common area of shopping centre

(Mike, 2014):

• Roofs and structure

• Entrances and malls Customer

area and facilities

• Service corridors and stairs

• Delivery and loading areas

• External walkways and

pavements

• Car park and roads

• Services supporting the above

• Mezzanine,

• exhibition space,

• lobbies,

• sidewalks or corridors

(a) Use for SEE




centre having an occupancy rate of over 80%

was selected. As per Richard Chan, Malaysia

Association for Shopping and High-Rise

Complex Management, a shopping centre with

an occupancy rate of over 80% is considered

well-performing (The Star Online, 2017). The

selected comparable groups comprised of

Galleria@Kotaraya, Angsana Johor Bahru Mall,

Paradigm Mall Johor Bahru, Sutera Mall,

Perling Mall, and Pelangi Leisure Mall. A

ground survey was conducted on those six

operational shopping centres, and all the data

collected to answer the research question

(Section 2.2, paragraph 9) were constantly

compared for their commonalities and

variations, then trace out to the emerging pattern

and theory. Finally, the theory of employment

generation was conceptualized. Data collection

was carried out in two stages.

The first stage dealt with the management

team of each comparable group. It has two

following objectives:

1) Identifying the components or retail mix

classes existing in a shopping centre and

determining the percentages distribution

placement of each retail mix classes from

the total NLA. Data on the retail mix and its

percentages distribution placement from the

total NLA were collected from the

documentation provided by the management

team of the CG6. By enlisting the

components or retail mix classes (Table 1)

and from the documentation obtained from

the management team, this study came up

with many classifications, making it difficult

to make a comparison of the results. To

overcome the problem, we developed a

concept of retail mix classes as per Table 3,

resulting in twenty-one components of retail

mix classes. The retail mix classes and its

corresponding percentage of placement for

each comparable group are shown in Table 3

and Figure 4.

2) Measuring autonomous employment. As

mentioned before, there were three

important elements that must be considered

in measuring the autonomous employment,

namely CA, EQ for SEE, and SEE intensity.

These data also collected from the

documentation provided by the management

team of the CG6. From all the CG6, only

three of them are productive in the context

of SEE, namely Galleria@Kotaraya,

Angsana Johor Bahru Mall, and Paradigm

Mall Johor Bahru. Table 4 shows the output

of the data obtained from the three

comparable groups aforementioned.

The second stage involves determining

employment density (ED) of every component

or retail mix classes. A simple structured verbal-

conversation or interview was conducted among

retailers or workers for each retail mix classes in

order to obtain the figure of ED. This simple

structured verbal-conversation or interview

conducted by addressing a specific aspect

namely the number of workers for the

respondents’ retail business. A dimensional

stratified random sampling procedure was used

for selecting the respondents. In this study, the

sample of respondents was arranged according

to ‘retail mix classes’ in the CG6; identifying the

dimensional strata as per Table 5, and then,

applying the random selection procedure to

select the subjects for each of the dimensional

stratum. The respondents were randomly chosen

but carefully selected based on their business

classification to ensure all the retail mix classes

were included. As many as 660 respondents

representing all retail mix classes were selected

from the CG6; 117 from Galleria@Kotaraya,

165 from Angsana Johor Bahru Mall, 295 from

Paradigm Mall Johor Bahru, 48 from Perling

Mall, and 35 from Pelangi Leisure Mall. All the

data collected were then compared constantly

among the CG6 to find a common pattern for

developing an equation of employment density

parameters. The ED of every components or

retail mix classes of each comparable group was

operationalized to calculate the EQ as per

Equation 1. The EQ by retail mix classes of each

comparable group are shown in Table 6.




