increase client payment error - mdpi · 2019. 2. 15. · marketing in a demanding technology...

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Special Issue Paper List (20 Papers)

No. Paper Title Authors(*

corresponding

Author)

First or

Corresponding

Author

1 Knowledge Diffusion Path

Generated by Technological

Collaborators: The Exploratory

Case of the Advanced Coal

Technology Consortium

Ben Zhang & Lei

Ma

Ben Zhang

2 Smart City Governance with

Sustainable System

Development Framework-An

Empirical Study of Taipei City

Min-Ren Yan,

Cheng-Sheng

Pong, Ahmad

Hadavi

Min-Ren Yan

3 A Study on the education

using intelligence information

technology on 4th industrial

revolution

Eun Soo Choi,

Hang Sik Park &

Min Soo Kang

Eun Soo Choi

4 The mechanism, progress and

enlightenment of National

Network for Manufacturing

Innovation: What can we learn

from Manufacturing USA?

Yunhao Feng &

Jinxi Wu*

Jinxi Wu*

5 The influence of R&D

collaboration structure on

open innovation performance

in bio-pharmaceutical industry

- focus on inter-organizational

implementation

Eungdo Kim;

KwangsooShin*

KwangsooShin*

6 Does administrative burden Sabinne Lee, Sabinne Lee

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increase client payment error

and fraud? The case of the US

Supplemental Nutrition

Assistance Program

Kwangho Jung*

7 Open Innovation, and

Creativity: Bureaucratic

Pathologies

Eunhyeong Park,

Kwangho Jung,

Hyue-Su Ha

Eunhyeong Park

8 Conservation plan of Eco-park

in Seoul with the habitat

suitability model for Korean

waterdeer

Sangdon Lee Sangdon Lee

9 The importance of carbon

sequestration for Greenbelt

region in the city of Seoul

Sangdon Lee &

Jiyoung Choi

Sangdon Lee

10 The effects of acquirer’s firm’s

capability and dyadic

knowledge characteristics on

the acquirer’s technological

innovation performance in

M&A: A case of

biopharmaceutical industry

YejinLee;

EungdoKim,

Kwangsoo Shin*

Kwangsoo Shin*

11 Structural effect of cluster

location of biotechnology firm

on open innovation and

technological innovation

performance: The case of

companies in US


Kwangsoo Shin;

EungdoKim*

EungdoKim*

12 The impact of Mergers and

Acquisitions (M&A) on Media

companies’ survival and

Jiyoon Chang;

NamjunCha;

JunseokHwang;

Sungdo Jung*

3

growth – Perspective on

dynamics of Media

convergence

Sungdo Jung*

13 The impact of cluster’s open

innovation types on RIS

productivity: Case study of US

pharmaceutical companies

Hana Kim; Eungdo

Kim*

Eungdo Kim*

14 Factors affecting Outbound

Open Innovation Performance

in Bio-Pharmaceutical Industry-

Focus on Out-Licensing Deals

Insu Lee;

Kwangsoo, Shin;

Eungdo Kim*

Eungdo Kim*

15 Topography of Post-Genomic

Researches in Korea:

Governance and Institutional

Polymorphism

June-Seok Lee June-Seok Lee

16 What factors are barriers to

open innovation in electricity

industry? : Lessons from

European utilities’ CVC

strategies

Hanee Ryu Hanee Ryu

17 Factors affecting M&A

performance of

biopharmaceutical firms: an

empirical analysis of

influencing factors

Jimin Choi;

Kwangsoo Shin*;

Eungdo Kim

Kwangsoo Shin*

18 Green Governance

Responsibility, Corporate

governance and Investors’

Reaction

Weian Li,

Guangyao Cui,

Minna Zheng*,

Yaowei Zhang

Minna Zheng*

19 Does open trade increase

China's carbon emissions?

Longzheng Du,

Xinyu Guo*

Xinyu Guo*

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20 Let's Consume the Green to

Save the Environment!-A

Comparative and Critical

Discursive Perspective on

Green Advertisings

Liu Shubo, Min-

Ren Yan, Anqi

Song

Liu Shubo

1.

Knowledge Diffusion Path Generated by Technological Collaborators: The

Exploratory Case of the Advanced Coal Technology Consortium

Ben Zhang (Corr.)

Ph.D., School of Management, Huazhong University of Science and Technology, P. R. China

Lei Ma

Professor/Ph.D., School of Public Affairs/School of Intellectual Property/Center for Innovation and

Development, Nanjing University of Science and Technology, P. R. China

※ Corresponding author should be indicated as in, for example, Gildong Hong(Corr.).

Abstract

Purpose/ Research Question: The study is aim to explore the knowledge diffusion path in

international technological cooperation especially in large-scale demonstration project. Three

research questions are considered based on the literature. First, how is the knowledge of project

formed and perceived? Second, what approaches of collaboration in project can be adopted for

knowledge diffusion? Third, how can the knowledge diffuse without infringing upon the

intellectual property?

Key Literature Reviews (About 3~5 papers): There are many literature talking about the

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knowledge diffusion or similar concepts which are in different ways of expression, such as

knowledge sharing, knowledge transfer or knowledge learning. Some research on knowledge

diffusion path is mainly based on path dependence theory. The common knowledge diffusion

path exists in the dissemination of literature (Yu et al., 2014). Therefore the characteristics of

knowledge diffusion can be represented by the references in the literature (Liu et al., 2016). Greve

et al. (2015) believed that innovative product and technology will have path dependence effect in

the process of diffusion, regardless of the success or failure in the product marketing or

technology application. However, this effect may be different in developed and developing

countries (Briggs et al., 2015). And the diffusion path will develop if knowledge has evolutions,

which makes the new technologies spreading faster and broader (Simmie et al., 2014). Mercure et

al. (2014) studied the proliferation of low-carbon technologies and argued that global climate

policy had a significant impact on the adoption of low-carbon technologies in the international

power industry. But the policy is only one factor affecting technology transfer, and knowledge

management and organizational structure are also important factors (Dosi et al., 2013). Park et al.

(2018) analyzed the energy management system in an open innovation view, and found that the

information sharing in the system affects the electricity consumption (Park et al., 2018).

Design/ Methodology/ Approach: To resolve the problem of knowledge diffusion which is

proposed in this study, we apply the embedded analysis for exploring the multilevel of knowledge

diffusion and how this process works. The embedded analysis of case study can refine and detail

the research questions, thus more revelations for resolving the overall research question of “what”

and “how” may be obtained (Yin, 1989). Hilt et al. (2007) proposed the “Level of Theory” and its

relative methodology for management research, which contributed to addressing practice

problems in many fields. By a way which is similar to hypothesis-testing research, the theories can

be constructed based on the questions that are distilled from practical situations (Eisenhardt,

1989). Bresman (2013) proposed a process model which is studying on vicarious learning, and this

model includes four sub-process. In the domain of innovation research, the multilevel includes

individual, group/team, organization or industry (Gupta et al, 2007). The research on cross-

business-unit provided many effective enlightenments for studying the knowledge diffusion, and

the most important one of them is to examine the collaborations performance and analyze the

key incentives in the case study (Martin et al, 2007). In this study we choose the Advanced Coal

Technology Consortium (ACTC) as the research sample which contains 10 cases came from 8

different technological themes. The data source is mainly from the Project Fact Sheets (PFS) in

Phase I of the ACTC. And there are also some other data sources including the Memorandum of

Agreement (MoA), Technology Management Plan (TMP), Agreement on IP (AoI) and the press

reports of the ACTC. All these data is available at the website of U.S.-China Clean Energy Research

Center (CERC).

(Expected) Findings/Results: Based on the literature review and theoretical model of three

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dimensions, three propositions proposed in this study are expected to be verified, which are also

corresponding to the three dimensions as following: (1) Proposition 1. At the R&D activity

dimension, the knowledge produced in project is came from both the participants’ own R&D

activities, which becomes the basis of joint tasks of collaborative project by perceiving the

connection between these knowledge and research objectives. (2) Proposition 2. At the

collaboration activity dimension, the knowledge that satisfies the research objectives and mutual

interest of all parties diffuses more intense based on the external collaborative network, and in

turn it means that the issues without jointly concerning by the participants can lack the process of

knowledge diffusion. (3) Proposition 3. At the intellectual property dimension, the knowledge of

intellectual property rights is diffused more than the knowledge of technology, and the

intellectual property mechanism is an important consider factor of participating the cooperation

of international joint research project.

Research limitations/ Implications: The main limitations in this study are as follows. First, t

he single case can indicate some patterns for the knowledge diffusion path in the activit

ies of technological collaborators, but the conclusions are still need more proofs to supp

ort. For this limitation we plan to investigate in the ACTC and prepare the following em

pirical research. Second, this study mainly focus on the generation mechanism of knowle

dge diffusion and perform the analysis based on only three dimensions, thus it is possib

le that some important dimensions are ignored. For this limitation we plan to study som

e new perspectives in terms of policy dimension or industry dimension.

Keywords: Technological Cooperation, Knowledge Diffusion, Large-Scale Demonstration Project,

Technology Transfer

Reference

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literature: A main path analysis. Journal of Informetrics, 8(3), 594–605.

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2.

Smart City Governance with Sustainable System Development Framework-An Empirical Study of Taipei City

Min-Ren Yan

Professor, Department of International Business Administration, Chinese Culture University, Taiwan

Cheng-Sheng Pong Commissioner, Department of Public Works, Taipei City Government, Taiwan

Ahmad Hadavi

Clinical Professor, Department of Civil and Environmental Engineering, Associate Director, Master of Project Management Program, Northwestern University, USA.

Abstract

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The “smart city” concept is helping countries develop strategic blueprints for transforming their economies. Many countries have started smart city development plans and became vanguards in the smart city movement. Two good plans include the “smart grid” in the United States and the “intelligent energy management” program in Amsterdam. A “smart city” is an innovative urban area that uses information and communication technology (ICT) and other means to improve a city’s quality of life, operational efficiency, and competitiveness. A “sustainable” smart city also strives to meet the economic, social and environmental needs in the present and works on plans to meet those same needs in the future.

Analyses of different definitions of the term “smart city” reveal that different definitions emphasize different needs. Therefore, governments and stakeholders need to work together to develop a common understanding of what “smart city” means in their specific national and city-level contexts. In this paper, the authors use a holistic viewpoint and cross-regional data analyses to sample the case of Taipei City, Taiwan, as an empirical study. The goal is to investigate the principles and practices of smart city governance and sustainable infrastructure management.

The smart city concept offers different opportunities for different countries. The immediate need for cities in developing countries is to provide adequate urban infrastructure to meet the increasing pace of urbanization. In the process of meeting infrastructure demands, smart infrastructure applications provide a way for such cities to achieve leapfrogging in technology. In developed countries, the challenge is often to maintain legacy infrastructure systems, which cannot be abandoned due to cost, space, and other considerations. In such countries, smart city applications may focus more on facilitating the optimal use of existing infrastructure resources and monitoring the operations of such legacy resources. However, in both developing and developed countries, the primary goal of smart infrastructure applications should be that they respond to the sustainable development needs of society.

Smart infrastructure provides the foundation for all of the key themes related to a smart city, including smart people, smart mobility, smart economy, smart living, smart governance, and smart environment. The core characteristic that underlies most of these components is that they are connected and that they generate data that may be used intelligently to ensure the optimal use of resources and to improve performance. Accordingly, a “framework” that combines smart city governance and sustainable infrastructure management is crucial for supporting the vision of a smart city. Facilitating such a vision requires a framework that combines open innovation and the features of a “knowledge city” to support the development of the concepts of smart mobility, smart environment, smart people, smart living, smart governance, and smart economy. Using this framework, a smart city can develop from the growth of knowledge city infrastructure and from a combination of open innovation strategies and policies.

Previous studies have proposed that some urban regions have reached the limit of their economic growth and have physically deteriorated because of a capitalist economy and urban development. To overcome the obstacles to growth, cities should be armed with knowledge and ideas that encourage open innovation. Open innovation will support a knowledge city to build a creative economy within a national innovation system that follows an open innovation paradigm. Furthermore, the combination of open innovation and knowledge-based urban development can lead to a smart city. When open innovation dynamics (which are based on knowledge city infrastructure concepts) occur at the micro and macro levels, a smart city will appear and grow (Yun, 2015). A smart city is very livable and economically prosperous owing to smart thinking and smart infrastructure, such as that found in an IT-based city.

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In the benchmarked case, the Taipei City government is developing the smart city concept. The government is developing smart solutions that satisfy public demands by building an “innovation matchmaking” platform that combines industry and government resources. Smart city development in Taipei City is aimed at promoting public participation and public-private partnership in order to create new technologies, innovative applications, and data/information to solve citizens’ problems. Before executing smart city concepts, officials should incorporate public suggestions into the decision-making process. There is a wealth of such input available from smart city seminars. Taipei City aims to construct an eco-system that the government, the industry, and citizens can benefit from. The first task in the blueprint is to establish a matchmaking platform that allows citizens access to necessary innovative technologies. This platform will gradually open up opportunities in many fields. As a result, this platform will allow industry to do trials and turn the city into a “living lab.” Ultimately, citizens will benefit from services that are “more intelligent” than current services, as well as smart city governance and sustainable infrastructure management. To develop policies, city officials will deliver empirical studies and comprehensive reviews of smart mobility, smart environment, smart people, smart living, smart governance, and smart economy.

The main goal of the proposed research is to introduce a system for smart city governance. This system would be a collaborative decision support system for urban infrastructure maintenance management. The proposed research would use case studies and applications of computer simulations.

Periodic infrastructure maintenance and rehabilitation programs are essential for efficiently managing large networks of infrastructure assets and sustaining their safety and operability for public services. Deterioration of an infrastructure system can affect industrial productivity, quality of life, and the regional economy. An effective urban infrastructure maintenance system helps advance smart city governance and facilities. Previous studies proposed that a major challenge for asset managers is to determine how to preserve the performance of rapidly deteriorating infrastructure over a long service life. The process of adequately budgeting and planning infrastructure rehabilitation programs is very important in achieving this objective. The management of infrastructure deterioration and rehabilitations has been extensively studied and a number of optimization models have been introduced for different assets. However, there is a need to further develop a collaborative urban infrastructure maintenance system from a holistic perspective instead of through considering individual contracts of maintenance projects. Handling deterioration and rehabilitation, budgeting for maintenance cost, and adequately maintaining public services are critical factors to consider in improving the design and management of an urban infrastructure maintenance system. The challenge to align effective urban infrastructure maintenance management decisions with the level of quality of public service, budget, and operations management is complicated. Combining systematic planning with life cycle cost management is needed. So is combining applications with simulation-based scenario analysis.

In order to better integrate computer models and real-world maintenance management practices, the Smart Flood Management System of Taipei City and the infrastructure as well as relevant facilities will be benchmarked for data collection and case studies. System Dynamics modeling and simulation techniques will be used as complementary methodologies for quantitative analysis and analysis of computer-aided scenarios. Strategy Dynamics principles such as resource-based planning, model-based management, and time-phase performance evaluation are also incorporated for improving strategic thinking related to

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maintenance management decisions. It is expected that the proposed model can practically support the dynamic risk analysis and life cycle cost assessment for strategic maintenance management decisions. Compared with previous studies, the research results would have the potential to contribute benefits for infrastructure maintenance management practices. The research results would: (1) systematically evaluate structural causes and the schedule/cost variances of maintenance plans (so as to enable better early warning systems); (2) dynamically support risk analysis and life cycle cost management (so as to enhance the performance of infrastructure maintenance); (3) strategically integrate resources from executive management and their management decisions with applicable simulation-based scenario analyses (so as to support rational expectations on maintenance project performance). In addition, the proposed model, which is a scientific and structured decision support system, can also help train the management team and teach engineering and management lessons. Keywords: smart city, infrastructure, sustainability, open innovation, knowledge city, systems thinking, policy.

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3. Leydesdorff, L., Ivanova, I., “Open innovation” and “triple helix” models of innovation: can synergy in innovation systems be measured? J. Open Innov. Technol. Mark. Complex. 2016, 2(3), 11.

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10. Yun, J.J.; Won, D.; Park, K.; Yang, J.; Zhao, X. Growth of a platform business model as an entrepreneurial ecosystem and its effects on reginal development. Eur. Plan. Stud. 2017, 25, 805–826.

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3.

A Study on the education using intelligence information technology on 4th

industrial revolution

Eun Soo Choi

Master Course of Medical IT Marketing, Eulji University, Seongnam 13135, Korea

Hang Sik Park

Prof of, Eulji University, Seongnam 13135, Korea

Min Soo Kang

Prof of Medical IT, Eulji University, Seongnam 13135, Korea

Abstract

The 4th Industrial Revolution will bring about great changes not only in the mental labor of human beings, but also in the decision-making process. The key keywords of the 4th Industrial Revolution are Hyper-Connectivity, Intelligence, and Automation. In other words, the 4th Industrial Revolution is not limited to simple calculation, accumulation and exchange of information, but it is promoting autonomy of everything connected through intelligence based on calculation, communication and convergence of mass information through full real-time connectivity. It is clear that artificial intelligence and the 4th industrial revolution will fundamentally change society. One of the areas where the biggest changes are expected is education. A new society needs new talent and then new education is needed. In this paper, I have looked at the three keywords of individualization, experience, and robotization of how education will change in the 4th industrial revolution era. In the age of the 4th Industrial Revolution, education with three keywords is expected to be more efficient and effective.

Keywords: 4th Industrial Revolution, Education, Intelligence Information Technology

Introduction Currently, we are living in the era of the 3rd Industrial Revolution where automation and information

technology are carried out based on computer and ICT technologies. If the 1st and 2nd industrial revolutions were the process of replacing a person's physical labor with a machine and maximizing productivity, the 3rd industrial revolution created the foundation of the knowledge-service industry by enabling automation and informationization based on digital technology and replacing part of the mental labor such as human computing power with machines. The emergence of 4th-generation AI, which can acquire data on its own, learn knowledge, reason, and communicate with humans with natural language processing capabilities, signals a revolutionary change in what is called the 4th industrial revolution, along with big data built in major sectors of society. The

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4th industrial revolution will bring about a huge change in the way machines have replaced not only human mental labor but also the decision-making process. [1]. The term "4th Industrial Revolution" became a global issue as Klaus Schuwab, chairman of the Forum, said at the World Economy Forum held in 2016, was a social change that mankind has never experienced so far [2]. The 4th industrial revolution is expressed in different countries, and Germany’s Industrial 4.0, the USA’s Industrial Internet, Japan’s Robot Strategy and China’s Manufacturing 2015 plan are some of the examples [3]. Information and automation based on IT technology, a key keyword of the current 3rd Industrial Revolution. However, the core keyword of the 4th industrial revolution is hyper-connectivity-based automation through intelligence [4]. In other words, there is a core of the 3rd industrial revolution in the calculation and exchange of various information based on computers and the Internet. By comparison, the 4th industrial revolution is not only about the simple calculation, accumulation and exchange of information, but also about the calculation, communication and convergence of mass information through complete real-time connectivity and the liberalization of everything connected through the intelligence based on it [5].

It is clear that artificial intelligence and the 4th industrial revolution will fundamentally change society. One of the areas in which the biggest changes are expected is education. A new society needs new talent and a new education [6]. Korea's DIGIST strives for next-generation research and education through open innovation [7]. In this paper, we will look at three keywords of individualization, experience, and robotization.

Intelligence Information Education in the Age of the 4th Industrial Revolution In the 4th industrial revolution, intelligence information technology is expected to drastically change the

overall human lifestyle by replacing the complex decisions of human society. By replacing humans with machines, the functions and knowledge required of humans are changed, which inevitably leads to a change in education as a whole. There is also concern that intelligence information technology in the era of the fourth industrial revolution will replace human jobs and increase the educational gap. However, there is an advantage that customized education that can not be realized until now is possible. Fig. 1 shows an intelligence educational case analysis model.

Fig 1. intelligence educational case analysis model

The unique characteristics of intelligence information technology can serve as a driving force for innovation in education. By collecting, learning, and deducing various information through individualization, it is possible to provide personalized service tailored to individual. Through experience, we can strengthen the life of the real world through the fusion of the real world and the virtual world, and present experiences and experiences that have never been possible to humanity. Finally, human intelligence and physical abilities can be replaced and supplemented by intelligence information technology through robotization [8].

1) Individualization The role of the school, which is made in accordance with the demands of the industrial age, has reached the

end of its life span. In terms of educational effectiveness, private tutoring is the best education model, but it is difficult to realize in the 1: N education system [9]. However, if personalized education through intelligence

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information technology in the era of the 4th industrial revolution is realized, education can be more effective. American content company Triump Learning has developed an online learning platform called GET Waggle,

an adaptive learning technology. It analyzes each student's best achievements in what they know and how they learn, giving them advice, directions, and hints. In addition, games, review corners, grades, and other diverse and exciting features encourage students to participate in learning and induce learning naturally [10].

In Japan, Qubena developed an AI-type learning system, not a class by instructor. When the student solves the problem of appearing on the tablet screen, Qubena accumulates the thought process or time spent solving not only the student's answer, but also analyzes the problems that help students overcome difficulties [11].

Korea's customized math education service Knowre offers customized mathematics education online. If you are studying in Knowre, you can study mathematics step by step as you would play a game according to the student's level. The teacher can see at a glance what level the students have solved the problem, which learning is lacking at a glance, and can send the necessary learning steps to the students with the one-click [12].

2) Experience

Recently, possibility of immersive realistic education using various technologies such as VR (Virtual Reality), AR (Augmented Reality), hologram, and 3D printer has been highlighted [13]. These techniques enable immersion and novel effects to enable efficient learning based on experience. In addition, the virtual game-based experience enhances learner's learning motivation and enables them to learn more vividly [14].

In January 2015, Microsoft Corp. released HoloLens, an HDM (Head Mounted Display) AR device. HoloLens is defined as a device that implements the world of a MR (mixed reality) between AR such as Google Glass and VR such as Oculus Rift [15]. Microsoft has announced examples of medical education use using HoloLens. The reason why medical and healthcare fields are suitable for education and training through VR / AR is that learners must have a knowledge of complex human bodies and a high degree of proficiency to deal with life. This is an application example that saves cost and time through VR / AR, which is difficult to experience in real life, and has the advantage of realistic training through realistic risk simulation [16]. Fig 2 shows Experience-based learning based on MR technology.

Fig 2. Experience-based learning based on MR technology

Recently 3D printing technology has been attracting attention as a food for the future along with the makers

boom. 3D printers are being used throughout the education, from producing ideas to textbooks as well as producing audiovisual textbooks.

Staten Island Technical High School in Ireland conducts 3D printing of 3D CAD artifacts directly designed according to the major. Student can enhance the comprehension of your major by creating a prototype model and realizing your design tasks by 3D printing the 3D CAD results you designed.

In addition, the Department of Computer Science at Durham University in the UK introduced a 3D printer for the production of educational equipment, and 3D printing of the geological data required for geology classes was supported as a textbook. Through this, students at Durham University have been able to effectively learn the principles of faults and erosion, and the conditions under which earthquakes occur, using 3D-printed lipid data [17].

3) Robotization

16

With the development of intelligence information technology, there have been increasing cases of introducing robots to educational sites. Robots can be an alternative to meet the needs of a variety of students that have been difficult to cope with in the past, and can tailor their responses to students with disabilities or difficulties in learning [18].

The University of Nottingham in the UK conducted a pilot-test using robots for learning with students with intellectual disabilities. Studies have shown that students with intellectual disabilities have a much higher participation rate in learning with robots. In addition, the staff members who participated in the research also evaluated robots as providing students with learning disabilities a lot because they could provide students with customized learning [19].

The Lycée la Martiniére Monplaisir high school in France uses a robot as a remote teaching tool for students who can not go to school, and the students see the contents of the class through the lens of the robot and participate in discussions. Remote attendance robots can connect students to difficult or dangerous places to go directly to experience and learning similar to those in the place [20].

Conclusion One of the areas where the greatest change is anticipated as the 4th industrial revolution era approaches is

education. A new society needs new talent and then new education is needed. We looked at the three keywords of individualization, experience, and robotization of how education will

change in the fourth industrial revolution era. The role of the school made to meet the demands of the industrial age has reached the limit of its life span and it is difficult to realize it in the educational system of 1: N type. However, if individualization education is realized through individualization, it is possible to educate more effectively. Through experience, immersion and novel effect enable efficient learning based on experience. In addition, the virtual game based experience can enhance learning motivation of learners and enable more vivid learning. Robotization can be an alternative to meet the needs of various students who have been difficult to cope with in the past, and can be tailored to students with disabilities or difficulties in learning.

