Apjie Vol.9 No.2 Final 1

Editor in Chief:

Bong Jin Cho (Korea, E-mail: [email protected])

Hermina Burnett (Australia)

Deepanwita Chattopadhyay (India)

Daniel L. Friesner (U.S.A)

Chih-Hung Hsieh (Taiwan)

Yun Hwangbo (Korea)

Tommi Aleksanteri Inkinen (Finland)

R.M.P. Jawahar (India)

Lynn Kahle (U.S.A)

Akkharawit Kanjana-Opas (Thailand)

Phillip Kemp (Australia)

Harald F.O. von Kortzfleisch (Germany)

Abdul Aziz Ab Latif (Malaysia)

Ki Seok Lee (Korea)

David A. Lewis (U.S.A)

Xiaoming Liu (China)

Zhao Min (China)

Patricia Ordoñez de Pablos (Spain)

Rosemarie Reynolds (U.S.A)

Aviv Shoham (Israel)

Zhen Wang (China)

Dong Kyu Won (Korea)

Benjamin J.C. Yuan (Taiwan)

Editorial Board:

Tanyanuparb Anantana (Thailand)

Dong Ok Chah (Korea)

Check Teck Foo (Singapore)

Jin Hwan Hong (Korea)

Ching Yao Huang (Taiwan)

Choong Jae Im (Korea)

Rajendra Jagdale (India)

Wen-Jang (Kenny) Jih (U.S.A)

Janekrishna Kanatharana (Thailand)

Tomoyo Kazumi (Japan)

William Walton Kirkley (New Zealand)

Hyeong San Kye (Korea)

In Lee (U.S.A)

Pui Mun Lee (Singapore)

Zhan Li (China)

Gilroy Middleton (Belize)

Karen E. Mishra (U.S.A)

Hadi K Purwadaria (Indonesia)

Saras D. Sarasvathy (U.S.A)

Enrico Plata Supangco (Philippines)

Richard White (New Zealand)

Chang Seob Yeo (U.S.A)

Yuli Zhang (China)

Associate Editors

Dinah Adkins (U.S.A) Richard P. Bagozzi (U.S.A)

Sun Young Park (Korea) Tan Yigitcanlar (Australia)

JinHyo Joseph Yun (Korea)

mailto:[email protected]

ISSN 2071 - 1395

Asian Association of Business Incubation

Copyrightⓒ2015 by AABI, All Rights Reserved

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CONTENTS

Preface

Editor in Chief, Bong Jin Cho

Australian Innovation Ecosystem: A Critical Review of the National

Innovation Support Mechanisms

Jamile Sabatini Marques, Tan Yigitcanlar, and Eduardo Moreira da Costa

An Empirical Analysis about the Equipment-Intensive Public

Enterprise's Innovation Performances Affected by Service Innovation

Activity and Cooperation

Byoung-sun Kim, Sun-young Park, and Young-whan “Nick” Lee

Evaluating the Performance of Disaster Recovery Systemic Innovations

by Using the Data Envelopment Analysis

Chia-lee Yang, Benjamin J.C. Yuan, Chi-yo Huang, and Chih-neng Chang

Fueling Economic Prosperity through Incubation System: A Case Study

from an Eastern Indian Province

Manisha Acharya and Subhransu S Acharya

The Effect of Innovation Activities and Governmental Support on

Innovation Performance: Comparison between Innovative SMEs and

General Companies

Juil Kim and Sun-young Park

Publication Ethics and Malpractice Statement for the Asia Pacific

Journal of Innovation and Entrepreneurship

Call for Papers 133

125

93

77

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Volume 9, No.2, 2015 1

Preface

Bong Jin Cho, Ph. D., Editor in Chief

Innovation is, we may think, more than often made by genius people, such as Edison, an

invention king, Michelangelo, an artistic genius, and Bill Gates and Steve Jobs, computer

prodigies. Professor David Burkus of Oral Roberts University, a strategic leadership expert in

innovation, claims1 that they are instead the figureheads of the strong teams, who have been

meticulously orchestrating the innovative work of their creative teams. The conditions that one

could be called as a genius require an excellent supporting team and excellent system that will

raise that genius, and incredible efforts performed by himself/herself.

For example, Michelangelo who was referred to as an artistic genius had a team with 13

highly talented artists. As well, Edison, who invented electric bulb, had the research team of

Monroe Park, and Steve Jobs also had an excellent a group of R&D team. Without a strong

support team, any of these figureheads could have not had such reputation. Not to say that the

figurehead was genius himself, rather s/he leaded the team in a creative and genius way. How

could Edison invent the electric bulb and many other inventions without his team including

Charles Bachelor and John Adams and many others? Steve Jobs and Bill Gates are known as the

inventors of the graphic computer interface; however, in reality the technology was developed by

the researchers of Xerox‘s Palo Alto Research Center (PARC) in 1970. Even before that the early

prototype of this technology had been developed by Burniva Bush, an army engineer in 1945. It is

not common to invent any breakthrough at once by oneself without combination of any existing

technology and devices. In this sense, the figureheads are the leaders of creative and genius teams.

In short, innovation is about a productive combination of existing technology and devices

developed earlier with a genius as the team leader, who creatively operates the efficient system of

combinations with extraordinary hard working group of talented team members.

Invention is like a team sport such as football, rugby, basketball and volleyball as well as a

musical symphony. It is a well-known fact that artistic genius Michelangelo has worked over 20

hours a day only taking bread with some wine. He said that ―if they have known how hard I have

worked for painting this picture, they will never call me a genius.‖ In the Lee Dynasty, there was

a scholar named Dasan Chung, Yak Yong who wrote more than 500 books in his life. Most of

these books had been written in his place of exile over a period of 18 years. It is believed that 25

Asia Pacific Journal of INNOVATION AND ENTREPRENEURSHIP 2

books have been written in a year or two books per month for 20 years continuously. How could

this many books be written without the help of his fellow students? Of course, he was a great

scholar working hard. This is an efficient system to work forward to achieve a common objective

that the genius leader has adopted. To come up with an innovative idea might take only a

moment, but that moment is the accumulation of incredible works and intensive pursuit of

thoughts about what he/she has been working hard for a very long time.

2015 AABI General Assembly and APJIE International Conference on ―Innovation and

Entrepreneurship in Creative Economy‖ was held in Korea on October 28 through 29, 2015 in

Daegu, Korea. The 2015 APJIE International Conference is focused on the innovation, Incubation,

Management of Technology and Service Innovation and Marketing in Creative Economy.

The main discussion of the conference is focused on investigating the main contributing

factors of innovation and entrepreneurship in creative economy with the performances affected by

the contributing factors. The APJIE Desk selected five papers, among the submitted 12 papers

from the conference, through a blind peer-review process.

The first paper introduces and reviews Australian innovation ecosystem for the national

innovation support mechanism. The second one focuses on the equipment–intensive public

enterprise‘s innovation performance affected by service activity and cooperation. The third paper

is about the evaluating the performance of disaster recovery systemic innovations by using the

data envelopment analysis. The fourth paper is on the topic of fueling economic prosperity

through incubation system with a case study investigation from an Eastern Indian province. The

last paper covers the effect of innovation activities and governmental support on innovation

performance. The APJIE Desk is most grateful to the authors of these five papers for their

contribution to the journal with their quality submissions. We believe our readers all across the

globe will enjoy reading these papers.

The APJIE Desk also cordially offers our special thanks to AABI (president Yeung-Shik

Kim), KOBIA (president, Hyeong-San Kye), ISBA (president, Rajendra Jagdale) and STIC

(president, Zhen-Hong Zhu, president-elect of AABI) for their financial support for the

publication of the APJIE by the Emerald Publishing House in 2016 through 2018.

Thank you!

1 Source: An interview with professor David Burkus by the reporter HyeHoon Lee in London. From the excerpt by Dr,

Kwang Lee, professor emeritus, Keimyung University, Daegu Korea.

Volume 9, No.2, 2015 3

Australian Innovation Ecosystem: A Critical Review of

the National Innovation Support Mechanisms

Jamile Sabatini Marques, Tan Yigitcanlar

,

and Eduardo Moreira da Costa

Abstract

Innovation is understood as the combination of existing ideas or the generation of new

ideas into new processes, products and services, and widely viewed as the main driver of growth

in contemporary economies. In the age of the knowledge economy, successful economic

development is intimately linked to a country’s capacity to generate, acquire, absorb, disseminate,

and apply innovation towards advanced technology products and services. This development

approach is labelled as knowledge-based economic development and highly associated with a

capacity embodied in a country’s national innovation ecosystem. The research reported in this

paper aims to critically review the Australian innovation ecosystem in order to provide a better

understanding on the potential impacts of policy and support mechanisms on the innovation and

knowledge generation capacity. The investigation places Australia’s innovation system and

national-level innovation support mechanisms under the microscope. The methodology of the

study is twofold. Firstly, it undertakes a critical review of the literature and government policy

documents to better understand the innovation policy and support mechanisms in the country. It,

then, conducts a survey to capture Australian innovation companies’ perceptions on the role and

effectiveness of the existing innovation incentive programs. The paper concludes with a

discussion on the key insights and findings and potential policy and support directions of the

country to achieve a flourishing knowledge economy.

Key words: Innovation; innovation ecosystems; national innovation systems; incentive

programs; knowledge economy; knowledge-based economic development; Australia.

Visiting Doctoral Researcher, School of Civil Engineering and Built Environment, Queensland University of

Technology, 2 George Street, Brisbane, QLD 4001, Australia, Tel: +61.7.3138.9124, E-mail: [email protected] Corresponding author, Associate Professor, School of Civil Engineering and Built Environment, Queensland University

of Technology, 2 George Street, Brisbane, QLD 4001, Australia, Tel: +61.7.3138.2418, E-mail: [email protected] Professor, Engineering and Knowledge Management, Federal University of Santa Catarina Campus Universitário,

Trindade, CEP 88040-900, Florianópolis, SC, Brazil, Tel: +55.48.3721.2449, E-mail: [email protected]





1. Introduction

Innovation basically means changing the way we do things (Zhao, 2005; Pancholi et al.,

2015). Australia‘s economy is at significant risk due to its lack of innovation-driven industries.

This includes those sectors related to information and communications technologies (ICT),

sciences, creative industries (e.g., media- and design-based industries) and others that rely on high

levels of knowledge and human capital. It is widely accepted that innovation has a significant

influence upon economic growth (Porter, 1990; Glaeser, 2011; Caragliu & Nijkamp, 2014; Romer,

2014). It is estimated that innovation can boost economic growth by as much as 50% (OECD,

2010). However, Australia currently struggles to capitalise on the innovation opportunity and

heavily relies on knowledge and innovations generated overseas (OECD, 2012). This poor

performance is evident in the recent Global Competitiveness Index, where in the innovation

category Australia only ranks 19th out of 34 OECD countries (WEF, 2014). Compounding the

lack of innovative industries, there is limited industry diversity and an overdependence on

commodity exports in the country. This condition creates a significant risk to Australia‘s medium-

and long-term productivity growth and the sustainability of its economy (DoIS, 2013).

Recognising these challenges, the Australian government has recently called for a new agenda for

industry innovation and competitiveness (Commonwealth of Australia, 2014).

Although since the 1990s Australian government has prepared a number of policy

initiatives for seeking to diversify economic activities and improve the use of innovation as a tool

to achieve global competitiveness (Yigitcanlar et al., 2008a, 2008b), the strength in the resource-

based economy held back most of these efforts to establish robust knowledge economy

foundations in the country. Australia, especially during the latest mining and energy boom era

(2005-2013)—due to heavy demand on Australian iron, coal, uranium, and gas—was one of the

world‘s fastest growing economies. During this boom period, a confluence of events has boosted

world mineral prices and mining investments. This has significantly increased the citizens‘

purchasing power—raising per capita household disposable income by 13% and real wages by

6%, and decreasing unemployment by 1.25 percentage points. The large volume of export

performance achieved during this period has impacted Australian economy to grow faster

(Downes et al., 2014).

Australia, today, invests and supports science, technology and innovation (STI) modestly.

Consequently the export of new technologies is insignificant—only producing 3% of world

knowledge, and heavily relying on innovations generated overseas (Commonwealth of Australia,

Volume 9, No.2, 2015 5

2014). However, the end of the mining and energy boom redirected the attention of government to

diversification of the economy and investing on other options to support the innovation ecosystem

in the country. Almost in consensus Australian scholars advocate that the only way to sustainable

growth of the country‘s economy is to increase individuals and businesses‘ competitiveness levels

(see Enright & Petty, 2013). This is to say, with policy and support mechanisms well designed

and distributed, such as in Finland (see Satarauta, 2012), Australian entrepreneurs will be able to

enjoy the opportunities created by the global knowledge economy. Otherwise, Australia‘s global

competition in the era of the knowledge economy may be harmed. With this idea in mind, in the

2014-2015 fiscal year the government allocated a budget of about $9.2 billion for supporting STI

education and R&D (for a detailed breakdown see DoIS, 2015). The budget is distributed through

the Commonwealth Government‘s Department of Industry and Science. This department is also in

charge of the development of the ‗Australian innovation system (AIS)‘, which an open network of

public and private organisations that produce and disseminate knowledge and practices that add

economic, social or environmental value to Australian products and services (DoIS, 2014).

Against this background, the study aims to provide a deeper understanding of the role and

effectiveness of existing policy and support mechanisms on Australia‘s innovation and knowledge

generation capacity. The research scrutinises Australia‘s innovation ecosystem thoroughly by

reviewing the literature and government policy documents, and surveying Australian innovative

companies. The study concentrates on the national scale, and undertaking explorations at the state

and local government level policy and incentive programs are beyond the scope of this research—

that is a limitation of the study. The results of the review and analysis generate insights on the

innovation policy and national-level innovation support instruments along with Australian

innovation companies‘ perceptions on the innovation incentive programs. Furthermore, the

findings pinpoint potential policy and support directions of the country to achieve a flourishing

knowledge economy performance.

2. Australian Innovation Support Programs

Australia was ranked as the 12th largest economy in the world with an estimated GDP of

around $1.5 trillion in 2014, and the 6th largest country with an area of over 7.5 million km2.

Amongst the developed countries, Australia positioned itself 5th in terms of its per capita income,

and took the 2nd position in the 2013 Human Development Index (HDI). On the one hand,

Australia is relatively disadvantaged globally due to its small population of slightly under 24

million people making it only the 51st most populated nation. This population size brings


limitations for the consumption economy and talent base of labour force. However, with

immigration policies, particularly the skilled migration scheme, Federal government targets to

support the required talented workforce and population increase. On the other, a reason for

Australia‘s such a high ranked position was that about 60% of productivity growth in the country

was driven from intangible capital investment—that is skills development, design and

organisational improvements and spill over effects. However, when compared to the other OECD

member countries, Australians are more likely to invest on machinery and equipment than

investing on intangibles (OECD, 2014). The main reason is that innovation in Australia is

generally practiced as concentrated efforts focusing on consolidating the competitive advantages

of sectors such as mining and agriculture, as opposed to investments on ICT, biotechnology,

nanotechnology and so on (Martinez-Fernandez, 2010). In other words, so far no other industry in

Australia has achieved a greater significance in economic development as much as mining and

agriculture. Particularly mining industries have built a national infrastructure throughout the

country for more than a century and Australia‘s mining boom has produced generations of mining

technology services companies. Despite this innovation focus, one of the strengths of Australia is

the ability to rather quickly transform its innovation governance and legislation systems in order

to be at par with the world trends (OECD, 2015). With such capability at the end of the mining

and energy boom Australia still has the potential to make its transformation into a knowledge

economy.

2.1 Governance of Innovation in a Nutshell

In Australia, a number of governmental organisations play a pivotal role in delivering the

innovation agenda. In an attempt to better understand how Australian innovation system works,

these organisations that have been providing innovation incentives to companies are introduced to

understand their roles in delivering the country‘s innovation policy.

The Department of Industry, Innovation and Science (DIIS): The mission of this

administrative office is to establish the connections between businesses, research institutes,

tertiary education bodies, government departments, and the society at large. Its main objective is

to sponsor and support productivity growth in Australia by means of developing human capital.

This department has seats in several national innovation committees to promote these networks,

according to the Australian Public Services Innovation Action Plan. The plan focuses on the

following four action areas: (i) Developing an innovation consciousness with the Australian

public services; (ii) Building innovation capacity; (iii) Leveraging the power of co-creation, and;

(iv) Strengthening leadership so there is courage to innovate at all levels. The aims of this plan

Volume 9, No.2, 2015 7

are: (i) Recognise innovation as a process that can and should be systematically pursued; (ii)

Involve the users and citizens in the design and development of services and policies; (iii) Pursue

open processes that encompass a wide range of experience and expertise; (iv) Generate results

through involvement utilising partnerships and collaboration; (v) Facilitate the creativity inherent

in organisations, and welcome tests, pilots and experiments; (vi) Recognise risk as an inherent

part of innovation; (vii) Promote and celebrate innovation successes; (viii) Acknowledge that not

all innovation will succeed, but we can also learn from failures; (ix) Use procurement to spur the

generation and uptake of innovative solutions, and; (x) Be accountable for delivering and

implementing the plan and successor initiatives (see http://www.industry.gov.au).

The Australian Research Council (ARC): ARC is the main office of the Australian

government for the investment on research and training in all fields of science, including social

and human sciences. It is also responsible for mediating the relation between researcher

communities and the industry, government, non-profit organisations and the international

community. The ARC aims to integrate researchers and the industry. ARC manages the following

programs as major incentive sources to develop knowledge, associated with research scholarships

for the formation of researchers, and with the universities: (i) The Linkage Projects scheme aims

to set up or develop strategic long-term research alliances between higher education institutions

and other organisations, including the industry and users; increases the scope and focus of

researches in National Research Priorities; sponsor opportunities for researchers to develop

internationally competitive researches in cooperation with organisations out of the higher

education sector; and produce a national network of world-class researchers to meet the broadest

demands of the Australian innovation system; (ii) The National Competitive Grants Program

(NCGP) is one of Australia‘s major investment mechanisms for R&D. This program grants

scholarships for basic and applied research, apart from funding research training in all academic

areas except clinical medicine and dentistry—the National Health and Medical Research Council

(NHMRC) looks after this area. (iii) The Excellence in Research for Australia (ERA), in turn, is

the program for evaluating the quality of researches conducted by the higher education institutions

of Australia. The ERA aims to guarantee the excellence of the conducted investigations. This

office publishes, for example, a comparison between the levels of researches carried out in the

country with international standards in each field (see http://www.arc.gov.au).

The Commonwealth Scientific and Industrial Research Organisation (CSIRO): CSIRO

aims to offer innovative solutions to the industry, society and the environment through the

development of cutting-edge science. The organisation employs over 6,500 workers and

researchers, distributed into 57 centres all across Australia, which dedicate to four programs: (i)


The national research flagships are multidisciplinary partnerships for large-scale research that use

the international-level expertise to serve the national priorities. The program commenced in 2003

and is one of the biggest efforts Australia has ever put into researching, with a total investment of

over $1.5 billion in the fiscal year of 2010-2011. The main sectors that has received support are:

climate adaptation, minerals down under, energy transformed, preventive health, food futures,

sustainable agriculture; future manufacturing, water for a healthy country, wealth from oceans and

light metals. (ii) The core research and services program comprises a series of research portfolios

that do not match the flagships. In 2010-2011, five CSIRO research groups managed 12 portfolios,

in the fields of energy, environment, food, health, life sciences, information sciences,

manufacturing, materials and minerals. (iii) The science outreach: education and scientific

publishing is a set of science education programs for primary and secondary school students and

teachers, as well as the general public. The maintenance of the CSIRO Discovery Centre in

Canberra is part of this program, and; (iv) National research infrastructure: national facilities and

collections is the CSIRO program responsible for the administration of two kinds of research

infrastructure: national research facilities and national biological collections. Apart from these

infrastructures, CSIRO comprises 30 other research installations, such as the Australian

Resources Research Centre (in Perth) and the High Resolution Plant Phonemics Centre (in

Canberra), and more than 30 collections of national importance, including the national tree seed

collection, the national soil archive and the cape grim air archive (see http://www.csiro.au).

The Chief Scientist for Australia: Apart from a large number of researchers focusing on

various R&D activities, Australia also has an Australian Chief Scientist, who provides high-level

independent counselling to the Prime Minister and other ministers on the issues related to STI.

The person in position, currently Professor Ian Chubb AC, is a defender of Australian science

worldwide and disseminates to the community and government the importance of STI, research

and empirical evidence. The Chief Scientist for Australia is also a spokesman for science to the

public in general, with the aim to promote the understanding, contribution and pleasure of science

as well as evidence-based reasoning (see http://www.chiefscientist.gov.au).

The Australian Taxation Office (ATO): As being the government taxation office ATO is

the main office that regulates the incentive programs related to innovation in the country (ATO,

2015). The incentives go through this taxation office takes place through tax reductions—i.e.,

R&D Tax Incentive Program (see https://www.ato.gov.au).

The Innovation Australia: Innovation Australia is an independent organisation created to

help the Australian government to manage its innovation programs and risky investment plans

Volume 9, No.2, 2015 9

designed to support industrial innovation through a number of programs: Clean Technology Food

and Foundries Investment Program; Clean Technology Innovation Program; Clean Technology

Investment Program; Climate Ready; Green Car Innovation Fund; Re-Tooling For Climate

Change; Renewable Energy Development Initiative (REDI); R&D Tax Concession (including the

R&D Tax Offset and 175% Premium Incremental Tax Concession); R&D Tax Incentive;

Commercialisation Australia Program (CA); Commercialising Emerging Technologies

(COMET); Commercial Ready (including Commercial Ready Plus); Industry Cooperative

Innovation Program (ICIP) and; R&D Start Program. There are also other similar Australian

venture capital programs including: Innovation Investment Fund (IIF); Innovation Investment

Follow-on Fund (IIFF); Early Stage Venture Capital Limited Partnerships (ESVCLP); Venture

Capital Limited Partnerships (VCLP); Pooled Development Funds (PDF); Pre-Seed Fund (PSF),

and; Renewable Energy Equity Fund (REEF) (see http://www.business.gov.au/grants-and-

assistance/innovation-rd/InnovationAustralia/Pages/default.aspx).

The Prime Minister’s Science, Engineering and Innovation Council (PMSEIC): The

Council is an eminent advisory body for counselling the government about scientific and

technological developments. It is presided by the Prime Minister and composed by ministers, the

Chief Scientist for Australia and a handpicked group of eminent experts. In 2009, Australian

government launched, so-called Powering Ideas: An Innovation Agenda for the 21st Century—a

10-year reform agenda with the aim of making Australia more competitive. This innovation

agenda is based on the assumption that there are two action fronts to strengthen the Australian

innovation system: strengthening its constituents (businessmen, public managers, researchers,

workers, and consumers) and strengthening the connections among these parties. With this in

mind, the Australian government has adopted seven National Innovation Priorities to guide its

innovation policies. All priorities are considered equally important and complement the

Australian National Research Priorities (see http://www.ausinnovation.org/articles/powering-

ideas.html).

Public research funding to support high-quality research that addresses national

challenges and opens up new opportunities.

Building a strong base of skilled researchers to support the national research effort in

both the public and private sectors.

Incentive to cutting-edge industries, securing value from the commercialisation of

Australian R&D.

More effective dissemination of new technologies, processes, and ideas to increase

innovation across the economy, with a particular focus on SMEs.

http://www.ausinnovation.org/articles/powering-ideas.html

http://www.ausinnovation.org/articles/powering-ideas.html


Encouraging a culture of collaboration within the research sector and between

researchers and industry.

More involvement of Australian researchers and businesses in international

collaborations on research and development.

Joint work of the public and private sectors in the innovation system to improve

policy development and service delivery.

The Australian government targets to establish its National Innovation System until 2020,

in which: (i) The country clearly articulates national priorities and aspirations to make the best use

of resources, drive change, and provide benchmarks against, which to measure success; (ii)

Universities and research organisations attract the best minds to conduct world-class research,

fuelling the innovation system with new knowledge and ideas; (iii) Businesses of all sizes and in

all sectors embrace innovation as the pathway to greater competitiveness, supported by

government policies that minimise barriers and maximise opportunities for the commercialisation

of new ideas and new technologies; (iv) Governments and community organisations consciously

seek to improve policy development and service delivery through innovation, and; (v)

Researchers, businesses and governments work collaboratively to secure value from commercial

innovation and to address national and global challenges, and to measure the progress of

Australian innovation system concerning priorities and objectives (see

http://www.ausinnovation.org/articles/powering-ideas.html).

2.2 Innovation Incentive Programs

Under the leadership of the aforementioned organisations innovation is supported through

a number of innovation incentives schemes. These schemes form the backbone of the Australian

innovation support mechanism. The incentive programs can be accessed through a single

government portal named Business. On this portal, entrepreneurs find the necessary information

to start a business as well as hints to guarantee the success of their enterprise (see

www.business.gov.au/Pages/default.aspx). The portal has a grants and assistance area including

several incentive programs. These programs are aimed at businesses of various sizes, in order to

generate productivity, innovation, competitiveness, and create new jobs. These programmes also

contain incentives for R&D, support for small businesses, tax and duty concessions, and

assistance for industries in transition. They support invention and technology development in

businesses by fostering collaboration between industry and researchers. The main incentive

programs include the followings.

Volume 9, No.2, 2015 11

The R&D Tax Incentives Program: This program the most popular one in the country, is a

broad-based, market-driven program accessible to all industry sectors. It provides a targeted tax

offset to encourage more companies to engage in R&D and help businesses offset some of the

costs of doing R&D. The program aims to help more businesses do R&D and innovate. It is a

broad-based entitlement program. This means that it is open to firms of all sizes in all sectors who

are conducting eligible R&D (see http://www.business.gov.au/grants-and-assistance/innovation-

rd/RD-TaxIncentive/Program-Information/Pages/default.aspx).

The Entrepreneurs’ Program: This program is Australian Government‘s major initiative to

promote business competitiveness and productivity at the firm level. It is part of the Australian

Government‘s new industry policy provided for in the Industry Innovation and Competitiveness

Agenda. This Agenda is part of the Economic Action Strategy of the Australian Government. It

unites and develops other economic reforms in order to foster Australia‘s strengths and promote

business opportunities (see http://www.business.gov.au/advice-and-support/EIP/Pages/default.aspx).

