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Does movement of inventors between companies affect their productivity? M. Hosein Fallah a , Piyasi Choudhury a , Tugrul U. Daim b, * a Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ 07030, USA b Department of Engineering and Technology Management, Portland State University,1900 SW 4th Suite #50, Portland, OR 97202, USA article info Article history: Received 25 February 2012 Received in revised form 16 April 2012 Accepted 17 April 2012 Keywords: Knowledge spillover Inventor movement R&D productivity abstract This paper examines the effect of the movement of inventors on their productivity (measured by the rate of patent production) as well as the effect of knowledge spillovers on the patenting behavior of the inventors. We studied the patenting behavior of a sample of inventors in the US Telecom industry. The study shows two signicant patterns: for the mobile inventors, there is a visible declining trend in the average patenting performance compared to those who stayed with the same company. This declining productivity trend is however compensated by an increased knowledge gain. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Studies of innovation have long highlighted the impor- tance of the management of technical personnel [44,57,67]. Social connections between the scientist and engineers offer opportunities for knowledge exchange, collaborative interactions and mutual learning. Researchers have long considered knowledge exchange and spillovers a signi- cant driver of innovation [2,4,5,15,16,27,36,41,49,53]. Hence, it is reasonable to assume that the movement of innovators among rms positively impacts knowledge creation and development of new products and services. A study of patent data for software inventors [59] concludes that mobility could result in knowledge spill- overs but also could be driven by knowledge spillovers. Hoisl [34] studied the relationship between inventors mobility and productivity. She found while inventorsmovement increases productivity, more productive inven- tors are less likely to move. The study also concludes that rm size affects the mobility of inventors. These ndings point to the complexity of why inventors may move and to what extent such moves drive knowledge spillovers and affect the productivity of inventors. Although the behavior of inventors and technologists has long been studied [8,9,20,48], the relationships between mobility, produc- tivity and knowledge spillover have not been explored in detail. One aspect of productivity is the value of the invention, represented by the intellectual value of the patent and there are a number of studies that have examined the value of patents [13,47]. While the intellectual value of a partic- ular patent depends on the specics of the invention, the mere patenting of an invention represent an intrinsic value perceived by the owner. Hence, the patent production rate (number of patents produced per year) can be a direct measure of the inventor productivity. Some researchers [34,37,45] have provided studies on this measure, vali- dating that inventor productivity increases with the size of the company, attributing the effect to the increased capa- bility and resources of the larger companies. However, the literature seriously lacks studies explicitly exploring the movement of inventors and its relationship to the inventor productivity measured in terms of patents. The only studies that explored these areas were those by Tzabbar [66] who explored scientist recruitment and its relationship to technological repositioning and by Kapur and Lim [43] who * Corresponding author. E-mail addresses: [email protected] (M. Hosein Fallah), [email protected] (P. Choudhury), [email protected] (T.U. Daim). Contents lists available at SciVerse ScienceDirect Technology in Society journal homepage: www.elsevier.com/locate/techsoc 0160-791X/$ see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.techsoc.2012.04.003 Technology in Society 34 (2012) 196206

Does movement of inventors between companies affect their productivity?

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Technology in Society

journal homepage: www.elsevier .com/locate/ techsoc

Does movement of inventors between companies affect theirproductivity?

M. Hosein Fallah a, Piyasi Choudhury a, Tugrul U. Daim b,*

aHowe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ 07030, USAbDepartment of Engineering and Technology Management, Portland State University, 1900 SW 4th Suite #50, Portland, OR 97202, USA

a r t i c l e i n f o

Article history:Received 25 February 2012Received in revised form 16 April 2012Accepted 17 April 2012

Keywords:Knowledge spilloverInventor movementR&D productivity

* Corresponding author.E-mail addresses: [email protected]

[email protected] (P. Choudhury), tugrul@etm

0160-791X/$ – see front matter � 2012 Elsevier Ltddoi:10.1016/j.techsoc.2012.04.003

a b s t r a c t

This paper examines the effect of the movement of inventors on their productivity(measured by the rate of patent production) as well as the effect of knowledge spilloverson the patenting behavior of the inventors. We studied the patenting behavior of a sampleof inventors in the US Telecom industry. The study shows two significant patterns: for themobile inventors, there is a visible declining trend in the average patenting performancecompared to those who stayed with the same company. This declining productivity trendis however compensated by an increased knowledge gain.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Studies of innovation have long highlighted the impor-tance of the management of technical personnel [44,57,67].Social connections between the scientist and engineersoffer opportunities for knowledge exchange, collaborativeinteractions and mutual learning. Researchers have longconsidered knowledge exchange and spillovers a signifi-cant driver of innovation [2,4,5,15,16,27,36,41,49,53].Hence, it is reasonable to assume that the movement ofinnovators among firms positively impacts knowledgecreation and development of new products and services.

