Technological Forecasting & Social Change 78 (2011) 945–952

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Technological Forecasting & Social Change

The typology of technology clusters and its evolution — Evidence from thehi-tech industries

Jiang He, M. Hosein Fallah⁎Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ 07030, USA

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +1 201 216 5018.E-mail addresses: jhe@stevens.edu (J. He), hfallah@

0040-1625/$ – see front matter © 2011 Elsevier Inc.doi:10.1016/j.techfore.2011.01.005

a b s t r a c t

Article history:Received 22 November 2009Received in revised form 29 November 2010Accepted 16 January 2011Available online 21 February 2011

Clustering is one of the key drivers for regional economic growth. Development of clusters is adynamic process shaped by a variety of internal and external factors such as availability ofskilled labor, presence of functioning networks and partnerships, technological changes, andmarket competition, etc. As a result, the patterns of cluster growthmay differ from one another.Although each cluster is unique in some way, previous research has attempted to identify fewsimplified models of evolution of clusters. In this study, we briefly reviewed the literature on avariety of models of clusters. Based on these models, we investigated 15 hi-performingmetropolitan-based clusters in the United States, covering communications equipmentmanufacturing, information technology, and biopharmaceutical industries, in order to findout the similarities and differences between real-world clusters. Specifically, by examining thecomposition of these high-tech clusters, we attempted to find out the following: 1) What arethe typologies of these technology clusters? 2) Whether different industries tend to supportdifferent cluster typologies? and 3) How do clusters change their typologies over time? Ouranalysis results suggest that the real-world clusters rarely feature any single type of typology; amixed type of typology is much more prevalent in reality.We also found that different industries tend to support different types of cluster typologies. Inother words, an individual cluster's typology is to some extent shaped by the industry group itbelongs to. In addition, we note that, as a cluster goes through different stages of its lifecycle, itstypology may change significantly.

© 2011 Elsevier Inc. All rights reserved.

Keywords:Technology clustersAgglomerationPatent analysisRegional planning

1. Introduction

Clusters are geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in relatedindustries, and associated institutions (for example, universities, standard agencies, and trade associations) in particular fields thatcompete but also cooperate [1]. We noted that, from literature research, there are many different ways of defining a cluster. In theearliest study in this field, conducted by Alfred Marshall [2], the notion of “industrial districts”was developed and it was referringto agglomerations of firms operating in one industry sector in a well-defined and relatively small geographic area. Building onMarshall's notion, other regional innovative models, such innovative milieu and technology districts have been offered asframeworks for discussing the pattern of agglomerations. What distinguishes clusters from these other models has not beenclearly defined [3]. Indeed, these concepts — clusters, industrial districts, innovative milieu, and technology districts — are oftenused almost interchangeably despite having origins in different conceptual contexts [4]. Although we've recognized that there arestudies attempting to distinguish the industrial clusters from technology clusters [5], the differences identified by certain authorswere not consistently acknowledged by other studies/researchers. In this study, we follow Porter's [1] definition of clusters, mainlybecause of the influence of Porter's research in this field.

stevens.edu (M.H. Fallah).

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Despite the potential confusion caused by the inconsistence in terminology, the contribution of clustering to regional economicdevelopment has been well accepted and documented [6,7]. Companies cluster together to benefit from the availability andquality of local labor pools, well-developed intermediate input suppliers, and better information flows which can facilitate thegeneration of new ideas. Among those factors, knowledge spillovers between economic actors are believed to be a particularimportant one promoting the development of modern clusters [8–10].

Because of the significant benefits brought by clustering, both academic researchers and regional planners seek to understandthe factors shaping the evolution of clusters. In many of the real-world cases, clusters have emerged unplanned and then gonethrough a dynamic lifecycle process, which typically consists of four stages: embryonic, established, mature and declining. It isnoteworthy that, when a cluster goes through its own evolution path, the forces that foster the subsequent growth of the clusterare not necessarily those that provide the cluster its initial advantage [11]. From a perspective of regional planning, it seems to berather challenging, if not impossible, to develop a cluster from scratch by planning. But policy interventions, when appropriate, canfacilitate the development of an existing cluster [12]. In order to come up with appropriate cluster policy interventions,understanding the driving forces of individual clusters would be fundamental, as clusters are all different from one another.

