A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method

Seong-Ho Cho

doi:doi:10.11648/j.ijefm.20180602.11

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A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method

Seong-Ho Cho

Published in International Journal of Economics, Finance and Management Sciences (Volume 6, Issue 2)

Received: 20 February 2018 Accepted: 11 March 2018 Published: 2 April 2018

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Abstract

In this exploratory paper, the dynamic stock return method (DSRM) initially proposed as an effective and replicable method by [14], [4], [5], [6] is deliberately applied to the US airline industry over the period from 1979 to 1992 (14 years). The longitudinal categorization or strategic group (SG) results from the DSRM show good face validity. They are consistent with the industry’s fact-based historical progress. We also observe that the operational measures such as market share or productivity tend to support the grouping results. Furthermore, the results of 15- and 7-year analysis of relative closeness of stock responsive movements between two representative airline firms (American and Hawaiian airlines, respectively) could be inferred that the SGs derived from the DSRM are valid and robust over a longer time span. We conclude that the DSRM could be a good alternative instrument for the longitudinal study of industry substructure.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 6, Issue 2)
DOI	10.11648/j.ijefm.20180602.11
Page(s)	35-42
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Categorization, Strategic Group, Niche, Industry Substructure, Cluster, US Airline Industry, Longitudinal Structural Dynamics, Longitudinal Study

References

[1]	Barney, J. B., and R. E. Hoskisson (1990), “Strategic groups: Untested assertions and research proposals”, Managerial and Decision Economics, 11, pp 187-198.
[2]	Calinski, R. B., and J. Harabasz (1974), “A dendrite method for cluster analysis,” Communications in Statistics, 3: 1-27.
[3]	Cattani, G., J. F. Porac, and H. Thomas (2017), “Categories and Competition”, Strategic Management Journal, 38, pp. 64-92.
[4]	Cho, S-H (2007), “On the Stock Return Method to Determining Industry Substructure: The Case of Airline, Banking, and Oil Industries,’ Journal of Strategic Management, 10:41-70.
[5]	Cho, S-H (2011), “Detecting Industry Substructure via Stock Return Method: NASDAQ Electronics Firms”, Journal of Strategic Management, 14 (1):77-103.
[6]	Cho, S-H (2017), “On the Dynamic Stock Return Method to Analyzing Longitudinal Industry Substructure”, Working Paper, Hongik University.
[7]	Cool, K., and D. Schendel (1987), “Strategic group formation and performance: The case of the U.S. pharmaceutical industry”, Management Science, 33, pp. 1102-1124.
[8]	DeSarbo, W. S., R. Grewal, and R. Wang (2009), “Dynamic strategic groups: deriving spatial evolutionary paths”, Strategic Management Journal, 30 (13), pp. 1420–1439.
[9]	Duda, R. O., and P. E. Hart (1973), Pattern Classification and Scene Analysis, Wiley, New York.
[10]	Fiegenbaum, A., and H. Thomas (1993), “Industry and strategic group dynamics: competitive strategy in the insurance industry, 1970-84. Journal of Management Studies, 30 (1): 69-97.
[11]	Fiegenbaum, A., and H. Thomas (1995), “Strategic groups as reference groups: Theory, modeling and empirical examination of industry and competitive strategy”, Strategic Management Journal, 16 (6), pp. 461–476.
[12]	Mas-Ruiz, F., J. Nicholau-Gonzalbez, and F. Ruiz-Moreno (2005), “Asymmetric Rivalry between Strategic Groups”, Strategic Management Journal, 26, pp. 713-745.
[13]	McKelvey, Bill (1982), Organizational Systematics, University of California, Berkeley, CA.
[14]	Ryans A. B., and D. R. Wittink (1985), “Security returns as a basis for estimating the competitive structure in an industry”, in H. Thomas and D. Gardner (Eds.), Strategic Marketing and Management, Wiley, New York.
[15]	Suarez FF, Grodal S, and A. Gotsopolous (2014), “Perfect timing? Dominant category, dominant design, and the window of opportunity for firm entry”. Strategic Management Journal, 36: 437–448.
[16]	Vergne J-P and T. Wry (2014) “Categorizing categorization research: review, integration, and future directions”. Journal of Management Studies, 51 (1): 56–94.