A study of patent data for software inventors [59]concludes that mobility could result in knowledge spill-overs but also could be driven by knowledge spillovers.Hoisl [34] studied the relationship between inventor’smobility and productivity. She found while inventors’movement increases productivity, more productive inven-tors are less likely to move. The study also concludes thatfirm size affects the mobility of inventors. These findingspoint to the complexity of why inventors may move and to

(M. Hosein Fallah),.pdx.edu (T.U. Daim).

. All rights reserved.

what extent such moves drive knowledge spillovers andaffect the productivity of inventors. Although the behaviorof inventors and technologists has long been studied[8,9,20,48], the relationships between mobility, produc-tivity and knowledge spillover have not been explored indetail.

One aspect of productivity is the value of the invention,represented by the intellectual value of the patent andthere are a number of studies that have examined the valueof patents [13,47]. While the intellectual value of a partic-ular patent depends on the specifics of the invention, themere patenting of an invention represent an intrinsic valueperceived by the owner. Hence, the patent production rate(number of patents produced per year) can be a directmeasure of the inventor productivity. Some researchers[34,37,45] have provided studies on this measure, vali-dating that inventor productivity increases with the size ofthe company, attributing the effect to the increased capa-bility and resources of the larger companies. However, theliterature seriously lacks studies explicitly exploring themovement of inventors and its relationship to the inventorproductivitymeasured in terms of patents. The only studiesthat explored these areas were those by Tzabbar [66] whoexplored scientist recruitment and its relationship totechnological repositioning and by Kapur and Lim [43] who

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206 197

explored the impact of acquisitions on the productivity ofinventors. We attempted to explore this area further byexamining the differences between inventors who haveremained in the same company all throughout their careersversus those who have moved multiple times amonga number of companies. We aim to complement our studyby also analyzing the effect of inventor mobility on anotherimportant measure: knowledge gain. In particular, weinvestigatewhether an inventormoving from one companyto another gains in terms of their technical expertise.

To remove the effect of the technology area, we focus onthe inventors in a single industry: telecommunications.Also, we stratify our dataset by company size, therebydistinguishing between inventors moving within large,medium and small companies. The paper is organized asfollows. First, we examine the current literature, high-lighting what other researchers have found related to theissue of inventor mobility and emphasizing the gap that ledus to pursue this study. In Section 2 we present our modelformulation and our hypotheses. This section is followed bya discussion of the data used in the study. The study find-ings and the implications for R&D management are dis-cussed in Section 4. We conclude the paper witha summary and direction for future research.

2. Literature review

While innovation is creating and bringing a new idea tomarket, the technological innovation process is the jointactivities between scientists and engineers to generate newtechniques and knowledge or improve on existing ones.Some of these mutual activities are the results of scientistsand engineers moving from one organization to another.

2.1. Mobility of inventors

Research on innovation and productivity examines therelationship between productivity and the mobility ofinventors as well as the effect of mobility on the innovationprocess. However, most of the current studies are empiricaland the theoretical ones are limited. The empirical work sofar has only examined different aspects and characteristicsof inventors’ mobility such as: the determinants andpatterns of inventor mobility [10,14,46]; the geographicalscope of workers’ mobility and its effect on the localizationof knowledge; the formation of clusters and whetherinventor movements can be used as an indicator of clusterdevelopment [5,6,32,33,41]; the role of job mobility inpromoting the regional innovation [4]; knowledge transferfrom universities to industry and themigration of scientistsbetween these two channels, as well as the creation of newcompanies where the knowledge from previous employersis transferred [17,69]. In fact, inventor mobility andproductivity did not gain much attention from researchersuntil recently. So literature in this area is limited and thereare many unexplored issues and questions. One of the coreissues in inventor mobility which we explore in this studyis the effect of mobility on the productivity of the inventorsthemselves. Is there any positive or negative effect of suchmovements from one firm to the other? What are thetrade-offs, if any, for the inventors?

2.2. Patents as productivity measure

We consider patent production a direct measure ofinventor productivity. In fact, there has been an increasingvolume of literature in the recent years utilizing patentdata. Giuri et al. [30] used patent surveys to gather infor-mation on the innovation process. Patent citation was usedto measure the importance and value of the innovations orto describe the direction and geographical scope ofknowledge flows and spillovers among inventors andpatent holders [7,40,41,63]. Patent count was used indifferent studies investigating technological change [1] aswell as inventors’ performance and mobility [12,40]. Someresearchers have examined the value of patents byanalyzing forward citations of the underlying patents[13,19]. Some researchers have also expressed concernswith the use of patents in measuring information flows asmany citations are added by the patent examiners or evensome of them are included after the patent is granted[4,60,61]. Subramanian and Soh [61] recently considerednon patent output as a metric as well.