In the age of the 4th Industrial Revolution, education with three keywords is expected to be more efficient and effective.

Reference [1] HyeongSang Han, Hyeon Kim, "Fourth Industrial Revolution and Knowledge Service", KEIT PD Issue Report Vol 17(2), 2017

[2] Schwab, K. The Fourth Industrial Revolution; Crown Business: New York, NY, USA, 2017.

[3] WonGyu Ha, Nam Heui Choi, “The Fourth Industrial Revolution. Seoul: Content.”, 2015

[4] HangSik Park , Technology convergence, open innovation, and dynamic economy. Journal of Open Innovation: Technology, Market, and Complexity, 3(4), 24, 2017

[5] SangHun Kim, "Major Concepts and Examples of the Fourth Industrial Revolution", KIET Industrial Economic Analysis, 2015

[6] YeonGu Choi, "Prediction and Prospect of Future Education in the Age of the Fourth Industrial Revolution", Future Research Focus, 2017

[7] JinHyo Joseph Yun, Xiaofei Zhao, Tan Yigitcanlar, DooSeok Lee & HeungJu Ahn, “Architectural Design and Open Innovation Symbiosis: Insights from Research Campuses, Manufacturing Systems, and Innovation Districts.” Sustainability, 10(12), 4495, 2018

[8] Beverly Park Woolf, H. Chad Lane, Vinay K. Chaudhri, & Janet L. Kolodner. (2013). “AI grand challenges for education.” AI Magazine, 34(4), 66.

[9] ChanSeung Lee, "Vision and Achievement Strategy of School Education in 2030", 2016

[10] http://www.ciokorea.com/print/21819

[11] JiYeong Kim, "2016 Overseas Educational Trends Planning Article", Korean Education Development Institute, 2016

[12] http://www.businesspost.co.kr/BP?command=naver&num=9364

[13] JaeHong Choi, “Present and Future of Convergence Education through Virtual / Augmented Reality”, Korea Internet & Security

17

Agency, 2016

[14] Dimiter Velev, & Plamena Zlateva, “Virtual reality challenges in education and training.” International Journal of Learning and Teaching, 3(1), 33-37, 2017

[15] http://www.bloter.net/archives/227004

[16] AReum Lee, "Mixed Reality (MR) Market and Industry Trends" Convergence Research Policy Center, 2018

[17] http://it.chosun.com/site/data/html_dir/2014/12/01/2014120185031.html

[18] ByeongJo Seo, “A.I Plus Series 2017”, NIA, 2017

[19] SuJin Choi, “Educational Use of Robots ... France, United States, United Kingdom and Australia “, Korean Educational Development Institute, 2016

[20] https://www.zdnet.com/article/ohmni-a-telepresence-robot-for-technophobes/

4.

The mechanism, progress and enlightenment of National Network for

Manufacturing Innovation: What can we learn from Manufacturing USA?

Yunhao Feng

Ph.D. Candidate, School of Social Sciences, Tsinghua University, China

Jinxi Wu*

Prof., Ph.D., School of Social Sciences, Tsinghua University, China

* Correspondence

Purpose/ Research Question:

Manufacturing industry is an important embodiment of a country’s comprehensive national

strength. With the advent of a new round of industrial revolution, world powers have placed

manufacturing industry in a crucial position. After the financial crisis, Europe, the United States

and other developed countries have put forward the strategy of "re-industrialization" to cultivate

and develop advanced manufacturing industry to seize the commanding heights of a new round

of science and technology. To this end, the United States has launched a series of measures to

pool the resources of the federal government, academia and the business community to build an

advanced manufacturing innovation network to ensure that a new round of industrial revolution

takes place in the United States.

18

In March 2012, the US government announced the launch of the national network for

manufacturing innovation (NNMI) plan, in the information network, intelligent manufacturing, new

energy and new materials in key areas such as construction of manufacturing innovation research

institute (IMIs), forming a national manufacturing industry the innovation of political participation

of enterprises in the field of ecological system, so as to promote the advanced manufacturing

technology to productivity.

In September 2016, NNMI was officially renamed "Manufacturing USA". According to its website,

the United States has established 14 national manufacturing innovation institutes so far.

The key questions of this paper are:

(1) What was the starting point of the us government's policy? What's the point?

(2) Progress and results in recent years, and plans for the future.

(3) NNMI's evaluation and comments by industry, academia and government.

(4) What are the practices and practices of the made in China 2025 innovation center?

Key Literature Reviews (About 3~5 papers):

There is not much literature on the national manufacturing innovation network in the United

States. Lin xueping, Benlin and Wang xiaoming (2017) believe that the national manufacturing

innovation network strategy of the United States is an important development plan that affects

the "reindustrialization" strategy of the United States, and specifically study how the American

manufacturing innovation network was born and conceived.How America's manufacturing

innovation network is built; The planning method of advanced manufacturing, the selection and

evaluation mechanism of innovation center's breakthrough technology field and so on. Ding

Minglei, Chen Zhi (2014) analyzed the plan of the background, the preliminary results and

development of resistance, sums up the national network for manufacturing innovation and the

main characteristics of manufacturing innovation center construction, based on this, advances the

United States government construction revelation of national network for manufacturing

innovation and policy recommendations to promote the development of China's advanced

manufacturing industry. Sun hengmei (2015) started from the establishment, development and

characteristics of the U.S. manufacturing innovation network plan, analyzed the influence and

inspiration of the U.S. manufacturing innovation network plan on China's advanced manufacturing

industry, aiming to find the path for China's advanced manufacturing industry to improve its

technological innovation ability.

Design/ Methodology/ Approach:

This study mainly adopts literature research method and case study method. Since August 2012,

when the first additive manufacturing innovation institute was identified as the leading innovation

institution of "manufacturing USA", the United States has successively established 14 innovation

institutions in different industrial technology fields. Each of these innovative institutions has a

different focus on technology. This study will focus on these innovation institutes focusing on

19

different technical fields, and specifically analyze the operation mechanism of some representative

innovation institutes to explore the "commercial transformation power of innovative technologies"

in the United States.

(Expected) Findings/Results:

Innovation is a complete chain, technology development and technology transformation is a

continuous process, technology must form a commercial application or into large-scale

production, in order to complete an innovation process. There is a gap between technology and

productivity, a gap between basic research and commercial production known as the valley of

death. How to transform the achievements of basic research into the commercial application of

new technologies is an obstacle that manufacturing industry must overcome to solve the

innovation problem.

While the United States still leads the world in basic research, scientific discovery, and the creative

spirit, because most of the research and development of large companies is focused on short-

term projects, there is a lack of specialized institutions like bell LABS in the early days and

fraunhofer institute in Germany to do transformational research.The "gap" between them has

caused great waste of innovation investment in the United States, and the manufacturing industry

lacks sufficient profit drive. The deep reason is mainly because the manufacturing technology is

relatively complex, and the investment risk is high, the enterprise research and development tends

to "short-term, flat and fast" projects. Therefore, the United States not only needs some

institutions to specialize in transformational research, but also needs to integrate the resources

and advantages of industry, academia, government and other aspects, brainstorm and participate

together.

As for how to bridge this gap, the countermeasures proposed by the United States are to

establish manufacturing innovation institutions in cooperation between industry and academia.

Through the "handshake mechanism", the government is responsible for the first half and the

second half to the industry.

This study will focus on the overall planning, progress and effectiveness of the national

manufacturing innovation network of the United States, as well as the cooperation mode within

the innovation network, fully absorb and learn from the United States' practice of developing

advanced manufacturing industry, and provide some reference for China to achieve the "China

manufacturing 2025" strategic goal.

Research limitations/ Implications:

Manufacturing USA is expected to play an active role as a bridge between innovative R&D and

advanced manufacturing in the United States. Through this program, innovative technologies of

scientific research institutions can be transformed into advanced manufacturing products by

enterprises.

America's national network for manufacturing innovation has much to offer. The United States

20

plans to establish 45 NNMI innovation institutes by 2025. It remains to be seen what

breakthrough areas (MTA) innovation institutes will be established and how the MTA system

framework will be continued.

Keywords: Manufacturing USA, NNMI, innovation strategy, industrialization

Reference

[1] Jia wei, Liu runsheng. Preliminary design scheme of American manufacturing innovation

network [J]. Chinese science, 2013(8):24-26.

[2] Lin xueping, Ben Lin, Wang xiaoming. Analysis on the national manufacturing innovation model

of the United States [J]. China industry review, 2017(10).

[3] Ding minglei, Chen zhi. Enlightenment and Suggestions on the construction of national

manufacturing innovation network in the United States [J]. Scientific management research,

2014(5):113-116.

[4] Huang jian. Enlightenment from American institute of manufacturing innovation [J]. High-tech

and industrialization, 2015, 11(3):56-59.

[5] Ma jun, Zhang wenkui, et al. Operation mode and inspiration of American manufacturing

innovation center [J]. Development research, 2017(2):4-7.

[6] Zhang hengmei. Research on the path of improving technological innovation ability of China's

advanced manufacturing industry -- based on the influence and inspiration of American

manufacturing innovation network plan [J]. Scientific management research, 2015(1):52-55.

21

5.

The influence of R&D collaboration structure on open innovation

performance in bio-pharmaceutical industry - focus on inter-organizational

implementation

Eungdo Kim

Assistant Professor, Department of Health Science Business Convergence, College of Medicine,

Chungbuk National University, Republic of Korea

Kwangsoo Shin(Corr.)



22

Abstract

Purpose/ Research Question

This study aims to analyze whether the R&D collaboration structure and structure of biopharmaceutical firms affect the performance of R&D stage (early stage/late stage). Based on the theory of inter-organizational Implementation by O'Toole & Montjoy, we will analyze how different collaboration structures among different institution affect performance.

Key Literature Reviews

1. Concept and type of R&D collaboration

R&D collaboration is a special relationship between at least two R&D actors, which is a mere market transaction for the creation, acquisition, exchange and utilization of technical knowledge. It is a kind of strategic alliance between participating institutions, (Chesbrough, 2003; Hagedoorn & Schakenrad, 1994; Kim Hyunmin et al., 2013; Kim & Kim, 2018; Kim & Kim, 2018). The main features of R&D collaboration are two or more organizations that collaborate and work through various types of collaborative effects, and their effects are most effective in improving the efficiency or efficiency of complementing the shortcomings of participating organizations (Shin et al., 2018). In addition, it is important to understand the relationship between the two countries.

R&D collaboration types can be categorized by various criteria. The results of this study are summarized as follows: (1) The level R&D collaboration, type of collaboration, type of R&D skill, R&D human resources capacity, number of cooperation, cooperation period, entrepreneurship, (Hagedoorn, 1993; Dowling & McGee, 1994; Shan, Walker & Kogut, 1994; Cooke, 1998; Ahuja, 2000; Freel, 2000; Shin et al., 2018)

2. Relationship between R&D collaboration performance system and performance

Through research and development cooperation activities, it is possible to share and combine mutually complementary resources among the cooperation partners, to enhance the accessibility of the key information of partner organizations, and to promote economies of scale and time, (Nieto & Santamaria, 2007; Lee, 2010; Yun et al., 2016, Lee et al., 2018), which has a positive impact on R&D performance. It is important to note that when research collaboration is viewed as an organized study among multiple research performing entities, it refers to the collective network of relationships or relationships of multiple research subjects, and performance. Therefore, it is necessary to examine existing researches on the effect of the difference in structure and relationship between the research cooperation partners on the research development performance.

Argyers & Silverman (2004) analyzed the relationship between innovation performance and the decentralized organization of research and the centralized organization of research in 71 companies, R&D activities can lead to more innovation than the result of more dispersed R&D activities. Lee and Bozeman (2005) analyzed the relationship between research collaboration and publishing productivity using the Two-Stage Least Squares (2SLS) method using questionnaire data from 443 researchers belonging to the US university research institutes. As a result of the analysis, there was a significant positive relationship between the number of collaborative researchers and the number of published

23

SCI papers, but there was no statistically significant relation with the number of cooperating researchers when the dependent variable was the ratio of SCI papers divided by the number of co-authors . Huang et al. (2010) analyzed the role that the structure of 167 companies (manufacturing plants) made possible the development of mass customization. In other words, as a result of the empirical analysis of whether the organic structure (faltness), dispersion type structure, and multi-functional structure played a role in leading to mass customized production, organic structure can affect mass custom production capacity.

3. O'Toole & Montjoy's Inter-organizational Implementation Theory: Interdependence Types and Performance

From the viewpoint of organizational theory, the interaction patterns among the individual organizations and the structural arrangement mode that governs the relationship types and the organization of the tasks can be influenced in carrying out the tasks cooperatively among the plural organizations. O'Toole & Montjoy (1984), on the basis of Thompson's (1967) interdependence, discusses inter-organizational interaction patterns as "pooled interdependence," "sequential interdependence, and "reciprocal interdependence".

First, "pooled interdependence" is a form in which individual organizations are required to make efforts to achieve each task, but there is little interaction between the organizations. In other words, there is no correlation between unit tasks and each participating organization contributes independently to achieving a common goal (O'Toole & Montjoy, 1984).

Second, "sequential interdependence" means that a series of related organizations are formed and arranged as a united system, so that the activities of one organizational unit are reintroduced to other organizational units and linked to the execution of the tasks (O'Toole & Montjoy, 1984). Therefore, each organization exchanges necessary resources and mutual influences to perform their tasks, and the degree of interdependence becomes higher than the type of "interdependence". In other words, "sequential interdependence" is a case where the output of one organization becomes the input of another organization. Under this type, delay or interruption at one point may affect the subunits in a chain.

Third, "reciprocal interdependence" refers to the interaction of the activities of various organizations at the same time in order to carry out a task, and mutual bargaining and mutual adjustment occur between the organizations (O 'Toole & Montjoy, 1984). In the case of a high level of interdependence, it is necessary to tie the constraints and networks of the organizations to each other and adjust to the coordination process. Therefore, the coordination for cooperation is high and uncertainty is high. Therefore, (Thompson, 1967).

24

In other words, according to O'Toole & Montjoy (1984), inter-organizational implementation has a risk due to constraints on coordination and lack of incentive for mutual cooperation. , But it can be seen that there are different organizational behaviors by structural type. Therefore, it is suggested that the design and management of the performance system considering characteristics of each type of R & D cooperation is needed because the research development performance may be different according to the coordination mode of inter-organizational relations.

Design/ Methodology/ Approach

Table 1. Description of variables used

Variables Definition Data Source

Control variables Internal

Age Age of firm Compustat R&D Intensity R&D expenditure divided by sales Compustat

Firm size External HHI

Full-time-equivalent(FTEs) employees Herfindahl-Hirschman index

Compustat

Collaboration Strategy Structure Independence

pooled interdependence sequential interdependence reciprocal interdependence

Metrack

Stage (Drug development phases)

Early : Discovery, Formulation, Lead molecule, Preclinical

Late : Phase I, Phase II, Phase III

Metrack

Partner University Research Institute Firm

Metrack

Firm Performance Innovation Performance Patent

New Product Lexis Nexis Medtrack/Orangebook

Financial Performance Revenue Compustat

Expected Findings/Results

This study aim to analyze firm’s collaboration structure, stage of collaboration and collaboration partner affect firm’s innovation and financial performances and expected to find three important findings; (i) which collaboration structure affects firm’s innovation/financial performance, (ii) In which stage of firm’s collaboration affects Firm’s innovation/financial performance. (ii) which collaboration partner has higher impact on Firm’s innovation/financial performance.

25

Research limitations/ Implications

Since R&D cooperation projects inevitably have mutual cooperation among different organization, in order to achieve higher results through research and development cooperation, it is necessary to maximize the synergy effect from collaboration by considering structural characteristics of collocation, stage of collaboration and collaborative partner. Therefore, this study seeks to explore the causality that can be attributed to the characteristics of the types in which the R&D project is structured, by structuring the structural characteristics of collaborations in R & D cooperation and analyzing the impact on firm performance by type. Keywords: R&D collaboration, inter-organizational implementation, collaboration structure, innovation performance, financial performance Reference Kim, S.Y.; Kim, E. “How Intellectual Property Management Capability and Network Strategy Affect

Open Technological Innovation in the Korean New Information Communications Technology Industry.” Sustainability 10 (2018): 2600.

Kim, H.; Kim, E. “How an Open Innovation Strategy for Commercialization Affects the Firm Performance of Korean Healthcare IT SMEs.” Sustainability 10 (2018): 2476.

Lee, J.H.; Sung, T.-E.; Kim, E.; Shin, K. “Evaluating Determinant Priority of License Fee in Biotech Industry.” J. Open Innov. Technol. Mark. Complex 4 (2018): 30.

Shin, K.; Lee, D.; Shin, K.; Kim, E. “Measuring the Efficiency of U.S. Pharmaceutical Companies Based on Open Innovation Types.” J. Open Innov. Technol. Mark. Complex 4 (2018): 34..

Shin, K.; Kim, E.; Jeong, E. “Structural Relationship and Influence between Open Innovation Capacities and Performances.” Sustainability 10 (2018): 2787.

Yun, J.J.; Won, D.; Park, K. “Dynamics from open innovation to evolutionary change.” J. Open Innov. Technol. Mark. Complex 2(2016): 7.

Banerjee, Tannista, and Ralph Siebert. "Dynamic impact of uncertainty on R&D cooperation formation and research performance: Evidence from the bio-pharmaceutical industry." Research Policy 46.7 (2017): 1255-1271.

Ding, Min, Jehoshua Eliashberg, and Stefan Stremersch. INNOVATION AND MARKETING IN THE PHARMACEUTICAL INDUSTRY. SPRINGER-VERLAG NEW YORK, 2016.

Freel, Mark S. "Perceived environmental uncertainty and innovation in small firms." Small Business Economics 25.1 (2005): 49-64.

Paul, Steven M., et al. "How to improve R&D productivity: the pharmaceutical industry's grand challenge." Nature reviews Drug discovery 9.3 (2010): 203.

Rafols, Ismael, et al. "Big Pharma, little science?: A bibliometric perspective on Big Pharma's R&D decline." Technological forecasting and social change 81 (2014): 22-38.

Shakeri, Roya, and Reza Radfar. "Antecedents of strategic alliances performance in biopharmaceutical industry: A comprehensive model." Technological Forecasting and Social Change 122 (2017): 289-302.

Yu, Xiaoyu, et al. "Managing uncertainty in emerging economies: The interaction effects between causation and effectuation on firm performance." Technological Forecasting and Social Change 135 (2018): 121-131.

26

6.

Does administrative burden increase client payment error

27

and fraud? The case of the US Supplemental Nutrition

Assistance Program

Sabinne Lee (Yonsei University)

Kwangho Jung (Seoul National University)

<Abstract>

In this study, we analyze the impact of Simplified Reporting (SR) on unintended and intended

payment error rates caused by clients of the Supplemental Nutrition Assistance Program

(SNAP). With the adoption of Simplified Reporting, the recipients of SNAP only have to

report their economic status over an extended period and in a more simplified form, to reduce

the administrative burden of the SNAP clients and agency. But reducing the administrative

burden in the social security policy field faces the criticism that it can increase the possibility

of fraud. Although there are some studies that deal with the concept of administrative burden,

most of them are only focused on the behaviors of public officials or general citizens who are

about to decide to enter the Social Security program. These limitations not only cause

obscure differences between the administrative burden and red tape, but also cannot provide

relevant policy implications for actual recipients of social security programs such as SNAP.

Also, despite myths that reduced administrative burden can cause fraudulent behavior, there

is no empirical evidence. By running several empirical models including a Panel Fixed Effect

Model using 14-year State panel data, we conclude that Simplified Reporting and the reduced

administrative burden do not increase fraudulent behavior but rather, help clients to receive

relevant benefits from the government.

Keywords: Administrative Burden, Simplified Reporting, SNAP, Payment Error, Fraud,

Management Innovation

References

Arendsen, R., Peters, O., Ter Hedde, M., & Van Dijk, J. (2014). Does e-government reduce the administrative burden of businesses? An assessment of business-to-government

28

systems usage in the Netherlands. Government information quarterly, 31(1), 160-169.

Heinrich, C. J. (2015). The bite of administrative burden: A theoretical and empirical investigation. Journal of Public Administration Research and Theory, 26(3), 403-420.

Herd, P., & Moynihan, D. P. (2018). Administrative Burden: Policymaking by Other Means. Russell Sage Foundation.

Jilke, S., Van Dooren, W., & Rys, S. (2018). Discrimination and Administrative Burden in Public Service Markets: Does a Public–Private Difference Exist?. Journal of Public Administration Research and Theory, 28(3), 423-439.

Jilke, S., Van Ryzin, G. G., Van de Walle, S., Davis, R. S., Stazyk, E. C., Andersen, S. C., ... & Ford, M. T. (2016). The Bite of Administrative Burden: A Theoretical and Empirical Investigation Carolyn J. Heinrich 403 Responses to Decline in Marketized Public Services: An Experimental Evaluation of Choice Overload. Journal of Public Administration Research and Theory, 26(3).

Jun, M. (2018). Blockchain government-a next form of infrastructure for the twenty-first century. Journal of Open Innovation: Technology, Market, and Complexity, 4(1), 7.

Jung, K., & Lee, S. (2015). A systematic review of RFID applications and diffusion: key areas and public policy issues. Journal of Open Innovation: Technology, Market, and Complexity, 1(1), 9.

Mustapha, S. (2018). E-Payment Technology Effect on Bank Performance in Emerging Economies–Evidence from Nigeria. Journal of Open Innovation: Technology, Market, and Complexity, 4(4), 43.

Moynihan, D., Herd, P., & Harvey, H. (2014). Administrative burden: Learning, psychological, and compliance costs in citizen-state interactions. Journal of Public Administration Research and Theory, 25(1), 43-69.

29

7.

Open Innovation, and Creativity: Bureaucratic

Pathologies

Eunhyeong Park (Seoul National University)

Kwangho Jung (Seoul National University)

Hyue-Su Ha (Kyungpook National University)

<Abstract>

This study explores the possibilities and limitations of open innovation and creativity in

public bureaucracies. Basically, public bureaucracy is perceived as having low innovation

and creativity. Particularly because of existing practices and organizational culture,

bureaucrats are expected to be negatively interested in introducing new ideas.

In this environment, public bureaucracy 's interest and enthusiasm for open innovation will

also be low. This study examines how open innovation and creativity are related to public

service bureaucracy and the bureaucratic characteristics related to it. In particular,

bureaucrats have a great desire for new knowledge and ideas, but innovation does not take

place due to the many bureaucratic constraints in adopting and implementing them. This

study examines how bureaucrats view open innovation and how to promote such open

innovation. To do this, we will collect surveys and interview data for public officials of

government departments in South Korea and attempts to conduct both qualitative and

quantitative methods.

Key words: Open Innovation, Public Bureaucracy, Bureau Pathologies, and Creativity

References

30

Agger, A., & Sørensen, E. (2018). Managing collaborative innovation in public bureaucracies.

Planning Theory, 17(1), 53-73.

Bushe, G. R. (1991). Parallel learning structures: Increasing innovation in bureaucracies.

Addison-Wesley.

Caniëls, M. C., & Rietzschel, E. F. (2015). Organizing creativity: Creativity and innovation

under constraints. Creativity and Innovation Management, 24(2), 184-196.

Hirst, G., Van Knippenberg, D., Chen, C. H., & Sacramento, C. A. (2011). How does

bureaucracy impact individual creativity? A cross-level investigation of

team contextual influences on goal orientation–creativity relationships.

Academy of Management Journal, 54(3), 624-641.

Hodgson, D. E. (2004). Project work: The legacy of bureaucratic control in the post-

bureaucratic organization. Organization, 11(1), 81-100.

Jun, M. (2018). Blockchain government-a next form of infrastructure for the twenty-first

century. Journal of Open Innovation: Technology, Market, and Complexity,

4(1), 7.

Kernaghan, K. (2000). The post-bureaucratic organization and public service values1.

International Review of Administrative Sciences, 66(1), 91-104.

Lee, K., & Jung, K. (2018). Exploring institutional reform of Korean civil service pension:

advocacy coalition framework, policy knowledge and social innovation.

Journal of Open Innovation: Technology, Market, and Complexity, 4, 1-23.

