Abstract
From an evolutionary economics perspective, knowledge networks are self-organizing systems. Therefore, studying changes of these systems requires an understanding of how such changes are influenced by both the behaviors and characteristics of key individual actors and the network structure. We apply this perspective to a network of investigators (i.e. lead scientists) and a sample of 9543 Phase 2 cancer clinical trials during the period 2002–2012, in order to examine the structure and explore the dynamics of the clinical trial network. Using temporal exponential random graph models, we examine whether preferential attachment, multi-connectivity, or homophily drive the formation of new collaborative relations to knowledge translators - i.e. investigators with basic and clinical research knowledge. Our results suggest that despite some increased connectivity over time the network remains fragmented due to the considerably growing number of investigators in the network. This fragmentation limits opportunities for knowledge transfer to advance clinical trials. We find that homophily in research fields and investigators’ country of affiliation and heterophily in terms of publication output promote the formation of ties to knowledge translators. We find also that multi-connectivity increases the probability of tie formation with knowledge translators while preferential attachment reduces this probability.
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The CHI classification of journals has been criticized due to methodological and other concerns (e.g., Tijssen 2010). Consequently, alternative approaches for classifying publications as basic or clinical research have been developed (e.g., Boyack et al. 2014). However, Anckaert et al. (2020) emphasize that different classification approaches lead to very consistent classification outcomes.
As some clinical trials do not report investigator names, the shares presented in Fig. 1 do not add up to 100.
It should be noted that compared to clinical and basic research investigators, knowledge translators have a higher median number of publications in the 5 years preceding the start of the trial they are involved in. We see this higher number of publications as an outcome of the collaborative role played by knowledge translators. The development of the unclassified investigators’ average degree is similar to the development of the other groups. This finding indicates that unclassified investigators connectivity is - on average – comparable to the connectivity of other groups.
Using the median degree instead of the mean shows a similar trend of increasing connectivity within the network and across the different types of investigators.
References
Adams J (2013) The fourth age of research. Nat 497:557 EP. https://doi.org/10.1038/497557a
Agha L, Molitor D (2018) The local influence of Pioneer investigators on technology adoption: evidence from new Cancer drugs. Rev Econ Stat 100:29–44. https://doi.org/10.1162/REST_a_00670
Anckaert P-E, Cassiman D, Cassiman B (2020) Fostering practice-oriented and use-inspired science in biomedical research. Res Policy 49:103900. https://doi.org/10.1016/j.respol.2019.103900
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509. https://doi.org/10.1126/science.286.5439.509
Barabási AL, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Physica A 311:590–614. https://doi.org/10.1016/S0378-4371(02)00736-7
Boschma R, Frenken K (2010) The handbook of evolutionary economic geography. In: Boschma R, Martin R (eds) Handbook of evolutionary economic geography. Edward Elgar Publishing, Cheltenham and Northhampton
Boyack KW, Patek M, Ungar LH, Yoon P, Klavans R (2014) Classification of individual articles from all of science by research level. J Informet 8:1–12. https://doi.org/10.1016/j.joi.2013.10.005
Bozeman B, Corley E (2004) Scientists’ collaboration strategies: implications for scientific and technical human capital. Res Policy 33:599–616. https://doi.org/10.1016/j.respol.2004.01.008