Despite the differences between clusters, there are some important features shared by certain groups of real-world clusters.Previous research has attempted to model the evolution of real-world clusters with those important cluster characteristics. In thefollowing, we will review the literature on cluster characteristics and a variety of cluster typologies. Following the literaturereview, we will examine 15 U.S.-based hi-tech geographical clusters, covering information technology, communicationsequipment manufacturing, and biopharmaceutical sectors. By examining the typologies of the select high-performing clusters, weaim to answer the following 3 questions: 1) What are the typologies of these industrial clusters? 2) Whether different industriestend to support different cluster typologies? and 3) How do clusters change their typologies over time? The reminder of this paperis organized as follows. Section 2 reviews the literature on industrial clusters and cluster characteristics. Section 3 describes ourmethodology for analyzing the select clusters. In Section 4, we present and interpret the data results. In Section 5, we concludewith a discussion about the implications of this study for cluster researchers and practitioners.

2. Cluster characteristics and the variety of cluster typologies

The studies of clusters date back to Marshall [2], who developed the agglomeration concept, based upon the cost-saving scaleeffects brought by industrial localization. According to Marshall, companies in a particular industry tend to cluster together fortaking advantage of external economies brought by co-locating, including reduced transaction costs, increased specialization, andefficient information flows. Although Marshall's original model successfully explained some earliest industrial clusters,observations from recent clusters suggest that the phenomenon of clustering appears to be much more complex than theoriginal Marshallian agglomeration model has suggested. For example, case studies on many recent clusters suggested that socialinteractions as well as knowledge flows between cluster members play an important role in the development of many modernclusters [8–10,13]. Social relations, however, were not considered an important element in Marshall's original cluster model.

FollowingMarshall'swork, Becattini [14], Brusco [15], and Sforzi [16] identified a newer type of cluster by examining the economicsuccess of several Italian cities and regions. ThemodifiedMarshallianmodel not only acknowledgesMarshall's original agglomerationconcept, it also recognizes the roles of social relations as well as inter-firm cooperation/competition within industrial clusters. WhileMarshall stressed the direct benefits of co-locating (e.g., input cost reduction, availability of labor, etc.); the modified Marshallianmodel (sometimes referred to as “Italian version of Marshallian model” in the literature) emphasized the role of social interactionsbetween co-located firms/individuals when explaining the success of clusters. The modified Marshallian model is important as itprovides a good foundation to explain many successful modern clusters including Silicon Valley and Orange County [9,10].

Even with its Italian version, the Marshallian formulation wasn't able to explain the flourishing of some clusters and demise ofothers. In order to address the increasing complexity and variety of real-world clusters, Markusen [17], through inductiveinquiries, broadened the picture by introducing additional models of clusters. Besides the Marshallian formulation, threeadditional models of clusters were proposed by Markusen — “hub-and-spoke”, “satellite platform”, and “state-centered”.

Fig. 1 illustrates the typology for each of the cluster models. The links depicted in this chart reflect regular inter-firm businessactivities such as supplier–customer transactions. In the categorisation shown in Fig. 1, Markusen [17] stressed the distinct roles playedby different groups of cluster members, as well as the way different players interact with each other. We noted there are othercategorisations proposed by different researchers; based on our understanding, other models are not as comprehensive as theMarkusen's model when it comes to capturing the structures of real-world clusters. For example, Lorenzoni [18] proposed acategorisationbasedonasymmetries among the clustermembers and introduced the concept of “leaderfirms” and “surroundingfirms”.

According to Markusen [17], a hub-and-spoke cluster, is structured around one or few dominant firms supporting the regionalcluster. In this type of cluster, suppliers and other entities spread around and are connected to the hubs like wheel spokes. In a simpleversion as depicted in Fig. 1, a giant hub firm trades with local small firms as well as external suppliers and sells mainly to externalcustomers, which could be large or small ones. In reality, single-hub-based clusters include the Seattle-based aircraft manufacturingcluster supported by Boeing, and the Toyota City-based carmanufacturing cluster supported by Toyota. Amulti-hub-based cluster canbe found in Detroit, where the “Big Three” auto manufacturers dominate the regional economy and are surrounded by a number ofsmall and large suppliers. In such a cluster, substantial trades take place between hub firms and local smaller suppliers. However,cooperative activities among competitors within this type of cluster are remarkably lacking. Cooperation may occur between hub-firms and surrounding suppliers, but the terms of cooperation are often set by the hub-firms. Due to its very nature, the dynamics of ahub-and-spoke cluster are largely determined by the hub firm(s), either via its success or failure.