2.3. Mobility and productivity

Outside patent information, a few studies have focusedon the inventors. However, these studies have used smallsamples of inventors in particular fields and countriesbecause of the limited availability of information on indi-vidual inventors [30]. Some recent studies [23,51] havesupported the notion that there exists a concurrent rela-tionship between inventor mobility and inventor produc-tivity. Movers are more productive than non-movinginventors and the relationship between inventor’s mobilityand productivity shows that while inventor’s movementincreases productivity, more productive inventors are lesslikely to move [34]. Almeida and Kogut [4] found thatskilled engineers who illustrate high rates of inter-firmmobility within the semiconductor industry have largenumber of patents. A number of researchers [64,65],focused on the causality betweenmobility and productivityof inventors listed on U.S. patent documents. Their inves-tigation revealed that about 35% of the inventors aremovers, and the patents of inventors who moved receivemore citations. Additionally, inventors who are responsiblefor a valuable patent are more likely to move [64]. A newstudy by Fallah and Choudhury within the telecommuni-cation industry shows that patent production ratedecreases with the number of moves [24,25]. Hence we canassume that there exists a relationship between innovativeperformance of inventors and their mobility. In particular,a study by Lenzi on a group of Italian inventors showed thatjob mobility is a frequent phenomenon and indicateda positive relationship between productivity and mobility[46]. Through an analysis derived from a survey of Germaninventors, Hoisl explains that inventors at the upper end ofthe performance distribution, in terms of patent produc-tion, are better able to benefit from amove. Alternatively, atthe bottomof the performance distribution a higher level ofeducation has a positive impact on inventive performance.However, education does not have a considerable effect atthe upper end of the performance distribution [1,35]. The

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literature also addresses the issue of knowledge transferthrough the mobility of academic inventors, exploring thecauses of a scientist’s move from a university to privateindustry [17,69], thereby supporting the fact that produc-tivity positively affects the probability of moving away fromacademia [69]. However, the literature that considers therelationship betweenmobility and productivity, has not yetexplored in depth the effect of the frequency of moves onthe inventor productivity.

2.4. Knowledge transfer

While considering knowledge transfer as a major driverof innovation [2,4,5,15,16,18,28,36,49,68], we often comeacross a particular circular structure: mobility of inventorscould be driven by knowledge spillover [59] and mobilitycan be a cause of R&D activities and innovation [15,34,39].Jaffe specifically mentioned that “knowledge spillovers alsooccur when researchers leave a firm and take a job atanother firm.” Through the analysis of patent citationpatterns in the semiconductor industry, Almeida and Kogutinvestigate the relationship between the mobility of patentholders for semiconductor innovation and the localizationof technological knowledge where they confirmed thatinter-firm mobility of engineers influences the local trans-fer of knowledge [4]. We also find Rosenkopf and Almeidaassociating mobility with inter-firm knowledge flowsregardless of geographic proximity [58].

2.5. Literature gaps

The mixed interplay among inventor mobility, produc-tivity and knowledge spillovers has not been treated inenough detail in the existing literature. To be morespecific, there are studies that explain why inventorsmove and how such moves drive knowledge spillovers[2,4,5,15,16,26,36,47,52,60]. There are also studies thatdiscuss the different factors influencing inventors’ deci-sions to move (e.g. risk aversion, localization sunkcosts, and so on) [11]. In addition, some studies thatshow movements often leads to knowledge transfers[3,4,50,56,58]. Some studies attributed inventor produc-tivity to the size of companies [21,34,37,45]. There are alsoa number of studies that attribute movement to better jobmatching [55,62], thereby supporting the fact that inven-tors who have already found a good match do not move[54], although Schankerman et al. [59] found no evidencethat matching is an important incentive for movement. Insummary, while researchers have examined variousaspects of inventor mobility, there is a lack of quantitativeanalysis of the relationship between inventors’ mobility,their productivity and their knowledge gains as they relateto the frequency of movement. In this paper, we aim toaddress this gap. Granted, these relationships are complexand not easy to analyze. However, we try to find an answerto this issue by providing a theoretical framework intendedto analyze the complex association between the mobilitymetric and the performance measures of an inventor. Thetreatment provided in this study is unique in the sense thatit considers both the positive and the not-so-positiveinfluences of inventor mobility, particularly, how the

knowledge gained with each movement gets compensatedby a declining patent productivity.

