帳號:guest(216.73.216.146)          離開系統
字體大小: 字級放大   字級縮小   預設字形  

詳目顯示

以作者查詢圖書館館藏以作者查詢臺灣博碩士論文系統以作者查詢全國書目
作者(中文):賴俊彥
作者(外文):Lai, Jiun-Yan
論文名稱(中文):探討技術變遷的過程:三個混合方法的研究
論文名稱(外文):Unraveling the Process of Technological Change: Three Mixed-Method Studies
指導教授(中文):洪世章
指導教授(外文):Hung, Shih-Chang
口試委員(中文):胡美智
劉顯仲
丘宏昌
譚丹琪
王振源
口試委員(外文):Hu, Mei-Chih
Liu, John S.
Chiu, Hung-Chang
Tan, Danchi
Wong, Chan-Yuan
學位類別:博士
校院名稱:國立清華大學
系所名稱:科技管理研究所
學號:103073801
出版年(民國):108
畢業學年度:108
語文別:英文
論文頁數:123
中文關鍵詞:定量方法定性方法三角驗證多重典範混合方法資料科學技術變遷非線性分析主路徑主題模型
外文關鍵詞:quantitative methodqualitative methodtriangulationmultiple paradigmsmixed-methoddata sciencetechnological changenonlinear analysismain pathtopic modeling
相關次數:
  • 推薦推薦:1
  • 點閱點閱:259
  • 評分評分:*****
  • 下載下載:0
  • 收藏收藏:0
根據Thomas Kuhn的科學典範,定量與定性方法涉及到研究者如何主觀地看待這個世界(本體論),以及相關的知識是如何產生(認識論),因此,定量與定性方法分別代表著兩種截然不同的價值體系。在過去,研究者通常只會選擇其中一種方法在他們的實證工作與論文出版。受到三角驗證與多重典範的影響,將定量與定性結合的方法論,也就是混合方法,受到愈來愈多的關注。在文獻中,已經累積相當多相關的討論與嘗試性的研究設計。然而,定量分析的部分仍然受限於傳統的統計工具,即迴歸與變異數分析。除此之外,對於長期受到文字與田野訓練的定性學者而言,進行混合方法研究似乎有較高的進入門檻。我們注意到近幾年的資料科學,像是文字探勘、資料視覺化、機器學習、大數據等概念,已經在組織與管理領域中受到廣泛的運用,而這樣的潮流似乎對於定性學者而言,提供一個有別於傳統統計方法的機會。為此,本論文選擇三個新興的資料科學工具,分別是非線性分析、主路徑、主題模型,來進行混合方法的研究。我們選擇探討技術變遷的過程,因為其充滿許多不確定因素,使得我們無法以單一方法或理論來全面瞭解它。此論文對於方法論的討論有所貢獻,且對於試圖從事混合方法研究的定性學者而言,給與一些實務上的建議。
Through the viewpoint of Thomas Kuhn’s scientific paradigm, quantitative and qualitative methods are involved with how researchers subjectively perceive the world (i.e., ontology) and how related knowledge is generated (i.e., epistemology). Therefore, quantitative and qualitative methods respectively represent two different value systems. In the past, researchers chose only one in their empirical studies and for their publishing. Inspired by triangulation and multiple paradigms, the methodology of combining them – mixed-method – has gained increasing attentions. In literature, there are increasingly more related discussions and research design tentatively proposed. However, the part of quantitative analysis is still largely confined to traditional statistic tools: regression and analysis of variance (ANOVA). In addition, it is more difficult to do mixed-method research for qualitative researchers who are disciplined by texts and fields. It is noted that some ideas of data science like text-mining, data visualization, machine learning, and big data have extensively used in organization and management studies. Such trend provides a promising opportunity beyond traditional statistic approaches to qualitative researchers. Thus, this doctoral theses chooses three emerging approaches of data science: nonlinear analysis, main path, and topic modeling, to do mixed-method research. We attempt to study the process of technological change which is full of uncertainties so that one single approach fails to understand it comprehensively. This theses contributes to the discussion of methodology, and it gives some practical suggestions to the qualitative researchers who would like to do mixed-method research.
中文摘要--------------------------------------------------I
ABSTRACT------------------------------------------------II
ACKNOWLEDGEMENTS---------------------------------------III
TABLE OF CONTENT----------------------------------------IV
FIGURE LIST---------------------------------------------VI
TABLE LIST---------------------------------------------VII

CHAPTER 1. INTRODUCTION----------------------------------1

CHAPTER 2. NONLINEARITY OF DISRUPTIVE INNOVATION---------6
2.1 MOTIVATION-------------------------------------------6
2.2 LITERATURE-------------------------------------------8
Disruptive Innovation------------------------------------8
Nonlinear Process---------------------------------------10
2.3 METHOD----------------------------------------------13
Correlation Dimension-----------------------------------13
Phase Space Reconstruction------------------------------15
Research Targets----------------------------------------18
Data Collection-----------------------------------------20
2.4 NONLINEAR ANALYSIS----------------------------------22
Unit Roots Test and ARIMA-------------------------------22
Phase Space Reconstruction------------------------------23
Estimation of Correlation Dimension---------------------25
Robustness Testing by Hurst Exponent--------------------28
2.5 RESULT----------------------------------------------29
2.6 DISCUSSION AND CONCLUSION---------------------------30
Theoretical Implications--------------------------------31
Managerial Implications---------------------------------32
Final Words---------------------------------------------33

CHAPTER 3. PATTERN OF TECHNOLOGICAL KNOWLEDGE FLOW------35
3.1 MOTIVATION------------------------------------------35
3.2 LITERATURE------------------------------------------36
Mapping Technological Trajectories as Knowledge Flows---36
Propositions--------------------------------------------38
3.3 RESEARCH DESIGN-------------------------------------41
Case: Thermal Inkjet Print Head-------------------------41
Method: Main Path Analysis------------------------------44
Data Collection-----------------------------------------46
3.4 CASE STUDY------------------------------------------48
Patterns of Divergence-Convergence----------------------48
Assignee Cross-Citation---------------------------------54
Technological Development-------------------------------58
3.5 DISCUSSION AND CONCLUSION---------------------------63
Theoretical Implications--------------------------------63
Policy and Managerial Implications----------------------64
Limitations---------------------------------------------65

CHAPTER 4. TOPIC MODELING OF INNOVATION DIFFUSION-------67
4.1 MOTIVATION------------------------------------------67
4.2 LITERATURE------------------------------------------69
Diffusion of Innovation---------------------------------69
Topic Modeling------------------------------------------71
4.3 METHOD----------------------------------------------74
LDA Algorithm-------------------------------------------74
Research Site-------------------------------------------78
Data Collection-----------------------------------------79
4.4 ANALYSIS--------------------------------------------81
Determining the Number of Topics------------------------81
Coding the Data-----------------------------------------82
4.5 THEORIZATION----------------------------------------92
4.6 DISCUSSION AND CONCLUSION---------------------------94
Theoretical Implications--------------------------------95
Practical Implications----------------------------------96
Limitations and Future Study----------------------------97

CHAPTER 5. CONCLUSION-----------------------------------98

REFERENCES---------------------------------------------101
1. Abrahamson, E., & Fombrun, C. J. 1994. Macrocultures: Determinants and consequences. Academy of Management Review, 19(4): 728-755.
2. Abrahamson, E., & Rosenkopf, L. 1993. Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. Academy of Management Review, 18(3): 487-517.
3. Abrahamson, E., & Rosenkopf, L. 1997. Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science, 8(3): 289-309.
4. Aden, J. S., Bohórquez, J. H., Collins, D. M., Crook, M. D., García, A., & Hess, U. E. 1994. The third-generation HP thermal inkjet printhead. Hewlett-Packard Journal, 45(1): 41-45.
5. Adner, R. 2002. When are technologies disruptive? A demand-based view of the emergence of competition. Strategic Management Journal, 23(8): 667-688.
6. Ahire, S., & Ravichandran, T. 2001. An innovation diffusion model of TQM implementation. IEEE Transactions on Engineering Management, 48(4): 445-464.
7. Aldrich, H. E., & Fiol, C. M. 1994. Fools rush in? The institutional context of industry creation. Academy of Management Review, 19(4): 645-670.
