在商業的市場中，計畫性過時的策略一直被許多公司所應用著，以其策略取得更大的利潤。而本研究看到微軟為了要推廣Win 10並使他們最穩定的作業系統Win 7退役，宣布了其停止支援Win 7 的時間表也運用了計畫性過時的手法。微軟的最新作業系統Win 10 在2015年推出，但在過了兩年之後，其市佔率並沒有達到微軟預期的目標，且Win 7 的用戶還佔居多數。眾所周知的，作業系統是個人電腦的核心，一台電腦必須要有作業系統才能運作，但作業系統的升級並不在許多使用者的計畫中，許多Win 7的使用者並沒有打算升級作業系統，即使微軟在 Win 10推出的第一年提出了免費升級的政策。這對於業者來說是個攸關產品獲利的重要課題。為何這些 Win 7的用戶不願意升級到 Win 10。這在學術上也是個有趣且值得研究的現象，為什麼更新穎，效能更好的產品而使用者卻不願意升級？
所以針對這個議題，本研究從計畫性過時的角度結合PPM理論框架，以推力-拉力-繫助力的概念，並參考許多資訊系統轉換的關鍵因素，如轉換成本，系統相容性，相對優勢及主觀規範等因素建立了一個關於系統升級的模型來探討使用者作業系統轉換的意圖，並運用PLS與SPSS統計軟體加以驗證模型與假說。本研究成果可供業界實際參考應用，而且在學術上也補強了關於資訊系統轉換的相關研究中所缺乏的「垂直升級」這一類型的實例。

In the commercial market, the "Planned Obsolescence" strategy has been applied by many companies to make more profits with its strategy. Microsoft announce its schedule to stop supporting Win 7 in an effort to promote Win 10 and retire its most stable operating system, Win 7. Microsoft's latest operating system, Win 10, was launched in 2015, but after two years, its market share fell short of Microsoft's target, and Win 7's users still make up the majority. As is well known, the operating system is the core of a personal computer. A computer must have an operating system to operate. However, the upgrade of the operating system is not in the plan of many users. Many users of Win 7 don’t intend to upgrade the operating system,even if Microsoft introduced a free upgrade policy in the first year of Win 10. This is an important issue for the industry to profit from the product. This is also an interesting and worthwhile phenomenon in the academic world. Why users reluctant to upgrade a newer and more effective OS?
For this issue,this study tired to combines the PPM Model framework from the perspective of planned obsolescence , with the concept of Push-Pull-Mooring Model, and refers to many key factors of information system switching, such as switching cost, system compatibility, relative advantage and subjective norms and other factors have established a model of system upgrade to explore user OS switching intention.This study use PLS and SPSS statistical software to verify the model and hypothesis. The results of this study can be used for reference by the industry, and academically reinforce the lack of "vertical upgrade" in the related research on IS switching.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 研究流程 6
第二章 文獻探討 7
2.1 軟體升級 7
2.2 計畫性過時 10
2.3 人口遷徙理論 14
第三章 研究方法 17
3.1 研究架構 17
3.2 研究假說 18
3.3 操作型定義與衡量 27
3.4 研究設計 30
第四章 實證分析 32
4.1 樣本基本資料分析 32
4.2 模型與假說驗證 34
4.2.1 測量模式分析 34
4.2.2 區別效度(Discriminant validity) 37
4.2.3 多元共線性(Multicollinearity)與共同方法偏誤(Common Method Bais)檢定 39
4.2.4 結構方程式分析與假說檢定： 41
第五章 結論與建議 44
5.1 研究結果與討論 44
5.2 意涵與未來研究方向 48
5.3 研究限制 50
5.4 未來研究方向 51
參考文獻 52
附錄一、問卷資料 62

1. 王豐勝（2011）。探討個人電腦作業系統升級意圖與行為之研究。國立中山大學資訊管理學系研究所博士論文，高雄市。
2. 林哲延（2013）。智慧型手機作業系統轉換意圖之探討。國立中山大學資訊管理學系研究所碩士論文，高雄市。
3. 潘民偉（2014）。整合規範焦點與人口遷移理論探討使用者於智慧型手機平台上之轉換意圖。國立中山大學資訊管理學系研究所碩士論文，高雄市。
4. Adamson, G., Gordon, D., (2003). Industrial Strength Design: how Brooks Stevens Shaped Your World. MIT press, Cambridge.
5. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice-Hall.
6. Anderson, J. C. and D. W. Gerbing (1988). "Structural equation modeling in practice: A review and recommended two-step approach." Psychological Bulletin 103(3): 411-423.
7. Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58(3), 53–66.
8. Arkes, H. R., & Ayton, P. (1999). The sunk cost and concorde effects: Are humans less rational than lower animals? Psychological Bulletin, 125(5), 591–600. doi: 10.1037/0033-2909.125.5.591
9. Au, Y. and Kauffman, R. (2001). Should we wait? Network externalities, compatibility, and electronic billing adoption, Journal of Management Information Systems. 18(2), 47-63.
10. Avus, C. & Hou, Y. (2010). ‘Migrating to a New Virtual World’: Exploring Social Network Sites Switching through Human Migration Theory. Department and Graduate Institute of Library and Information Science, National Taiwan University.
11. Bayus, Barry L. (1988), “Accelerating the Durable Replacement cycle with Marketing Mmix Variables.” Journal of Product Innovation Management, 5(3), 216-26.
12. Bansal, H. S., Taylor, S. F., & James, Y. S. (2005). ’Migrating' to new service providers: toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96–115. doi: 10.1177/0092070304267928
13. Bogue, D. J. (1969). Principles of demography, New York: Wiley.
14. Boyle, P. J., Halfacree, K., and Robinson, V. (1998). Exploring Contemporary Migration. New York: Longman.
15. Bundschuh, R.G., and Dezvane, T.M.,(2003) How to make after sale services pay off, The McKinsey Quarterly., 4, 116–127
16. Burnham, T. A., Frels, J.K. and Mahajan, V. (2003). Consumer Switching Costs: A Typology, Antecedents, and Consequences, Journal of the Academy of Marketing Science. 31(2), 109-126
17. Champion, T. & Fielding, T. (1992). Editorial Introduction. Migration Processes and Patterns, Vol. 1: Research Progress and Prospects. Eds. T. Champion and T. Fielding. London: Belhaven Press, 1–14.
18. Chang, I. C., Liu, C. C., & Chen, K. (2013). The push, pull and mooring effects in vir- tual migration for social networking sites. Information Systems Journal, 24(4), 323–346. doi: 10.1111/isj.12030
19. Chiu, H. C., Hsieh, Y. C., Roan, J., Tseng, K. J., & Hsieh, J. K. (2011). The challenge for multichannel services: Cross-channel free-riding behavior. Electronic Commerce Research and Application, 10(2), 268–277. doi:10.1016/j.elerap.2010.07.002
20. Clark, W. A. V. (1986). Human Migration. Beverly Hills: Sage Publications.
21. Clark, D. E., & Knapp, T. A., & White, N. E. (1996). Personal and location-specific characteristics and elderly interstate migration. Growth & Change, 27(3), 327–351. doi:10.1111/j.1468-2257.1996.tb00909.x
22. Cooper, R.B and Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach, Management Science. 36(2), 1990, pp.123-139
23. Cooper, T. (2004). Inadequate life? Evidence of consumer attitudes to product obsolescence. Journal of Consumer Policy, 27(4), 421-449.
24. Cohen, R. (1996). Theories of migration. Cheltenham, UK: E. Elgar.
25. Creusen M. (1998), “Product Appearance and Consumer Choice,” Delft University of Technology: Delft, The Netherlands.
26. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-340.
27. Davis, F.D., Bagozzi, R. P. and Warshaw, P. R. (1989) User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science. 35(8), 982-1003
28. Dess, G. G., Lumpkin, G. T., and Eisner, A. B. (2007). Strategic Management: Text and Cases, 3 ed. New York: McGraw-Hill Irwin.