3. Model formulation and hypotheses

Invention is the process of discovering or creating newideas that can result in the development or enhancement ofnew products, services or processes. If the invention ismaterialized in a marketable “product” it is called innova-tion [31]. Invention is also an evolutionary process inwhichan inventor combines his/her own knowledge with theknowledge gained from others in the form of tacit and orexplicit knowledge to create new knowledge. Inventionshave intrinsic value to the inventors and could havesignificant commercial and economic value as well ifturned into innovations. Garcia and Calatone [29] providea detailed empirical study of the economic value associatedwith patents. Therefore, inventors have interest in pro-tecting their inventions through patents, copyrights ortrademarks. A patent formally documents an invention andprovides legal protection to the inventor(s) as the soleowner of the invention for a given period of time, normally20 years in the United States. The underlying theoreticalconcept of the knowledge creation and spillovers can beillustrated by Fig. 1. In this illustration, inventor A workingfor company 1 produces an invention captured by patent Jin patent class i, which represents an area of technologicalexpertise of inventor A. Later on, Inventors B and C workingfor company 2 collaborating with each other create patentK in the same patent class i. Since patent K cites patent J,there is flow of knowledge from inventor A to inventors Band C. Also, there is a knowledge flow between company 1that owns patent J and company 2 that owns patent K. Now,let’s assume that inventor C then moves to company 3 andin collaboration with inventor D produces patent M inpatent class j. Since patent M is in a different patent classfrom patent K, it could indicate an increase in inventor C’sspread of knowledge. This process could continue with theinventor movement and expansion of the technologicalexpertise by patenting in other patent classes.

Some inventions however may not be patented fora variety of reasons including the cost of patenting andmaintaining the patent right. Despite this problem, manyresearchers have used patents as measures of innovation,R&D productivity and intellectual capital of a firm. Anotherreason for using patent is the availability of the data fromthe United States Patent and Trademark Office (USPTO).

Therefore, inventors move from one company toanother for a variety of reasons. Financial incentives, betterpositions, opportunity to work in more exciting areas,collaboration with other inventors working in the samearea, stability of the organization and geographical locationare among the factors cited by researchers studyinginventors’ mobility. According to Jinyoung and Gerald [42],sometimes “if the scientist finds the external value of theinnovation sufficiently attractive, he sets up or joins a rival.”The knowledge spillovers, when an inventor moves fromone company to another, contribute to the R&D capabilityof the receiving company and could increase the organi-zation’s innovation output. As expected, inventors whomove also absorb new knowledge from the new

Fig. 1. Innovation and knowledge flow.

1 Assignee is normally the company of the inventor. If an individualinventor files for the patent, the inventor owns the patent and there is noassignee.

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environment that could expand their technical ability. Theexpanded capability could be measured in the inventor’sproductivity as it could result in producing higher qualitypatents and/or producing more patents [34].

It is not, however, clear if the rate of patent production byan inventor who has moved from one company to anotheractually increases or declines. Hoisl confirms that inventorsin larger firms are less likely to move [34]. The question wewant to answer is: Would the movement of inventors fromcompany to company increase or decrease the inventorperformance in patent production. Keeping these facts inmind, we propose the following hypotheses, consideringinventors in a single industry, telecommunications:

Hypothesis 1. Inventor movement is negatively correlatedwith patent production. For inventors who move repeatedlyamong different telecom companies, the average rate of patentproduction is lower than those inventors who do not move.

Hypothesis 2. The rate of decline in patent production varieswith the size of the companies. The rate of decline is higher forinventors moving among large companies compared to thosemoving among medium and small companies.

Hypothesis 3. Inventor mobility is positively correlated withknowledge spillovers as measured by patenting in new tech-nology classes. The more the number of career moves, themore is the gain in the technical expertise of the inventor.

Hypothesis 4. The rate of increase in cognitive knowledgevaries according to the size of the companies. Inventorsmoving among larger companies experience greater overallgrowth in technical expertise than those moving amongmedium/small size companies.

Wedefine amobile inventor as an inventorwith frequentmoves. Topel and Ward [62] point to the compatibilitybetween employee and employer as one of the reasons for

job movement. Hence, a better match as a result of a movecould improve the productivity of the employee. Of coursethe reverse could also be true. While there could be manyreasons for a scientist or an engineer to change employer,our study focuses not on the cause of the move. Instead itanalyzes the consequences for the person moving.

4. Data collection

The patent data for this study was extracted from theUnited States Patent and Trademark Office (USPTO) data-base. First we extracted all the patents that came under thearea of telecommunications using Jaffe and Trajtenberg’sdefinition [38]. Each patent in the USPTO database containsdetailed information about the invention with some of thecharacteristic items being:

Patent number that identifies the patent within the USPTOdatabaseDate of application, when the request for patent was filedDate of grant of the patentName and address of the inventor and any co-inventorsName and address of the assignee1

Patent main class and subclasses, that defines thetechnologyForward and backward citations if anyDescription of the invention

Initially we extracted information about 133,489 suchtelecom patents belonging to a number of inventors. The

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206200

filing dates of these patents were between 1954 and 2005.We excluded those inventors who had only self filedpatents (not assigned to any company).