The Entrepreneurs’ Infrastructure Program: This program counts on a national network of

over 100-experienced private sector advisers and it offers support to businesses through three

components: (i) Business management, which provides support for business to improve and grow;

(ii) Research connections, which promotes the collaboration of SMEs with the research sector as a

way to develop new ideas with commercial potential, and;(iii) Accelerating commercialisation,

which helps entrepreneurs, researchers, start-ups and businesses face key challenges when trading

new products, processes and services. The program uses quality facilitators and advisers with

expertise in the industry, to ensure that businesses receive all necessary information to better their

competiveness and productivity. It focuses primarily on providing information—rather than

financial assistance—so entrepreneurs can find solutions to their problems. The support offered to

businesses includes advice from experienced people from the private sector, co-funded grants to

trade new products, processes and services, funding to help businesses grow, and connection and

collaboration opportunities (see http://www.australianbusiness.com.au/entrepreneurs-

infrastructure-programme).

The Industry Skills Fund Growth Stream: The $476 million Industry Skills Fund is a key

component in the Industry Innovation and Competitiveness Agenda of the Australian Government

and will provide up to 200,000 training places and support services over the next four years. The

fund prioritises SMEs, including micro businesses, and is delivered through the single business

service, which favours the access to essential information for all Australian businesses. The fund

offers assistance to the industry so it can invest in training and support services, as well as


develop innovative training solutions. The fund helps forming a highly skilled workforce that can

have access to new opportunities due to business growth, and that can adapt to rapid technological

change (see http://www.business.gov.au/grants-and-assistance/Industry-Skills-Fund/Pages/default.aspx).

Innovation and R&D Program R&D Tax Incentive: It aims to boost competitiveness and

improve productivity across the Australian economy by: (i) Encouraging industry to conduct

R&D that may not otherwise have been conducted; (ii) Providing business with more predictable,

less complex support, and;(iii) Improving the incentive for smaller firms to engage in R&D. The

R&D Tax Incentive replaces the R&D Tax Concession for R&D in income years commencing on

or after 1 July 2011. The R&D Tax Concession continues to be administered for R&D in income

years commencing prior to 1 July 2011.The R&D Tax Incentive provides benefits in two core

components (AusIndustry, 2012). A 45% refundable tax offset (equivalent to a 150% deduction)

for eligible entities with a turnover of less than $20 million per annum, provided they are not

controlled by income tax exempt entities, and; A non-refundable 40% tax offset (equivalent to

133% deduction) for all other eligible entities. Unused non-refundable offset amounts may be able

to be carried forward to future income years (see http://www.business.gov.au/grants-and-

assistance/innovation-rd/Pages/default.aspx).

In order to give special attention to the technology sector and considering that the tax

benefit is open to all sectors, software is subject to the same eligibility tests as other forms of

R&D, with the exception of certain software activities, which are excluded from being a core

R&D activity. This exclusion covers activities related to the development, modification or

customisation of software where the software is for the dominant purpose of internal

administration by the entity (or connected entities or affiliates) for which it was developed,

modified or customised. Software for ‗internal administration‘ includes management information

systems and enterprise resource planning software that is for use in the day-to-day administration

of a business. The software exclusion does not apply to software developed in-house that is of an

applied nature, forming an integral part of an electrical or mechanical device (such as home

appliances or industrial equipment). In general only R&D activities conducted in Australia or the

external Territories qualify for the R&D Tax Incentive. However in certain circumstances, R&D

activities conducted overseas may also qualify. For example, a company intending to claim a tax

offset for R&D activities conducted overseas must apply to Innovation Australia for a decision

(called a ‗finding‘) about the eligibility of these overseas activities. Innovation Australia can issue

a finding that overseas activities are eligible for the R&D Tax Incentive (see

http://www.business.gov.au/grants-and-assistance/innovation-rd/RD-TaxIncentive/Pages/default.aspx).

http://www.business.gov.au/grants-and-assistance/Industry-Skills-Fund/Pages/default.aspx

http://www.business.gov.au/grants-and-assistance/innovation-rd/Pages/default.aspx

http://www.business.gov.au/grants-and-assistance/innovation-rd/Pages/default.aspx

http://www.business.gov.au/grants-and-assistance/innovation-rd/RD-

Volume 9, No.2, 2015 13

The government also provides financial support for private firms to conduct innovation

projects. Nevertheless, there is less evidence that such investment—about $1 billion every year—

is justified by the extra innovation it helps produce. The largest government support for private

sector innovation is the R&D Tax Credit. The largest 3% of innovative firms take in 60% of the

credit—over $1 billion per year. Nonetheless, there is little evidence that this tax credit

substantially increases the amount of actual R&D activity in large firms. By contrast, there is

good evidence that improving the framework conditions for innovation, particularly by reducing

the corporate tax rate, would have a significant impact on innovation in the long run. A lower

corporate tax rate encourages foreign direct investment (FDI), which in turn increases innovative

activity and encourages the diffusion of ideas from other countries. Australia would probably see

more innovation—and increase living standards accordingly—if the R&D Tax Credit for large

firms and much of the direct support for private firm innovation were redirected into funding a

reduction in the corporate tax rate of up to 1.5%.Whereas governments should support innovation,

they should ensure public money is invested where it makes the biggest difference (see

http://www.business.gov.au/grants-and-assistance/innovation-rd/RD-TaxIncentive/Pages/default.aspx).

3. Australian Firm Awareness on Incentive Programs

The study undertook an online survey exercise to capture Australian innovation

companies‘ perceptions on the role and effectiveness of the innovation incentive programs. The

survey contains six key questions and circulated through Survey Monkey online survey tool (see

https://www.surveymonkey.com) to the directors of Australian technology and innovation

companies. The survey prepared by the authors was sent out to the firms through email with help

from the Australian Information Industry Association (AIIA) and the Cooperative Research

Centres Association (CRCA) between May and August 2015. Contact details of the targeted

companies were obtained from the Australian Business Directory (see http://abdo.com.au). In

total 75 valid responses received, during the four month period that the survey was open, out of

surveys sent to 379 companies (19.79% response rate). The responses to survey questions are

presented below.

Q1. Are you aware that Australia has an Industry Innovation and Competitiveness

agenda? Of the 75 firms that responded to the survey, 38 claim to know the government‘s agenda

and 37 of them state that they are unaware of this agenda (see [Table 1]). Although the number is

balanced, considering that it is a relevant issue for the development of innovative firms, firm

owners should be more aware of governmental programs and seek more information about them.

It seems to be that the Australian government does not make much effective use of trade


associations to disseminate its programs and plans. The general understanding of the government

officials‘ is that government‘s website is a good enough source of information. They seem to

believe that it is the businessmen‘s duty to find out about programs and support to which they are

entitled.

[Table 1] Results of the Survey Question 1

Q2. Are you aware that there are refundable, non-refundable and subsidised resources

that your business can use for innovation and R&D? Out of 75, 74 firms responded this question.

Amongst them 47 claimed to know about the available resources, whereas 27 declared not

knowing about the incentive lines (see [Table 2]). The number of firms (63.51%) that know about

the availability of Federal incentive programs to innovation is relevant, considering that Australia

makes little use of trade associations and barely conducts presentations to firms on this topic. The

survey findings show that, although the number is relevant when compared to the little effort put

on promotion, the government must focus on spreading the word about its sources of incentive

and public policies.

Volume 9, No.2, 2015 15


Q3. Has your company ever used these types of resources for innovation and R&D? In

total 68 firms answered this question, and the alternatives listed Federal programs of incentive to

innovation (see [Table 3]). It was also possible to check the answer ‗other‘ with an option to

specify the program the entrepreneur had used. More than half of the firms that answered the

questionnaire (54.41%) do not use the incentive sources, including tax incentives, which is a

flagship of the Australian government program. R&D Taxes Incentives is the main program, used

by 35.29%; the program is considered simple and not very bureaucratic by government officials

for it can be applied for online. The Entrepreneurs–Infrastructure Program comes in third, used by

4.41%. This is a four-pillar line that contributes to the commercialisation of generated

goods/services. Lines such as the Linkage Projects Scheme (LPS), The National Competitive

Grants Program (NCGP), which are university-related programs, reached a very low rate of

response, 2.94% each. The Industry Skill Fund program did not produce any answer (0%). The

reason for no one choosing this program in this question is given by the Australian government

itself: since the name of the program was changed by the new administration, entrepreneurs did

not recognise it when it was renamed (formerly known as National Workforce Development

Program).This question gave respondents the choice to include other incentive lines in the field

other/specify. Nine answers came up: Export Market Development Grants/Austrade (EMDG),

Accelerating Commercialisation, and Commercialisation Australia Early Stage Grants, state

programs such as the Canberra Innovation Network, Commercial Ready and Climate Ready.

These programs were not originally listed as alternatives in this question since they are not

Federal programs with focus on innovation.



Q4. If you have tried but have not been successful, please indicate the reasons. Although

Australia is not a very bureaucratic country—ranked 11th less bureaucratic country in the world—

entrepreneurs believe that government programs are bureaucratic. The alternative complex

application process/bureaucracy was checked by 47.06% of the respondents. 34 firms answered

this question (see [Table 4]).Two other answers to this question are worth mentioning, each one

highlighted by 23.53% of the respondents: the lack of personnel to prepare the application and the

high cost in application preparation. The cost of labour in Australia is very high and the incentive

program is not attractive since Australians believe the process is highly bureaucratic. The lack of

information about the programs and the lack of guarantees were highlighted by 17.65% of the

respondents. The reasons presented in the others, with 35.29% are: (i) Registered Research

Agency went into administration, and ATO penalised my application; (ii) Each successive

program gets smaller and smaller and the return on investment is such I cannot be bothered

anymore; (iii) Have not tried; (iv) No time to apply as being a small start-up company; (v) Not

tried; (vi) Commercialisation Australia ‗need for funding‘ criteria is hard to meet; (vii)

Requirements on matching funding are ‗impossible‘ to meet. You have to show you have

matching funds but why the funders of matching funds cannot meet the whole cost. You cannot

use future sales for matching funds; (iix) Unaware of what options were available and how to

prepare a successful submission; (ix) 9 of them not applied for; (x) Not know, and;(xxi) I have not

tried.

Volume 9, No.2, 2015 17


Q5. For what purpose is your firm interested in this type of resource? This question is

useful to guide legislators that design public policies, since it shows the actual current needs of

the firms. This question was attended by 67 of the surveyed firms (see [Table 5]).Support for

R&D tops the list of needs (with 62.69%); Marketing, Sales and Fairs activities come in second

that demonstrates the importance of support to the commercialisation of goods. This information

reinforces innovative firms‘ high dependency on human capital and know-how. These firms differ

from the traditional industry, whose capital is guaranteed by machinery and equipment. Therefore,

in the innovation and technology sector, talented labour is specialised and highly costly.

Incentives to the R&D of products and services are important in order to guarantee the continuous

process of innovation in the firm, very often anticipating the needs of the market. Of all

respondents, 46.27% highlighted the incentive to commercialisation. Internationalisation comes in

third (with 32.84%). This is an interesting fact that this alternative completes the top two

demands—since Australia is a vast country with little population, internationalisation is an

important aspect for sending products and services out to foreign markets. Australia has no

dedicated development bank (such in the case of many developing nations), so businessmen turn

to investment funds for financial resources. Inflation rates are low in the country and traditional

banks operate at low interest rates. Working capital comes in fourth in the survey; it was selected


by 29.85% of the respondents. The reasons presented in the others, with 4,48% are: (i) Innovation

and entrepreneurship—no one calls it 'R&D' in start-ups; (ii) Developing intellectual property in

emerging areas such as cloud technologies, and;(iii) Engaging young innovators and students.


Q6. Please indicate on which incentive programs you would be interested in applying in

future. This is another answer that can guide the Federal government and contributes to designing

policies, since it demonstrates the firms‘ expectations towards the incentive lines they intend to

use in the future. In total 62 firms answered this question (see [Table 6]). R&D Tax Incentive is

still the government‘s master program, according to the results of question 3. Answered by

54.84% of the respondents, Entrepreneurs Program comes in second, although this program was

selected by 4.41% in question 3. This shows that it is little used at the moment but entrepreneurs

are interested in knowing it better. Private Funds comes next, selected by 30.65%, which shows

that it is possible to integrate investment funds and firms through trade associations, by organising

Seed and Venture Forums. As mentioned earlier, in question 3 the program focused on Skills

Funds was not used widely (0% of responses) because the program name was changed by the new

administration. However, since 25.81% of the firm owners‘ highlighted this answer, it

demonstrates an interest in using it in the near future. The same occurs with the Australian

Research Council‘s programs that reached a 25.81% rate of interest and demand by firm owners.

Volume 9, No.2, 2015 19

Nevertheless, these days it is used by only 1.47%.A reasonable number of entrepreneurs (12.9%)

did not show interest in having access to incentive lines. It can be noticed that the firm owners or

managers have not been seriously considered the benefit of incentive, through programs such as

the R&D Tax Incentive. The main reason for this is that them not being able to spare time from

their business and clients to allocate time for an application preparation. The open-ended feedback

section of the question, ‗other/specify‘, originated 12.9% of suggestions of state programs,

commercialisation and exportation, as well as feelings about the programs and disbelief in the

government: comments were, as written by respondents: (i) Accelerating Commercialisation,

QLD State Grants; (ii) Would not bother unless totally reformed to take into account available

resources of start-ups; (iii) The Entrepreneurs Program is hopeless and full of all the wrong

organisations; I am not the person responsible for this within the company, so I am not able to

speculate; (iv) I would love this information to be disseminated properly; (v) Commercialisation

Australia; (vi) Too much bureaucracy and therefore a waste of time. Also, I do not trust the

government to choose whom to give the grant to. Would only be interested in automatic self-

selection grants; (vii) EMDG, and; (iix) Do not know enough about them to decide.



4. Conclusions and Discussion

The review of the literature indicates that throughout the history knowledge—outcome or

product of intellectual capital—has always been an important driver of growth and development

(Carrillo et al., 2014). In the age of knowledge economy, the role of knowledge generation and

innovation has even become more prominent (Pancholi et al., 2014). Today innovation through

generation of new marketable knowledge is a primary driver of growth, both growth of nations

and growth of businesses (Brown, 2010; Drucker, 2014). Especially, today rising expectations

about future demand for new technologies increase the incentives for investments in innovation

by enlarging payoffs to successful innovations (Nemet, 2009).At present, many governments

around the world that aim to replicate the success of innovation nations—e.g., the USA, Japan,

Germany, Finland, Israel, Estonia, and Korea—are making sure innovation activities are

incentivised and a sound innovation ecosystem is established (Wallsten, 2000; Coates & Holroyd,

2007; Kao, 2007; Wandersman et al., 2012; Breznitz & Ornston, 2013; Makkonen & Inkinen,

2014). As underlined by Maxwell (2015),firms perform innovation in order to reduce risk, reduce

costs, increase market share, increase margins and create new market opportunities, which leads

to increased profits and enterprise value. Furthermore, today, it is highly rare that a firm can

flourish or even survive without continuous innovation (Maxwell, 2015).

The review of the Australian innovation support schemes reveals that Australia has the

required basic foundation and infrastructure for the governance of the innovation ecosystem.

However, a closer and deeper look into individual policy and support programs along with the

results of the Australian technology company surveys reported in this paper reveal the following

invaluable insights on the opportunities and constraints of the Australian innovation ecosystem.

Firstly, both Federal and State levels policy documents indicate that innovation is not at

the forefront of the development agenda. Furthermore, there is no policy targeting to raise

awareness within the public and business circles to invest in the innovation economy. Australia

needs to communicate what innovation is, and start a national conversation, involving more

people, government, associations, universities and the broader society. The investigation has

shown that half of the innovative companies are not involved with the innovation conversation

and this is a serious problem. For instance, some of the successful initiatives or projects can be

used as communicating systems to create a culture of innovation and performance (see

Johannessen & Olsen, 2011).

Secondly, almost all of the universities in the country are public universities; nevertheless,

their research activities are not well integrated with companies and the innovation sector and their

Volume 9, No.2, 2015 21

priorities. Most of the Australian universities have no real incubators; as they are seen as ‗white

elephant‘—a business or investment that is unprofitable and is likely to remain unprofitable

(Roberts, 1996).Universities, with financial support from government, and collaboration with

industry and businesses, should play a more active role in developing knowledge and innovation

spaces—such as incubators, accelerators, and knowledge precincts—for innovation in the country

to take off. This is to say, the way of conducting research at the universities has to change and

evolve into collaborative activities with government, industry and community—i.e., quadruple

helix model research partnership (Alfonso et al., 2012). Currently available incentive programs

are not aligned well with the universities, communities and companies‘ needs. The required

mechanisms are not in place for university professors and researchers to engage and work closely

with businesses for new product, process or service development; rather the system motivates and

awards scholars for their academic writings. Creatively employing funding to universities in order

to support innovation is needed (see Millard & Hargreaves, 2015).Investigation of Finnish model

innovation collaboration would create some pathways for Australia (see Uotila et al., 2012).

Thirdly, Czarnitzki and Lopes-Bento‘s (2014) research on the effectiveness of innovation

support in Germany finds that innovation subsidies increase innovation intensity and performance.

However, in order to apply and receive the funding entrepreneurs need to know about available

schemes. This can be challenging at times. For instance, it is common in Australia that with every

new administration in office many of the departments are restructured. This restructuring also

applies to the innovation support schemes. These rather frequent changes leave entrepreneurs with

confusion and not much knowledge about the new innovation incentive programs. For those who

are keen to apply, the application process causes spending longer time in search of to find the new

schemes and their eligibility to apply. The outcome of these frequent changes is entrepreneurs‘

lack of knowledge on the innovation support programs; and, therefore, lesser applications to the

programs—for instance the Industry Skills Program.

Fourthly, today, the way firms are chosen to receive support is not transparent to

entrepreneurs. Some are chosen to grow—i.e., pick winners—where this model is considered as

political and to a degree biased. There needs to be more transparency at the selection criteria and

how the applications are evaluated against these criteria. Australia loses its talent and innovative

entrepreneurs to other regions of the world, such as South East Asia, Europe and North America,

where they can find more lucrative and more transparent innovation support programs. Unlike

Australia, some other governments share the risk of investment with the firm owners.

Fifthly, it can be said that the major reason of innovation failure in the country is the lack

of innovation culture and a healthy ecosystem. Australia‘s tolerance for business risk of failure is


very low, and this is reflected in the fact that there is a general reluctance of talented scientists and

researchers to make a move from the tertiary education sector to private R&D sector organisations.

For instance to support the innovation culture and knowledge-based economic development in the

country, Australia can develop new programs to attract bright minds to become entrepreneurs

similar to those in Canada and Chile—Quebec First (http://www.quebecfirst.com/en/) and Start

up Chile (http://www.startupchile.org), respectively—since attracting and retaining talented

people is directly associated with the raise in job creation and economic growth.

Sixthly, the lack or limited support to innovation in many countries, including Australia,

has led entrepreneurs to investigate new ways to support their marketable ideas. Crowdsourcing is

a new method to fill the void of funding need to innovate, especially for open innovation—during

the last few years open innovation has gained increasing attention as a potential paradigm for

improving innovation performance (see Marjanovic et al., 2012; Chebulski, 2013). This new

funding mechanisms can also be supported by Federal policies and incentives as part of the efforts

in forming a prosperous innovation ecosystem in Australia.

Seventhly, even though the importance of innovation to generate competitiveness is

acknowledged, the government confesses that currently Australia‘s support to innovation is still

poor (see DoIS, 2014). Therefore, in addition to abovementioned insights, we conclude the paper

with some strategic suggestions for the country to advance its innovation ecosystem, and moving

economic focus from resource-based economy to knowledge economy:

Australia must develop or adopt a more informed and systematic approach for building

innovation and creativity in the country (Baum et al., 2009). This is to say; Australian

innovation system needs to be design to work more effectively, if the country really

desires to maintain the standard of living achieved during the recent resources boom

period.

Australia needs to further invest on its talent base and endogenous assets (see Lonnqvist

et al., 2014; Yigitcanlar, 2014),and work more focused to maintain its global economic

position in a world of rapidly emerging economies and tough competition. Australia can

learn from the other countries, such as the US, Germany, Singapore, and Finland

(Yigitcanlar, 2009; Yigitcanlar &Lonnqvist, 2013; Yigitcanlar et al., 2015), that are

taking risks with their entrepreneurs to further advance their innovative edges.

In order to improve the effectiveness of the Australian innovation ecosystem, the gap

between scientific research and market needs to be mapped carefully. That is getting the

right high value added products out of the brains and laboratories and placing into the

global market place. This requires further human and intellectual capitals investments in

http://www.quebecfirst.com/en/

http://www.startupchile.org/

Volume 9, No.2, 2015 23

the forms of financial and infrastructural support for higher education, R&D institutes,

and innovation companies particularly in the fields of STI. Rather than recently

introduced budget cuts to these critical sectors by the Federal government (Daley et al.,

2013), further support is crucially needed to establish a global competitive innovation

edge.

Lastly, the new Prime Minister Malcolm Turnbull‘s National Innovation and Science

Agenda is a welcome initiative, bringing hope to Australian entrepreneurs, researchers and

innovators in general. After a few years of lacking direction in this space, we might begin to see

the light at the end of the tunnel. Although, it is too early to comprehensively assess the impact

the new agenda will have on the Australian economy, one thing is certain that this initiative gives

hope that Australian economy will again accelerate and catch up with most developed digital

economies in the world (CiDE, 2015).


Acknowledgements

The authors wish to acknowledge the financial and/or in-kind contributions of Queensland

University of Technology, Federal University of Santa Catarina, and Ministry of Education of

Brazil (CAPES-PDSE: 99999.004527/2014-03)in jointly supporting the research project.

Biographical notes

Jamile Sabatini Marques is a Visiting Doctoral Researcher at the School of Civil

Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia,

and a PhD Researcher at the Federal University of Santa Catarina, Florianopolis, Brazil. Her

research focuses on government innovation incentive systems for technology company growth.

Tan Yigitcanlar is an Associate Professor at the School of Civil Engineering and Built

Environment, Queensland University of Technology, Brisbane, Australia. The main foci of his

research are clusters around three interrelated themes: Knowledge-based urban development;

Sustainable urban development, and; Smart urban technologies and infrastructures.

Eduardo Moreira da Costa is a Professor at the Graduate Program on Knowledge

Management at the Federal University of Santa Catarina Florianopolis, Brazil, and founder of the

Pi-Academy, a private company that promotes innovation for large corporations. His main

research area focuses on the development of more humane, smart and sustainable cities.

<received: 2015. 09. 11>

<accepted: 2015. 09. 27>

Volume 9, No.2, 2015 25

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Volume 9, No.2, 2015 29

An Empirical Analysis about the Equipment-Intensive

Public Enterprise's Innovation Performances Affected by

Service Innovation Activity and Cooperation

Byoung-sun Kim, Sun-young Park

, and Young-whan “Nick” Lee

Abstract

This paper presents empirical performance improvements found as the result of the service

innovation activities in Seoul Metropolitan Rapid Transit (SMRT), which is considered as an

“equipment-intensive pubic enterprise (EIPE hereunder)”. It also presents implications that may

be utilized in the fields of service innovation for public enterprises from the viewpoint of

Management of Technology. In trying to increase the effect of service innovations in SMRT, the

researchers assume that the same kind of efforts may be applied to many areas of Korea’s

economy as a part of the recent campaign of “creative economy” driven by the government.

Important implications found in this study as following: First, it is necessary to build at least two

platforms for service innovation: to share technologies and to facilitate in-house communications.

Second, it is necessary to implement value-driven idea-sharing as corporate culture. Third, it is

necessary to support big data facility for service innovation as corporate policy.

Keywords: EIPE (equipment-intensive public enterprise), social infrastructure, service

innovation, innovation activity, innovation performance.

Ph.D., Instructor, Miller MOT School, Konkuk University, E-mail: [email protected] Corresponding Author, Professor, Miller MOT School, Konkuk University, E-mail: [email protected] Assistant Professor, Miller MOT School, Konkuk University, E-mail: [email protected]





1. Introduction

Due to economic growth, urbanization, and population increase, the social infrastructures

of the Republic of Korea have been increased rapidly. However, the government has not been able

to match up to the level of the expectations in response to ever increasing the demands because of

low budget, the lack of service standards, and continuously aging facilities. Demands for effective,

efficient, and economical facility managements and investments have been increasing. The social

infrastructure facilities are generally managed by Equipment-Intensive Public Enterprises (EIPEs

hereunder). Naturally, EIPEs have been playing prominent roles in the economy. According to

Sung, T. K. (2006), the technology innovation competence of enterprises is important not only for

their survival and business development, but also for their financial growth. He also pointed out

that the studies about service innovation are not sufficient enough to reflect the up-to-date

situation created by the rapid expansion of Korea‘s economy. He postulated that the reason is that

systemized databases for service innovation efforts, particularly for its service industry have never

been created and utilized.

In attempts to build such databases, Science and Technology Policy Institute, STEPI

(2010) ran a survey about the status of the overall innovation activities in manufacturing and

service industries in Korea and collected data sets hoping to help national policy-making and

research efforts for innovations. The survey was aimed to provide data sets for the purpose of

statistical analyses.

In such a background, the researchers in this study undertook an empirical analysis of the

service innovation performances and the improvements in Seoul Metropolitan Rapid Transit

(SMRT), an EIPE, which manages and operates some railroad infrastructure facilities in the

Republic of Korea. The researchers in the study gave questionnaires to the employees of SMRT,

and ran a peer evaluation survey for the service innovation activities performed by the internal

departments of the enterprise. They also ran a peer evaluation survey for the innovative

cooperation with external collaborating partners. Based on the data obtained from the surveys,

they analyzed the service innovation performances of SMRT as an example of EIPEs in Korea so

that the implications learned may be applied to others.

2. Theoretical Background and Previous Research

2.1 Theoretical Background

Volume 9, No.2, 2015 31

2.1.1 Social Infrastructure

Social infrastructures in general mean basic facilities that operate as the foundation of

economic activities. Social overhead capitals such as roads, airports, bridges, etc. are closely

related with economic activities and defined as infrastructures traditionally. Recently, public

facilities for sustainable living environment, such as parks, schools, and hospitals are also

included as other kinds of social infrastructures. This study focuses on SMRT, an EIPE, which

manages a social infrastructure. On the base of service innovation activity in EIPE, the effect of

performance improvement done by the activity was studied.