Fig. 1. Categorization of technology clusters.

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Another type of cluster typology proposed by Marsuken is the satellite platform. This type of cluster typically consists of acongregation of branch facilities of externally based multi-plant firms. The Research Triangle Park located in North Carolinarepresents a high-end example of satellite platform cluster, where a number of high-tech multinational firms have establishedtheir R&D centers there. In such a cluster, the remotely located parent companies make important decisions for the local branchesthus influence the regional cluster. A key characteristic for this type of cluster is the absence of any sort of connections or networkswithin the regional cluster, as the local branches maintain regular business relations mainly with their remote headquarters orpartners. The future growth for such a cluster is largely determined by the region's ability of keeping its competitive advantage. Forhigh-end satellite platforms, themost challenging issue is tomaintain and develop the pool of skilled professionals; while low-endplatforms are more vulnerable to increases in the cost of running business. Given its nature, a satellite platform is less sustainableover time if the cluster fails to develop a more diversified local economy.

The last type of cluster typology proposed by Marsuken [17] features a state-centered structure. The local business structure inthis case is dominated by the presence of one or few large public or non-profit entities, such as universities, public researchinstitutions, or military bases. The key public entities are typically surrounded by smaller firms/organizations, thus forming a hub-based structure similar to the previously described hub-and-spoke typology. In fact, many fast-growing cities in the U.S. andoverseas, to a large extent, owe their successes to the presence of government-supported entities. For example, cities such asMadison, Wisconsin and Ann Arbor, Michigan benefit greatly from the presence of the state universities in region. Over the longrun, the growth outlook for such a cluster is very much dependent on two factors: 1) the size and power of the dominating publicentities; and 2) the region's capability of supporting start-ups and entrepreneurship. Actually, the second would be an importantfactor facilitating the region to transform itself into a more dynamic and diversified regional economy.

It is noteworthy that the categorization proposed by Markusen [17] provides only a much simplified framework to examinecluster structures. It helps cluster researchers/practitioners understand how a cluster comes about and what it thrives on, but itmay not always reflect the realities of cluster development. As acknowledged by Markusen [17], real-world clusters often featurecharacteristics of a mix of multiple typologies. In addition, clusters evolve over time; therefore one may change its typology fromone to another as it moves through different stages of its lifecycle.

In the following sections, we examine 15 real-world strong clusters to find out how well the above-mentioned cluster modelsworks in reality and discuss the implications of cluster typologies for regional planning.

3. Methodology

Based on the objective of this study and the research questions proposed in Section 1, the first step of our investigation is toidentify clusters.

Using the data from Cluster Mapping Project, which is led by Professor Michael Porter at Harvard University, we selected 15strong U.S. metropolitan city-based clusters covering information technology, communications equipment manufacturing, and

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biopharmaceutical sectors. We decided to focus on the hi-tech industries because these newer industries are more dynamic thantraditional ones and have generated some of the country's strongest clusters such as Silicon Valley.

The Cluster Mapping Project assembles a detailed account of the location and performance of industries in the United States,with a special focus on the linkages or externalities across industries that give rise to clusters. Economies are analyzed at variousgeographic levels, including states, economic areas, metropolitan areas, and counties. The economic performance of a technologycluster in a particular region for a particular year is measured by multiple indicators including number of employment, number ofjob creation, and level of wages. For each of the three industries being investigated, we identified the top-5 strongest clusters basedon number of cluster employment in year 2004. Geographically, the top-performing clusters were identified at the level ofmetropolitan area, a geographic measure used by U.S. Census as well.

After identifying the 15 high-performing clusters, we then observed the composition of each cluster and its evolution withrespect to typology over time. As suggested by Markusen [17], a crucial factor determining the typology of a cluster is theasymmetries between cluster members in their roles of supporting the regional cluster. Therefore, in the analysis, we areparticularly interested in finding the importance of “leader-firms” relative to that of “surrounding smaller firms”.