Since we are comparing the effect of movement acrosslarge versus medium and small sized companies, weexplain here the definition of “large” for a company in thisstudy. The “largeness” of a company is attributed to theannual net sale figure of a company. A company is assumedto be “large” if it has average net sales of more than 100million dollars. A medium sized company is assumed tohave a net sales of anywhere between 10 and 100 milliondollars. Small companies are those with annual sales of lessthan 10 million dollars. To extract the sales information ofthe companies, we first identified “The North AmericanIndustry Classification System (NAICS) codes for the tele-communication industry and its sub sectors from the U.S.Census Bureau. According to the U.S. Census Bureau, NAICSis the standard used by Federal statistical agencies inclassifying business establishments for the purpose of col-lecting, analyzing, and publishing statistical data related tothe U.S. business economy.” The U.S. companies listedunder these codes and their net sales data for each yearwere then obtained from the Compustat database. Theaverage sales amount was calculated for each company andbased on this, companies were categorized as “large”,“medium” or “small”. Since the sales data were availableonly for the years 1990–2005, we excluded all the patentsgranted for years prior to 1990. Finally our dataset con-sisted of patents in Table 1 collected from a total of 1504companies.

5. Methodology

Since we are analyzing the effect of frequent inter-company movement on an inventor’s performance, wedefine “move” as a change in the assignee or the name ofthe company that files the patent for the inventor. Sucha change occurs when R&D inventors change their currentemployer and move to some other employer for bettercareer options [42] or simply because they find bettercompatibility [62], or other reasons. In our current study,we keep track of the number of such career moves thateach inventor had gone through. However, counting thenumber of moves had to be done carefully due to theexistence of anomalies in the way patent data are docu-mented and stored [25]. In the examples shown below, the1st column is the patent number, the second column is thegrant date, the third column is application date. Note thatboth columns 2 and 3 are in yyyy-mm-dd format. The nexttwo columns are the assignee (or company name) and theassignee code (shown by “A code”: a numeric code assignedby the USPTO for each company). The last two columns

Table 1Details of the data collected.

Company size No. of patents No. of inventors

Large 82,422 17,635Medium 10,960 2182Small 8430 1801

show the first and last names of the first inventor (changedin the example below to preserve anonymity):

Anomaly Case (i). Name of the company (from where theinventor filed patents) matches immediately before and aftera self filed patent (see Table 2a)

Anomaly Case (ii). Inventor files different patents fromdifferent assignees on the same day (see Table 2b)

In Table 2a, the presence of the same assignee(Motorola Inc.) immediately before and after the self filedpatent 5551062 indicates that the inventor did notchange his employer at that time and filed the patenthimself probably because the company was not inter-ested in filing that particular patent. In such cases, weassume that the inventor did not change his employer,but was somehow compelled to file the patent himself.Hence, this is not counted as a “valid move” In Table 2,we see that two patents (patent nos. 5002361 and5007696) were applied for by the same inventor undertwo different assignees: Hoechst Celanese Corporationand Lockheed Missiles, on the same day: January 1, 1990.We assume that this occurred due to possible researchcollaboration between the two assignees and the inven-tor’s involvement in both inventions (usually noticed incase of companies that later on merge or get acquired byone another). So it is assumed that the inventor did notmove from Hoechst Celanese Corporation to LockheedMissiles. Cases like this one are anomalous and are notconsidered as “valid moves” After explaining what wemean by a “valid move” in our research, we now proceedto provide a measure for our inventor performance. Weassess the term “performance” from two differentperspectives:

(i) Counting the number of patents produced per unit oftime (in this case, year) provides a simple and directmeasure of productivity; it measures the rate of patentproduction of the inventor. A java programwaswrittento calculate the rate of productivity for each inventor,along with the number of valid career moves that theinventor has gone through.

(ii) An indirect and a more convoluted measure of“performance” would be calculating the inventor’sgain in knowledge (referred to as “cognitive spread-over of knowledge” or “knowledge spillover” [25].While moving from one company to the other, thismetric is measured by looking at the new patentclasses in which the inventor files patents with eachmove. Hence knowledge spillover is an indication ofthe inventor’s expansion of technical expertise bypatenting in patent classes different from the classesunder which he/she filed previously. We call this the“cognitive spread-over of knowledge” To illustrate thisconcept, let us consider the following example fromour dataset. A new column called “Patent class” isintroduced here. This refers to a unique class numberunder which a particular patent is assigned by theUSPTO in order to uniquely identify the technologicalarea of the invention (for example, class 385 repre-sents inventions in optical waveguides, while class 250

Table 2aAnomaly case (i).