29. Dick, A. S., and Basu, K. (1994) . Customer loyalty: Toward an integrated conceptual framework, Journal of the Academy of Marketing Science. 22(2), 99-114
30. Dishaw, M. T. and Strong, D. M. (1998). Supporting software maintenance with software engineering tools: A computed task-technology fit analysis, The Journal of System and Software. 44(2), pp.107-120
31. D’Ambra, J. and Rice, R. E. (2001) Emerging Factors in User Evaluation of the World Wide Web, Information & Management. 38(6), 373-384
32. Eres, R, Louis, W. R., Molenberghs, P. (2017). "Why do people pirate? A neuroimaging investigation." Social Neuroscience 12(4): 366-378. DOI: 10.1080/17470919.2016.1179671
33. Farrell, J., Klemperer, P. Coordination and Lock-In: Competition with Switching Costs and Network Effects. Competition Policy Center. 2006
34. Farrell, J., and Saloner, G. (1985). Standardization, compatibility, and innovation, The RAND Journal of Economics. 16(1), 70-83.
35. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior. An Introduction to Theory and Research 1st Edn. Addison-Wesley, Reading, MA.
36. Fishman, A., Gandal, N., and Shy, O., (1993). Planned obsolescence as an engine of technological progress. J. Ind. Econ. 41 (4), 361–370.
37. Fornell, C. (1992). A National Customer Satisfaction Barometer: The Swedish Experience. Journal of Marketing, 56(1), 6–21. doi: 10.2307/1252129
38. Fornell, C. and D. F. Larcker (1981). "Evaluating structural equation models with unobservable variables and measurement error." Journal of Marketing Research 18(1):39-50.
39. Fu, J. R. (2011). Understanding career commitment of IT professionals: Perspectives of push–pull–mooring framework and investment model. International Journal of Information Management, 31(3), 279–293. doi: 10.1016/j.ijinfomgt.2010.08.008
40. Germani, G. (1965). Migration and Acculturation. In Handbook for Social Research in Urban Areas. Ed. Philip M. Hauser. Brussels, Belgium: UNESCO, 159–178.
41. Gershoff, A.D., Kivetz, R., and Keinan, A., (2012). Consumer response to versioning: howbrands' production methods affect perceptions of unfairness. J. Consum. Res. 39 (2),382–398
42. Goodhue, D. L and Thompson, R.L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly. (19:2), pp.213-236
43. Granberg, H. (1997). The quality re-evaluation process: product obsolescence in a consumer-producer interaction framework. Stockholm: University of Stockholm Department of Economic History.
44. Guielford, J. P. (1965). Fundamental Statistics in Psychology and Education. New York: McGraw-Hill.
45. Guiltinan, J. (2009) Creative Destruction and Destructive Creations: Environmental Ethics and Planned Obsolescence, Journal of Business Ethics, Vol. 89, September, 19-28
46. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New Jersey: Prentice-Hall.
47. Heide, J. B., and Weiss, A. M.,(1995) ,Vendor consideration and switching behavior of buyers in high technology markets, Journal of Marketing. , 59(3),.30-43.
48. Hennington, A., Janz B., Poston, R. (2011) I’m just burned out: Understanding information system compatibility with personal values and role-based stress in a nursing context Computers in Human Behavior. ,27 , .1238–1248
49. Hou, A. C. Y., Chern, C. C., Chen, H. G., & Chen, Y. C. (2009). Using demographic migration theory to explore why people switch between online games. 42nd Hawaii International Conference on System Sciences, 1–9. doi: 10.1109/HICSS.2009.493
50. Hou, A. C. Y., Chern, C. C., Chen, H. G. & Chen, Y. C. (2011). 'Migrating to a new virtual world': Exploring MMORPG switching through human migration theory. Computers in Human Behavior, 27(5), 1892–1903. doi: 10.1016/j.chb.2011.04.013
51. Hsieh, J. K., Hsieh, Y. C., Chiu, H. C., & Feng, Y. C. (2012). Post-adoption Switching Behavior for Online Service Substitutes: A Perspective of the Push-Pull-Mooring Framework. Computer in Human Behavior, 28(5), 1912–1920. doi: 10.1016/j.chb.2012.05.010
52. Hsu, J. S. C. (2014). Understanding the role of satisfaction in the formation of perceived switching value. Decision Support Systems, 59, 152–162. doi: 10.1016/j.dss.2013.11.003