8. Allen, T. J. 1966. Studies of the problem-solving process in engineering design. IEEE Transactions on Engineering Management, 13(2): 72-83.
9. Anderson, P. 1999. Complexity theory and organization science. Organization Science, 10(3): 216-232.
10. Anderson, P., & Tushman, M. L. 1990. Technological discontinuities and dominant designs: A cyclical model of technological change. Administrative Science Quarterly, 35(4): 604-633.
11. Ansari, S., Fiss, P., & Zajac, E. J. 2010. Made to fit: How practices vary as they diffuse. Academy of Management Review, 35(1): 67-92.
12. Ansari, S., Garud, R., & Kumaraswamy, A. 2016. The disruptor's dilemma: TiVo and the U.S. television ecosystem. Strategic Management Journal, 37(9): 1829-1853.
13. Ansoff, H. I., Avner, J., Brandenburg, R. G., Portner, F. E., & Radosevich, R. 1977. Does planning pay? The effect of planning on success of acquisitions in American firms. Long Range Planning, 3(2): 2-7.
14. Anthony, S. D., Johnson, M. W., Sinfield, J. V., & Altman, E. J. 2008. The innovator's guide to growth: Putting disruptive innovation to work. Boston, MA: Harvard Business Press.
15. Antohe, B. V., & Wallace, D. B. 2002. Demand mode piezoelectric ink jet printer. Journal of Imaging Science and Technology, 46(5): 409-414.
16. Antons, D., Joshi, A. M., & Salge, T. O. 2019. Content, contribution, and knowledge consumption: Uncovering hidden topic structure and rhetorical signals in scientific texts. Journal of Management, 45(7): 3035-3076.
17. Appleyard, M. M. 1996. How does knowledge flow? Interfirm patterns in the semiconductor industry. Strategic Management Journal, 17(S2): 137-154.
18. Archibugi, D., & Pianta, M. 1996. Measuring technological change through patents and innovation surveys. Technovation, 16(9): 451-468.
19. Arndt, J. 1967. Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research, 4(3): 291-295.
20. Arora, A., & Gambardella, A. 1994. The changing technology of technological change: General and abstract knowledge and the division of innovative labour. Research Policy, 23(5): 523-532.
21. Arthur, W. B. 1989. Competing technologies, increasing returns, and lock-in by historical events. Economic Journal, 99(394): 116-131.
22. Ashmos, D. P., Ducheon, D., McDaniel, R. R., & Huonker, J. W. 2002. What a mess! Participation as a simple managerial rule to "complexify" organizations. Journal of Management Studies, 39(2): 189-206.
23. Askeland, R. A., Childers, W. D., & Sperry, W. R. 1988. The second-generation thermal inkjet structure. Hewlett-Packard Journal, 39(4): 28-31.
24. Attewell, P. 1992. Technology diffusion and organizational learning: The case of business computing. Organization Science, 3(1): 1-19.
25. Austin, R. D., Devin, L., & Sullivan, E. E. 2012. Accidental innovation: Supporting valuable unpredictability in the creative process. Organization Science, 23(5): 1505-1522.
26. Bacchiocchi, E., & Montobbio, F. 2010. International knowledge diffusion and home-bias effect: Do USPTO and EPO patent citations tell the same story? Scandinavian Journal of Economics, 112(3): 441-470.
27. Bacharach, S. B. 1989. Organizational theories: Some criteria for evaluation. Academy of Management Review, 14(4): 496-515.
28. Bagheri, A., Saraee, M., de Jong, F. 2014. ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5): 621-636.
29. Bamberger, P., & Ang, S. 2016. The quantitative discovery: What is it and how to get it published. Academy of Management Discovery, 2(1): 1-6.
30. Bansal, P., & Corley, K. 2011. The coming of age for qualitative research: Embracing the diversity of qualitative methods. Academy of Management Journal, 54(2): 233-237.
31. Bao, Y., & Datta, A. 2014. Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6): 1371-1391.
32. Barberá-Tomás, D., Jiménez-Sáez, F., & Castelló-Molina, I. 2011. Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks. Research Policy, 40(3): 473-486.
33. Barney, J. 1997. On flipping coins and making technology choices: Luck as an explanation of technological foresight and oversight. In R. Garud, P. R. Nayyar, & Z. B. Shapira (Eds.). Technological innovation: Oversights and foresights: 13-19. New York, NY: Cambridge University Press.
34. Bass, F. M. 1969. A new product growth model for consumer durables. Management Science, 15(1): 215-227.
35. Batagelj, V. 2003. Efficient algorithms for citation network analysis. University of Ljubljana, Institute of Mathematics, Physics and Mechanics, Department of Theoretical Computer Science.
36. Batagelj, V., Ferligoj, A., & Squazzoni, F. 2017. The emergence of a field: A network analysis of research on peer review. Scientometrics, 113(1): 503-532.
37. Baumer, E. P. S., Mimno, D., Guha, S., Quan, E., & Gay, G. K. 2017. Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? Journal of the Association for Information Science and Technology, 68(6): 1397-1410.
38. Behling, O. 1980. The case for the natural science model for research in organizational behavior and organization theory. Academy of Management Review, 5(4): 483-490.
39. Bekkers, R., & Martinelli, A. 2012. Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators. Technological Forecasting and Social Change, 79(7): 1192-1216.
40. Beltagui, A., Rosli, A., & Candi, M. 2020. Exaptation in a digital innovation ecosystem: The disruptive impacts of 3D printing. Research Policy, 49(1): 103833.
41. Bhupatiraju, S., Nomaler, Ö., Triulzi, G., & Verspagen, B. 2012. Knowledge flows - Analyzing the core literature of innovation, entrepreneurship and science and technology studies. Research Policy, 41(7): 1205-1218.
42. Bingham, C. B., & Eisenhardt, K. M. 2011. Rational heuristics: The ‘simple rules’ that strategists learn from process experience. Strategic Management Journal, 32(13): 1437-1464.
43. Birkinshaw, J., & Sheehan, T. 2002. Managing the knowledge life cycle. MIT Sloan Management Review, 44(1): 75-83.
44. Blalock, M. 1984. Basic dilemmas in the social sciences. Beverly Hills, CA: Sage.
45. Blanchard, S. J., Aloise, D., & Desarbo, W. S. 2017. Extracting summary piles from sorting task data. Journal of Marketing Research, 54(3): 398-414.
46. Blei, D. M. 2012. Probabilistic topic models. Communications of the ACM, 55(4): 77-84.
47. Blei, D. M., & Lafferty, J. D. 2007. A correlated topic model of science. Annals of Applied Statistics, 1(2): 634-642.
48. Blei, D. M., Ng, A. Y., & Jordan, M. I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3: 993-1022.
49. Blumer, H. 1969. Symbolic interactionism: Perspective and method. Englewood Cliffs, NJ: Prentice-Hall.
50. Boes, D. C., & Salas, J. D. 1978. Nonstationarity of the mean and the Hurst phenomenon. Water Resources Research, 14(1): 135-143.
51. Bohlmann, J. D., Calantone, R. J., & Zhao, M. 2010. The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach. Journal of Product Innovation Management, 27(5): 741-760.
52. Boisot, M., & Child, J. 1999. Organizations as adaptive systems in complex environments: The case of China. Organization Science, 10(3): 237-252.
53. Box, G. E. P., & Jenkins, G. M. 1970. Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day.
54. Box, G. E. P., & Pierce, D. A. 1970. Distribution of residual autocorrelations in autoregressive-integrated moving average methods. Journal of the American Statistical Association, 65(332): 1509-1526.
55. Burns, T., & Stalker, G. 1966. The management of innovation. London, UK: Tavistock.
56. Burrell, G., & Morgan, G. 1979. Sociological paradigms and organisational analysis: Elements of the sociology of corporate life. London, UK: Heinemann.
57. Burton, R., & Obel, B. 2011. Computational modeling for what-is, what-might-be, and what-should-be studies - and triangulation. Organization Science, 22(5): 1195-1202.
58. Campbell, D. T., & Fiske, D. W. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2): 81-105.
59. Carayannopoulos, S. 2009. How technology-based new firms leverage newness and smallness to commercialize disruptive technologies. Entrepreneurship Theory and Practice, 33(2): 419-438.