Patent No Grant yr Appln yr Assignee A code F name L name

5313652 19940517 19920325 MOTOROLA, INC. 386735 John Clark5530908 19960625 19920626 MOTOROLA, INC. 386735 John Clark5551062 19960827 19930722 0 John Clark6862273 20050301 20010110 MOTOROLA, INC. 386735 John Clark6928063 20050809 20010316 MOTOROLA, INC. 386735 John Clark6944177 20050913 20030509 MOTOROLA, INC. 386735 John Clark6882855 20050419 20030509 MOTOROLA. INC. 386735 John Clark

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206 201

stands for radiant energy). In Table 3, taken from ourdataset, the inventor is seen to have worked for 5different companies during our observation period:

The 1st assignee to file patent for this inventor is AmocoCorporation under which patent number 5276756 has beenfiled under the patent classes 385 and 398. Similarly, whileworking with his 2nd employer, E-Systems Inc, patentnumber 5644668 was filed under the class 385. The 3rd,4th and 5th assignees (Molex Fiber Optics Inc, LucentTechnologies Inc and Vantageport Inc respectively) filedpatents under classes 385, 385, 250, 73, 324, 367 and 342respectively. The last employer of this inventor was LucentTechnologies under which patents were filed under theclasses 359, 385, 398. Let Yi be the set of unique patentclasses filed by an inventor under the i-th company. In thisexample, the 1st employer is Amoco Corporation, soY1 ¼ {385, 398}. Similarly, Y2 ¼ {385}, Y3 ¼ {385}, Y4 ¼ {385,250, 73, 324, 367}, Y5 ¼ {342}, Y6 ¼ {359, 385, 398}.

Cognitive spread-over of knowledge or knowledgespillover is defined as.

S ¼Xðn�1Þ

ði¼1Þ

����Yðiþ1Þ � Wi

j¼1Yj

����

(1)

where jYj denotes the cardinality of set Y. To put moresimply, ðYiþ1 �Wi

j¼1 YjÞ denotes contribution made by theresearcher upon joining the (i þ 1)th firm, without anyrecourse to the hitherto earned cumulative knowledge(that he/she gained while working with the previous ifirms). When ðYiþ1 �Wi

j¼1 YjÞ is summed overall theassignees, it gives us a metric of the cognitive spread-overof knowledge of the inventor. In this example, cognitivespread-over of knowledge “S” is calculated as follows:Spread-over of knowledge (S)

Table 2bAnomaly case (ii).

Patent No Grant yr Appln yr Assignee

5006285 19910409 19880701 LOCKHEED M4932737 19900612 19890601 HOECHST CE4936644 19900626 19890601 HOECHST CE4932738 19900612 19890601 HOECHST CE4936645 19900626 19890801 HOECHST CE5002361 19910326 19900101 HOECHST CE5007696 19910416 19900101 LOCKHEED M5039186 19910813 19900801 HOECHST CE5044725 19910903 19901101 HOECHST CE5143577 19920901 19910208 HOECHST CE

¼ ðY2 � Y1Þ þ fY3 � ðY1WY2Þg þ fY4 � ðY1WY2WY3ÞþfY � ðY W:::WY Þg þ fY � ðY W:::WY Þgg

5 1 4 6 1 5

¼ 0þ 0þ 4þ 1þ 1 ¼ 6

To calculate the values of cognitive spread against themoves for all the inventors, we wrote an extensive javaprogram to examine the phenomenon across our entiredataset.

6. Results

We generated the following table according to theoutputs of our programs described in the previous sections:the “Freq” column stands for the frequency or the numberof inventors, “Prod” stands for inventor productivity (asmeasured by the number of patents per year) and “Spill”denoted the knowledge spillover or cognitive spread-over(as measured by our metric in the methodology section).All the characteristics measured were categorized by thenumber of moves for inventors between large companies,medium companies and small companies – all in the tele-com industry.

We make the following immediate observations fromTable 4:

(i) The average productivity rate decreases with increasednumber of moves in all the three cases of large,medium and small sized companies. This decline inperformance measure is accompanied by a corre-sponding “compensatory” increase in the knowledgespillover metric.

(ii) In all three cases, the frequency of inventors is seen todiminish with and increased number of moves. Wefind majority of the R&D inventors to be staying in the

A code F name L name

ISSILES 340915 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim DoeISSILES 340915 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim DoeLANESE CORP 254155 Tim Doe

Table 3Illustration of cognitive spread-over of knowledge with the No. of moves.

Patent No Patent class Grant yr Appln yr Assignee A code F name L name

5276756 355,398 19940104 19911206 AMOCO CORP 27895 Mike Smith5644668 385 19970701 19931108 E-SYSTEMS, INC. 181120 Mike Smith5993074 385 19991130 19970620 MOLEX FIBER OPTICS 761832 Mike Smith6075909 385 20000613 19980626 LUCENT TECH 722326 Mike Smith6194706 73,250,324,367 20010227 19990519 LUCENT TECH 722326 Mike Smith6420994 342 20020716 19990614 VANTAGEPORT, INC 801833 Mike Smith6453085 359,385,398 20020917 20001011 LUCENT TECH 722326 Mike Smith

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206202

same company throughout our observation period,with fewer No. of inventors undergoing repeatedcareer moves.

(iii) In small/medium sized companies, the percentage ofnon-mobile inventors is much more than that in largecompanies. Specifically, in the case of large companies,14,024 out of 17,635 inventors (79.52%) have under-gone no career moves. In medium sized companies,1910 out of 2182 inventors stayed in the samecompany (87.53%). Lastly, in small sized companies, wefind 1555 out of 1801 inventors did not moveaccounting for 86.34% of the inventors for thiscategory.

