CitationDownload as .RIS
Emerald Group Publishing Limited
Copyright © 2010, Emerald Group Publishing Limited
Article Type: Guest editorial From: Kybernetes, Volume 39, Issue 5
About the Guest Editor Desheng Dash Wu is the Affiliate Professor at RiskLab of University of Toronto and Director of RiskChina Research Center at University of Toronto. He also served as the research fellow and Affiliated Lecturer at Rotman School of Management, University of Toronto. His research interests focus on enterprise risk management, performance evaluation, and decision support system. He has published more than 40 journal papers appeared in such journals as Risk Analysis, Decision Support Systems, International Journal of Production Research, European Journal of Operational Research, Expert Systems with Applications, Socio-Economic Planning Sciences, International Journal of Production Economics, Annals of Operations Research, Journal of Operational Research Society, IEEE Transactions on Knowledge and Data Engineering, Computers and Operations Research, International Journal of System Science, etc. He has coauthored three books with David L. Olson. He has served as editor/guest editors/chairs for several journals/conferences. The special issues he edited include those for Human and Ecological Risk Assessment (2009, 2010), Production Planning and Control (2009), Computers and Operations Research (2010), International Journal of Environment and Pollution (2009) and Annals of Operations Research (2010). He is a member of PRMIA (the Professional Risk Managers’ International Association) Academic Advisory Committee and steering committee member.
During the last decade, service science and service engineering have emerged as a new area attracting lots of attentions. In the current uncertain world, risk management has become more and more important. New risks in many practical service industry problems require new computational methods and tools.
Risk management has a long history in services that can be dated back to time memorial. Chinese traders, Egyptians and Babylonians prior to AD undoubtedly coped with the risk of sailing trade. The coffee trading of Lloyds of London is another example of using insurance for hedging risks of trading the seeds.
Risks can be viewed as threats, but business exists to cope with risks in services (Wu and Olson, 2009, 2010). Researchers declared risk-taking as the essential function of the entrepreneur, and thus the basis of his income (Hawley, 2009) and profit (Knight, 2006). There are many different ways of classifying risks (Majumder and Dutta, 2007; Hutton, 2007). In Wu and Olson (2010), we propose the following general way to classify risks: field based and property based.
“Risk in services”: an introduction
Financial risks, which basically includes all sorts of risks related to financial sectors and financial aspects in other sectors; these are, but not restricted to, market risk, credit risk, operational risk, and liquidity risk.
Nonfinancial risks, which includes risks from sources that are not related to finance. these are, but not restricted to, political risks, reputational risks, bioengineering risks, and disaster risks.
We think risks can have four properties: probability, dynamics, and dependence. The first two properties have been widely recognized in inter-temporal models from behavior decision and behavior economics area; the last property is well studied in finance disciplinary.
Probability of risks mainly proposes the utilization of probability theory and various distributions to model risks. This can be dated back to 1700s where Bernoulli, Poisson, Gauss are used to model normal events and general Pareto distributions and general extreme value distribution are used to model extreme events.
Dynamics of risks mainly proposes using stochastic process theory in risk management. This can be dated back to 1930s where Markov processes, Brownian motion and Levy processes are developed.
Dependence of risks mainly deals with correlation among risk factors. Various Copulas functions are built and Fourier transformations are also used here.
Risk management has not only developed a control focus, but most importantly it remains a tool to enhance the value of systems, both commercial and communal.
This special issue seeks to shed light on service area and its associated risks. We seek to provide the primary forum for both academic and industry researchers and practitioners to propose and foster discussion on state-of-the-art research and development quantitative risk analysis in the areas of risk management. It includes the broad coverage we were seeking, with a review work of enterprise risk management in supply chains, a modeling research using fuzzy Bayesian least squares support vector machine for urban power network planning, a survey study of risk management using qualitative data collected through surveys, a methodology paper for using regime-switching models and GARCH to model and explain the behavior of crude oil prices, a real options pricing approach for assessing R&D projects, a field study of a risk evaluation rule using a fuzzy Delphi method, and modeling research of using stochastic game-theoretic approach for modeling attacker-defender conflicts:
David L. Olson and Desheng Dash Wu review published approaches to supply chain risk management in services, to include identification and classification of types of risks, cases, and models. The authors identify a generic framework, then compare categorizations of supply chain risks. They briefly review cases and models applied to the study of supply chain risk.
Yongxiu He, Weijun Tao, Aiying Dai, Lifang Yang, and Rui Fang examined a fuzzy Bayesian least squares support vector machine (LS_SVM) model, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.
