Coherent diversification in corporate technological portfolios
Autoři:
Emanuele Pugliese aff001; Lorenzo Napolitano aff001; Andrea Zaccaria aff001; Luciano Pietronero aff001
Působiště autorů:
Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
aff001; International Finance Corporation, World Bank Group, 20433 Washington, United States of America
aff002; European Commission, Joint Research Centre (JRC), Seville, Spain
aff003; Istituto di Economia, Scuola Universitaria Superiore Sant’Anna, Pisa, Italy
aff004; Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
aff005
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223403
Souhrn
We study the relationship between the performance of firms and their technological portfolios using tools borrowed from complexity science. In particular, we ask whether the accumulation of knowledge and capabilities associated with a coherent set of technologies leads firms to experience advantages in terms of productive efficiency. To this end, we analyze both the balance sheets and the patenting activity of about 70 thousand firms that have filed at least one patent over the period 2004-2013. We define a measure of corporate coherent diversification, based on the bipartite network linking companies with the technological fields in which they patent, and relate it to firm performance in terms of labor productivity. Our measure favors technological portfolios that can be decomposed into large blocks of closely related fields over portfolios with the same breadth of scope, but a more scattered diversification structure. We find that the coherent diversification of firms is quantitatively related with their economic performance and captures relevant information about their productive structure. In particular, we prove on a statistical basis that a naive definition of technological diversification can explain labor productivity only as a proxy of size and coherent diversification. This approach can be used to investigate possible synergies within firms and to recommend viable partners for mergers and acquisitions.
Klíčová slova:
Cell phones – Computers – Economics – Evolutionary systematics – Labor economics – Taxonomy – Patents – Economic agents
Zdroje
1. McNamee RC. Can’t see the forest for the leaves: Similarity and distance measures for hierarchical taxonomies with a patent classification example. Research Policy. 2013;42(4):855–873. doi: 10.1016/j.respol.2013.01.006
2. Lee YN, Walsh JP, Wang J. Creativity in scientific teams: Unpacking novelty and impact. Research Policy. 2015;44(3):684–697. doi: 10.1016/j.respol.2014.10.007
3. Keijl S, Gilsing V, Knoben J, Duysters G. The two faces of inventions: The relationship between recombination and impact in pharmaceutical biotechnology. Research Policy. 2016;45(5):1061–1074. doi: 10.1016/j.respol.2016.02.008
4. Battke B, Schmidt TS, Stollenwerk S, Hoffmann VH. Internal or external spillovers—Which kind of knowledge is more likely to flow within or across technologies. Research policy. 2016;45(1):27–41. doi: 10.1016/j.respol.2015.06.014
5. Patel P, Pavitt K. The technological competencies of the world’s largest firms: complex and path-dependent, but not much variety. Research policy. 1997;26(2):141–156. doi: 10.1016/S0048-7333(97)00005-X
6. Brusoni S, Prencipe A, Pavitt K. Knowledge specialization, organizational coupling, and the boundaries of the firm: why do firms know more than they make? Administrative science quarterly. 2001;46(4):597–621. doi: 10.2307/3094825
7. Dosi G, Grazzi M, Moschella D. What do firms know? What do they produce? A new look at the relationship between patenting profiles and patterns of product diversification. Small Business Economics. 2017;48(2):413–429. doi: 10.1007/s11187-016-9783-0
8. Tacchella A, Cristelli M, Caldarelli G, Gabrielli A, Pietronero L. A new metrics for countries’ fitness and products’ complexity. Scientific reports. 2012;2. doi: 10.1038/srep00723 23056915
9. Zaccaria A, Cristelli M, Tacchella A, Pietronero L. How the Taxonomy of Products Drives the Economic Development of Countries. PLoS ONE. 2014;9(12):1–17. doi: 10.1371/journal.pone.0113770
10. Tacchella A, Mazzilli D, Pietronero L. A dynamical systems approach to gross domestic product forecasting. Nature Physics. 2018;14(8):861. doi: 10.1038/s41567-018-0204-y
11. Anderson PW. More is different. Science. 1972;177(4047):393–396. doi: 10.1126/science.177.4047.393 17796623
12. Pietronero L. Complexity ideas from condensed matter and statistical physics. Europhysics news. 2008;39(6):26–29. doi: 10.1051/epn:2008603
13. Pugliese E, Cimini G, Patelli A, Zaccaria A, Pietronero L, Gabrielli A. Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production. arXiv preprint arXiv:170705146. 2017;.