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Seong-Ho Cho. (2018). A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. International Journal of Economics, Finance and Management Sciences, 6(2), 35-42. https://doi.org/10.11648/j.ijefm.20180602.11

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ACS Style

Seong-Ho Cho. A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. Int. J. Econ. Finance Manag. Sci. 2018, 6(2), 35-42. doi: 10.11648/j.ijefm.20180602.11

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AMA Style

Seong-Ho Cho. A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. Int J Econ Finance Manag Sci. 2018;6(2):35-42. doi: 10.11648/j.ijefm.20180602.11

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@article{10.11648/j.ijefm.20180602.11,
  author = {Seong-Ho Cho},
  title = {A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {6},
  number = {2},
  pages = {35-42},
  doi = {10.11648/j.ijefm.20180602.11},
  url = {https://doi.org/10.11648/j.ijefm.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20180602.11},
  abstract = {In this exploratory paper, the dynamic stock return method (DSRM) initially proposed as an effective and replicable method by [14], [4], [5], [6] is deliberately applied to the US airline industry over the period from 1979 to 1992 (14 years). The longitudinal categorization or strategic group (SG) results from the DSRM show good face validity. They are consistent with the industry’s fact-based historical progress. We also observe that the operational measures such as market share or productivity tend to support the grouping results. Furthermore, the results of 15- and 7-year analysis of relative closeness of stock responsive movements between two representative airline firms (American and Hawaiian airlines, respectively) could be inferred that the SGs derived from the DSRM are valid and robust over a longer time span. We conclude that the DSRM could be a good alternative instrument for the longitudinal study of industry substructure.},
 year = {2018}
}

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TY  - JOUR
T1  - A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method
AU  - Seong-Ho Cho
Y1  - 2018/04/02
PY  - 2018
N1  - https://doi.org/10.11648/j.ijefm.20180602.11
DO  - 10.11648/j.ijefm.20180602.11
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 35
EP  - 42
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20180602.11
AB  - In this exploratory paper, the dynamic stock return method (DSRM) initially proposed as an effective and replicable method by [14], [4], [5], [6] is deliberately applied to the US airline industry over the period from 1979 to 1992 (14 years). The longitudinal categorization or strategic group (SG) results from the DSRM show good face validity. They are consistent with the industry’s fact-based historical progress. We also observe that the operational measures such as market share or productivity tend to support the grouping results. Furthermore, the results of 15- and 7-year analysis of relative closeness of stock responsive movements between two representative airline firms (American and Hawaiian airlines, respectively) could be inferred that the SGs derived from the DSRM are valid and robust over a longer time span. We conclude that the DSRM could be a good alternative instrument for the longitudinal study of industry substructure.
VL  - 6
IS  - 2
ER  -

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Author Information

Seong-Ho Cho

School of Business Administration, Hongik University, Seoul, Korea

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APA Style

Seong-Ho Cho. (2018). A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. International Journal of Economics, Finance and Management Sciences, 6(2), 35-42. https://doi.org/10.11648/j.ijefm.20180602.11

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ACS Style

Seong-Ho Cho. A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. Int. J. Econ. Finance Manag. Sci. 2018, 6(2), 35-42. doi: 10.11648/j.ijefm.20180602.11

Copy | Download

AMA Style

Seong-Ho Cho. A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method. Int J Econ Finance Manag Sci. 2018;6(2):35-42. doi: 10.11648/j.ijefm.20180602.11

Copy | Download

@article{10.11648/j.ijefm.20180602.11,
  author = {Seong-Ho Cho},
  title = {A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {6},
  number = {2},
  pages = {35-42},
  doi = {10.11648/j.ijefm.20180602.11},
  url = {https://doi.org/10.11648/j.ijefm.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20180602.11},
  abstract = {In this exploratory paper, the dynamic stock return method (DSRM) initially proposed as an effective and replicable method by [14], [4], [5], [6] is deliberately applied to the US airline industry over the period from 1979 to 1992 (14 years). The longitudinal categorization or strategic group (SG) results from the DSRM show good face validity. They are consistent with the industry’s fact-based historical progress. We also observe that the operational measures such as market share or productivity tend to support the grouping results. Furthermore, the results of 15- and 7-year analysis of relative closeness of stock responsive movements between two representative airline firms (American and Hawaiian airlines, respectively) could be inferred that the SGs derived from the DSRM are valid and robust over a longer time span. We conclude that the DSRM could be a good alternative instrument for the longitudinal study of industry substructure.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - A Longitudinal Categorization of US Airline Industry via Dynamic Stock Return Method
AU  - Seong-Ho Cho
Y1  - 2018/04/02
PY  - 2018
N1  - https://doi.org/10.11648/j.ijefm.20180602.11
DO  - 10.11648/j.ijefm.20180602.11
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 35
EP  - 42
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20180602.11
AB  - In this exploratory paper, the dynamic stock return method (DSRM) initially proposed as an effective and replicable method by [14], [4], [5], [6] is deliberately applied to the US airline industry over the period from 1979 to 1992 (14 years). The longitudinal categorization or strategic group (SG) results from the DSRM show good face validity. They are consistent with the industry’s fact-based historical progress. We also observe that the operational measures such as market share or productivity tend to support the grouping results. Furthermore, the results of 15- and 7-year analysis of relative closeness of stock responsive movements between two representative airline firms (American and Hawaiian airlines, respectively) could be inferred that the SGs derived from the DSRM are valid and robust over a longer time span. We conclude that the DSRM could be a good alternative instrument for the longitudinal study of industry substructure.
VL  - 6
IS  - 2
ER  -

Copy | Download