(iv) We do not find a large number of moves in small/medium sized companies as compared to the largecompanies. In particular, we find very few inventorswith 3,4 or 5 moves in these two categories while inlarge companies, we detected a very high number ofmoves, some of them moving as much as 9 or even 10times.

We will provide further rationale behind each of theseindividual phenomena in the next section.

Graphical plots of the two inventor performancemetrics(viz. “productivity” and “knowledge gain”) against the “No.of moves” (number of moves) are shown in Fig. 2. Thetrends for the inventors in large companies are shown inFig. 2a. Readers should note that to avoid the confusion ofterms, the authors henceforth prefer to use the term“knowledge gain” to mean the cognitive spread of knowl-edge or knowledge spillover. We plotted the “No. of moves”on the horizontal axis and.

“Productivity”/“knowledge gain” on the vertical axis.The solid red lines correspond to the “productivity” metricwhile the solid green lines correspond to “knowledge gain”.Additionally, we also tried to estimate the trend lines by

Table 4Descriptive statistics for productivity and cognitive spread-over categorized by th

Large Medium

Moves Freq Prod Spill Moves Freq

0 14,024 3.78 0 0 19101 2502 2.57 1.12 1 2172 573 2.52 1.68 2 323 300 1.72 2.09 �3 234 99 1.69 2.83 – –

�5 137 0.91 3.78 – –

fitting linear functions for the model estimates of“productivity” and “knowledge gain” vs. “move” Thefigures thus show the regression lines in dotted format: reddotted line for the “productivity” estimate and green dottedline for the “knowledge gain” estimate.

A similar analysis for the medium sized companiesproduced the results shown in Fig. 2b.

Finally, Fig. 2c shows this phenomena in the small sizedcompanies:

The analyses shown above can be represented by thefollowing linear regression models:Case 1: “No. of moves”vs. “productivity” for large, medium and small sized com-panies.The equations of the three linear models are asfollows:

y ¼ �0:41x þ 3:1ðlargeÞ (2)

y ¼ �0:27x þ 1:34ðmediumÞ (3)

y ¼ �0:26x þ 1:2ðsmallÞ (4)

where x is the number of moves for the inventors and y isthe average productivity value. The R2 values for the threeequations are 0.912, 0.992 and 0.982.

Case 2: “No. of moves” vs. “knowledge gain” for large,medium and small sized companies.

The equations of the three linear models are as follows:

z ¼ 0:65xþ 0:36ðlargeÞ (5)

z ¼ 0:625x� 0:067ðmediumÞ (6)

z ¼ 0:59x� 0:037ðsmallÞ (7)

where x is the number of moves for the inventors and z isthe average knowledge gain value. The R2 values for thethree equations are 0.981, 0.994 and 0.997.

e No. of moves.

Small

Prod Spill Moves Freq Prod Spill

1.37 0 0 1555 1.10 01.08 0.6 1 203 0.97 0.580.78 1.1 2 22 0.63 1.090.54 1.85 �3 21 0.46 1.76– – – – – –

– – – – – –

Fig. 2. a: Move vs. performance metrics for inventors in large sized companies b: Move vs. performance metrics for inventors in medium sized companiesc: Move vs. performance metrics for inventors in small sized companies.

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206 203

Another way to visualize the effects would be bycomparing the effects of all in a single graph with theperformance metrics across the three types of firms arerepresented in a single figure. In Fig. 3a, we compare the“productivity” of the inventors in large, medium and smallsized companies, all in the same graph so that the readergets a feel about the relative performance of the inventorsin these three types of companies.

A similar graph shows the “knowledge gain” metric inFig. 3b.

7. Discussion

Our study shows a declining trend in patent productionwith an increasednumberofmoves. Therefore, the inventorswho prefer staying longer in large companies tend toproduce more patents on average than the “mobile” inven-tors who go on moving across multiple companies. Thistrend remains consistent across the three types of compa-nies. However, the decline in the rate of patent production iscompensated by an increased in the knowledge gain metric.

Webelieve there couldbeanumberof reasons for thedeclinein patent production trend. In particular:

(i) It takes 18 months from the date of application fora patent to be published. The actual discovery happenssome time before the application date. Hence rapidmovement from one company to another couldimpede the invention process of patenting for theinventor.

(ii) An inventor moving to a new organization needs sometime to get up to speed in the new environment,particularly if working on a different type of project.This in turn, could slow down the invention process.

(iii) Non-competing clauses in employment contractsoften prevents an inventor from applying the knowl-edge gained in previous company to the job in the newcompany for at least two years.

These impediments could extend the time before theinventor gets a patent in the new company. Hence, a move

Fig. 3. a: Move vs. productivity comparison for inventors in all the threetypes of company sizes b: Move vs. knowledge gain comparison for inven-tors in all the three types of company sizes.

