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四、針對個別零售商，本研究發現不同的競爭市場特性，最佳價格調整策略也不相同，
在供應商無競爭行為下，零售商採取開放式價格調整策略能獲得較高的獲利，尤其 消費者為「群體式學習行為高理性程度」市場中，開放式價格調整策略具有明顯優 勢，因此建議個別零售商應穩定供應商之價格競爭，採取開放型策略，獲得最大的 獲利。
五、在供應商呈現競爭行為下，開放式價格調整策略在「無學習」及「群體式學習高理 性程度」行為市場仍為優勝策略，在「自我學習」及「群體式學習低理性程度」下，
保守型價格調整策略則表現較佳。整體而言，競爭市場中在供應商無競爭行為及競 爭零售商採取保守型策略下，該零售商採取開放式價格調整策略之獲利最高，與對 手的差距也最大。
六、本研究所完成之零售商價格競爭市場模擬系統提供考慮個體心理層陎來解釋動態的 市場資料，可作為實務管理者之參考架構，幫助其對於在市場或產業中資料的了解，
可事前針對所設計的價格策略或外部干涉進行所有可能的評估，觀察決策可能的影 響，藉此產生資訊與策略的意涵支援價格決策的制定，以進行相關的策略設計或配 套措施，提供定價策略測試之決策輔助模式。
第四節 未來研究方向
本研究提出一個涵蓋供應商、零售商與消費者之價格競爭市場之模擬模式，未來可根據 此價格競爭模式針對相關議題繼續延伸與擴展，以下為後續研究可能的研究方向：
一、 針對供應商商品特性繼續延伸，本研究考量供應商商品為完全同質性，未來可考量 產品具有品牌異質性，因此消費者進入零售商後，將根據品牌傾向進行商品購買決 策，並可藉由對商品使用後產生熟悉度表示消費過該品牌的報酬，而調整供應商商 品選擇決策。例如在日常生活用品，不具求新求變的消費型態，反而著重於產品熟 悉與安心的感覺，在消費者消費該品牌產品後，將增加對該產品之熟悉度，此熟悉 度的增加將增加其購買傾向，因此利用熟悉度代表所購買品牌之報酬，同時熟悉度
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隱含忠誠度的含意，當消費者愈熟悉或了解該商品，對該商品傾向增加下，價格的 影響力將逐漸縮小。藉此觀察商品品牌異質對零售商價格競爭所帶來的影響，此部 分研究目前已著手進行中。
二、 本研究價格競爭主軸下，主要針對價格變數作為價格決策法則之觀察與決策變數，
未來可根據實務情況加入重要之觀察與決策變數加以考量，例如存貨水準、訂購量 等。
三、 本研究主要聚焦在對零售商價格競爭的探討，未來可分析相同情境下，對供應商價 格競爭的影響。
四、 針對消費者個人特質與學習行為繼續擴展。依據不同的零售產業特性，所聚焦的個 人特質與行為將有所不同，個人特質上，可繼續延伸針對價格敏感度、忠誠度、社 群網絡、不同文化等深入分析。在消費者學習行為上，同樣不侷限本研究所提之三 種行為，可繼續擴展測試其他消費者行為，如策略式學習等，觀察對零售商價格競 爭的影響。
五、 針對零售商與供應商之間不同「價格主導權」（Leadership）進行探討(S. C. Choi, 1996;
Pan, et al., 2010)，本研究是假設在零售商與供應商具有相同價格主導權下進行價格 競爭。然而價格制定主導權可以區分為兩種：1、供應商權力大於零售商，例如可口 可樂大於零售商。2、零售商權力大於製造商，例如沃爾瑪（Wal-Mark），對於不 同價格主導權下如何影響零售商競爭行為，也是一個有趣的議題。
六、 本研究主要聚焦在二個供應商與二個零售商的競爭市場中，廠商數目可表示「競爭 強度」之程度，競爭市場中零售商陎臨對手之數目愈多，表示競爭強度愈大，未來 可針對競爭強度不同的情境下，測試供應商家數:零售商家數為 4:2，2:4 及 4:4 的情 形。
七、 本研究主要在零售商競爭觀點下探討其價格競爭行為。未來可將此模式與非競爭觀 點下的定價行為(Greetham & Sengupta, 2007; Tatsuo & Tamotsu, 2010) 進行比較。
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八、 本研究並未針對特定「定價策略」進行測試，未來可在實務上針對特定零售商價格 策略，例如「短視型定價策略」(Benchekroun, et al., 2009)進行測試，觀察其如何影 響零售商的競爭型態。
九、 最後期望實務業者提供或應用歷史資料進行模式參數調整，使本模式進而成為實務 導向的之價格策略決策支援系統。
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參考文獻
1. Amaral, L. A. N., & Uzzi, B. (2007). Complex systems--A new paradigm for the integrative study of management, physical, and technological systems. Management Science, 53(7), 1033-1035.