2.1.2 Equipment-Intensive Public Enterprise (EIPE) Defined

According to Korean Administrative Dictionary (2009) compiled by Korean Society of

Administration, an ―organization‖ is defined to be a social unit with operating systems, for

specialization and integration, to achieve common goals. With its structures, processes and norms,

it interacts with environment. Thus, organizations may be categorized into two, depending on the

pursuit of profits, private and public. According to Korean Administrative Dictionary (2010), a

similar concept, quasi-government organization, exists. It is not a legal government organization,

but often referred as public as it performs functions that are considered to be public. A quasi-

governmental organization is founded and operated independently from the departments of

governments. It is loosely controlled by of the departments of governments, and performs limited

administrative functions. It became emerged because the allocation of public goods and services

can be efficient and impartial. It is also considered as an alternative solution to autonomous

operations that often show many limitations in the ones run by government authorities.

Although the importance of the EIPEs to the national economy can‘t be overemphasized,

previous studies on EIPEs are hardly found. The researchers in this study define EIPE as

following:

“EIPE is an enterprise to provide the service for public economic activities based

on equipment-intensive facilities and organizations”.

2.1.3 Various Concepts of Innovation

The researchers in STEPI (2010) proposed to collect data for the following types of

innovation activities: ―R&D Including Internal and External Activities‖, ―Four Major Activities of

Innovation‖, ―Source of Information‖, ―Source of Funding‖, and ―Gathering Information Related


to Innovation Costs‖. In addition, Oslo Manual (2005) defines an innovation as following:

―An innovation is the implementation of a new or significantly improved product (good or

service), or process, a new marketing method, or a new organizational method in business

practices, workplace organization or external relations”.

Oslo Manual also divides innovation into four types: ―Service Innovation‖,

―Organizational Innovation‖, ―Process Innovation‖, and ―Marketing Innovation‖. Adapting these

concepts, the researchers in this study empirically categorized the innovation performances of

SMRT into two ways to fulfill the purpose of the study: ―Performed Level of the Innovation

Activities‖ and ―Collaboration Level of Internal & External Activities‖. With these, the

researchers in the study attempted to find the ways to improve the service innovation

performances in SMRT.

2.1.4 Service Innovation

As for the concept of service innovation, ever since it was raised by Miles (1993) the first

time, it has been referred and therefore, evolved in a large number of disciplines over the last 20

years. Oslo Manual (2005) proposed to categorize service innovation into the followings: ―The

Essential Characteristics of Product‖, ―Technical Information‖, ‖Software‖, ―User Friendliness‖,

and ‖Usage‖. It also stated that service innovation means the cases either a service is introduced in

the market, or it affects the sales of the company by introducing ―a new or considerably improved

products or services‖. A more comprehensive definition for service innovation was proposed by

Van Ark, et al. (2003). According to them, Service innovation is defined as following: ‖a new or

considerably changed service concept‖, ‖client interaction channel ‖, ―service delivery system ‖,

and ‖technological concept.‖

Each of them individually, but most likely in combination, leads to one or more (re)new

(ed) service functions that are new to the firm and do change the service/goods offered on the

market and do require structurally new technological, human or organizational capabilities of the

service organization. The definition also covers, according to the paper, the notions of

technological and non-technological innovation.

2.2 Previous Research

2.2.1 Previous Research on Service Innovation

Volume 9, No.2, 2015 33

The previous studies about the service innovation appear in various fields. Philippe Aghion,

et al. (2009) wrote, in their paper ―The Study on the Entry Effects on Existing Innovation and

Productivity‖, that entry effects were a long-term concern, and had been widely recognized as a

major driving force of economic growth. And the entry can cause the re-allocation of entries and

exits. And it can become a starting point to trigger the diffusion of knowledge. Also it affects the

innovation incentives of the existing companies.

David H. Henard, et al. (2010) said, in ―The Study on Reputation for Product Innovation:

Impact on Consumers‖ that companies also compete to win the prestige of the relevant

configuration group, just as they fight each other to win customers. In Korea, Yu, P. J. et al.

(2011) studied the service innovation of Incheon International Airport Corporation, a public

enterprise. While it was on the public enterprise, the study is on the same subject as ours: it

focused on service innovation to overcome the service inseparability of consumption and

production and attempted to draw consumer engagement in the service production. In fact, SMRT

has been pursuing service diversification to maximize customer satisfaction by replacing out

dated legacy services with something new having the inseparability into the consideration.

2.2.2 Previous Research on Innovation Activity and Performance

As for a previous research related innovation activity, Anna Bergek et al. (2008) found that

many researchers and policy analysts performed empirical studies about innovation systems to

understand the present state of innovation structure to track the dynamics. Bergek pointed out that

the researchers unfortunately ended up experiencing difficulties in extracting practical guidelines

from this kind of research. It is because they took functional approaches to analyze the dynamics

of innovation systems in their analysis plans taken from previous studies.

Innovation performance may be represented as intellectual property rights such as patents,

utility models, and designs that bring revenue increases in business performance. Ji, S. G. et al.

(2005) stated that innovation resistance is not merely the opposition of innovation but the

reformation enforcer that changes the course of innovation performance. They pointed out that, to

enhance the impact of innovation performance, the analysis of innovation resistance is very

important. Kim, B. S. et al. (2013) did a research that was about the process innovation of a social

infrastructure as EIPE. Through this study, they proposed the budget optimization for operating of

the public facility. Lee, J. D. et al. (2014) did a research about business performances (financial

performance, etc.). They tried to verify influences of the marketing & technology competency as a

business core competency.


In Leem, T. J. et al. (2012), they classified performance measurement systems (PMS) into

the ―diagnostic utilization‖ and ―interaction utilization‖. They also classified innovation types into

the ―exploring innovation‖ and ―practical innovation‖. As for organizational performance, they

analyzed the relationships among the ―PMS utilization‖, ―innovation types‖ and ―organizational

performance‖, and then classified them into financial and non-financial performance.

3. Hypotheses and Research Model

3.1 Hypotheses

In this study, the level of service innovation activity was surveyed for three years

(2011~2013) in SMRT. The survey respondents were asked to evaluate about the department they

belong to. They also were asked to evaluate about the cooperation level of innovation partners in

external organizations as applicable. Based on the survey, the service innovation performance of

SMRT was studied. Performance indicators used were of 5-point Likert scale to represent the

impact of the innovation performance of EIPE in accordance with service innovation activities.

3.1.1 Service Innovation Activity

To verify how the service innovation activities within the departments respondents belong

to, affected its performance, the researchers set the first set of hypotheses as shown in the [Table

3-1].

[Table 3-1] Hypotheses 1

H1. Service innovation activity in a public enterprise will affect to improve its

service performance.

H1-1. Service innovation activity will bring service quality improvement.

H1-2. Service innovation activity will bring either cost saving or revenue increase.

According to the research of Choi, B et al. (2006), the services of a company become

competitive after service innovation activities that improve its customer satisfaction. Also, Yu, P.

J et al. (2011) found, although results obtained in many previous studies may look different in

some degree, they could be integrated into five (5) types that brought the improvement of

customer satisfaction. To verify H1, the researchers defined the following dependent variables:

Volume 9, No.2, 2015 35

service quality improvement (SQI)１

and cost saving or revenue increases (SCR). From the H1,

one may draw a proposition that the performance level of service innovation activity will affect its

quality improvement (SQI) and cost saving or revenue increases (SCR).

As for the remark, the variable, cost saving or revenue increase (SCR), includes the

concepts of cost saving and revenue increase combined together throughout this paper because

the results may be measured only in the form of profit increase.

3.1.2 Cooperation with Innovation Partners

Concerning the policy, STEPI (2010) included ―quality improvement”, ―cost saving” as

the items in the survey, ―Korean Innovation Survey of the Republic of Korea‖, from which the

data this study used. As for the practice, SMRT announced ―SMRT Transportation Plan (2013),‖

that declared its new demand excavation as the mission, and profit-making as the goal. To verify

how cooperation with innovation partner(s) in a public enterprise affected its performance, the

researchers used the second set of hypotheses as shown in the [Table 3-2].


H2. Cooperation with innovation partner(s) in a public enterprise will affect to

improve their service performance.

H2-1. Cooperation with innovation partner(s) will bring their service quality improvement.

H2-2. Cooperation with innovation partner(s) will bring either cost reduction or revenue increase.

From the H2, one may draw propositions that the degree of cooperation with the

innovation partner(s) will have an impact on service quality improvement (SQI) in regard with the

service innovation performance. Also, as for the innovation performance of a public enterprise, it

will bring its cost saving or revenue increase (SCR).

3.1.3 General Characteristics of SMRT Employees

To verify how general characteristics of employees in a public enterprise affected its

performance, the researchers hypothesized that general characteristics of SMRT employees will

affect the service innovation as shown in the [Table 3-3].



H2. Cooperation with innovation partner(s) in a public enterprise will affect to

improve their service performance.

H2-1. Cooperation with innovation partner(s) will bring their service quality improvement.

H2-2. Cooperation with innovation partner(s) will bring either cost reduction or revenue increase.

3.2 Research Model

To test the hypothesis through empirical analysis, the research model was used as shown in

[Figure 3-1].

ACS: Activity for the customer satisfaction's

improvement

ASD: Activity for the service diversity

ARE: Activity for replacing the existing old

service

AAE: Activity for adopting the external

Knowledge & technology

ABE: Activity for buying the external

mechanical equipment & capital goods

ICC: Innovation cooperation with cooperative

vendors

ICO: Innovation cooperation with other

companies and competitors within the

same industry

ICP: Innovation cooperation with private service

firm(consulting, a private research institute)

ICU: Innovation cooperation with University,

Higher Institute

ICG: Innovation cooperation with Government-

funded research institute, Institute for public

[Figure 1] Research Model

Volume 9, No.2, 2015 37

4. Empirical Analysis

4.1 Data Collection and Analysis Methodology

4.1.1 Data Collection

This study surveyed the employees of SMRT. The survey respondents were categorized

into five groups: office staffs, locomotive engineers, rolling stock engineers, technical engineers,

and building maintenance engineers. A set of survey questionnaire was sent via E-mail or in

person to every employee. The ones responded were 271 of 6,463 total employees.

4.1.2 Analysis Methodology

As for the data analysis of this study, STATA 11 was used. To analyze the service

innovation performance of SMRT the survey respondents were asked to evaluate the service

innovation activities that were performed in their departments for the last 3 years (2011-2013)

using 5-point Likert scale. They also asked to evaluate the cooperation with innovation partner(s)

and service innovation performance as appropriate. With the data, the reliability and the

multicollinearity tests against each and every variable were performed.

The correlation analysis and the multiple regression analysis were also done to empirically

analyze the impact of the service innovation performance affected by the service innovation

activity and service innovation cooperation.

4.2 Analysis Results

4.2.1 General Characteristics of Data

From the analysis results, the researchers were able to get the general characteristics for the

survey respondents as shown in [Table 4-1].


[Table 4-1] General Characteristics of Data

(N=271)

Variables Contents Population Distributions (%) Variables Contents Population Distributions

(%)

Gender male 243 89.7

Education

Level

high school 19 7.0

female 28 10.3 junior college 68 25.1

Department

office 48 17.7 university 177 65.3

locomotive 4 1.5 master 7 2.6

rolling stock 14 5.2 ph.Dr. 0 0

technical & building

205 75.6 Head Office Experience

yes 140 51.7

no 131 48.3 Years of

Work less 10yrs 49 18.1

over 10yrs 222 81.9 Official

Certificate yes 243 89.7

Job Grade

3 22 8.1 no 28 10.3

4 45 16.6 Working

Type

general 133 49.1

5 73 26.9 shift 129 47.6

6 83 30.6 Modified-daywork 9 3.3

7 41 15.1 Work Hours

per Week

40hr general/modified

142 52.4

8 0 0 48hr(shift) 129 47.6

9 7 2.6 Union Affiliation

yes 228 84.1

Age

21～30 4 1.5 no 43 15.9

31～40 70 25.8 Job

Satisfaction

min～mean 74 27.3

41～50 167 61.6 mean(69.8) - -

51～58 30 11.1 mean～max 197 72.7

4.2.2 Correlation Analysis Result

Among the service innovation variables, the ones with correlation coefficient over 0.70 are

as shown in the [Table 4-2]: SQI and SCR (0.73), ACS and ASD (0.74), ASD and ARE (0.71),

AAE and ABE (0.73), ICC and ICO (0.74), ICO and ICU (0.71), ICP and ICU (0.79), ICP and

ICG (0.77), ICU and ICG (0.85).

Volume 9, No.2, 2015 39

[Table 4-2] Correlation Analysis Result

Service Innovation ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑪ ⑫

Performance ① SQI 1

② SCR .73* 1

Innovation

Activity

③ ACS .57* .48* 1

④ ASD .62* .54* .74* 1

⑤ ARE .60* .55* .60* .71* 1

⑥ AAE .62* .60* .55* .62* .65* 1

⑦ ABE .58* .63* .49* .53* .63* .73* 1

Innovation

Cooperation

⑧ ICC .58* .55* .45* .46* .48* .53* .53* 1

⑨ ICO .52* .54* .41* .43* .49* .48* .50* .74* 1

⑩ ICP .51* .53* .39* .49* .54* .52* .59* .63* .67* 1

⑪ ICU .47* .51* .32* .41* .47* .49* .59* .63* .71* .79* 1

⑫ ICG .48* .53* .36* .42* .46* .46* .58* .62* .68* .77* .85* 1

Remark) Reliable Level of 95%

SQI: Service quality improvement

SCR: Service cost reduction or revenue increase

ACS: Activity for the customer satisfaction's improvement

ASD: Activity for the service diversity

ARE: Activity for replacing the existing old service

AAE: Activity for adopting the external knowledge &

technology

ABE: Activity for buying the external mechanical equipment

&

capital goods

ICC: Innovation cooperation with cooperative vendors

ICO: Innovation cooperation with other companies and

competitors within the same industry

ICP: Innovation cooperation with private service firm

(consulting, a private research institute)

ICU: Innovation cooperation with University, Higher

Institute

ICG: Innovation cooperation with Government-funded

research institute, Institute for public

4.2.3 Analysis Result of Factors Affecting Innovation Performances

Before measuring the innovation performance of SQI and SCR for the service innovation,

Cronbach Alpha Coefficient, the result of measuring the reliability of variables are at 0.9237 for

the innovation activities, 0.8925 for the innovation cooperation, 0.8424 for the innovation

performances.

The researchers obtained numbers are shown in [Table 4-3]. It shows to effects on SQI and

SCR via the multiple regression analysis.


[Table 4-3] Multiple Regression Analysis Result

(N=271)

Service innovation performance

Innovation activity & cooperation

Service quality

improvement

(SQI)

Service cost saving

or revenue increase

(SCR)

Coef. t Coef. t

Service

Innovation Activity

Activity for the customer satisfaction's improvement 0.084 1.28 0.007 0.10

Activity for the service diversity 0.210*** 2.75 0.164* 1.96

Activity for replacing the existing old service 0.080 1.12 0.044 0.56

Activity for adopting the external knowledge &

technology 0.148** 2.31 0.111 1.59

Activity for buying the external mechanical equipment

& capital goods 0.109* 1.71 0.230*** 3.28

Cooperation

With Partners

Innovation cooperation with cooperative vendors 0.212*** 3.40 0.084 1.23

Innovation cooperation with other companies and

competitors within the same industry 0.054 0.79 0.169** 2.24

Innovation cooperation with private service firm

(consulting, a private research institute) -0.010 -0.15 -0.004 -0.05

Innovation cooperation with University, Higher

Institute -0.052 -0.64 -0.052 -0.58

Innovation cooperation with Government-funded

research institute, Institute for public 0.051 0.71 0.093 1.16

General Characteristics

GC1: Gender (male) 0.209 1.34 0.116 0.68

GC2: Department (office sector) 0.246* 1.87 0.031 0.22

GC3: Work Experience(more than 10) 0.058 0.30 -0.023 -0.11

GC4: Year of Education (with a college) 0.019 0.19 0.084 0.79

GC5: Union Affiliation -0.108 -0.84 -0.081 -0.58

GC6: Job Satisfaction(less than 70) -0.058 -0.53 -0.055 -1.31

GC7: Job Grade 4～6 0.226 0.78 -0.662** -2.09

7～9 0.064 0.31 -0.170 -0.75

GC8: Age less than 50 0.064 0.46 -0.038 -0.25

more than 51 -0.009 -0.04 0.520** 2.23

VIF

maximum value 4.98 4.98

Minimum 1.30 1.30

Mean 2.92 2.92

R2 0.574 0.544

Adj-R2 0.540 0.508

F-value 16.82 14.92

Number of Observation 271 271

Remarks: 1) *p<.1, **p<.05, ***p<.01, 2) omitted variables: Job grade(1～3), Age(less than 40)

Volume 9, No.2, 2015 41

The result of the service innovation performance is shown in, <Table 4-3>. It shows the

explanation-power of SQI, service innovation performance, is at 0.574. And the marginal effects

show significant values for service innovation activities, ASD (0.210), AAE (0.148), and ABE

(0.109). Also the innovation cooperation (ICC), with the service partners shows a significant

value at 0.212.

As for the general characteristics among the departments, the office sector is shown to be

significant (0.246) for service innovation performance compared to other non-office sectors

which include the locomotive, the rolling stock, the technical, and the building maintenance. With

the exception of the office sector, other general characteristics are not shown to be significant.

The explanation-power of SCR, the other service innovation performance was at 0.544. The

marginal effects show significant values for service innovation activities that are ASD (0.164) and

ABE (0.230). The innovation cooperation with the service partners is shown to be significant,

ICO (0.169).

As for the general characteristics of the organization, the job grade is shown to be

significant from the 4-6 group (-0.662) compared to the 1-3 group. And the age was shown to be

significant from the group of the more than 51 compared to the less than 40. However, other

general characteristics were not shown to be significant. As for the activity for the service

innovation of SMRT, the analysis result above indicates that the service quality improvement and

the service cost saving (or revenue increase) are found to be significant.

The researchers concluded that, SMRT needs to identify customer needs, and put

significant efforts to improve innovation performance. Also, in search of cooperation with the

external organization, the cooperation shows positive significance in the service innovation

performance only when the cooperation is done with service partners or competitors within the

same industry. Philippe Aghion, et al. (2009) stated that entry could lead the trigger of the

diffusion of knowledge in the paper, ―The Effects of Entry on Incumbent Innovation and

Productivity‖.

In this study, SMRT got the performance improvement for the service innovation from

cooperation with the external organization. Although somewhat limited, since the innovation

cooperation is confirmed to play a role as a trigger for the diffusion of knowledge, SMRT needs

not only to promote the internal innovation competency, but also n to encourage external

innovation competency. Considering the general characteristics of SMRT to service innovation


performance, the effect was shown to be significant only limited to department (office sector),

grade (4-6), and age (more than 51).

The researchers confirmed that other non-office sectors compared to office sector for the

innovation performance, age less than 40 compare to the ones more than 51 for service innovation

performance are small. From the findings, the researchers concluded that innovation performance

promotion plan may be necessary. For the innovation performance, negative effect of -0.662 for

cost saving (or revenue increase) was found in the Grade (4-6). The distribution ratio of the

Grade (4-6) was found at 74.1%.

Although the distribution ratio of the Age(less than 50) was found at 88.9%, it didn't affect

the service innovation performance. The researchers believe that SMRT must give efforts to find

ways to improve its service innovation performance.

4.2.4 Hypotheses Verification

To verify the hypotheses from the empirical analysis, the results are shown in the tables

from [Table 4-4] to [Table 4-6].

[Table 4-4] Hypotheses Verification for H1

Hypotheses Result Adopted

variables

H1. Service innovation activity in a public enterprise will affect to improve its service performance.

H1-1 Service innovation activity will bring the service quality

improvement. partial

ASD, AAE, ABE

H1-2 Service innovation activity will bring either cost reduction or

revenue increase. partial ASD, ABE

The hypothesis H1 was partially confirmed. The H1 was to verify how the internal

innovation competency of SMRT effects on each innovation performance. Furthermore, the

internal competency of SMRT effects on the innovation activity for each innovation.

The researchers in this study found the strengths and the weaknesses in the utilization

level of the internal innovation competency of SMRT. Also, the implications for developing the

internal innovation competency in the financial and non-financial side is found and offered.

Volume 9, No.2, 2015 43



variables

H2. Cooperation with innovation partner(s) in a public enterprise will affect to improve their

service performance.

H2-1 Cooperation with innovation partner(s) will bring their service

quality improvement. partial ICC

H2-2 Cooperation with innovation partner(s) will bring either cost

reduction or revenue increase. partial ICO

As shown in [Table 4-5], H2 states ―Cooperation with innovation partner(s) in a public

enterprise will affect to improve their service performance‖ and it was confirmed in a very

restrictive sense. Generally, the organizational culture of a public enterprise is conservative

against the changes caused by the external environment. This study confirmed that external

innovation competency to improve the innovation performance is needed to improve the business

performance by reacting to the changes of the external environment. The obstacles from

adopting external innovation competency appear both in the financial and non-financial

innovation performance. As for the innovation of organizations, Henry Chesbrough (2010) stated

that successful leadership overcomes the obstacles of organizational change.

Victoria Joy G. Staplan (2007) studied a comparative analysis to build a model to

understand the factors that affect the job-related knowledge sharing in Knowledge Management

Systems. The research compared TAM (Technology Acceptance Model), TRA (Theory of

Reasoned Action), and TPB (Theory of Planned Behavior).



variables

H3. The general characteristics of SMRT will have an impact on the service innovation of public

enterprise.

H3-1. The general characteristics of SMRT will have an impact on the

service quality improvement. partial GC2

H3-2. The general characteristics of SMRT will have an impact on the

service cost saving or revenue increasing. partial GC7, GC8


5. Conclusion

5.1 Summary and Implications

5.1.1 Summary

In this study, the researchers categorize SMRT as an EIPE that is consisted of the groups

of locomotives engineers, rolling stock engineers, technical engineers, and building maintenance

engineers. The results of the empirical analysis were summarized to help to improve the

innovation performance of SMRT.

Concerning Hypotheses 1, service innovation performance may be classified into

financial and non-financial, according to the emerging types of internal innovation competency.

The results found are as following:

- Financial: ASD (Activity for the service diversity, 0.164), and ABE (Activity for buying

the external mechanical equipment & capital goods, 0.230) show significant effects on SCR

(service cost saving or revenue increase)

- Non-Financial: ASD (Activity for the service diversity, 0.210), AAE (Activity for

adopting the external knowledge & technology, 0.148), and ABE (Activity for buying the external

mechanical equipment & capital goods, 0.109) show significant effects on SQI (Service quality

improvement).

From this study to enhance the innovation performance through SMRT's innovation

activity, the researchers found that two things must be considered.

1) SMRT must consider that it is mainly an enterprise of engineers of the locomotive,

the rolling stock, the technical engineering, and the building maintenance.

2) After the establishment of social infrastructure, it tends to be non-replaceable

complementary goods.

Therefore, as the primary party of operation for the social infrastructure, SMRT has the

characteristics of equipment-intensive public organization with non-replaceable complementary

goods. The pursuit of ―value-oriented shared value (VOSV, hereafter)‖ as its enterprise culture

has to be recognized to maximize the development of SMRT.

Volume 9, No.2, 2015 45

Concerning Hypothesis 2, in the case of the collaborative development with external

organization as the information source for innovating, Researchers have analyzed, that the

cooperation with innovation partners affects for the innovation performance. As in Hypothesis 1,

service innovation performance was classified into the financial innovation performance and the

non-financial innovation performance in accordance with the happening type of external

innovation competency against Hypothesis 2. As for the financial services innovation

performance, the cooperation with service innovation partners had given significant impact ICO

(Innovation cooperation with other companies and competitors within the same industry, 0.169)

against SCR (Service cost reduction or revenue increase). And as non-financial service innovation

performance, the cooperation with service innovation partner(s) had given significant impact ICC

(Innovation cooperation with cooperative vendors, 0.212) against SQI (Service quality

improvement).

Concerning Hypothesis 3, this study analyzed the general characteristics of SMRT which

affect the service innovation performance of public enterprise, and tried to propose SMRT's

direction from the results. In analyzing the service innovation performance, it was classified into

financial and non-financial. As for the financial service innovation performance, service

innovation had significant impacts to Job Grade (4~6, -0.662) and Age (more than 51, 0.520)

against SCR (Service cost reduction or revenue increase). As for the non-financial service

innovation performance, service innovation showed more significant impact from Department

(Office sector, 0.246) than the other, against SQI (Service quality improvement). As for the result,

innovation in non-Office sectors of SMRT needs to be preceded to improve the service quality as

the non-financial service innovation performance.

5.1.2 Implications

The focus of this study is on improving the service innovation performance of public

enterprise, an EIPE. The implications learned can be summarized as following:

First, this study suggests the needs for building a system for technology creation,

acquisition, and utilization for wealth creation. For wealth creation that occurred from 'the

competency of each organization' or 'the competitive advantage'. SMRT must build 'TSS

(Technology Sharing System)' to maximize the service innovation performance. The TSS is to

increase service competitiveness and also to enhance internal service innovation competency.


Second, in the case of jointly developing the innovation with external organizations, the

researchers found that, to maximize the innovation performance, Communication System (CS) is

important to enhance the cooperation with innovation partners.

Third, big data for service innovation including periodic information is necessary by

policy support. It is for evaluating the effectiveness of a system to enhance the service innovation

performance. The adducible policy implication of the study is the need of a big data processing

system. The purpose of the system is to improve the service innovation performance for

maximizing 'the business performance' and 'the development of EIPE'.

5.2 Limitations and Future Research

This study focused on the differentiation of service innovation performance of SMRT.

Especially, it is significant in the sense that the service innovation of EIPE was somewhat

neglected by research community at large in the past. It also has some limitations as following.

First, there are problems about the sample data. The survey respondents‘ answers were

somewhat in low quality. Since the survey was about SMRT's each sector(the office sector, the

locomotive engineering sector, the rolling stock engineering sector, the technical engineering

sector, the building maintenance engineering sector), the sample size for empirical analysis was

not sufficient to analyze each service innovation performance. Thus, it was difficult to get

statistically significant values.

Second, the service innovation performance was measured by the subjective indicators

from the staff of SMRT, and not the objective indicators. Therefore, the subjectivity of the staff

was concerned in the interpretation of innovation performance indicators. To eliminate the

possibility of subjectivity from being measured, the future studies may need to add objective

estimations such as 'Experts', 'External cooperation organization agents', 'External partners',

'General consumer', etc. To maximize the business performance (and the organization

development) of public sector, the service innovation research previously requires the

establishment & utilization of big data for each public sector.