Park, E., & Lee, J. W. (2015). A study on policy literacy and public attitudes toward

government innovation focusing on Government 3.0 in South Korea. Journal

of Open Innovation: Technology, Market, and Complexity, 1(2), 23.

Shao, Y., & Shi, L. (2018). Cross-border open innovation of early stage tech incubation: A

case study of forge, the first UK-China accelerator program. Journal of Open


Thompson, V. A. (1965). Bureaucracy and innovation. Administrative science quarterly, 1-20.

31

8.

Conservation plan of urban ecological park in the City of Seoul and

Identification of Habitat Suitability Model for Waterdeer

Sangdon Lee*

Prof. Environmental Sciences & Engineering, College of Engineering,

Ewha Womans University, Seoul, Korea

Abstract : Land Use Planning should be an important factor for a decision

making process in


Identify endangered species, vegetation mapping, footprints, etc

- green-networking to connect forests and wetlands nearby

- Guideline for habitat management

- Waterdeer: HSI (Habitat Suitability Index)

- MCP (Minimum Convex Polygon) for home range


32

• Lee SD and S. Kwon. 2018. Carbon Sequestration in the Urban Areas of

Seoul with Climate Change: Implication for Open Innovation in

Environmental Industry. J. Open Innov. Technol. Mark. Complex. 2018, 4,

58.

• Cooke, A and Farrell, L. 1998. Chinese Water Deer, The Mammal Society,

London and the British Deer Society, Fording bridge, pp. 1-32.

• Jung J., Y. Shimizu, K. Omasa, S. Kim and S.D. Lee. 2016. Developing and

testing a habitat suitability index model for Korean water deer (Hydropotes

inermis argyropus) and its potential for landscape management decisions

in Korea. Animal Cells and Systems

Study Areas

• Gildong Ecological Park

• City of Seoul (1999.05.20)

Size : 80,683㎡

Various habitat with wetlands

Biodiversity

Bird Observatory :

Wetland Plants

(reedbed, water dropwort, cattail)

Forests and Grasslands, etc

33

Study Areas and Methods

Habitat Suitability Index

Analyzing suitable habitat for waterdeer

Anthropogenic factors considering due to location in urban areas

34

Using GIS, each habitat variable => HIS model resulted in SI

35

Categories Habitat variable

Water(V1) Accessibility to water(≤

2km)

Food(V2) Distribution ratio of

vegetation type to feed

Cover

Hiding Cover(V3) forest tree canopy cover

Resting Cover(V4)

Aspect(southern, northern,

western, eastern,

non-aspect)

Anthropogenic

disturbance

Land Use(V5) Distribution ratio of the

development area

Road(V6) Distance to road(≤ 2km)

36

Habitat characteristics of waterdeer HSI analysis

37

Predicted SI maps of

l

38

Area and distribution ratio of SI value for habitat variables

Habitat variables average

VI (Water) 0.70 (0.0-0.8)

V2 (Food) 0.25 (0.0-1.0)

V3 (Hiding Cover) 0.17 (0.0-1.0)

V4 (Resting Cover) 0.61 (0.3-1.0)

V5 (Land Use) 0.36 (0.0-1.0)

V6 (Roads) 0.18 (0.0-0.4)

Overall mapping of HSI for waterdeer in Gangdong-gu

39

Area and distribution ratio of HSI value for Korean water deer in GangDong-gu

Habitat variables average

VI (Water) 0.70 (0.0-0.8)

V2 (Food) 0.25 (0.0-1.0)

V3 (Hiding Cover) 0.17 (0.0-1.0)

V4 (Resting Cover) 0.61 (0.3-1.0)

V5 (Land Use) 0.36 (0.0-1.0)

V6 (Roads) 0.18 (0.0-0.4)

40

> Minimum Convex Polygon with Kernel Modelling with 95%, 50%


Kernel 95 Kernel 50

Day night Whole

period Day night

Whole

period

Land use-

value Area(km2) Area(km2) Area(km2) Area(km2) Area(km2) Area(km2)

Urban area 0.00009 - - - - -

Agricultural

area 0.00018 0.00006 0.00006 - - -

Forest 0.00156 0.00078 0.00081 0.00027 0.00018 0.00018

Glass land - - - - - -

Wetland - - - - - -

Bare land - - - - - -

Waters - - - - - -

total 0.00183 0.00084 0.00087 0.00027 0.00018 0.00018

41

• Quantitative Measure for IUCN endangered species of waterdeer with

Habitat Suitability Model Identified

• Indicator species of waterdeer should be managed as a key species for

ecological conservation and identify home range

• For ecological restoration by identifying green-networking with

surrounding areas (Amenities, Forest restoration, Habitat conservation)

43

References

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in fragmented deciduous forests. Journal of Wildlife Management. 62: 1076-1085.

Brown, R. E. (1991) The Biology of Deer. Springer-Verlag, New York, Berlin, Tokyo.

Butzler, W. (1990) Grzimek's Encyclopedia Mammals. Volume five. McGraw-Hill Publishing

Company, New York.

Congalton, R. G., Stenback, J. M. and Barrett, R. H. (1993) Mapping deer habitat suitability

using remote sensing and geographic information system. Geocarto International. 3: 23-

33.

Cook, A. and Farrell, L. (1983) Chinese water deer. The British Deer Society.

Crawford, H. S. (1984) Habitat management. pages 629-646 in L.K.Halls, ed. White-tailed

deer : ecology and management. Stackpole Book, Harrisburg, PA.

Corbet, G.B. 1978. The mammals of the Palearctic region: a taxonomic review. Cornell

Univ. Press. 314pp.

44

Cooke, A and Farrell, L (1998). Chinese Water Deer, The Mammal Society, London and the

British Deer Society, Fording bridge, pp. 1-32.

Gibson, L. A., B. A. Wilson, D. M. Cahill and J. Hill(2004), Modelling habitat suitability of

the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of

southern Victoria. Austr. Biol. Conserv., Vol. 117, pp. 143-150.

Gough, L. A. and Rushton, S. P. (2000) The application of GIS-modeling to mustelid

landscape ecology. Mammal Review. 30: 197-216.

Guo, G. and Zhnag, E. (2005) Diet of the Chinese water deer(Hydropotes inermis) in

Zhoushan Archipelago, China. Acta Theriologica Sinica 25(2): 122-130.

Marchinton, R L. and Hirth, D. H. (1984) Behavior. pages 129-168 in L.K.Halls, ed. White-

tailed deer : ecology and management. Stackpole Book, Harrisburg, PA.

Jung J., Y. Shimizu, K. Omasa, S. Kim and S.D. Lee. 2016. Developing and testing a habitat

suitability index model for Korean water deer (Hydropotes inermis argyropus) and its

potential for landscape management decisions in Korea

Kim, BJ and Lee, SD (2011). Home range study of the Korean water deer (Hydropotes

inermis agyropus) using radio and GPS tracking in South Korea: comparison of daily and

seasonal habitat use pattern, J. Ecol. Field Biol, 34(4), pp. 365-370.

Kim, BJ. N. Lee and Lee, SD (2011) Feeding diets of the Korean water deer (Hydropotes

inermis argyropus) based on a 202 bp rbcL sequence analysis. Conservation Genetics

12:851-856

Koh, H.S., B.K. Lee, J. Wang, S.W. Heo and K.H. Jang(2009) Two sympartic phylogroups of

the Chinese water deer(Hydropotes inermis) identified by mitochondrial DNA control

region and cytochrome b gene analyses. Biochemical Genetics 47: 860-867.

Ministry of Environment(2006) Nature Conservation Act, Seoul.

Park, J, Kim, B, Oh, D, Lee, H and Lee, SD (2011). Food analysis of waterdeer in Korea,

Korean J Environmental Ecology, 25(6), pp. 896-845.

Schadt, S., Revilla, E., Wiegand., T., Knauer, F., Kaczensky, P., Breitenmoser, U., Bufka, L.,

45

Huber, T., Stanisa, C. and Trepl, L. (2002) Assessing the suitability of central European

landscapes for the reintrodution of Eurasian Iynx. Journal of Applied Ecology. 39: 189-203.

Schilling, A. and G.E. Rossner. 2017. The (sleeping) Beauty in the Beast – a review on the

water deer, Hydropotes inermis. Hystrix, Italian J. Mammalogy (doi:10.4404/hystrix-28.2-

12362)

Short, H. L. (1986) Habitat suitability index model; White-tailed deer in the Gulf of mexico

and south atlantic coastal plains. U.S. Fish and Wilidlife Service Biological report 82.

Thomas, J. W. (1979) Wildlife habitats in managed forests. USDA Forest Service.

Williams, K. C. and Todd, S. F. (2007) Tree seeding and sapling density and deer browsing

incidence on recently logged and mature non-industrial private forestlands in Virginia,

USA. Forest Ecology and Management. 242: 671-677.

Wilson, D. (1993) Mammal Species of the World. Second edition. Smithsonian Institution

Press, Washington.Won, C and Smith, KG (1999). History and current status of mammals

of the Korean Peninsula, Mammal Rev., 29(1), pp. 3–.33.

Won PH. 1967. Illustrated flora and fauna of Korea: mammals. Seoul: Korean Ministry of

Education.

Woo, HC, Lee, JI, Son, SW and Park, HS (1990). Ecological survey of mammals in South

Korea (IV), Ministry of Environment, Seoul.

U. S. Fish and Wildlife Service. (1981) Habitat Evaluation Procedures Handbook.

U. S. Fish and Wildlife Service. (1986) Habitat Suitability Index Models: White-tailed deer

in the Gulf of Mexico and South Atlantic Coastal Plains.

Zhang, E. (2000) Daytime activity budget in the Chinese water deer. Mammalia. 64: 163-

172.

Zhang E, Teng L, Wu Y. 2006. Habitat selection of the Chinese water deer (Hydropotes

inermis) in Yancheng Reserve, Jiangsu Province. Acta Theriol Sin. 26:49-53.

46

9.

The importance of carbon sequestration for Greenbelt region in the city of

Seoul

Sangdon Lee*

Prof. Environmental Sciences & Engineering, College of Engineering,


47

Jiyoung Choi

Ph.D. Student, Environmental Sciences & Engineering, College of Engineering,



• Urban development should lead to various changes in Land Use Plan, thus

influencing BES (Biodiversity and Ecosystem Services)

• In the urban Greenbelt areas, it is important to save forests and carbon

fixation for adaptation and ecosystem service as well as intimacy to nature

for urban residents

• This study will focus on the amount change of carbon fixation with a

InVEST model so that one can select the importance of saving greenbelt

areas. This model is a standard in REDD+ for UN climate change

adaptation

Methods

InVEST Carbon model

Land use plan, Land coverage map with carbon pool, economic value

Arc GIS Map : Natural Capital Project, and land coverage map

48

Figure 1. InVEST Carbon model Process (Natural Capital Project 2012)

Input data File type Program

Land coverage map Raster file Arc GIS Map 10.3

Carbon table Table(.csv)

Lee et al. 2015; Kim et

al. 2017; Natural

Capital Project;

Carbon price Number(7.6) EEX (European Energy

Exchange)

Carbon discount rate Number(5) Government post

Table 1. InVEST Carbon MODEL

49


Lee SD and S. Kwon. 2018. Carbon Sequestration in the Urban Areas of Seoul

with Climate Change: Implication for Open Innovation in Environmental Industry.

J. Open Innov. Technol. Mark. Complex. 2018, 4, 58.

50

Natural Capital Project. 2012. Informing Land-Use Plans in Central Sumatra.

INVEST User Guide Release +VERSION.

Vigerstol KL, Aukema JE. 2011. A comparison of tools for modeling freshwater

ecosystem services. Journal of Environmental management. 92(10): 2403-2409.

Kim JS, Kim CK, Yoo KJ, Hwang SI. 2017. A Preliminary Study for Identifying Soil

Management Area in Environmental Impact Assessment of Development Projects.

J. Environmental Impact Assessment. 26(6): 457-469. [Korean Literature]

Natural Capital Project. https://naturalcapitalproject.stanford.edu/invest/

[2018.08.29.].

Methods

Study Area

-GwaChun City and Anyang City with comparison of urban greenbelt areas

Past (1990 and 2000) and Future (2020)

Analyzing Carbon Fixation

51

Table 4. A Value of Carbon Fixation Amount and Change in Research Period

(Expected) Findings/Results:

Area

1990 2000 2030 2000-2030

Amount (Mg of C)

Amount (Mg of C)

Amount (Mg of C)

Change (Mg of C)

Rate (%)

Gwacheon-

si 649,266.44 640,774.25 536,230.04 -140,536.21

-

16.32

Anyang-si 881,866.88 833,381.81 702,775.41 -130,606.40 -

15.67

52

Table 3. A Value and Result Map of Carbon Fixation Amount and Change in

Research Period

Change of Carbon Fixation


• Quantitative Measure with InVEST Model with implication of Change of

Carbon Fixation depending on Land Use Plan

53

• In Greenbelt areas, Gwachon projected ‘8,492.19 Mg of C’ will be

reduced; Anyang, 48,485.05 Mg of C during 1990-2000; 130,606 Mg of

C (2000-30)

• Adaptation of climate change and Environmental Policy Should be most

appropriate

References

Anyang City. 2107. Major Work Plans for 2017. Anyang City Report. 20-26 [Korean

Literature]

Anyang City. Available from: http://www.anyang.go.kr/anyang/main.do. (Cited

2018 Jan 30)

Bhagabati NK, Ricketts T, Sulistyawan TBS, Conte M, Ennaanay D, Hadian O, Wolny

S. 2014. Ecosystem services reinforce Sumatran tiger conservation in land use

plans. Biological Conservation. 169: 147-156.

Choi HA, Lee WK, Jeon SW, Kim JS, Kwak HB, Kim MI, Kim JT. 2014. Quantifying

Climate Change Regulating Service of Forest Ecosystem - Focus on Quantifying

Carbon Storage and Sequestration. Journal of Climate Change Research. 5(1): 21-

36. [Korean Literature]

Gyeonggi Development Institute. 2016. A Study on the Industrial Space

Improvement and the Establishment of the Development Restriction Area.

Gyeonggi Development Institute. 251. [Korean Literature]

Gwacheon City. Available from:

http://www.gccity.go.kr/main/page.do?mCode=A030060000&cIdx=558. (Cited

2018 Jan 30)

Kim CK. 2014. Use the Framework of Ecosystem Service to Inform Sustainable

Development of Marine and Coastal Environment. J. Society for Marine

Environment and Energy. 11: 4-4. [Korean Literature]

54

Kim JS, Kim CK, Yoo KJ, Hwang SI. 2017. A Preliminary Study for Identifying Soil

Management Area in Environmental Impact Assessment of Development Projects.

J. Environmental Impact Assessment. 26(6): 457-469. [Korean Literature]

Kim TY, Kim CK, Maeng JH, Jang SJ, 2015. A Study on Strategic Envrionmental

Assessment Guideline for Site Selection of Offshore Wind Farm Project. Korea

Environment Institute. [Korean Literature]

Korea Environment Institute 2015 Development of Decision Supporting

Framework to Enhance Natural Capital Sustainability: Focusing on Ecosystem

Service Analysis. Korea Environment Institute. [Korean Literature]

Lee HW, Kim CK, Hong HJ, Roh YH, Kang SI, Kim JH, Shin SC, Lee SJ. 2015.

Development of Decision Supporting Framework to Enhance Natural Capital

Sustainability: Focusing on Ecosystem Service Analysis. Korea Environment

Institute. 2015(0): 3479-3651. [Korean Literature]

Natural Capital Project 2012. Informing Land-Use Plans in Central Sumatra.

INVEST User Guide Release +VERSION.

Nelson E, Sander H, Hawthorne P, Conte M, Ennaanay D, Wolny S, Manson S,

Polasky S. 2010. Projecting Global Land-Use Change and Its Effect on Ecosystem

Service Provision and Biodiversity with Simple Models. PLoS One. 5(12): e14327.

Roh YH. 2016. Introduction to the Estimation of Carbon Storage and Space

Distribution. Science and Technology Policy Institute. 26(5): 46-51. [Korean

Literature]

Ryu DH, Lee DK. 2013. Evaluation on Economic Value of the Greenbelt’s

Ecosystem Services in the Seoul Metropolitan Region. J. National Association for

Urban Planning and Design. 48(3): 279-292. [Korean Literature]

Sharp R, Tallis HT, Ricketts T, Guerry AD, Wppd SA, Chaplin-Kramer R, Nelson E,

Wolny S, P;wero N, Vigerstol K, Pennington D, Mendoza G, Aukema J, Foster J,

55

Forrest J, Cameron D, Arkema K, Lonsdorf E, Kennedy C, Verutes G, Kim CK,

Guannel G, Papenfus M, Tofr J, Marsik M, Bernhardt J, Griffin R, Glowinski K,

Chaumount N, Perelaman A, Lacayo M, Mandle L, Hamel P, Vogl AL, Rogers L,

Bierbower W. 2015. InVEST User’s Guide. Stanford University, University of

Minnesota, The Nature Conservancy, World Wildlife Fund.

Vigerstol KL, Aukema JE. 2011. A comparison of tools for modeling freshwater

ecosystem services. Journal of environmental management. 92(10): 2403-2409.

Yoon JJ. 2011. Development Restriction Area. 1971-2011. Korea Land and Housing

Institute. [Korean Literature]

56

10.

The effects of acquirer’s capability and dyadic knowledge characteristics on

the technological innovation performance of acquirer in M&A: A case of


Ye-Jin Lee

Master Candidate, Chungbuk National University, the Republic of Korea

Eungdo Kim

Prof., Chungbuk National University, the Republic of Korea

Kwangsoo Shin (Corr.)


Extended Abstract


In recent years, technological mergers and acquisitions (hereafter M&As) have become

important strategic tools for companies to access and utilize new external knowledge (Azan &

Huber Sutter, 2010). Scholars have examined why firms choose M&A as open innovation or

growth strategies. Companies use them to obtain complementary resources and capabilities, or to

resolve uncertainties from transactions with other companies (Shin, Kim, & Jeong, 2018). Through

57

M&A, acquirers can make their resources and capabilities more productive, and expand their

technology depth or breadth to the extent that their absorption capacity allows (Tani, 2018). In

particular, there have been many studies to predict whether open innovation through M&A will

give good rewards because of the high risks in R&D for technological innovation in science-based

industries such as biopharmaceutical industry (Hwang et al. 2017).

Previous M&A studies have focused on the impact of the absorptive capability and M&A

management capacity on the acquirer's performance from the viewpoint of the acquirer, or the

relationship of knowledge bases of two firms between the acquirer and the target (similarity,

digestibility, etc.) from the dyadic perspectives. However, despite the importance of these two

perspectives, there are a few studies from a mixed point of view. Therefore, this study focuses on

how the technological innovation performance of the acquirer appears from the mixed

perspective of the acquirer's capability's perspective and the dyadic perspective between acquirers

and targets. In addition, this study divides the technological innovation performance of acquiring

firms into exploratory innovation performance and exploitative innovation performance. This

approach can provide implications for the creation of explorative or exploitative innovation

performance, depending on the capabilities of the acquirer and the relationship of the acquirer

and the target's knowledge base.


The degree of acquisition of external knowledge is directly related to absorptive capacity

and indirectly related to innovation. Absorptive capacity of acquirer is the ability to recognize new

external knowledge, and transform and further exploit it for technological innovation.

Accumulated prior knowledge acts as absorptive capacity, facilitating the process of recognize,

transform, and exploit potentially useful knowledge that exists in the external organization.

(Hussinger, 2012)

Acquirers with a large number of M&As are more likely to acquire subsequent

acquisitions because they can develop knowledge integration process based on know-how or

organizational learning, gained through past acquisitions. Furthermore, the previous M&A

experience can be a management capability to manage the risk factors in the M&A integration

process and can positively affect the technological innovation performance of the acquiring

company. (Trichterborn, 2016)

58

Discussions on the relationship between acquirer’s and target's knowledge base can be

narrowed down to the point of similarity and differentiation between the two companies. The

similarity between the two firms can be attributed to the advantages of the initial acquiring firm's

organizational learning, which can increase the exploitative innovation performance of the

acquiring firm by operating the technological complement. However, if the similarity of the two

firms' knowledge bases is high, acquirers may disperse the resources and capabilities for

management by M&A, not complementing useful knowledge and technologies, and further

reduces the technological innovation performance of the acquiring company. (Carayannopoulos

and Auster, 2010)

On the other hand, the differences of knowledge-base between the acquirer and the

target enables exploratory innovation, enabling acquirers to explore in the knowledge-domain

that they do not already have. But, the bigger the differentiation, the more difficult it is to learn

and it can hinder the technological innovation performance of the acquiring company. (Makri,

2010)

Knowledge dimension of integrated operations by investigating the exploitation of

existing practices and the exploration of new possibilities in complex adaptive processes. Findings

of this study suggested a complex interplay between the exploitation of already existing practices

and local adaptations emerging from processes of interactions, and ambidexterity in integrated

operations. (Bento, 2018)

Finally, it is necessary to consider various endogenous and exogenous factors in the

context of 1) open innovation strategy, 2) industry condition, and 3) time scope to find out the

concrete open innovation effects on firm performance. Yun, et al. (2017) found out that the

dynamics of open innovation is not just an inverted U-curve but also fluctuated, and diverse,

according to several factors including the open innovation strategy, the specific industry, and the

time scope for analysis.

Design/ Methodology

[Research Design]

59

[H1a] The acquirer’s technological capability has a positive effect on the technological explorative

innovation performance after M&A.

[H1b] The acquirer’s technological capability has a positive effect on the technological exploitative

innovation performance after M&A.

[H2a] The acquirer’s M&A management capability has a positive effect on the technological

explorative innovation performance after M&A.

[H2b] The acquirer’s M&A management capability has a positive effect on the technological

exploitative innovation after M&A.

[H3] The similarity of knowledge-base between acquirer and target has an inverted U relationship

with the exploitative innovation performance.

[H4] The difference of knowledge-base between acquirer and target has an inverted U relationship

with the explorative innovation performance.

[Methodology]

- Method: Negative Binomial Regression

Data: Medtrack Data, WARDS Data, GPASS Data

Variables:

Variables Measurement

Dependent Variables

Technological

Innovation

Performance (t+5)

<Exploitation performance>

Number of registered patents in IPC code of acquirer’s

patents registered prior to M&A

<Exploration performance>

Number of patents registered in IPC codes excluding

IPC codes of acquirer’s patents registered prior to M&A

Independent Variables

60

Acquirer’s Technological

Capability (t)

Acquirer’s R&D intensity (R&D expenditure / revenue),

Total number of acquirer’s registered patents prior to

M&A

Acquirer’s M&A

Management Capability (t)

Number of acquirer’s M&A prior to M&A

The similarity of

knowledge-base between

acquirer and target (t)

The number of identical IPC codes that exist both in

the acquirer and target prior to M&A

The difference of

knowledge-base between

acquirer and target (t)

(The number of IPC codes that exist in the acquirer

prior to M&A) minus (The number of identical IPC

codes that exist both in the acquirer and target prior to

M&A)

Control Variables

Size (t) Log value of number of acquirer’s employee

Age (t) Number of years since acquirer’s founding

Business Diversification (t) Number of acquirer’s businesses

(Expected) Findings/Results


This study gives to implications of mixed perspectives considering the acquirer’s capabilities and

the dyadic characteristics of knowledge base of acquirer and target. Furthermore, it can give

practical instruction to M&A managers or R&D managers of acquiring company by examining

how each factor affect the technological explorative innovation performance and the exploitative

innovation performance of them. However, although this study focuses on technological M&A, it

can be not known whether the purpose of M&A is friendly or hostile through data. Due to these

limitations, attention should be paid to the interpretation of this study.

61

Keywords: Technological M&A, Exploitative innovation performance, Explorative innovation

performance, Acquirer’s capability, Dyadic characteristics in M&A, Biopharmaceutical industry

Reference

Azan, W., & Huber Sutter, I. (2010). Knowledge transfer in post-merger integration management:

case study of a multinational healthcare company in Greece. Knowledge Management

Research & Practice, 8(4), 307-321.

Bento, F. (2018). Complexity in the oil and gas industry: a study into exploration and exploitation

in integrated operations. Journal of Open Innovation: Technology, Market, and

Complexity, 4(1), 11.

Carayannopoulos, S., & Auster, E. R. (2010). External knowledge sourcing in biotechnology through

acquisition versus alliance: A KBV approach. Research Policy, 39(2), 254-267.