To measure the relative importance of different firms in supporting a regional cluster, we examined the individual firms' patentcontribution relative to the total patent output of the particular cluster. We acknowledge that number of patents cannot be an idealmeasurement for company performance, because only a portion of business activities are patented.Nevertheless, number of patents isa reasonable indicator for company innovation activities [19], which are fundamental for the hi-tech enterprises' success.

Individual companies' historical patent output in each year can be obtained from the USPTO (United States Patent andTrademark Office) database [20]. For the “leader-firms”, in this study defined as the top-10 patent-generating firms/organizationsin a regional cluster, we checked their background individually in order to find out their nature (i.e., locally headquarteredcompany, externally headquartered company, or state-supported institution). Then, for each cluster, we calculated the relativepatent contribution by each group of cluster members (i.e., locally headquartered large firms, externally headquartered largefirms, state-supported large organizations, and local small/medium firms). The relative patent contribution by different categoriesof cluster members enabled us to observe whether a cluster is close to any of the typical typologies depicted in Fig. 1.

Patents within USPTO database are originally classified by “main classes”, rather than by broadly defined “industries”. In orderto identify patents in each of the three industries, we followed Jaffe's [21] approach of categorizing patents. In Table 1, we list themain patent classes belonging to each industry (i.e., information technology, communications equipment manufacturing, andbiopharmaceutical).

4. Data analysis

Using the methodology described in Section 3, we identified 15 top-performing US-based clusters, covering informationtechnology, communications equipment manufacturing, and biopharmaceutical sectors. The select clusters for each industry arelisted in Table 2, and the ranking of cluster performance is based on number of cluster employment measured in year 2004.

In Fig. 3, we use a clustered-stacked column chart to demonstrate the composition of each cluster, and different types of firms (i.e.,locally headquartered large firms, externally headquartered large firms, state-supported large organizations, and local small/mediumfirms) are represented by different colors. From this chart, we observed a few interesting characteristics for these strong clusters.

First, it appears that local small/medium firms dominatedmost of the clusters. This observation is consistent with the literaturewhich indicates that an Italian version of Marshallian cluster is more sustainable over the long run when compared with othertypes of typologies.

Second, we observed hub-and-spoke clusters in few cases, and the most significant one is the Seattle-based IT cluster (“IT-Cluster-2”). Given Microsoft's presence in Seattle, WA and its influence, our finding is not surprising at all. A similar clustercomposition was observed for “Communications-Cluster-4”, representing the communications equipment manufacturing clusterlocated in the metropolitan area of New York–North New Jersey–Long Island. While in the latter case, the hub firms' power wasless significant than the one's in Seattle. The situation observed for “Communications-Cluster-4” is understandable as well,because Bell Laboratory (later changed to Lucent Technologies and Alcatel Lucent), located in North New Jersey, has long been animportant hub for innovations in telecommunications.

Table 1Categorization of patents by industry.

Industry Classifications by Jaffe Main patent classes by USPTO

Information technology Cat.22 — Computer hardware and software 341, 380, 382, 395, 700, 701, 702, 704, 705, 706, 707, 708,709, 710, 712, 713, 714

Cat.23 — Computer peripherals 345, 347Cat.24 — Information storage; 360, 365, 369, 711Cat.41 — Electrical devices 174, 200, 327, 329, 330, 331, 332, 334, 335, 336, 337, 338,

392, 439Cat.46 — Semiconductor devices 257, 326, 438, 505

Communications equipment Cat.21 — Communications 178, 333, 340, 342, 343, 358, 367, 370, 375, 379, 385, 455Biopharmaceuticals Cat.31 — Drugs 424, 514

Cat.33 — Biotechnology 435, 800Cat.39 — Miscellaneous—drug and med 351, 433, 623

Table 2Top-performing US-based technology clusters in 2004.