Finally, the service innovation research about EIPE that manages social infrastructure is

somewhat scarce.

The researchers hope that this study serve as the starting point for the large increase of

innovation research for the same or similar industry in the future.

Volume 9, No.2, 2015 47

<received: 2015. 10. 07>

<revised: 2015. 12. 06>

<accepted: 2015. 12. 14>

１ The numbers such as SQI, SCR and so on in parentheses are denotations to be used in tables later in the paper.


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Volume 9, No.2, 2015 51

Evaluating the Performance of Disaster Recovery Systemic

Innovations by Using the Data Envelopment Analysis

Chia-lee Yang, Benjamin J.C. Yuan

, Chi-yo Huang

,

and Chih-neng Chang

Abstract

Over the past decades, evolutions in disaster recovery (DR) technologies have proven to be

typical systemic innovations. Such innovations consist of various interrelated changes in information

technology (IT), telecommunications, Internet service provider management, backup server product

design, and so on. As data is regarded as a strategic corporate asset that must be protected, selecting a

reliable innovative IT DR system has become high priority for many organizations. Such decision-

making problems usually face the challenges of seeking the most efficient DR system(s) to fulfill

requirements of specific tasks or projects. Typical examples include the backup of big data, the

recovery of operations in the case of emergencies being caused by natural or man-made disasters,

cyber-attacks, etc. Therefore, how to evaluate and select an appropriate innovative IT DR system is

critical for modern organizations. However, no existing studies have evaluated the performance of

systemic innovation, in general, and IT DR systemic innovations especially. Thus, this paper aims to

propose an analytic framework to evaluate the performance of IT DR systems. Given that IT DR system

performance evaluation problems are indistinct and involve various considerations, this paper

introduces data envelopment analysis (DEA) methods with derivations of the efficiency achievement

measure (EAM). An empirical study on evaluating three DR systems belonging to a Taiwanese

research institute will be used to demonstrate the feasibility of the analytic framework. This framework

can serve as an appropriate method for evaluating the performance of DR system efficiency and then

develop strategic plans for enhancing their performance.

Key words: Disaster Recovery (DR); Information Technology; Performance Evaluation; Data

Envelopment Analysis (DEA).

Doctoral Candidate, Institute of Management of Technology, National Chiao-Tung University, Taiwan; Principle

Engineer, National Center for High-Performance Computing, Taiwan. E-mail: [email protected] Professor, Institute of Industrial Economics, Jinan University, China; Institute of Management of Technology, National

Chiao-Tung University, Taiwan. E-mail: [email protected] Corresponding author; Professor, Department of Industrial Education, National Taiwan Normal University, Taiwan. E-

mail: [email protected] Institute of Industrial Economics, Jinan University, Guangzhou, China. E-mail: [email protected]






1. Introduction

Over the past few years, there has been growing interest in system (or systemic)

innovations (Mulgan & Leadbeater, 2013), which are defined as large-scale transformations in the

way societal functions such as transportation, communication, housing, and feeding are fulfilled

(Geels, 2004). Such innovations are not matters of solving isolated problems but of overhauling

the entire system (de Bruijn, van der Voort, Dicke, De Jong, & Veeneman, 2004). Systemic

innovations by nature require ―interrelated changes in product design, supplier management,

information technology (IT), and so on‖ (Teece, 2003). Many heterogeneous elements change

during a systemic innovation; such innovation is a coevolutionary process. Thus, these changes

involve technical innovations and innovations on the user side (Geels, 2005). In general, systemic

innovations can take place at different aggregation levels, but they always share the following

aspects: they are comprehensive, with a long-time horizon, requiring the efforts of many

stakeholders, and a change of perspective and a cultural shift among these stakeholders (de Bruijn

et al., 2004). The distinction between systemic innovations and autonomous innovation applies to

not just the manufacturer but also services (Chen, Wen, & Yang, 2014; Vesa, 2005).

Systemic innovations are embedded in certain institutions, structures, and values that will

have to change as well (de Bruijn et al., 2004). The idea behind systemic innovation is that regular

change will not suffice to solve them (de Bruijn et al., 2004). Chesbrough and Teece (1996)

initiated discussions on systemic innovations versus autonomous innovations, in terms of the

choice of innovation governance of internalization versus outsourcing in manufacturing

(Chesbrough & Teece, 2002). A system can be quite small and localized (for example, the system

of housing allocation or food sourcing in one town or city), and it can be national or global

(Mulgan & Leadbeater, 2013).

Innovations in the information technology (IT) disaster recovery (DR) systems are typical

systemic innovation since such innovations consist of various interrelated changes in information

technology (IT), carrier and support, backup servers design, and so on. During the past decades,

the IT DR system has been innovated, from a set of procedures to recover and protect IT

infrastructure when computer(s) shut down accidentally (Yang, Yuan, & Huang, 2015), to modern

systems in the cloud computation and big data era, which such innovative systems can cope with

disasters effectively and efficiently (Sahebjamnia, Torabi, & Mansouri, 2015). Disaster recovery

in the modern age is a detailed, step-by-step course of actions for quickly recovering after a

natural or manmade disaster; the details may vary depending on the business needs, and can be

developed in-house or purchased as a service (TechAdvisory.org, 2010). IT DR systems need to

Volume 9, No.2, 2015 53

be able to restore data and backup systems for the organization to ensure continuous operation,

even in the presence of extensive failures that may render an entire system un-operational and for

which local replication may be inadequate (Kant, 2009; Lumpp, Schneider, Holtz, Mueller, Lenz,

Biazetti & Petersen, 2008). Further, the primary DR system objectives – reducing costs and

increasing efficiency while mitigating risk and better aligning IT with business initiatives – are

often stymied by today‘s datacenter challenges (DuBois & Amatruba, 2013). A strategic approach

to evaluate the performance of innovative DR systems will help organizations meet their business

continuity objectives while considering both performance and cost issues.

Practical problems regarding performance evaluations of DR systems are complicated and

usually involve massive subjectivities and uncertainties (Claunch, 2004; Johnston, 2014; Ueno,

Miyaho, Suzuki, & Ichihara, 2010). A few researchers have studied issues related to performance

evaluations of DR infrastructure solutions (Chen, 2001; Covas, Silva, & Dias, 2013; Kant, 2009).

However, to the best of the present authors‘ knowledge, very few or no researchers have explored

DR system performance in detail. To fill the research gap, this paper aims to propose an analytic

framework to evaluate DR system performance.

This study will first review the literature regarding possible aspects and criteria that could

be used in evaluating the performance of a DR system. We will then seek to confirm these aspects

and criteria through focus group interviews with experts. Because the IT DR system performance

evaluation problems are indistinct and involve various considerations, this paper introduces data

envelopment analysis (DEA) methods with derivations of the efficiency achievement measure

(EAM). An empirical study evaluating three DR systems belonging to a Taiwanese governmental

research institute will be used to demonstrate the feasibility of the proposed framework.

The rest of this paper is organized as follows. A literature review on DR systems, DR

objectives, and performance evaluations is presented in Section 2. The research methods of the

DEA are presented in Section 3. The empirical study evaluating DR systems‘ performance for the

Taiwanese research institute is presented in Section 4. Advances in management practices and

comparisons between the empirical study results and past research results are discussed in Section

5. Finally, Section 6 summarizes the results and concludes the paper.

2. Literature Review

In order to review the latest DR system performance research and to construct an analytic

framework accordingly, related literature is reviewed and summarized below. This literature


review focuses on past studies related to the concepts of systemic innovations, DR, DR systems,

objectives of DR systems, and performance evaluation criteria for DR systems.

2.1 Systemic Innovation

The initiation of systemic innovation study dates back to the late 1960s. According to

Marquis, innovations can be divided into three categories based on the scale of impacts: radical,

incremental, and systems (Marquis, 1969). However, according to Betz (2011), what Marquis

called a system innovation has not proven to be a useful distinction at that that moment because

all technologies are systems. Later, scholars (e.g., Chesbrough and Teece, 1996) initiated

discussions on systemic innovations versus autonomous innovations, in terms of the choice of

innovation governance of internalization versus outsourcing in manufacturing filled the gap and

compared systemic innovations with autonomous innovations.

Systemic innovation is defined as innovation related to complex systems such as

communication networks, which takes many years to develop and costs millions of dollars

(Marquis, 1969). According to Geels (2004), systemic innovations are defined as large-scale

transformations in the way societal functions such as transportation, communication, housing, and

feeding are fulfilled (Geels, 2004). Such innovation is not a matter of solving an isolated problem

but of overhauling the entire system (de Bruijn et al., 2004). Systemic innovations by nature

require ―interrelated changes in product design, supplier management, information technology

(IT), and so on.‖ In addition, the distinction between systemic innovations and autonomous

innovation applies to not just the manufacturer but also to services (Chen et al., 2014; Vesa, 2005).

Chesbrough and Teece (1996) initiated discussions on systemic innovations versus

autonomous innovations, in terms of the choice of innovation governance of internalization versus

outsourcing in manufacturing (Chesbrough & Teece, 2002.) As with the broad concepts of

innovation, focus and definition have been placed on technological innovation to associate the

organization system. Further, complex product system innovations are also defined as high cost,

engineering-intensive products, systems, networks, and constructs (Thota & Munir, 2011). They

are business-to-business capital goods, which form the backbone of modern economy and society

(Ren & Yeo, 2006).

A system can be quite small and localized (for example, the system of housing allocation

or food sourcing in one town or city), and it can be national or global (Mulgan & Leadbeater,

2013). Many heterogeneous elements change during a systemic innovation; the systemic

Volume 9, No.2, 2015 55

innovation is a coevolutionary process; these heterogeneous elements involve technical

innovations and innovations on the user side (Geels, 2005). Systemic innovations are embedded

in certain institutions, structures, and values that will have to change as well (de Bruijn et al.,

2004). In general, systemic innovations can take place at different aggregation levels, but they

always share the following aspects: they are comprehensive innovations, with a long-time horizon,

requiring the efforts of many stakeholders, along with a change of perspective and a cultural shift

among these stakeholders (de Bruijn et al., 2004).

Processes of system change have a longtime horizon and are complex because of the many

interrelated actors and factors (van Mierlo, Arkesteijn, & Leeuwis, 2010). New research has been

undertaken, and theories have demonstrated that systemic innovations are characterized by

fundamental uncertainties, chaos, unintended consequences, conflicts, and unpredictable

trajectories of change, which cannot be understood from a reductionist perspective, or, for that

matter, from the perspective of direct cause–effect relations that seem to be at the core of former

problem-solving approaches (Prigogine & Stengers, 1990; Rotmans, Loorbach, & van de Brugge,

2005; (van Mierlo et al., 2010). Along with the vision on how systemic innovation takes place, the

ideas about how systemic innovation might be stimulated and managed have evolved

considerably (van Mierlo et al., 2010). Systemic innovation projects need not only to be reflexive

in design, planning, and management but also to be accompanied by a monitoring and evaluation

approach that supports and maintains such reflexivity (van Mierlo et al., 2010).

2.2 DR and DR systems

The terrorist attacks, data assaults, and natural catastrophes that occurred in recent years

have taught us that many types of disasters can hit organizations of every size, threatening to

disrupt operations and potentially even destroying those that are not fully prepared (Wallace &

Webber, 2010). As information systems continue to advance at a formidable speed and become an

ever-more universal and powerful tool in organizations, the influences of disasters can directly

injure information systems and impact the business continuity (Spillan & Hough, 2003;

Anthopoulos, Kostavara, & Pantouvakis, 2013). As the IT services without interruption are

expected, the supporting infrastructure has become critical to the success of most enterprises. To

meet the continuity needs of most organizations, one popular strategy is to set up a secondary DR

system and DR site that can support operations until the primary data center can be returned to

normal operations. Therefore, information infrastructure availability has become a critical issue,

attracting increasing attention from both IT researchers and practitioners. In general, the


importance of IT DR can hardly be overestimated (Fothergill & Peek, 2004; Rose, 2007; Feng &

Li, 2011).

Modern information infrastructure is full of uncertainties. Such uncertainties are due to

years of acquisitions, investments, building and rebuilding, partial improvements, and temporary

fixes that finally become obsoletet (Clitherow, Brookbanks, Clayton, & Spear, 2008). Thus, more

organizations have begun to consider using DR system(s) being located in remote sites, which can

restore data and maintain IT system operations during or after occurrences of disasters (Lumpp et

al., 2008; Kant, 2009; Sembiring & Siregar, 2013). An IT DR site is a backup data center that

restores the full data and all functionalities of communications or equipment to a remote location

and synchronizes those data with the primary site in the event of primary site failure (King, Halim,

Garcia-Molina, & Polyzois, 1991; Sembiring & Siregar, 2013). DR sites should be as separate

from the primary site as possible (Ellis & Collins, 2013).

Numerous researchers have examined IT DR concepts and technologies, such as network

technology, storage, and so forth (Serrelis & Alexandris, 2006; Clitherow et al., 2008; Bianco,

Giraudo, & Hay, 2010;Ueno et al., 2010). However, very few previous research works have

focused on IT DR site selection and evaluation in general or on performance evaluation of DR

sites in particular. The DR site performance evaluation problems require multiple-criteria decision

making (MCDM), which usually involves multi-disciplinary knowledge including IT technology,

business continues process and disaster management (Bryson, Millar, Joseph, & Mobolurin, 2002;

Cegiela, 2006; Daim, Bhatla, & Mansour, 2013). A strategic approach to evaluate the

performance of DR sites will help organizations meet their business continuity objectives while

addressing both performance and cost issues.

2.3 Objectives of DR Systems

The most important key metrics for business continuity objectives to fulfill the

requirements of data dependability are Recovery Time Objectives (RTOs) and Recovery Point

Objectives (RPOs). RTOs define how quickly information systems and services must be

operational after a disaster, including recovery of applications and data and end-user access to

secondary-site applications. Thus, RTOs refer to the periods of time to recover from a disaster.

Meanwhile, Recovery Point Objectives (RPOs) define the point in time from which data must be

restored in order to resume processing transactions. RPOs are measured backward in time from

the instant at which that the organization‘s failure occurs as a result of the disaster event (Claunch,

2004; Johnston, 2014).

Volume 9, No.2, 2015 57

RTO and RPO requirements are defined in the business impact analysis (BIA) stage of the

business continuity plan process. The BIA identifies what is at risk in the enterprise and which

business processes are most critical, thereby prioritizing risk management and recovery

investments. The direct and indirect impact of business interruptions is assessed over time. RTOs

and RPOs depend on the organization‘s ability and willing to protect data in a way that limits the

impact of disruption to the business (Claunch, 2004).

The definitions of RPOs and RTOs not only influence business risks and losses in or after

disasters, but also influence DR costs. Organizations often face the dilemma between business

losses and IT over-investment when decide RPOs and RTOs. On the one hand, business operation

downtimes, as defined by RPOs and RTOs, adversely affect recovery time costs, opportunity

losses, company reputation, legal considerations for breached service levels, customer confidence,

and drops in stock price (Garg, Curtis, & Halper, 2003; Wiboonrat, 2008). For example, Garg et

al. analyzed the financial impact of information breaches on corporations. The results show that

the average loss in share price on the day of an event‘s occurrence was nearly 5.6 percent over a

three-day period after the event (Garg et al., 2003). On the other hand, the DR technologies are

selected to meet the data protection needs of the organization based on RTO and RPO. DR

systems are generally the best, albeit the most expensive, solution if no data loss is an issue

(Broder & Tucker, 2012). The RTO and RPO metrics will define the media size for backup, the

location where data is being recovered, and the type of the IT infrastructure. A short RTO and

RPO or lower outage tolerance will result in a higher cost of IT solutions. The DR objectives and

costs of investment are trade-off problems that have attracted researchers‘ interests. For example,

Wiboonrat simulated the reliability and cost issues of two data centers and proposed an optimal

balance between data center system reliability and investment costs for each case (Wiboonrat,

2008).

2.4 Performance Evaluation of DR Systems

Within a business context, performance is defined in terms of efficiency and effectiveness.

Effectiveness is compliance with customer requirements, while efficiency is how the

organization‘s resources are used to achieve customers‘ satisfaction levels (Neely, Gregory, &

Platts, 1995). Performance measures are the metrics used to quantify the efficiency and/or

effectiveness of actions of part or all of a process or system in relation to a pattern or target

(Fortuin, 1988; Neely et al., 1996). Performance measures should be chosen, implemented, and

monitored to capture the essence of organizational performance (Neely, 1999; Gunasekaran, Patel,

& McGaughey, 2004;Braz, Scavarda, & Martins, 2011; ).


As DR is becoming increasing complicated and costly, the need to evaluate DR systems

performance has become more critical. For corporate IT decision makers, evaluation of DR

systems is extremely complicated, always involving various input and output factors. A lack of

well-defined criteria will result in wrong solutions and ultimately misle decisions.

A limited number of scholars have tried to study performance evaluations of DR

technologies. Early research focused on the performance evaluation of information systems or

backup technology (Bryson et al., 2002; King et al., 1991; Patterson et al., 2002), while recent

works have mainly focused on the DR mechanisms in the cloud computing environment (Ueno et

al., 2010; Wood et al., 2010; Ichihara, Miyaho, Ueno, & Suzuki, 2013). Some recent studies have

started to examine DR system location evaluation and selection. Such research works have

usually developed mathematical models and techniques to select DR sites locations (Dekle,

Lavieri, Martin, Emir-Farinas, & Francis, 2005; Ratick, Meacham, & Aoyama, 2008). While

some scholars have started to study the performance evaluation of DR mechanisms, to the best of

the authors‘ knowledge, little to no research to date has evaluated the performance of DR systems.

3. Analytical Methods: DEA Method

DR system performance evaluation problems include a complex array of evaluation

aspects and criteria, which always include multiple inputs and outputs. The input criteria are

usually related to economic, technical, and risk management issues, while the output criteria may

include RTOs and RPOs. In order to define a reasonable analytic framework, the authors propose

a DEA method-based approach, which will be verified using an empirical study case.

The DEA is a mathematical programming approach that deals with the problem of

measuring the productive efficiency. The DEA is a non-parametric approach (Farrell, 1957) that

aims to build mathematical programming models to derive the comparative efficiency. The DEA

has become an important performance evaluation tool since its introduction by Charnes et al.

(Charnes, Cooper, & Rhodes, 1978) and allows constant returns to scale (called the DEA-CCR

model). The DEA-based approach involves the measurement of efficiency for a given decision

making unit (DMU) without any prior assumptions about the inputs or outputs. The DEA uses

linear programming methodology to define a production frontier for DMUs. Since DEA

incorporates multiple inputs and outputs of DMUs into an efficiency measure, which is consistent

with real-world situations, there has been rapid and continuous growth in the field. As a result, a

considerable amount of published research has appeared, with a significant portion focused on the

Volume 9, No.2, 2015 59

applications of DEA for efficiency and productivity evaluations in both public and private sectors

(Emrouznejad, Parker, & Tavares, 2008).

There are several models of DEA, such as the DEA-CCR model (Charnes et al., 1978); the

DEA-BCC model (Banker, Charnes, & Cooper, 1984); the Cross-Efficiency DEA model (Doyle

& Green, 1994; Sexton, Silkman, & Hogan, 1986); and the Super-Efficiency DEA model

(Andersen & Petersen, 1993). The CCR model (Charnes et al., 1978) assumes that production

exhibits constant returns to scale. Banker et al. (1984) extended the original CCR model by

considering variable returns to scale. For company managers, controlling the inputs is easier than

increasing total sales. Because both CCR and BCC models are input-oriented, which is consistent

with the evaluation needs of data centers, both the CCR and BCC models are introduced in this

research. The formulations of the research works are based on the authors‘ earlier works (Chen &

Huang, 2012; Huang, Tzeng, Chen, & Chen, 2012).

3.1 DEA-CCR Model

The DEA-CCR model computes relative efficiency score ( )ih based on selected s outputs

( 1,..., )r s and m inputs ( 1,..., )i m using the following linear programming expression:

max

1 1

/s m

r rj i ij

r i

u y v x

s .t.

1 1

/ 1s m

r rj i ij

r i

u y v x

(1)

, 0; 1,..., ; 1,..., ; 1,...,r iu v r s i m j n .

It assumes the DMU has s outputs and m inputs, and there are n DMUs. The ru and iv

are not zero, calculating as , 0r iu v , is non-Archimedean number and is-610 .

3.2 DEA-BCC Model

The DEA-BCC model is input-oriented and has a variable 0u (returns to scale). The

equations show as follows:

1

( )=1m

i ij

i

v x


max hij 0

1 1

( ) / ( )s m

r rj i ij

r i

u y u v x

s.t. 0

1 1

( ) / ( ) 1,s m

r rj i ij

r i

u y u v x

(2)

, 0; 1,..., ; 1,..., ; 1,..., .r iu v r s i m j n

The Equation (2) was changed to 오류! 참조 원본을 찾을 수 없습니다. for solving

formula by using the fractional mathematical programming approach as follows:

max gj 0

1

( )s

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=1m

i ij

i

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0

1 1

0s m

r rj i ij

r i

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(3)

, 0; 1,..., ; 1,..., ; 1,..., .r iu v r s i m j n .

The dual formula:

min Zj

1 1

( )m s

i r

i r

s s

s.t.

1

0,n

j ij ij i

j

x x s

1

,n

j rj i rj

j

y s y

1

1n

j

j

(4)

, , 0; 1,..., ; =1,..., ; 1,..., .j i rs s r s i m j n

4. Evaluating DR Systems of a Taiwanese Research Institute by DEA Methods

In this section, an empirical study of the proposed DEA-based evaluation framework will

be presented. First, the background of the empirical study case will be introduced and the DMUs

will be decided. Then, the input and output criteria will be derived based on experts‘ opinions.

Volume 9, No.2, 2015 61

Finally, an evaluation of three DMUs will be used to demonstrate the feasibility of the proposed

framework.

4.1 Selection of decision making unit (DMU)

We first select three DR systems belonging to a Taiwanese Research Institute (X center) as

DMUs for verifying the feasibility of the proposed framework. The X center is the most advanced

Taiwanese research institution providing super computation, cloud based computation and big

data services. Since the establishment in 1991, the X center has provided DR services for

governmental and academic research institutes.

There are three DR systems – systems A, B, and C – belonging to the X center. All three

systems provide remote backup systems and reliable DR services. The three DR systems are

linked together by using a 100 Gbps backbone. The Storage Area Network (SAN) structure is

used along with the dual backbone optical fiber network, which is characterized by uninterrupted

data transmissions and thus not only significantly increases the data back up speed but also

achieves rapid data recovery in case of losses or failures.

4.2 Aspects and Criteria Derivations Using the Focus Group Method

The possible aspects and criteria for evaluating DR system performance will be derived

based on the comprehensive literature review results [Table 1]. First, government and industry

standards and existing enterprise IT architectures were considered. Then, experts were invited to

provide their opinions. Details regarding every aspect and criterion were derived based on the

experts‘ opinions. The aspects of tangible and intangible resource are viewed as inputs, whereas

the aspects of DR objectives are considered outputs.

Ten experts related to the DR business in X center were invited to provide opinions using

the modified Delphi method. The experts selected include five senior IT managers who are

responsible for DR decisions in the X center, as well as five IT managers who use DR services

provided by the X center. All the experts have more than five years of work experience in the

related fields of business continuity management and/or DR plan definition.


[Table 1] Candidate Aspects and Criteria for Evaluating DR Systems’ Performance

Volume 9, No.2, 2015 63

4.3 Evaluating Performance of DR Sites Using the DEA

First, the experts‘ opinions regarding the input and output criteria are summarized in the

following [Table 2] as input datasets of the DMUs. Then, the analysis will proceed as follows: (i)

statistical selection of inputs and outputs; (ii) evaluating performance of the DMUs; and (iii)

conducting the sensitivity analysis.

[Table 2] Input Datasets of the DMUs

DMUs Inputs 1: Tangible Resource(A) Inputs 2: Intangible Resource(B)

Outputs: DR

Objectives (C)

a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 c1 c2

System A 7.0 6.6 6.5 7.2 6.8 6.6 6.7 6.3 6.9 6.9 6.1 6.1

System B 6.6 6.7 6.7 6.9 6.3 6.1 6.0 5.9 6.1 5.7 6.2 6.0

System C 6.4 6.4 6 7.2 6.7 6 6.5 5.8 6.2 5.5 5.7 5.6

4.3.1 Statistical Selection of Inputs and Outputs

Using the Pearson‘s coefficient, we will test the bivariate correlation of the variables

relating to inputs and outputs, with the objective of detecting factors with the same significance so

as to eliminate them [Table 3]. We will redefine variables that do not fulfil the isotonic property,

which requires that there should be no negative correlation between inputs and outputs

(GarcER‐Sánchez, 2006). Since the input criteria ―Telecommunication Infrastructure ( 4a )‖,

―Carrier and Support ( 5a )‖ and ―Information Security Management Procedure ( 2b )‖ are

negatively related to the output criteria, the three criteria are eliminated due to their violation of

the isotonic property.

4.3.2 Evaluating the Performance of the DMUs

After justifying the variables theoretically and statistically, we will start with the definite

analysis of the intervening service by applying the BCC model. According to the empirical proof

obtained by various authors, BCC model will provide a level of pure technical efficiency. In turn,

we will estimate the CCR-efficiency index, which will allow us to calculate scale efficiency from

the quotient of both indexes (GarcER‐Sánchez, 2006). The performance evaluation results have

been derived in comparison to those derived by the CCR and BCC models (Table 4). The

efficiency scores for the three systems are calculated utilizing both the CCR and BCC models.

http://www.sciencedirect.com/science/article/pii/S0307904X08000395#tbl1


The performance evaluation results present two major findings. Based on the evaluation

results derived using the CCR and the BCC models, all three of the DR systems achieved optimal

performance. These three DR systems can thus be seen as performance maximizers based on the

results derived by using the CCR and BCC DEA models. Some reasons for this are

straightforward. First, these three DR systems belong to same data center, share some common IT

characteristics and demonstrate the same tendencies in performance. The X center designed

similar and interconnected IT infrastructures and backup system architecture in order to provide

multi-system backup service. Second, the X center implements information security management

standards, such as ISO 27001 and CSA Star, to guarantee the same RTO and RTO service level

agreement across the three systems.