2. Anand, K. S., & Goyal, M. (2009). Strategic information management under leakage in a supply chain. Management Science, 55(3), 438-452.
3. Anderson, E., & Kumar, N. (2007). Price competition with repeat, loyal buyers. Quantitative
Marketing and Economics, 5(4), 333-359.
4. Anderson, P. (1999). Complexity theory and organization science. Organization Science, 10(3), 216-232.
5. Benchekroun, H., Martin-Herran, G., & Taboubi, S. (2009). Could myopic pricing be a strategic choice in marketing channels? A game theoretic analysis. Journal of Economic Dynamics &
Control, 33(9), 1699-1718.
6. Bernstein, F., & Federgruen, A. (2003). Pricing and replenishment strategies in a distribution system with competing retailers. Operations Research, 51(3), 409-426.
7. Chaharsooghi, S. K., Heydari, J., & Zegordi, S. H. (2008). A reinforcement learning model for supply chain ordering management: An application to the beer game. Decision Support Systems,
45(4), 949-959.
8. Chen, S. H., & Chie, B. T. (2008). Lottery markets design, micro-structure, and macro-behavior:
An ACE approach. Journal of Economic Behavior & Organization, 67(2), 463-480.
9. Chintagunta, P. K., & Rao, V. R. (1996). Pricing strategies in a dynamic duopoly: A differential game model. Management Science, 42(11), 1501-1514.
10. Choi, S. C. (1991). Price-competition in a channel structure with a common retailer. Marketing
Science, 10(4), 271-296.
11. Choi, S. C. (1996). Price competition in a duopoly common retailer channel. Journal of Retailing,
72(2), 117-134.
12. Choi, T., Dooley, K., & Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: Control versus emergence. Journal of Operations Management, 19(3), 351-366.
13. Cowan, R., Jonard, N., & Zimmermann, J.-B. (2007). Bilateral collaboration and the emergence of
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
129
innovation networks. Management Science, 53(7), 1051-1067.
14. Delre, S. A., Jager, W., Bijmolt, T. H. A., & Janssen, M. A. (2007). Targeting and timing promotional activities: An agent-based model for the takeoff of new products. Journal of Business
Research, 60(8), 826-835.
15. Engelbrecht, A. (2005). Fundamentals of computational swarm intelligence: Wiley New York.
16. Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 88(4), 848-881.
17. Furtado, V., Melo, A., Coelho, A. L. V., Menezes, R., & Perrone, R. (2009). A bio-inspired crime simulation model. Decision Support Systems, 48(1), 282-292.
18. Gilbert, G., & Troitzsch, K. (2005). Simulation for the social scientist: Open Univ Pr.
19. Greetham, D. V., & Sengupta, A. (2007). Evolutionary brand competition with consumers
interactions Paper presented at the The second workshop on Market Dynamics
20. Hanaki, N., Peterhansl, A., Dodds, P. S., & Watts, D. J. (2007). Cooperation in evolving social networks. Management Science, 53(7), 1036-1050.
21. Harrington, J. E., & Chang, M. H. (2005). Co-evolution of firms and consumers and the implications for market dominance. Journal of Economic Dynamics & Control, 29(1-2), 245-276.
22. Heath, B., Hill, R., & Ciarallo, F. (2009). A survey of agent-based modeling practices (january 1998 to july 2008). Journal of Artificial Societies and Social Simulation, 12(4), 143-177.
23. Hehenkamp, B. (2002). Sluggish consumers: an evolutionary solution to the Bertrand paradox.
Games and Economic Behavior, 40(1), 44-76.
24. Hopkins, E. (2007). Adaptive learning models of consumer behavior. Journal of Economic
Behavior & Organization, 64(3-4), 348-368.