Dexiang Wu, Desheng Dash Wu, and Liang Liang connect the PCA method with the DEA method to estimate the online banking performance. Data are collected from 2007 annual reports of giant banks in the USA and UK including both financial and nonfinancial variables. It was found that most giant banks are performing well based on DEA analysis. Different DEA models can be classified into cost-oriented and online-oriented models, which is consistent with existing work based on data from other nations.
Klemen KavcČicČ and Andrej Bertoncelj examine the strategic orientation of organizations and importance of risk management. Their research methodology is based on the analysis of qualitative data collected through surveys. Furthermore, a theoretical framework is introduced based on a study conducted in the transition economy of Slovenia. Their study finds that companies in Slovenia, a transition economy within the European Union, often enter contractual relationships without sufficient strategic long-term assessments and are thus faced with high risks.
Cuicui Luo, Luis A. Seco, Haofei Wang, and Desheng Dash Wu present a comparison study of using both regime-switching models and GARCH to model and explain the behavior of crude oil prices in order to forecast their volatility. In regime-switching models, the oil return volatility has a dynamic process whose mean is subject to shifts, which is governed by a two-state first-order Markov process. It was found that the GARCH models are very useful in modeling a unique stochastic process with conditional variance; regime-switching models have the advantage of dividing the observed stochastic behavior of a time series into several separate phases with different underlying stochastic processes.
Lieh-Ming Luo and Her-Jiun Sheu present a real options pricing approach as an applicable assessment method for R&D projects that can jointly consider the aforementioned two types of risk management activities. The authors also investigate the value-enhancing effects of R&D risk management activities via interviews survey and secondary data analyses in the pharmaceutical industry of Taiwan. The value-enhancing effect of the hedging management on R&D projects is examined through numerical analyses. The results indicate that the hedging management can serve to be a useful mechanism for risk reduction as well as value enhancement for R&D projects. Additionally the value-enhancing effect would be more significant for those R&D projects with even higher risk-level.
Lawrence W. Judge, David Bellar, Jeffrey Petersen, and Elizabeth Wanless examine the level of compliance with National Collegiate Athletic Association and International Amateur Athletics Federation track and field hammer facility recommendations at division I universities in the USA, and to determine factors related to perceptions of facility safety. A 35-item survey instrument was distributed to 279 applicable schools with a 28 per cent response rate. Multiple regression analysis was utilized to determine factors significantly related to overall perception of safety. Such a research work creates another application of statistical analyses for risk perception in a specific sport setting.
Ming-Kuen Chen and Shih-Ching Wang derive a risk evaluation rule by applying Bayesian decision analysis to mitigate the risk and lower the cost in their outsourcing policy; and they use Delphi method to extract out 11 DC service quality evaluation indicators and also use these indicators to conduct a benchmark in Taiwan. Furthermore, they apply the proposed framework to figure out critical service advantages as well as suggestions for the DC involved in the benchmark. The results of their framework point out that enterprises should monitor the four operation elements (facility and infrastructure, server system management, information security management, disaster recovery mechanism) to ensure and improve their data integrity; and DC firms need to build robust facilities and services in the five operation elements (customizability, serviceability, IT infrastructure, security management and knowledge intensity).
Jing Zhang, Shifei Shen, and Rui Yang propose a stochastic game-theoretic approach for modeling attacker-defender conflicts. Attackers and defenders are supposed both to be strategic decision makers and partially aware of adversary’s information. The result shows that the intentional risk would be elevated by adaptive attack strategies.
We hope that these papers provide views of how contemporary service decision making under risks can be supported with innovative use of analytic techniques.
Desheng Dash WuGuest Editor
Hawley, F.B. (2009), Enterprise and the Productive Process, BiblioBazaar, Charleston, SC (original 1907)
Hutton, D.M. (2007), “Review: risk management for IT projects – how to deal with over 150 issues and risks”, Kybernetes, Vol. 36 Nos 5/6, pp. 824–6
Knight, F.H. (2006), Risk, Uncertainty, and Profit, Signalman Publishing, Orlando, FL (original 1921)
Majumder, D.K.D. and Dutta, S.K. (2007), “A new look at the Heisenberg’s uncertainty principle: a cybernetics and general dynamical systems approach”, Kybernetes, Vol. 36 Nos 5/6, pp. 754–67
Wu, D. and Olson, D.L. (2009), “Introduction to the special section on optimizing risk management: methods and tools”, Human and Ecological Risk Assessment, Vol. 15 No. 2, pp. 220–6
Wu, D. and Olson, D.L. (2010), “Introduction to the special section on Chinese earthquake risk management”, Human and Ecological Risk Assessment (in press)