Table 3: Percentages Distribution Placement of Retail Mix Classes of CG6

Retail Mixes Classification Galleria@

Kotaraya

Angsana

Johor

Bahru

Mall

Paradigm

Mall

Johor

Bahru

Sutera

Mall

Perling

Mall

Pelangi

Leisure

Mall

Mum & Baby Product 0.00% 0.47% 0.09% 0.00% 0.00% 0.00%

Clothing/Fashion wear 7.51% 16.48% 14.39% 20.30% 4.04% 0.06%

Shoe ware 1.00% 2.26% 1.31% 2.49% 4.95% 0.00%

Pet shop 0.00% 0.00% 0.14% 0.00% 0.00% 0.00%

Supermarket 10.97% 29.36% 21.07% 19.96% 26.60% 56.24%

Health & Beauty 4.80% 2.93% 3.36% 6.61% 5.90% 1.67%

Furniture 0.21% 0.00% 0.93% 0.00% 0.00% 0.82%

Sport & Fashion 0.00% 0.76% 1.08% 0.00% 0.00% 0.12%

Showroom 0.00% 0.00% 1.65% 0.00% 0.00% 0.00%

Book & gift 0.73% 3.78% 4.05% 0.00% 8.11% 0.42%

Hardware store/DIY 4.73% 0.00% 0.49% 0.00% 13.77% 3.30%

Mobile phone/CD/DVD 0.68% 1.91% 0.97% 1.96% 3.45% 0.39%

Home furnishing 0.00% 13.10% 13.34% 3.85% 0.00% 0.00%

Jewelry/accessories 1.41% 1.07% 0.97% 0.56% 1.18% 0.00%

Services 44.28% 1.18% 0.36% 1.45% 3.85% 0.96%

F & B (restaurant, food court) 9.37% 6.39% 10.14% 13.97% 7.15% 2.07%

F & B (coffee, bakery, cake) 1.47% 0.56% 1.67% 9.77% 0.00% 0.27%

Entertainment 0.00% 11.51% 5.58% 4.84% 0.24% 14.97%

Bag/Luggage 0.00% 0.58% 0.30% 0.72% 0.37% 0.10%

Optic/Watch 0.00% 1.52% 0.77% 1.27% 2.18% 0.41%

Cinema 0.00% 0.00% 4.88% 0.00% 10.40% 0.00%

Vacant 12.83% 6.12% 12.46% 12.25% 7.81% 18.21%

Others Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Figure 4: Percentages Distribution Placement of Retail Mix Classes of CG6

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

PERCENTAGES DISTRIBUTION PLACEMENT OF RETAIL MIX CLASSES OF CG6

Paradigm Mall Johor Bahru

Galleria@Kotaraya

Perling Mall

Pelangi Leisure Mall

Sutera Mall

Angsana Johor Bahru Mall




Table 4: CA, ED for SEE, and SEE intensity of CG6

Element Collected Data and Calculation Outcome (Average of output)

Concourse Area (CA)

Comparable Group 1

CA = 6,000 sqft

NoLA = 1,031,795 – 401,324.5 = 630,470.5 sqft

GFA NLA

% of CA = (6,000/630,470) x 100 = 0.95%

% of CA = 1.43% x NoLA

Comparable Group 2

CA = 0.8% x 1,310,950 = 10,488 sqft

GFA

% of CA = 0.8%

Comparable Group 3

CA = 9,219 sqft

NoLA = 546,530 – 184,383 = 362,147 sqft

GFA NLA

% of CA = (9,219/362,147) x 100 = 2.55%

Employment quotient (EQ)

of special event

entertainment (SEE)

Average worker per tenant

= 2 people

Comparable Group 1

Average number of tenant per SEE = 50 tenant

Average number of worker per SEE = 50 x 2 = 100

EQ for SEE = 100/6,000 = 0.0167

EQ for SEE = 0.0104



EQ for SEE = 60/10,488 = 0.0057

Comparable Group 3



EQ for SEE = 80/9,219 = 0.0087

Special event entertainment

intensity (SEEi)