14. Pugliese E, Chiarotti GL, Zaccaria A, Pietronero L. Complex economies have a lateral escape from the poverty trap. PloS one. 2017;12(1):e0168540. doi: 10.1371/journal.pone.0168540 28072867
15. Dosi G. Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Research policy. 1982;11(3):147–162. doi: 10.1016/0048-7333(82)90016-6
16. Dosi G, Faillo M, Marengo L. Organizational capabilities, patterns of knowledge accumulation and governance structures in business firms: an introduction. Organization Studies. 2008;29(8-9):1165–1185. doi: 10.1177/0170840608094775
17. Teece DJ, Rumelt R, Dosi G, Winter S. Understanding corporate coherence. Journal of Economic Behavior & Organization. 1994;23(1):1–30. http://dx.doi.org/10.1016/0167-2681(94)90094-9.
18. Hausmann R, Klinger B. Structural transformation and patterns of comparative advantage in the product space. 2006.
19. Hausmann R, Hidalgo CA. The network structure of economic output. Journal of Economic Growth. 2011;16(4):309–342. doi: 10.1007/s10887-011-9071-4
20. Granstrand O, Patel P, Pavitt K. Multi-Technology Corporations: Why They Have Distributed Rather Than Distinctive Core Competencies. California Management Review. 1997;39(4):8–25. doi: 10.2307/41165908
21. Schumpeter JA. Business cycles: a theoretical, historical, and statistical analysis of the capitalist process. McGraw-Hill New York; 1939.
22. Nelson RR, Winter SG. An evolutionary theory of economic change. harvard university press; 1982.
23. Henderson RM, Clark KB. Architectural innovation: The reconfiguration of existing. Administrative science quarterly. 1990;35(1):9–30. doi: 10.2307/2393549
24. Arthur WB. The structure of invention. Research policy. 2007;36(2):274–287. doi: 10.1016/j.respol.2006.11.005
25. Savino T, Messeni Petruzzelli A, Albino V. Search and recombination process to innovate: a review of the empirical evidence and a research agenda. International Journal of Management Reviews. 2017;19(1):54–75. doi: 10.1111/ijmr.12081
26. Fleming L. Recombinant uncertainty in technological search. Management science. 2001;47(1):117–132. doi: 10.1287/mnsc.47.1.117.10671
27. Kogut B, Zander U. Knowledge of the firm, combinative capabilities, and the replication of technology. Organization science. 1992;3(3):383–397. doi: 10.1287/orsc.3.3.383
28. Lichtenthaler U. Open innovation in practice: an analysis of strategic approaches to technology transactions. IEEE transactions on engineering management. 2008;55(1):148–157. doi: 10.1109/TEM.2007.912932
29. Ardito L, Natalicchio A, Messeni Petruzzelli A, Garavelli AC. Organizing for continuous technology acquisition: The role of R&D geographic dispersion. R&D Management. 2018;48(2):165–176. doi: 10.1111/radm.12270
30. Rothaermel FT, Deeds DL. Exploration and exploitation alliances in biotechnology: A system of new product development. Strategic management journal. 2004;25(3):201–221. doi: 10.1002/smj.376
31. Sampson RC. R&D alliances and firm performance: The impact of technological diversity and alliance organization on innovation. Academy of management journal. 2007;50(2):364–386. doi: 10.5465/amj.2007.24634443
32. Capaldo A, Messeni Petruzzelli A. Origins of knowledge and innovation in R&D alliances: a contingency approach. Technology Analysis & Strategic Management. 2015;27(4):461–483. doi: 10.1080/09537325.2015.1011612
33. Elia S, Petruzzelli AM, Piscitello L. The impact of cultural diversity on innovation performance of MNC subsidiaries in strategic alliances. Journal of Business Research. 2019;98:204–213. doi: 10.1016/j.jbusres.2019.01.062
34. Strumsky D, Lobo J, Van der Leeuw S. Measuring the relative importance of reusing, recombining and creating technologies in the process of invention. Working Paper; 2011.