60. Carbone, A., Castelli, G., & Stanley, H. E. 2004. Time-dependent Hurst exponent in financial time series. Physica A: Statistical Mechanics and its Applications, 344(1/2): 267-271.
61. Cassiman, B., & Veugelers, R. 2006. In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52(1): 68-82.
62. Castiaux, A. 2007. Radical innovation in established organizations: Being a knowledge predator. Journal of Engineering and Technology Management, 24(1/2): 36-52.
63. Caviggioli, F. 2016. Technology fusion: Identification and analysis of drivers of technology convergence using patent data. Technovation, 55/56: 22-32.
64. Ceja, L., & Navarro, J. 2011. Dynamic patterns of flow in the workplace: Characterizing within-individual variability using a complexity science approach. Journal of Organizational Behavior, 32(4): 627-651.
65. Chandra, Y., Jiang, L. C., & Wang, C.-J. 2016. Mining social entrepreneurship strategies using topic modeling. PLos ONE, 11(3): e0151342.
66. Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S., & Blei, D. 2009. Reading tea leaves: How humans interpret topic models. In Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neutral information processing systems, vol. 22. https://http://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models.pdf. Accessed June 23, 2019.
67. Chen, H., Wang, X., Pan, S., & Xiong, F. 2019. Identify topic relations in scientific literature using topic modeling. IEEE Transactions on Engineering Management. In Press.
68. Chen, K., Zhang, Y., & Fu, X. 2019. International research collaboration: An emerging domain of innovation studies? Research Policy, 48(1): 149-168.
69. Cheng, Y.-C., & Van de Ven, A. H. 1996. Learning the innovation journey: Order out of chaos. Organization Science, 7(6): 593-614.
70. Chiva, R., Ghauri, P., & Alegre, J. 2014. Organizational learning, innovation and internationalization: A complex system model. British Journal of Management, 25(4): 687-705.
71. Choudhury, P., Wang, D., Carlson, N. A., & Khanna, T. 2019. Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles. Strategic Management Journal. In Press.
72. Christensen, C. M. 1992. Exploring the limits of the technology S-curve. Part I: Component technologies. Production and Operations Management, 1(4): 334-357.
73. Christensen, C. M. 1993. The rigid disk drive industry: A history of commercial and technological turbulence. Business History Review, 67(4): 531-588.
74. Christensen, C. M. 1997. The innovator's dilemma: When new technologies cause great firms to fail. Boston, MA: Harvard Business School Press.
75. Christensen, C. M. 2006. The ongoing process of building a theory of disruption. Journal of Product Innovation Management, 23(1): 39-55.
76. Christensen, C. M., & Bower, J. L. 1996. Customer power, strategic investment, and the failure of leading firms. Strategic Management Journal, 17(3): 197-218.
77. Christensen, C. M., & Overdorf, M. 2000. Meeting the challenge of disruptive change. Harvard Business Review, 78(2): 66-76.
78. Christensen, C. M., & Raynor, M. E. 2003. The innovator's solution: Creating and sustaining successful growth. Boston, MA: Harvard Business School Press.
79. Christensen, C. M., Grossman, J. H., & Hwang, J. 2009. The innovator's prescription: A disruptive solution for health care. New York, NY: McGraw-Hill.
80. Christensen, C. M., McDonald, R., Altman, E. J., & Palmer, J. E. 2018. Disruptive innovation: An intellectual history and directions for future research. Journal of Management Studies, 55(7): 1043-1078.
81. Christensen, C. M., Raynor, M., & McDonald, R. 2015. What is disruptive innovation? Harvard Business Review, 93(12): 44-53.
82. Clymer, N., & Asaba, S. 2008. A new approach for understanding dominant design: The case of the ink-jet printer. Journal of Engineering and Technology Management, 25(3): 137-156.
83. Cohen, W. M., & Levinthal, D. A. 1990. Absorptive capability: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1): 128-152.
84. Cook, T. D., & Campbell, D. T. 1979. Quasi-experimentation: Design & analysis issues for field settings. Boston, MA: Houghton Mifflin.
85. Cooper, A., & Schendel, D. 1976. Strategic responses to technological threats. Business Horizons, 19(1): 61-69.
86. Creswell, J. W., & Clark, V. L. P. 2007. Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.
87. Danneels, E. 2004. Disruptive technology reconsidered: A critique and research agenda. Journal of Product Innovation Management, 21(4): 246-258.
88. Dedehayir, O., Nokelainen, T., & Mäkinen, S. J. 2014. Disruptive innovations in complex product systems industries: A case study. Journal of Engineering and Technology Management, 33: 174-192.
89. Deephouse, D. L. 1999. To be different, or to be the same? It’s a question (and theory) of strategic balance. Strategic Management Journal, 20(2): 147-166.
90. Deerwester, S., Dumais, S., Furnas, G. W., Landauer, T. K., & Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6): 391-407.
91. Delre, S., Jager, W., Bijmolt, T. H., & Janssen, M. A. 2010. Will it spread or not? The effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management, 27(2): 267-282.
92. Denzin, N. K. 1970. The research act: A theoretical introduction to sociological methods. New York, NY: McGraw Hill.
93. Denzin, N. K., & Lincoln, Y. S. 1998. Strategies of qualitative inquiry. Thousand Oaks, CA: Sage.
94. Dew, N. 2009. Serendipity in entrepreneurship. Organization Studies, 30(7): 735-753.
95. Dewald, J., & Bowen, F. 2010. Storm clouds and silver linings: Responding to disruptive innovations through cognitive resilience. Entrepreneurship Theory and Practice, 34(1): 197-218.
96. Dhanaraj, C., & Parkhe, A. 2006. Orchestrating innovation network. Academy of Management Review, 31(3): 659-669.
97. DiMaggio, P., Nag, M., & Blei, D. 2013. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding. Poetics, 41(6): 570-606.
98. Ding, M., Grebogi, C., Ott, E., Sauer, T., & Yorke, J. A. 1993. Estimating correlation dimension from a chaotic time series: When does plateau onset occur? Physica D: Nonlinear Phenomena, 69(3/4): 404-424.
99. Dodson, J. A., & Muller, E. 1978. Models of new product diffusion through advertising and word-of-mouth. Management Science, 24(15): 1568-1578.
100. Dooley, K. J., & Van de Ven, A. H. 1999. Explaining complex organizational dynamics. Organization Science, 10(3): 358-372.
101. Dooley, K., & Van de Ven, A. 2017. Cycles of divergence and convergence: Underlying processes of organization change and innovation. In A. Langley, & H. Tsoukas (Eds.). The SAGE handbook of process organization studies: 574-592. Thousand Oaks, CA: Sage.
102. Dosi, G. 1982. Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 11(3): 147-162.
103. Dosi, G. 1988. Sources, procedures, and microeconomic effects of innovation. Journal of Economic Literature, 26(3): 1120-1171.
104. Dosi, G., & Grazzi, M. 2006. Technologies as problem-solving procedures and technologies as input-output relations: Some perspectives on the theory of production. Industrial and Corporate Change, 15(1): 173-202.
105. Dougherty, D., & Heller, T. 1994. The illegitimacy of successful product innovation in established firms. Organization Science, 5(2): 200-218.
106. Eckmann, J.-P., & Ruelle, D. 1985. Ergodic theory of chaos and strange attractors. Reviews of Modern Physics, 57(3): 617-656.
107. Epicoco, M. 2013. Knowledge patterns and sources of leadership: Mapping the semiconductor miniaturization trajectory. Research Policy, 42(1): 180-195.
108. Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. 2013. Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1): 225-242.
109. Evered, R., & Louis, M. R. 1981. Alternative perspectives in the organizational sciences: "Inquiry from the inside" and "inquiry from the outside." Academy of Management Review, 6(3): 385-395.
110. Feitzinger, E., & Lee, H. 1997. Mass customization at Hewlett Packard: The power of postponement. Harvard Business Review, 75(1): 116-122.
111. Ferlie, E., Fitzgerald, L., Wood, M., & Hawkins, C. 2005. The nonspread of innovations: The mediating role of professionals. Academy of Management Journal, 48(1): 117-134.