Hussinger, K. (2012). Absorptive capacity and post-acquisition inventor productivity. The Journal of

Technology Transfer, 37(4), 490-507.

Hwang, B. Y., Jun, H. J., Chang, M. H., & Kim, D. C. (2017). A case study on the improvement of

institution of “High-Risk High-Return R&D” in Korea. Journal of Open Innovation:


Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness,

and invention outcomes in high technology mergers and acquisitions. Strategic

Management Journal, 31(6), 602-628.

Shin, K.S., Kim, E.D., Jeong, E.S. (2018) Structural Relationship and influence between Open

Innovation Capacities and Performances. Sustainability, 10(8), 2787.

Tani, M., Papaluca, O., & Sasso, P. (2018). The system thinking perspective in the open-innovation

research: A systematic review. Journal of Open Innovation: Technology, Market, and


Trichterborn, A., Zu Knyphausen‐Aufseß, D., & Schweizer, L. (2016). How to improve acquisition

performance: The role of a dedicated M&A function, M&A learning process, and M&A

capability. Strategic Management Journal, 37(4), 763-773.

Yun, J.H., Won, D.K., Jeong, E.S., Park, K.B., Lee, D.S., Yigitcanlar, T. (2017) Dismantling of the

Inverted U-Curve of Open Innovation. Sustainability, 9(8), 1423.

62

11.

Structural effect of cluster location of biotechnology firm on open innovation

and technological innovation performance: The case of companies in US

63


Kwangsoo Shin


Eungdo Kim (Corr.)


Extended Abstract


The bio-economy is considered a new growth engine for developed and developing countries

that focus on knowledge-based industries. Based on the cases of developed countries such as US,

UK, Germany, etc., it is a bio-cluster that is considered to be a common factor that affected the

development of the bio-industry. Of course, there is no doubt that clusters are an important

factor in the development of the biotechnology industry, although the causes themselves are

divided by the voluntary occurrence of clusters based on universities and the formation of clusters

based on national policy efforts (Su & Hung, 2009). The main reason for the bio-cluster to lead

the development of the bio-industry is the composition of the industrial ecosystem formed by

various supporting organizations including companies. The network for acquiring complementary

resources and capabilities among bio-companies in the bio-cluster is further facilitated by the

advantages of physical distance, and networks with support organizations for technology

innovation and commercialization of bio-companies such as venture capital, legal support

organization and technology transfer organization can also be made easier than other companies

not in the cluster (Cooke, 2016). Due to these various factors in technology innovation, the region

can be likely to capitalize on technological as well as organizational and institutional opportunities

that facilitated the growth of technology-based firms (Feldman & Francis, 2003).

Previous studies have addressed the advantages of cluster location of bio companies to

the firm’s innovation performance due to regional integration and efficiency. However, scholars

have mainly described the effects of clusters qualitatively, and there are a few studies that analyze

quantitatively. In addition, they have not shown in what R&D stage the cluster location is useful,

and it is available for which open innovation type. From fundamental R&D resources and

capability and open innovation strategy at the R&D stage, ultimately to technological innovation

performance of the firms, there is no comprehensive study on the effects of R&D input-process-

output and cluster location of the firm. Therefore, this study examines the effect of cluster

location of bio companies on technological innovation performance through structured analysis

64

considering R&D input, process, and output. Specifically, it considers 2 elements of R&D input,

R&D intensity, cumulative patents, 4 types of open innovation in R&D process (1. Research Inside-

out, 2. Research Outside-in, 3. Development Inside-out, 4. Development Outside-in), and patent

and new product to market as output.


Prevezer (1997) identified the forces of attraction to new companies to a cluster in

biotechnology in the U.S. as it grows. He found that the main agent of attraction to new firms to

enter the biotechnology industry is the presence of a strong science base at that location. In

particular, it found that there is positive attraction and feedback of sectors in the biomedical

industry – namely the therapeutics, diagnostics and the equipment/research tools sector than in

other sectors of the industry – chemicals, food, agriculture. Cooke (2002) also found that the main

organization for bio cluster formation is a university or a research hospital for clinical trials, which

is a characteristic of the science-based industry of the bio-industry. Casper and Karamanos (2003)

examined the variety of linkages firms have established with university science. These included

using universities as a source of ideas for start-ups, scientific collaboration between firms and

laboratories, the role of scientists on the scientific advisory boards of firms, and the role of

universities in supplying firms with a labour market for talented scientists.

Bagchi-Sen (2004) examined the difference between collaborators and non-collaborators,

in clusters and elsewhere, in the US biotechnology industry. In doing so, the relationship among

R&D intensity, collaboration, innovation, and location is examined. Firms with higher levels of R&D

intensity are more intent on engaging in R&D alliances, especially research collaborations with

universities. Many alliances occur with scientists located outside of the local area, including other

countries. More firms located in defined clusters of the biotechnology industry engage in

collaborative R&D than do firms located elsewhere. University scientists are the main research

partners (although not necessarily locally based) and the main purpose of collaboration by a

cluster firm is access to basic research. One of the main purposes of such collaborations for a

non-cluster firm is product development. Firms engaged in collaborative R&D exhibit better

innovation performance.

Hendry & Brown (2006) presented evidence from two surveys, one national and one

regionally based, of networking patterns in UK biotechnology, focusing on how firms engage with

other small firms, with large pharmaceuticals, and with research centers, and how far these

interactions are regional, national or international in character. It suggested that where companies

do collaborate, there was a tendency for the intensity of activity to increase as the location of the

partner moves from the local to the international space. This study evaluated the obvious

interpretation that local linkages might be satisfactory for idea generation and early product

development, but that national and international connections were more important for

65

manufacturing, marketing and distribution activities. However, this study concluded that a broader

set of largely industry factors accounted for the networking patterns – namely, the

science‐technology base, research funding, firms' business models, and competitor strategies in

evolving markets.

Segers (2016) observed that biotechnological firms have been engaged in open

innovation for a long time by clustering and intensive partnering to innovate with knowledge

from inside and outside the firms. In other words, they have been engaging in open innovation.

This paper presented a case study of biotechnology clusters in Belgium and Germany. The focus

was on the interplay between new biotechnology firms, strategic alliances and open innovation,

within a regional system of innovation context.

Leydesdorff and Inga Ivanova (2016) and Leydesdorff (2017) emphasized that innovation

at the regional level is synergistic with the national level innovation system. In particular, in

science-based industries such as the bio-industry, regional innovation is very important and it can

include the triple helix model which emphasizes the university-enterprise-government relationship.

Pyka, et al. (2018) introduce a policy laboratory in which innovation processes can be

analyzed in depth to see the impact of different innovation policy instruments in-silico, using the

example of regional innovation policy. They emphasized that companies in the region are facing

competition for innovation to keep pace with competitors. The second feature is to consider the

dynamics and scarcity of related knowledge. Furthermore, they expected that successful

knowledge might be readily available to other actors in the region, as companies in one region

can observe knowledge of other companies at least to a shallow level.

Rothgang et al. (2017) found that, in case of German cluster, through analysis of network

relations, intensive networking between innovative stakeholders in the cluster regions had been

achieved. In addition, they found that the cluster increased firm-level R&D expenditure, but also

that the programme design influenced the programme impulse, e.g. by promoting additional

activities of SMEs

66

Design/ Methodology

[Research Design]

[H1] Strategic alliances for technological development of bio companies will have a positive

impact on technological innovation performance of them.

[H2] Cluster position of bio companies will have a positive impact on technological innovation

performance.

[H2A] The cluster position of bio companies will have a positive impact on R&D resources and

capacity of enterprises.

[H2B] The cluster position of bio companies will have a positive impact on strategic alliances.

[H2A1] The R&D resources and capabilities of bio companies will have a positive impact on the

technological innovation performance of them.

[H2A2] The R&D resources and capabilities of bio companies will have a positive impact on

strategic alliances

[Methodology]

- Data: Medtrack Data (information of firm location, strategic alliance, new product), WARDS

Data (information of R&D intensity), GPASS Data (information of patent)

- Target Firm: Firms in US biopharmaceutical industry

- Target Cluster: Greater Boston, San Diego, San Fransisco Bay Area, Raleigh-Durham,

Philadelpia, Suburban Maryland / DC / Arilington, New Jersey / New York City, Los Angeles /

Orange County, Minneapolis-St. Paul, Seattle (10 Clusters)

- Model: Structural Equation Model

67

0 1 2 3

4 5 6

_ &

Size Age Business areait it it it

it it it it

Inno Performance R D StrategicAlliance Cluster

⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ Eq.

(1)

0 1 2

3 4 5

&

Size Age Business areait it it

it it it it

Cluster R D StrategicAlliance

⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ Eq.

(2)

0 1

2 3 4

&

Size Age Business areait it

it it it it

StrategicAlliance R D

⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ Eq.

(3)

(Expected) Findings/Results

1. [Basic Statistics] The Number of Collaboration inside/outside Cluster according to R&D

Stage and Open Innovation Type

R&D Stage Open Innovationl

Type

Collaboration inside

Cluster

Collaboration outside

Cluster

Research Stage Inside-Out # of collaboration # of collaboration

Research Stage Outside-In # of collaboration # of collaboration

Development Stage Inside-Out # of collaboration # of collaboration

Development Stage Outside-In # of collaboration # of collaboration

2. [Expected Results of SEM]

68


The results of this study are as follows: 1) the direct effect of cluster on the innovation

performance of bio-companies; and 2) the indirect effects of both R&D resources and capabilities,

and open innovation stage and type. This can help decision-making when policy makers build and

manage clusters for the development of the bio-industry. Nonetheless, this study is confined to

US clusters and does not reflect environment or institutional requirements that may vary from

country to country. Therefore, caution should be taken in interpreting the results of this study.

Keywords: Bio-Cluster, Innovation performance., Open innovation type, R&D stage,

Biopharmaceutical industry

Reference

Bagchi-Sen, S. (2004). Firm-specific characteristics of R&D collaborators and non-collaborators in

US biotechnology clusters and elsewhere. International Journal of Technology and

Globalisation, 1(1), 92-118.

Casper, S., & Karamanos, A. (2003). Commercializing science in Europe: the Cambridge

biotechnology cluster. European Planning Studies, 11(7), 805-822.

Cooke, P. (2002). Regional innovation systems: general findings and some new evidence from

biotechnology clusters. The Journal of Technology Transfer, 27(1), 133-145.

Cooke, P. (2016). The virtues of variety in regional innovation systems and entrepreneurial

ecosystems. Journal of Open Innovation: Technology, Market, and Complexity, 2(1), 13.

Feldman, M. P., & Francis, J. L. (2003). Fortune favours the prepared region: The case of

69

entrepreneurship and the capitol region biotechnology cluster. European Planning Studies,

11(7), 765-788.

Hendry, C., & Brown, J. (2006). Organizational networking in UK biotechnology clusters. British

Journal of Management, 17(1), 55-73.

Leydesdorff, L., & Ivanova, I. (2016). “Open innovation” and “triple helix” models of innovation: can

synergy in innovation systems be measured?. Journal of Open Innovation: Technology,

Market, and Complexity, 2(1), 11.

Leydesdorff, L. (2018). Synergy in Knowledge-Based Innovation Systems at National and Regional

Levels: The Triple-Helix Model and the Fourth Industrial Revolution. Journal of Open


Prevezer, M. (1997). The dynamics of industrial clustering in biotechnology. Small business

economics, 9(3), 255-271.

Pyka, A., Mueller, M., & Kudic, M. (2018). Regional Innovation Systems in Policy Laboratories.

Journal of Open Innovation: Technology, Market, and Complexity, 4(4), 44.

Rothgang, M., Cantner, U., Dehio, J., Engel, D., Fertig, M., Graf, H., ... & Töpfer, S. (2017). Cluster

policy: Insights from the German leading edge cluster competition. Journal of Open


Segers, J. P. (2016). Regional systems of innovation: lessons from the biotechnology clusters in

Belgium and Germany. Journal of Small Business & Entrepreneurship, 28(2), 133-149.

Su, Y. S., & Hung, L. C. (2009). Spontaneous vs. policy-driven: The origin and evolution of the

biotechnology cluster. Technological Forecasting and Social Change, 76(5), 608-619.

70

12.

71

13.

The impact of cluster’s open innovation types on RIS productivity: Case study of US

pharmaceutical companies

Hana Kim Ph.D., Technology Management Economics and Policy Program, Seoul National University,

1 Gwanak-ro Gwanak-gu, Seoul 08826, Republic of Korea

Eungdo Kim(Corr.)



72

Abstract


In order to have better efficiency in R&D, which has high risk, pharmaceutical companies are using various

strategies. Among them, the open innovation strategy, in which they share core techniques with other companies,

is being used in various ways. Thus, the regional innovation system (RIS) is critical as a government policy to

foster pharmaceutical companies by establishing an efficient ecosystem for organically collaborating within the

cluster. The cluster itself would have the characterized innovation type across the RIS development stage.

In the United States, there are ten representative pharmaceutical clusters. This study examines the firm's open

innovation type change according to the RIS development stage of clusters and analyzes the effect of this open

innovation type change on cluster productivity.

First, the study examines the open innovation type at each stage, dividing the 2001-2016 period of the US

pharmaceutical cluster into the evolutionary stages of the RIS as open innovation types are divided into firm

level and cluster level. The open innovation types are inbound/outbound/coupled. Second, firm level and cluster

level productivity is calculated by SFA and Meta-frontier analysis. Third, this study examines how open

innovation types affect cluster productivity and firm productivity through tobit analysis

Key Literature Reviews: The emergence of the regional innovation policy is a result of more than 40 years and

is now attracting attention as an innovation in economic development (McCann & Ortega-Argiles, 2013; Yun et

al., 2016, Yun el al., 2015, Kim & Kim, 2018). Spatial proximity and social embeddedness are locally rooted

advantages (Belussi & Sedita, 2009; Cooke et al., 2004). Previous RIS studies have emphasized local sources,

but in fact, global companies are emerging by leading knowledge transmission within RIS (Owen-Smith and

Powell, 2004). The RIS strength is overcoming the limitations of a single firm and emphasizing the advantages

of spatial proximity by supporting the building the ecosystem to collaboration. In order to maximize the

advantages of RIS, it is necessary to understand and support the collaboration that increases the efficiency of the

enterprise across RIS development stage.

Systemic innovation (SI) corresponds to the type of innovation that only generates value if accompanied by

complementary innovations. The approach has proven useful to derive intervention strategies for stimulating

innovation in sectors (Malerba, 2002), and regions (Cooke et al., 1997). A sectoral system of innovation can fail

73

(Hu and Hung, 2014) so that the government should intervene based on the productivity analysis.

Identifying the most productive collaboration for each RIS development stage would suggest RIS policy to

support collaboration to enhance cluster and firm productivity, Collaboration types can be distinguished based

on open innovation.

Open innovation means the innovation strategy by collaboration with external resources (Chesbrough, 2006;

Enkel,et al, 2009; Torres et al., 2015). Since it is hard to innovate only with internal knowledge, they need to

collaborate with external institutes. For this point of view, there have been researches, in which OI strategies are

divided into three types of inside-out, outside-in and coupled (Kou et al., 2016; Al-Refaie et al., 2018) at both

firm and cluster level. The efficient OI type would be different across RIS development stage.

Design/ Methodology/ Approach:

This study analyzes the productivity of representative ten clusters. The analysis is consisted of three steps as

follows.

Methodology 1: Analysis of clusters’ partnership behavior by different RIS stages.

Methodology 2: Analysis of clusters’ and firms’ productivity using SFA and MFA.

74

Previous studies limited the study to some global pharmaceutical companies or only small and medium-sized

pharmaceutical companies in one country. Even though data envelopment analysis (DEA) and meta-frontier

analysis can be used to analyze the OI performance, most of them used DEA (Gascón et al., 2017; Emrouznejad

& Yang, 2018; Al-Refaie et al., 2018). The research using Meta-frontier analysis is not only limited in number of

companies but also has a limited usefulness because the analysis period is short (Chen et al., 2014). This study

analyzes R & D performance depending on strategic OI types of pharmaceutical companies using meta-frontier

analysis, as Liu & Lu (2010) analyzed in terms of R&D performance using DEA.

The cluster level and firm level open innovation type is divided into inside-out, outside-in, coupled, and no

activity according to OI type and then meta-frontier analysis is performed after stochastic frontier analysis of

each group.

The efficiency of each group is measured by stochastic frontier analysis (SFA), and the efficiency comparison

between the groups is made using meta-frontier analysis (MFA).

SFA estimates technological efficiency using the frontier production function, which represents the

relationship between input and output factors as a production function and represents the maximum output

relative to input. Technical efficiency (TE) shows the relative technology level of a given firm’s actual

production compared to the frontier production function. The further away the technological level of the

company is from the frontier production function, the less efficient the company is.

In this study, we use the SFA model based on Battesse & Coelli (1992) to measure the efficiency (Battese

& Coelli, 1992).

TEit, TE of firm i at time t is given by the following equation (1):

(1)

In particular, assuming a random effects time-varying production model and assuming a production

function of the translog form:.

75

(3)

where x1it represents the amount of capital (K) at time t of the ith firm, x2it represents the amount of cost

(M) at time t of the ith firm, and x3it represents the number of employees at time t of the ith firm. In this study,

total capital stock is used for K, other expenditures is used for M, labor is used for L, and net sales is used for

output Y.

Moreover, we use a meta-frontier production function that wraps the production function of all groups to

compare the efficiency levels of other groups operating under different technical conditions (Battese & Rao,

2002). MFA has recently been applied to various industry analysis including the information and

communication technology (ICT) industry (e.g., see Yang et al., 2013; Kim et al., 2018).

Methodology 3: Tobit analysis of how the characteristics of corporate partnership behavior affect the

productivity of firms

(Expected) Findings/Results: The cluster OI type would be different in ten clusters and at the RIS development

stage. The more the inner collaboration type is the main, the higher RIS productivity would be.

Research limitations/ Implications: This study expands the open innovation concept from firm level to cluster

level by not only explored the effectiveness of cluster level OI type at the RIS development stage, but also

revealed what type of OI is effective at each cluster. However, this study has the following limitations. Because

innovation clusters exist in each state, the characteristics of firms in a particular region may be included in the

representation. Therefore, it is necessary to analyze the characteristics of the region in the further study. In

addition, small firms are not considered because open innovation is mainly performed by large firms and SMEs

are exempted from the analysis. Therefore, further research is needed on the OI strategy of SMEs.

Keywords: regional innovation system, productivity, opens innovation type, firm and cluster level,

pharmaceutical industry

76

References

McCann, P., & Ortega-Argilés, R. (2013). Modern regional innovation policy. Cambridge Journal of Regions,

Economy and Society, 6(2), 187-216.

Yun, J.J.; Won, D.; Park, K. (2016). Dynamics from open innovation to evolutionary change. J. Open Innov.

Technol. Mark. Complex, 2, 7.

Yun, J.J. (2015). How do we conquer the growth limits of capitalism? Schumpeterian Dynamics of Open

Innovation. J. Open Innov. Technol. Mark. Complex, 1, 17.

Kim, S.Y.; Kim, E. (2018). How Intellectual Property Management Capability and Network Strategy Affect

Open Technological Innovation in the Korean New Information Communications Technology Industry.

Sustainability, 10, 2600.

Belussi, F., Sammarra, A., & Sedita, S. R. (2010). Learning at the boundaries in an “Open Regional Innovation

System”: A focus on firms’ innovation strategies in the Emilia Romagna life science industry. Research

Policy, 39(6), 710-721.

Cooke, P. N., Heidenreich, M., & Braczyk, H. J. (Eds.). (2004). Regional Innovation Systems: The role of

governance in a globalized world. Psychology Press.

Belussi, F., & Sedita, S. R. (2009). Life cycle vs. multiple path dependency in industrial districts. European

Planning Studies, 17(4), 505-528.

Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of

spillovers in the Boston biotechnology community. Organization science, 15(1), 5-21.

Chesbrough, H.W. and Teece, D.J. (2002): Organizing for Innovation: When is Virtual Virtuous? (HBR Classic),

Harvard Business Review, August, pp. 127-134.

Malerba, F. (2002). Sectoral systems of innovation and production. Research policy, 31(2), 247-264.

Cooke, P., Uranga, M. G., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organisational

dimensions. Research policy, 26(4-5), 475-491.

Hu, M. C., & Hung, S. C. (2014). Taiwan's pharmaceuticals: A failure of the sectoral system of

innovation?. Technological Forecasting and Social Change, 88, 162-176.

Chesbrough, H. W. (2006). Open innovation: The new imperative for creating and profiting from technology.

Harvard Business Press.

77

Enkel, E., Gassmann, O., & Chesbrough, H. (2009). Open R&D and open innovation: exploring the

phenomenon. R&D Management, 39(4), 311-316.

Torres, L. T. R., Ibarra, E. R. B., & Arenas, A. P. L. (2015). Open innovation practices: a literature review of

case studies. Journal of Advanced Management Science Vol, 3(4).

Kou, M., Chen, K., Wang, S., & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-division

systems associated with DEA: An application to OECD countries’ national innovation systems. Expert Systems

with Applications, 46, 494-510.

Al-Refaie, A., Wu, C. W., & Sawalheh, M. DEA window analysis for assessing efficiency of blistering process

in a pharmaceutical industry. Neural Computing and Applications, 1-15.

Gascón, F., Lozano, J., Ponte, B., & de la Fuente, D. (2017). Measuring the efficiency of large pharmaceutical

companies: an industry analysis. The European Journal of Health Economics, 18(5), 587-608.

Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in

DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.

Chen, C. M., Sheng, T. C., & Yang, Y. L. (2014). Cost efficiency analysis of Taiwan biotech and pharmaceutical

industry: The application of stochastic meta frontier model. International Journal of Economics and

Finance, 6(11), 131.

Liu, J. S., & Lu, W. M. (2010). DEA and ranking with the network-based approach: a case of R&D

performance. Omega, 38(6), 453-464.

Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with

application to paddy farmers in India. Journal of productivity analysis, 3(1-2), 153-169.

Battese, G. E., & Rao, D. P. (2002). Technology gap, efficiency, and a stochastic metafrontier

function. International Journal of Business and Economics, 1(2): 87-93.

Yang, A., Lee, D., Hwang, J., & Shin, J. (2013). The influence of regulations on the efficiency of

telecommunications operators: A meta-frontier analysis. Telecommunications Policy, 37(11), 1071-1082.

Kim, H., Lee, D., & Hwang, J. (2018). The effect of online platform maturity on the efficiency of offline

industry. Telematics and Informatics, 35(1), 114-121.

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14.

Factors Affecting the Outbound Innovation Strategies: Focused on the

Pharmaceutical Industry

Insu Lee

Master Student, Technology management, economics and Policy program, College of Engineering,

79

Seoul National University, Republic of Korea

Eungdo Kim(Corr.)



Abstract

Purpose/ Research Question

The pharmaceutical industry is an area where the various open innovation strategies are activated than any other industries due to its high R&D cost, long development period, and complex knowledge required for development (E.Petrova, 2014; Shin et al., 2018). In spite of the growing importance of the licensing activities, the hurdle for the firms is that licensing out their technology as strategies of outbound innovation is quite challenging. The attrition rate between the decision to out-license a technology and the actual conclusion of the deal is nearly below 40% (Gambardella et al.2007). This results from the complexities of the activities attributed to the information asymmetry problems.

Under this circumstance, the ‘Inventive Capacity’ and ‘Desorptive Capacity’ as dynamic capabilities of firms have been the main determinants of the out-licensing propensity (Hu et al., 2015; Shin et al., 2018). Inventive Capacity refers to the firms’ capabilities to generate new knowledge inside the firms. This capacity is related to the prestige, noticeability, and visibility of the licensors to the potential licensees. The Desorptive Capacity is related to the firms’ knowledge exploitation capabilities (Lichtenthaler et al., 2009; Shin et al., 2018). The capacities which firms should build up under the open innovation systems are systematically suggested by Lichtenthaler and Lichtenthaler(2009) which is called ‘Knowledge Management Capacities’ framework. This mainstream does not include the knowledge retention capability which is called ‘Connective Capacity.’

In accordance with this trend, this research particularly focuses on the number of out-licensing decisions as a means of outbound innovation strategies by adopting the Knowledge Management Capacities framework (Lichtenthaler et al., 2009) based on the dynamic capabilities.