Comparable 1

SEEi = at least 30 times

SEEi = 25 Comparable 2


Comparable 3


Therefore, autonomous employment () = 𝐂𝐀 × 𝐄𝐐 × 𝐒𝐄𝐄𝐢 = 1.43% (NoLA) x 0.0104 x 25 = 0.0037 NoLA




Table 5: Sample Size of CG6 Using Stratified Random Sampling Procedure

CG6 Galleria@Kotara

ya Johor Bahru

Mall

Angsana Mall

Johor Bahru

Paradigm Mall Perling Mall Pelangi Leisure

Mall

Retail Mix Classes Total Sample Total Sample Total Sample Total Sample Total Sample

Mum & Baby Product 0 0 2 2 1 1 0 0 0 0

Clothing/Fashion wear 20 20 59 53 48 48 9 9 1 1

Shoe ware 2 2 10 10 14 14 3 3 0 0

Pet shop 0 0 0 0 1 1 0 0 0 0

Supermarket 0 2 1 1 12 3 1 1 1 1

Health & beauty 8 8 12 12 36 35 8 8 6 6

Furniture 1 1 0 0 1 1 0 0 1 1

Sport & Fashion 0 0 3 3 3 3 0 0 1 1

Showroom 0 0 0 0 1 1 0 0 0 0

Book & gift 1 1 4 4 22 16 1 1 3 3

Hardware store/DIY 2 1 0 0 2 1 1 1 2 2

Mobile phone/CD/DVD 4 4 9 9 24 23 6 6 3 3

Home furnishing 0 0 13 13 18 17 0 0 0 0

Jewel/accessories 7 7 6 6 9 9 3 3 0 0

Services 37 37 9 9 10 8 7 4 6 4

F & B (restaurant, food court) 24 24 34 22 60 48 5 5 3 3

F & B (coffee, bakery, cake) 10 10 3 3 49 46 0 0 3 3

Entertainment 0 0 4 4 7 4 2 2 3 3

Bag/Luggage 0 0 2 2 4 4 1 1 1 1

Optic/Watch 0 0 12 12 12 11 3 3 3 3

Cinema 0 0 1 1 1 1

Vacant 36 24 110 13 12

Total 154 117 207 165 445 295 65 48 49 35 Notes: Due to the complex analysis where more than one category of an independent variable was measured, the sample size was calculated using

a sample proportion, which was the ratio of the sample size to the population size (Chua, 2016). Using this concept, the sample size of respondent aforementioned is sufficient, as the value of the sample proportion is more than 5%.

Table 6: Employment Quotient (EQ) of the CG6

Retail Profile

Galleria@

Kotaraya

Johor Bahru

Mall

Angsana Mall

Johor Bahru

Paradigm

Mall

Perling

Mall

Pelangi

Leisure

Mall

Mum & Baby Product 0.0036 0.0036 Clothing/ Fashion wear 0.0046 0.0049 0.0030 0.0064 0.0155

Shoe wear 0.0022 0.0036 0.0030 0.0026 Pet shop 0.0033 Supermarket 0.0027 0.0011 0.0021 0.0012 0.0007

Health & Beauty 0.0062 0.0054 0.0051 0.0067 0.0152

Furniture 0.0076 0.0016 0.0021

Sport & Fashion 0.0050 0.0023 0.0110

Showroom 0.0006 Book & gift 0.0030 0.0035 0.0056 0.0012 0.0062

Hardware store/ DIY 0.0013 0.0009 0.0010 0.0018

Mobile phone/ CD/ DVD 0.0101 0.0042 0.0099 0.0052 0.0091

Home furnishing 0.0028 0.0047 Jewellery/ accessories 0.0056 0.0102 0.0040 0.0104 Services 0.0064 0.0080 0.0086 0.0088 0.0063