112. Fitzgerald, L., Ferlie, E., Wood, M., & Hawkins, C. 2002. Interlocking interactions, the diffusion of innovations in health care. Human Relations, 55(12): 1429-1449.
113. Fleming, L. 2001. Recombinant uncertainty in technological search. Management Science, 47(1): 117-132.
114. Fleming, L. 2002. Finding the organizational sources of technological breakthroughs: The story of Hewlett-Packard's thermal ink-jet. Industrial and Corporate Change, 11(5): 1059-1084.
115. Fleming, L., & Sorenson, O. 2001. Technology as a complex adaptive system: Evidence from patent data. Research Policy, 30(7): 1019-1039.
116. Fontana, R., Nuvolari, A., & Verspagen, B. 2009. Mapping technological trajectories as patent citation networks. An application to data communication standards. Economics of Innovation and New Technology, 18(4): 311-336.
117. Foster, R. 1986. Innovation: The attacker's advantage. New York, NY: Summit Books.
118. Fraser, A. M., & Swinney, H. L. 1986. Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2): 1134-1140.
119. Fremeth, A. R., Holburn, G. L. F., & Richter, B. K. 2016. Bridging qualitative and quantitative methods in organizational research: Applications of synthetic control methodology in the U.S. automobile industry. Organization Science, 27(2): 462-482.
120. Fuentelsaz, L., Gómez, J., & Palomas, S. 2016. Interdependences in the intrafirm diffuse of technological innovations: Confronting the rational and social accounts of diffusion. Research Policy, 45(5): 951-963.
121. Fujimoto, M., Miyazaki, K., & von Tunzelmann, N. 2000. Technological fusion and telemedicine in Japanese companies. Technovation, 20(4): 169-187.
122. Fulco, M. 2019. Taiwan’s mobile payments market heats up. https://topics.amcham.com.tw/2019/05/taiwans-mobile-payments-market-heats-up/. Accessed July 11, 2019.
123. Garud, R., Gehman, J., & Kumaraswamy, A. 2011. Complexity arrangements for sustained innovation: Lessons from 3M Corporation. Organization Studies, 32(6): 737-767.
124. Garud, R., Tuertscher, P., & Van de Ven, A. H. 2013. Perspectives on innovation processes. Academy of Management Annals, 7(1): 775-819.
125. Gephart, R. P. 2004. Qualitative research and the Academy of Management Journal. Academy of Management Journal, 47(4): 454-462.
126. Geroski, P. A. 2000. Models of technology diffusion. Research Policy, 29(4/5): 603-625.
127. Geva, H., Oestreicher-Singer, G., & Saar-Tsechansky, M. 2019. Using retweets when shaping our online persona: Topic modeling approach. MIS Quarterly, 43(2): 501-524.
128. Gioia, D. 2014. A 1st-order/2nd-order qualitative approach to understanding strategic management. http://smj,strategicmanagement.net. Accessed August 31, 2019.
129. Gioia, D. A., Corley, K. G., & Hamilton, A. L. 2012. Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1): 15-31.
130. Giorgi, S., & Weber, K. 2015. Marks of distinction: Framing and audience appreciation in the context of investment advice. Administrative Science Quarterly, 60(2): 333-367.
131. Goldstein, J. 1988. A far-from-equilibrium systems approach to resistance to change. Organizational Dynamics, 17(2): 16-26.
132. Gort, M., & Klepper, S. 1982. Time paths in the diffusion of product innovations. Economic Journal, 92(367): 630-653.
133. Govindarajan, V., & Kopalle, P. K. 2006. The usefulness of measuring disruptiveness of innovations ex-post in making ex-ante predictions. Journal of Product Innovation Management, 23(1): 12-18.
134. Grassberger, P., & Procaccia, I. 1983. Measuring the strangeness of strange attractors. Physica D: Nonlinear Phenomena, 9(1/2): 189-208.
135. Green, R. 2018. Taiwan incentivizes mobile payments. https://www.businessinsider.com/taiwan-incentivizes-mobile-payments-2018-1. Accessed July 11, 2019.
136. Griffiths, T. L., & Steyvers, M. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl. 1): 5228-5235.
137. Guba, E. G., & Lincoln, Y. S. 1994. Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research: 105-117. Thousand Oaks, CA: Sage.
138. Guttentag, D. 2013. Airbnb: Disruptive innovation and the rise of an informal tourism accommodation sector. Current Issues in Tourism, 18(12): 1192-1217.
139. Guttentag, D. A., & Smith, S. L. J. 2017. Assessing Airbnb as a disruptive innovation relative to hotels: Substitution and comparative performance expectations. International Journal of Hospitality Management, 64: 1-10.
140. Gwak, J. H., & Sohn, S. Y. 2018. A novel approach to explore patent development paths for subfield technologies. Journal of the Association for Information Science and Technology, 69(3): 419-419.
141. Haack, S. 2009. Evidence and inquiry: A pragmatist reconstruction of epistemology. Amherst, NY: Prometheus Books.
142. Haans, R. 2019. What's the value of being different when everyone is? The effects of distinctiveness on performance in homogeneous versus heterogeneous categories. Strategic Management Journal, 40(1): 3-27.
143. Hall, B. H., Jaffe, A. B., & Trajtenberg, M. 2001. The NBER patent citations data file: Lessons, insights and methodological tools. NBER Working Paper NO. 8498.
144. Hannigan, T., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V., Wang, M., Kaplan, S., & Jennings, P. D. 2019. Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2): 586-632.
145. Hargadon, A. B. 1998. Firms as knowledge brokers: Lessons in pursuing continuous innovation. California Management Review, 40(3): 209-227.
146. Hargadon, A. B., & Douglas, Y. 2001. When innovations meet institutions: Edison and the design of the electric light. Administrative Science Quarterly, 46(3): 476-501.
147. Haupt, R., Kloyer, M., & Lange, M. 2007. Patent indicators for the technology life cycle development. Research Policy, 36(3): 387-398.
148. Heidenreich, S., & Kraemer, T. 2015. Innovations - Doomed to fail? Investigating strategies to overcome passive innovation resistance. Journal of Product Innovation Management, 33(3): 277-297.
149. Henderson, R. M. 2006. The innovator's dilemma as a problem of organizational competence. Journal of Product Innovation Management, 23(1): 5-11.
150. Henrique, B. M., Sobreiro, V. A., & Kimura, H. 2018. Building direct citation networks. Scientometrics, 115(2): 817-832.
151. Hofmann, T. 1999. Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International SIGIR Conference: 289-296.
152. Hollenstein, H., & Woerter, M. 2008. Inter- and intra-firm diffusion of technology: The example of e-commerce: An analysis based on Swiss firm-level data. Research Policy, 37(3): 545-564.
153. Horn, S. A. 2017. Non-English nativeness as stigma in academic settings. Academy of Management Learning & Education, 16(4): 579-602.
154. Horsky, D., & Simon, L. S. 1983. Advertising and the diffusion of new products. Marketing Science, 2(1): 1-17.
155. Hummon, N., & Doreian, P. 1989. Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1): 39-63.
156. Hung, S.-C., Liu, J. S., Lu, L. Y. Y., & Tseng, Y.-C. 2014. Technological change in lithium iron phosphate battery: The key-route main path analysis. Scientometrics, 100(1): 97-120.
157. Hurst, H. E. 1951. Long-term storage capacity of reservoirs. Transactions of American Society of Civil Engineers, 116: 770-808.
158. Hwang, S., & Shin, J. 2019. Extending technological trajectories to latest technological changes by overcoming time lags. Technological Forecasting and Social Change, 143: 142-153.
159. Jaffe, A. B., & Trajtenberg, M. 2002. Patents, citations and innovation: A window on the knowledge economy. Cambridge, MA: MIT Press.
160. Jao, N. 2018. Explainer: Why Taiwan is slow to adopt mobile payments. https://technode.com/2018/03/26/explainer-taiwan-mobile-payments/. Accessed June 23, 2019.
161. Järvenpää, H. M., Mäkinen, S. J., & Seppänen, M. 2011. Patent and publishing activity sequence over a technology’s life cycle. Technological Forecasting and Social Change, 78(2): 283-293.
