Service Innovation Decision Analysis Based on Influence Diagrams
Keywords:
influence diagram, service innovation, decision-makingAbstract
The influence diagram is a probabilistic model for presenting decision problems as a directed graph. In this study, the dynamic influence diagram and the interactive dynamic influence diagram are used to model the three parties to service innovation: customers, suppliers, and service enterprises. The models analyze the decisions of these dierent parties and describe the process by which service enterprises should consider their own innovation conditions as well as those of the other parties, that is, customers and suppliers. Moreover, during the process of service innovation, service enterprises should be in constant communication with customers and suppliers. After the customers and suppliers respond, service enterprises can modify their innovation decision-making, and improve service innovation quality and income.References
Aranda, D.A.; Molina{Fernandez L.M. (2002); Determinants of innovation through a knowledgebased theory lens, Industrial Management & Data Systems, 102(5), 289-296, 2002. https://doi.org/10.1108/02635570210428320
Bilderbeek, R.; Hertog, P.D.; Marklund, G.; Miles I. (1998); Services in innovation - Knowledge intensive business services (KIBS) as co-producers of innovation, Synthesis report, SI4S project, 1998.
Burns, T.E.; Stalker, G.M. (2009); The Management of Innovation, Quarterly Journal of Economics, 109(4), 1185-1209, 2009.
Doshi, P.; Zeng, Y.F. (2009); Improved Approximation of Interactive Dynamic Influence Diagrams Using Discriminative Model Updates, International Conference on Autonomous Agents and Multiagent Systems, 907-914, 2009.
Doshi, P.; Zeng, Y.F.; Chen, Q. (2009); Graphical models for interactive POMDPs: representations and solutions, Autonomous Agents and Multi-Agent Systems, 18(3), 376-416, 2009. https://doi.org/10.1007/s10458-008-9064-7
Gallouj, F. (2015); Innovation in the Service Economy-The New Wealth of Nations, Post Print, 2015.
Gong, D.; Tang, M.; Liu, S. (2017); Reconsidering Production Coordination: A Principal Agent Theory Based Analysis, Advances in Production Engineering & Management, 12(1), 51-61, 2017. https://doi.org/10.14743/apem2017.1.239
Gmytrasiewicz, P.J.; Doshi, P. (2005); A Framework for Sequential Planning in Multi-Agent Settings, Journal of Artificial Intelligence Research, 24(1), 49-79, 2005.
Kaelbling, L.P.; Littman, M.L.; Cassandra, A.R. (1998); Planning and Acting in Partially Observable Stochastic Domains, Artificial Intelligence, 101(1-2), 99-134, 1998. https://doi.org/10.1016/S0004-3702(98)00023-X
Koller, D.; Milch, B.(2001) ; Multi-agent influence diagrams for representing and solving games, International Joint Conference on Artificial Intelligence, 1027-1034, 2001.
Ojasalo, K.; Koskelo, M.; Nousiainen, A.K. (2015), Foresight and Service Design Boosting Dynamic Capabilities in Service Innovation, The Handbook of Service Innovation, Springer London, 193-212, 2015.
Pan, Y.H.; Luo, J.; Zeng. Y.F. (2012); Modeling Method of Multi - Agent Interactive Dynamic Impact Diagram, Journal of Xiamen University (Natural Science), 51(6), 985-990, 2012.
Pan, Y.H.; Zeng, Y.F. (2018); Interactive Dynamic Influence Diagram Research Summary and New Solutions on Top-K Model Selection, Chinese Journal of Computer, 41(1), 28-46, 2018.
Pan Y.H.; Zeng Y.F.; Xiang Y.P.; Sun L.; Chen X.F. (2015); Time-Critical Interactive Dynamic Influence Diagram, International Journal of Approximate Reasonin, 57, 44-63, 2015. https://doi.org/10.1016/j.ijar.2014.11.004
Ryu, H.S.; Lee, J.N.; Choi, B. (2015); Alignment Between Service Innovation Strategy and Business Strategy and Its Eect on Firm Performance: An Empirical Investigation, IEEE Transactions on Engineering Management, 62(1), 100-113, 2015. https://doi.org/10.1109/TEM.2014.2362765
Tang, M.; Qi, Y.; Zhang, M. (2017); Impact of Product Modularity on Mass Customization Capability: An Exploratory Study of Contextual Factors, International Journal of Information Technology & Decision Making, 16, 939-959, 2017. https://doi.org/10.1142/S0219622017410012
Zeng, Y.F.; Doshi, P. (2012); Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams, Journal of Articial Intelligence Research, 43(1), 211-255, 2012.
Zeng Y.F.; Doshi P.; Chen Y.K.; Pan Y.H.; Mao H.; Chandrasekaran M. (2016); Approximating behavioral equivalence for scaling solutions of I-DIDs, Knowledge and Information Systems, 49(2), 511-552, 2016. https://doi.org/10.1007/s10115-015-0912-x
Zhang, H.Q.; Lu, R.Y. (2013); The impact of customer participation on employees innovation behavior in the service industry, Science Research Management, 34(3), 99-105, 2013.
Zhang, R.Y.; Liu, X.M.; Wang, H.Z.; Nie, K. (2010); Customer-Firm Interaction and Service Innovation Performance: A Perspective of Organizational Learning from Customers, Chinese Journal of management, 7(2), 218-224, 2010.
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