Identifying Emerging Industries through Meso-Micro Level Analysis

Tim Mazzarol, R.D. Patmore, N. Van Heemst

Research output: Contribution to journalArticle

Abstract

This paper examines the methodology employed in a project undertaken in Western Australia to identify and map the existence of industry clusters within the creative digital industries. Utilising a standard industry concentration and location quotient technique, the study team identified above average industry and employment concentrations in 59 selected industries considered to have high levels of digital content intensity. The study found industry and employment concentrations in seven key areas: i) spatial sciences; ii) engineering; Hi) construction; iv) education and training; v) creative; vi) media and vii) medical science. These were then grouped into two potential industry clusters. The first focused on the Digital Spatial Industries and the second on the Digital Creative Industries. Analysis of longitudinal data sets found emerging and established industry segments within these two clusters and case study and survey findings identified issues facing businesses within these sectors. These findings point to the need for cluster development to be based on both statistical foundations from established employment and industry data sets, and an in-depth assessment of the activities at the business unit level gathered through survey and case studies.
Original languageEnglish
Pages (from-to)49-56
JournalJournal of New Business Ideas and Trends
Volume3
Issue number2
Publication statusPublished - 2005

Fingerprint

Industry
Industry cluster
Longitudinal data
Western Australia
Digital content
Industry location
Location quotient
Industry concentration
Industry studies
Cluster development
Creative industries
Methodology
Education
Industry data

Cite this

@article{89a355d6388a45bb903af6bcf84033fb,
title = "Identifying Emerging Industries through Meso-Micro Level Analysis",
abstract = "This paper examines the methodology employed in a project undertaken in Western Australia to identify and map the existence of industry clusters within the creative digital industries. Utilising a standard industry concentration and location quotient technique, the study team identified above average industry and employment concentrations in 59 selected industries considered to have high levels of digital content intensity. The study found industry and employment concentrations in seven key areas: i) spatial sciences; ii) engineering; Hi) construction; iv) education and training; v) creative; vi) media and vii) medical science. These were then grouped into two potential industry clusters. The first focused on the Digital Spatial Industries and the second on the Digital Creative Industries. Analysis of longitudinal data sets found emerging and established industry segments within these two clusters and case study and survey findings identified issues facing businesses within these sectors. These findings point to the need for cluster development to be based on both statistical foundations from established employment and industry data sets, and an in-depth assessment of the activities at the business unit level gathered through survey and case studies.",
author = "Tim Mazzarol and R.D. Patmore and {Van Heemst}, N.",
year = "2005",
language = "English",
volume = "3",
pages = "49--56",
journal = "Journal of New Business Ideas and Trends",
issn = "1446-8719",
number = "2",

}

Identifying Emerging Industries through Meso-Micro Level Analysis. / Mazzarol, Tim; Patmore, R.D.; Van Heemst, N.

In: Journal of New Business Ideas and Trends, Vol. 3, No. 2, 2005, p. 49-56.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Identifying Emerging Industries through Meso-Micro Level Analysis

AU - Mazzarol, Tim

AU - Patmore, R.D.

AU - Van Heemst, N.

PY - 2005

Y1 - 2005

N2 - This paper examines the methodology employed in a project undertaken in Western Australia to identify and map the existence of industry clusters within the creative digital industries. Utilising a standard industry concentration and location quotient technique, the study team identified above average industry and employment concentrations in 59 selected industries considered to have high levels of digital content intensity. The study found industry and employment concentrations in seven key areas: i) spatial sciences; ii) engineering; Hi) construction; iv) education and training; v) creative; vi) media and vii) medical science. These were then grouped into two potential industry clusters. The first focused on the Digital Spatial Industries and the second on the Digital Creative Industries. Analysis of longitudinal data sets found emerging and established industry segments within these two clusters and case study and survey findings identified issues facing businesses within these sectors. These findings point to the need for cluster development to be based on both statistical foundations from established employment and industry data sets, and an in-depth assessment of the activities at the business unit level gathered through survey and case studies.

AB - This paper examines the methodology employed in a project undertaken in Western Australia to identify and map the existence of industry clusters within the creative digital industries. Utilising a standard industry concentration and location quotient technique, the study team identified above average industry and employment concentrations in 59 selected industries considered to have high levels of digital content intensity. The study found industry and employment concentrations in seven key areas: i) spatial sciences; ii) engineering; Hi) construction; iv) education and training; v) creative; vi) media and vii) medical science. These were then grouped into two potential industry clusters. The first focused on the Digital Spatial Industries and the second on the Digital Creative Industries. Analysis of longitudinal data sets found emerging and established industry segments within these two clusters and case study and survey findings identified issues facing businesses within these sectors. These findings point to the need for cluster development to be based on both statistical foundations from established employment and industry data sets, and an in-depth assessment of the activities at the business unit level gathered through survey and case studies.

M3 - Article

VL - 3

SP - 49

EP - 56

JO - Journal of New Business Ideas and Trends

JF - Journal of New Business Ideas and Trends

SN - 1446-8719

IS - 2

ER -