162. Jayanthi, S., & Sinha, K. K. 1998. Innovation implementation in high technology manufacturing: A chaos-theoretic empirical analysis. Journal of Operations Management, 16(4): 471-494.
163. Jick, T. D. 1979. Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4): 602-611.
164. Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. 2007. Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2): 112-133.
165. Joseph, G. 2002. Predicted and unpredicted changes in non-impact printing: 1981-2001. Journal of Imaging Science and Technology, 46(4): 292-299.
166. Kahl, S. J., & Grodal, S. 2016. Discursive strategies and radical technological change: Multilevel discoursive analysis of the early computer (1947-1958). Strategic Management Journal, 37(1): 149-166.
167. Kao, S. 2019. Mobile payment transactions surge on rapid adoption. http://www.taipeitimes.com/News/biz/archives/2019/04/04/2003712744. Accessed July 11, 2019.
168. Kaplan, D. 2004. The Sage handbook of quantitative methodology for the social sciences. Thousand Oaks, CA: Sage.
169. Kaplan, S. 2016. Mixing quantitative and qualitative research. In K. D. Elsbach & R. M. Kramer (Eds.), Handbook of qualitative organizational research: Innovative pathways and methods: 423-433. New York, NY: Routledge.
170. Kaplan, S., & Henderson, R. M. 2005. Inertia and incentives: Bridging organizational economics and organizational theory. Organization Science, 16(5): 509-521.
171. Kaplan, S., & Vakili, K. 2014. The double-edged sword of recombination in breakthrough innovation. Strategic Management Journal, 36(10): 1435-1457.
172. Kauffman, S. 1993. The origins of order: Self organization and selection in evolution. New York, NY: Oxford University Press.
173. Keller, A., & Hüsig, S. 2009. Ex ante identification of disruptive innovations in the software industry applied to web applications: The case of Microsoft's vs. Google's office applications. Technological Forecasting and Social Change, 76(8): 1044-1054.
174. Kennel, M. B., Brown, R., & Abarbanel, H. D. I. 1992. Determining embedding dimension using a geometrical construction. Physical Review A, 45(6): 3403-3411.
175. Ketchen, D. J., Boyd, B. K., & Bergh, D. D. 2008. Research methodology in strategic management: Past accomplishments and future challenges. Organizational Research Methods, 11(4): 643-658.
176. Kim, H., Ahn, S.-J., & Jung, W.-S. 2019. Horizon scanning in policy research database with a probabilistic topic model. Technological Forecasting and Social Change, 146: 588-594.
177. Kim, E., Cho, Y., & Kim, W. 2013. Dynamic patterns of technological convergence in printed electronics technologies: Patent citation network. Scientometrics, 98(2): 975-998.
178. Kim, J., & Lee, S. 2015. Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO. Technological Forecasting and Social Change, 92: 332-345.
179. Kim, J., & Shin, J. 2018. Mapping extended technological trajectories: Integration of main path, derivative paths, and technology junctures. Scientometrics, 116(3): 1439-1459.
180. King, A. A., & Baatartogtokh, B. 2015. How useful is the theory of disruptive innovation. MIT Sloan Management Review, 57(1): 77-90.
181. King, G. P., & Stewart, I. 1992. Phase space reconstruction for symmetric dynamical systems. Physica D: Nonlinear Phenomena, 58(1/4): 216-228.
182. Klenner, P., Hüsig, S., & Dowling, M. 2013. Ex-ante evaluation of disruptive susceptibility in established value networks: When are markets ready for disruptive innovation? Research Policy, 42(4): 914-927.
183. Kobayashi, V., Mol, S. T., Berkers, H., Kismihók, G., & Den Hartog, D. N. 2018. Text mining in organizational research. Organizational Research Methods, 21(3): 733-765.
184. Kodama, F. 1995. Emerging patterns of innovation: Sources of Japan’s technological edge. Boston, MA: Harvard Business Press.
185. Koput, K. W. 1997. A chaotic model of innovative search: Some answers, many questions. Organization Science, 8(5): 528-542.
186. Kostoff, R. N., Boylan, R., & Simons, G. R. 2004. Disruptive technology roadmaps. Technological Forecasting and Social Change, 71(1/2): 141-159.
187. Kpoczak, L. R., & Lee, H. 2001. Hewlett-Packard Co.: DeskJet printer supply chain (A). Harvard Business School Case Study #GS-3A.
188. Krippendorff, K. 1980. Content analysis. Beverly Hills, CA: Sage.
189. Kuhn, T. S. 1962. The structure of scientific revolutions. Chicago, IL: The University of Chicago Press.
190. Lakshmanan, M., & Rajasekar, S. 2012. What is nonlinearity? In M. Lakshmanan, & S. Rajasekar (Eds.). Nonlinear dynamics: Integrability, chaos and patterns: 1-15. New York, NY: Springer.
191. Lanjouw, J. O., & Schankerman, M. 1999. The quality of ideas: Measuring innovation with multiple indicators. NBER Working Paper NO. 7345. National Bureau of Economic Research.
192. Lathabai, H. H., George, S., Prabhakaran, T., & Changat, M. 2018. An integrated approach to path analysis for weighted citation networks. Scientometrics, 117(3): 1871-1904.
193. Layton, E. T. 1974. Technology as knowledge. Technology and Culture, 15(1): 31-41.
194. Le, H. P. 1998. Progress and trends in ink-jet printing technology. Journal of Imaging Science and Technology, 42(1): 49-62.
195. Lee, A. S. 1989. Case studies as natural experiments. Human Relations, 42(2): 117-137.
196. Lee, H., & Kang, P. 2018. Identifying core topics in technology and innovation management studies: A topic model approach. Journal of Technology Transfer, 43(5): 1291-1317.
197. Lee, H., Smith, K. G., & Grimm, C. M. 2003. The effect of new product radicality and scope on the extent and speed of innovation diffusion. Journal of Management, 29(5): 753-768.
198. Lee, J.-D., Yoon, J.-B., Kim, J.-K., Chung, H.-J., Lee, C.-S., Lee, H.-D., Lee, H.-J., Kim, C.-K., & Han, C.-H. 1999. A thermal inkjet printhead with a monolithically fabricated nozzle plate and self-aligned ink feed hole. Journal of Microelectromechanical Systems, 8(3): 229-236.
199. Lee, S. K. J. 1992. Quantitative versus qualitative research methods: Two approaches to organization studies. Asia Pacific Journal of Management, 9(1): 97-94.
200. Lee, S.-Y., & Wang, Z.-L. 2018. LinePay and Jkopay: Who will win in the end? Business Weekly, 1585: 70-79.
201. Lee, W. S., Han, E. J., & Sohn, S. Y. 2015. Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100: 317-329.
202. Leonard-Barton, D. 1992. Core capabilities and core rigidities: A paradox in managing new product development. Strategic Management Journal, 13(2): 111-125.
203. Leung, K. 2007. The glory and tyranny of citation impact: An East Asian perspective. Academy of Management Journal, 50(3): 510-513.
204. Levy, D. 1994. Chaos theory and strategy: Theory, application, and managerial implications. Strategic Management Journal, 15(2): 167-178.
205. Leydesdorff, L., Rotolo, D., & de Nooy, W. 2013. Innovation as a nonlinear process, the scientometric perspective, and the specification of an ‘innovation opportunities explorer.’ Technology Analysis & Strategic Management, 25(6): 641-653.
206. Liebowitz, S. J., & Margolis, S. E. 1995. Path dependence, lock-in, and history. Journal of Law, Economics, & Organization, 11(1): 205-226.
207. Light, D. 1979. Surface data and deep structure: Observing the organization of professional training. Administrative Science Quarterly, 24(4): 551-559.
208. Lim, D.-J., & Anderson, T. R. 2015. Technology trajectory mapping using data envelopment analysis: The ex ante use of disruptive innovation theory on flat panel technologies. R&D Management, 46(5): 815-830.
209. Lincoln, Y. S., & Guba, E. G. 1985. Naturalistic inquiry. Newbury Park, CA: Sage.
210. Linton, J. D. 2002. Forecasting the market diffusion of disruptive and discontinuous innovation. IEEE Transaction on Engineering Management, 49(4): 365-374.