35. Strumsky D, Lobo J, van der Leeuw S. Using patent technology codes to study technological change. Economics of Innovation and New Technology. 2012;21(3):267–286. doi: 10.1080/10438599.2011.578709
36. Youn H, Strumsky D, Bettencourt LMA, Lobo J. Invention as a combinatorial process: evidence from US patents. Journal of The Royal Society Interface. 2015;12(106):20150272. doi: 10.1098/rsif.2015.0272
37. Griliches Z. Patent statistics as economic indicators: a survey. Journal of Economic Literature. 1990;28(4):1661–1707.
38. Hall BH, Jaffe AB, Trajtenberg M. The NBER patent citation data file: Lessons, insights and methodological tools. National Bureau of Economic Research; 2001.
39. Knecht M. Diversification, Industry Dynamism, and Economic Performance: The Impact of Dynamic-related Diversification on the Multi-business Firm. Springer Science & Business Media; 2013.
40. Penrose E. The theory of the growth of the firm. New York: John Wiley; 1959.
41. Penrose ET. The growth of the firm—a case study: the Hercules Powder Company. Business History Review. 1960;34(01):1–23. doi: 10.2307/3111776
42. Gort M. Diversification and Integration in American Industry. National Bureau of Economic Research, Inc; 1962. Available from: http://EconPapers.repec.org/RePEc:nbr:nberbk:gort62-1.
43. Rumelt RP. Strategy, structure, and economic performance. 1974.
44. Berry CH. Corporate Growth and Diversification. The Journal of Law & Economics. 1971;14(2):371–383. doi: 10.1086/466714
45. Montgomery CA. Corporate diversification. The Journal of Economic Perspectives. 1994;8(3):163–178. doi: 10.1257/jep.8.3.163
46. Palepu K. Diversification strategy, profit performance and the entropy measure. Strategic management journal. 1985;6(3):239–255. doi: 10.1002/smj.4250060305
47. Palich LE, Cardinal LB, Miller CC. Curvilinearity in the diversification–performance linkage: an examination of over three decades of research. Strategic management journal. 2000;21(2):155–174. doi: 10.1002/(SICI)1097-0266(200002)21:2%3C155::AID-SMJ82%3E3.0.CO;2-2
48. Miller DJ. Firms’ technological resources and the performance effects of diversification: a longitudinal study. Strategic Management Journal. 2004;25(11):1097–1119. doi: 10.1002/smj.411
49. Rycroft RW, Kash DE. The complexity challenge: Technological innovation for the 21st century. Cengage Learning EMEA; 1999.
50. Cohen WM, Nelson RR, Walsh JP. Protecting their intellectual assets: Appropriability conditions and why US manufacturing firms patent (or not). National Bureau of Economic Research; 2000.
51. Pavitt K. Technologies, products and organization in the innovating firm: what Adam Smith tells us and Joseph Schumpeter doesn’t. Industrial and Corporate change. 1998;7(3):433–452. doi: 10.1093/icc/7.3.433
52. Fai F. Technological Diversification, its Relation to Product Diversification and the Organisation of the Firm. Unitversity of Bath School of Management Working Paper Series. 2004.
