طراحی شبکه های زنجیره ی دانش در چین - یک پیشنهاد برای سیستم مدیریت ریسک با استفاده از تصمیم گیری زبانشناختی

Designing Knowledge Supply Networks (KSN) with universities and research institutes has become a key source of technological innovations in Mainland China. In order to explore the key design principles, we first present typologies within KSN and explain the factors that can push, guide, or support the innovation process in such a network. Second, we identify and classify the particular risks that prevail when KSN are designed in an emerging region. To assess these risks, we next propose an advanced method that takes into consideration typical problems in group decision-making processes by applying linguistic operators derived from the field of decision theory and fuzzy-sets theory. The risk evaluation method is illustrated with a case study. Fourth, we offer advice on the mitigation of risks in KSN. Finally, we provide insights into the implementation of the risk evaluation method and its automation using Stakeholder Information Systems.

1.1. Problem situation During the global economic downturns of 2008 and 2009, top managers have become increasingly aware of their (global) supply networks. They have to cope with longer lead times due to distant transportation from suppliers on the other side of the globe, customer demand forcing shortened product-life cycles, and increased risks and uncertainties streaming from technological changes. For example, managers need to decide upon: when to switch production from hard disk to flash memories; to what extent telecommunication technology should be integrated in cars to enhance passenger safety; which environmentally-friendly (“green”) technology should be selected in a nuclear power plant or chemical plant; the pros and cons of having an advanced electronic payment system to allow customers to pay bills with mobile devices (financial services). We have observed that many companies are hesitant to bear the risks of developing innovations or adopting leading-edge technologies, particularly ‘strategic’ innovations. There is evidence in Mainland China that companies are systematically under-investing in technologies and failing to commercialize technical products, and thereby losing business opportunities. In contrast to incremental product and process innovation, decisions about the development and/or usage of strategic innovations can particularly shape a company's future. Outcomes determine revenue growth, cash flow, and competitive position, especially in emerging or poorly defined industries. The same management decision also changes risk exposure because strategic innovations are expensive, research and development takes longer, and the proportion of results with ambiguity is high. To help reduce these risks, collaboration strategies can be employed. Although it is possible that when companies seek to cooperate with technology partners, there can be unintended knowledge “spillovers” that often prevent cooperation, or make it less successful, nevertheless, we have identified a real benefit to companies working with universities and research institutions in the dissemination of knowledge. Technological innovations have another “drawback”: the risks associated with such science-based innovations are often hard to verbalize, circumscribe, and quantify. As Larry Jarrett, Vice President of Witco Corporation (chemicals), pointed out, the quantification of technical risk is as much an art as it is a science [1]. Although assessment questions may sound simple, they are not so simple to answer; for top managers: What is the benefit of the innovation?; for government: What are the health and safety effects of innovations and technology? How safe is safe enough? (see details in [2]). The inherent risk can lead to inconsistent decisions best illustrated by the well-known example from the 1980s in the UK: spending on safety measures per life saved in the pharmaceutical industry was 2500 times and in the steel industry 1000 times that in agriculture. An additional challenge is to assign weights to risk factors that reflect the relative importance. 1.2. Research questions In the face of the transition of Mainland China to a more market-oriented economy, the question arises about how to build up and ensure efficient national innovation processes while at the same time avoiding innovation that is managed in a largely random fashion. To cope with uncertainties and risks, we propose analyzing “knowledge networks” from a Supply Chain Management (SCM) perspective. Selected SCM methods and tools might prove to be useful in improving effectiveness and efficiency not only stage-by-stage but more importantly, over the entire chain or network. The focal point of this paper is the Knowledge Supply Network (KSN) in which “knowledge” is the flow unit. Similar in concept to the familiar physical “supply chain”, we can identify suppliers, i.e. technology and innovation sources, and customers. We also see analogies in dealing with risk situations because technology transfer alone does not automatically enable revenue or reduce costs. It is the design, configuration, and architecture of KSN that is crucial for the entire supply chain performance. So far, research in the fields of KSN has been limited, especially when KSN are designed in Mainland China, using universities and research institutes as a source of innovation [3]. Recently, entrepreneurs are increasingly being identified as “enablers” in these networks. However, several particular risks are faced by young managers when they try to commercialize innovative products and services. They can very easily be overwhelmed by large and established companies if the latter enterprises manage the leveraging of their enormous assets and capabilities. Moreover, young entrepreneurs often lack necessary business relations (“guanxi”), communication skills, and business know-how because designing innovation processes and networks are a relatively new approach in China. Important factors to consider are who are the entrepreneurs, and how to support the innovation process of these entrepreneurs in the described setting. Another vital question is how to establish a risk-mitigating mechanism in order to achieve the so-called win–win situation for business partners. A prerequisite for managing risks is that the overall risk value of a KSN can be measured. Having identified this research gap, we evaluated relevant decision methods, and present a model that identifies and evaluates risks for the design of KSN (Section 5). An example in this section illustrates the idea. In addition, we propose a mechanism to mitigate those risks (Section 6). Finally, we outline how to implement the proposed risk evaluation method with a special type of information system (Section 7).

We anticipate that systematic thinking in KSN designs provides a first step for decision makers to evaluate the risks and benefits of technological innovations more precisely, and thus foster the transformation of scientific achievements into product and process developments. The contribution of this paper to risk management research is threefold: 1. We extended the theory of physical supply chains to knowledge supply networks (KSN) by integrating a risk perspective. Specifically, we have presented two typical types of KSN in Mainland China, i.e., the national innovation system (macro level) and a system from an entrepreneurial perspective (micro level). Major risk factors when designing such KSN have been identified and described. 2. Given the economic, corporate and governmental uncertainties in Mainland China, together with the constraints and sometimes inadequate knowledge of some experts, we have frequently observed that experts in group decision-making processes do not give exact numbers to express their judgment but can only vaguely establish opinions. To cope with the problem of such fuzzy information we have presented an advanced method, based on linguistic decision analysis using LWD and LOWA operators, in order to assess an overall risk value. Due to the inherent goal of mitigating KSN risks, we have outlined practical ways of dealing with influencing risk factors. 3. Turning our ideas into practice, we suggest taking advantage of the years-long experience with knowledge-based Stakeholder Information Systems as a means to automate expert “opinion polls” via the Internet. The design of a ‘rational risk assessment system’ could be a possible direction for future fruitful research in risk communication.