We analyze the global pharmaceutical industry network using a unique database that covers strategic transactions (i.e. alliance, financing and acquisition collaborations) for the top 90 global pharmaceutical firms and their ego-network partnerships totaling 4735 members during 1991-2012. The network evolution is traced via a novel method based on the concept of dynamicity that quantifies individual network members (i.e. actors) contribution to the longitudinal period. Specifically, we observe dynamic embeddedness defined for key network centrality measures, and capture the impact of the 2007-2008 global financial crises and the subsequent global and Eurozone recession effects on the strategic transaction flows between the industry's key players as well as their partners. Results suggest the feasibility of dynamicity as a dynamic network indicator as well as the importance of constellation strategic transactions in the study of large network perturbations.
Organizations are inherently embedded actors of social networks, whose
structures evolve dynamically, and as a result of each actor’s involvement
offer important clues on organizational strategic behavior. Inside a dynamic
network, organizations exist as highly mobile entities with their relationships
and positional structures continuously changing in time. As such,
understanding organizational behavior involves first and foremost capturing
organizational dynamics often done by analyzing the longitudinal context where
network dynamics is observed. While most literature on longitudinal
networks focuses on a more holistic evolution of their structure [
However, quantifying actor involvement and contribution in
longitudinal networks, and modeling its behavior against specific perturbations has
been limited, with research confined to the effect that organizational crisis
has had on organizational communication networks [
Furthermore, the majority of research on embeddedness has
approached the concept from a dyadic (i.e. a group consisting of only two
actors) perspective, bypassing multiple types of firm interaction. Even those
studies that focus on the so-called constellation (i.e. interactions between
more than two actors) perspective [
We fill these shortcomings by focusing on longitudinal networks generated from strategic transactions, a conceptualization of interorganizational collaborations engaged by a firm with its network partners including strategic alliances, acquisitions and financing collaborations, analyzed under both a dyadic and constellation lens. By doing so, we contribute not only to the literature of alliance collaborations but enhance the currently undernourished network literature on acquisition and financing collaborations as well which play an important role in the dynamics of strategic organizational behavior.
Our study addresses the above gaps by developing and testing a theoretical framework that links the concepts of dynamicity, embeddedness and strategic transactions. By doing so, we uncover the dynamic evolution of the global pharmaceutical industry chosen for its intensive collaboration environment. Given the novel nature of dynamicity as a concept, we attempt to examine some fundamental questions that develop a theory-based understanding of dynamicity and its relationship with embeddedness, as well as analyze the impact that strategic transactions have on such structure: How does dynamicity of centrality measures evolve in a longitudinal network? What is the role of strategic transactions in the evolution of such dynamicity? How does actor´s dynamicity behave in the presence of exogenous events that critically alter the network structure?
Specifically, we expand the actor-level approach on dynamic networks
by introducing the concept of dynamic embeddedness, defined as the
individual actor’s central position variability in a longitudinal network setting
compared to its central position variability in a aggregated network. For the
purpose of this paper, our focus is exclusively on the dynamicity of structural
embeddedness and particularly on the dynamicity of key network centrality
measures such as degree, betweenness and closeness. Specifically, we build
on a dynamicity model [
We conduct our analysis on a longitudinal dataset (t = 22 years, 1991-2012) comprising the strategic transactions of 90 leading firms from the pharmaceutical industry in Western Europe, United States, Asia, Africa and Australia. The sample is selected by identifying those firms that appear at least once in the top 50 of the Pharmaceutical Executive Magazine yearly editions for the period 2002-2013. We then use the Pharma and Medtech Business Intelligence database to collect all the strategic transactions that involved the firms in question from 1991 to 2012. During this period, the 90 firms of the sample engaged in alliance, financing and acquisition collaborations with 4645 other firms and institutions creating a total of 12055 strategic transactions.
Due to our selection process, we consider two types of firms, the core firms comprised of the top 90 pharmaceuticals and the periphery firms including the rest of the actors, with a total population of 4735 firms whose full list is available from the authors. The obtained longitudinal data for both core and periphery firms presents missing actors, since some firms are acquired by others, or simply are not active for any particular year. Our analysis also includes financial data obtained from COMPUSTAT and DATASTREAM databases, supplying missing data when possible using company annual reports. Since the financial data concerns firms from different countries, we convert all currencies to USD with an exchange rate based on the particular year the data is retrieved.