F & B (restaurant, food court) 0.0097 0.0122 0.0063 0.0108 0.0066

F & B (coffee, bakery, cake) 0.0162 0.0080 0.0091 0.0135

Entertainment 0.0009 0.0010 0.0200 0.0011

Bag/luggage 0.0021 0.0033 0.0065 0.0083

Optic/watch 0.0070 0.0060 0.0071 0.0072

Cinema 0.0005 0.0015




In the GTM, reducing the data into

manageable units and coding information are an

integral part of the analysis process (Miles and

Huberman, 1994). The data in this study were

analyzed using the descriptive statistics which

comprises graphs, charts, tables and also the

calculation of various descriptive measures such

as average, variation as well as percentiles

(Larson, 2006). In analyzing data, each

interpretation and finding were compared to find

the commonality, variation, then, emerging

pattern and theory. Based on the outputs as per

Table 4 and Table 6, the analysis proceeded to

developing EDE for estimating employment

opportunities from the case study. Discussion

follows in the next section.

4.0 RESULT AND DISCUSSION

4.1 Retail Mix Classes

Table 3 and Figure 4 show the retail mix classes

of the CG6. Each percentage of placements from

the total NLA was different among them.

However, four of them have quite similar retail

mix placement, namely Angsana Johor Bahru

Mall, Perling Mall, Sutera Mall and Paradigm

Mall Johor Bahru. Retail mix placement of

Galleria@Kotaraya showed a significant

difference because of its function, mainly as a

shopping centre, besides functioning as an

Urban Transformation Centre of Johor (UTC

Johor) (providing government and private

sector’s core services to the community in a one-

stop centre). Therefore, the placement of retail

mix was more for services.

Appropriate retail mix placement has been a

long-term concern for the shopping centre’s

management team and researchers. However,

there is no hard-and-fast rule for the best

strategy for retail mix placement, so the

management team merely sticks to the

fundamental concept, whereby a good retail mix

should include a variety of compatible

retail/service providers, sufficient public

facilities and services. Nevertheless, the most

important aspect is the anchor tenant as per

Kyriazis and Cloete (2018). An anchor tenant

will have the most floor space and generates the

largest of a shopping centre’s business space.

Before analyzing the retail mix placement,

there was one important component that should

be excluded from the analysis, i.e. cinema. It

needs a different calculation for determining

floor space. Generally, the number of halls or

screens determines the cinema floor space (Park

and Ham, 2016; website of Independent Cinema

Office. There is no specific measurement for

determining the number of halls or screens for

cinema. It depends on the consideration and

decision of the developer for the said project.

However, the existing information pertaining to

the details of cinema in Malaysia (Wikipedia

Website, 2019) (Table 7) can be adopted as a

basis of measurement.

Table 7: Several Cinemas in Johor Bahru (Wikipedia Website, 2019)

Cinema Total Halls Seats Operator

City Square 14 2776 mmCineplexes

Paradigm Mall 16 2107 Golden Screen Cinemas

AEON Tebrau City 10 1731 TGV Cinema

Toppen Shopping Centre 11 1716 TGV Cinema

AEON Bukit Indah 9 1614 TGV Cinema

KSL City 8 1378 MBO Cinema

Plaza Tasek/Tasek Central 3 649 Lotus Five Star

Perling Mall 3 582 Blockbuster Cineplex

Table 7 shows the total halls for cinemas in

Johor Bahru are in the range of 3 to 16 halls.

Results from the ground survey show that the

Paradigm Mall Johor Bahru has the largest

cinema which comprises 16 halls and covering

an area of 64,038 sqft, while, Perling Mall has

the smallest cinema which comprises only 3

halls and covering an area of 8,762 sqft.




