Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 2 Sayı: 2, 140 - 150, 31.10.2019

Öz

Kaynakça

  • Alexander, J., Chase, J., Newman, N., Porter, A., & Roessner, J. D. (2012). Emergence as a conceptual framework for understanding scientific and technological progress. Picmet '12: Proceedings - Technology Management for Emerging Technologies, 1286-1292.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(4-5), 993-1022. doi:DOI 10.1162/jmlr.2003.3.4-5.993
  • Chen, C. (2006). Information visualization: Beyond the horizon: Springer Science & Business Media.
  • Chen, C. M. (2006). Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. doi:10.1002/asi.20317
  • Dernis, H., Squicciarini, M., & de Pinho, R. (2016). Detecting the emergence of technologies and the evolution and co-development trajectories in science (detects): A 'burst' analysis-based approach. Journal of Technology Transfer, 41(5), 930-960. doi:10.1007/s10961-015-9449-0
  • Ding, W. Y., & Chen, C. M. (2014). Dynamic topic detection and tracking: A comparison of hdp, c-word, and cocitation methods. Journal of the Association for Information Science and Technology, 65(10), 2084-2097. doi:10.1002/asi.23134
  • Fuchs, S. (1993). A sociological-theory of scientific change. Social Forces, 71(4), 933-953. doi:Doi 10.2307/2580125
  • Garfield, E. (2006). Citation indexes for science. A new dimension in documentation through association of ideas (reprinted from science, vol 122, pg 108-11, 1955). International Journal of Epidemiology, 35(5), 1123-1127. doi:10.1093/ije/dyl189
  • Garner, J., Carley, S., Porter, A. L., & Newman, N. C. (2017). Technological emergence indicators using emergence scoring. Paper presented at the Proceedings of PICMET'17: Technology Management for Interconnected World.
  • Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49-72.
  • Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895-900. doi:10.1038/nature03288
  • Hofmann, T. (1999). Probabilistic latent semantic analysis. Uncertainty in Artificial Intelligence, Proceedings, 289-296.
  • Iwami, S., Mori, J., Sakata, I., & Kajikawa, Y. (2014). Detection method of emerging leading papers using time transition. Scientometrics, 101(2), 1515-1533. doi:10.1007/s11192-014-1380-x
  • Khanagha, S., Volberda, H., & Oshri, I. (2017). Customer co-creation and exploration of emerging technologies: The mediating role of managerial attention and initiatives. Long Range Planning, 50(2), 221-242. doi:10.1016/j.lrp.2015.12.019
  • Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373-397. doi:Doi 10.1023/A:1024940629314
  • Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J. (2004). A survey of emerging trend detection in textual data mining. Survey of Text Mining, 185-224.
  • Kuhn, T. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.
  • Li, M., Porter, A. L., & Suominen, A. (2017). Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2017.09.032
  • Nagy, D., Schuessler, J., & Dubinsky, A. (2016). Defining and identifying disruptive innovations. Industrial Marketing Management, 57, 119-126. doi:10.1016/j.indmarman.2015.11.017
  • 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. doi:10.1002/asi.20342
  • Pottenger, W. M., & Yang, T. H. (2001). Detecting emerging concepts in textual data mining. Computational Information Retrieval, 89-105.
  • Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827-1843. doi:10.1016/j.respol.2015.06.006
  • Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274-282. doi:10.1016/j.techfore.2010.07.006
  • Shneider, A. M. (2009). Four stages of a scientific discipline; four types of scientist. Trends in Biochemical Sciences, 34(5), 217-223. doi:10.1016/j.tibs.2009.02.002
  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450-1467. doi:10.1016/j.respol.2014.02.005
  • Tu, Y. N., & Seng, J. L. (2012). Indices of novelty for emerging topic detection. Information Processing & Management, 48(2), 303-325. doi:10.1016/j.ipm.2011.07.006

EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES

Yıl 2019, Cilt: 2 Sayı: 2, 140 - 150, 31.10.2019

Öz

Increasing intensity and rapid shifts on technology domain force policymakers and managers to think more on adaptive strategies by understanding the nature of emergence. However, even there were many conceptual models without consensus, understanding the nature of emergence may not lead to decision for managers or policymakers. There were some proposals aiming to design practical solutions but different fields, experts, or subjects may alter these proposed solutions and sometimes make them biased. In this study, it is aimed to propose a conceptual model by using combination of scientometrics and fuzzy Multi-Criteria Decision Making for evaluating emerging topics holistically. By using fuzzy approach, it is thought that expert decisions can be enhanced and with applying decision making process a compromise solution can be reached.  Consequently, conceptual model is proposed and step-by-step methodology discussed.