Brass DJ, Galaskiewicz J, Greve HR, Tsai W (2004) Taking stock of networks and organizations: a multilevel perspective. Acad Manag J 47:795–817. https://doi.org/10.5465/20159624
Bromham L, Dinnage R, Hua X (2016) Interdisciplinary research has consistently lower funding success. Nat 534:684 EP. https://doi.org/10.1038/nature18315
Cantner U, Rake B (2014) International research networks in pharmaceuticals: structure and dynamics. Res Policy 43:333–348. https://doi.org/10.1016/j.respol.2013.10.016
Contractor N (2013) Some assembly required: leveraging web science to understand and enable team assembly. Philos Trans R Soc Lond A Math Phys Eng Sci 371. https://doi.org/10.1098/rsta.2012.0385
Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28:1557–1575. https://doi.org/10.1016/j.jedc.2003.04.002
Cranmer SJ, Leifeld P, McClurg SD, Rolfe M (2017) Navigating the range of statistical tools for inferential network analysis. Am J Polit Sci 61:237–251. https://doi.org/10.1111/ajps.12263
D’Este P, Ramos-Vielba I, Woolley R, Amara N (2018) How do researchers generate scientific and societal impacts? Toward an analytical and operational framework. Sci Public Policy 45:752–763. https://doi.org/10.1093/scipol/scy023
Davidoff F, DeAngelis CD, Drazen JM, Hoey J, Højgaard L, Horton R, Kotzin S, Nicholls M, Nylenna M, Overbeke JP, Sox HC, van der Weyden MB, Wilkes MS (2001) Sponsorship, authorship, and accountability. JAMA 286:1232–1234. https://doi.org/10.1001/jama.286.10.1232
FitzGerald GA (2005) Anticipating change in drug development: the emerging era of translational medicine and therapeutics. Nat Rev Drug Discov 4:815–818. https://doi.org/10.1038/nrd1849
Flowers CR, Melmon KL (1997) Clinical investigators as critical determinants in pharmaceutical innovation. Nat Med 3:136–143. https://doi.org/10.1038/nm0297-136
Gittelman M (2016) The revolution re-visited: clinical and genetics research paradigms and the productivity paradox in drug discovery. Res Policy 45:1570–1585. https://doi.org/10.1016/j.respol.2016.01.007
Glückler J (2007) Economic geography and the evolution of networks. J Econ Geogr 7:619–634. https://doi.org/10.1093/jeg/lbm023
Glückler J (2010) The evolution of a strategic Alliance network: exploring the case of stock photography. In: Boschma R, Martin R (eds) Handbook of evolutionary economic geography. Edward Elgar Publishing, Cheltenham and Northhampton, pp 298–315
Haeussler C, Assmus, A (2021) Bridging the gap between invention and innovation: Increasing success rates in publicly and industry-funded clinical trials. Res Policy, 50:1041555, https://doi.org/10.1016/j.respol.2020.104155
Haeussler C, Rake B (2017) The changing geography of clinical research: a critical analysis of its drivers. Ind Corp Chang 26:285–310
Hamilton KS (2003) Subfield and level classification of journals. CHI Research Inc CHI No 2012-R
Hoekman J, Frenken K, de Zeeuw D, Lambers Heerspink H (2012) The geographical distribution of leadership in globalized clinical trials. PLoS One 7:e45984. https://doi.org/10.1371/journal.pone.0045984
Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008) ergm: ergm: A package to fit, simulate and diagnose exponential-family models for networks. J Stat Softw. 24(3):ihpa54860. https://doi.org/10.18637/jss.v024.i03
Jeong H, Néda Z, Barabási A-L (2003) Measuring preferential attachment in evolving networks. Europhys Lett 61:567
Jha Y, Welch EW (2010) Relational mechanisms governing multifaceted collaborative behavior of academic scientists in six fields of science and engineering. Res Policy 39:1174–1184. https://doi.org/10.1016/j.respol.2010.06.003
Jones BF (2009) The burden of knowledge and the “death of the renaissance man”: is innovation getting harder? Rev Econ Stud 76:283–317. https://doi.org/10.1111/j.1467-937X.2008.00531.x
Kim JY, Michael H, Emily CP, Warren B (2016) Understanding network formation in strategy research: exponential random graph models. Strateg Manag J 37:22–44. https://doi.org/10.1002/smj.2454