211. Liu, J. S., & Lu, L. Y. Y. 2012. An integrated approach for the main path analysis: The development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63(3): 528-542.
212. Liu, J. S., Lu, L. Y. Y., & Ho, M. H.-C. 2019. A few notes on main path analysis. Scientometrics, 119(1): 379-391.
213. Lorenz, E. 1963. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20(2): 130-141.
214. Lori, G. 1994. Five inkjet printers that let you colorize your work. PC Magazine, 13(18): 37-39.
215. Lucio-Arias, D., & Leydesdorff, L. 2008. Main-path analysis and path-dependent transitions in HistCite (TM)-based historiograms. Journal of the American Society for Information Science and Technology, 59(12): 1948-1962.
216. Mahajan, V., & Muller, E. 1979. Innovation diffusion and new product growth models in marketing. Journal of Marketing, 43(4): 55-68.
217. Mahajan, V., Muller, E., & Bass, F. 1990. New product diffusion models in marketing: A review and directions for research. Journal of Marketing, 54(1): 1-26.
218. Mandelbrot, B. B. 1982. The fractal geometry of nature. New York, NY: W. H. Freeman.
219. Mane, R. 1980. On the dimension of the compact invariant sets of certain non-linear maps. In D. A. Rand, & L.-S. Young (Eds.). Lecture notes in mathematics: Dynamic systems and turbulence: 230-242. Coventry, UK: Springer.
220. Mansfield, E. 1961. Technical change and the rate of imitation. Econometrica, 29(4): 741-766.
221. March, J. G. 1991. Exploration and exploitation in organizational learning. Organization Science, 2(1): 71-87.
222. Martin, J. A., & Eisenhardt, K. M. 2010. Rewiring: Cross-business-unit collaborations in multibusiness organizations. Academy of Management Journal, 53(2): 265-301.
223. Martinelli, A. 2012. An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry. Research Policy, 41(2): 414-429.
224. Martinelli, A., & Nomaler, Ö. 2014. Measuring knowledge persistence: A genetic approach to patent citation networks. Journal of Evolutionary Economics, 24(3): 623-652.
225. Maruyama, M. 1963. The second cybernetics: Deviation-amplifying mutual causal processes. American Scientist, 5(2): 164-179.
226. McCarthy, I. P., Tsinopoulos, C., Allen, P., & Rose-Anderssen, C. 2006. New product development as a complex adaptive system of decisions. Journal of Product Innovation Management, 23(5): 437-456.
227. McDonald, K. C. 2013. Innovation: How innovators think, act and change our world. London UK: Kogan Page.
228. Meade, N., & Islam, T. 2006. Modelling and forecasting the diffusion of innovation: A 25-year review. International Journal of Forecasting, 22(3): 519-545.
229. Mei, Q., Shen, X., & Zhai, C. 2007. Automatic labeling of multinomial topic models. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 490-499.
230. Mejía, C., & Kajikawa, Y. 2019. Technology news and their linkage to production of knowledge in robotics research. Technological Forecasting and Social Change, 143: 114-124.
231. Menke, M., Xu, Q., & Gu, L. 2007. An analysis of the universality, flexibility, and agility of total innovation management: A case study of Hewlett-Packard. Journal of Technology Transfer, 32(1/2): 49-62.
232. Merton, R. K., & Barber, E. 2004. Travels and adventures of serendipity. Princeton, NJ: Princeton University Press.
233. Meyer, A. D., Gaba, V., & Colwell, K. A. 2005. Organizing far from equilibrium: Nonlinear change in organizational fields. Organization Science, 16(5): 456-473.
234. Miller, G. A. 1995. WordNet: A lexical database for English. Communications of ACM, 38(11): 39-41.
235. Miller, D. J., Fern, M. J., & Cardinal, L. B. 2007. The use of knowledge for technological innovation within diversified firms. Academy of Management Journal, 50(2): 308-326.
236. Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. 2011. Optimizing semantic coherence in topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing: 262-272.
237. Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. 2007. Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5): 789-806.
238. Mody, C. C. M. 2016. The long arm of Moore's Law: Microelectronics and American science. Cambridge, MA: MIT Press.
239. Mohr, J. W., & Bogdanov, P. 2013. Introduction - Topic models: What they are and why they matter. Poetics, 41(6): 545-569.
240. Molina-Azorin, J. 2012. Mixed methods research in strategic management: Impact and applications. Organizational Research Methods, 15(1): 33-56.
241. Molina-Azorin, J. F., Bergh, D. D., Corley, K. G., & Ketchen, D. J. 2017. Mixed methods in the organizational sciences: Taking stock and moving forward. Organizational Research Methods, 20(2): 179-192.
242. Momeni, A., & Rost, K. 2016. Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling. Technological Forecasting and Social Change, 104: 16-29.
243. Momtazi, S., & Lindenberg, F. 2016. Generating query suggestions by exploiting latent semantics in query logs. Journal of Information Science, 42(4): 437-448.
244. Monge, P., Cozzens, M. D., & Contractor, N. S. 1992. Communication and motivational predictors of the dynamics of organizational innovation. Organization Science, 3(2): 250-274.
245. Moore, G. A. 1991. Crossing the chasm: Marketing and selling high-tech products to mainstream customers. New York, NY: Harper Business.
246. Morse, J. 2003. Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research: 189-208. Thousand Oaks, CA: Sage.
247. Mowery, D. C., Oxley, J. E., & Silverman, B. S. 1996. Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2): 77-91.
248. Muller, E. 2019. Delimiting disruption: Why Uber is disruptive, but Airbnb is not? International Journal of Research in Marketing. In Press.
249. Müller, O., Junglas, I., vom Brocke, J., & Debortoli, S. 2016. Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems, 25(4): 289-302.
250. Murthy, D. 2016. The ontology of Tweets: Mixed-method approaches to the study of Twitter. In L. Sloan & A. Quan-Haase (Eds.), The Sage handbook of social media research methods: 559-572. Thousand Oaks, CA: Sage.
251. Myers, R. A., & Tamulis, J. C. 1984. Introduction to topical issue on non-impact printing technologies. IBM Journal of Research and Development, 28(3): 234-240.
252. Nag, R., Corley, K. G., & Gioia, D. A. 2003. Innovation tensions: Chaos, structure, and managed chaos. In L. V. Shavinina (Eds.), The international handbook on innovation: 607-618. Oxford, UK: Elsevier Science.
253. Narin, F., Noma, E., & Perry, R. 1987. Patents as indicators of corporate technological strength. Research Policy, 16(2/4): 143-156.
254. Natale, F., Fiore, G., & Hofherr, J. 2012. Mapping the research on aquaculture: A bibliometric analysis of aquaculture literature. Scientometrics, 90(3): 983-999.
255. Ndofor, H. A., Fabian, F., & Michel, J. G. 2018. Chaos in industry environments. IEEE Transactions on Engineering Management, 65(2): 191-203.
256. Nelson, R. R. 2003. On the uneven evolution of human know-how. Research Policy, 32(6): 909-922.
257. Nerkar, A. 2003. Old is gold? The value of temporal exploration in the creation of new knowledge. Management Science, 49(2): 211-229.
258. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. 2012. Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3): 521-543.
259. Newman, D. J., & Block, S. 2006. Probabilistic topic decomposition of an Eighteenth-century American newspaper. Journal of the American Society for Information Science and Technology, 57(6): 753-767.
260. Newman, D., Noh, Y., Talley, E., Karimi, S., & Baldwin, T. 2010. Evaluating topic models for digital libraries. Proceedings of the 10th Annual Joint Conference on Digital Libraries: 215-224.
261. Nickerson, J. A., & Zenger, T. R. 2004. A knowledge-based theory of the firm: The problem-solving perspective. Organization Science, 15(6): 617-632.
262. Nielsen, N. 1985. History of ThinkJet printhead development. Hewlett-Packard Journal, 36(5): 4-10.
263. Nieto, M., Lopéz, F., & Cruz, F. 1998. Performance analysis of technology using the S curve model: The case of digital signal processing (DSP) technologies. Technovation, 18(6/7): 439-457.
264. Nilakanta, S., & Scamell, R. W. 1990. The effect of information sources and communication channels on the diffusion of innovation in a data base development environment. Management Science, 36(1): 24-40.