Generally, a cinema has an average of 8 unit of

halls (as per Wikipedia website, 2019), thus, this

study assumed a standard number of halls in a

cinema to be 8 units. The calculation of halls

quotient is expressed as follows:

Qsh = TSH/TCin (Equation 4)

where,

Qsh = Halls quotient,

TSH = Total unit of halls,

TCin = Total floor area of cinema

Thus, from these data, an analysis was made

to calculate the quotient of halls giving an

average figure of 0.00029. Accordingly, the total

sqft of floor space for cinema with 8 halls is

expressed as follows:

TCin =8

0.00029

= 27,590 sqft

Therefore, before calculating the percentage

placement of retail mix classes, the total NLA

for the subject property should deduct the total

standard area of cinema, as expressed as

follows:

TNLA − TCin = TsqftExCIN

TsqftExCIN = R1,2,3….(%) (Equation 5)

where,

TNLA = Total sqft of NLA of the

subject property,

TCin = Are defined above,

TsqftExCIN = Total sqft of NLA without

cinema,

R1,2,3… = Percentages retail mix based

on its classes

In this study, retail mix placement was

determined by analysing the percentages derived

from the CG6. From the analysis, a figure of

percentage placement for every retail mix in a

shopping centre was calculated. Although the

percentages placement of retail mix can change

according to any situation and factor, Figure 5

can be used as a basis for determining the

percentage placement of retail mix. Furthermore,

this figure seemed to be ideal because it

comprised all retail mix classifications with the

consideration of market trend.

Figure 5 show that the anchor tenant

(supermarket) occupied the largest percentages

of distribution placement. This was aligned with

Kyriazis and Cloete (2018), and Mouton (2014).

Generally, anchor tenant is pivotal in the

creation of the image of the shopping centre,

attracting shopper traffic and other tenants to the

shopping centre (Kyriazis and Cloete, 2018).

The anchor tenant is acknowledged as an

instrumental in positioning the shopping centre

in relation to its competitors, and therefore, it

allocates the largest space for distribution

placement in a shopping centre. Although the

percentages distribution placement of retail mix

classes in Figure 5 was not exactly the same to

Mouton (2014), the pattern of distribution

placement between the two quite similar and

parallel.




Figure 5: Percentages Distribution Placement of Retail Mix Classes

4.1.1 Validation Result: Percentages

Distribution Placement of Retail Mix

Classes

There are a few techniques of validation and one

of them is a comparison to other models

(Sargent, 2011) whereby various results (e.g.,

outputs) of the simulation model being validated

are compared to results of other (valid) models.

In the context of this study, the percentages of

retail mix placement in Figure 5 were validated

by comparing them with the actual percentages

of retail mix placement of the paradigm mall

Johor Bahru. The Paradigm Mall Johor Bahru

was adopted as a comparison because it was a

new shopping mall, growing and becoming a

focus and the divisions of retail mix placement

consist of the variety of compatible retail and

service providers.

Figure 6 and Table 8 show the percentages

of retail mix classes developed in this study

against the actual percentages of retail mix

classes within the paradigm mall. It shows a

similar pattern and a small range of differences

in percentages.

Figure 6: Percentages Distribution Placement of Retail Mix Classes (Output vs. Real Situation)

0.26%10.31%

2.56%

0.13%

28.59%

3.19%

0.81%0.61%1.54%3.81%

5.46%

1.62%

9.42%

0.88%

1.46%

7.41%

2.86% 6.93%

0.39%

1.15%

10.61%

PERCENTAGES DISTRIBUTION PLACEMENT OF RETAIL MIX CLASSES

Mum/Baby (0.26%) Clothing/Fashion (10.31%) Shoe (2.56%) Pet shop (0.13%)Supermarket (28.59%) Health/Beauty (3.19%) Furniture (0.81%) Sport/Fashion (0.61%)Showroom (1.54%) Book/gift (3.81%) Hardware/DIY (5.46%) Mobile/CD (1.62%)Homefurnishing (9.42%) Jewel/accessories (0.88%) Services (1.46%) F&B(1) (7.41%)F&B(2) (2.86%) Entertainment (6.93%) Bag/luggage (0.39%) Optic/watch (1.15%)Vacant (10.61%)

0%

5%

10%

15%

20%

25%

30%

35%

PERCENTAGES DISTRIBUTION PLACEMENT OF RETAIL MIX CLASSES

(OUTPUTS VS. REAL SITUATION)