53. Rumelt RP. Diversification strategy and profitability. Strategic management journal. 1982;3(4):359–369. doi: 10.1002/smj.4250030407
54. Engelsman EC, van Raan AF. A patent-based cartography of technology. Research Policy. 1994;23(1):1–26. doi: 10.1016/0048-7333(94)90024-8
55. Piscitello L. Relatedness and coherence in technological and product diversification of the world’s largest firms. Structural Change and Economic Dynamics. 2000;11(3):295–315. doi: 10.1016/S0954-349X(00)00019-9
56. Leten B, Belderbos R, Van Looy B. Technological diversification, coherence, and performance of firms. Journal of Product Innovation Management. 2007;24(6):567–579. doi: 10.1111/j.1540-5885.2007.00272.x
57. Joo SH, Kim Y. Measuring relatedness between technological fields. Scientometrics. 2010;83(2):435–454. doi: 10.1007/s11192-009-0108-9
58. Rigby DL. Technological relatedness and knowledge space: entry and exit of US cities from patent classes. Regional Studies. 2015;49(11):1922–1937. doi: 10.1080/00343404.2013.854878
59. Breschi S, Lissoni F, Malerba F. Knowledge-relatedness in firm technological diversification. Research Policy. 2003;32(1):69–87. doi: 10.1016/S0048-7333(02)00004-5
60. Nesta L, Saviotti PP. Firm knowledge and market value in biotechnology. Industrial and Corporate Change. 2006;15(4):625–652. doi: 10.1093/icc/dtl007
61. Balland PA, Boschma R, Crespo J, Rigby DL. Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification. Regional Studies. 2018; p. 1–17.
62. Bottazzi G, Pirino D. Measuring industry relatedness and corporate coherence. Available at SSRN 1831479. 2010.
63. Napolitano L, Evangelou E, Pugliese E, Zeppini P, Room G. Technology networks: the autocatalytic origins of innovation. Royal Society open science. 2018;5(6):172445. doi: 10.1098/rsos.172445 30110482
64. Hidalgo CA, Klinger B, Barabási AL, Hausmann R. The product space conditions the development of nations. Science. 2007;317(5837):482–487. doi: 10.1126/science.1144581 17656717
65. Hidalgo CA, Hausmann R. The building blocks of economic complexity. Proceedings of the National Academy of Sciences. 2009;106(26):10570–10575. doi: 10.1073/pnas.0900943106
66. Saracco F, Di Clemente R, Gabrielli A, Pietronero L. From innovation to diversification: a simple competitive model. PloS one. 2015;10(11):e0140420. doi: 10.1371/journal.pone.0140420 26544685
67. Balassa B. Trade liberalisation and “revealed” comparative advantage. The Manchester School. 1965;33(2):99–123. doi: 10.1111/j.1467-9957.1965.tb00050.x
68. Zhou T, Ren J, Medo M, Zhang YC. Bipartite network projection and personal recommendation. Physical Review E. 2007;76(4):046115. doi: 10.1103/PhysRevE.76.046115
69. Zaccaria A, Mishra S, Cader M, Pietronero L. Integrating services in the economic fitness approach. World Bank Policy Research Working Paper No 8485. 2018;.
70. Di Guilmi C, Clementi F, Di Matteo T, Gallegati M. Social networks and labour productivity in Europe: an empirical investigation. Journal of Economic Interaction and Coordination. 2008;3(1):43. doi: 10.1007/s11403-008-0034-6
71. Ribeiro SP, Menghinello S, De Backer K. The OECD ORBIS Database. 2010.
72. Archibugi D, Planta M. Measuring technological change through patents and innovation surveys. Technovation. 1996;16(9):451519–468. doi: 10.1016/0166-4972(96)00031-4
73. Bogliacino F, Perani G, Pianta M, Supino S. Innovation and development: The evidence from innovation surveys. Latin American Business Review. 2012;13(3):219–261. doi: 10.1080/10978526.2012.730023
74. Martinez C. Insight into different types of patent families. 2010.
75. Dernis H, Khan M. Triadic patent families methodology. 2004;.
76. Cristelli M, Gabrielli A, Tacchella A, Caldarelli G, Pietronero L. Measuring the intangibles: A metrics for the economic complexity of countries and products. PloS one. 2013;8(8):e70726. doi: 10.1371/journal.pone.0070726 23940633
77. Caldarelli G, Cristelli M, Gabrielli A, Pietronero L, Scala A, Tacchella A. A network analysis of countries’ export flows: firm grounds for the building blocks of the economy. PloS one. 2012;7(10):e47278. doi: 10.1371/journal.pone.0047278 23094044
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