265. No, H. J., & Park, Y. 2010. Trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology. Technological Forecasting and Social Change, 77(1): 63-75.
266. Omar, M., On, B.-W., Lee, I., & Choi, G. S. 2015. LDA topics: Representation and evaluation. Journal of Information Science, 41(5): 662-675.
267. O'Reilly, C. A., & Tushman, M. L. 2008. Ambidexterity as a dynamic capability: Resolving the innovator's dilemma. Research in Organizational Behavior, 28: 185-206.
268. O'Reilly, C. A., & Tushman, M. L. 2016. Lead and disrupt: How to solve the innovator's dilemma. Stanford, CA: Stanford Business Books.
269. Osiyevskyy, O., & Dewald, J. 2015. Explorative versus exploitative business model change: The cognitive antecedents of firm-level responses to disruptive innovation. Strategic Entrepreneurship Journal, 9(1): 58-78.
270. Parayil, G. 2003. Mapping technological trajectories of the Green Revolution and the Gene Revolution from modernization to globalization. Research Policy, 32(6): 971-990.
271. Peres, R., Muller, E., & Mahajan, V. 2010. Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2): 91-106.
272. Peterson, M., & Meckler, M. R. 2001. Cuban-American entrepreneurs: Chance, complexity and chaos. Organization Studies, 22(1): 31-57.
273. Pfeffer, J., & Salancik, G. 1978. The external control of organizations: A resource dependence perspective. New York, NY: Harper & Row.
274. Pinkse, J., Bohnsack, R., & Kolk, A. 2014. The role of public and private protection in disruptive innovation: The automotive industry and the emergence of low-emission vehicles. Journal of Product Innovation Management, 31(1): 43-60.
275. Plowman, D. A., Baker, L. T., Beck, T. E., Kulkarni, M., Solansky, S. T., & Travis, D. V. 2007. Radical change accidentally: The emergence and amplification of small change. Academy of Management Journal, 50(3): 515-543.
276. Popp, D., Juhl, T., & Johnson, D. K. N. 2003. Time in purgatory: Determinants of the grant lag for U.S. patent application. NBER Working Paper NO. 9518. National Bureau of Economic Research.
277. Popper, K. 1959. The logic of scientific discovery. London, UK: Hutchinson.
278. Popper, K. 1972. Objective knowledge: An evolutionary approach. Oxford, UK: Clarendon Press.
279. Porter, M. 1980. An algorithm for suffix stripping. Program, 14(3): 130-137.
280. Powell, T. C. 1992. Strategic planning as competitive advantage. Strategic Management Journal, 13(7): 551-558.
281. Pratt, M. G. 2009. For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52(5): 856-862.
282. Prigogine, I., & Stengers, I. 1984. Order out of chaos: Man’s new dialogue with nature. Boulder, CO: New Science Library.
283. Pritchard, D. 2006. What is this thing called knowledge? New York, NY: Routledge.
284. Pröllochs, N., & Feuerriegel, S. 2020. Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1): 103070.
285. Provenzale, A., Smith, L. A., Vio, R., & Murante, G. 1992. Distinguishing between low-dimensional dynamics and randomness in measured time series. Physica D: Nonlinear Phenomena, 58(1-4): 31-49.
286. Puyot, M. 2017. Memjet's inkjet printhead technology and associated printer components. In W. Zapka (Ed.). Handbook of industrial inkjet printing: A full system approach: 335-349. Weinheim, Germany: Wiley-VCH.
287. Quinn, J. B. 1985. Managing innovation: Controlled chaos. Harvard Business Review, 63(3): 73-84.
288. Rabinowitz, V. C., & Weseen, S. 1997. Elu(ci)d(at)ing epistemological impasses: Re-viewing the qualitative/quantitative debates in psychology. Journal of Social Issues, 53(4): 605-630.
289. Ram, S. 1989. Successful innovation using strategies to reduce consumer resistance: An empirical test. Journal of Product Innovation Management, 6(1): 20-34.
290. Ray, G. F. 1989. Full circle: The diffusion of technology. Research Policy, 18(1): 1-18.
291. Ritzer, G. 1975. Sociology: A multiple paradigm science. Boston, MA: Allyn and Bacon.
292. Roach, M., & Cohen, W. M. 2013. Lens or prism? Patent citations as a measure of knowledge flows from public research. Management Science, 59(2): 504-525.
293. Rogers, E. 1962. Diffusion of innovation. New York, NY: Free Press.
294. Roy, R., Lampert, C. M., & Stoyneva, I. 2018. When dinosaurs fly: The role of firm capabilities in the 'avianization' of incumbents during disruptive technological change. Strategic Entrepreneurship Journal, 12(2): 261-284.
295. Ruckman, K., & McCarthy, I. 2017. Why do some patents get licensed while others do not? Industrial and Corporate Change, 26(4): 667-688.
296. Rudolph, J. W., & Repenning, N. P. 2002. Disaster dynamics: Understanding the role of quantity in organizational collapse. Administrative Science Quarterly, 47(1): 1-30.
297. Ruelle, D. 1989. Chaotic evolution and strange attractors. Cambridge, UK: Cambridge University Press.
298. Ruelle, D. 2006. What is... a strange attractor? Notice of the AMS, 53(7): 764-765.
299. Ruelle, D., & Takens, F. 1971. On the nature of turbulence. Communications in Mathematical Physics, 20(3): 167-192.
300. Rycroft, R. W., & Kash, D. E. 2002. Path dependence in the innovation of complex technologies. Technology Analysis & Strategic Management, 14(1): 21-35.
301. Said, S. E., & Dickey, D. A. 1984. Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3): 599-607.
302. Sale, J. E., Lohfeld, L. H., & Brazil, K. 2002. Revisiting the quantitative-qualitative debate: Implications for mixed-methods research. Quality & Quantity, 36(1): 43-53.
303. Salvato, C., & Rerup, C. 2011. Beyond collective entities: Multilevel research on organizational routines and capabilities. Journal of Management, 37(2): 468-490.
304. Sandoz, P. 1997. Canon: Global responsibilities and local decisions. London, UK: Penguin Books.
305. Schilling, M. A., & Esmundo, M. 2009. Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government. Energy Policy, 37(5): 1767-1781.
306. Schmidt, G. M., & Druehl, C. T. 2008. When is a disruptive innovation disruptive? Journal of Product Innovation Management, 25(4): 347-369.
307. Schmiedel, T., Müller, O., & vom Brocke, J. 2019. Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4): 941-968.
308. Schmittlein, D. C., & Mahajan, V. 1982. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Science, 1(1): 57-78.
309. Schoenmakers, W., & Duysters, G. 2010. The technological origins of radical inventions. Research Policy, 39(8): 1051-1059.
310. Shane, S., & Venkataraman, S. 2000. The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1): 217-226.
311. Shannon, C. E. 1948. A mathematical theory of communication. Bell System Technical Journal, 27(3): 379-423.
312. Si, S., & Chen, H. 2020. A literature review of disruptive innovation: What it is, how it works and where it goes. Journal of Engineering and Technology Management, 56: 101568.
313. Siggelkow, N. 2007. Persuasion with case studies. Academy of Management Journal, 50(1): 20-24.
314. Simske, S. J. 2017. Hewlett Packard's inkjet printhead technology. In W. Zapka (Ed.). Handbook of industrial inkjet printing: A full system approach: 313-333. Weinheim, Germany: Wiley-VCH.
315. Soete, L., & Turner, R. 1984. Technology diffusion and the rate of technical change. Economic Journal, 94(375): 612-623.
316. Sood, A., & Tellis, G. J. 2005. Technological evolution and radical innovation. Journal of Marketing, 69(3): 152-168.
317. Sood, A., & Tellis, G. J. 2011. Demystifying disruption: A new model for understanding and predicting disruptive technologies. Marketing Science, 30(2): 339-354.
318. Sprott, J. C. 2014. A dynamical system with a strange attractor and invariant tori. Physics Letters A, 378(20): 1361-1363.