Rank Information technology Communications equipment manufacturing Biopharmaceuticals

1 San Jose–Sunnyvale–Santa Clara, CA(57,149)

Los Angeles–Long Beach–Santa Ana, CA (28,576) New York–Northern New Jersey–Long Island,NY-NJ-PA (68,309)

2 Seattle–Tacoma–Bellevue, WA (41,503) San Jose–Sunnyvale–Santa Clara, CA (17,150) Chicago–Naperville–Joliet, IL-IN-WI (25,263)3 San Francisco–Oakland–Fremont, CA

(38,105)Chicago–Naperville–Joliet, IL-IN-WI (11,968) Los Angeles–Long Beach–Santa Ana, CA (17,181)

4 Boston–Cambridge–Quincy, MA-NH(35,619)

New York–Northern New Jersey–Long Island, NY-NJ-PA(11,298)

Philadelphia–Camden–Wilmington, PA-NJ-DE-MD(12,108)

5 Los Angeles–Long Beach–Santa Ana, CA(28,945)

Dallas–Fort Worth–Arlington, TX (9,522) San Francisco–Oakland–Fremont, CA (11,153)

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Third, when making comparisons between different industries, we noted that externally headquartered large firms played animportant role in the communications equipment manufacturing clusters; while this type of firms were less important for theother two industries. In addition, we observed that state-supported institutions constituted an important force supporting thebiopharmaceutical clusters, a situation not observed for the other two industries. This finding may reflect the fact that manyinventions in biopharmaceutical industry rely heavily on R&D resources made available by public organizations.

Fig. 2 suggests that the type of industry may be a factor influencing cluster typologies, which is an important finding for oursecond research question. In order to confirm that different industries tend to support different cluster typologies, we performedthe discriminant function analysis. The discriminant analysis helps us analyze differences between groups by distinguishing thosevariables on which groups differ [22]. This is an appropriate technique for our analysis because here wewant to confirm clusters indifferent industries differ with respect to their typology. Specifically, by using the discriminant analysis, we attempted to quantifythe degree to which the continuous variables (i.e., relative patent contribution by locally headquartered large firms, relative patentcontribution by externally headquartered large firms, and relative patent contribution by state-supported large organizations) canbe used to discriminate between the groups (i.e., three types of industries). To perform the discriminant analysis, we measuredthree types of ratios (Ratio-1 represents the relative patent contribution by locally headquartered large firms, Ratio-2 representsthe relative patent contribution by externally headquartered large firms, and Ratio-3 represents the relative patent contribution bystate-supported large organizations) for each of the select clusters, and we used GroupID as the dependent variable to representdifferent industries (i.e., 1 represents IT, 2 represents communications equipment manufacturing, and 3 representsbiopharmaceutical).

Fig. 2. Breakdown of the top-performing US-based technology clusters.

Table 3Group statistics (discriminant analysis).

GroupID Mean Std. Deviation

1 IT Ratio1 0.3058 0.19179Ratio2 0.0838 0.06299Ratio3 0.03 0.03062

2 Communications equipment manufacturing Ratio1 0.314 0.11059Ratio2 0.244 0.12178Ratio3 0.012 0.02683

3 Biopharmaceuticals Ratio1 0.234 0.07301Ratio2 0.0928 0.10899Ratio3 0.1208 0.06936

Table 4Tests of equality of group means (discriminant analysis).

Wilks' Lambda F Sig.

Ratio1 0.918 0.535 0.599Ratio2 0.602 3.961 0.048Ratio3 0.432 7.887 0.007

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The discriminant analysis results are reported in Tables 3, 4, and 5. To find out whether the three types of industries differ withrespect to cluster typology, we first examined the results of “Group Statistics” and “Tests of Equality of Group Means”. The “GroupStatistics” in Table 3 suggest there are noticeable differences betweenmeans of different groups for Ratio-2 and Ratio-3. The “Testsof Equality of GroupMeans” (Table 4) further confirm this situation by rejecting the null hypothesis that all groupmeans are equal.The Wilks' Lambda value reported in Table 4 is a multivariate measure of group differences over several variables, it is concernedwith the ratio between the within group variance and the overall variance. A ratio close to 1 is an indication of equality of groupmeans, whereas a lower ratio indicates a larger difference between the various groupmeans.We observed that theWilks' Lambdasfor Ratio-2 and Ratio-3 are relatively low and the results are statistically significant.

Table 5 reports the eigenvalues and Wilks' Lambdas for the discriminant functions. As we have three groups for analysis, twodiscriminant functions were produced in this case. The eigenvalues and canonical correlations reported in Table 5 are used tomeasure the discriminating power of the discriminant functions. A high eigenvalue along with a high canonical correlationindicate a more discriminating function. Based on this, we concluded that the discriminating power of the first discriminantfunction is high (eigenvalue=1.383, canonical correlation=0.762). In addition, the Wilks' Lambda reported in Table 5 indicatesthe significance of the first discriminant function. Accordingly, we concluded that the three different industries tend to supportdifferent cluster typologies.