53. Jackson, J. A. (1986). Migration. In Aspects of Modern Sociology: Social Processes, Longman, London and New York.
54. Jacoby, J., Berning, C., and Dietvorst, T. F. (1977), “What about Disposition?” Journal of Marketing, 41(2), 22-28.
55. Jones, M. A., Reynolds, K. E., Mothersbaugh, D. L., & Beatty, S. E. (2007). The Positive and Negative Effects of Switching Costs on Relational Outcomes. Journal of Service Research, 9(4), 335–355. doi: 10.1177/1094670507299382
56. Jones, M.A., Mothersbaugh, D.L.and Beatty, S.E. (2000) Switching barriers and repurchase intentions in services, Journal of Retailing. 76(2), 259–274
57. Jones, M.A., Mothersbaugh, D.L. and Beatty, S.E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes, Journal of Business Research. (55), 441-450
58. Juglas, I.A., and Waston, R. T.,(1997), The consequences of information technology acceptance on subsequent individual performance, Information & Management ,32(3),113-121
59. Junglas, I & Watson, R. (2003). U-Commerce: A Conceptual Extension of E-Commerce and M-Commerce.. 667-677.
60. Karahanna, E., Agarwal, R., and Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research, MIS Quarterly, 30(4), 781–804.
61. Keaveney, S. M. (1995). Customer switching behavior in service industries: An explor- atory study. Journal of Marketing, 59(2), 71–82. doi: 10.2307/1252074
62. Kende, M. (1994). A note on backward compatibility, Economics Letters. ,45 385-389
63. Kim, H., and Kankanhalli, A. (2009). Investigating user Resistance to information system implementation: A Status Quo Bias Perspective, MIS Quarterly., 33(3), 567-582.
64. Kim, G., Shin, B., and Lee, H. (2006). A study of factors that affect user intentions toward email service switching, Information & Management. (43), 884–893
65. Kim, S. and Son J. (2009). Out of Dedication or Constraint? A Dual Model of Post-Adoption Phenomena and Its Empirical Test in the Context of Online Services, MIS Quarterly., 33(1), 49-70
66. Klemperer, P.,(1987), Markets with Consumer Switching Costs, Quarterly Journal of Economics. (102), 375–394.
67. Klemperer, P., (1995), Competition when customers have switching costs: an overview with applications to industrial organization, macroeconomics, and international trade. Review of Economic Studies., 62(4),515–539.
68. Kuppelwieser, Volker G., Klaus, P., Manthiou , A., Boujena, O. (2019). "Consumer responses to planned obsolescence." Journal of Retailing and Consumer Services 47, 157-165.
69. Katz, M.L., and Shapiro, C.,(1985) , Network Externalities, Competition, and Compatibility,American Economic Review. (75), 424-440.
70. Lee, Y.E., and Benbasat, I. (2004). A framework for the study of customer interface design for mobile commerce, International Journal of Electronic Commerce., 8(3), 79-102
71. Lee, E. S. (1966). A theory of migration., Demography, 3(1), 47–57.
72. Lewis, G. J. (1982). Human Migration: A Geographical Perspective. London and Can- berra, Australia: Croom Helm.
73. Lui, S. M. (2005). Impacts of information technology commoditization: selected studies from ubiquitous information service. in Department of Information and Systems Management. Hong Kong: Hong Kong University of Science and Technology, 139.
74. Mariñoso, B. C. (2001). Marketing an upgrade to a system: compatibility choice as a price discrimination device, Information Economics and Policy. (13), 377–392
75. Longino, C. F. (1992). The forest and the trees: micro-level considerations in the study of geographic mobility in old age. In Rogers. A (Ed.), Elderly Migration and Population Redistribution, Bellhaven, London, 23–34.
76. Miao, C.-H., (2011). Planned obsolescence and monopoly undersupply. Inf. Econ. Policy 23(1), 51–58.
77. Moon, B. (1995). “Paradigms in Migration Research: Exploring 'Moorings' as a Schema”, Progress in Human Geography, 19(4), 504-524.
78. Morgan, R. M., and Hunt, S. D. (1994). The commitment–trust theory of relationship marketing. Journal of Marketing, (58:3), pp.20–38.
79. Nicole van Nes and Jacqueline Cramer, SKIM Analytical, Rotterdam (2005), “Influencing Product Lifetime Through Product Design. Business Strategy and the Environment Bus. Strat,” Env. 14, 286–299
80. Nahm, J. (2008). The effects of one-way compatibility on technology adoption in systems markets, Information Economics and Policy. (20), 269–278
81. Nunnally, J.C., (1978), Psychometric Theory, New York: McGraw-Hill.
82. Okada EM. (2001), “Trade-ins, Mental Accounting, and Product Replacement Ddecisions.” Journal of Consumer Research, 27(March), 433–446.