[Table 3] Correlation Matrix

a 1 a 2 a 3 a 4 a 5 b 1 b 2 b 3 b 4 b 5 c 1 c 2

a 1 1.000

a 2 0.500 1.000

a 3 0.545 0.999 1.000

a 4 0.189 -0.756 -0.721 1.000

a 5 0.371 -0.619 -0.577 0.982 1.000

b 1 0.984 0.339 0.388 0.359 0.529 1.000

b 2 0.454 -0.545 -0.500 0.961 0.996 0.604 1.000

b 3 0.990 0.371 0.419 0.327 0.500 0.999 0.577 1.000

b 4 0.901 0.075 0.127 0.596 0.737 0.963 0.795 0.954 1.000

b 5 0.980 0.317 0.366 0.381 0.549 1.000 0.623 0.998 0.970 1.000

c 1 0.619 0.990 0.996 -0.655 -0.500 0.470 -0.419 0.500 0.217 0.449 1.000

c 2 0.866 0.866 0.891 -0.327 -0.143 0.764 -0.052 0.786 0.564 0.749 0.929 1.000

[Table 4] Efficiency Scores for DEA Models and Sensitivity Analysis Results

c 1 c 2 a 1 a 2 a 3 b 1 b 3 b 4 b 5

CCR Site A 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.695 1.000 1.000 0.461 1.000 1.000

Site B 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.740 1.000 1.000 0.491 1.000

Site C 1.000 1.000 1.000 1.000 1.000 0.976 1.000 1.000 1.000 1.000 1.000 1.000 0.687 1.000 1.000 0.456

BCC Site A 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.793 1.000 1.000 0.646 1.000 1.000

Site B 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.832 1.000 1.000 0.678 1.000

Site C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.950 1.000 1.000 0.737

DMU3

(Extreme

Case 1)

DMU1

(Extreme

Case 2)

DMU2

(Extreme

Case 2)

DMU3

(Extreme

Case 3)

DEA

ModelDMU Efficiency

Sensivity Analysis Results by Excluding One Criteria DMU1

(Extreme

Case 1)

DMU2

(Extreme

Case 1)

Volume 9, No.2, 2015 65

4.3.3 Sensitivity Analysis Results

To demonstrate the discrimination capability of the proposed framework, we conduct a

sensitivity analysis by (1) increasing the value corresponding to any one criterion to the highest

goal to which organization can aspire (called aspired level), (2) simultaneously decreasing 10% of

both outputs and increasing 10% of all inputs of some specific DMU, and (3) simultaneously

decreasing 20% of both outputs and increasing 20% of all inputs of some specific DMU.

The sensitivity analysis results are also demonstrated in Table 4. At first, the results show

that increasing the value of any other criteria to the aspired level will not influence the

performance except the CCR results will be slightly biased for increasing the ―backup system

architecture (a3)‖. Then, by simultaneously decreasing 10% of both outputs and increasing 10% of

all inputs of some specific DMU and also simultaneously decreasing 20% of both outputs and

increasing 20% of all inputs of some specific DMU, significant differences can begin to be

observed in cases in which the outputs decrease while the inputs increase. System C is the most

sensitivity system at extreme case, with the value of 0.456. Please refer to Table 4 for the

sensitivity analysis results.

5. Discussion

In the following discussion section, the managerial implications from the systemic

innovation perspective and from those of evaluation criteria and objectives of IT DR systems

performance will be discussed. Limitations and future research possibilities will be addressed.

5.1. Managerial Implications from Systemic Innovation Perspective

In the past, the DR was often regarded as the computer system or location problem

belonging to emergency management in organizations. However, regardless of the nature of the

customers and end users, many operating businesses need to be prepared for the unexpected

downtime, whether it is via natural disaster or human error. Thus, modern DR systems play roles

in ensuring business continuity has increased substantially. Hence, DR technology has evolved

significantly.

Today‘s IT DR systems are becoming increasingly complicated. Such systems integrate

various contexts including IT, business operations, management disciplines, etc. For example, in

the past, a private hot site, which is a mirror of the primary data center infrastructure in order to


take over immediately after the disaster, was extremely expensive because duplicate equipment

must be purchased and replaced at the same time. Now, modern hot site innovations allow users

to connect different equipment or share the same server space without concern for equipment

specification. Further, project management and information security management procedures are

also changing for the distributed backup. Top management support and commitment to DRP

operations, which include allocation of the time and resources required in the DR plan, also

change because the level of redundancy is affordable. Therefore, there are necessities for

evaluating systemic innovation performance.

The analytic procedure and results demonstrate that evolutions of DR systems are systemic

innovations, which include an interconnected set of innovations. According to the aspects and

criteria for evaluating DR system performance, which is summarized in [Table 1], almost all

technologies and management systems have been innovated during the past 5 to 10 years, no

matter from the aspect of backup servers ( 2a ), backup system architecture ( 3a ), the information

security management procedure ( 2b ), DR procedure ( 3b ), etc.

For example, Teefe (2014) recently summarized three novel DR technologies: server

virtualization, cloud computation, and mobile devices. Innovations not only exist in each

technology, the innovations also connect with each other. According to Teefe (2014), the novel

server virtualization, the process by which multiple servers are designed to operate out of the

same piece of hardware, has been innovated as new DR backup server ( 2a ) technology (Teeft,

2014). Cloud computing is another new DR innovation. According to Dix (2013), the cloud can

serve as a backup system architecture ( 3a ), which gives companies backups of data, failover of

servers, and the ability to have a secondary center far enough away to allow for regional disaster

recovery (Dix, 2013). Cloud computation provides backups and restorations to and from the cloud,

along with replication to virtual machines in the cloud, etc. Further, mobile devices can be an

intuitive part of any disaster recovery plan [DR Procedure ( 3b )], simply because almost everyone

has a cell phone or tablet on or near them at all times. Communication is key to disaster recovery,

so the connection with as many players as possible mitigates downtime (Teeft, 2014). Further,

organizations should assure to outline security protocols [Information Security Management

Procedure ( 2b )] regarding company data accessed on private devices in your business‘s bring

your own device (BYOD) device policy (Teeft, 2014).

Volume 9, No.2, 2015 67

The Internet also connects the three DR innovations (server virtualization, cloud

computation, and mobile devices). These innovations also influence each other. For example,

Shafer noted that cloud computation is gaining popularity as a way to virtualize data centers

(Shafer, 2010). Some recent cloud computation innovation [e.g., CloneCloud (Chun, Ihm,

Maniatis, & Naik, 2010)] can automatically transform a single mobile device computation into a

distributed execution (mobile device and cloud computation). Apparently, advances in the DR

innovations are interlinked and influence each other. DR innovations are typical systemic

innovations.

According to Schilling (Schilling, 2005), the DEA is a useful quantitative method for

choosing innovation projects or systems. This study adopted the DEA as a systemic method to

evaluate innovative DR system performance and obtained reasonable and satisfactory results.

5.2 Managerial Implications from Evaluation Criteria of IT DR System

Performance

Based on the analytic results being demonstrated in [Table 3], we find that the input

criteria ―Telecommunication Infrastructure‖ ( 4a ) and ―Carrier Support‖ ( 5a ) are negatively

related to the output criteria. Both of ―Telecommunication Infrastructure‖ and ―Carrier Support‖

are related to the Internet bandwidth. In general, critical business applications require high-speed

network to synchronous or replication data in order to meet RPO and RTO objectives. Enterprises

add their network and redundancy facilities for DR plans by leasing slow, copper-based T1 or E1

lines. Therefore, the Internet transaction fees are usually one of the major costs of DR sites

(Rajagopalan, Cully, O'Connor, & Warfield, 2012). However, the X center is a national research

institute with high-speed optical fiber backbone which support by government founding. The DR

Systems A, B, and C are linked together by dual optical fiber backbone network. The capacity of

optical fiber is large enough to achieve rapid data recovery with flexible and scalable ability.

Therefore, network capacity may not be the constraints in this situation. It depends on network

technology and organizations ability.

5.3 Managerial Implications from Objectives of IT DR Systems Performance

According to business continuous process, the DR objectives (RPOs and RTOs) are

determined by the Business Impact Analysis (BIA) phase, which differentiates critical and non-

critical functions. A business function may be considered critical if such function is regulated by


mission, business and operationally based on business loss concerns (Claunch, 2004; Garg, Curtis,

& Halper, 2003; Wiboonrat, 2008). It implies that once the DR objectives have been defined, IT

departments evaluate the DR system performance by meeting their RPOs and RTOs goal.

However, this research observes that the DR system performance would be significantly different

if the outputs decrease while inputs increase [Table 4]. Inputs of DR system are also important for

DR system performance since DR system investment is undoubtedly the most costly task. Yang et

al. found that the DR objectives receive influences from IT system availability (Yang et al. 2015).

Wiboonrat found that 50% of DR solution for business unit applications is overinvestment for

system reliability. Thus, IT managers define critical business functions not only in consideration

of reputation, operation, and regulation, but also finance cost (Wiboonrat, 2008). The findings of

this work are consistent with the research results by Wiboonrat. M and Yang et al (Wiboonrat,

2008; Yang et al, 2015).

5.4 Limitations and Future Research Possibilities

There are two limitations of using the proposed MCDM framework of this study.

First, albeit some exiting researches have provided valuable insights for DR site selections, this

research is the first attempt to evaluate the performance of DR system(s) and site(s) via the Data

Envelopment Analysis (DEA) method. The raw data was derived based on Taiwanese experts‘

opinions, which may be controversial. In this regard, future DR system performance evaluation

researches may include studies of larger economies with more available experts.

Second, in the era of Big Data, more and more organizations are facing the needs of DR

for Big Data applications. The Big Data, which is characterized by data volume, variety, velocity,

variability, veracity, and complexity, have imposed heavy burdens on IT system performance in

data centers. The data volume may exceed the traditional IT system capability. Innovative IT

techniques and infrastructures may appear in the near future. Therefore, the novel aspects and

criteria maybe required for evaluating the performance of the DR systems in the era of Big Data.

In this regard, the current DR system performance evaluation framework may be challenged in the

future due to the emergence of novel IT applications.

6. Conclusion

As IT systems have become increasingly critical to the smooth operations of modern

organizations, the importance of ensuring the continuous operation and recovery of IT systems

http://en.wikipedia.org/wiki/Information_technology

Volume 9, No.2, 2015 69

has increased. Although management understands the importance of DRs, adequate allocation of

budgets and resources is still worthy of investigation. In order to determine the DR site‘s

investment, management should appropriately evaluate the efficiency of DR sites‘ performance. A

holistic approach to DR site performance evaluation must evaluate multiple factors on the

technical and business front.

The contributions of this research are twofold. On the one hand, existing research on DR

sites allows for assessment of the technological performance only. This research goes further by

providing methods to evaluate the overall DR site‘s performances. This study provides a

performance evaluation analytic framework of IT DR sites for data center and corporate IT

decision makers. The contributions of the studies that focus on a particular technological area are

very significant but are nonetheless limited from the viewpoint of investment since they do not

provide a complete picture. This research provides a complete picture of DR sites and

demonstrates the feasibility of the DEA method on related research topics.

On the other hand, the nature of DR is indistinct and involves various considerations to

identify and backup the data into new insights. This study defines the DR site efficiency factor

from the aspects of business continuity management as well as the enterprise IT infrastructure.

According to the performance evaluation results, firms can compare their DR site‘s efficiency

with that of others in the same industry and then develop strategic plans for enhancing their

performance. Thus, this research can provide future researchers with an overview of the DR site

with appropriate performance evaluation results.

<received: 2015. 05. 26>

<revised: 2015. 07. 24>

<accepted: 2015. 07. 30>


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Volume 9, No.2, 2015 77

Fueling Economic Prosperity through Incubation

System: A Case Study from an Eastern Indian

Province

Manisha Acharya and Subhransu S Acharya

Abstract

Entrepreneurship has been accepted as one of the best answers to mitigate the problem of

unemployment and is a cause of economic prosperity of any country. Thus, to create more jobs,

more entrepreneurs are needed in the society. Hence, various Government and non-Government

agencies are making efforts towards promoting entrepreneurship with an objective to create jobs.

Therefore, promoting innovation, entrepreneurship and resultant economic growth in all

countries have become issues of national significance. In India, different agencies of Government

of India are doing good work in helping business incubators to promote entrepreneurship with an

objective of economic prosperity of the country. The present paper gives a whole some idea about

the role of Incubation centres in converting Innovators to entrepreneurs in the Indian context. The

work involves a case study of a Technology Incubation centre in the Eastern Indian province of

Odisha and it has examined its role in creation of entrepreneurship in this part of the country.

The data source was mainly primary. The incubatees from the technology incubation centre were

subjected to a structured questionnaire survey. The financial and economic impact of the new

start-ups has been studied and it is inferred that there is justification in investing in promotion of

incubation system in a country.

Keywords: Innovation, Entrepreneurship, Business Incubation, Knowledge economy

Ph. D., Senior Incubation Manager, KIIT Technology Business Incubator, KIIT University, Bhubaneswar, India, E-mail:

[email protected] Ph. D., Dy. General Manager, SIDBI MSME International Training Institute, Bhubaneswar, India, E-mail:

[email protected]




Introduction

Micro and Small Enterprises (MSEs) are important to almost all developing economies in

the world, having major employment, import dependency, and income distribution challenges.

Entrepreneurs and Economic development are two sides of the same coin. Entrepreneurs are key

to economic development of a country. Entrepreneurship and Economic development of a country

are inter-related. Entrepreneurs play a pivotal role not only in the development of industrial sector

of a country but also in the development of farm and service sector. Establishing Micro and Small

Enterprises (MSEs) is not as easy despite having innovative inventions or ideas. It takes more

than just having an idea of establishing a start-up. Planning and arrangement of scarce resources

like Finance, Infrastructure, Technology, Sourcing of raw materials, Market places (buyers) and

organizing Sales distribution channels are the major challenges for establishment and survival of

any enterprise. Majority of start-ups fail in their first year of inception. Many of these failures can

possibly be prevented if entrepreneurs get hand-holding support by an Institution having

specialized Incubation programs. An incubator's main goal is to produce successful Micro and

Small Enterprises with an array of targeted resources and services. These incubates grown in the

incubators have the potential to create jobs, develop technology for import substitution,

commercialize new technologies, and strengthen local and national economies. "Business

Incubators‖ are playing a catalytic role in realization of dreams of innovators leveraging certain

congenial policy initiatives from Government.

Objective of the Present Study

The objective of this work is to ascertain whether the Technology Incubation process has had a

positive impact on the financial and economic indicators of success of incubated start-up enterprises.

These would include empirical analysis of turnover, employment and investment data over a period of

three years in respect of the incubated enterprises. In addition, the paper shall also deal with the

understanding of certain aspects related to the process of incubation. They are as under:

1. The process of promoting entrepreneurship through incubation centres.

2. The period of incubation support.

3. The incubation infrastructure usually made available to incubated companies.

4. Outside investment support available to incubated companies.

Volume 9, No.2, 2015 79

Review of Literature

Incubators can be described as an institutional start-up enterprise support system. Like real

life incubators which provide a controlled and protective environment in which premature small

babies are placed for care, the Business incubators provide the ideators and innovators a chance to

adjust to outside environment, and grow stronger before they face the outside world. In a similar

fashion, the start-up entrepreneur‘s business idea is incubated in the incubation centers (Gupta K,

Rathore Shivali 2015 ) .

Entrepreneurship has been conventionally rated as risky career, to break the myth and to

augment the supply of new entrepreneurs through education, research and training, the incubation

centers have been established. Their objective is to help create and grow young businesses by

providing them with necessary support and financial and technical services. The start-up

companies spend on an average two years in a business incubator during which physical

infrastructure like office space, equipments etc. and most importantly funding support and need

based mentoring is provided by the incubators to the start-up businesses.

The concept of technology and business incubation first emerged in the United States in

the 1960s. It is widely believed that the Batavia Industrial Center, opened in Batavia2, New York

in 1959 was the first business incubator outside of the academic environment (Alseikh, 2009). A

Business Incubator was a facility designed to assist infant businesses to become established and

sustainable during their start-up phase. However, this concept did not gain much outreach until

the late 1970s. During the 80s, the industry experienced significant growth as many recognized

the value of creating and expanding new businesses to maintain local economies. There were a lot

of people who got fascinated by the idea and started developing incubators to support new

ventures and entrepreneurship. The concept gradually started gaining ground and about a dozen

incubators took shape in the U.S. However, in those initial years of evolution of the concept of

Incubation, a few from the community restrained themselves from supporting the concept as they

felt that it would restrict common economic development strategies related to the expansion of

large companies. During the same period, the concept reached UK and Europe by means of

innovation centres and technology parks. In the late 1990s, there were nearly twenty five

incubation environments in the U.K (Sahay, 2008). The establishment and nurturing of Small and

Medium Enterprises (SMEs) is a vital input in creating dynamic market economies in the

economic and social development of transition countries. Entrepreneurs are the key drivers of

economic growth, innovation, balanced regional development and job creation.


Innovation, as widely believed, is the driver of an idea into a marketable commodity or

process having potential for wider acceptance by the intended target consumers. An entrepreneur

has the vision to identify and ability to bring together these two and converts it to a successful

Enterprise. He has the unique ability to bring along all co-ordinates on same plane to achieve the

desired objectives. The famous robotics engineer Joseph F. Engelberger asserts that innovations

require only three things i.e. a recognized need, competent people with relevant technology and

financial support. In order to synthesise the benefits of Innovation and Entrepreneur towards

larger goal of meeting welfare objectives of opportunity multiplication through Enterprise

creation, Governments across the world adopt various policies. They include delineation of

policies for creating National competitiveness which includes building educational, R&D and

Physical and healthcare infrastructures. However, the single most important and decisive Policy

intervention towards this end is the Institution of ‗Business Incubators. Business Incubation is an

important tool at fostering innovative enterprise creation and growth. The Business Incubators are

an important cog in the wheel of the Ecosystem bringing together finance, academics, policy and

business. When the said instrument of intervention is to promote ‗Technology Innovations‘, they

are christened as ‗Technology Business Incubators‘ or TBIs.

The Business Incubators bring together the advantages of providing shared physical

infrastructure including office space, R&D facility, communication facilities, finance through tie

ups with seed funds for start-up capital. The Technology Business Incubator [TBI] is an initiative

by Technical Institutions / Universities, Public research institutes, Government and private

institutions to promote new technology intensive or knowledge driven enterprise. A TBI

intervenes in the spatial processes of a knowledge economy, integrates innovation and enterprise

development policy and fosters innovative start-up entities. Technology Business Incubators are a

powerful economic development tool. They promote the concept of growth through innovation

and application of technology, support economic development strategies for small business

development, and encourage growth from within local economies, while also providing a

mechanism for technology transfer. Business incubation is the temporary, facilitative support

provided to start-up enterprises through the delivery of complex services and special environment

with the aim of improving their chance of survival in the early phase of the life span and

establishing their later intensive growth.

Indian Incubation Scenario-An Overview

http://en.wikipedia.org/wiki/Joseph_F._Engelberger

Volume 9, No.2, 2015 81

India after independence has initiated a series of steps towards promoting Science and

Technology, Research & Development as also promoting Enterprises especially in Small Scale

Sector. A chain of Research Laboratories, Entrepreneurship Development initiatives including

setting up of Institutions like Entrepreneurship Development Institute of India (EDII), start up

financing initiatives like the State Finance Corporations etc. were set up. During the eighties, with

support from UN Fund for Science and Technology, three pilot Technology Business Incubators

(TBIs) were commissioned in India. The National Science & Technology Entrepreneurship

Development Board (NSTEDB), established in 1982 by the Government of India (GOI) under the

aegis of Department of Science & Technology, is the institutional mechanism to help promote

knowledge driven and technology intensive enterprises. Besides NSTEDB, other Institutional

mechanisms to promote Business Incubator support systems are piloted by Biotechnology

Industry Research Assistance Council (BIRAC) of Department of Biotechnology (DBT), Ministry

of Micro, Small & Medium Enterprises (MSME), GOI and Technological Incubation and

Development of Entrepreneurs (TIDE), Department of Electronics and Information Technology

(DeitY), ICAR (Indian Council of Agricultural Research) etc. (Acharya, M, 2013) .\

Source: http://www.nstedb.com/booklet.pdf 2014

Outcome of Incubation system in India

At a macro level, as per a 2014 report of National Science & Technology Entrepreneurship

Development Board (NSTEDB), India, over 32,000 persons were employed by incubated and

graduated enterprises while a Turnover of over INR 15 billion was generated with more than 450

copyrights or patents.

So far, out of about 130 TBIs / STEPs in the country, 86 have been set up under the aegis

of NSTEDB. As per a tentative estimate, about 500 enterprises graduate out of Incubators and


roughly 60 per cent of them are Technology based. As per the said estimate, about 500 enterprises

graduate out of Incubators and roughly 60 per cent of them are Technology based. The emergence

of service sector as a very strong contributor in the overall economic canvass of the country

coupled with emergence of knowledge based Enterprises has given a perfect dream platform for

the TBIs to incubate such Enterprises. The ICT based enterprises and ITESs, Tele-medicine and

Bio-informatics etc. are increasingly in demand for Incubation besides the frontier areas of Bio-

Technology, Nano-Technology, Energy & Environment.

A Brief on Business Incubator Support Schemes in India

1. The National Science and Technology Entrepreneurship Development

Board (NSTEDB), Department of Science and Technology (DST), Government of

India :

The NSTEDB, aims to create "job-generators" through Science & Technology (S&T)

interventions. Major objectives of NSTEDB are to promote and develop entrepreneurship by

using scientific methods and utilizing science and technology infrastructure in the country. The

NSTEDB has already catalyzed and supported several Science and Technology Entrepreneurs

Parks (STEPs), Technology Business Incubators (TBIs) etc. in addition to many Entrepreneurship

Development Cells (EDCs), in different parts of the country. According to ―Incubator Conclave

of STEPs and TBIs report‖ (2012), there are 52 numbers of TBIs and STEPs in India who have

incubated over 1700 start-up companies. Some of the well know TBIs and STEPs are Centre for

Innovation and Incubation (CIIE) @ IIM Ahmedabad, SIDBI Innovation & Incubation Centre

(SIIC) @ IIT Kanpur, Venture Centre, NCL, Pune, IKP Knowledge Park, Hyderbad, TREC-

STEP, Trichy, Science & Technology Park, University of Pune, SINE @ IIT Bombay, Techno-

park TBI, Trivandrum, KIIT-TBI, Bhubaneswar @ KIIT University, JSSATE-STEP, Noida,

STEP @ IIT Kharagpur, VIT-TBI, Vellore, ICRISAT, Hyderabad etc. The major thrust areas in

these incubation centers are IT & ICT, Healthcare, Biotechnology, Bioinformatics, Nano-

technology, Waste management, Electronics & Embedded system, other Engineering

manufacturing sectors, Renewable energy, Aerospace, Chemical and Material Science, Industrial

design etc.

Volume 9, No.2, 2015 83

Source: http://www.nstedb.com/booklet.pdf 2014

2. “Support for Entrepreneurial and Management Development of SMEs

through Incubators” of Ministry of MSME, Govt. of India:

Under the scheme "Support for Entrepreneurial and Management Development of SMEs

through Incubators" of Development Commissioner (DC) - MSME, 100 "Business Incubators"

(BIs) are being set up under Technology (Host) Institutions and each BI is expected to help the

incubation of about 10 new ideas or units. This scheme is operational since April 2008. The main

objective of this scheme is to assist incubation of innovative business ideas that could be

commercialized in a short period of time, resulting in the formation of MSMEs that have

distinctive presence in the market. Under this scheme, each BI will be given between INR 400

thousand to INR 800 thousand per idea/unit nurtured by them. The BIs are present in different

institute like IITs, NITs, Engineering Colleges, Technology Development Centers, Tool Rooms,

R&D and/or Technical Institutes etc. As per date available over their Website (http:// www.

dcmsme.gov.in/ schemes/incubator.htm), presently 129 BIs are approved under this scheme.

3 . Technology Incubation and Development of Entrepreneurs (TIDE)

Scheme of Department of Electronics and Information Technology’s (DIT) :

Technological Incubation and Development of Entrepreneurs (TIDE) scheme of DIT was

launched in the year 2008. The Scheme has multipronged approach in the area of Electronics, ICT


and Management. The following support for the Incubated Companies are available from TIDE

Centers:

i. The TIDE Centre provides operating space to the selected companies on rent for a

period of up to 2 years (extendable by one year).

ii. Each company selected for incubation gets financial support, in the form of soft loan,

of up-to INR 2.5million over a two (or three) year period (subject to satisfactory

performance). The amount of loan would be up to a maximum of 80% of the project

cost of the incubating company. The loan can be used for equipment and

consumables essential for the implementation of the project, subsistence allowance

of the promoters, staff salaries and other contingencies.

There are 27 total TIDE centers in India and around 80 start ups have been supported by

this scheme in India.

4. Bio-Incubator Support Scheme (BISS) of BIRAC, DBT

For empowering and enabling the Bio-tech based Innovation Eco-system in India, BIRAC is

playing a major role in India. There are twelve (12) number of BIRAC funded Bio-incubators in India

(Source: http://www.birac.nic.in/programmes.php). The BIRAC Bio-incubators focus on, inter alia,

medical devices, Bio-pharma vaccines and diagnostics, industrial Biotechnology etc. The Bio-incubators

endeavor to provide incubation space and other required services for entrepreneurs and start-up

companies for their initial growth. There are different schemes of BIRAC, like Biotechnology Ignition

Grant (BIG), Small Business Innovation Research Initiative (SBIRI), Biotechnology Industry

Partnership Program (BIPP), Contract Research Scheme (CRS) etc, which are the main source of

funding for Bio based Entrepreneurs.

Business Incubation Scenario in Odisha State, India

Odisha state, in the eastern coast of India lies between 17.49N latitude to 22.34N latitude

and from 81.27E longitude to 87.29E longitude. The state is well known for its rich mineral

resources. However, the state also is known as one of the lowest ranked states in terms of poverty.

Few of the challenges before the state economy, affecting its growth are as below:

http://www.birac.nic.in/programmes.php

Volume 9, No.2, 2015 85

The incidence of poverty in Odisha is high at 32.59% (2011-12) despite plenty of

natural resources in the state. The All India average is 21.92% for the corresponding

period.

State per capita income is Rs.25,891/- as against national average of Rs.39,961/-

during 2013-14.

Human Development Index at 0.30 vs. 0.55 at national level in 2011.

Creation of more livelihood and employment opportunities.

Lack of quality roads, ports, rail network, irrigation and market and storage

infrastructure for agriculture produce.

19 out of 30 districts are affected by Left Wing extremism making it difficult for

infrastructure and other developments.

Frequent natural calamities.