As discussed earlier, clusters evolve over time and may change their typology from one to another. Next we examine theevolution of the select clusters by observing how compositions of the clusters changed from one time-window to another. In Fig. 3,we show the composition for each of the 15 clusters. For each cluster, its compositionwas examined on 3 different time-windows—1995, 2000, and 2005. Based on this chart, we observed that few clusters indeedwent through significant changes in their typologyover time. The “Communications-Cluster-1” provides us with a good example to observe such changes. The “Communications-Cluster-1” in this study represents the communications equipment manufacturing cluster based in Los Angeles–Long Beach–SantaAna metropolitan area. In early 1990s, that LA-based cluster was more close to the satellite platform typology, as much of itsperformance was attributed to the presence of a few large externally headquartered defense contractors including HughesElectronic Devices, TRW, and Boeing. Over the past decades, the cluster has successfully converted itself into a mixed typology. Forthe recent period, year 2005,weobserved that small/mediumfirms and locally headquarteredfirms became increasingly importantin supporting the regional cluster.

Table 5Eigenvalues and Wilks's Lambdas for discriminant functions.

Function Eigenvalue % of variance Cumulative % Canonical correlation

1 1.383(a) 75.6 75.6 0.7622 .445(a) 24.4 100 0.555

Test of function(s) Wilks' Lambda Chi-square df Sig.

1 through 2 0.29 13.604 6 0.0342 0.692 4.052 2 0.132

Fig. 3. Compositions of the select clusters in different periods.

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5. Conclusions and policy implications

Our analysis results suggest that the real-world clusters rarely feature any single type of typology; a mixed type of typology ismuch more prevalent in reality.

Second, we found that different industries tend to support different types of cluster typologies. In other words, an individualcluster's typology is to some extent shaped by the industry group it belongs to. In this regard, cluster practitioners/planners wouldbe better off examining a cluster's evolution by comparing it with peer clusters in the same industry.

Third, as a cluster goes through different stages of its lifecycle, its typology may change as well. Our study indicates that acluster may characterize one of the prototypical models proposed by Markusen [17], for example a satellite platform type, at itsinitial stage of development. Given the fact thatmost of the high-performing clusters feature amixed typology, we argue that, overthe long run, the success of a cluster is largely determined by the growth potential of its small-and-medium size firms. Therefore,for cluster policy makers, one of the top priorities is to promote local entrepreneurship and enable smaller firms, we refer to theseas “toddlers”, to benefit from the available resources within the cluster. In this regard, the significant success of Silicon Valley andthe recent trouble for Detroit confirmed our observation.

This study comes with certain limitations which are noteworthy. The study attempted to capture the typology for each of theselect clusters. We acknowledge that the typology of a cluster should bemeasured with multiple indicators, due to the complexityof real clusters. However, in the studywe focused on the asymmetries among the cluster members, and only examined the relativepatent contribution by different cluster groups. Other factors shaping a cluster's typology, for example how the cluster membersnetworkwith each other, were not taken into consideration in this study. Therefore, we suggest that future research should seek tounderstand the roles of social interactions in the transformation of cluster typology.

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Dr. Hosein Fallah is an Associate Professor of Technology Management at Stevens Institute of Technology. He teaches graduate courses in Telecom Managementand Technology Management programs. His research interest is in the area of innovation management with a focus on the communications industry. Prior tojoining Stevens, Dr. Fallah was Director of Network Planning and Systems Engineering at Bell Laboratories. He has over 30 years of experience in the areas ofsystems engineering, product/service realization, software engineering, project management, and R&D effectiveness. Dr. Fallah has a B.S. in Engineering and a MSand a Ph.D. in applied science from the University of Delaware.

Jiang He has received his Ph.D. in Technology Management from Stevens Institute of Technology. His research interest is in the area of technology clusters andsocial networks with a focus on the telecommunications industry. He had worked in the telecommunications industries for over 3 years. Dr. He also holds a MSdegree in Telecommunications from State University of New York Institute of Technology at Utica.