We model each year over the sample period as a separate social
network and analyze each network based on a similar approach for the
global banking network analysis [
Network indicators. The network measures of our analysis include
three centrality variables (degree, betweenness and closeness centrality) and
the dynamicity variable representing the variability of the structural positions
of an actor in all short-interval networks compared to its structural position
in the aggregated network [
m
t
where DDAi is the degree of dynamicity shown by ith actor, OVAN is the
observed value (i.e. degree centrality) for the aggregated network, OVt is
the observed value (i.e. degree centrality) for tth yearly network for the ith
actor, m is the number of yearly networks considered in the analysis, and
t,t 1 is a constant valued according to whether the actor is present or
missing in the current and previous short-interval network. The presence of this
constant is of crucial important to properly count for actors that disappear
from the network due to simple inactivity or possible lack of presence due to
network dynamics. The possible combination values that t,t 1 can take are
For the first short-interval network (i.e. i,0 for t = 0) of our analysis, the
value of the constant depends on the presence or absence of each actor (i.e.
either 0 or 1) at that particular period. The dynamicity model [
where DDN t is the average degree of dynamicity shown by an actor of the tth short-interval network meaning the contribution of each actor to the short-interval network´s dynamicity, and wt is the total number of actors in the tth short-interval (i.e. yearly) network. Therefore, our analytical approach is based on three variables: degree dynamicity, betweenness dynamicity and DDN t t (2) closeness dynamicity constructed by substituting each obtained centrality value to equations 1 and 2.
Industry indicators. In order to analyze the effect of exogenous critical events such as financial crises and recessions on the global pharmaceutical industry, we construct two main effect variables: (i) global crisis representing the combined effect of the 2007-2008 financial crisis and the global recession of 2008-2009 that followed as a direct consequence, and constructed as a dummy variable that takes the value of 1 for the years 20072009 and zero for the rest, and (ii) local crisis representing the exogenous effect of the Eurozone recession during 2011-2013, and constructed as a dummy variable that takes the value of 1 for the years 2011-2012 and zero for the rest.
Control indicators. We use several actor-specific measures such as strategic transaction frequency, R&D intensity, profitability, headquarters (HQ) location and financial leverage age and size. Strategic transaction frequency represents the relative frequency in percentage with which firms engage in strategic transactions. In the analysis, we differentiate between the frequency in percentage of firms engaging in alliance, financing and acquisition collaborations. R&D intensity represents the firm’s R&D expenditure scaled by total sales while profitability is measured for each firm by computing the ratio of net income to total assets (ROA). We define financial leverage as the debt-to-total assets ratio including both short- and long-term debt and control for the age of the firms, operationalized as the foundation year minus the year considered in the 2002-2012 panel analysis, and size operationalized as the natural logarithm of company’s employees. Finally, since our data consists of multinational firms and knowing that the majority of the top 90 firms are US- or EU-based, we control for headquarters (HQ) location based on two separate dummy variables representing whether firms are U.S. or EU-based.
Model approach. By using a two-step approach to our analysis, first we assess the stability of dynamicity distributions in selected years to capture statistical differences throughout our data using Kolmogorov-Smirnov (henceforth, KS) tests for both core and full networks, second by controlling for firm-specific effects, we investigate the effect that the global crisis (including the 2007-2008 financial crisis and the great 2008-2009 recession), and the local crisis referring to the Eurozone recession, observed for 20112012, have on degree, betweenness and closeness dynamicity. For our second step, we run a panel regression model based on random effects (henceforth, RE) with robust estimations based on the model seen below:
Yit k X it i uit it , i 1,..., 90, t 1,...,10, where Yit is firm´s dynamic embeddedness considered as a dependent variable, X it is a vector of firm and industry-specific independent variables including global and local crises, age, size, profitability, financial leverage, R&D intensity, transaction frequency and firm location, i is the unknown intercept for each firm, uit is the between-firm error, it is the within-firm error, k is the coefficient for each k independent variable, i is the number of firms (90 in total) and t is period of time considered (10 years in total or +/- 5 years window before and after the offset of the 2007-2008 financial crisis). 3
We describe the dynamics of the global pharmaceutical industry using four key estimates: (i) tracking dynamic embeddedness evolution based on average dynamicity estimate plots, (ii) monitoring the stability variation of actors’ dynamic embeddedness based on KS-tests, (iii) constructing the top five firm rankings based on yearly network average dynamicity estimates, and (iv) understanding the global and local crises causative effect on dynamic embeddedness based on panel regression estimates. Results (i) – (iii) concern the total panel period 1991-2012 while results (iv) concern the panel period 2002-2012.