The sectoral performance reflects the change in the magnitude and composition of GSDP

of the state economy over time. From a pre-dominantly agricultural economy, the state GDP

during 2013-14 clearly marked a shift of the economy becoming more oriented towards industrial

and services sectors. The services sector, in tune with national economy, has exhibited dominance

with 59.02 per cent of state GDP. The share of agriculture sector in the GSDP, which was over

70% in the early 1950s, has come down to 15.58%, as per advanced estimates of 2013-14. About

60% population of the state draws its sustenance fully or partly from the agriculture sector.

In Odisha, there are eleven Business Incubation centers which are supported by DC-

MSME. However, there is only one Technology Business Incubation center (TBI) viz. KIIT

Technology Business Incubator supported by National Science and Technology Entrepreneurship

Development Board (NSTEDB), DST, GOI. This Incubation center is also supported by DBT

(BIRAC), DeitY(TIDE) and DC-MSME schemes of Govt. of India.

TBI for Enterprise Creation – A Case Study of KIIT-TBI,

Bhubaneswar, Odisha

Established in year 2009, KIIT Technology business Incubator (KIIT- TBI), an initiative of

KIIT University, supported by the National Science and Technology Entrepreneurship

Development Board [NSTEDB] and Department of Science & Technology [DST], Government

of India is the only Technology Business Incubator in the state of Odisha and one amongst the

fifty-odd in the country. Besides NSTEDB, the KIIT-TBI is now recognized by other Central

Government agencies such as TIDE (DeitY), DC-MSME, BIRAC (DBT), TDB etc. The principal


thrust of KIIT-TBI is Biotechnology and Information & Communication Technology (ICT).

Besides, KIIT-TBI is also focusing on other emerging areas in Engineering & Technology and

Rural Innovations.

During a very short period of its existence, KIIT-TBI has so far incubated 37 business

entities out of which nine have graduated out to set up full scale commercial enterprises. KIIT-

TBI accelerates the development of entrepreneurial businesses by providing them with assistance,

which addresses the particular needs of new companies during the key stages of development

such as product characterization, prototyping, market development and continuous innovation etc.

KIIT-TBI provides various promotional supports like product launches, product seminars,

exhibitions and media interviews to facilitate product promotion and marketing activities for

incubated enterprises. It also has been conducting various capacity building and training programs

like Entrepreneurship Awareness Camps (EAC), Entrepreneurship Development Programs (EDP),

Techno Entrepreneurship Development Programs (TEDP), Women Entrepreneurship

Development Programs (WEDP) etc.

Impact Analysis of Incubation

Methodology

The present study has been carried out by empirical analysis of survey results obtained

through Interview method. The participants of the survey included the CEOs of around 35

Incubated start-up companies at KIIT-TBI. The sample constituted over 85% of the universe. The

targets were administered a structured questionnaire and their responses were recorded. A detailed

analysis of the survey was made and extrapolated to compile and arrive at the findings to

conclude the effect of Incubation intervention on the incubated enterprises.

The following hypotheses were proposed to be tested by way of the empirical analysis.

Null Hypothesis 1: There is no increase in sales revenue of start-ups with incubation

support.

Alternate Hypothesis 1: There is significant increase in sales revenue of start-ups with

incubation support.

Null Hypothesis 2: There is no increase in employment generation by start-ups with

incubation support.

Volume 9, No.2, 2015 87

Alternate Hypothesis 2: There is significant increase in employment generation by start-

ups with incubation support.

Null Hypothesis 3: There is no increase in total investments in the start-ups with

incubation support.

Alternate Hypothesis 3: There is significant increase in total investments in the start-ups

with incubation support.

For the purpose of analysis, the data in respect of turnover, employment generation and

investments were obtained for first year and third year. This was done so because the incubation

support is available through a period of three years. The data so collected were summarized and

were subjected to ―test for equality of means‖. Further, they were also treated with ―dummy

variable analysis to test structural change‖.

Data Analysis: The results of the empirical analysis are presented below.

Test for Equality of Means of Incubated Enterprises in two periods

Variable Mean p-value

Sales Revenue P1 1891279.0 0.0477

Sales Revenue P2 4634835.0

Employment Generation P1 5.206897 0.0001

Employment Generation P2 12.000000

Total Investments P1 954310.3 0.0006

Total Investments P2 2617586.0

Significance during Period 2

Dummy Variable analysis to test the structural change

Variable Coefficient p-value

Sales Revenue 2743556.0 0.0347

Employment Generation 6.793103 0.0000

Total Investments 1663276.0 0.0048

Data Interpretation:

As can be observed from the above test results, there is a significant variation in sample

means of sales revenue for period 2 (end of Year 3) and period 1 (end of Year 1) with p value of


0.047 i.e. less than 0.05 (significance less than 5%). Similarly, there is a significant variation in

sample means of employment generation with p value of 0.0001 i.e. less than 0.05 (significance

less than 5%). As regards investments too, significant variation in sample means of employment

generation was observed with p value of 0.0006 i.e. less than 0.05 (significance less than 5%).

Thus, the null hypotheses are rejected.

When subjected with dummy variable analysis, the coefficients of Sales revenue,

Employment generation and total investments remained at INR 2.7 million, 6.8 persons per

enterprise and investment of INR 1.7 million with p value of 0.035, 0.00 and 0.0048 respectively.

Discussion and Conclusion

The Incubators are the most vital cog in the Innovation Eco-system in the country. India

has consciously evolved a policy to encourage commercialization of Technologies and

Innovations from Research Institutions and other Institutions of higher learning. Now time is ripe

to rapidly upscale the model to galvanize more such engines of growth for capitalizing the benefit

of technological advancements for larger societal benefits!

The Indian economy in general and economy of Odisha state in particular, has undergone a

shift in composition, where the services sector is assuming a significant proportion. The share of

services sectors, especially IT, ITES, Telecom etc. are growing which indicates a shift towards a

knowledge driven economy. The state of Odisha is counted as one of the bottom ranking states in

terms of important development indices. However, of late, the state has initiated a number of steps

those have started showing encouraging results in terms of its ranking amongst Indian states in

―ease of doing business‖ index etc. With its capital city emerging as an educational hub of eastern

India and with quality Research and Development output from these institutions of excellence

coupled with contribution of national level R&D institutions in the state, there is a very good

opportunity for commercialization of such research outputs. With innovations from students,

faculties and others, there is a need to capitalize these and convert them to commercial enterprises

for societal benefits.

The Technology Business Incubators could be the force multipliers in the efforts of the

state to create an ambient eco-system for technology commercialization. The study attempted to

empirically map and quantify to an extent the contributions of the only TBI in the state. The

analysis of data from the TBI has revealed that there have been positive effects of incubation

Volume 9, No.2, 2015 89

intervention. The impact has been both financial improvements (increase in sales revenue) and

economic contributions in terms of creation of additional employment and additional investments.

Thus, it can be inferred that the intervention through the incubation system has been yielding

dividends and there is a need to upscale the incubation system in the state and also the whole

country so as to meet the objectives of making the country a truly technology driven knowledge

economy and creating more employment opportunities.

<received: 2015. 09. 28>

<revised: 2015. 11. 25>

<accepted: 2015. 12. 05>


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The Effect of Innovation Activities and Governmental

Support on Innovation Performance: Comparison between

Innovative SMEs and General Companies

Juil Kim and Sun-young Park

Abstract

Creating innovation performance through technological innovation and managerial

innovation and securing competitive advantage are coming to the fore as national issues beyond

organizational and individual dimensions. Korea is also putting much effort into strengthening

competitiveness of small and medium enterprises (SMEs) and creating national wealth by

aggressively introducing and implementing the innovation certification system for venture companies

and technological and/or managerial innovative SMEs to boost innovation of small companies

systematically.

This study analyzed the effects of various factors such as firm size, innovation activities and

governmental support on various types of innovation performance; product innovation, process

innovation, organizational innovation and marketing innovation, and derived differentiated

characteristics of innovative SMEs and discuses in-depth implications through comparison with

general companies.

As a result, the following implications were derived from the empirical analysis. First, there is

a need for differentiated management to motivate corporate entities to engage in the four major

innovation activities to improve innovation performance which is directly related to corporate

performance. In other words, corporate resources need be managed appropriately for the purposes

of innovation. This is important to a manager in charge of management, introduction and execution

of innovation seeks, since the effects of factors vary with different types of innovation. Second, the

factors affecting innovation performance differed between innovative SMEs and general companies.

Therefore, in relation to management and policy of SMEs, it is necessary to differentiate innovation

strategies according to different types of enterprises.

Keywords: venture business, innovative SMEs, firm size, innovation activity, governmental

support, innovation performance

Researcher, Korea Institute of S&T Evaluation and Planning (KISTEP), E-mail: [email protected] Corresponding author: Professor, Miller MOT School, Konkuk University, E-mail: [email protected]




1. Introduction

Since Schumpeter proposed a theory of technological innovation, innovation has been

perceived as the driving force of capitalism and economic growth. Now, innovation is mentioned in

not only economics and business administration but also enterprises, hospitals, and the public and

third sectors as a determinant of organizational and individual competitive advantages. In the same

vein, researchers consider the true nature of innovation represented by technological and management

innovation, empirically analyse its determinants, processes and outcomes, and apply the findings to

organizational management processes in practice.

Innovative SMEs (small and medium-sized enterprises) have been regarded as important for

effectively implementing innovation and creating outcomes. Innovative SMEs refer to SMEs having

innovative capabilities, surpassing general SMEs in terms of innovative performance through

technological and managerial innovation, and promising to create added value in the foreseeable

future (Hicks & Hegde, 2005; Kang & Lee, 2012). Korean economic system is characterized by the

substantial roles of SMEs in employment and their measurable importance in all economic activities.

Therefore, enhancing national competitive advantages by strengthening SMEs' innovativeness has

become an issue of national significance.

In this context, policy, support and development measures for innovative SMEs have been

established. Particularly, an array of relevant policy initiatives have been taken since the enactment of

the 「Act on the Promotion of Technology Innovation of SMEs」1in 2013. Specifically, prior to the

current system, the venture business certification criteria were formulated in 1997, followed by the

InnoBiz2 System for certifying technologically innovative SMEs in 2001 and the certification system

for managerial innovative SMEs in 2006 based on the Comprehensive Plan for Strengthening SMEs‘

Competitiveness framed by the Participatory Government in 2004.

Diverse research has been conducted concerning the measures for improving innovative

SMEs' innovation performance, which is perceived as a challenge at the national level. Yet, previous

studies have revealed the following limitations. First, a significant number of those studies are

concerned with the factors influencing management outcomes including sales or revenues without

considering the essential value of innovative SMEs, i.e. the roles of innovation performance and the

factors influencing the innovation performance (Sung, 2013; Lee & Chung, 2008; Chung, 2011; Jin et

al., 2012; Kwak & Suh, 2010; Yi, 2009).

Volume 9, No.2, 2015 95

Second, some studies are focused only on particular types of innovative SMEs, and thus their

findings are far from generalizability (You, 2010; Yang & Song, 2007; Yoo & Yang, 2009; Yoon &

Seo, 2011; Kim, 2013). Also, those studies fail to compare innovative SMEs with general firms, and

have limitations in deriving differentiated characteristics and in-depth implications. Lee et al. (2008)

compare innovative SMEs with general SMEs in light of technological innovation performance and

managerial outcomes without verifying the influence of explanatory variables.

Third, as the proxy variables of innovation performance, only partial indicators of

technological innovation performance including patents and outcomes of new products are used,

which makes it difficult to discuss extensive outcomes of corporate innovation encompassing product

innovation, process innovation, organizational innovation and marketing innovation (Kim, 2013; Lee

et al., 2008; Yoo & Yang, 2009).

Thus, the present study verifies the effects of firm size, innovation activities and governmental

supports as the determinants of innovative SMEs‘ innovation performance, and compares the findings

with those of general firms with a view to presenting differentiated theoretical and practical

implications for innovative SMEs. Moreover, factors influencing the comprehensive innovation

performance including product innovation, process innovation, organizational innovation and

marketing innovation are verified from multiple angles to derive some relevant implications for

innovation performance.

2. Rational and Literature

2.1 Innovation Theory

Innovation is a multidisciplinary topic discussed from academic and practical perspectives,

which is why the concept varies with different authors and contexts. As a pioneer who introduced the

comprehensive concept of innovation to economics and business administration, Schumpeter (1934)

defines innovation as a new combination, or a creative destruction of the established market

equilibrium. He emphasizes technology as a method of the new combination, and entrepreneurs as

the principal agents of the creative destruction. Building on Schumpeter's definition, Van de Ven

(1986) defines innovation as the process of new ideas being developed or filled by multiple members

engaging in transactions and lines within institutional environment. Amablie (1988) defines

innovation as the selection of creative ideas and a shift toward useful products, services and processes.


Drawing on the extensive definitions of innovation, this study is to consider four principal

concepts that underscore the targets of innovation such as product innovation, process innovation,

organizational innovation and marketing innovation.

2.1.1. Product Innovation

Product innovation typifies the technological innovation, referring to creating unprecedented

novel products or altering and improving existing products. Tung (2012) defines product innovation

as applying differentiated technologies to introduce new products capable of delivering higher levels

of utility to customers than the old ones in the market. Similarly, according to Kristina & Dean

(2005), significant changes in technological features of products are an essential prerequisite for

product innovation.

A host of studies confirm the effects of product innovation on corporate market performance

(Yalcinkaya et al., 2007; Govindarajan & Kopalle, 2006; Sivadas & Dwyer, 2000; Henard &

Szymanski, 2001). For instance, Aboulnasr et al. (2008) argue that continuing efforts for innovation

by applying new ideas to products help extend their life cycles and ultimately sustain their

competitive advantages in the market. According to Kim & Huarng (2011), introducing new

knowledge and products to the market makes it possible to achieve innovation, which in turn allows

corporate competencies including financial performance and customer satisfaction to build up. In

short, against the backdrop of tougher competition, shorter lifecycle of products and ongoing

technological advancement, product innovation is viewed as ever more important (Ziamou &

Ratneshwar, 2003; Godener & Soderquist, 2004).

2.1.2. Process Innovation

In parallel with the product innovation, process innovation is viewed as representing the

technological innovation (Barney & Griffin, 1992; Dewar & Dutton, 1986; Tushman & Nadler, 1986).

Trejo et al. (2012) define the process innovation as the implementation of a new approach or the

significant improvement of production or delivery. That is, process innovation refers to introducing a

commercially worthwhile new production technique (Schumpeter, 1934), and is broadly applicable to

the process value chain involving production, data processing, delivery and service (Zaltman et al.,

1973). The OECD (2005) includes any substantive changes in process-related technology, tools and

software in the process innovation framework.

Papinniemi (1999) proposes a process innovation model from the perspective of business

reengineering, which is equivalent to management innovation, and emphasizes three core components

Volume 9, No.2, 2015 97

of process innovation; enablers, targets and outcomes. First, the author attributes the process

innovation to technological enablers, dissatisfaction with the performance of existing products and

processes, and resource opportunities. Also, the author describes that shifts occur in such targets of

process innovation as processes, product structures, production organizations, man-machine-

computer connectivity, core sectors, user-friendliness, management and control. Lastly, as the

outcomes of process innovation, the author stresses such qualitative outcomes as flexibility,

improvement of quality and customer relationship and cost savings in addition to the betterment of

diverse quantitative indicators.

2.1.3. Organizational Innovation

Organizational innovation refers to introducing new methodologies to business practices,

internal organizations, and external relations with a view to implementing progressive or radical

changes (OECD, 2005). Organizational innovation is focused on organizations as both the principal

agents and targets of innovation, and is applied to multiple aspects including business practices,

knowledge management and interactions with external parties.

Organizational innovation is defined largely from two perspectives. First, studies considering

macroscopic innovation theories focus on not individuals but organizations as principal agents of

innovation. These studies regard organizational innovation as the overall process of introducing,

utilizing and commercializing new ideas in connection with products, services, processes,

technologies and systems for the purpose of organizational goal achievement, performance

improvement and customer satisfaction (Choe & Lee, 1997; Han, 2010, Park & Kim, 2008; Kessler,

2004; Kim, 1998). In other words, organization-led product innovation, process innovation, and

marketing innovation are described within a broad spectrum of organizational innovation framework.

Second, organizational innovation is defined in a narrow sense focused on changes and

improvement in managerial aspects and intangible methodologies involving organizational structure,

system, institution, consciousness, culture and behavior rather than technological aspects (OECD,

2005; Kenneth, 1967; Tidd & Bessant, 2009; Sundbo & Gallouj, 1998; Durst & Newell, 2001).

Although this definition may sound relatively narrower in scope than the abovementioned one, it

helps clarify the features of organizational innovation vis-a-vis other innovation targets such as

products, process and marketing. This study builds on the latter to define organizational innovation as

changing and improving business practice, knowledge management, flexibility in fulfilling business

practices, and external relations as part of new approaches to organizational operation (Ha et al.,

2010).


Positive effects of organizational innovation on corporate performance have been verified by

many studies. Organizational innovation reduces the cost of corporate management and transactions,

and improves job satisfaction and labor productivity (OECD, 2005). Downs and Mohr (1979)

elucidate three benefits of organizational innovation, i.e. programmatic benefits, prestige benefits,

and structural benefits. Choe & Lee (1999) build on Downs & Mohr (1979) to ascribe financial,

symbolic and behavioral outcomes to organizational innovation.

2.1.4. Marketing Innovation

Marketing innovation has been less widely investigated and more ambiguously defined than

product innovation, process innovation and organizational innovation (Naidoo, 2010). Tidd &

Bessant (2009) define marketing innovation as an activity that takes place in market settings where

products and services of a firm are available, and name it ‗position innovation‘ which implies any

changes of the company‘s position in the market. Also, the authors include the innovation of essential

aspects of products involving customization together with changes in target market, pricing system

and distribution practice in the category of marketing innovation. The OECD (2005) defines

marketing innovation as a new method of marketing encompassing the innovation of product design,

package, distribution, promotion and pricing policy. Also, they describe that marketing innovation

helps respond effectively to customer needs, create a new market, position a product anew and

ultimately increase sales.

Given that marketing innovation is referred to as market innovation, it is perceived as a

determinant for new market creation and market success of products and services (Trienekens et al.,

2008). In a conceptual study on the sources of competitive advantage, Ren et al. (2010) assert that

marketing innovation can serve as a latent factor influencing the creation of sustainable competitive

advantage. Indeed, reviewing empirical analyses of marketing innovation and corporate performance,

Yang (2013) analyzes that market orientation increases marketing innovation, and ultimately

improves financial and non-financial corporate performance. Likewise, according to Hong & Chaiy

(2008), marketing innovation exerts positive effects on overall corporate performance. Naidoo (2010)

observes that marketing innovation influences the competitive advantages in differentiation and

pricing, which in turn becomes a pivotal factor of corporate survival.

2.2 Innovative SMEs

Innovative SMEs (small and medium-sized enterprises) are extensively defined across

academic disciplines and government policies. The OECD (2005) defines an innovation-active firm

Volume 9, No.2, 2015 99

as engaging in one or more innovation activities relevant to product innovation, process innovation,

marketing innovation and organizational innovation. An innovative indicates when it successfully

innovates and create performance. The definition includes the two concepts in an inclusive category

of innovative firms. Chung (2011) defines a firm as an innovative SME when it proves superior to

other SMEs in view of technological innovation activities and relevant outcomes.

Based on the aforementioned rationale, innovative SMEs are defined, in general, as a policy-

related concept in Korea. Specifically, innovative SMEs are perceived to create more value through

innovation activities in terms of technology and management than general firms (Kang & Lee, 2012;

Lee, 2007). Currently, venture businesses, technological innovative SMEs and managerial innovative

SMEs are classified as innovative SMEs as per local policy initiatives. Venture businesses have been

lawfully stipulated since the latter half of the 1990s, whereas the lawful foundation for technological

and managerial innovative SMEs was not laid until 2013 when the 「Act on the Promotion of

Technology Innovation of SMEs」 was enacted. According to the applicable provision, innovative

SMEs including venture businesses are defined as the SMEs that are capable of securing

competitiveness or well-positioned for future growth potential by means of technology, management

and other innovation activities.

This study defines innovative SMEs from the perspective of local policy initiatives. That is,

innovative SMEs in this study include venture businesses, technological innovative SMEs and

managerial innovative SMEs that create more value than general firms through technological and

managerial innovation activities.

2.3 Literature

2.3.1. Firm Size and Innovation Performance

Effects of firm size on innovation activities and outcomes have been considered by many

researchers. First, the postulation that firm size serve as a positive factor of innovation is based on

Schumpeter (1942). Schumpeter (1942) posits that large enterprises are favorably positioned for

technological innovation with their capacity for implementing the economies of scale in terms of

R&D, production, marketing and financing. Schumpeter‘s assertion that a large-scale company can

gain the market power in proportion to its size which in turn facilitates its technological innovation

activities has been widely known as the Schumpeterian hypothesis among the later generation of

researchers.


The finding that the firm size positively affects innovation supports the Schumpeterian

hypothesis, forming the mainstream in the literature (Cohen, 1995; Yin & Zuscovitch, 1998; Cohen

& Klepper, 1992). Studies highlighting the positive effects of firm size on innovation provide the

following grounds (Arias-Aranda et al., 2001). First, large enterprises have competitive advantages in

respect of the stability and availability of internal resources, which allows them to effectively support

R&D projects. Second, companies boasting of high sales figures can appropriately distribute the fixed

innovation cost, e.g. process innovation, and thus improve the efficiency of R&D.

Third, effective trade-offs between non-production components, e.g. R&D, marketing and

finance, increase the chance of innovation, which is likely to arise more efficiently in large

companies. Fourth, as large enterprises are fitted with diversified divisions and thus can achieve the

economies of scope, they are highly likely to effectively reduce any innovation-related risk factors.

Conversely, some argue that the firm size has negative effects on innovation (Scherer & Ross,

1990; Acs & Audretsch, 1990, 1991; Pavitt et al., 1987; Bound et al., 1984) based on the following

grounds. First, corporate growth is likely to cause losses in terms of management, lessening the

efficiency of R&D activities. Second, large enterprises are characterized by bureaucratic management

across the board, which is detrimental to R&D activities. Third, individual entrepreneurs‘ and

researchers‘ contribution to innovation may not be properly rewarded in large enterprises, which

decreases individual members‘ efforts for innovation and undermines the opportunities for innovation.

As for the relation between the firm size and innovation performance, research findings

mostly converge upon their positive (+) relationship (Sung, 2005; Kim, 1992; Yoon, 2006; Jang,

2011; Choi & Lee, 2011; Kim, 2009). Conversely, some report on their negative (-) relationship

(Kang, 1994; Scherer & Ross, 1990), while others argue the firm size influences some aspects of

innovation performance (Hong & Kim, 2009). Still others suggest both positive and negative effects

of firm size on innovation (Scherer, 1965). In addition, some researchers report on the insignificant

effects of firm size (Kamien & Schwartz, 1982).

Taken together, the relationship between firm size and innovation performance varies with

study targets, measures and analysis methods. Hence, it is unreasonable to affirm any unidirectional

relationship between them without allowing for relevant conditions. This study sets up a hypothesis

on the relationship between firm size and innovation performance as below based on the previous

findings.

Hypothesis 1. Firm size will influence innovation performance.

Volume 9, No.2, 2015 101

2.3.2. Innovation Activities and Innovation Performance

Innovation activities collectively refer to scientific, technological, organizational, financial and

commercial processes implemented to practice innovation (OECD, 2005). More often than not, it is

easy to associate innovation with technological innovation, and to narrow technological innovation

activities down to R&D in discourses. Yet, the inclusive concept of innovation activities is not

limited to R&D (Arias-Aranda et al., 2001). Innovation activities are also seen in functional divisions

such as marketing and management (Lawrence & Lorsch, 1967; Song et al., 1997), which implies

that a series of innovative activities intended to meet market needs can be viewed as innovation

activities in terms of technological and managerial aspects (Tushman & Nadler, 1986).

Depending on targets and goals of activities, innovation activities are sub-divided into

technological innovation activities and managerial innovation activities. Technological innovation

activities are meant to implement and facilitate technological innovation involving product

innovation and process innovation, and typified by R&D. According to Kennedy & Thirlwall (1972),

the essence of R&D consists in research, i.e. exploration into new knowledge, and development, i.e.

technological activities for transforming research findings or scientific knowledge into new products

or processes.

Technological innovation activities are known to provide two benefits at two different levels

(Kim & Yun, 2009). First, technological innovation activities improve the performance, price and

quality of a product to increase customer satisfaction, to prevent customer churn, to secure new

customers, and ultimately to strengthen the competitiveness in the market. Second, technological

innovation activities improve business processes, enhance technological capabilities, increase the

responsive and absorptive capacities toward new technologies, and thus enable sustainable and stable

creation of profits.

Technological innovation activities concern the alteration and improvement of technologies

applicable to products and processes, whereas managerial innovation activities are defined as efforts

for making creative changes of corporate management system with intent to strengthen

competitiveness and to survive (Amabile, 1988). Pierce & Delbecq (1977) regard the managerial

innovation as a social process of accepting and applying new things within an organization,

emphasizing the difference from scientific discovery. Kimberly & Evanisko (1981) define managerial

innovation as the innovation of managerial skills and of programs, products and technologies that

influence the essence, quality and quantity of information acquired in the course of a decision making

process.


Cho & Shin (1996) define managerial innovation as essentially altering the important parts of

a firm by deliberately carrying out plans or programs intended to change organizational members,

management system, structure, production process technologies and new products and services.

Taken together, managerial innovation activities may be construed as a series of activities intended

for effective organizational changes by responding to the dynamic internal and external environment

surrounding an organization.

Ultimately, innovation activities are intended to derive innovation performance, improve

managerial performance and attain competitive advantages. Accordingly, extensive empirical

analyses of the relationship between innovation activities and innovation performance mostly indicate

positive effects. First, R&D activities presented as the most dominant technological innovation

activities in many studies are found to be positively related to innovation performance (Kwon, 2011;

Kim, 2011; Ban & Kim, 2012; Lee & Limb, 2012; Serrano-Bedia et al., 2012; Hadjimanolis, 2000;

Romijin & Albaladejo, 2002; Lin et al., 2012; Wang & Kafouros, 2009; Becker & Dietz, 2004; Freel,

2003). In particular, many recent studies distinguish internal from external innovation activities, and

derive the findings that highlight the synergetic knowledge creation drawing on collaborative

innovation networks and coordinating the internal and external innovation capabilities and

knowledgeability (Kim, 2011; Lee & Limb, 2012; Serrano-Bedia et al., 2012; Hadjimanolis, 2000;

Lin et al., 2012; Becker & Dietz, 2004).