Kleinbaum AM, Stuart TE, Tushman ML (2013) Discretion within constraint: Homophily and structure in a formal organization. Organ Sci 24:1316–1336. https://doi.org/10.1287/orsc.1120.0804
Kossinets G, Watts DJ (2009) Origins of Homophily in an evolving social network. Am J Sociol 115:405–450. https://doi.org/10.1086/599247
Lazarsfeld PF, Merton RK, others (1954) Friendship as a social process: a substantive and methodological analysis. In: Berger MA, Abel T, Page C (eds) Freedom and control in modern society. Van Nostrand, New York, pp 18–66
Leahey E, Beckman CM, Stanko TL (2017) Prominent but less productive: the impact of Interdisciplinarity on scientists’ research. Adm Sci Q 62:105–139. https://doi.org/10.1177/0001839216665364
Leifeld P, Cranmer SJ, Desmarais BA (2018) Temporal exponential random graph models with btergm: estimation and bootstrap confidence intervals. J Stat Softw 83(6). https://doi.org/10.18637/jss.v083.i06
Leong SPL (2007) The role of the principal investigator in Cancer clinical trials. In: Leong SPL (ed) Cancer clinical trials: proactive strategies, vol 132. Springer, New York, pp 157–177
Llopis O, D’Este P (2016) Beneficiary contact and innovation: the relation between contact with patients and medical innovation under different institutional logics. Res Policy 45:1512–1523. https://doi.org/10.1016/j.respol.2016.03.004
Lomi A, Lusher D, Pattison PE, Robins G (2013) The focused Organization of Advice Relations: a study in boundary crossing. Organ Sci 25:438–457. https://doi.org/10.1287/orsc.2013.0850
Lusher D, Robins G (2013) Formation of social network structure. In: Lusher D, Koskinen J, Robins G (eds) Exponential random graph models for social networks: theory, methods, and applications. Cambridge University Press, New York, pp 16–28
Malterud K (2001) The art and science of clinical knowledge: evidence beyond measures and numbers. Lancet 358:397–400. https://doi.org/10.1016/S0140-6736(01)05548-9
Martin R, Sunley P (2006) Path dependence and regional economic evolution. J Econ Geogr 6:395–437. https://doi.org/10.1093/jeg/lbl012
Mathieu J, Marks M, Zaccaro S (2001) Multiteam systems. In: Anderson N, Ones DS, Sinangil HK, Viswesvaran C (eds) Handbook of Industrial, Work & Organizational Psychology: Volume 2: Organizational Psychology. Sage, pp 289–313
McKelvey M, Orsenigo L (2006) Introduction. In: McKelvey M, Orsenigo L (eds) The economics of biotechnology. Edward Elgar, Cheltenham, UK, pp. xi–xiv
McKelvey M, Orsenigo L, Pammolli F (2004) Pharmaceuticals analyzed through the lens of a sectoral innovation syste. In: Malerba F (ed) Sectoral Systems of Innovation Sectoral Systems of innovation: concepts. Cambridge University Press, Issues and Analyses of Six Major Sectors in Europe. Cambridge et al., pp 157–198
McPherson JM, Smith-Lovin L (1987) Homophily in voluntary organizations: status distance and the composition of face-to-face groups. Am Sociol Rev 52:370–379. https://doi.org/10.2307/2095356
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annu Rev Sociol 27:415–444
Metcalfe JS, James A, Mina A (2005) Emergent innovation systems and the delivery of clinical services: the case of intra-ocular lenses. Res Policy 34:1283–1304
Moody J (2004) The structure of a social science collaboration network: disciplinary cohesion from 1963 to 1999. Am Sociol Rev 69:213–238. https://doi.org/10.1177/000312240406900204
Moody J, White DR (2003) Structural cohesion and Embeddedness: a hierarchical concept of social groups. Am Sociol Rev 68:103–127
Nebus J (2006) Building collegial information networks: a theory of advice network generation. Acad Manag Rev 31:615–637. https://doi.org/10.5465/amr.2006.21318921
Nelson RR (1995) Recent evolutionary theorizing about economic change. J Econ Lit 33:48–90
Newman M (2001a) Clustering and preferential attachment in growing networks. Phys Rev E 64:1–4