The objective of this study is to identify the required capabilities by pharmaceutical firms to carry out active outbound innovation. As forming a variety of partnerships including out-licensing is driving firms’ performance and survival, analysis on the determinants for out-licensing will be a guide for firms to cope with dynamic open innovation ecosystems.

The research questions of this study are as follows;

(1) What capacities does a particular firm need to have in order to be able to actively out-license to other firms?

(2) Does knowledge retention gives an actual effect on the out-licensing decisions?

80


1) Out-Licensing in Pharmaceutical Industry Out-licensing can play a critical role in accessing the diverse sources of innovation in the new pharmaceutical R&D landscape (Allarakhia et al., 2011). As mentioned above, the overall pharmaceutical industry is in the crisis of cutting down of their R&D productivities. Firms seek to lower their total costs and risks of new drug creations and shorten the time to reach market through strategic alliances and licensing agreements (E.Petrova.2014). In particular, the licensing agreements between pharmaceutical firms and research-intensive biotech firms are being active and strengthened.

As pharmaceutical firms strive to maintain their annual revenue-growth rates, they strive to focus on improving the flow of new drug candidates into their research pipeline and increasing the number of products for commercial launches each year. To achieve these objectives, a growing number of pharmaceutical firms are licensing proprietary compounds or drug discovery related technologies from other pharmaceutical firms to supplement their internal R&D efforts (Wong, 2008). On top of that, they license out their technologies or products to supplement financial resources and organizing product that have become less or less important to the firms.

In contrast to the pharmaceutical firms, the biotech firms in general lack the resources to maintain a diverse project portfolio and would often lack the downstream assets such as marketing skills, networks due to their small sizes. Therefore, licensing out their newly developed technologies are their only viable route to market, as the majority of them have no significant sales structure or marketing capacity in place. Thus, licensing fees constitute their main source of revenue (E.Petrova, 2014).

It has been demonstrated that when firms establish partnerships, including out-licensing, they have a higher success rate in the drug development process. According to Danzon et al. (2005), the inter-firms cooperation in phase 3 of clinical tests show 15% greater probability of approval compared to independent efforts. In addition, drug developed by partnerships, indicates more significant success rate in passing through the phase 2 and 3 of clinical tests.

As mentioned above, biotech firms are small and medium-sized enterprises specialized in research, so they seek out the appropriate partners such as other biotech firms or pharmaceutical firms to out-license their technologies. It makes them recover their investment costs and develop new compounds through exit strategies.

2) Previous Research on Determinants of Out-Licensing For pharmaceutical firms, they try to secure their profits by purchasing technologies from biotech firms and other pharmaceutical companies for their depleting R&D productivity. According to Motohashi (2012), the wider the R&D pipelines the pharmaceutical firms possess, the more likely they are to success at commercializing the drug compounds. Furthermore, pharmaceutical firms are also securing their profitabilities by licensing out their less important products or technologies to other firms.

However, the hurdle for the firms is that licensing out their technology as strategies of outbound innovation is quite challenging. The attrition rate between the decision to out-license a technology and the actual conclusion of the deal is nearly below 40% (Gambardella et al.2007). This results from the complexities of the activities. Previous research regarding the outbound open innovation are dissected

81

into ‘Inventive Capacity’ in the technology exchange markets and ‘Connective Capacity’ of the licensors which is theoretically first suggested by Lichtenthaler (2009).

2.1) Inventive Capacity According to Lichtenthaler and Lichtenthaler (2009), the knowledge management capacity is defined as ‘a firm’s ability to dynamically manage its knowledge base over time by reconfiguring and realigning the processes of knowledge exploration, retention, and exploitation inside and outside the organization. They built up the framework which supplements the existing absorptive capacity (Cohen and Levinthal, 1990) and also stressed out of the necessity of knowledge retention.

Inventive Capacity is defined as ‘a firm’s capability to generate new knowledge inside the firm (Lichtenthaler et al., 2009).’ Creating new knowledge is generally the outcome of perceiving opportunity or unmet needs for that knowledge. Therefore, the creation of new knowledge is affected by the firm’s existing knowledge base (Shin et al., 2018). As the new knowledge and technologies arise from the firms’ knowledge bases, this is highly reflected to the patent characteristics of firms such as forward citations, technological breadth and depth.

Licensing technologies in the technology-intensive environments across the firms are complex due to cognitive, intangible, tacit nature of technological knowledge (Hu et al., 2015). Limited transparency and inefficiencies in the technology market impede the identification of potential partners. On top of that, the process of contracting and negotiating with partners are not that easy task because of the problem of information asymmetry (Kani et al., 2012).

Under this condition of market, Inventive Capacity is related ‘prestige’ of the licensors and serve as a sign of the competencies in terms of the resources or capabilities firms possess. Gambardella et al. (2007) listed patent characteristics affecting the licensing propensity, including the generality of a technology along the spectrum of potential applications, the economic value of a technology, and patent breadth measured by technology classes covered by the patents.

There are several reasons why the Inventive Capacity of licensors makes them more attractive to the potential licensees. First, the patent stocks or famous researchers possessed by the licensors act as a “halo effect”1 that makes the licensee view the potential of the licensor's resource management capabilities or potentials. It provides the collective perceptions of potential partners with the trustworthiness and promising opportunities. This leads to the high “noticeability” and “visibility” of licensors to the licenses.

Second, licensees consider their own prestige to be higher by making transactions with licensors with higher inventive capacity. Having deal with a firm of high social status means that it is perceived as being equal trust relationship from the standpoint of other firms. For example, biotech firms borrow prestige of well-known large pharmaceutical firms by forming partnerships (Ruckman et al.2016). In sum, licensors with high Inventive Capacity will have a higher chance of out-licensing because licensees are more likely to recognize of and be attracted to them due to the increases in noticeability,

1 This effect is a cognitive bias about a firm’s reputation, which allows firms to better attract resources and

opportunities (Ruckman et al. 2016)

82

trustworthiness, and the benefits (Sine et al., 2003).

2.2) Desorptive Capacity The second stream of previous research, Desorptive Capacity, defined as ‘an organization’s ability to identify technology transfer opportunities based on a firm’s outward technology transfer strategy and to facilitate the technology’s application at the recipient’.

Desorptive Capacity is related to the external knowledge exploitation which refers to the outward knowledge transfer. It is also a type of dynamic capabilities2 as it indicates that the firms intentionally create, extend or modify their resource bases (Helfat et al.2007). According to Teece(2007), Dynamic capabilities can be disaggregated into sensing, seizing, and transforming capacity.

To build up strong Desorptive Capacity, it requires sufficient prior experience (Fosfuri, 2006). As mentioned above, the problem of information asymmetry is prevailed in the technology market, prior exposure to dealing with the out-licensing can lower the transaction cost. Experience in gathering information about expenditures of industry prospective licensees, negotiating, writing contracts will cut down the cost of out-licensing for the licensors (Vornotas et al., 2006).

The way to build strong desorptive capacity is by learning from its own technological trajectory (Dosi, 1982). The firms usually face their own problems in reactions with the turbulent and competitive environments. According to Rosenberg (1982), the innovation here can be defined as a cumulative and firm-specific process of problem defining and solving activities. Due to the uniqueness and cumulativeness of a firms learning experience, their technological trajectories feature distinctiveness and path-dependent (Garud et al., 2002).

2.3) Connective Capacity Regarding firm’s knowledge management processes, several authors have distinguished knowledge exploration or creation on the one hand, and knowledge exploitation on the other, sometimes mentioning the need for retaining knowledge over time (Nonaka et al., 1994; Lichtenthaler et al., 2009; Shin et al., 2018).

As mentioned above, Lichtenthaler and Lichtenthaler (2009) proposed the framework of knowledge management capacities to give guidance to the firms how to manage their knowledge related capacities and embraced the standpoints of exploration, exploitation and retention.

Connective Capacity refers to a firm’s ability to retain knowledge in interfirm relationships; it consists of alliance capability and relational capability (Lichtenthaler et al., 2009). In contrast to desorptive capacity, external knowledge retention does not assume inward knowledge transfer as absorptive capacity. Instead, Licensors are ensured having privileged access to external knowledge without completely acquiring it. The more alliances firms form, the easier for them to manage

2 The dynamic capability theory assumes that organizations need to adapt to changing business environments

and renew their competences in order to stay competitive (Teece et al. 2007). Thus, firms in open innovation systems need to develop capabilities to internalize and externalize knowledge to gain competitive advantage.

83

interfirm relationships and to profit from external knowledge retention (Lichtenthaler et al., 2009).

2.4) Other Determinants Aside from this main classification, previous studies on out-licensing decisions, they have focused on other firm-level determinants. In this study these determinants are controlled for their effects on the out-licensing decisions. First, the size of licensor, which is known as ‘firm size’ have been considered(Arora et al. 2005; Vonortas et al. 2006; Gambardella et al. 2007; Kani et al. 2012; Nishimura et al. 2014; Kim & Kim, 2018; Kim & Kim, 2018) The firm size was used as an indicator of the degree of complementary assets held by the firms.

The second determinant is ‘R&D Intensity’. Basically, the innovative outputs stems from the R&D activities of firms(Cohen et al. 1990) and the R&D intensity indicates the concentration of biopharmaceutical firm’s total R&D investments regarding their innovation process. For technology-based firms, the amount of internal R&D tends to promote inter-firm relationships and stimulate the firms’ motivation to license out (Ruckman et al. 2016). In sum, the licensor R&D intensity is expected to have a positive effect on licensing likelihood (Kani et al., 2012).

Design/ Methodology/ Approach

1) Hypotheses As licensing technologies is the activities between the licensors and licensees, there always exist the inefficiency and information asymmetry problems. This is attributed to the cognitive, intangible, tacit nature of technological knowledge (Hu et al.2015) and the licensing activity consists of the various sub-activities such as evaluation of technologies and negotiation with the potential partners (Wong, 2008). Limited transparency in the technology market impede the identification of potential partners, and this leads to the 40% attrition rate in regard to the licensing decisions and actual contraction conclusions.(Gambardella et al., 2007).

Under this condition of market, Inventive Capacity is related to the ‘prestige’ of the licensors and serve as a sign of the quality in terms of the resources or capabilities firms possess. As mentioned earlier, the prestige is deeply associated with the ‘noticeability’, ‘visibility’, ‘trustworthiness’ of the licensors. By previous studies it is measured as the value of the firms’ patents, technological breadth and technological depth and it has positive effect on the out-licensing decisions.

Gambardella et al. (2007) listed patent characteristics affecting the licensing propensity, including the economic value of a technology, and patent breadth measured by technology classes covered by the patents. Hu et al. (2015) have identified the licensors’ prestige by forward citation and figured out it enhances the licensing propensity. Ruckman and Mccarthy (2016) also measured the determinants of out-licensing as number of forward citation, technology depth and breadth.

First, the patent value is measured as the number forward citations of the licensors’ patents (Gambardella et al., 2007; Hu et al., 2015; Ruckman et al.,2016). A large number of forward citations imply the outstanding status in the knowledge domain, giving signals to potential licensees that the patents underpinning the firm’s out-licensing activities ensures generating more economic returns (Hu et al., 2015). Therefore the firms with high forward citations of patents lead to the more out-licensing deals.

84

H1: For Licensors, the number of forward citations of their patents have positive effects on the number of out-licensing deals.

Regarding the technological depth and technological breadth, these are deeply related to the ‘noticeability’ of the licensors to the licensees. The former refers to the degree of firms’ specialization in an area of technological knowledge, and it helps potential partners to seek the relevant technological areas of licensors matching with them(Ruckman et al., 2016). The licensors with higher technological depth of their patents are therefore more noticed and chosen by the potential partners than the licensors with less technological depth.

The latter is related to the variety and scope of technological areas the firms have dealt with (Ceccagnoli and Battaggion, 2015; Ruckman et al., 2016). The licensors with a broad technological knowledge base are more adept at disseminating their technologies to external parties. This can be also interpreted as the attractiveness to the potential licensees.

H2: For Licensors, the technological depth of their patents have positive effects on the number of out-licensing deals.

H3: For Licensors, the technological breadth of their patents have positive effects on the number of out-licensing deals.

According to the Lichtenthaler and Lichtenthaler(2009), the Connective Capacity implies the external knowledge retention which means the firms extend their knowledge bases by forming interfirm relationships. It is constituted with the alliance capability and relational capability.

The mainstream of determinants of out-licensing have neglected this point of view. Firms not only conduct the complete inward knowledge transfer, they also make various alliances with external parties to have privileged access to their knowledge base. Therefore, by extending their knowledge by this capacity firms can efficiently enjoy specialization in the creation of new knowledge (Gulati, 1999). In other words, the licensors with stronger Connective Capacities are likely to show stronger Desorptive Capacities.

Previous studies related to the Connective Capacity measured it as the number of backward citations of the patents or the number of collaboration on R&D (Mudambi et al., 2010; Ahn et al., 2015; Shin et al., 2018)

H4: For Licensors, the number of backward citations of their patents have positive effects on the number of out-licensing deals.

H5: For Licensors, the number of R&D collaborations have positive effects on the number of out-licensing deals.

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Figure1. Research Framework

2) Data The data this research utilizes are extracted from three sources. Medtrack, Wharton Research Data Services (WRDS) and LexisNexis Total Patent data. Firstly, the Deal data is extracted form Medtrack data from the UK INFORMA. This database covers various deal information about 16,000 bio-pharmaceutical industry firms such as deal year, deal industry, deal value, and so on according to the each deal type. Deal type is confined to the partnerships and licensing agreement, and specifically, deal year, deal phase, number of alliances data are utilized. Second, the WRDS database is related to the financial information of each firm in a given year. R&D investments, sales, assets, debts, employees information is contained. I extracted R&D investments, sales, and employees for the econometric analysis. Lastly, the LexisNexis Total Patent data covers the patent information of data.

Table 1. Data Sources

Database Extracted Data

Medtrack Deal information(Deal year, number of R&D collaboration)

WRDS Financial information(R&D investment, sales, employees)

LexisNexis Total Patent Patent information(citation, reference, granted year, IPC counts)

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3) Variables 3.1) Dependent Variable The main interest of this study is to figure out why some firms are superior to successfully out-license than any others in spite of the fact that the licensing activities are confronted with high attrition rate. As this is related to the licensors’ competencies to exploit their technology it could be seen in the context of Desorptive Capacity. By definition, the Desorptive Capacity refers to the capabilities of firms exploiting their resources to the external partners (Lichtenthaler et al., 2009) and features the path-dependencies which come from the previous experiences. Previous studies have used the previous number of out-licensing deals(Gambardella et al. 2006; Fosfuri, 2008; Lichtenthaler et al. 2010; Shin et al., 2018) and the number of alliances(Hu et al.,2015) as the indicator of Desorptive Capacity.

In this study, it is measured by the number of out-licensing deals of licensors in a given year (DC). This is because the number of alliances is more directly related to the Connective Capacity by its definition. In addition, the number of licensing deals are classified into early stage (Early_L) and

later stage (Later_L). The early stage includes the research, preclinical stage, and phaseⅠof clinical

trials, and the later stage contains the phaseⅡ, phase Ⅲ of the clinical trials. This classification

follows the Higgins and Rodriquez(2006) and (Nishimura and Okada 2014)

There are three rationales for this classification. First, the clinical trials at the late stage(phaseⅡ,

phase Ⅲ) necessitates much higher cost than that of the early stages(research, preclinical, and phase

Ⅰ). Second, related to the first reason, the success rate of clinical trials from phaseⅠto phaseⅡ is

much lower than that of the subsequent stages(DiMasi et al., 2003). Lastly, a fast-track clinical trials process for life-threatening or highly effective drug candidates such as anti-cancer drugs and orphan drugs makes classification of drug candidates between phase II and phase III obscure and virtually impossible(Nishimura et al., 2014).

3.2) Explanatory Variables 3.2.1) Inventive Capacity

Inventive Capacity in this study is measured by patent characteristics of each firm; the forward citation number of granted patents (FC), technological depth (TD) and the technological breadth (TB) of licensors. Inventive Capacity is strongly related to the licensors’ visibility, noticeability to the potential licensees and reflects the quality of their technologies (Ruckman et al., 2016). Previous studies have measured the quality of patents in perspective of the number of forward citations(Gambardella et al.,2006; Hu et al.,2015;), technological depth (Ruckman et al., 2016) and technological breadth(Gambardella et al.,2006; Kani et al.,2012; Ceccagnoli et al., 2015; Ruckman et al., 2016) of licensors.

In detail, FC is computed by the sum of forward citations number of patents for 5 years before the execution of out-licensing deals. The TD is calculated by the number of identical IPC codes and TB by the number of different IPC codes. To measure this, I followed the Harhoff(1999)’s approach as the number of identical four-digit IPC classification codes in the granted patents. To be specific, TD is measured by the accumulative number of identical 4-digit IPC codes of patents for 5 years before the execution of out-licensing deals, and TB is measured by the accumulative number of different 4-digit

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IPC codes of patents for 5 years before the execution of out-licensing deals.

3.2.2) Connective Capacity According to Lichtenthaler and Lichtenthaler(2009), the Connective Capacity is the firms’ ability to

link with other external partners to facilitate the innovation process. It is associated with the knowledge retention which excludes the complete knowledge transfer. Connective capacity includes not only relational capability but also alliance capability, which further ensures access to external knowledge bases.

Previous studies used the number of R&D collaboration at firm-levels, or the number of backward citations of firms to measure Connective Capacity (Mudambi et al., 2010; Shin et al., 2018). Therefore, in accordance with these mearsurements, this study measures the Connective Capacity in two ways. The number of R&D collaboration (CN) is computed by the sum of R&D collaboration number for 5 years before the execution of out-licensing deals. And the number of backward citation (BC) is calculated by the sum of backward citation number for 5 years before the execution of out-licensing deals3.

3.3) Control Variables This research included two control variables; (1) Firm size and (2) R&D Intensity. The number of employees is used to measure firm size. Usually, the size of firms is considered as degree of firms’ complementary assets. Therefore, the firm size is proportionally associated with the their technological innovation (Shin et al., 2018). The R&D intensity is usually measured by R&D investment divided by the firm size (Ruckman et al., 2016). It represents concentration of their innovation activities. Thus, the higher R&D investment biopharmaceutical firms have, the more likely they are to increase their technological innovation or financial performance. In this context, the R&D Intensity in this study is calculated by the R&D expenditure of each firm in a given year normalized by firm size.

Table 1. Definition of variables

Variable Definition Source of data

Dependent Variable

Early Stage Out-Licensing(Early_L)

The number of out-licensing in the stage of Research, Pre-clinical , and

PhaseⅠof drug development process

Medtrack

Late Stage Out-Licensing(Late_L)

The number of out-licensing in the

stage of PhaseⅡ, and PhaseⅢ of drug development process

3 From this measurement, the number of self-citations is subtracted.

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Inventive

Capacity

Number of

Forward Citations(FC)

Accumulative number of ForwardCitation of each firm for 5 yearsbefore the execution of deal

Lexis Nexis

Total Patent

Technological

Depth(TD)

Accumulative number of identical IPC codes of each firm for 5 yearsbefore the execution of deals.

Technologial

Breadth(TB)

Accumulative number of differentIPC codes of each firm for 5 yearsbefore the execution of deals.

Explanatory

Variable

Connective Capacity

Number of

Backward Citations(BC)

Accumulative number ofBackward Citation of each firm for5 years before the execution ofdeal

Lexis Nexis

Total Patent

Number of

R&D Collaborations(CN)

Accumulative number of R&Dcollaboration of each firm for 5 years before the execution of deal

Medtrack

Control

Variable

Firm Size(FS)

The average of sum of employeesfor 5 years before the executionout-licensing deals

WRDS

R&D Intensity(RND)

The average of sum of R&Dexpenditure for 5 years before the execution out-licensing deal

3.4) Econometric Model Considering the dependent variable is a countable, nonnegative, and integer variable (the number of firm i’s out-licensing deals in a given year t), the conventional linear regression models are not appropriate for the analysis. The simplest model to deal with countable data is Poisson regression model. However, the Poisson distribution estimation should meet the property of the equality between mean and variance. This condition, however, is has been criticized for the problem of ‘overdispersion’. This occurs when the conditional variance is larger than the conditional mean, which is attributed to the unobserved ‘heterogeneity’.

The solution for this problem is to include ‘fixed’ or ‘random’ effects into the Poisson model. As the sample mean is smaller than the sample variance in the descriptive statistics in Table 5, the negative binomial model is specified in this study (Hausman et al., 1984). The more efficient estimator is used in the situation of overdispersion by adding a parameter that reflects unobserved

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heterogeneity among observations (Vonortas et al., 2006). I adopted the most general negative binomial model used in econometric applications with mean function λi and variance function λi + α λi 2(Cameron & Trivedi, 1986):

With model used in econometric applications with mean function λit = exp(xit’ ), where xit’ denotes a matrix of explanatory variables(FC, CN, BC, FS, RND) and denotes a vector of unknown parameters. The estimation method is conducted through MLE.

Expected Findings/Results

The pharmaceutical industry is a high technology industry that requires a combination of in-depth knowledge of various fields and is characterized by high cost, high risk and long term due to high regulations. In addition, there is a problem that R&D productivity is deteriorating in the industry as a whole. Under these conditions, the importance of open innovation strategies has been emphasized than any other industries, and under this open innovation system, it is essential for firms to develop several dynamic capabilities that can effectively manage their resources internally and externally. Lichtenthaler and Lichtenthaler (2009) suggested the systematic framework for the dynamic capabilities.

As mentioned above, the research flow of Open Innovation has moved from the inbound process to outbound process and a number of studies dealt with the effect of dynamic capabilities on the firm performance have been conducted(Shin et al.,2018). Among the capabilities, the Absorptive Capacity of firms related to the inbound process and external exploration had been spotlighted from 1990s. As firms have shifted their focuses to outbound innovation, several studies have been conducted regarding the Desorptive Capacity (Hu et al.,2015). However, as Lichtenthaler and Lichtenthaler (2009) pointed out, the capability associated with knowledge retention has been ignored.

Therefore, the implications of this study are as follows. First, it differs from the previous studies which have focused on the effects of dynamic capabilities on firm performance. The number of out-licensing used as dependent variable corresponds to a Desorptive Capacity that indicates how much the firms can actively perform outbound innovation. Therefore, it could say that it is inter-capabilities analysis which differs from the mainstream of dynamic capabilities studies.

Second, previous studies dealing with the determinants of the number of out-licensing are limited to Inventive Capacity and Desorptive Capacity (Hu et al., 2015) which are related to the knowledge exploration and exploitations. The perspective of the knowledge retention is not considered as a determinant of out-licensing decisions. These days, it is not hard to see the landscape which biotech firms’ knowledge is externally retained from the pharmaceutical firms without immediate knowledge internalization. However, the pharmaceutical firms have ensured exclusive access to the results of the partners’ R&D in this field by establishing collaboration agreements (Shin et al., 2018).

According to Lichtenthaler and Lichtenthaler (2009), the Inventive Capacity is related to the firm’s internal knowledge exploration, and in case of Desorptive Capacity, it is related to external

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exploitation of the firms. In sum, this research is inter-capabilities analysis regarding how the Inventive Capacities and Connective Capacities of licensors affect their Desorptive Capacities.

By the empirical analysis this research identified the required capabilities by pharmaceutical firms to carry out active outbound innovation. As forming a variety of partnerships including out-licensing is driving firm’s performance and survival, analysis on the determinants for out-licensing will be a guide for firms to cope with dynamic open innovation ecosystems.


This study identified which competencies the licensors should built up in the pharmaceutical industry for their active out-licensing deals. Inventive Capacity is basically the capacities of licensors, but they are associated with their prestige, noticeability, and attractiveness. This means that this could be criteria for identifying the licensors in perspective of the licensees. In this regard, Inventive Capacity can be regarded as considering the demand side of out licensing deals (Ruckman et al.,2016). And Connective Capacity means the ability of licensors to form alliances with external firms, which in turn, makes their knowledge base richer by sharing R&D results with other firms (Lichtenthaler et., 2009). This is the supply aspect of licensing, so this study comprehensively covers the supply and demand aspects related to out-licensing.

The limitation of this study is that the determinants of out-licensing are confined to the firm-level knowledge management capacities. It has been proven through several previous studies that the effects of industry-level characteristics also affect out-licensing(Arora et al., 2005; Fosfuri, 2006; Vonortas et al., 2006; Kani et al.,2012) According to them, the licensors should consider not only their capabilities but also the characteristics of the industry when out-licensing. Depending on how many competitors are in the market, the licensor's out-licensing incentive will vary, with two effects; The revenue effect (the degree of profits they earn from out-licensing) and the rent dissipation effect(the extent to which market share is reduced by increasing competitors in the market).