We track dynamic embeddedness evolution by plotting the
crosssectional averages of dynamic indicators during 1991-2012 as seen in Figure
1. Both panels show that dynamicity values present relative stability before
2007 for degree centrality but vary substantially for betweenness and
closeness centrality throughout the study period. Specifically, for the core
network, degree and betweenness dynamicity drop respectively 20 percent and
17 percent while closeness dynamicity is almost halved by 40 percent during
the global crisis. The more local Eurozone crisis of 2011-2013 (of which we
analyze only one year due to sample structure) shows a similar pattern with
both networks’ dynamicity severely reduced. An exception is closeness
centrality, whose dynamicity shows an upward trend for the core network, with
signs of a more clustering-oriented tendency.
We monitor the stability of both core and full networks by comparing the
dynamicity distribution in the first year of each decade including last
available year’s data (1991, 2001 and 2012) with subsequent years in the same
decades, a procedure seen in global banking network analysis [
Table 2. Empirical distribution stability for dynamic embeddedness Looking at the main effects of the regression analysis, we observe the negative effect of the global crisis on dynamicity indicators except degree dynamicity, for which the effect is not significant, meaning that the combined effect of the 2007-2008 crisis and the subsequent global recession of 20082009 have not significantly affected the number of strategic transactions originating from each of the core network members. Moreover, we find strong statistical significance for the negative effect that the local Eurozone crisis has had on firms’ dynamic embeddedness. Additionally, the type of strategic transaction is found to influence dynamic embeddedness. This effect is understandable considering the relatively high distribution of alliance transactions in the sample (about 75 percent). However, the positive and significant effect of acquisition transactions on degree dynamicity is interesting considering that both acquisition and financing transactions show similar distributions in the sample (about 12.5 percent each). Finally, the observed low R-squared is not necessarily a drawback for the chosen model particularly if we consider that the results present statistically significant predictors and the regressors are used in a panel setting. 4
With respect to the analyses’ objective, the results on firm´s dynamic embeddedness suggest that prior to the global crises the global pharmaceutical industry has been relatively stable, with firms’ centrality reflecting their market position. Specifically, the top pharmaceutical firms that rank high in terms of sales have a noticeable central position in both core and full networks as observed in the firm rankings. Dynamically speaking, the global pharmaceutical industry has reduced its activity to even lower levels than the beginning of our sampling data, year 1991. While the reduction varies for specific centrality measures, its effect is more prominent after 2007, which coincides with the offset of the 2007-2008 financial crises. The regression results confirm this by showing significant dynamicity reduction during both crises. Furthermore, the regression results indicate that the Eurozone recession has had a far deeper negative effect on global pharmaceutical industry than the global recession.
This study also highlights the importance of acquisition transactions in the expansion of the firms’ importance as central hubs. Specifically, the significant effect of acquisitions on degree dynamicity demonstrates the impact that different strategic transactions have on centrality indicators and further reinforces the reasoning behind our choice to study the centrality measures evolution via the dynamicity concept. However, this also raises questions as to why comparable effects of strategic transaction types (i.e. acquisitions and financings) respond differently to centrality-based dynamicity.
Our study’s limitations could potentially provide interesting areas of future research. First, we should be careful when generalizing our results about the global pharmaceutical industry, knowing that not all firms in both core and periphery networks are dedicated to pharmaceuticals but come from other adjacent industries such as biotechnology and chemicals. Second, dynamicity measure calculation is based on a novel design which takes into account missing actors during network evolution using a specific constant which should be subject to further research for proper values’ assignment. Finally, the dynamicity measure could be used for other centrality measures (i.e Eigenvector, Bonacich Power) or be included in the analysis of network measures such as actor’s structural similarity, structural holes and brokerage elasticity.
Acknowledgments. The authors wish to acknowledge the financial support provided by the Spanish Ministry of Science and Education under projects ECO2010-21393-C04-01 and ECO2013-48496-C4-4-R.