Newman MEJ (2001b) The structure of scientific collaboration networks. Proc Natl Acad Sci 98:404. https://doi.org/10.1073/pnas.98.2.404
O’ Connell D, Roblin D (2006) Translational research in the pharmaceutical industry: from bench to bedside. Drug Discov Today 11:833–838. https://doi.org/10.1016/j.drudis.2006.07.009
Orsenigo L, Pammolli F, Riccaboni M, Bonaccorsi A, Turchetti G (1998) The evolution of knowledge and the dynamics of an industry network. J Manag Gov 1:147–175. https://doi.org/10.1023/A:1009991701667
Orsenigo L, Pammolli F, Riccaboni M (2001) Technological change and network dynamics: lessons from the pharmaceutical industry. Res Policy 30:485–508. https://doi.org/10.1016/S0048-7333(00)00094-9
Pammolli F, Magazzini L, Riccaboni M (2011) The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 10:428 EP. https://doi.org/10.1038/nrd3405
Patel V, Arocha JF, Kaufman DR (1999) Expertise and tacit knowledge in medicine. In: Sternberg RJ (ed) Tacit knowledge in professional practice: researcher and practitioner perspectives. Erlbaum, Mahwah, pp 75–99
Perry-Smith JE, Mannucci PV (2015) From creativity to innovation: the social network drivers of the four phases of the idea journey. Acad Manag Rev 42:53–79. https://doi.org/10.5465/amr.2014.0462
Phillips N, Tracey P, Karra N (2013) Building entrepreneurial tie portfolios through strategic homophily: the role of narrative identity work in venture creation and early growth. J Bus Ventur 28:134–150. https://doi.org/10.1016/j.jbusvent.2011.12.002
Powell WW, White DR, Koput KW, Owen-Smith J (2005) Network dynamics and field evolution: the growth of Interorganizational collaboration in the life sciences. Am J Sociol 110:1132–1205
Rake B, D'Este P, McKelvey M, Assmus A (2017) How do networks affect knowledge generation for drug development? Acad Manag Proc 2017:15393. https://doi.org/10.5465/AMBPP.2017.15393abstract
Rasmussen N (2005) The drug industry and clinical research in interwar America: three types of physician collaborator. Bull Hist Med 79:50–80
Reagans R, McEvily B (2003) Network structure and knowledge transfer: the effects of cohesion and range. Adm Sci Q 48:240–267. https://doi.org/10.2307/3556658
Rivera MT, Soderstrom SB, Uzzi B (2010) Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu Rev Sociol 36:91–115. https://doi.org/10.1146/annurev.soc.34.040507.134743
Robins G, Lusher D (2013) Simplified account of an exponential random graph model as a statistical model. In: Lusher D, Koskinen J, Robins G (eds) Exponential random graph models for social networks: theory, methods, and applications. Cambridge University Press, New York, pp 29–36
Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Networks 29:173–191. https://doi.org/10.1016/j.socnet.2006.08.002
Rogers EM, Bhowmik DK (1970) Homophily-Heterophily: relational concepts for communication research. Public Opin Q 34:523–538
Ruef M, Aldrich HE, Carter NM (2003) The structure of founding teams: Homophily, strong ties, and isolation among U.S. entrepreneurs. Am Sociol Rev 68:195–222. https://doi.org/10.2307/1519766
Schumpeter JA (1934) The theory of economic development: an inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press, Cambridge
Sie RLL, Drachsler H, Bitter-Rijpkema M, Sloep P (2012) To whom and why should I connect? Co-author recommendation based on powerful and similar peers. Int J Tech Enhanc Learn 4:121–137. https://doi.org/10.1504/IJTEL.2012.048314
Stokes DE (1997) Pasteur’s quadrant: basic science and technological innovation. Brookings Institute, Washington DC
Tijssen RJW (2010) Discarding the ‘basic science/applied science’ dichotomy: a knowledge utilization triangle classification system of research journals. J Am Soc Inf Sci 61:1842–1852. https://doi.org/10.1002/asi.21366
Tijssen RJW (2018) Anatomy of use-inspired researchers: from Pasteur’s quadrant to Pasteur’s cube model. Res Policy 47:1626–1638. https://doi.org/10.1016/j.respol.2018.05.010