Previous studies on the relationship between technological innovation activities and

innovation performance employ such diverse dependent variables as new product sales (Kwon, 2011;

Serrano-Bedia et al., 2012; Wang &Kafouros, 2009), new product development and launch (Kim,

2011; Ban & Kim, 2012; Hadjimanolis, 2000; Becker & Dietz, 2004), product innovation (Kim,

2011; Ban & Kim, 2012; Romijin & Albaladejo, 2002; Freel, 2003), process innovation (Kim, 2011;

Ban & Kim, 2012; Freel, 2003), quality improvement (Ban & Kim, 2012), innovation certification

(Lee & Limb, 2012), patent counts (Romijin & Albaladejo, 2002), acquisition of new technology

(Lin et al., 2012), and R&D intensity (Becker & Dietz; 2004).

In contrast to the diverse measurements and empirical analyses of technological innovation

activities and resultant innovation performance, there is a paucity of empirical analyses of the

relationship between managerial innovation activities and managerial innovation performance.

Particularly, the performance of technological innovation can be measured in terms of patents, R&D

cost and new product launch (Tidd, 2001), whereas no clear-cut quantitative measurement for

managerial innovation exists (Damanpour & Aravind, 2011). Yet, many economists expound on the

socio-economic importance of managerial innovation (Sanidas, 2005), and its positive effects on

Volume 9, No.2, 2015 103

productivity and employment (Edquist et al., 2001; Nyholm, 1995). Still, empirical analyses of the

relationship are insufficient. That said, some researchers have conducted empirical analyses. Park &

Lim (2013) focus on knowledge management and TQM (total quality management) among other

managerial innovation activities, and analyze that such activities are significant factors influencing

both product innovation and process innovation.

Yang and Song (2007) draw upon local indicators and data used in practice to select

managerial innovative SMEs for empirical analyses, and explicate such managerial innovation

activities as knowledge and information management, organizational management, personnel

management and customer management have statistically significant effects on quantitative and non-

quantitative managerial innovation performance. Hong (2007) analyzes the effects of managerial

innovation activities on managerial performance and reports that the levels of application,

sustainment, support and progress of, as well as engagement in, managerial innovation activities are

significant variables, where the level of support exerts the most significant effects.

Based on the abovementioned findings, this study sets up a hypothesis on the effects of

innovation activities on innovation performance as below.

Hypothesis 2. Innovation activities will influence innovation performance.

2.3.3. Governmental Support and Innovation Performance

Most innovation activities incur costs. In addition, the immediate return on initial investment

in such activities is unlikely and any subsequent commercial success is often uncertain. Therefore, it

is difficult for principal agents of innovation to proceed with all innovation activities by relying

completely on internal resources, which is why financial support from external sources is needed. In

the same vein, each central government has established a range of financial and administrative

systems and laws to support innovation activities and made diversified efforts to elicit innovation

performance at the national level.

Innovation-related governmental support refers to government-led tax cuts, financing, and

provision of technological information and skilled human resources so that companies can unfold

R&D activities and thus effectively create innovation (OECD, 2005). Kirzner (1985) classifies the

governmental support for innovation activities into financing and personnel training. Particularly,

financing is the most common governmental support at home and abroad, providing practical aids

that helps overcome uncertainties and barriers that might arise in the course of commercializing


innovation leading to market performance (Svensson, 2008). Also, financial support is known to

benefit SMEs and start-ups, in particular.

Studies on the relationship between governmental support and innovation performance are

divided largely into three types. First, most studies describe the positive effects of governmental

support (Audretsch et al., 2002; Yoon & Yoon, 2013; Yoon, 2006; Yoon et al., 2011; Chung et al.,

2013; Kim, 2008). Audretsch et al. (2002) analyze the performance of the Small Business Innovation

Research program led by the US Department of Defense and highlight the positive effects of

governmental support on R&D in the private sector. Yoon & Yoon (2013) use national R&D project

data and Korean patent data to analyze 8,729 private companies and emphasize that governmental

financial support for R&D effectively facilitates innovation-related exploration and innovation

capabilities.

Yoon (2006) surveys 907 manufacturers to comparatively analyze the effects of financial

support for research, tax incentives and other governmental support measures, and confirms the

positive effects of financial support on R&D activities. Yoon et al. (2011) verify the difference in

technological innovation performance between beneficiaries and non-beneficiaries of governmental

support among 2,056 venture businesses. According to Chung et al. (2013), governmental support

benefits external R&D collaboration and ultimately creates innovation performance. Kim (2008)

compares the corporate participants in a 4-year project under the auspices of governmental initiatives

with non-participant corporate entities to analyze the effects of governmental support on

technological innovation and corporate survival, and sheds light on the positive effects of

governmental support on technological projects and economic outcomes.

Next, other studies are skeptical of the relationship between governmental support and

innovation performance (Svenssen, 2008; Lichtenberg & Siegel, 1991; Park, 1995). Notably,

Svenssen (2008) surveys 1,678 patent-holding companies and 1,082 individual innovators and

concludes that governmental financial support exerts negative effects on innovation performance on

account of the generosity of governmental support that hinders the selection of appropriate market-

oriented R&D projects. Still other studies derive no significant relationship between governmental

support and innovation performance as it varies with analysis targets and methods (Lichtenberg &

Siegel, 1991; Park, 1995).

Lastly, some studies focus on the complementary relationship between governmental support

and corporate R&D (Leyden & Link, 1991; David et al., 2000). Leyden & Link (1991) investigate

137 R&D centers in the US to compare the regression models between public and private R&D

Volume 9, No.2, 2015 105

sectors. They derive a complementary relationship from each input value, which is ascribable to the

technological complementarity arising in the production phase. David et al. (2000) review empirical

studies in economics and report that governmental support for private R&D is likened to a substitute

rather than a complement.

Referring to previous studies classifying the governmental support for innovation into tax

incentives and direct support, this study formulates the following hypothesis (David et al., 2000;

Yoon, 2006; Chung et al., 2013).

Hypothesis 3. Governmental support will influence innovation performance.

3. Method

3.1 Model

The present empirical analysis model is shown in [Figure 1]. First, to compare the factors

influencing the innovation performance between innovative SMEs and general firms, three

independent variables are considered; firm size, innovation activities, and governmental support. In

reference to the OECD‘s Oslo Manual, the dependent variable, innovation performance, is explained

as the sum of four types of innovation performance; product innovation, process innovation,

organizational innovation, and marketing innovation. Next, the independent variable, firm size, is

measured based on sales figures. Regarding the innovation activities, seven specific variables are

provided such as the percentage of R&D personnel, internal R&D cost, external R&D cost, other

innovation activity cost, ownership of R&D centers, number of patents, and diversity of information

sources for innovation. Governmental support is classified into tax incentives and financing.

[Figure 1] Analysis Model


3.2 Analysis

3.2.1. Data Collection

The empirical analysis draws on the data from the Science and Technology Policy Institute‘s

KIS 2010 (Korean Innovation Survey 2010: manufacturing sector). The technological innovation

survey was first conducted in the domestic manufacturing industry in accordance with the OECD‘s

Oslo Manual in 1996. The survey was conducted biennially by 2002, and triennially since 2005.

Since 2003, the service sector has been surveyed separately.

The 2010 technological innovation survey was based on the Korea National Statistical

Office‘s National Establishment Survey, where 3,925 out of 4,000 responses were extracted with a

multistage systematic sampling method from a population of 41,485 manufacturers nationwide that

had been founded three years before the survey and hiring over 10 full-time employees. The survey

was conducted from May to October, 2010. The sampling error was within ±3.34% at a 95%

confidence interval.

To identify the differences in the factors influencing the innovation performance between

innovative SMEs and general companies, this study extracts the targets separately from the original

data. Excluding those firms with no innovation activities and large enterprises, a total of 1,083

entities consisting of 252 innovative SMEs holding both venture and InnoBiz certificates and 831

general companies with no innovation certificates are analyzed here.

3.2.2 Operational Definition and Measurement of Variables

First, referring to Cho (2010) analyzing the factors influencing the product, process,

organizational and marketing innovation performance, the question items about the launch and

introduction of each innovation are defined as variables in line with the dependent variable,

innovation performance. The product innovation performance is defined as either or both of the

following: (1) Launching a new product completely different from the existing product or (2)

Launching a new product significantly improved over existing products over the past three years

prior to the survey date. The process innovation performance refers to introducing one or more items

from the following to the corporate operation in practice: (1) A whole new or significantly improved

production process, (2) A whole new or significantly improved logistics, and (3) A whole new or

significantly improved support system. The organizational innovation performance refers to

introducing one or more items from the following to the corporate operation: (1) Change in business

Volume 9, No.2, 2015 107

practices, (2) Change in knowledge management, (3) Change in business organizations e.g. flexibility

in business practices and inter-departmental integration, and (4) Change in relationships with external

organizations. The marketing innovation is defined as introducing one or more items from the

following to corporate practices: (1) Substantial change in product design and package, (2) Launching

a new brand for product promotion, and using new concepts of ad media and PR strategies, (3) Using

new sales strategies, e.g. product displays and new sales channels, and (4) Using new pricing tactics

including discounts and differentiation.

Next, by referring to Arias-Aranda et al. (2001) and Scherer (1965) regarding the independent

variable, firm size, this study validates the effects of firm size based on financial metrics. To be

specific, the log value of the mean sales figure for three years from 2007 to 2009 is used as the

independent variable.

The innovation activities consist of the percentage of R&D personnel, the cost of innovation

activities, the ownership of R&D centers, the number of patents, and the diversity of information

sources for innovation. The percentage of R&D personnel is calculated by dividing the mean number

of R&D personnel among the full-time internal employees for three years from 2007 to 2009 by the

total number of employees. The cost of innovation activities is categorized into the costs of internal

R&D activities, external R&D activities and other innovation activities spent for three years from

2007 to 2009. The cost of other innovation activities is the sum of the following three items

excluding R&D costs: (1) Cost of introducing capital goods including machine, equipment and

software, (2) Cost of introducing external knowledge and technology, and (3) Cost of preparation and

job training programs.

The log value of the cost for each of three innovation activities is used. The ownership of

R&D centers refers to operating any R&D centers. Any forms of departments responsible for R&D or

irregular R&D activities are considered as the non-ownership of any R&D centers. The number of

patents refers to the patents registered as of the end of 2009. The diversity of information sources for

innovation means the number of information sources used for corporate innovation activities from

2007 to 2009. Specifically, 12 information sources are presented: (1) In-house sources, (2) Group

affiliates, (3) Suppliers, (4) Companies in demand and customers, (5) Competitors and other firms in

the same industry, (6) External meetings, e.g. associations and co-ops, (7) New employees, (8)

Private service providers, (9) Universities, (10) Government-funded research centers and

national/public institutes, (11) Conferences, fairs and expos, and (12) Journals and books. The

number of information sources for innovation used is defined as a variable on an interval scale.


The governmental support is classified into tax incentives and financing. The tax incentives

refer to the total amount of tax reduction for technological development supported from 2007 to 2009.

The financing refers to the total amount supported for technological development and projects. The

log values of tax incentives and financing are used as variables with respective deviations taken into

account.

3.2.3. Methodology

A statistical package program State 11.0 is used for the empirical analysis. Specifically, first,

descriptive statistical analysis involving means, frequencies and standard deviations is performed to

understand the factors influencing the innovation performance of innovative SMEs and general

companies. Also, the innovation performance is sub-divided into product innovation, process

innovation, organizational innovation and marketing innovation performance. Then, logistic analysis

is used to find out the factors influencing each innovation performance.

4. Result and Interpretation

4.1. Basic Statistical Analysis

252 entities are defined here as innovative SMEs holding certificates for both venture

businesses and technological innovative SMEs, accounting for 23.27% of the 1,083 firms surveyed.

As for the sales figures used to distinguish innovative SMEs from general firms, the general firms‘

yearly mean is 36.06 billion Won, which is larger than that of innovative SMEs by approximately 17

billion Won. As for the number of employees, general companies hire more employees than

innovative SMEs by about 34 individuals.

The number of R&D personnel as an indicator of the innovation activities is 9.24 in innovative

SMEs in comparison to 5.77 in general firms. Other indicators of innovative SMEs‘ innovation

activities such as the cost of internal and external R&D activities, the cost of other innovation

activities and the number of patents exceed those of general firms.

In light of the governmental support, the three-year tax incentives for innovative SMEs

amount to 11.264 billion Won in comparison to 3.595 billion Won for general firms. In contrast, the

amount of financing for general firms is marginally greater than that for innovative SMEs.

Volume 9, No.2, 2015 109

[Table 1] General Characteristics of Targets

Item Total

(n=1,083)

Innovative

SMEs

(n=252)

General

firms

(n=831)

Means

compared

(t-value)

General

Sales

Mean

(Stan

dard

dev

iation

)

32098.55

(61933.00)

19034.47

(31681.00)

36060.22

(68038.33) -3.45***

Employees 95.26

(185.67)

69.27

(74.53)

103.14

(207.34) -2.54*

Innovation

activities

R&D

personnel

6.58

(11.66)

9.24

(12.75)

5.77

(11.20) 4.17***

Cost of

internal R&D

activities

864.72

(3214.22)

1102.15

(2357.85)

792.72

(3429.92) 1.34

Cost of external R&D

activities

125.60

(691.61)

215.61

(775.74)

98.30

(662.09) 2.36*

Cost of other

innovation

activities

1072.63

(4495.47)

1141.50

(3751.80)

1051.74

(4699.62) 0.28

Number of

patents

5.73

(18.16)

8.96

(18.93)

4.75

(17.81) 3.24**

Governmental

support

Tax

incentives

53.80

(210.84)

112.64

(322.55)

35.95

(158.49) 5.11***

Financing 469.82

(6819.61)

469.28

(1438.37)

469.98

(7746.07) -0.001

※ *p < .05; **p < 0.01; ***p < .001

※ Unit: Million Won / Personnel: number of persons

In terms of the number of innovative SMEs and general firms with manifested innovation

performance, innovative SMEs‘ innovation rates are higher than those of general firms in the four

areas of innovation performance. The highest innovation rate is found in innovative SMEs‘ product

innovation with 82.54% of innovative SMEs manifesting the product innovation performance. On the

contrary, the lowest innovation rate is found in general firms‘ marketing innovation with only

31.53% of general firms engaging in marketing innovation.

Both innovative SMEs and general firms show the highest innovation rates in the product innovation,

followed by process innovation, organizational innovation and marketing innovation in the order

named.


[Table 2] Innovation Performance

Item

Total

(n=1,083)

Innovative SMEs

(n=252)

General firms

(n=831)

# of

firms Innovation rate

# of

firms Innovation rate

# of

firms

Innovation

rate

Product innovation 730 67.41% 208 82.54% 522 62.82%

Process innovation 591 54.57% 158 62.70% 433 52.11%

Organizational innovation 505 46.63% 152 60.32% 353 42.48%

Marketing innovation 380 35.09% 118 46.83% 262 31.53%

※ # of firms: the number of firms with the given innovation performance manifested

※ Innovation rate: the percentage of firms with the given innovation performance manifested (= # of innovative

firms/n)

4.2. Hypothesis Test Result

[Table 3] Relationship between Product Innovation Performance and Firm Size, Innovation

Activities, and Governmental Support

Item

Total

(n=1,083)

Innovative SMEs

(n=252)

General firms

(n=831)

Coef. i / Coef. i / Coef. i /

Firm size -0.12** -0.02 -0.22 -0.01 -0.09 -0.02

R&D personnel 0.17 0.24 3.06 0.21 0.41 0.09

Internal R&D 0.10*** 0.02 0.20* 0.01 0.10*** 0.02

External R&D 0.06*** 0.01 0.17*** 0.01 0.04*** 0.01

Other innovation activities 0.05** 0.01 0.15* 0.01 0.04* 0.01

Ownership of R&D centers 0.14 0.03 -0.39 -0.03 0.15 0.03

Number of patents 0.02** 0.48e-02 -2.58e-03 -0.02e-02 0.03** 0.01

Information sources 0.09*** 0.02 0.16** 0.01 0.08*** 0.02

Tax incentives 0.02 0.32e-02 -0.01 -0.07e-02 0.02 0.37e-02

Financing 0.14e-02 0.03e-02 7.84e-04 0.01e-02 -3.98e-03 -0.09e-02

Constant 1.10 - 1.86 - 0.69 -

Log Likelihood -574.00 -81.91 -482.10

Chi² 219.34*** 69.59*** 132.60***

Pseudo R² 0.16 0.30 0.12

※ *p < .10; **p < 0.05; ***p < .01

The logistic analysis in [Table 3] is used here to test the hypothesis concerning the factors

influencing the product innovation performance.

Volume 9, No.2, 2015 111

According to the analysis, innovative SMEs‘ cost of internal and external R&D and other

innovation activities as well as diversity of information sources for innovation serve as positive

factors of their product innovation performance. These independent variables exert significant effects

on the product innovation performance of general firms as well. The number of patents is

insignificant for innovative SMEs, whereas it has significant effects as positive factors on general

firms‘ product innovation performance.

Likewise, the number of patents proves to be a positive factor of the product innovation

performance in the model of all firms surveyed as in the general firms. Also, the firm size proves to

be a negative factor of the product innovation performance of all firms surveyed.

[Table 4] Relationship between Process Innovation Performance and Firm Size, Innovation


Item

Total

(n=1,083)

Innovative SMEs

(n=252)

General firms

(n=831)


Firm size -0.02 0.01 0.15 0.04 0.01 0.35e-02

R&D personnel -2.34*** -0.58 -2.63** -0.60 -1.42 -0.36

Internal R&D -0.03* -0.01 0.01 0.27e-02 -0.04** -0.01

External R&D 0.06*** 0.01 0.05*** 0.01 0.06*** 0.01

Other innovation activities 0.26*** 0.06 0.24*** 0.06 0.25*** 0.06

Ownership of R&D centers 0.21 0.05 -0.22 -0.05 0.26 0.07

Number of patents 1.58e-03 0.04e-02 -3.76e-03 -0.09e-02 2.90e-03 0.07e-02

Information sources 0.09*** 0.02 0.01 0.18e-02 0.11*** 0.03

Tax incentives 0.01 0.29e-02 0.01 0.26e-02 0.02 0.40e-02

Financing 6.89e-04 0.02e-02 -1.39e-03 -0.03e-02 1.11e-03 0.03e-02

Constant -1.10** - -1.27 - -1.01* -

Log Likelihood -625.61 -145.87 -473.40

Chi² 241.07*** 41.18*** 203.73***

Pseudo R² 0.16 0.12 0.18

※ *p < .10; **p < 0.05; ***p < .01


influencing the process innovation.

The percentage of R&D personnel and the costs of external R&D and other activities are the

factors influencing the process innovation of innovative SMEs. Particularly, the R&D personnel

proves to be a negative factor of the process innovation with a rather high marginal effect, –0.60%. In


the model of general firms, four variables have significant effects, i.e. the costs of internal and

external R&D and other innovation activities and the diversity of information sources for innovation.

Notably, the cost of internal R&D activities proves to exert negative (-) effects.

In the model of all firms, the percentage of R&D personnel and the cost of internal R&D

activities have negative effects, whereas the costs of external R&D and other innovation activities as

well as the diversity of information sources for innovation prove to be the factors exerting positive

effects on the process innovation.

[Table 5] Relationship between Organizational Innovation Performance and Firm Size,

Innovation Activities, and Governmental Support

Item

Total

(n=1,083)

Innovative SMEs

(n=252)

General firms

(n=831)


Firm size 0.10* 0.03 -0.14 -0.03 0.14** 0.03

R&D personnel 0.92 0.23 0.25 0.06 1.06 0.26

Internal R&D 0.01 0.13e-02 0.06 0.01 2.01e-03 0.05e-02

External R&D 0.05*** 0.01 0.05*** 0.01 0.06*** 0.01

Other innovation activities 0.10*** 0.03 0.20** 0.05 0.09*** 0.02

Ownership of R&D centers 0.22 0.05 0.41 0.10 0.16 0.04

Number of patents -1.70e-03 -0.04e-02 -0.01 -0.12e-02 1.36e-04 0.00

Information sources 0.15*** 0.04 0.14*** 0.03 0.15*** 0.04

Tax incentives 0.03** 0.01 0.02 0.39e-02 0.03** 0.01

Financing 6.76e-04 0.02e-02 -0.01 -0.20e-02 2.67e-03 0.06e-02

Constant -2.05*** - -0.72 - -2.26 -

Log Likelihood -608.45 -142.21 -462.99

Chi² 279.54*** 54.12*** 207.16***

Pseudo R² 0.19 0.16 0.18

※ *p < .10; **p < 0.05; ***p < .01


influencing the organizational innovation performance.

The costs of external R&D and other activities and the diversity of information sources for

innovation serve as the positive factors influencing the innovative SMEs‘ organizational innovation

performance. In contrast, these three variables in tandem with the firm size and the tax incentives

have positive effects on general firms‘ organizational innovation performance. In comparison to the

negative effects of the firm size in the model of all firms, it serves as a positive factor influencing the

Volume 9, No.2, 2015 113

general firms‘ process innovation. Likewise, the firm size has positive effects on all firms with a

comparable marginal effect, 0.03.

[Table 6] Relationship between Marketing Innovation Performance and Firm Size, Innovation


Item

Total

(n=1,083)

Innovative SMEs

(n=252)

General firms

(n=831)


Firm size -0.16*** -0.03 -0.29** -0.07 -0.12** -0.03

R&D personnel 0.16 0.04 0.13 0.03 0.08 0.02

Internal R&D 0.03 0.01 -0.02 -0.37e-02 0.03 0.01

External R&D 0.03*** 0.01 0.02 0.01 0.03*** 0.01

Other innovation activities 0.10*** 0.02 0.08 0.02 0.11*** 0.02

Ownership of R&D centers -0.14 -0.03 -0.09 -0.02 -0.19 -0.04

Number of patents 0.01 0.11e-02 0.01 0.21e-02 4.22e-03 0.09e-02

Information sources 0.14*** 0.03 0.18*** 0.04 0.13*** 0.03

Tax incentives 0.02** 0.46e-02 0.01 0.24e-02 0.03** 0.01

Financing 4.07e-03 0.09e-02 -0.01 -0.33e-02 0.01 0.22e-02

Constant -0.29 - 0.75 - -0.34 -

Log Likelihood -630.26 -158.88 -467.49

Chi² 143.01*** 30.58*** 100.89***

Pseudo R² 0.10 0.08 0.10

※ *p < .10; **p < 0.05; ***p < .01


influencing the marketing innovation performance.

The firm size and the diversity of information sources for innovation are the factors

influencing the innovative SMEs‘ marketing innovation performance. The diversity of information

sources for innovation serves as a positive factor, whereas the introduction and implementation of

marketing innovation decreases as the firm size increases. By contrast, the information sources for

innovation, the costs of external R&D and other innovation activities and the tax incentives have

positive effects on general firms‘ marketing innovation. The firm size exerts negative effects on

marketing innovation in the model of all firms and those of innovative SMEs and general firms.

5. Conclusion and Implication

5.1 Conclusion


The following conclusions are derived from the present empirical analysis. First, the firm size

has mixed effects on innovation performance. Specifically, the firm size exerts positive effects on

general firms‘ organizational innovation, whereas it has negative effects on the marketing and

product innovation of all firms surveyed here.

Second, among the innovation activities, the percentage of R&D personnel, the cost of R&D

activities, the number of patents and the diversity of information sources for innovation influence the

innovation performance, whereas the ownership of R&D centers has insignificant effects on all types

of performance. The percentage of R&D personnel exerts negative effects on innovative SMEs‘

process innovation, whereas the number of patents has positive effects on general firms‘ product

innovation. In case of the cost of R&D activities, the costs of external R&D and other innovation

activities exert positive effects on all models, excluding the innovative SMEs‘ marketing innovation.

The cost of internal R&D activities is positively related to the product innovation only, whereas it is

negatively related to the general firms‘ process innovation. The diversity of information sources for

innovation has positive effects on all types of innovation excluding the innovative SMEs‘ process

innovation.

Third, concerning the governmental support, the tax incentives prove to be a positive factor of

general firms‘ organizational innovation and marketing innovation, whereas the financing proves

insignificant.

5.2 Implication

The following implications are derived from the present findings.

First, differentiated management specific to each type of innovation is required to improve the

innovation performance directly associated with corporate performance. As described in the section

on the rationale, innovation is largely divided into technological innovation and managerial

innovation. The technological innovation is subdivided into product innovation and process

innovation. Similarly, the managerial innovation is subdivided into organizational innovation and

marketing innovation. The extent to which the factors affect each type of innovation may vary.

Likewise, the signs of effects may vary with the different types of innovation. Indeed, according to

the empirical analysis, the firm size is a positive factor of the product innovation, whereas it has

negative effects on the marketing innovation. In the same vein, the number of patents influences the

product innovation, whereas it has insignificant effects on the other types of innovation. As for the

governmental support, the tax incentives have statistically significant effects only on general firms‘

Volume 9, No.2, 2015 115

organizational and marketing innovation. In that each type of innovation is influenced by specific

factors, it is necessary to take differentiated managerial approaches to corporate capabilities and

resources in accordance with the direction of innovation pursued from the perspective of managers in

charge of managing, introducing and implementing different types of innovation. In addition, optimal

government policy measures specific to the nature of a given type of innovation need to be developed.

Second, the factors influencing the innovation performance differ between innovative SMEs

and general firms. The present empirical analysis highlights that the percentage of R&D personnel

and the cost of internal R&D activities are negative factors of the process innovation in innovative

SMEs and general firms, respectively. Also, the number of patents and the costs of external R&D and

other innovation activities prove to influence the general firms‘ product and marketing innovation

only. Besides, the effects of the firm size on the organizational innovation and those of the tax

incentives on organizational and marketing innovation are significant in general firms only. Thus, it

is necessary to map out differentiated innovation strategies for different types of firms from the

perspective of management and policy measures for SMEs.

Third, the SMEs‘ limited internal innovation activities and resources attributable to SMEs-

specific smallness are manifest here. The percentage of R&D personnel and the cost of internal R&D

activities prove to be negative factors of innovative SMEs‘ and general firms‘ process innovation

performance, whereas the external innovation activities have positive effects on the relevant

indicators of diverse innovation performance. Also, the cost of internal R&D activities serves as a

positive factor of the product innovation only. These findings suggest that it is often harsh and

challenging for small and medium-sized manufacturers to achieve innovation performance with their

limited internal capabilities, and that collaboration with external parties is indispensable for SMEs to

attain any process innovation.