83. Packard, V., 1960. The Waste Makers. Pelican, Harmondsworth.
84. Ping, R. A. (1993). The effects of satisfaction and structural constraints on retailer exiting, voice, loyalty, opportunism, and neglect, Journal of Retailing. (69:3), pp. 320–352.
85. Porter M. (1980) Competitive strategy: techniques for analyzing industries and competitors. New York: The Free Press.
86. Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3. Hamburg: University of Hamburg.
87. Rogers, E. (1995). Diffusion of innovations. New York. Free press.
88. Segoro, W. (2013). The Influence of Perceived Service Quality, Mooring Factor, and Relationship Quality on Customer Satisfaction and Loyalty. Procedia Social and Behavioral Science, 81, 306–310.
89. Shapiro, C and Varian H.R.,(1999), Information Rules: A Strategic Guide to the New Economy, Boston: Harvard Business School Press.
90. Stewart, J.B., (1959). Planned obsolescence. Harv. Bus. Rev. 37 (5), 14–174.
91. Sun, Y., Bhattacherjee, A., and Ma, Q. (2009). Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation, Information & Managemen.t (46), 351–356
92. Sutton, J. (1991). Sunk Costs and Market Structure. The MIT Press, Cambridge, MA
93. Sharma, N., and Patterson, P. (2000). Switching costs, alternative attractiveness and experience as moderators of relationship commitment in professional consumer service, International Journal of Service Industry Management., 11(5), 470–490
94. Teo, T., and Pok, S. (2003). Adoption of WAP-enabled mobile phones among Internet users, Omega. 31(6), 483-498.
95. Tsuda, T. (1999). The Motivation to Migrate: The Ethnic and Sociocultural Constitution of the Japanese-Brazilian Return-Migration System?” Economic Development and Cultural Change, 49 (1), 1–31.
96. Venkatesh, V., and Davis, F. D.,(2000), A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science. (45:2), 186-204.
97. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly., 27(3), pp. 425-478.
98. Wieringa, J. E., and Verhoef, P. C. (2007). Understanding Customer Switching Behavior in a Liberalizing Service Market. Journal of Service Research, 10(2), 174–186. doi: 10.1177/1094670507306686
99. Wu, J. H., Chen, Y. C., and Lin, L. M. (2007). Empirical evaluation of the revised end user computing acceptance model, Computers in Human Behavior. (23:1), pp.162–174.
100. Xu, Y., Yang, Y., Cheng, Z., & Lim, J. (2014). Retaining and attracting users in social networking services: An empirical investigation of cyber migration. Journal of Strategic Information Systems. doi: 10.1016/j.jsis.2014.03.002
101. Zengyan, C., Yinping, Y., & Lim, J. (2009). Cyber migration: An empirical investiga- tion on factors that affect users switch intentions in social networking sites. In proceedings of 42nd Hawaii International Conference on System Sciences. Ha- waii: IEEE Computer Society.
102. Zhang, Kem ZK, Cheung, Christy MK, & Lee, Matthew KO. (2012). Online Service Switching Behavior: The Case of Blog Service Providers. Journal of Electronic Commerce Research, 13(3), 184-197.

網路來源
103. iThome,(民106年3月)。微軟：採用英特爾、AMD第7代處理器的PC將無法更新Windows 7、8.1。取自https://www.ithome.com.tw/news/112838
104. Windows Taiwan,(民105年1月)。Windows 10 全面擁抱晶片創新。取自https://blogs.windows.com/taiwan/2016/01/18/windows-10-embracing-silicon-innovation/
105. 曹家榮(2016年4月15日) 更新，還是汰舊？消費者，還是冤大頭？擷取自數位時代 https://www.bnext.com.tw/article/39178/BN-2016-04-11-154322-197
106. Hadhazy, A. (2016年6月30日). 科技產品「計劃性報廢」的真相。擷取自BBC https://www.bbc.com/ukchina/trad/vert_fut/2016/06/160630_vert_fut_heres-the-truth-about-the-planned-obsolescence-of-tech