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15.

Topography of Post-Genomic Researches in Korea:

Governance and Institutional Polymorphism

June-Seok Lee (Corr.)

Invited Lecturing Professor, DGIST, South Korea

Abstract

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Human Genome Project was a big science done by United States, U.K., France, China, Germany and Japan. But in Korea HGP was not constructed because of lack of governmental funding and failure to attract relevant actors’ attention in spite of small voices from early genome researchers and some family members of patients with incurable diseases. This article does not argue that HGP in Korea was an undone science, a concept claimed by Scott Frickel, et al. Instead, it shows the historical fact that HGP was not constructed in Korea in 1990s and analyzes how genomic researches could become possible in Korea in the post-genomic age using the framework of triple-helix. In Korea, researchers have constructed hybrid networks and organizations that intermingles laboratories of university, industry, and government to conduct genomic researches which requires a lot of financial funding. This structure is different from the entrepreneurial university seen in developed countries such as the United States. Using two examples, this article shows that founding a start-up company by university researchers was not an option as in the United States, but a necessity in order to obtain enough funding to conduct genomic researches in Korea. Otherwise, researchers in Korean universities had to form hybrid networks with government to obtain small amount of funds to conduct researches. I argue that this phenomenon shows multifaceted characteristics of institutional structures regarding genomic researches in Korea.

Keywords: Genomic medicine, Triple helix, Research assemblage,

Technoscientific governance, Institutional polymorphism,

Undone science

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16.

What factors are barriers to open innovation in electricity industry? : Lessons

100

from European utilities’ CVC strategies

Hanee Ryu

Research Fellow, Hanyang University, South Korea

Abstract

Purpose/ Research Question: After renewable energy sources (RES) has exploded, wholesales

market price has been descending and electricity generation decreased, which has attacked

profitability of conventional plants of big major utilities in European countries. European major

utilities have been forced to shift their strategy from the traditional business mainly depended on

fossil fuel- fired generation, to reinforcement of RES and retail. Thus, they choose cooperate

venture capital(CVC) strategies to get the business area that advanced energy management and

optimization across a wide range of areas using ICT are emerging; investing in 16 start-ups of

E.ON,‘Innovation Hub’ and‘Innogy Venture Capital’ of Innogy,‘Engie fab’,‘call for project’and ‘Engie

New Venture’of Engie,‘PERSEO International Startup program’by Iberdrola and‘Innovation Hub &

Lab’ of Enel.

In Europe, originally liberalization has initiated a fundamental restructuring in the energy

sector, industry structure has an impact on the innovation activities of firms that are monopoly or

vertically integrated structure as it started. This paper focuses on figuring out whether the

liberalization draw the innovation and change the strategies of the utilities. Further, the argument

lead to encourage the other countries, those who pursue to way to advance in the middle of

liberalization, to build the innovation ecosystem. Purpose of this paper is building up the model

to investigate the factors that affect the open innovation among the potential variable to be

considered and selecting barriers with figuring out its coefficient to open innovation. The factors

determined innovation activities are considered as having many potential variables of which only a

few factors are significant. Thus, research questions are as follows ;“ Does market liberalization

drive the innovation in electricity industry?”and“ Which factors are obstacles to overcome

challenges through the open innovation?”.

Key Literature Reviews (About 3~5 papers): Lee et al. (2018) and Yun et al.(2016) explore the

ideological foundation of open innovation strategies and discover concrete bases for open

innovation. With the review of literature, Yun and Yigitcanlar(2017) and Trąpczyński et al. (2018)

develop a research framework for open innovation in the value chain and propose five ways of it.

Simona O et al. (2012) present an overview of typical systemic problems in the development of

innovation systems around renewable energy technologies. They categorize the innovation system

101

failures that hamper the development and diffusion of RETs. Park et al. (2018), Kolloch and

Dellermann(2018) and Battistella et al.(2018) contributes to a better understanding of managerial

challenges associated with digital innovation and their respective ecosystems in the electricity

industry such as exhibiting path dependencies and high barriers for radical innovation. Markard

and Truffer(2006) examine how liberalization has altered innovation processes in the field of

electricity supply. They argued that market liberalization could be regarded as an external driver,

which brings about innovation behavior, especially radical organizational innovation, of electric

utilities. Dzhengiz(2018) focus on the how a portfolio of alliances, collaborative partnership

motivated as business solution, is configured. They argue that organizational value frame play a

key role in the selection of alliance partners and hence the configuration of alliance portfolios.

Design/ Methodology/ Approach: This paper introduces Least Absolute Shrinkage and Selection

Operator (LASSO) analysis and double selection in order to establish the factors among potential

variables that affect innovation activities, especially CVC. This alternative approach is motivated to

figure out the determining factors in strategic approaches of utilities that were vertically

integrated firms before liberalization that display high-dimensional data and that underwent

difficulty in conventional estimation. Belloni et al.(2014) pointed out two reasons why high-

dimensional data arise. First, the data may be inherently high dimensional in that many different

characteristics per observation are available. Second, even when the number of available variables

is relatively small, researchers rarely know the exact functional form with which the small number

of variables enters the model of interest. Researchers are thus faced with a large set of potential

variables formed by different ways of interacting and transforming the underlying variables

(Belloni et al.,2014). They proposed lasso estimator defined as

where is the penalty level, which controls the degree of penalization. Let be the full

least squares estimates which means and let to . Values of will cause

shrinkage of the solutions towards 0, and some coefficients may be exactly equal to 0.

(Expected) Findings/Results: Electric market liberalization could draw utilities’ strategic alliance

such as CVC under regulation on the vertical integrating. It would be the barriers to open

innovation with narrow field for emerging start-ups to collaborate at the earlier stage of

liberalization.

Research limitations/ Implications: For other countries that in the middle of the liberalization,

the factors selected as the barrier to open innovation can have implication to make a favor

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circumstance to foster the innovation ecosystem.

Keywords: Electricity Market Liberalization, Open Innovation, Cooperate Venture Capital, Least

Absolute Shrinkage and Selection Operator, Double Selection

Reference

Battistella, C., De Toni, A., & Pessot, E. (2018). Framing Open Innovation in Start-Ups’ Incubators: A

Complexity Theory Perspective. Journal of Open Innovation: Technology, Market, and


Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on

structural and treatment effects. The Journal of Economic Perspectives, 28(2), 29-50.

Dzhengiz, T. (2018). The Relationship of Organisational Value Frames with the Configuration of

Alliance Portfolios: Cases from Electricity Utilities in Great Britain. Sustainability, 10(12), 4455.

Kolloch, M., & Dellermann, D. (2018). Digital innovation in the energy industry: the impact of

controversies on the evolution of innovation ecosystems. Technological Forecasting and Social

Change, 136, 254-264.

Lee, M., Yun, J., Pyka, A., Won, D., Kodama, F., Schiuma, G., & Yan, M. R. (2018). How to respond to

the Fourth Industrial Revolution, or the Second Information Technology Revolution? Dynamic new

combinations between technology, market, and society through open innovation. Journal of Open


Markard, J., & Truffer, B. (2006). Innovation processes in large technical systems: Market

liberalization as a driver for radical change?. Research policy, 35(5), 609-625.

Negro, S. O., Alkemade, F., & Hekkert, M. P. (2012). Why does renewable energy diffuse so slowly?

A review of innovation system problems. Renewable and Sustainable Energy Reviews, 16(6), 3836-

3846.

Park, E., Kim, B., Park, S., & Kim, D. (2018). Analysis of the Effects of the Home Energy

Management System from an Open Innovation Perspective. Journal of Open Innovation:


Trąpczyński, P., Puślecki, Ł., & Staszków, M. (2018). Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?. Sustainability, 10(12), 4517.

Yun J.H.J., and Tan Yigitcanlar (2017). "Open Innovation in Value Chain for Sustainability of Firms".

Sustainability, 9(811), P. 1-8.

103

Yun, J. J., Park, K., Yang, J., & Jung, W. (2016). The philosophy of “open innovation” Historical

development of the philosophy of open innovation and its reflection from Taoism. Journal of

Science and Technology Policy Management, 7(2), 134-153.

17.

Factors affecting M&A performance of biopharmaceutical firms: an empirical

analysis of influencing factors

Jimin Choi Researcher, Graduate School of Biomedical Convergence, College of Medicine,

Chungbuk National University, South Korea

Kwangsoo Shin, Ph.D. (Corr.) Assistant Professor, Graduate School of Health Science Business Convergence, College of Medicine,


Eungdo Kim, Ph.D. Assistant Professor, Graduate School of Health Science Business Convergence, College of Medicine,


Abstract

Purpose/ Research Question: Mergers and acquisitions (hereinafter ‘M&A’), though the terms are often used interchangeably, they are considered as one of the most popular and indispensable survival strategies of biopharmaceutical firms, along with concept of ‘Open Innovation’ (hereinafter ‘OI’), due to the inherent properties – high value, high uncertainty – of biopharmaceutical industry (Shin et al., 2018), (Lee et al., 2018), (Shin et al., 2018). Given this high‐risk environment, M&A has been leveraged from both small and medium‐sized enterprises (hereinafter ‘SME’s), and large enterprises. SMEs use M&A not only as a survival strategy but as an exit strategy, which would enable them to maintain their momentum toward a new product development, whereas large enterprises use M&A as a way of finding new drug candidates. Therefore, there has been consistent efforts and studies to reveal factors that affect the outcome of mergers and acquisitions. These efforts and studies have struggled to explain the outcome and the influencing factors, in various ways – in terms of acquirer’s experience, size, the strategic fit of acquirer and acquiree, and more – but only few have consolidated the factors, especially in the biopharmaceutical industry. Thus, this is the point where our question starts: what are pre‐M&A factors that affect the outcome (performance) of M&A deal and can the relationship between the factors and the outcome be empirically proved? Key Literature Reviews (About 3~5 papers): King et al. (2004), have analyzed 93 published studies through meta‐analytic review, categorizing

104

commonly used factors into 4 different groups: conglomerate firms, related acquisitions, method of payment, acquisition experience. The ‘conglomerate firms’ factor investigates acquirer’s organizational and business structure of a firm, whether the acquiring firm consists of two or more completely different area of business, whereas related acquisitions, also defined as business relatedness or industry familiarity, investigates to which extent the area of business of both the acquirer and acquired firm is similar. The method of payment mentioned is two‐fold: to pay with cash or to pay with stock shares (equity). Regarding the acquisition experience, the experience can be broadly divided into two different sectors: individual experience and organizational experience. In a similar vein to King et al.’s approach, through a systematic investigation of state‐of‐the‐art studies in leading journals, Gomes et al. (2013) have organized the critical success factors into 6 distinct groups: ‘choice and evaluation of the strategic partner’, ‘pay the right price’, ‘size mismatches and organization’, ‘overall strategy and accumulated experience on M&A’, ‘courtship’, ‘communication before the merger’, and ‘future compensation policy’. Firstly, ‘Choice of partner’ argues that the degree of ‘strategic fit’ and ‘organizational fit’ is one of the most important measures to determine successful M&A. Also, excessive payment of M&A deals are frequently known as the cause of the failure of a deal, therefore 'pay the right price' highlights the size of the premium paid for M&A deals or method of payment (cash/stock). For ‘size mismatches and organization’, on the other hand, it has been believed that the relative size of the two companies participating in M&A will affect the performance of the deal. If the size of the acquirer is too small or too large, the possibility of the amount of interest paid in mergers and acquisitions can be small or too large rises, which will eventually end up in apathy or to a political fight. ‘Overall strategy and accumulated experience on M&A’ claims that M&A experience is a critical factor, and ‘courtship’ can be understood in a similar manner. The factor, ‘courtship’ argues that the ‘courtship period’ – a time when companies can get to know each other – plays a pivotal role in leading a successful deal. Factors such as ‘communication before the merger’, or ‘Future compensation policy’ also are taking up a crucial part of the successful deal, in that they prevent forming of uncertain rumors, the clash between employees which could result in detrimental effect in the deal. Especially regarding organizational experience, Al‐Lahama et al. (2010) deliver more detailed definitions on the organizational side of the experience, breaking down organizational experience one step further, to general acquisition experience of the acquirer, and pre‐acquisition alliance experience with the target firm (acquiree). Design/ Methodology/ Approach: From the aforementioned factors, we have piled up quantifiable factors and categorized into 4 sectors, technology, business, experience, and firm size. Specifically, inspired by Al‐Lahama’s idea that both general and mutual experience of the firms are important, we divided all influencing factors within each sector into two different groups: general and mutual factors.

105

Experience

Acquirer’s M&A

experience

Acquirer and Acquiree’s mutual alliance

experience

General Mutual

Business

Acquirer’s business area

diversity

Acquirer and Acquiree’s

business area relatedness

General Mutual

Technology

Acquirer’s technology diversity

Acquirer and Acquiree’s technology relatedness

General Mutual

Firm Size

Absolute size of acquirer

Relative size of acquirer and acquiree

General Mutual

Merger andAcquisitionperformance

106

Summary of independent variables derived from each factor is listed in the table below. Table1. Summary of (independent) variables

Category Sub

Category Variable Description Reference

Technology

General Acquirer’s

technology diversity

Number of acquirer’s International Patent Classification

(IPC) code [9]

Mutual

Acquirer and acquiree’s technology relatedness

Proportion of number of common International Patent Classification (IPC) code to number of total IPC code of acquirer and acquiree

[9]

Business

General Acquirer’s business

area diversity

Diversification classified into two‐by‐two matrix of based on broad spectrum diversification (BSD) and mean narrow spectrum

diversification (MNSD)

[7]

Mutual Acquirer and

acquiree’s business area relatedness

Proportion of number of common FTC/SIC codes to number of total

FTC/SIC codes [1], [5]

Experience

General Acquirer’s M&A and alliance experience

Numbers of acquisitions and alliances in the 5 preceding years

of acquisition [3]

Mutual Acquirer and

acquiree’s mutual alliance experience

Numbers of alliances between the acquirer and acquire in the 5 preceding years of acquisition

[2], [3]

Firm Size

General Absolute size of

acquirer Market value / Total revenue [2], [7]

Mutual Relative size of acquirer and acquiree

Proportion of acquirer’s market value or total revenue to sum of acquirer and acquiree’s market

value or total revenue

[2], [7]

In addition, the outcome of mergers is measured in both financial and non‐financial domain, using variables derived from stock value, accounting, patent, and new product factors. Table2. Summary of (dependent) variables

Category Variable Description Reference

Financial Stock Value

Acquirer’s short-term abnormal stock returns [8]

Accounting Acquirer’s Return‐on‐asset (ROA) [4]

Non‐financial

Patent Acquirer’s patenting speed [3]

Product Number of new products developed ‐

Although there have been endeavors to measure outcome with sample cases of M&A (Demirbag, 2007), (James, 2002), (Cloodt, 2006), only a limited numbered of research, specifically in the

107

biopharmaceutical industry, has provided the empirical evidence by fully leveraging commercial databases. Our study is conducted with large commercial databases such as Medtrack, Wharton Research Data Services (WDRS), GPASS of LexisNexis, each having approximately 240,000, 36,000, and 840,000 sets of data. The regression for the performance will be induced with the equation below, each individual variables representing 8 factors from 4 different sectors.

(Expected) Findings/Results: With the increase in both general and mutual factors of 4 sectors will result in an increment of merger and acquisition performance.

Business

Acquirer’s business area

diversity

Acquirer and Acquiree’s

business area relatedness

General

Mutual

Technology

Acquirer’s technology diversity

Acquirer and Acquiree’s technology relatedness

General Mutual

Firm Size

Absolute size of acquirer

Relative size of acquirer and acquiree

General Mutual

Merger andAcquisitionperformance

Experience

Acquirer’s M&A

experience

Acquirer and Acquiree’s mutual alliance

experience

General

Mutual

+ +++

+

+

+

+

Research limitations/ Implications: The scope of this study is limited to solely quantitative pre‐M&A factors. Post‐M&A factors, as well as qualitative factors, and their interactions effects are left for future studies. This study will help the establishment of the direction of M&A deals, especially associated within biopharmaceutical industry.

Keywords: Pharmaceutical industry, Mergers and acquisitions, M&A performance framework,

empirical study, pre‐merger factors

Reference

[1] King, D.R., Dalton, D.R., Daily, C.M., Covin, J.G. (2004). Meta‐analyses of Post‐acquisition

Performance: Indications of Unidentified Moderators. Strategic Management Journal, Vol. 25, No. 2,

108

pp 187–200.

[2] Gomes, E., Angwin, D.N., Weber, Y., Yedidia Tarba, S. (2013). Critical Success Factors through the

Mergers and Acquisitions Process: Revealing Pre‐ and Post‐M&A Connections for Improved

Performance. Thunderbird International Business Review, 55(1), pp. 13‐35.

[3] Al‐Laham, A., Schweizer, L., Amburgey, T.L. (2010). Dating before marriage? Analyzing the

influence of pre‐acquisition experience and target familiarity on acquisition success in the "M&A as

R&D" type of acquisition. Scandinavian Journal of Management, 26(1), pp. 25‐37.

[4] Meglio, O., Risberg, A. (2011). The (mis)measurement of M&A performance – A systematic

narrative literature review. Scandinavian Journal of Management, 27(4), pp. 418‐433.

[5] Rumelt, R.P. (1974) Strategy, Structure, and Economic Performance. Harvard Business Press,

Cambridge.

[6] Shelton, L.M. (1988) Strategic business fits and corporate acquisition: empirical evidence.

Strategic Management Journal, 9 (3), pp. 279–287.

[7] Varadarajan, P., Ramanujam, V. (1987) Diversification and performance: a reexamination using a

new two‐dimensional conceptualization of diversity in firms. Academy of Management Journal, 30

(2), pp. 380–393.

[8] Barkema, H.G., Schijven, M. (2008) How do firms learn to make acquisitions? A review of past

research and an agenda for the future. Journal of Management, 34(3), pp. 594‐634.

[9] Garcia‐Vega, M. (2006) Does technological diversification promote innovation? An empirical

analysis for European firms. Research Policy, 35(2), pp. 230‐246.

[10] Shin, K.S., Lee, D.H., Shin, K.S., Kim, E.D. (2018) Measuring the efficiency of U.S. pharmaceutical

companies based on open innovation types. Journal of Open Innovation, 4(3), 34.

[11] Lee, J.H., Kim, E.D., Sung, T.E., Shin, K.S. (2018) Factors affecting pricing in patent licensing

contracts in the biopharmaceutical industry. Sustainability, 10(9), 3143.

[12] Shin, K.S., Kim, E.D., Jeong, E.S. (2018) Structural Relationship and influence between Open

Innovation Capacities and Performances. Sustainability, 10(8), 2787.

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18.

Green Governance Responsibility, Corporate governance and

Investors’ Reaction

Weian Li

Professor, Business School, Nankai University, China Academy of Corporate Governance, Tianjin, China

Guangyao Cui

Ph.D, Business School, Nankai University, China Academy of Corporate Governance, Tianjin, China

Minna Zheng※ (Corr.)

Ph.D, Business School, Nankai University, China Academy of Corporate Governance, Tianjin, China

Yaowei Zhang

Associate Professor, Business School, Nankai University, China Academy of Corporate Governance, Tianjin, China

Abstract (including the following aspects)

Purpose/ Research Question: With the deterioration of natural environment, enterprises are

required to undertake more responsibilities in green governance. From the perspective of short-

term benefits, enterprises have to afford additional cost when they actively carry out green

responsibility. However, the long-term value of green governance responsibility still needs further

research. This research mainly explores the long-term values of enterprises’ green governance

responsibility behavior from the perspective of investors’ reaction. Through this research, we

expected to find that enterprises would gain positive long-term values when they undertake green

responsibility. If enterprises could gain long-term value return, they would have motivation to take

green responsibility. Thereby, this research will provide enterprises valuable suggestions when they

deal with green responsibility decisions, and help to improve the natural environment finally.

Considering the different types of investors, this paper further discusses the impact of

110

different types of investors on the relationship between corporate green governance responsibility

and investor response. The empirical results show that compared with individual investors,

institutional investors have a significantly positive impact on the relationship between corporate

green governance responsibility and investor response. This is because institutional investors have

more advantages in information acquisition and enterprise supervision. Moreover, this paper also

investigates the influence of corporate governance level on the relationship between green

governance responsibility and investors’ reaction.

Key Literature Reviews (About 3~5 papers): In the short term, undertaking green responsibility

usually means a cost to the enterprises (Tang, et al, 2013). However, it may also additional bring

benefits to enterprises when they actively take responsibility for the environment from the long-

term perspective, such as government subsidies, bank loans and so on (Zhang, et al, 2011). Some

researches have found that investors will concern corporate green responsibility, and make their

invest decision according to the enterprise's green responsibility (Heinkel R, et al, 2001; Li and Lu,

2015; Martin and Moser, 2016).

Through literature review, we have found that most of existing researches put attention o

n the cost of enterprises’ green governance responsibility, neglecting the long-term value to s

ome degree. In recent years, although some scholars begin to concern about the value return

of enterprises’ green responsibilities, relevant researches are still insufficient. Some researches

have explored the relationship between corporate social responsibilities and the invest decisio

ns of investors, but there are no researches that attach importance of green responsibilities a

nd investors’ reactions. In order to fill this theorical gap, this paper mainly empirically studies

the relationship between green responsibility and investor response from the perspective of gr

een governance.

Design/ Methodology/ Approach: To begin with, this paper first introduces the research

background in detail and put forward the research significance and innovation. Then, review the

relevant literature and propose theoretical development context of green governance and green

governance responsibility. Next, this paper mainly take data from China listed companies to

undertake empirical research. This research obtains data from Corporate Social Responsibility of

China listed companies through manual collection and arrangement. Based on the data of China

listed companies from 2015 to 2017, we have established a set of comprehensive green

governance responsibility indexes through principal component analysis and experts’ Delphi

method. On the basis of such index, this paper investigates the investors’ reaction on corporate

green responsibility behavior and further explores the impact of different types of investors and

corporate governance level. To be specific, this research mainly uses multiple regression, firm

fixed-effect regressions, Heckman two-stage regressions to verify the relationship between green

governance responsibility and investors’ reaction. Then, test the effect of investors’ type and

corporate governance level through both interaction and grouping regression. At last, we provide

111

corresponding suggestions to enterprises on green responsibility decisions according to the

research results and realistic situation.

(Expected) Findings/Results: Through empirical analysis, this paper expects to find that

enterprises with good green responsibilities will gain more the favor of investors, namely, there is

a significantly positive relationship between green governance responsibility and investors’

reaction. The better the enterprises’ green responsibility, the higher the shareholding ratio of

investors. What’s more, the type of investors would have a significant impact on the relationship

between green governance responsibility and investors’ reaction. Compared to the individual

institutional investors, this paper also found that institutional investor has a significant positive

influence on enterprises’ green responsibility. Besides, we also find that the corporate governance

level will have a positive influence on the relationship between green governance responsibility

and investors’ reaction.

Expected results: Regression results show that there is a significant positive impact of investor’

s shareholding ratio on corporate green governance responsibility. In terms of investors’ types,

when the investor type is institutional investor, the correlation between investor response and

corporate green governance responsibility is more significant and the coefficient is larger. Ind

ividual investors have no such effect. That is, the type of investors will also have an influence

on the relationship of green governance responsibility and investors’ reaction. Moreover, the

corporate governance level has a positive correlation with the relationship between green gov

ernance responsibility and investors’ reaction.

Research limitations/ Implications: Firstly, the concept of green governance has been proposed

in just recent years, and the corresponding theoretical research is not mature enough. Therefore,

the theoretical basis of this paper may need to be further improved. Secondly, there is no

consistent conclusion on the definition and measurement of corporate green governance

responsibilities, so future studies can be improved in this aspect. Thirdly, this research only

considers the long-term value of enterprises’ green responsibility behavior from the perspective of

investors, thereby other long-term values could be further explored from multidimensions, such as

the support of stakeholders, government, bank, media and so on.

Keywords: Green governance responsibility; shareholding ratio; investors’ type; corporate

governance level

Reference

[1] Cooke P. Green governance and green clusters: regional & national policies for the climate

change challenge of Central & Eastern Europe[J]. Journal of Open Innovation: Technology,

Market, and Complexity, 2015, 1(1): 1.