319. Stone, M. D. 2007. The future of ink jet printing? PC Magazine, 26(11): 65-66.
320. Stoorvogel, A. 2018. Mobile payment: NFC, QR codes, in-app and beyond. https://www.fintechweekly.com/magazine/articles/mobil-payment-nfc-qr-codes-in-app-and-beyond. Accessed July 12, 2019.
321. Strauss, A., & Corbin, J. M. 1990. Basics of qualitative research: Grounded theory procedures and techniques. Thousand Oaks, CA: Sage.
322. Strogatz, S. H. 1994. Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering. Reading, MA: Perseus Books.
323. Subramaniam, M., & Youndt, M. A. 2005. The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3): 450-463.
324. Sull, D., & Eisenhardt, K. M. 2015. Simple rules: How to thrive in a complex world. New York, NY: Houghton Mifflin Harcourt.
325. Suominen, A., & Toivanen, H. 2016. Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification. Journal of the Association for Information Science and Technology, 67(10): 2464-2476.
326. Sutton, R. I., & Staw, B. M. 1995. What theory is not. Administrative Science Quarterly, 40(3): 371-384.
327. Szmigin, I., & Foxall, G. 1998. Three forms of innovation resistance: The case of retail payment methods. Technovation, 18(6/7): 459-468.
328. Taalbi, J. 2017. What drives innovation? Evidence from economic history. Research Policy, 46(8): 1437-1453.
329. Takens, F. 1980. Detecting strange attractors in turbulence. In D. A. Rand, & L.-S. Young (Eds.). Lecture notes in mathematics: Dynamic systems and turbulence: 366-381. Coventry, UK: Springer.
330. Tapio, P., Paloniemi, R., Varho, V., & Vinnari, M. 2011. The unholy marriage? Integrating qualitative and quantitative information in Delphi processes. Technological Forecasting and Social Change, 78(9): 1616-1628.
331. Tashakkori, A., & Creswell, J. W. 2007. The new era of mixed methods. Journal of Mixed Methods Research, 1(1): 3-7.
332. Tashakkori, A., & Teddlie, C. B. 1998. Mixed methodology: Combining qualitative and quantitative approaches. London, UK: Sage.
333. Teisberg, E. O., & Clark, T. H. 1994. The desktop printer industry in 1990. Harvard Business School Case Study #9-390-173.
334. Tellis, G. J. 2006. Disruptive technology or visionary leadership? Journal of Product Innovation Management, 23(1): 34-38.
335. Theiler, J. 1987. Efficient algorithm for estimating the correlation dimension from a set of discrete points. Physical Review A, 36(9): 4456-4462.
336. Thiétart, R. A. 2016. Strategy dynamics: Agency, path dependency, and self-organized emergence. Strategic Management Journal, 37(4): 774-792.
337. Thiétart, R. A., & Forgues, B. 1997. Action, structure and chaos. Organization Studies, 18(1): 119-143.
338. Thrane, S., Blaabjerg, S., & Møller, R. H. 2010. Innovative path dependence: Making sense of product and service innovation in path dependent innovation processes. Research Policy, 39(7): 932-944.
339. Tonidandel, S., King, E. B., & Cortina, J. M. 2018. Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21(3): 525-547.
340. Toubia, O., Iyengar, G., Bunnell, R., & Lemaire, A. 2019. Extracting features of enertainment products: A guided latent Dirichlet allocation approach informed by the psychology of media consumption. Journal of Marketing Research, 56(1): 18-36.
341. Tripsas, M., & Gavetti, G. 2000. Capabilities, cognition, and inertia: Evidence from digital imaging. Strategic Management Journal, 21(10/11): 1147-1161.
342. Tsuji, Y. S. 2001. Product development in the Japanese and US printer industries. Technovation, 21(5): 325-332.
343. Turner, S. F., Cardinal, L. B., & Burton, R. M. 2017. Research design for mixed methods. Organizational Research Methods, 20(2): 243-267.
344. Turney, P. D., & Pantel, P. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37: 141-188.
345. Tushman, M. L., & Anderson, P. 1986. Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3): 439-465.
346. Utterback, J. M. 1994. Mastering the dynamics of innovation: How companies can seize opportunities in the face of technological change. Boston, MA: Harvard Business School Press.
347. Utterback, J., & Acee, H. J. 2005. Disruptive technologies: An expanded view. International Journal of Innovation Management, 9(1): 1-17.
348. Vaara, E., Aranda, A. M., Etchanchu, H., Sele, K., & Guyt, J. 2019. From big data to rich theory: Combining structural topic modeling and critical discourse analysis. The 78th Academy of Management Annual Meeting, Boston, USA.
349. Van de Ven, A., & Poole, M. S. 1995. Explaining development and change in organizations. Academy of Management Review, 20(3): 510-540.
350. Van de Ven, A., Polley, D., Garud, R., & Venkataraman, S. 1999. The innovation journey. Oxford, UK: Oxford University Press.
351. van Eck, P. S., Jager, W., & Leeflang, P. S. H. 2011. Opinion leaders' role in innovation diffusion: A simulation study. Journal of Product Innovation Management, 28(2): 187-203.
352. Van Maanen, J. 1998. Different strokes: Qualitative research in the Administrative Science Quarterly from 1956 to 1996. In J. Van Maanen (Ed.), Qualitative studies of organizations: ix-xxxii. Thousand Oaks, CA: Sage.
353. Vecchiato, R. 2017. Disruptive innovation, managerial cognition, and technology competition outcomes. Technological Forecasting and Social Change, 116: 116-128.
354. Venkatesh, V., Brown, S. A., & Bala, H. 2013. Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1): 21-54.
355. Venugopalan, S., & Rai, V. 2015. Topic based classification and pattern identification in patents. Technological Forecasting and Social Change, 94: 236-250.
356. Verspagen, B. 2007. Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Advances in Complex Systems, 10(1): 93-115.
357. Vincenti, W. G. 1990. What engineers know and how they know it. Baltimore, MD: Johns Hopkins University Press.
358. von Hippel, E. 1994. "Sticky information" and the locus of problem solving: Implications for innovation. Management Science, 40(4): 429-439.
359. von Wartburg, I., Teichert, T., & Rost, K. 2005. Inventive progress measured by multi-stage patent citation analysis. Research Policy, 34(10): 1591-1607.
360. Wallach, H. M., Mimno, D., & McCallum, A. 2009. Rethinking LDA: Why priors matter. Proceedings of Neural Information Processing Systems: 1973-1981.
361. Walsh, S. T., Boylan, R. L., McDermott, C., & Paulson, A. 2005. The semiconductor silicon industry roadmap: Epochs driven by the dynamics between disruptive technologies and core competencies. Technological Forecasting and Social Change, 72(2): 213-236.
362. Watanabe, C., Lei, S., & Ouchi, N. 2009. Fusing indigenous technology development and market learning for greater functionality development: An empirical analysis of the growth trajectory of Canon printers. Technovation, 29(4): 265-283.
363. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. 1966. Unobtrusive measures: Nonreactive research in the social sciences. Chicago, IL: Rand McNally.
364. Yin, R. K. 1984. Case study research: Design and methods. Newbury Park, CA: Sage.
365. Young, H. P. 2009. Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, 99(5): 1899-1924.
366. Yu, D., & Hang, C. C. 2010. A reflective review of disruptive innovation theory. International Journal of Management Reviews, 12(4): 435-452.
367. Zable, J. L., & Lee, H.-C. 1997. An overview of impact printing. IBM Journal of Research & Development, 4(6): 651-668.
368. Zhang, M., & Berger, P. 2009. The influence of technology evolution on technology adaptation: A study of digital cameras. International Journal of Technology Intelligence and Planning, 5(3): 258-275.
369. Zhong, N., & Schweidel, D. A. 2020. Capturing changes in social media content: A multiple latent changepoint topic model. Marketing Science. In Press.
370. Zhou, K. Z., & Li, C. B. 2012. How knowledge affects radical innovation: Knowledge base, market knowledge acquisition, and internal knowledge sharing. Strategic Management Journal, 33(9): 1090-1102.
371. Ziman, J. 2000. Selectionism and complexity. In J. Ziman (Ed.), Technological innovation as an evolutionary process: 41-51. Cambridge, UK: Cambridge University Press.
(此全文未開放授權)
電子全文
中英文摘要
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
* *