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206204

can adversely affect and inventor’s productivity, in thesense that following any move, his/her invention will takemore time to see the light of the day. On the other hand,had the inventor stayed with the same employer, he/shewould have adapted one-time to the organization for theentire stay with the employer thereby having higher rate ofoutput. These are possible explanations for the underlyingmodel and our data analysis supports the Hypothesis 1.

On the other hand as we have shown, while theproductivity declines with an increased number of moves,the inventors seem to gain in their breadth of technicalexpertise (represented by the “knowledge gain” metrics)with each new move. With an increased number of moves,the inventors’ receptivity to external knowledge is facili-tated. In particular, inter-firm moves help the transfer oftacit knowledge, which otherwise is immobile [22]. Thus,mobility within companies creates some localized knowl-edge spillovers which has been measured here in terms of“knowledge gain” Our analysis supports the work of Hoisl[34] where the author has argued that “knowledge spill-overs and labor mobility are inter-related phenomena” and

the transfer of tacit knowledge, can occur through“knowledge flow resulting from skilled workers’ mobility”.The mobile inventors are found to possess expertise overvaried technological areas often very different from theirinnovations in the previous firms where they have workedearlier. Again, we believe this is a valid explanation and ourdata and analyses support Hypothesis 3.

We also observe that the rate of decline in patentproductivity is much more pronounced in the largercompanies than in the medium/small ones (evident fromthe slopes of the three linear equations: 2, 3 and 4). Thisprobably happens due to the enforcement of non-competing clauses in employment by the large compa-nies. Hence, when the inventor has repeated career moves,he/she seems to suffer the most in case of large companies.Smaller or medium sized companies may not have theresources or inclination to hold defecting inventorsaccountable for violation of non-competing clauses. Thisserves to support our Hypothesis 2. Another importantobservation is that the rate of increase in knowledge gainwith movement is much more prominent in largercompanies in comparison to the medium/small sizedcompanies (evident from the slopes of the three linearequations: 5, 6 and 7). In large companies where there arebetter financial incentives, job stability and better oppor-tunity to work, the inventors seem to have more conduciveatmosphere for their intellectual stimulation which isreflected by an increased number of patents in differentclasses. More importantly, large companies offer a widervariety of projects to work on and also encourage collabo-ration with other R&D inventors. This perhaps helps theinventors to spread out their knowledge of technicalexpertise with each move. In contrast, smaller or mediumsized companies, in general, mostly focus on their currentproduct lines and may not offer much variety in theirprojects. Hence, even though the inventors in thesecompanies enjoy an increase in knowledge gain with eachmove, the rate of knowledge growth remains less thanthose moving among the large companies. This argumentand the data support our Hypothesis 4.

Although we have recognized the complexity of thedynamics of inventors’ movement among differentcompanies, our study, based on a relatively large dataset,provides conclusive evidence of the effect of moves on twospecific measures of inventor performance.

8. Conclusion

This paper provides an empirical analysis of theinventor performance on two dimensions based on thenumber of inter-company moves: that of productivity andalso knowledge gain. The data analysis suggests an almostlinear relationship between the number of moves andthese dimensions of performance. That is, with anincreased number of moves, there is a decline in the patentproduction rate and an increase in the span of technicalexpertise.Our study however has some limitations:

1- We only examined the effect of mobility within thetelecommunications industry. To generalize these

M. Hosein Fallah et al. / Technology in Society 34 (2012) 196–206 205

findings, the study needs to be extended to otherindustries. Consideration should also be give to differ-ences between public and private enterprises and areascontrolled by government regulations such as energy ordefense.

2- In calculating the moves of an inventor, we did notdifferentiate between moves initiated by the inventorsand those caused by mergers, acquisition or divesti-tures. The nature of the moves can be another factorimpacting productivity.

3- We analyzed the moves between similar sized compa-nies. We did not account for inventors moving fromlarge to medium or small companies and vice-versa toavoid dealing with the complex effect of the companysize.

4- This studywas conducted for mobility within the UnitedStates. We did not consider the effect of mobility glob-ally or in other national contexts. Inventors’ produc-tivity in global moves could also be affected by cultureand language differences as well.

5- Finally, a major assumption in this study was thatpatents can be used to measure inventors’ productivity.Other metrics could be considered to enhance theanalysis and the validity of our findings.

These limitations offer opportunities for furtherresearch in this area. Findings from this study and addi-tional research topics identified could have a significantimpact on R&D management and career planning forscientists and engineers. Individuals interested in expand-ing their areas of expertise could find our findingsencouraging for more career moves. Similarly, R&Dmanagers find further data driven support that hiring fromoutside their current area of technology could be instru-mental for rapid innovation and strategic expansion oftheir products and technologies. This research also indi-cates that R&D employees hired from other companies maybe less productive in terms of patent productions rate, atleast for the initial years.

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