Tomasello MV, Napoletano M, Garas A, Schweitzer F (2017) The rise and fall of R&D networks. Ind Corp Chang 26:617–646. https://doi.org/10.1093/icc/dtw041
Uzzi B, Lancaster R (2003) Relational Embeddedness and learning: the case of Bank loan managers and their clients. Manag Sci 49:383–399. https://doi.org/10.1287/mnsc.49.4.383.14427
Veugelers R, Wang J (2019) Scientific novelty and technological impact. Res Policy 48:1362–1372. https://doi.org/10.1016/j.respol.2019.01.019
Wagner CS, Leydesdorff L (2005) Network structure, self-organization, and the growth of international collaboration in science. Res Policy 34:1608–1618
Wang J, Hicks D (2015) Scientific teams: self-assembly, fluidness, and interdependence. J Informet 9:197–207. https://doi.org/10.1016/j.joi.2014.12.006
Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge et al
White DR, Harary F (2001) The cohesiveness of blocks in social networks: node connectivity and conditional density. Sociol Methodol 31:305–359. https://doi.org/10.1111/0081-1750.00098
Witt U (2008) Evolutionary economics. In: Durlauf SN, Blume LE (eds) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke
Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316:1036. https://doi.org/10.1126/science.1136099
Xie W-J, Li M-X, Jiang Z-Q, Tan Q-Z, Podobnik B, Zhou W-X, Stanley HE (2016) Skill complementarity enhances heterophily in collaboration networks. Sci Rep 6:18727. https://doi.org/10.1038/srep18727
Zaheer A, Soda G (2009) Network evolution: the origins of structural holes. Adm Sci Q 54:1–31. https://doi.org/10.2189/asqu.2009.54.1.1
Zappa P (2016) Toward a multilevel social exchange theory of advice relations in organizations
Zhang C, Bu Y, Ding Y, Xu J (2018) Understanding scientific collaboration: Homophily, transitivity, and preferential attachment. J Assoc Inf Sci Technol 69:72–86. https://doi.org/10.1002/asi.23916
Zucker LG (1986) Production of trust: institutional sources of economic structure, 1840–1920. Res Organ Behav 8:53–111
Acknowledgements
This work was supported by the Swedish Research Council Distinguished Professor’s Programme, Contract number 2017-03360, awarded to Professor McKelvey, for the research program “Entrepreneurial Ecosystems: Transforming society through knowledge, innovation and entrepreneurship”. We thank Anne Assmus for sharing data on investigators’ publication activities. We thank Mark Bagley and participants in the Workshop on Medical Innovation 2017 held in Reykjavik for discussion of the research idea, and participants in the 17th International Joseph A. Schumpeter Society Conference held in Seoul in 2018, the Workshop in Memory of Luigi Orsenigo held in Milan in December 2018 and the research seminar held in Spring 2019 at the University of Gothenburg for stimulating discussions and valuable suggestions on earlier versions of the paper.
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This work was supported by the Swedish Research Council Distinguished Professor’s Programme, Contract number 2017–03360, awarded to Professor McKelvey, for the research program “Entrepreneurial Ecosystems: Transforming society through knowledge, innovation and entrepreneurship”.
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Appendix
Appendix
Visualization of the investigator network’s largest component in 2002
Visualization of the investigator network’s largest component in 2007
Visualization of the investigator network’s largest component in 2012
Goodness-of-fit assessment of model 4 in Table 3
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Rake, B., D’Este, P. & McKelvey, M. Exploring network dynamics in science: the formation of ties to knowledge translators in clinical research. J Evol Econ 31, 1433–1464 (2021). https://doi.org/10.1007/s00191-020-00716-1
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DOI: https://doi.org/10.1007/s00191-020-00716-1
Keywords
- Network dynamics
- Preferential attachment
- Homophily
- Multi-connectivity
- Clinical trials
- Knowledge translators