25. Hwarng, H. B., & Xie, N. (2008). Understanding supply chain dynamics: A chaos perspective.
European Journal of Operational Research, 184(3), 1163-1178.
26. Ingene, C. A., & Parry, M. E. (1995). Channel coordination when retailers compete. Marketing
Science, 14(4), 360-377.
27. Izquierdo, S. S., & Izquierdo, L. R. (2007). The impact of quality uncertainty without asymmetric
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
130
information on market efficiency. Journal of Business Research, 60(8), 858-867.
28. Janssen, M., & Jager, W. (1999). An integrated approach to simulating behavioural processes: A case study of the lock-in of consumption patterns. Journal of Artificial Societies and Social
Simulation, 2(2), 21-35.
29. Janssen, M. A., & Jager, W. (2003). Simulating market dynamics: Interactions between consumer psychology and social networks. Artificial Life, 9(4), 343-356.
30. Jiang, L., & Wang, Y. Z. (2010). Supplier competition in decentralized assembly systems with price-sensitive and uncertain demand. M&Som-Manufacturing & Service Operations Management,
12(1), 93-101.
31. Kennedy, J. (1999). Minds and cultures: Particle swarm implications for beings in sociocognitive space. Adaptive Behavior, 7(3-4), 269-287.
32. Kennedy, J., Eberhart, R., & Shi, Y. (2001). Swarm intelligence: Springer.
33. Khouja, M., Hadzikadic, M., & Zaffar, M. A. (2008). An agent based modeling approach for determining optimal price-rebate schemes. Simulation Modelling Practice and Theory, 16(1), 111-126.
34. Kim, W. S. (2009). Effects of a trust mechanism on complex adaptive supply networks: An agent-based social simulation study. Journal of Artificial Societies and Social Simulation, 12(3).
35. Kimbrough, S. O., Wu, D. J., & Zhong, F. (2002). Computers play the beer game: Can artificial agents manage supply chains? Decision Support Systems, 33(3), 323-333.
36. Kirman, A. (1993). Ants, rationality, and recruitment. Quarterly Journal of Economics, 108(1), 137-156.
37. Kirman, A., & Tuinstra, J. (2005). Bounded rationality, heterogeneity and market dynamics - Introduction. Journal of Economic Dynamics & Control, 29(4), 595-600.
38. Kirman, A. P., & Vriend, N. J. (2001). Evolving market structure: An ACE model of price dispersion and loyalty. Journal of Economic Dynamics & Control, 25(3-4), 459-502.
39. Leng, M. M. (2005). Game theoretic applications in supply chain management: A review. Infor,
43(3), 187-220.
40. Levin, Y., McGill, J., & Nediak, M. (2009). Dynamic pricing in the presence of strategic
‧
consumers and oligopolistic competition. Management Science, 55(1), 32-46.
41. Li, G., Ji, P., Sun, L. Y., & Lee, W. B. (2009). Modeling and simulation of supply network evolution based on complex adaptive system and fitness landscape. Computers & Industrial
Engineering, 56(3), 839-853.
42. Li, G., Yang, H., Sun, L., Ji, P., & Feng, L. (2010). The evolutionary complexity of complex adaptive supply networks: A simulation and case study. International Journal of Production
Economics, 124(2), 310-330.
43. Li, L. D., & Zhang, H. T. (2008). Confidentiality and information sharing in supply chain coordination. Management Science, 54(8), 1467-1481.
44. Marsh, L., & Onof, C. (2008). Stigmergic epistemology, stigmergic cognition. Cognitive Systems
Research, 9(1-2), 136-149.
45. McCarter, M. W., & Northcraft, G. B. (2007). Happy together? Insights and implications of viewing managed supply chains as a social dilemma. Journal of Operations Management, 25(2), 498-511.
46. Midgley, D., Marks, R., & Kunchamwar, D. (2007). Building and assurance of agent-based models:
An example and challenge to the field. Journal of Business Research, 60(8), 884-893.
47. Miller, J., & Page, S. (2007). Complex adaptive systems: Princeton Univ. Press.
48. Mishra, B. K., Raghunathan, S., & Yue, X. H. (2009). Demand forecast sharing in supply chains.
Production and Operations Management, 18(2), 152-166.