5.3 Limitation and Future Studies

Noting the difference between innovative SMEs and general firms, this study empirically

analyzes the factors such as firm size, innovation activities, and governmental support that influence

corporate innovation performance. The effects of each factor on corporate innovation performance

vary with different types of firms based on the analysis findings, from which the suggestion and

implications are derived. Yet, despite some meaningful findings, this study has the following

limitations. First, due to the limited empirical analysis based on the secondary data, limited variables

are selected and analyzed here. Given that a range of quantitative and qualitative factors including

specific collaboration networks, entrepreneurship, innovative disposition and intention for innovation


may exist as the factors influencing the innovation performance in addition to firm size, innovation

activities and governmental support, relevant theoretical consideration and validation is limited here.

Next, although this study extensively deals with different types of innovation performance, it

measures the likelihood of implementation and introduction of innovation in terms of performance

indicators without shedding light on the relevance to financial and non-financial performance which

may be considered an ultimate goal of innovation performance.

These limitations warrant extensive further studies on the effects of qualitative factors

associated with innovative SMEs‘ innovation performance and on their relationship with

management outcomes as well as some comparative analyses of diverse types of firms except SMEs.

The findings will serve as the reference data for implementing the strategies for creating competitive

advantages of all types of SMEs including innovative SMEs, and be conducive to deriving

implications for rejuvenating the national economy.

<received: 2015. 10. 14>

<revised: 2015. 11. 23>

<revised: 2015. 12. 06>

<accepted: 2015. 12. 07>

1 A law established to make and support policies for technological innovation of SMEs in Korea 2 Name of certification for innovative SMEs in Korea

Volume 9, No.2, 2015 117

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Volume 9, No.2, 2015 125

Publication Ethics and Malpractice Statement for the

Asia Pacific Journal of Innovation and Entrepreneurship

The Asia Pacific Journal of Innovation and Entrepreneurship, known as APJIE (the Journal

hereunder), is a refereed and highly professional journal covering entrepreneurship, innovation,

incubation and related topics. It aims to establish channels of communication and to disseminate

knowledge among policy makers, experts and professionals working in universities, government

departments, research institutions, as well as industry and related business.

The Journal publishes original papers, theory-based empirical papers, review papers, case

studies, conference reports, relevant reports and news, book reviews and briefs.

Articles in the Journal set out to validate and preserve the records of research so that ideas

and experiences may evolve to innovations in the long run. It is very important that the records

are accurate and trustworthy. Many parties, each of which plays an important role in achieving

these aims, are involved in the act of publishing. Therefore, it entails that the authors, the editor,

the peer-reviewers, and the publisher of the Journal have the responsibility to meet expected

ethical standards at all stages in their involvement with the publication of an article.

We at APJIE are committed to ensuring ethical standards at all stages of the publication

process. We follow the industry associations closely, such as the Committee on Publication Ethics

(COPE), and many international prestigious associations that set standards and provide guidelines

for best practices in order to meet these requirements.

A summary of our key expectations of editors, peer-reviewers, authors and publishers is as

follows:

1. Publication and Authorship

List of References and Financial Support

Published articles should have the relevant list of references. Additionally, sources of funding

for the research should always be disclosed by authors.

Originality and Plagiarism

The authors should ensure that they have written entirely original works, and if the authors


have used the work and/or words of others, they must ensure that this has been appropriately cited

or quoted. Papers found with such problems are automatically rejected and authors are so advised.

Multiple, Redundant or Concurrent Publication

When a paper is submitted for possible publication, the submitting author, as per the APJIE

Publishing Agreement and the APJIE Policies, informs that the paper has not been published and

is not currently under publication with any other journal. Simultaneous submission is considered

unethical and is therefore unacceptable.

Peer Review Process

All submitted papers are subject to a peer-review process. The factors that are taken into

account in the review are relevance, originality, organization, completeness, significance of the

findings, clarity, and overall quality. The possible decisions include honors quality, acceptable,

acceptable with minor revisions, acceptable with major revisions and not acceptable.

2. Author's Responsibilities

Peer Review Process

It is compulsory for all authors seeking to have their paper published in the journal to

participate in the peer review process.

Authorship of the Paper

The authors‘ names must to be listed on the paper in order of their contribution to the article,

and all authors take responsibility for their own contributions. Only those who have made a

substantive contribution should be listed as authors. The corresponding author must ensure that

all appropriate co-authors and no inappropriate co-authors are included on the article, and that all

listed co-authors have seen and approved the final version of the article and agreed to its

publication.

Reporting Standard

Authors should present an accurate account of the work performed as well as an objective

discussion of its significance. Underlying data should be represented accurately in the paper. A

paper should contain sufficient detail and references to permit others to replicate the work

wherever possible. Fraudulent or knowingly inaccurate statements constitute unethical behavior

and are unacceptable.

Volume 9, No.2, 2015 127

Data Statement

All data in the submissions must be real and authentic as noted in the APJIE Policies and

agreed to in the APJIE Publishing Agreement.

Fundamental Errors in Published Works

When an author discovers a significant error or inaccuracy in his/her own published work, it

is the author‘s obligation to promptly notify the journal editor or publisher and cooperate with the

editor to publish corrections, clarifications, retractions and apologies, where this is deemed

necessary.

Disclosure and Conflicts of Interest

All authors should disclose in their manuscript all affiliations, funding sources, and financial

or management relationship that could be perceived as a substantive conflict of interest that might

be construed to influence the results or interpretation of their manuscript. All sources of financial

support for the project should be disclosed.

3. Peer Reviewer’s Responsibility

Standards of Objectivity

The editors and reviewers are required to evaluate papers based on the content. The review

comments must be respectful of authors. The reviewers are required to justify their decision and

recommendation.

Disclosure of Conflict of Interest

Reviewers should not consider manuscripts in which they have conflicts of interest resulting

from competitive, collaborative, or other relationships or connections with any authors, research

funders, companies, or institutions connected to the papers.

Acknowledgement of Sources

Reviewers should identify relevant published work that has not been cited by the authors.

Any statement that an observation, derivation, or argument had been previously reported should

be accompanied by the relevant citation. A reviewer should also call to the editor's attention to

any substantial similarity or overlap between the manuscript under consideration and any other

published paper of which they have personal knowledge.


Confidentiality

Privileged information or ideas obtained through peer review must be kept confidential and

not used for personal advantage. The reviewers must not disclose any information about a

submitted manuscript to anyone other than the corresponding author, reviewers, potential

reviewers, editorial team, and the publisher, as appropriate.

Promptness

The journal editors are committed to provide timely review to the authors. If a reviewer does

not submit his/her report in a timely manner, the paper may be sent to another qualified reviewer.

Any selected referee who feels unqualified to review the research reported in a manuscript or

knows that its prompt review will be impossible should notify the editor and withdraw from the

review process.

Contribution to the Editorial Decisions

The journal uses the double-blind review process. The reviewers advise the editor-in-chief in

making the editorial decision. The editor-in-chief communicates with authors, as required, and

helps them in improving the quality of their research paper.

Non-Discrimination

The reviewers will evaluate manuscripts without regard to the authors' race, gender, sexual

orientation, religious belief, ethnic origin, citizenship, or political philosophy. The decision will

be based on the paper‘s relevance, originality, organization, completeness, significance, clarity,

overall quality and its relevance to the journal's scope.

4. Editorial Responsibilities

Complete Responsibility and Authority to Reject or Accept an Article

The editor has ultimate responsibility for deciding which of the articles submitted to the

journal should be published, and in doing so is guided by the APJIE Policies. Additionally, the

decision will be based on the recommendation of the journal's editorial board members and

reviewers. The journal abides by legal requirements regarding libel, copyright infringement and

plagiarism.

Disclosure of Conflict of Interest

Editors should have no conflict of interest in reference to articles they reject or accept.

Reviewers should not consider manuscripts in which they have conflicts of interest resulting from

Volume 9, No.2, 2015 129

competitive, collaborative, or other relationships or connections with any authors, research

funders, companies, or institutions connected to the papers.

Acceptance of Papers

Editors should only accept a paper when they are reasonably certain about the content.

Furthermore, the editor should accept papers for publication ―subject to revision‖ only when

he/she is reasonably certain that the paper will become fully publishable in a reasonable amount

of time.


When the editor is informed of or discovers a significant error or inaccuracy in a published

work, it is the editors‘ obligation to promote the publication of corrections, clarifications,

retractions and apologies, where this is deemed necessary.

Anonymity of Reviewers

The editors will make every effort to ensure the integrity of the review process by not

revealing the identity of the reviewers of a manuscript to the author of that manuscript.

Non-Discrimination

The editor and editorial staff will evaluate manuscripts without regard to the authors' race,

gender, sexual orientation, religious belief, ethnic origin, citizenship, or political philosophy. The

decision will be based on the paper‘s relevance, originality, organization, completeness,

significance, clarity, overall quality and its relevance to the journal's scope.

Confidentiality

Privileged information or ideas obtained through peer review must be kept confidential and

not used for personal advantage. The editor and editorial staff must not disclose any information

about a submitted manuscript to anyone other than the corresponding author, reviewers, potential

reviewers, editorial team, and the publisher, as appropriate.

5. Publishing Ethics Issues

Monitoring/Safeguarding Publishing Ethics by the Editorial Board

Double–blind peer review should be conducted for each paper to avoid academic dishonesty.

Furthermore, all files related to each paper should be kept properly, atleast including the paper,

the APJIE Publishing Agreement, and the APJIE Paper Review Form.


Guidelines for Retracting Articles

1) The retraction of an article will be considered if there is clear evidence that the findings

are unreliable, either as a result of misconduct or an honest error, or if the findings have

previously been published elsewhere without proper cross-referencing, permission or justification,

or if the paper constitutes plagiarism, or if the paper reports unethical research.

2) The main purpose of retractions is to correct the literature and ensure its integrity rather

than to punish authors who misbehave.

3) The retracted article will not be removed from the APJIE online archives. However,

notices of retraction will be promptly published and linked to the retracted article, accurately

stating the information of the retracted article, the reason(s) for the retraction and who retracted

the article, which will be freely available to all readers.

4) Articles may be retracted by their author(s) or by APJIE. In some cases retractions are

issued jointly. Additionally, APJIE has the final decision on retractions. APJIE will retract

publications even if all or some of the authors refuse to retract the publication themselves once the

unethical behavior is confirmed.

Maintain the Integrity of Academic Record

All authors will make a commitment of the integrity of the academic record, including the

integrity of the data and figures in the paper, when they sign the APJIE Publishing Agreement.

Furthermore, the peer review will help the editors to verify the originality and integrity of the

submitted paper.

Preclude Business Needs from Compromising Intellectual and Ethical Standards

All business needs should be precluded from compromising intellectual and unethical

standards.


When the author discovers a significant error or inaccuracy in a published work, it is the

author‘s obligation to formally notify the editor promptly. The editors and editorial staff should

always be willing to publish corrections, clarifications, retractions and apologies where deemed

necessary.

No Plagiarism and No Fraudulent Data

Plagiarism and fraudulent data are forbidden. When such a case is brought up after the

Volume 9, No.2, 2015 131

paper‘s publication in APJIE, a preliminary investigation will be conducted by APJIE. The

author will be informed by APJIE. In the event that the plagiarism and/or the fraudulent data are

confirmed, APJIE will contact the author‘s institute and funding agencies. Furthermore, APJIE

will mark the plagiarism paper on the PDF file of this paper or formally retract the paper.

Volume 9, No.2, 2015 133

Call for Papers

Introduction about APJIE

Asia Pacific Journal of Innovation and Entrepreneurship is a refereed and highly

professional journal covering entrepreneurship, innovation, incubation and related topics. It aims

to establish channels of communication and to disseminate knowledge among policymakers,

experts and professionals working in universities, government departments, research institutions,

as well as industry and related business.

The Journal publishes original papers, theory-based empirical papers, review papers, case

studies, conference reports, relevant reports and news, book reviews and briefs.

Commentaries on papers and reports published in the Journal are encouraged. Authors will

have the opportunity to respond to the commentary on their work before the entire treatment is

published.

Subject Coverage for Vol.9, No.3

This journal focuses on the strategy and management methods of business innovation and

Entrepreneurship. Subjects include, but are not limited to:

• Case Study in Following Fields Respectively &

• Innovation Management

• Incubation Management

• Economic Development

• Entrepreneurship

• Strategy and System Development

• Entrepreneurial Marketing

• Entrepreneurial Business Environment

• Business Ethics

Submission Ways of Paper

1) APJIE (Asia Pacific Journal of Innovation and Entrepreneurship) officially announce a

Call for Papers for the Volume 9, No.3 which will be published in December 2015. The deadline

for manuscripts is October 15, 2015, respectively, which must be received on the Desk of APJIE

through electronic mailing system: [email protected]


Requirements of Papers

1) Formal conditions for acceptance

Papers will only be published in English. Each typescript must be accompanied by a

statement that it has not been submitted for publication elsewhere in any languages. Previous

presentation at a conference in any language should be disclosed.

All papers are refereed by three blind reviewers (the third blind reviewer will review the

manuscript only in case the two reviewers are split), and the Chief Editor reserve the right to

refuse any typescript, whether on invitation or otherwise, and to make suggestions, editorial

changes, and/or modifications on grammatical errors before publication. Typescripts that have

been accepted become the property of the publisher. It is a condition of acceptance that copyright

shall be vested in the publisher.

The publisher shall furnish authors of accepted papers with proofs for the correction of

printing errors. The proofs shall be returned within 14 calendar days of submittal. The publisher

shall not be held responsible for errors that are the result of authors‘ oversights.

2) Typescript preparation

The original typescript and two other copies should be submitted on A4 or similar-size

paper, following with the APA style and using 10-point size and Times New Roman font type

with single-spaced typing and a wide margin on the left. Any paper that would occupy more than

20 pages of the Journal may be returned for abridgement.

A complete typescript should include in the following order: title, author(s), address(es),

abstract, keywords, biographical notes, introduction, text, acknowledgements, references and end

notes, tables, figure captions, figures, appendix.

3) Electronic copy

Authors are asked to supply their articles, where possible, on CD-R (Compact Disc-

Recordable). Please state the word processing program used (Microsoft Word is preferred).

4) International context

APJIE is an international journal, and authors should be aware of the worldwide readership.

Authors are encouraged to approach their chosen topic with an international perspective.

It should not be assumed that the reader is familiar with specific national institutions or

corporations. Countries and grouping of countries should be referred to by their full title (for

Volume 9, No.2, 2015 135

example, ‗America‘, ‗China‘ and ‗Europe‘ are all ambiguous). Special attention should be paid to

identifying units of currency by nationality. Acronyms should be translated in full into English.

5) Title, abstract, keywords, addresses, biographical notes

Please assist us by following these guidelines:

Title: as short as possible

Abstract: approximately 200 words, maximum 300

Keywords: approximately 10 words or phrases

Address: position, department, name of institution, full postal address, e-mail address &

telephone number

Biographical notes: approximately 50 words per author, maximum 100

6) References

APJIE uses an alphabetical system in references order. References should be made only to

works that are published, accepted for publication (not merely ―submitted‖), or available through

libraries or institutions. Any other source should be qualified by a note regarding availability. Full

reference should include all authors‘ names and initials, date of publication, title of paper, title of

publication (underlined), volume and issue number (of a journal), publisher and form (books,

conference proceedings), page numbers.

7) Figures

All illustrations, whether diagrams or photographs, suitable for printing in black and white,

are referred to as Figures and are numbered sequentially. Please place them at the end of the paper,

rather than interspersed in text.

Originals of line diagrams will be reduced and used directly, so please prepare them to the

highest possible standards. Bear in mind that lettering may be reduced in size by a factor of 2 or 3,

and that fine lines may disappear. Electronic copies of the figures are also required.

8) Translated works

Difficulty often arises in translating acronyms, so it is best to spell out an acronym in

English (for example, IIRP-French personal income tax).

Similarly, labels and suffixes need careful attention where the letters refer to words that have

been translated.


The names of mathematical functions may change in translation-check against an English or

American mathematical reference text.

9) Units of measurement

APJIE follows the Système International for units of measurement. Imperial units will be

converted, except where conversion would affect the meaning of a statement, or imply a greater or

lesser degree of accuracy.

10) Fees

The authors are subject to pay the submission fee when they submit their manuscript to the

APJIE Desk for the review process and the publication fee after their manuscript has been

accepted for publication. If all the co-authors do not have any affiliation in Korea, they are

exempted from the following fees.

Submission fee of KW100,000

Publication fee of KW200,000 (in case of without financial support)

Publication fee of KW300,000 ( in case of with financial support)

Bank Account: Daegu Bank 086-13-399521 (under name of: Cho, Bong Jin (APJIE))

The receipt of fee(s) may be issued upon request to:

Email: [email protected]

Phone: 82-10-5105-7693 or 82-70-7568-6371

Address: Mokwon Univ. O1-510, Doanbuk-ro 88, Seo-gu, Daejeon, Korea

AABI Presidential Board Member:

AABI President

Yeung-Shik Kim (Korea, [email protected])

Honorary President, Korea Business Incubation Association (KOBIA)

President, Kumoh National Institute of Technology

AABI Vice President

Lin Xu Wei, Director Shanghai Technology Business Incubation Association http://www.incubator.sh.cn

AABI Honorary President

R.M.P. Jawahar (India, [email protected])

Executive Director of Tiruchirappalli Regional Engineering College - Science and

Technology Entrepreneurs Park (TREC-STEP)

AABI Advisor

Benjamin Yuan (Chinese Taipei, [email protected])

President of Chinese Business Incubation Association (CBIA)

Wang Rong (China, [email protected])

Honorary President, Shanghai Technology Business Incubation Association

Rustam Lalkaka (USA, [email protected])

President of Business & Technology Development Strategies LLC

Hong Kim (Korea, [email protected] )

Dean of Graduate School of Global Entrepreneurship, Hoseo University


http://mail2.daum.net/hanmail/mail/[email protected]



AABI Association Members:

Australia Phillip Kemp, President

Business Innovation & Incubation Australia

http://www.businessincubation.com.au

China Duan Junhu, Deputy Director General

Torch High-Tech Industry Development Center, Ministry of Science and

Technology, China

http://www.ctp.gov.cn

Zhen Hong Zhu, President

Shanghai Technology Innovation Center

Shanghai Technology Business Incubation Association

http://www.incubator.sh.cn

Hong Kong, China Allen T.B. Yeung, Representative

Hong Kong Science and Technology Parks Corporation

http://www.hkstp.org

India Dr. Rajendra Jagadale, President

Indian STEPs and Business Incubators Association

http://www.isba.in

Indonesia Asril Fitri Syamas Syamas, Chairman

Association of Indonesian Business Incubation

Japan Satoshi Hoshino, President

Japan Business Incubation Association

http://www.jbia.jp

Kazakhstan Yerik Dukenbayev, President

The Kazakhstan‘s Association of Business Incubators and Innovation Centers

http://www.kabic.kz

Korea Hyeongsan Kye, President

Korea Business Incubation Association

http://kobia.or.kr, http://kised.or.kr

http://www.businessincubation.com.au/

http://www.ctp.gov.cn/

http://www.hkstp.org/

http://www.isba.in/

http://www.jbia.jp/

http://www.kabic.kz/

http://kobia.or.kr/

http://kised.or.kr/

Kyrgyz Republic General secretary, Zamira Akbagysheva

Union of Business Incubators and Innovation Centers of the Kyrgyz Republic

http://www.cango.net.kg

Malaysia Andrew Wong, President

Multimedia Development corporation Sdn. Bhd(MSC central Incubator/

Accelerator, National Incubator Network Association)

http://www.mdc.com.my

New Zealand Hamish Campbell, Representative

New Zealand Trade and Enterprise

http://www.nzte.govt.nz

Steve Corbett, Representative

Incubator New Zealand

http://www.incubators.org.nz

Pakistan Akhtar Ali Qureshi, Representative

Technology Incubation Centre, National University of Sciences and Technology

http://www.tic.org.pk

Philippines Mercedes M. Barcelon, Representative

Ayala Technology Business Incubator Network, Ayala Foundation, Inc.

http://www.ayalatbi.org‘

Singapore Cham Tao Soon, Representative

Nanyang Technological University

http://www.ntu.edu.sg

Chinese Taipei Benjamin Yuan, President

Chinese Business Incubation Association

http://www.cbia.org.tw

Ching-Yao Huang, Representative

NCTU Center of Academia and Industry Collaboration

http://www.iic.nctu.edu.tw

http://www.cango.net.kg/

http://www.mdc.com.my/

http://www.nzte.govt.nz/

http://www.tic.org.pk/

http://www.ayalatbi.org/

http://www.ntu.edu.sg/

http://www.cbia.org.tw/

http://www.iic.nctu.edu.tw/

Thailand

Chusak Limsakul, President

Thai Business Incubators and Science Parks Association (Thai-BISPA)

http://www.thaibispa.or.th

Uzbekistan Valijon Amanlikov, Representative

Association of Business Incubators and Technoparks of the Republic of

Uzbekistan

http://www.abit.uz

Vietnam Representative, Dr. Pham Minh Tuan,

Vietnam Business Incubation Club

http://www.topica.edu.vn/incubation

http://www.thaibispa.or.th/

http://www.abit.uz/

http://www.topica.edu.vn/incubation

Review Board:

Dinah Adkins (U.S.A)

Richard P. Bagozzi (U.S.A)

Dong Ok Chah (Korea)

James K.C. Chen (Taiwan)

Jing-Yau Chen Cheng (Taiwan)

Man Kee Choe (Korea)

Check Teck Foo (Singapore)

Daniel L. Friesner (U.S.A)

C. Young Hong (Taiwan)

Satoshi Hoshino (Japan)

Ching Yao Huang (Taiwan)

Seok Joon Hwang (Korea)

Choong Jae Im (Korea)

Rajendra Jagdale (India)

Wen-Jang (Kenny) Jih (U.S.A)

Lynn Kahle (U.S.A)

Akkharawit Kanjana-Opas (Thailand)

Phillip Kemp (Australia)

William Walton Kirkley (New Zealand)

Fredric Kropp (U.S.A)

Rustam Lalkaka(U.S.A)

Hyoung Tark Lee (Korea)

Ki Seok Lee (Korea)

David A. Lewis (U.S.A)

Xiaoming Liu (China)

Gilroy Middleton (Belize)

Karen E. Mishra (U.S.A)

Kee Bong Park (Korea)

Tanyanuparb Anantana (Thailand)

Hermina Burnett (Australia)

Deepanwita Chattopadhyay (India)

Alfred Li-Ping Cheng (Taiwan)

Yoon Shik Cho (Korea)

Myeong Gil Choi (Korea)

Rolf P. Friedrichsdorf (Germany)

Ramasamy Ganesan (India)

Jin Hwan Hong (Korea)

Chih-Hung Hsieh (Taiwan)

Yun Hwangbo (Korea)

Ahmad Ibrahim (Malaysia)

Tommi Aleksanteri Inkinen (Finland)

R. M. P. Jawahar (India)

Seoung Min Kang (Korea)

Janekrishna Kanatharana (Thailand)

Tomoyo Kazumi (Japan)

Kyung Ho Kim (Korea)

Harald F.O. von Kortzfleisch (Germany)

Hyeong San Kye (Korea)

Abdul Aziz Ab Latif (Malaysia)

In Lee (U.S.A)

Pui Mun Lee (Singapore)

Zhan Li (China)

P. K. B. Menon (India)

Zhao Min (China)

Patricia Ordoñez e Pablos (Spain)

Sun Young Park (Korea)

ISSN 2071 – 1395

AABI Secretariat Office:

Asian Association of Business Incubation (AABI)

100 Qin Zhou Road, Shanghai, 200235

Tel: 86-1304-4118-085

Web site: www.aabi.info Email: [email protected]

Editorial Office:

Korea Business Incubation Association (KOBIA)

The APJIE Desk extends a hearty thanks to all of you

for your financial support!

Asian Association of Business Incubation (AABI) Yeung-Shik Kim, President

http://www.aabi.info

Small and Medium Business Administration (SMBA) Jungwha Han, Adiministrator

http://www.ctp.gov.cn

Korea Business Incubation Association (KOBIA) Hyeongsan Kye, President

http://www.kobia.or.kr

Indian STEPs and Business Incubator’s Association (ISBA) Rajendra Jagadale, President

http://isba.in

Young Jeon Co., Ltd. Hong Jang Lee, President

e-mail: [email protected]

Proofreading: Gilroy Middleton (Belize, Professor, University of Belize)

Graphic Design: Seong Jae Song (Korea, Professor, Hoseo University)

Secretariat General: Dr. Eun Sook Son & Secretariat: Ms. Hye Run Jeong

Review Board: continued

Hadi K Purwadaria (Indonesia)

Saras D. Sarasvathy (U.S.A)

Aviv Shoham (Israel)

Ming Yen Wang (China)

Richard White (New Zealand)

Andrew Wong (Malaysia)

Ho Tack Yi (Korea)

Benjamin J. C. Yuan (Taiwan)

Frederick W. S. Yung (Hong Kong)

Rosemarie Reynolds (U.S.A)

Michael Schaper (Australia)

Enrico Plata Supangco (Philippines)

Zhen Wang (China)

Dong Kyu Won (Korea)

Chang Seob Yeo (U.S.A)

Tan Yigitcanlar (Australia)

JinHyo Joseph Yun (Korea)

Yuli Zhang (China)

http://www.aabi.info/



http://www.ctp.gov.cn/

http://www.kobia.or.kr/

http://isba.in/


ISSN 2071 – 1395

AABI Secretariat Office:

Asian Association of Business Incubation (AABI)

100 Qin Zhou Road, Shanghai, 200235

Tel: 86-1304-4118-085

Web site: www.aabi.info Email: [email protected]

Editorial Office:

Korea Business Incubation Association (KOBIA)

Mokwon Univ. O1-510, Doanbuk-ro 88, Seo-gu,

Daejeon, Korea

Tel: +82-70-7568-6371 Mobile: +82-10-7190-1258

Web site: www.kobia.or.kr

Email: [email protected]

Home Page: www.apjie.org

www.apjie.net

Vol. 9, No. 2



http://www.kobia.or.kr/


http://www.apjie.org/

http://www.apjie.net/

Apjie Vol.9 No.2 Final 1

Documents

Transcript of Apjie Vol.9 No.2 Final 1