[2] Guoping Tang, Longhui Li, Dejun Wu. Environmental Regulation，Industry Attributes and

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Corporate Environmental Investment[J]. Accounting Research, 2013(6):83-89. (In Chinese)

[3] Gupta A, Dey A, Singh G. Connecting corporations and communities: Towards a theory of

social inclusive open innovation[J]. Journal of Open Innovation: Technology, Market, and

Complexity, 2017, 3(1): 17.

[4] Lee, B., Park, J. H., Kwon, L., Moon, Y. H., Shin, Y., Kim, G., & Kim, H. J. About relationship

between business text patterns and financial performance in corporate data[J]. Journal of

Open Innovation: Technology, Market, and Complexity, 2018, 4(1): 3.

[5] Wenjing Li, Xiaoyan Lu. Do Institutional Investors Care Firm Environmental Performance?

Evidence from the Most Polluting Chinese Listed Firms[J]. Journal of Financial Research,

2015(12):97-112. (In Chinese)

[6] Patrick R. Martin, Donald V. Moser. Managers’ green investment disclosures and investors’

reaction[J]. Journal of Accounting and Economics,2016,61:239-254.

[7] Robert Heinkel, Alan Kraus, Josef Zechner. The effect of Green Investment on Corporate

Behavior[J]. The Journal of Financial and Quantitative Analysis,2001,36(4):431-449.

[8] Zhang Wei, Wen Hongyu, Zhang Dandan. Discussion on the Role of Chinese Government in

Strengthening Environmental Protection Investment[J]. Energy Procedia 2011,5: 250–254.

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19.

Does open trade increase China's carbon emissions?

DU LONGZHENG

Position(Ph.D.),Affiliation(Inner Mongolia University of Finance and Economics),CHINA

GUO XINYU(Corr.)

Position (Master),Affiliation (Inner Mongolia University of Finance and Economics),CHINA

Abstract

1. Research Question

This article will focus on the following questions: (1) is trade openness having a positive or

negative impact on China's carbon dioxide emissions? What is the intensity of the impact? (2) is

the "pollution shelter" hypothesis caused by foreign direct investment established in China? (3)

Whether there are significant regional differences in trade openness in China due to differences in

the natural environment and the characteristics of foreign location selection, and will this

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difference have an impact on carbon dioxide emissions? (4) The construction of new urbanization

is a powerful engine of China's economic growth and social development, and has the rapid

development of urbanization in China in recent years had an important impact on carbon dioxide

emissions? (5) Import and export trade is the booster of China's economic development. has trade

transactions resulted in the transfer of pollutants to China?

2. Key Literature Reviews

The literature on trade and environmental pollution can be broadly divided into two categories:

support for Copeland and Taylor (1994) [1]to propose the famous "pollution paradise Hypothesis"

when studying the relationship between North and South trade and environment. Then more

and more scholars use inter-provincial data to explore the impact of FDI on China's carbon

emissions also found that there is a positive correlation between carbon emissions and FDI inflows

in China (Zhou Jieqi, Wang Tongsan, 2014)[2].

Another type of scholar study found that foreign direct investment is a positive effect on China's

pollution emissions. The inflow of FDI has improved the environmental quality of China to a

certain extent, and has certain positive significance for the reduction of carbon emission intensity,

The hypothesis has not been confirmed in China.(Song Deyong, 2011; Guo Pei, 2015)[3-4].

Academia has recognized that trade contains implicit carbon pollution, The impact of trade

between China and major trading countries on China's pollution emissions has attracted the

attention of relevant scholars (Huang Yongming, Chen Xiaofei, 2018)[5], further discussing the

implied carbon flows accompanying cross-border trade and consumer responsibility for global

carbon emissions, providing theoretical support for more effective climate policy and international

cooperation .

3. Methodology

(一) Static model settin

(二) Dynamic model setting

where i represent the Provincial section unit, i = 1,2...,29; t represents the time; cit is expressed in

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CO2 emissions and carbon intensity, respectively; yit represents the actual GDP per capital in each

province, reflecting the per capital income of each province; Tradeit indicates the openness of the

regions. Delta T represents time non-observational effects, ηi represent regional non-observational

effects, and εit are random error items unrelated to time and region. X is other control variables,

including the proportion of foreign direct investment, the proportion of industry, the structure of

primary energy consumption, the level of urbanization and R&D intensity. (Philip Cooke，2015；

Evelin Priscila Trindade et al.，2017)[6-7]

(三) Multi-region input-output model

In a non-competitive input-output model, the input-output relationship of a country is as:

Where, aij is the output of a unit of the department that needs to consume the direct input of the

department, xij /X j . Yi is the final demand of the i department, xi is the total output of the i

department. On the basis of the non-competitive input-output table of a country, the input-

output model of many regions can be extended. Assuming that there are N countries (or regions),

the model MRIO can be expressed as:

Among them, formula (4) equal sign left column vector for the national Q of the various

departments (Department 1,2, ..., n) of the total output. The first item to the right of the equal

sign is the A matrix, which is the direct consumption matrix. The second item is the total output

of each sector of the country Q. The last column vector is the final demand of national Q for

Domestic and other national products. It was not only possible to estimate the direct and indirect

environmental impact of GHG emissions, but also allocate total pollution and resource use

embodiments of traded commodities.(ChangKeun Park et al.，2017)[8]

4. Results

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This paper first estimates the CO2 emissions of 30 provinces and regions in China in 2005-2016,

and uses panel data to focus on the relationship between trade openness and China's CO2

emissions has been rich and meaningful conclusions:

First, for the absolute CO2 emission level, the model has confirmed the environmental

kuznets curve hypothesis, and the trade opening has significantly reduced CO2 emissions. FDI was

significantly positive, and the "pollution sanctuary" hypothesis still holds in China. The change of

urbanization level is significantly negative, indicating that China's current sustainable green

urbanization path is appropriate.

Second, for the relative CO2 emission level, the estimated results show that there is an inverted u-

shaped relationship between China's carbon intensity and economic growth. After controlling the

per capital GDP variable, trade liberalization will reduce the carbon intensity of Chinese provinces

and regions. In addition, the high proportion of coal consumption and the increasing proportion

of industrialization will be obstacles to the reduction of carbon intensity.

Third, the industrial proportion and primary energy consumption structure in eastern and western

regions have a significant positive impact on carbon emissions. Trade openness is better for the

east and central, reducing the region's carbon emissions while growing the economy. Urbanization

rate is significantly negative in east and central China, which inhibits the total carbon emission,

indicating that the new urbanization construction is intensive and environmentally friendly.

Fourth, China is a net exporter of trade pollution, with much higher implied carbon emissions

from exports than from imports. When China trades with developed countries, the carbon

emission from domestic production is greater than that from consumption, which belongs to the

mode of "domestic commitment and foreign consumption" with implicit carbon. Developed

countries such as the European Union, the United States and Japan have transferred more

pollution to China.

5. Research limitations

The current research cycle of this paper is relatively short, and the whole process since the reform

and opening up has not been comprehensively studied.

Due to the limitation of data collection, Tibet has not been included in the research scope.

The Angle of variable selection is not comprehensive enough and needs to be improved.

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Only the implied carbon in trade between countries was measured, and no different industry

studies were conducted.

Key words: carbon emission；Trade means pollution；Economic growth；MRIO model

6. Reference

[1] Brian，R.Copeland，and M.Scott Taylor，1994，“North-South trade and the

environment”，Quarterly Journal of Economics，109(3)：755-787.

[2] Zhou Jieqi，Wang Tongsan，2014，“Foreign Direct Investment, Economic Growth and

CO2 Emissions—Based on Empirical Research on China's Provincial Panel Data ”， Journal of

Beijing Institute of Technology (Social Science Edition)，16(3)：30-37.

[3] Song Deyong，Yi Yanchun，2011，“Foreign Direct Investment and China's Carbon

Emissions”，China Population Resources and Environment，21(1)：49-52.

[4] Guo Pei，Yang Jun， 2015：“The Impact of FDI on Carbon Emission Intensity in China's

Industrial Sector”， Economic Issues，(8)：76-85.

[5] Huang Yongming，Chen Xiaofei，2018，“Research on Implicit Pollution Transfer in China”，

China Population Resources and Environment，28(10)：112-120.

[6] Philip Cooke，2015，“Green governance and green clusters：regional & national policies

for the climate change challenge of Central & Eastern Europe”，Journal of Open Innovation：

Technology，Market，and Complexity， 1(1)：1-17.

[7] Evelin Priscila Trindade，et al“. Sustainable development of smart cities：a systematic

review of the literature”，

Journal of Open Innovation：Technology，Market，and Complexity，3(11)：1-14.

[8] ChangKeun Park，JiYoung Park，and Simon Choi，2017，“Emerging clean transportation

technologies and distribution of reduced greenhouse gas emissions in Southern California ” ，

Journal of Open Innovation ： Technology，Market，and Complexity，3(8)：1-19.

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20.

Let’s Consume the Green to Save the Environment! – A Comparative and Critical

Discursive Perspective on Green Advertisings

Shubo Liu Assistant Professor, PhD, Central University of Finance and Economics, China

Min‐Ren Yan (Corr.)

Professor, PhD, Chinese Culture University, Taiwan

Anqi Song

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Master, Central University of Finance and Economics, China

Abstract

As environmental concerns began to emerge, companies started to target at the growing

‘green market’ and launch their green products. Corporate advertisings as service

industry played an important role in facilitating corporate green marketing and fuelling

the desire for environmental-friendly commodities. Applying a Critical Discursive

Perspective, this study focuses on the corporate environmental advertisings in order to

illuminate their discursive strategies and the process that corporate green advertisings

generate and symbolically structure the customer perceived value of green

consumption. In addition, this study pays special attention on the constructive

characteristics and constructed meanings of green advertisings collected in China

market, where environmental awareness just began to rise. Research findings suggest

that a collaborative ecosystem is needed to enhance the green consumption.

Keywords: Green advertisement; Environmental Marketing; Critical discourse

analysis; China

Introduction

Public concerns over environmental issues have produced a dramatic increase in the

introduction of ‘green’ or environmentally friendly product, and many companies are

engaged in environmental marketing (Bahn & Wright, 2001; Leonidou et al., 2013; Wang &

Ju, 2017; Wang et al., 2018). In this background, corporate green advertisings emerge to

manifest the combination of the globalized ‘green movement’ and corporate marketing.

‘Green advertising’ is defined as commercial advertising that uses an environmental theme to

promote products, services, or corporate public images (Banerjee et al., 1995). In developing

economies such as China, marketers also begin to make effort to target the increasingly

lucrative green segment of the Chinese population (Chan, 2000). Like their counterparts in

the West, these ‘green pioneer firms’ rely on environmental advertising to communicate the

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eco-friendly aspects of their green products.

Green advertising, by claiming its promoted brand or product as ‘green’, seems to

have removed its negative environmental impacts. But a growing number of studies (e.g.,

Alcott, 2005; Bäckstrand & Lövbrand, 2006; Banerjee, 2003; Böhm & Brei, 2008; Carvalho,

2001; Raineri & Paillé, 2016) have suggested, it is still not a fact that business has become

reconciled with the environment. Therefore, commercial green advertisings are likely to

cover the conflict between environmental protection and business production and

consumption. And the corporate green advertising discourse connects consumerism to

environmentalism and seems to deliver a particular kind of green delusion to consumers.

To be more specific in this research context – China, it has been observed that green

advertising as well as green consumption is rising phenomenally, despite it being at its

beginning stage in this emerging economy. However, existing studies (e.g., Child et al., 2007;

Tsai, 2001; Weller, 2006) have suggested that the environmental protection institutions as

well as Chinese people’s understanding of the environment vary from its Western

counterpart. Similarly, as Corbett (2006) contends, ‘the social construction of nature or the

definitions and meanings, which people tend to build through social interaction about nature,

can be quite different from culture to culture’. Therefore, it can be proposed that firms tend to

adjust their environmental messages to their target audience, especially via the use of green

advertising. And the representation of the ‘greenness’ constructed by firms operating in

China is likely to be influenced by Chinese contexts.

As both multinational corporations and Chinese indigenous companies are launching

their green products and producing green advertisings in Chinese market, the discourses of

their green advertisings and green consumption might be featured differently, based on

differences in their understanding and experiences on green marketing. In such context, the

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main focus of this study is on discourses active on corporate websites as an advertising

channel in the digital era, and aims to explore in what ways do companies use both texts and

visuals to represent their green products and themselves as environmentally responsible.

Literature Review

The Constructive Role of Green Advertising in Green Consumption

Based on positivist assumption, there have been studies exploring the characteristics of green

advertising content. For example, researchers applying content analysis explored the

functional dimension of green advertisings and examine if the green advertising can induce

consumers’ purchase decision (McGowan, 2000). On the consumer side, such studies aim to

measure consumer attitudes toward green advertising and environmental attitudes (e.g.,

Haytko & Matulich, 2008; Nyilasy, Gangadharbatla & Paladino, 2014). In this paradigm of

research, the green advertising is assumed to be an instrumental role and the method of

content analysis is mostly used to comprehend the nature of green advertisings, namely their

composition and functionality. However, the positivist/structuralist approach faces difficulties

in explanations on the construction characteristics and constitutive components of the green

advertisings. Noticing the incompleteness, other scholars seeing advertising in its active form

examine the phenomenon for advertising’s meanings by using. For example, via a qualitative

and discursive research approach, Garland et al. (2013) and Chen (2016) studied the

configuration features of hybrid car advertisings. Such studies found that using ambiguous

messages in the green advertisings can promote socially and politically charged products for

consumers’ understanding and imagination, which lead to green consumption desires.

Similarly, drawing on findings from a rhetorical analysis of advertising and branding efforts

by an environmentally conscious cleaning product company, Ryan (2012) claimed that the

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role of advertising was shifting: the advertisements nowadays, besides disseminating material

lifestyle aspirations and product information, has been utilized as ‘Agenda-Setting socio-

political tools’ enabling private firms to incorporate social issues, such as the environmental

movement, into their advertising messages (Ryan, 2012: 73).

Above perspective assumed that companies hold an active stance to shape their

legitimacy through communication and, thereby, influence public perceptions. In this respect

then, corporate green advertising as a way of corporate communication, can be seen as ‘a

public relations vehicle’ aimed at influencing people’s perceptions (e.g., Dutton & Dukerich,

1991; Hooghiemstra, 2000). This stream of research on corporate advertising assumes an

active and constructing role. However, corporate advertising’s cultural/political role and its

semiotic effect have been neglected in the field of green marketing/consumption and

advertising research. As Caruana and Crane (2008, 2014), Glozer, Caruana and Hibbert

(2014) pointed that, to date, the majority of studies on consumer responsibility have relied on

the assumption that responsibility is ‘an objectively identifiable trait of sovereign consumers’

(Caruana & Crane, 2008), despite the recently emerged researches focusing on the discursive

and cultural aspect of corporate advertising and communication (e.g., Böhm & Brei, 2008;

Saint, 2008; Hansen, 2010; Eyles & Fried, 2012; Ryan 2012; Tregidga et al., 2014). This

research will adopt a critical discursive perspective to study the green advertisings.

Examining Green Advertising in Critical Discursive Perspective

In the critical discursive perspective, green advertising as an active discourse represents an

attempt to fix a web of specific meanings within a particular domain, and it is developed in

different social contexts and in a specific manner which will keep the needs of certain social

actors. To be more specific, the discourse of green advertising articulates people’s

understanding of the natural environment. And the discourse should be treated as

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heterogeneous and its embodied meanings as diversified. As Banerjee et al. (1995) claim,

although the green discourse was initially rooted in environmental activism, it has been

undergone semantic broadening and disseminated through other domains of public discourse

(such as social media and corporate marketing). In this sense and in lots of cases, the meaning

of ‘greenness’ has been extended, or even transformed, and thus appears much less evident in

its link to environmental issues.

Following Mühlhäusler and Peace (2006), ‘green discourse’ is defined as

environmental discourse as comprising the linguistic devices articulating arguments about the

relationship between humans and the natural environment. And there is a variety in

environmental management and governance discourse as green discourse. From a politics

viewpoint, Bäckstrand and Lövbrand (2006) argue that environmental discourse can be

generally categorized as ecological modernization, green governmentality, and civic

environmentalism. Each category of green discourse has different perspectives towards

environmental problem solving and environmental protection. These green discourses have

impact on corporate green advertising.

It is expected that corporate green advertising discourse recruits elements from

existing meta-green discourses, but it is not clear that in what way such discursive elements

are arranged into corporate green advertisings and how companies make efforts to contribute

to the meaning of green consumption through their green advertisings. Especially in the

Chinese social context, both the industrial development and people’s understanding of natural

environment are very different from their Western counterparts (Tsai, 2001; Weller, 2006).

It can be hypothesized that green advertising discourse as a sub-category of

commercial discourse bears the same characteristic of practicality. However, as mainstream

advertisings are for promoting consumptions and thus potentially involved in materialism,

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and materialism is intrinsically contradictory to environmentalism (Banerjee & McKeage,

1994), how do firms compromise the conflict in their green marketing discourse? In addition,

as discourse plays a vital role in representing firms’ green brand and products and

constructing meanings of green consumption for consumers, and commercial advertisings can

be seen as a manifesto of companies’ perceptions towards environmental issues, it is

important to understand the representation of commercial greenness. In sum, the discursive

process of firm constructing the meanings of greenness through their advertisings, as well as

this process how green advertisings help firms play their authoritative role in the construction

of eco-knowledge and informing consumption practices dealing with environmental

degradation is worth investigating.

Methodology –Critical Discourse Analysis

Based on Critical Discourse Analysis perspective, this study analyses both the texts and

visuals in an effort to understand how companies use language, and explores the types of

messages that firms communicate via websites. Methodologically, Fairclough (1992; 1993;

1995a; 2001) provides an analytic framework researchers using CDA (Critical Discourse

Analysis) can employ to illuminate representations within the text. Fairclough’s three-

dimensional discourse analysis framework (see Figure 1) provides a systematic set of

inquiries to analyse both textual and visual constructs in relation to social phenomena.

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Figure 1. Fairclough’s Three-dimensional Model (Fairclough 2001: 21).

Adopted from: Fairclough (2001)

Data Collection

As corporate websites serve as one of the main channels and as a form of broadcasting green

advertisements, websites provide rich data sets for this research. It is therefore worth

investigating the linguistic or discursive features of corporate online green advertisings.

In order to select the most suitable samples, companies are selected according to the

following four criteria: firstly, the company should have a series of green products (products

are communicated as having environmental protection features, such as pollution reduction or

energy efficiency enhancement), and should have launched its green campaigns for

advertising their green products in the Chinese market. Secondly, the firms should be from

resources-based industries which have received the most environmental pressure and have

had prominent environmental impact. Such industries can be real estate development,

automobile manufacturing, chemical industry or machinery manufacturing. Thirdly, the

company should have a strong environmental performance in its industry and should have

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been rated as the top green firms in China for consecutive years (from 2014-2018) according

to China’s Green Company rating.

Based on the selection criteria, four companies that advertise their environmental

friendly product through corporate websites have been selected for data collection through

their corporate websites: General Electric in China, Unilever in China, BYD Auto, and

Landsea Real Estate. These four companies are categorized into two groups for comparative

analysis: Category 1 as MNCs subsidiaries (GE and Unilever) and Category 2 as Chinese

indigenous companies (BYD and Landsea). To study these corporate websites in details, we

focus on discourse from Products/Services Introduction page. In addition to textual

information, visual information from product introduction page and from webpage embedded

video clips was collected and analysed. In sum, there are 76 advertising samples collected for

analysis.

Data Analyses and Findings

The Descriptive/textual Analysis

The Product Introduction Page provides detailed textual information on firms’ green products

and services. The promotional texts of the product advertisings not only stress the

‘environmental-friendly’ facet, but also emphasize the facet of ‘hi-technology’ in their green

products. Such technological advancement is always linked to innovation, improved

efficiency and economic advantages. Additional functional facets of green products are also

often found in the discourse, such as ‘safety’, ‘convenience’, and ‘cosiness’.

In addition, new green words/terms have been coined by the MNCs to name their

green products/service or green projects. For example, GE coins the word of ‘ecomagination’

and Unilever brings forward its ‘Sustainability Living Plan’. In the Product page of GE’s

website, all green products are introduced as a subfield category under the main theme of

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‘ecomagination’. GE launched their ‘ecomagination’ campaign in 2005 in order to promote

their energy-efficient technology products and services, and to construct the company’s

public image as a leading socially responsible company. In comparison, the Chinese firms are

less active in ‘green vocabulary’. In their advertised product names, the green factors are

literally presented. For example, the advertised green cars from BYD are named as ‘pure

electric car e6’, ‘DM dual-mode electric car’ and ‘K9 pure electric bus’4. Landsea simply

named its green housing products according to their different market sectors, such as ‘green

house for first-time house buyers’ and ‘green residence for the aged people’5.

Exhibit 1. ‘Wind Turbines’ Ad 1 for GE.

Websites addresses: http://www.ge.com/about-us/ecomagination6;

http://www.ge-energy.com/wind7

4 http://www.bydauto.com.cn/energy.html 2015/11/23

5 http://landsea.cn/Group/RealEstate.aspx 2015/11/23

6 Accessed 2015/11/18.

7 Accessed 2015/11/18

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Exhibit 2.’Wind Turbines’ Ad 2 for GE.

Websites addresses: http://www.ge-energy.com/about/index.jsp8

The Descriptive/visual Analysis

In addition to textual information, visual information can be identified. A typical example is

from GE’s ‘Wind Power’ (Exhibit 1). The picture of the green product features a close-up

shot of a beautiful view of nature. In the foreground, clean and trimmed grasses, standing in

the grassland, waive gently in the breeze. In the background, the sky is blue and clear. The

two wind turbines stand on the horizon and the line between the grassland and the sky. All

the features combined together in this advertising signify a harmonious relationship between

the life on Earth and ‘wind power’ generated by human technology. Besides, as the human

made participants (two wind turbines) are placed behind the other visual participants (natural

objects: trees, grasses), this sequencing of information suggests a sequencing of importance

(Kress and van Leeuwen, 2006).

A simple semiotic reading would argue that this ad tries to construct a utilitarian

fantasy of technology putting natural resources to use. These two wind turbines represent the

scientific power which intrudes into the natural territory and frames it as a resource for

8 Accessed 2015/11/18

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human use. The sky and the invisible wind which are made visible through the presentation

of gently waving grasses are presented as a tamed object of consumption, capable of

providing ‘proven performance, availability and reliability.’ It also turns into a commercial

and privatized sense which brings ‘more value for our customers.’ Corbett (2002) illustrates

this point of green advertisement: ‘Advertising commodifies the natural world and attaches

material value to non-material goods, treating natural resources as private and ownable, not

public and intrinsic’ (p. 146).

However, the green products and technology here in the advertising are structured a

little differently from the usual fashion, especially compared with the portrayal of technology

which appears in traditional advertisings or advertisings from C2 companies (which will be

presented later). In this green advertising, the intrusion of human technology, the two wind

turbines, is played down and naturalized by placing them into the secondary position to the

primary natural landscape in the foreground and background (See Exhibit 1). This is very

different from many other new technology product advertisings in which technology or the

product, as well as their function description, is usually represented in the central position of

the advertising.

The setting in the GE green advertising also tries to de-materialize the technology and

green product – the wind turbine – by presenting simplicity in the visual composition: there is

neither sophisticated technological description nor information about the products. Instead,

the wind turbines in the picture look like natural objective. Similarly, another advertising

picture of the wind turbine is positioned together with coconut trees (See Exhibit 2); the

contrast between GE’s products and the natural trees sends a message to the audience: the

green product is just another object in the eco-system, same as the trees standing on the sea,

and it causes no harm to the natural environment. This parallel strategy, together with the

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overall campaign theme of ‘ecomagination’, can be read as a corporate defence against the

environmentalist critique of technology – how could the green technology such as the wind

turbine possibly cause harm to nature if it can exist in such a perfect and tranquil scene?

A very similar presentation of the green product from Unilever can be found in

Unilever’s green product (See Exhibit 3). The product is even invisible in this advertising;

the purifying effect of the product is visually shown in a purifying process, and the ‘U’ shape

water represents the brand of Unilever. The invisibility of advertised products further reduces

people’s concern about the technological intrusion into the natural environment – how can

technology cause harm if it has nothing but a purifying effect?