Kaynakça

  • Alexander, J., Chase, J., Newman, N., Porter, A., & Roessner, J. D. (2012). Emergence as a conceptual framework for understanding scientific and technological progress. Picmet '12: Proceedings - Technology Management for Emerging Technologies, 1286-1292.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(4-5), 993-1022. doi:DOI 10.1162/jmlr.2003.3.4-5.993
  • Chen, C. (2006). Information visualization: Beyond the horizon: Springer Science & Business Media.
  • Chen, C. M. (2006). Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. doi:10.1002/asi.20317
  • Dernis, H., Squicciarini, M., & de Pinho, R. (2016). Detecting the emergence of technologies and the evolution and co-development trajectories in science (detects): A 'burst' analysis-based approach. Journal of Technology Transfer, 41(5), 930-960. doi:10.1007/s10961-015-9449-0
  • Ding, W. Y., & Chen, C. M. (2014). Dynamic topic detection and tracking: A comparison of hdp, c-word, and cocitation methods. Journal of the Association for Information Science and Technology, 65(10), 2084-2097. doi:10.1002/asi.23134
  • Fuchs, S. (1993). A sociological-theory of scientific change. Social Forces, 71(4), 933-953. doi:Doi 10.2307/2580125
  • Garfield, E. (2006). Citation indexes for science. A new dimension in documentation through association of ideas (reprinted from science, vol 122, pg 108-11, 1955). International Journal of Epidemiology, 35(5), 1123-1127. doi:10.1093/ije/dyl189
  • Garner, J., Carley, S., Porter, A. L., & Newman, N. C. (2017). Technological emergence indicators using emergence scoring. Paper presented at the Proceedings of PICMET'17: Technology Management for Interconnected World.
  • Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49-72.
  • Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895-900. doi:10.1038/nature03288
  • Hofmann, T. (1999). Probabilistic latent semantic analysis. Uncertainty in Artificial Intelligence, Proceedings, 289-296.
  • Iwami, S., Mori, J., Sakata, I., & Kajikawa, Y. (2014). Detection method of emerging leading papers using time transition. Scientometrics, 101(2), 1515-1533. doi:10.1007/s11192-014-1380-x
  • Khanagha, S., Volberda, H., & Oshri, I. (2017). Customer co-creation and exploration of emerging technologies: The mediating role of managerial attention and initiatives. Long Range Planning, 50(2), 221-242. doi:10.1016/j.lrp.2015.12.019
  • Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373-397. doi:Doi 10.1023/A:1024940629314
  • Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J. (2004). A survey of emerging trend detection in textual data mining. Survey of Text Mining, 185-224.
  • Kuhn, T. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.
  • Li, M., Porter, A. L., & Suominen, A. (2017). Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2017.09.032
  • Nagy, D., Schuessler, J., & Dubinsky, A. (2016). Defining and identifying disruptive innovations. Industrial Marketing Management, 57, 119-126. doi:10.1016/j.indmarman.2015.11.017
  • 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. doi:10.1002/asi.20342
  • Pottenger, W. M., & Yang, T. H. (2001). Detecting emerging concepts in textual data mining. Computational Information Retrieval, 89-105.
  • Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827-1843. doi:10.1016/j.respol.2015.06.006
  • Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274-282. doi:10.1016/j.techfore.2010.07.006
  • Shneider, A. M. (2009). Four stages of a scientific discipline; four types of scientist. Trends in Biochemical Sciences, 34(5), 217-223. doi:10.1016/j.tibs.2009.02.002
  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450-1467. doi:10.1016/j.respol.2014.02.005
  • Tu, Y. N., & Seng, J. L. (2012). Indices of novelty for emerging topic detection. Information Processing & Management, 48(2), 303-325. doi:10.1016/j.ipm.2011.07.006
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Serhat Burmaoğlu 0000-0002-5537-6887

Esra Dündar Aravacık Bu kişi benim 0000-0002-6504-6283

Yayımlanma Tarihi 31 Ekim 2019
Gönderilme Tarihi 4 Ağustos 2019
Kabul Tarihi 13 Eylül 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 2 Sayı: 2

Kaynak Göster

APA Burmaoğlu, S., & Dündar Aravacık, E. (2019). EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES. İzmir Katip Çelebi Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 2(2), 140-150.
AMA Burmaoğlu S, Dündar Aravacık E. EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES. IKCIIBFD. Ekim 2019;2(2):140-150.
Chicago Burmaoğlu, Serhat, ve Esra Dündar Aravacık. “EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES”. İzmir Katip Çelebi Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 2, sy. 2 (Ekim 2019): 140-50.
EndNote Burmaoğlu S, Dündar Aravacık E (01 Ekim 2019) EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES. İzmir Katip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2 2 140–150.
IEEE S. Burmaoğlu ve E. Dündar Aravacık, “EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES”, IKCIIBFD, c. 2, sy. 2, ss. 140–150, 2019.
ISNAD Burmaoğlu, Serhat - Dündar Aravacık, Esra. “EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES”. İzmir Katip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2/2 (Ekim 2019), 140-150.
JAMA Burmaoğlu S, Dündar Aravacık E. EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES. IKCIIBFD. 2019;2:140–150.
MLA Burmaoğlu, Serhat ve Esra Dündar Aravacık. “EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES”. İzmir Katip Çelebi Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, c. 2, sy. 2, 2019, ss. 140-5.
Vancouver Burmaoğlu S, Dündar Aravacık E. EVALUATING TECHNOLOGICAL EMERGENCE FOR STRATEGIC TECHNOLOGY (ETEST) MANAGEMENT: A HYBRID MODEL OF SCIENTOMETRICS AND MCDM APPROACHES. IKCIIBFD. 2019;2(2):140-5.
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