Paradigm

Mall

Output




Table 8: Differences Percentages Distribution Placement of Retail Mix Classes (Output vs. Real Situation)

Retail Mixes Classification Differences

Mum/Baby 0.18%

Clothing/Fashion -4.08%

Shoe ware 1.26%

Pet shop -0.01%

Supermarket 7.52%

Health/Beauty -0.17%

Furniture -0.11%

Sport/Fashion -0.47%

Showroom -0.11%

Book/gift -0.24%

Hardware/DIY 4.97%

Mobile/CD 0.65%

Home furnishing -3.92%

Jewel/Accessories -0.09%

Services 1.09%

F & B (restaurant, food court) -2.73%

F & B (coffee, bakery, cake) 1.19%

Entertainment 1.35%

Bag/Luggage 0.09%

Optic/Watch 0.38%

Cinema -4.88%

Vacant -1.85% Notes: The differences between them are small, less than 5% except for the supermarket. However, the differences of less than 10% for the anchor tenant (supermarket) are considered acceptable. Usually, percentages placement of the anchor tenant is depending on the size of a

shopping centre; the larger the size of a particular shopping centre, the greater of the percentage placement is given to the anchor tenant.

4.2 Employment Density Parameter

The EQ in Table 6 was calculated by dividing

the total number of workers of a retail business

class with the total square footage of floor space

of the said retail business class. ED parameter

was then derived from the set of employment

quotients as mentioned in the previous section.

The employment quotient was computed as

follows:

EQr = ∑NWr

Tsqftr (Equation 6)

where,

EQr = Employment quotient

according to retail mix classes,

NWr = Total number of worker

according to retail mix classes,

Tsqftr = Total net-lettable are of floor

space by retail mix classes,

Table 6 above shows the EQ for each retail

mix classes of the CG6, and therefrom, the data

were analyzed and then, a set of EQ by retail

mix classes was developed. The development of

the EQ figure was based on the average EQ for

each retail mix of all comparable groups (Table

9).

Table 9: EQ by Retail Mix Classes

Retail Profile EQ

Mum & Baby Product 0.0036

Clothing/Fashion wear 0.0069

Shoe wear 0.0029

Pet shop 0.0033

Supermarket 0.0016

Health & Beauty 0.0077

Furniture 0.0038

Sport & Fashion 0.0061

Showroom 0.0006

Book & gift 0.0039

Hardware store/DIY 0.0013

Mobile phone/CD/DVD 0.0077

Home furnishing 0.0038

Jewelry/accessories 0.0076

Services 0.0076

F & B (restaurant, food court) 0.0091

F & B (coffee, bakery, cake) 0.0117

Entertainment 0.0058

Bag/Luggage 0.0051

Optic/Watch 0.0068

Cinema 0.0010




There were twenty-one predictor variables

for one criterion variable. The employment

density equation (EDE) was estimated as per

Equation 2 above, expressed as:

EDE = + NLAj

The value of autonomous employment () is as

per Table 4 above. Therefore:

ED = (0.0037 x NoLA) + (TsqftMB x 0.0036) + (TsqftCF x 0.0069) + (TsqftSW x 0.0029)

+ (TsqftPS x 0.0033) + (TsqftS x 0.0016) + (TsqftHB x 0.0077) + (TsqftF x 0.0038)

+ (TsqftSF x 0.0061) + (TsqftSR x 0.0006) + (TsqftBG x 0.0039) + (TsqftDIY x 0.0013)

+ (TsqftMC x 0.0077) + (TsqftHF x 0.0038) + (TsqftJA x 0.0076) + (TsqftSV x 0.0076)

+ (TsqftF&B1 x 0.0091) + (TsqftF&B2 x 0.0117) + (TsqftE x 0.0058) + (TsqftBL x 0.0051)