Exhibit 3. Unilever’s Water Purifier Ad 1.

Websites addresses: http://www.unilever.com.cn/brands-in-action/detail/pureit/332166/9

In addition to the ‘de-materialization’ strategy of representation, a promotional nature

of advertising discourse is reflected in its effect of decoration which glorifies the promoted

corporate greenness. For example, GE’s ‘ecomagination’ campaign advertising videos

9 Accessed 2015/10/9

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represent the utopian version of corporate green advertisings which are replete with of

‘imagination’, ‘invention’, ‘ideas’, etc.

Exhibit 4. BYD’s Pure Electric Bus ‘K9’ and ‘e6’.

Website address: http://www.bydauto.com.cn/energy.html

Another example is from the visual presentation. Exhibit 4 shows two advertisings of

BYD’s pure electric bus, the K9, and the pure electric car, the e6. In these advertisings, the

car’s physical presence takes up nearly 1/3 of the advertisings (the bus takes 1/2) and is in the

very central position. Its chrome outlook appears shiny and sleek. It is also surrounded by

radiating ‘swoosh’ lines, suggesting extreme speed. The fluorescent lines with the light green

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of the enveloping city background make the green bus/car look technologically advanced; the

green leaves decorating and surrounding the bus/car replace its emission pollution.

Coupled with the streamline design of the bus, these features suggest futurism – a

significant Western artistic style employed in, for example, painting, film, architecture,

industrial design, and fashion. Futurism stresses speed, technology, youth and the triumph of

humanity over nature (Marinett, 1973).

The textual information of the green car advertisings read as:

‘BYD: as the very first pure electric car in the world, e6 has the most advanced

technologies. Its energy transforming rate reaches 90%, which is much higher than the

one from traditional cars; the battery can be re-charged with more than 4,000 times

while still keep its capacity of 80%.’10

Thus, one can see that the environmental value of the electric is rarely represented in

the product introduction texts. In the abstract sketch of the background of the advertising, one

sees a slightly green city outline and bright sky; the environmental protection factor is

positioned as a sort of decoration - nature is down-played and only presents itself through the

scattered leaves flying around the bus/car driving in the highway.

So, in the sequencing of information which appears in such advertisements, the

product itself is presented as ‘high’ while the environmental factors are ‘low’ (Kress &

Leeuwen, 2006). In the background, the fictitious sketch of the city as the car’s embedding

environment also help to play down the environment in the background. The same tendency

to trivialize environmental values can also be observed in the product introduction: The BYD

is called ‘the car of tomorrow’, but the ad does not specify what kind of ‘tomorrow’ it is. Is it

the ‘tomorrow’ of environmental harmony? Or economic prosperity? Or technological

10 http://www.bydauto.com.cn/energy.html accessed 2015/11/21

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development? The answer is unclear. But whatever it is, the advertising suggests that it ‘has

not stopped amazing the automobile world’.

Exhibit 5. Landsea’s Green Houses: Landsea Countryside-shire.

Websites addresses: http://landsea.cn/Group/RealEstate.aspx11

The character that protruding product as the central while marginalizing natural

environment can also be found in the green real estate developer’s product advertisements

(See Exhibit 5). In the visual part of the advertising, it shows a view overlooking the whole

architectural complex. The advertising does highlight the ‘environmental value’ of the

housing complex, but the ‘environmental value’ is not aligned to notions of environmental

protection or pollution reduction. Instead it shows it as an environmental aesthetic value. The

houses are situated at the foot of a green hill and beside a tranquil stream. Ironically, the vast

mountain covered by forest and the fantastic view of a clear river in the advertising seems to

be unrealistic for ordinary Chinese consumers living an urban life, although the housing

product in the ad is targeting them.

11 Accessed 2015/10/10

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In addition, the name of the green building complex (‘Landsea Countryside-shire’) is

also implying an unrealistic sense of a pleasant bucolic lifestyle. The slogan on the top left of

the picture – ‘healthy technology houses’ – suggests the functional aspects of the product: it

can bring health and housing technologies. Again, the issue of environmental protection is

not mentioned.

By calling on the urban or new urban rich to live in the not-yet-polluted land of the

rural region or the rural poor, this advertising exacerbates the already serious environmental

inequality along class and geographical lines. It seems the green house is presented in the

advertising as a way to escape the pollutions of city life: as long as you can afford to buy this

‘green’ house, you will live in a clean environment.

Compared to the last advertising, this green apartment advertising contains more

explicit environmental references, in both images and words. The building complex is named

as ‘Countryside-shire’, which explicitly implies its connection with the countryside, or the

bucolic lifestyle. The ‘shire’ is originally a noun defining an administrative district of

England. The direct translation and application of this English word into the name of

Landsea’s green product is emblematic for a desired ‘Western lifestyle’. The green

mountains, blue sky, and the clear river construct a fantasy of eco-utopia—a ‘pure world’ into

which people from severely polluted cities can escape. This eco-utopia contrasts sharply with

Chinese ordinary urban people’s familiar, polluted environment.

However, while this advertising addresses the public’s increasing environmental

concerns, it proposes an extremely individualistic solution – to run away. Escape into pristine

nature all by yourself, simply through purchasing an advertised ‘green house’. In this sense,

instead of marketing the house as a solution for saving the environment, the green product is

portrayed as a ‘parachute’ or ‘escape pod’ in which the urban rich can flee from pollution.

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They can then explore their private eco-utopia, which is to be enjoyed alone or/and with

families. The utopia portrayed here constructs a fantasy of human nature harmony to cover

the rising social anxiety about environmental pollution. And the utopia is envisioned through

the expansion of consumption: to consume more and consume the advertised green products.

Interpretative Analysis – themes and discursive strategies

Referring to Fairclough (2001)’s interpretative analysis framework, discursive aspects such

as the contents of the language, the subjects, and the relationship existing among the subjects

need to be analysed in order to decode the tissues of meaning in the narrative construction.

Based on descriptive and interpretive analyses of representative green advertisings, three

common themes or discursive strategies can be identified (See Table 1).

The first discursive strategy is to re-color the greenness. The studied firms are all

found to communicate green in a more-than-green way and their green products are not only

marketed on their eco-friendliness (to protect the planet earth) but also on attributes such as

products’ property of pleasantness, high-technology, fuel efficiency and the likelihood of

reduced fuel costs. A few quotes mention the eco-benefits for the environment, and in many

cases the green discourse blur the boundary of between conventional and green product; this

in order to give more breadth to the idea of commercial greenness, and to fit green products

into a hi-tech, and holistic designs which are deemed more about nature than just their

‘greenness’.

The explicit unbalance between environmental-protection or eco-benefits for the

environment of the green products and other features in green products in the descriptive

texts reflects the discursive strategy which embeds greenness/green consumption in the

context of functionality and instrumentality.

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Along with constructing green products as multi-facets, corporate green advertising

discourse defines the green consumer subject, and the texts inform their audience that being

green is not just about being responsible for the environment, but entails multiple roles: they

are not only consumers, but also ‘environmentally concerned contributors’, ‘responsible

participants’ and ‘caring family members’. One subject that companies particularly frame and

align with consumers is the value-advocate/contributor/participant/patron of responsible and

savvy practices. The semantic power in advertising discourse leads interpretations that the

audiences are not just consumers.

Secondly, by presenting the technological competitiveness and economic advantages

with statistical data, the advertising discourse ostensibly makes sense of and portrays the

commercial greenness as a must-take option for consumers by forecasting future standards. In

shaping green products as a necessary solution to consumer’s problems and a contributor to

everyday life, companies are constructed as the provider of the achievement, thus they obtain

power to control what is needed/desired for green consumption and green consumers.

Corporate communication discourse strategically poses the environmental and resource-

related challenges as undoubted upcoming realities and thus presents their green products as

the inevitable choice for the audience. Firms’ green products play a role as saviour helping

solve clients’ environmental pressure; by relating to and stressing the macro environment

threats such as the slow-down of economic growth, the endangering natural environment, and

the depletion of resources, being green and consuming greenness seems to be the only choice

left. The discursive effect of future tense such as ‘will’, ‘be going to’ used in the sentences is

to centre the green products and green consumption as an essential approach for consumers to

achieve this end: choosing greenness means choosing the future. In addition to using the

future tense statements which shape greenness as necessary for the future, firms also

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strategically place discourse which highlights that the greenness has become a fad in society

and draw on the contextual background. So appropriating green consumption is necessary.

The promotional consumption discourse produces the possibility of an ‘ideal self’ of

consumer culture (Holt and Thompson, 2004; Thompson, 2004) and implies to the audience

that being a consumer can also be a contributor in environmental protection, as long as

consumers choose the advertised green products. And, the more you consume, the more you

can contribute to environmental benefits.

In the end and based on the previous two strategies, a pacification and perfecting

strategy emerges. This can be found in the green advertisings shaping a utopian version of the

consumption world in which advertised corporate greenness helps to meet environmental

challenges, and corporate green product/technology serves as a panacea for environmental

threats.

In a nutshell, a common connection exists between corporate green advertising

discourse and a broader societal context. The discursive strategies such as positioning,

embedding and idealizing (See Table 1) are in order to represent green consumption as a

feasible way to environmental problem, and a direction to the future, or a green consumption

lifestyle. Themes 1 and 2 are about positioning environmental responsibility with green

returns (such as functional aspects of green products), and embedding ‘greenness’ into a

broader context and making worldly meanings of the corporate greenness (such as economic

benefits, people’s health, technocratic). In such positioning and embedding process through

intertexutality and interdiscursivity, people’s direct concern to the environment is diluted and

people’s attention redirected to consumptions through the all-around green products. Theme 3

is for idealizing the corporate greenness by constructing utopian versions of green future. The

corporate greenness discourse in a promotional style naturalizes the intrusion of human’s

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industrial practices to the nature and therefore seems to guarantee a problem-free version of

green consumption.

Different to the traditional environmentalist understanding of ‘greenness’, which

holds the opinion that radical changes to the current lifestyle and economic systems should

take place in order to reverse environmental degradation and reduce industrial damages to the

nature (Catton & Dunlap, 1994), and greenness is to protect the environment itself, in the

corporate understanding of ‘greenness’, being green is re-articulated as a way of consuming:

consumption turns to be environmental responsible and problem-free as long as people

consume the green products. In addition, as the advertising discourse implies, consuming

green is also rational (because it helps to reduce cost and protect health) and modern (because

it has the advanced technologies and is able to solve current environmental threats).

Therefore, greenness in green products is not simply a responsibility anymore, but an

attraction for consumers. The green products in advertisings are more like a new choice for

living a lifestyle, a consumption lifestyle. As a means for the ends of living a green lifestyle,

to protect the environment is not an end anymore.

Table 1. Common Themes and Discursive Strategies

Common themes Strategies 1. Re-coloring the corporate greenness Positioning - intertextuality and interdiscursivity 2. Making sense of the corporate

greenness Embedding - embedding the greenness into the existing discourses; rationalism; futurism

3. Perfecting the corporate greenness Idealizing - Pacification, topic avoidance, utopian version

This lifestyle is not necessarily related to reducing over-consumptions, but a new

approach to consumption and a way of extricating both consumers and the consumerism

society from environmental worries, although the environmental threats remain. This can be

explained as a reflection of social ideological thought of ecological modernization (Hajer

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1995; Coffey & Marston, 2013) and is connected to social change towards post-materialism

(Inglehart, 1981).

Differences between the two categories

In addition to common discursive strategies above, differences can be found among the

discourses from the two categories of firms. Applying Kress and van Leeuwan’s (2006)

visual analysis framework on reading the advertising images from aspects of angle, distance,

and size, it can be found that differences existing between the two categories of firms’

advertisings: C1 advertising visuals tend to naturalize the green products while C2 ones tend

to centralize the green products. Naturalization is achieved by both distance and size visual

strategies. With regard to distance, the C1 firms either position green products and natural

objects in an equal position (See Exhibit 6: Unilever), or position natural objects in a closer

position than products, to viewers (See Exhibit 7: GE). Similarly, the natural objects appear

to be a larger size than products in C1 firms’ green advertisings. Conversely, C2 firms

apparently give prominence to products instead of natural objects by positioning products in

the middle and closer to viewers, and by presenting products in a larger size (See Exhibit 8,

9). Such differences also signify the different relationships between green products and

nature/environment: C1 advertisings treat products and nature as equal and relate green

products closely to the natural environment while C2 advertisings value products more

highly.

140

Exhibit 6. Unilever’s Green Advertisngs.

Website address: http://www.unilever.com.cn/brands-in-action/detail/pureit/332166/

Exhibit 7. GE’s Green Advertisings.

Website address: http://www.ge-energy.com/wind

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Exhibit 8. BYD’s Hybrid Cars.

Website address: http://www.bydauto.com.cn/car-360-F3DM.html

Exhibit 9. Landsea’s Green Advertings.

Website address: http://landsea.cn/Group/RealEstate.aspx

The Chinese local firms (C2)’ online advertising discourse has largely presented

elements such as the being functional, utilitarian and economic. Furthermore, the

environmental protection value of corporate greenness is placed in a peripheral position and

becomes simply decorative in the composition of the product advertisement.

Compared with Chinese indigenous firms, MNCs (C1)’ green advertising discourse

reflects a higher level in sophistication in C1 firms which have integrated greenness into their

development strategies. For example, GE’s Ecomagination and Unilever’s Sustainability

Living Plan both represent a strategic purpose which not only talks about protecting the

142

natural environment but also makes business sense. Such linguistic novelty can be seen to

demonstrate an intention for change, a change from a singular focus on environmental

protection to a joint (and more importantly) concern for business gains and

market/consumption growth. In addition, in the product introduction advertisings, the

discourse aims to dematerialize and naturalize human technologies in the composition of

greenness, and thus the discourse is attached to an ecocentrism perspective (Thompson &

Barton, 1994).

Moreover, as C2 advertising discourse attributes their environmental responsibility

partly to the governmental and civil demand, and thus external political appeals, C1

advertising discourse expresses environmental responsibility as intrinsically motivated. In C1

advertisings, it is not mainly the outside pressures or appeals leading to corporate green turn,

but also, and more importantly, the companies’ understanding of the business opportunities in

future green market.

In addition, a strategy-oriented approach towards green business is found in C2’s

green discourse surrounding the idea of ‘sustainability’. The term of sustainability

development is better articulated and integrated by considering environmental protection and

meanwhile making business sense: for GE, sustainability means that green is green: green

business brings dollars. For Unilever, under the umbrella idea of Sustainability Living Plan,

the advertising discourse interprets sustainability in one hand as consuming environmentally

friendly products to a green lifestyle, and in the other hand as opportunity for growing the

business. In such discursive approach, the term of sustainability development is made

comprehensible and provides guidance for practices.

143

Compared with the MNCs green discourse, it seems that the Chinese corporate green

discourse is more oriented toward a culture of benefits – emphasizing the instrumental

greenness for the consumers while erasing the green benefits for the environment.

Explaining the Differences

The differences existing in the studied corporate green discourses mark the contextual

influences (such as historical and social differences) shaping the construction of greenness

and the objective of environmental protection.

In the first general difference, the MNCs’ green advertisings discourse is greener than

the Chinese one. Such differences can be further explained by probing into the contextual

backgrounds. First, in the political realm, China did not experience a powerful and consistent

environmental movement as did the U.S. and Europe. Instead, Chinese politics have been

deeply influenced by Confucianism, which proposes strict social hierarchy and demarcates

the responsibilities of the ruler and the subject. This belief extricates ordinary Chinese

citizens from concerns about public issues such as environmental problems (Weller, 2006).

Under Mao’s reign, the socialist egalitarianism and Mao’s call on the Chinese to participate

in collective actions eventually coalesced into a collective violence against nature (Shapiro,

2001). Post-Mao Chinese society relapsed into the Confucian tradition and citizens again

became indifferent toward public affairs. Most citizens believe that environmental protection

is the government’s business (Weller, 2006; Zhang, 2002).

Also in China, the state regime directs and coordinates institutional change, i.e. the

Chinese Communist Party and the state administrative bodies are the rule-makers, and others

bodies such as companies and non-government organizations follow the rules (Tsai, 2001;

Child, Lu & Tsai, 2007). The dominant and repressive role that the state plays in constructing

regulatory pillars for the system of environmental protection, and the follower’s role played

144

by the Chinese indigenous companies, helps to shape the discursive features of their green

advertising. For example, C2 firms’ green advertising discourse bears a ‘political accent’

(compared with C1 firms’ business strategic orientation) and attaches much importance to

government policies and regulations regarding environmental protection and responsibility.

Factors from the economic realm play another role in restraining the constitution of

green norms. While the Western consumers have achieved an elevated place in post-

industrial life and begun to pursue post-materialism consumption such as green consumption

(Ger & Belk, 1996), China’s bourgeoning capitalism has not reached the stage of mass

consumerism which has paved the road for green consumption in the West. China’s rapid

economic development polarizes the society into the poor and the rich. The poor, comprising

most of the population, are still struggling to enhance their very low living standards. As

users but not consumers, they seek to fulfil needs from commodities’ functions. Although

China’s rising middle class is influenced by the imported environmentalism from the West

through media and education, their green consumption for a green lifestyle is comparably in

an early stage and their demand for expensive status-symbols such as green products is

limited.

Discussion and Conclusion

Given the findings reviewed above, the research can now summarize the findings. It is found

that in representing their greenness, firms apply both textual and visual languages to construct

corporate green hegemony and maintain their power by developing a corporate

environmental discourse.

To be more specific, in a CDA lens, the descriptive analyses suggest that firms shape

themselves as environmental responsible and authoritative, and represent themselves as

145

taking a green leader’s position. Based on the interpretative analyses, the themes as the

‘meaning tissues’ of green consumption have been identified. Firstly, a recurring theme in

corporate greenness discourse is the subversion of subjects: the corporate discourse reframes

consumers as non-commercial. A particular subject that companies frame and align with their

targeted consumers is the value-advocate/contributor/participant/patron of responsible and

savvy practices. The semantic power in advertising discourse frames audiences’

interpretations that they are not just consumers, but also having other subjective positions. In

such way, the promotional consumption discourse produces the possibility of an ‘ideal self’

of consumer culture (Thompson, 2004) and implies to the audience that being a consumer can

also be a contributor in environmental protection, as long as consumers choose the advertised

green products. Secondly, the object of ‘consuming green’ is structured as being morally

superior to the ‘other’. Such constructed dualisms and built boundaries also play a role to

mythologies and idealize the greenness in the green products/firms (Thompson, 2004).

Indeed, the discursive practices of corporate green advertising rely on mixture of these

elements to imbue the green consumption with meanings and make it interpretable.

In conclusion, compared with the ‘deep green’ advocated by environmental activists,

the advertised greenness is the ‘in-breadth green’, which helps to balance consumerism with

environmental conservation. Therefore, the components in green products are presented both

horizontally via interdiscursivity and vertically via intertextuality. Such process of meaning-

making has been enabled through both ‘intertextual’ (e.g., newly produced texts are from

fragments of existing, conventional ones) and ‘interdiscursive’ (e.g., texts are drawn from

texts from other domains of discourses) properties of discourse, enables the audience to draw

upon a wider range of social-historical backgrounds.

146

There are three theoretical contributions in this study. Firstly, this research extends the

corporate environmental responsibility literature by showing how firms are discursively

constructed through their commercial green advertisings. Secondly, linked into consumer

culture theory and by accepting that corporations are capable to influence the meaning of

environmental responsibility, this study advances a critical understanding of how firms

influence the nature, meaning, and knowledge of environmental consumption. Thirdly, this

research contributes to literature of green marketing by finding out how green advertising

practices vary and identifying the characteristics of green advertisings in a developing

country context.

Divergence of Commercial Greenness

Although the transnational advertising industry tries to spread a universal version of green

consumerism around world, this universalizing scheme is found to be localized by local firms

in their green marketing and advertising (Li, 2010). This shows that advertising, in spite of its

dazzling visual power and excellent outreach capability, is not fully transferred across

borders. Instead, the green advertising discourses are embedded in a society’s particular

cultural-historical and institutional conditions, or so called ‘ecosystem’ conditions.

As Corbett (2006) suggests, ‘the social construction of nature or the definitions and

meanings, which people tend to build through social interaction about nature, can be quite

different from culture to culture’, and furthermore, all environmental messages ‘have

ideological roots that are deep and that are influenced by individual experience, geography,

history, and culture’ (Corbett, 2006:6). Based on such point, firms are expected to adjust their

environmental messages to their target audience, especially via the use of green advertising.

And the representation of the ‘greenness’ constructed by firms operating in China is likely to

be influenced by Chinese ecosystem contexts.

147

In this study, the analysis findings have indicated differences exist between MNCs’

and Chinese firms’ websites (See Table 2). Such finding supports the argument that the

globalization process of environmentalism is not homogeneous or unitary (Weller, 2006).

And the globalization of environmentalism as well as green discourse is not simply a

diffusion process from a single core to the rest of the world. Instead, the green discourse is

influenced by external influences and bears specific characteristics.

Compared with the MNCs green discourse, it seems that the Chinese corporate green

discourse is more oriented toward a culture of benefits – emphasizing the instrumental

greenness for the consumers while erasing the green benefits for the environment. The co-

existing of pragmatist attitude and philanthropy feature as one of the indigenous

characteristics of Chinese corporate environmental responsibility is found as influenced by

the emerging market economy in China, and is closely related to China’s social and cultural

backgrounds (Xu & Yang, 2010). In conclusion, although firms have their communication

channels and discursive power to shape and present their green innovativeness, the corporate

greenness is in certain extent subject to external constraints from existing social structures

and ecosystem.

Table 2. Dissimilarities between Green Consumption Discouses constructed by C1 and C2.

Dissimilarities C1: MNCs C2: Indigenous Firms Level of greenness

High Low

Green Vocabulary (Linguistic Novelty)

Coinage of new green terms such as ‘ecomagination’ (GE), ‘Sustainability Living Plan’ (Unilever); consistently presenting

No newly coined green words; Different and inconsistent presenting

148

Focus To manifest the environmental friendliness of the product; Dematerializing the product

To highlight the functionality of the product (e.g. energy reduction, high performance); Marginalizing the environmental factors; instrumental view; More anthropocentrism.

Level of Integration

High (strategic, organizational level)

Low (PR, product level)

Features Sustainability: reservation/protection and business growth

Responsibility: only emphasizes the responsibility for environment (national pride, patriotism, promote national environment, harmony, and prosperity)

Political Orientation

Environmental governance Environmental legitimacy

This research finding suggests that, in order to avoid ‘superficial’ or low level of

green perception and consumption, and improve firms’ integrated environmental practice and

innovation, it is necessary to develop a comprehensive approach within the organization, as

well as a holistic and systematic perspective and supporting ecosystem among stakeholders

(Cocca & Ganz, 2014; Yan et al., 2018). As green innovation and sustainability development

involve multiple stakeholders (Banerjee, 2003), a collaborative ecosystem (government-

academia-industry) needs to be adopted and connected to green consumption: firstly, a

nationwide macro viewpoint is necessary for the planning of green innovation norms and

environmentally friendly industrial developments, and the idea of collaborative ecosystem

should be embedded in governmental and public policies. Secondly, once governmental

authorities and agencies take environmental impact and sustainability development model as

an important criterion of national/regional economy, the green innovation/technology of

industrial practices can be better assessed and encouraged. An appropriate green innovation

index system can also be established during this process. Thirdly, an industrial clustering

mechanism can be developed and additionally facilitate the green industrial practices to

149

enhance the level of sustainability development. In the end, a feedback structure is essential

for identifying the critical success factors and trends of consumer expectations. Based on the

identified information, firms conducting green innovation are able to better construct their

green advertising content. The commercial green discourse therefore can deepen green

consumption - helps enhance consumers’ environmental awareness as well as the value of

green innovation products.

Limitations and future research

There are several limitations to this study. First, the nature of qualitative and interpretivism

research limits a great level of generalizability. Secondly, as this study is exploratory in a

new research context, only a few green pioneer companies were selected. It is expected that

future scholarly investigations will include and focus on other categories of firms. Finally, in

qualitative studies, the researcher is positioned as the primary instrument of data collection,

analysis, and interpretation (Creswell, 2003). In discourse studies, as discourse is socially

constructed and constantly changing, it is impossible that researchers can be immune to the

influences from their surrounding discourses and other social constructions. Therefore, it has

to be admitted that the research findings, as well as the interpretations and conclusions

within, can be limited to some extent.

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