49. Nagarajan, M., & Sosic, G. (2009). Coalition stability in assembly models. Operations Research,
57(1), 131-145.
50. Nair, A., Narasimhan, R., & Choi, T. Y. (2009). Supply networks as a complex adaptive system:
Toward simulation-based theory building on evolutionary decision making. Decision Sciences,
40(4), 783-815.
51. North, M., & Macal, C. (2007). Managing business complexity: Discovering strategic solutions
with agent-based modeling and simulation: Oxford University Press, USA.
52. Pan, K. W., Lai, K. K., Leung, S. C. H., & Xiao, D. (2010). Revenue-sharing versus wholesale price mechanisms under different channel power structures. European Journal of Operational
Research, 203(2), 532-538.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
132
53. Pathak, S., Dilts, D., & Mahadevan, S. (2009). Investigating population and topological evolution in a complex adaptive supply network. Journal of Supply Chain Management, 45, 54.
54. Pathak, S. D., Day, J. M., Nair, A., Sawaya, W. J., & Kristal, M. M. (2007). Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective. Decision Sciences, 38(4), 547-580.
55. Pathak, S. D., Dills, D. M., & Biswas, G. (2007). On the evolutionary dynamics of supply network topologies. Ieee Transactions on Engineering Management, 54(4), 662-672.
56. Rivkin, J. W. (2000). Imitation of complex strategies. Management Science, 46(6), 824-844.
57. Rivkin, J. W., & Siggelkow, N. (2007). Patterned interactions in complex systems: Implications for exploration. Management Science, 53(7), 1068-1085.
58. Roach, A., & Gilbert, N. (2007). ConSims: Simulating consumer behaviour. Paper presented at the The second workshop on Market Dynamics
59. Sarin, R., & Vahid, F. (2001). Predicting how people play games: A simple dynamic model of choice. Games and Economic Behavior, 34(1), 104-122.
60. Schenk, T. A., Loffler, G., & Rauh, J. (2007). Agent-based simulation of consumer behavior in grocery shopping on a regional level. Journal of Business Research, 60(8), 894-903.
61. Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113-1126.
62. Siebers, P., Aickelin, U., Celia, H., & Clegg, C. (2009). Simulating customer experience and word-of-mouth in retail-a case study. Simulation, 86(1), 5-30.
63. Surana, A., Kumara, S., Greaves, M., & Raghavan, U. (2005). Supply-chain networks: A complex adaptive systems perspective. International Journal of Production Research, 43(20), 4235-4265.
64. Tatsuo, Y., & Tamotsu, O. (2010). Dynamics of market structure driven by the degree of consumer's rationality. Physica A: Statistical Mechanics and its Applications, 389(5), 1041-1054.
65. Tay, N. S. P., & Lusch, R. F. (2005). A preliminary test of Hunt's general theory of competition:
Using artificial adaptive agents to study complex and ill-defined environments. Journal of
Business Research, 58(9), 1155-1168.
66. Tay, N. S. P., & Lusch, R. F. (2007). Agent-based Modeling of ambidextrous organizations:
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
133
Virtualizing competitive strategy. IEEE Intelligent Systems, 22(5), 50-57.
67. Tykhonov, D., Jonker, C., Meijer, S., & Verwaart, T. (2008). Agent-based simulation of the trust and tracing game for supply chains and networks. Journal of Artificial Societies and Social
Simulation, 11(3).
68. Varga, L., Allen, P., Strathern, M., Rose-Anderssen, C., Baldwin, J., & Ridgway, K. (2009).
Sustainable supply networks: A complex systems perspective. Emergence: Complexity &
Organization 11(3), 16-36.
69. Vila, X. (2008). A model-to-model analysis of Bertrand competition. Journal of Artificial Societies
and Social Simulation, 11(2).
70. Yue, X. H., Mukhopadhyay, S. K., & Zhu, X. W. (2006). A Bertrand model of pricing of complementary goods under information asymmetry. Journal of Business Research, 59(10-11), 1182-1192.
71. Zhang, H. T. (2002). Vertical information exchange in a supply chain with duopoly retailers.
Production and Operations Management, 11(4), 531-546.
72. Zhang, T., & Brorsen, B. (2009). Particle swarm optimization algorithm for agent-based artificial markets. Computational Economics, 34(4), 399-417.
73. Zhang, T., & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research, 60(8), 912-922.