+ (TsqftOW x 0.0068) + (TsqftCN x 0.0010)

where, ED = total employment density

NoLA = non-lettable area

TsqftMB = total sqft Mum & Baby Product

TsqftCF = total sqft Clothing/Fashion TsqftSW = total sqft Shoe ware TsqftPS = total sqft Pet shop TsqftS = total sqft Supermarket TsqftHB = total sqft Health & Beauty TsqftF = total sqft Furniture TsqftSF = total sqft Sport/Fashion TsqftSR = total sqft Showroom TsqftBG = total sqft Book & Gift

TsqftDIY = total sqft Hardware store/DIY

TsqftMC = total sqft Mobile/CD/DVD TsqftHF = total sqft Home Furnishing TsqftJA = total sqft Jewelry/Accessories TsqftSV = total sqft Services TsqftF&B1 = total sqft F&B (restaurant, food

court) TsqftF&B2 = total sqft F&B (coffee, bakery,

cafe) TsqftE = total sqft Entertainment TsqftBL = total sqft Bag/Luggage TsqftOW = total sqft Optic/Watch TsqftCN = total sqft Cinema

4.2.1 Validation of Employment Density

The EDE abovementioned was validated by two

following techniques:

a) Case Simulation

A brief statement of the case study is given here.

This example gives some perspective of the

‘problem’ at hand; how a shopping centre

development generates employment

opportunities. The proposed development

(hereinafter referred to as the project) was a

shopping centre at Jalan Tampoi, Johor. It was

built on a 3 ha. flat commercial land. The project

was planned to be a three-storey building having

a gross floor area of 620,000 square feet and net

let-table area of 480,000 square feet. It was a

multi-purpose, one-stop shopping centre that

will offer a range of consumer products.

From the analysis above, the retail mix

classification placement and employment

density were as follows (Table 10):

Table 10: Simulation of the Proposed Project

Gross floor area = 620,000 sqft

Net lettable area = 480,000 sqft Non-lettable area (NoLA) = 620,000 sqft – 480,000 sqft = NLA without cinema (a) = 480,000 sqft - 27,590 sqft = 452,590 sqft

Retail Mix Classes

Percentages

placement(b)

Total

(sqft)(c) EQ(d)

ED

(person)(e)

ED

(person/sqft)(f)

Mum & Baby Product 0.26% 1,177 0.0036 4 278

Clothing/Fashion wear 10.31% 46,662 0.0069 322 145

Shoe wear 2.56% 11,586 0.0029 34 345

Pet shop 0.13% 588 0.0033 2 303

Supermarket 28.59% 129,395 0.0016 207 625




Health & Beauty 3.19% 14,438 0.0077 111 130

Furniture 0.81% 3,666 0.0038 14 263

Sport & Fashion 0.61% 2,761 0.0061 17 164

Showroom 1.54% 6,970 0.0006 4 1667

Book & gift 3.81% 17,244 0.0039 67 256

Hardware store/DIY 5.46% 24,711 0.0013 32 769

Mobile phone/CD/DVD 1.62% 7,332 0.0077 56 130

Home furnishing 9.42% 42,634 0.0038 162 263

Jewelry/accessories 0.88% 3,983 0.0076 30 132

Services 1.46% 6,608 0.0076 50 132

F&B (restaurant, food court) 7.41% 33,537 0.0091 305 110

F&B (coffee, bakery, cake) 2.86% 12,944 0.0117 151 85

Entertainment 6.93% 31,364 0.0058 182 172

Bag & luggage 0.39% 1,765 0.0051 9 196

Optic & watch 1.15% 5,205 0.0068 35 147

Cinema 27,590 0.0010 28 1000

Vacant 10.61% 48,020 0 Total 100% 480,000 1824

Notes: The figures were based on (b)(d)ground survey of CG6; (c)Total sqft of each retail mix classes =

(b)x(a); (e)Employment density = (c)x(d); (f)Employment density per person = (c)/(e)

From the EDE, the total employment density of

the case study was as follows:

ED = (0.0037 x 140,000) + (1,177 x 0.0036)

+ (46,662 x 0.0069) + (11,586 x 0.0029)

+ (588 x 0.0033) + (129,395 x 0.0016)

+ (14,438 x 0.0077) + (3,666 x 0.0038)

+ (2,761 x 0.0061) + (6,970 x 0.0006)

+ (17,244 x 0.0039) + (24,711 x 0.0013)

+ (7,332 x 0.0077) + (42,634 x 0.0038)

+ (3,983 x 0.0076) + (6,608 x 0.0076)

+ (33,537 x 0.0091) + (12,944 x 0.0117)

+ (31,364 x 0.0058) + (1,765 x 0.0051)

+ (5,205 x 0.0068) + (27,590 x 0.0010)

= 2,342 workers

As per International Finance Corporation

(IFC) (2008), a shopping centre typically

requires one worker per 20 sqm of space, thus, a

medium-sized, 50,000 sqm of shopping centre

could have employed approximately 2,500 full-

time retail staff. In the case of ‘subject

property’, with the total of 57,600 sqm., as many

as 2,342 of full-time retail employment could

have been generated. This figure was equivalent

to 24 sqm of space per worker. A small

difference between the outcome (subject

property) and the figure cited from literature was

considered acceptable.

b) Comparison to the Existing Literature

Employment density proposed by Deloitte

(2010) and Dancer (2015) as per Table 02 were

compared with the outcome of this study (Table

11) as follows:

Table 11: Comparison of Employment Density

Retail mix

classification

Sub-sector Employment

Density by

Deloitte &

Dancer

(sqm)

This

Study

(sqm)

Retail (NIA) High Street 15 - 20 12 - 32

Food store 15 - 20 12 - 58

Retail

Warehouse 90 24 - 71

Finance & Professional

Services (NIA) 16 12

Restaurants & Cafes (NIA) 15 - 20 8 - 10 (a)Fitness

Centre

Budget 100

108 Mid-

Market 65

Family

Cinema (GIA) 90-200 93 (b)Visitor & Cultural

Attractions (GIA) 30 – 300 87- 155

(c)Amusement &

Entertainment Centre (GIA) 70 16 - 93

Note: The item of (a), (b) and (c) were separated to shows the

comparison value of square meters per person between the existing

literature and the outcome. However, in this study, those items are classified into the same class and therefore, the employment

quotient gives the same value.




As per Table 11, the differences were

considered acceptable for not being too far

between the two. Furthermore, the employment

density of different country might vary

according to several aspects such as; energy

efficiency (sustainable design attempt to reduce

energy consumption such as using natural

ventilation rather than energy-intensive air-

conditioning), economic cycles, the unique

historical background in income, etc (Deloitte,

2010).

5.0 LIMITATION

This study attempted to estimate employment

opportunities generated by a proposed shopping

centre development project using physical

parameters. Generally, the development of a

shopping centre will be able to create a wide

range of employment opportunities. In this

study, however, the analysis did not cover all

types of employment. It only measured

employment opportunities generated on the basis

of physical space (net-lettable area – NLA)

available within a particular shopping centre.

Other elements of potential employment such as

those from kiosks, cleaning, security,

maintenance, gardening, and parking were not

included in the analysis. Differences in

autonomous employment among shopping

centres were also not analyzed.

Nevertheless, employment density equation

developed in this study can be used as a basis for

estimating the number of employment that can

be generated based on the NLA of a new

proposed shopping centre.

6.0 CONCLUSION

This study has demonstrated the application of

GTM for estimating employment opportunities

that can be generated from a proposed shopping

centre using employment density equation. This

equation was derived from a set of variables

analyzed from selected comparable groups. The

GTM was an important tool that has contributed

to the development of this equation.

Besides the employment density equation

developed in, this paper has also suggested an

ideal set of retail mix in a particular shopping

centre. The outcome of this study made a

contribution to the body of knowledge,

particularly in the employment analysis of retail

industry.

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