A MODEL FOR QUANTIFYING EFFECTS OF VIRTUALPROXIMITY ON INNOVATION

Tom CoughlanUniversity of Phoenix

ABSTRACT

Over time, innovation typically proves itself as a critical factor in the successof most organizations, and the proximity to key innovation resources has significanteffect on the level and quality of innovation within a geographic region. This studyproposes that the existence of virtual proximity, or the use of computer andinformation technology, reduces the psychic distance and develops a feeling ofcloseness to resources used in the innovation process. Utilizing a developed model forquantifying virtual proximity and innovation, this study examines the effects ofvirtual proximity on innovation.

Key words: Virtual proximity, Innovation, Virtual presence, Psychic Distance, Model.

INTRODUCTION

A focus on innovative thinking and its effects are notnew. In his classic 1776 work, The Wealth of Nations, Adam Smithpoints out that a nations wealth is dependent on “the skill,dexterity, and judgment with which its labor [and resources]”are applied to industrious purposes. Smith's work defined thevery essence of capitalism. More importantly, it set thefoundations for a revolution in economic thought. Smithsuggested that man needed to constantly rethink every productand process - in other words to innovate - in order toprosper. In many ways, Smith set the foundational thinking forfuture innovation thinkers such as Schumpeter (1942).Schumpeter posited that capitalism could never be stationary,and envisioned a process of creative destruction where the old andless effective will constantly be replaced by new, higher, andbetter uses of the available resources.

Skills that improve an organization's level of innovationhave become critical commodities, and anything that canenhance these abilities quickly becomes highly sought after.However, defining what innovation is, how it can be measured,and what can be done to promote it has been the subject ofmuch academic research and disagreement. Utilizing a developed

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model for quantifying virtual proximity and innovation, thisstudy examines the effects of virtual proximity on innovation.

This study defines the term virtual proximity as the useof computer and information technology to reduce the psychicdistance thereby developing a feeling of closeness toresources used in the innovation process. Further, it proposesthat virtual proximity exists, and that its existence affectsthe level and the quality of innovation that the firmproduces. In addition, it discusses the implications formanagers and makes some recommendations on how to best managethe firm’s virtual proximity.

BACKGROUND OF THIS STUDY

Modern thinkers and researchers (e.g., Chesbrough &Appleyard, 2007; Doloreux, 2004; Drejer & Vinding, 2005;Muscio, 2006; Oerlemans & Meeus, 2005; Wong & He, 2005) havefound methods for measuring innovation a constant bone ofcontention. Most would agree that innovation is a black box,inputs and outputs can be measured; however, the process ofinnovation itself is extremely difficult to define and evenharder to quantify. Many of the metaphors that have been used,such as research spending, or patent output, come up short asa true measure of innovation or innovation activity even thosewho use patents as a gauge of innovation acknowledge thesevere limitations to this metric. In addition, there does notseem to be a direct correlation between those inputs that aregenerally recognized as critical to the process and the outputof measurable innovation.

In a 2006 Booz Allen Hamilton study, of 1,000 companieswith the world’s largest research and development budgets, thetop 500 performers from a revenue perspective often spent only3.5% of sales compared to an average of 7.6% for the 500smallest firms and yet the top revenue firms significantlyoutperformed other top players in their fields. According tothe study's authors, it is common for larger firms to spend alower percentage of sales and revenues than smaller firms, yetproduce better results from both a return on investment and atotal innovation level viewpoint (Jaruzelski, Dehoff, &Bordia, 2006).

In measuring the results or outputs of the innovationprocess, the two primary schools of thought are either to

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quantify the number of patents or to aggregate a series ofcompany-reported innovation statistics to generate a totalinnovation quotient. The patent technique is a method used bya number of leading researchers including Christensen (1997),Florida (2002),and Porter (2001). As briefly discussed above,the technique does not account for the broad number ofinnovations that are not or cannot be patented in U.S. orEuropean patent offices. In addition, according to Chesbrough(2006), “seventy-five percent to ninety-five percent ofpatented technologies simply lie dormant” (p. 6) and are nevercommercialized. Noting there is no clear connection betweenregulatory instruments and innovation, the Organisation forEconomic Co-Operation and Development (OECD) is clearly in theaggregation camp as its preferred technique for measuringinnovation. The OECD developed a series of techniques for thequantification and tracking of innovation activities thatoccur both inside and outside the protection of regulatoryinstruments. The results were published in the Oslo Manual(Organisation for Economic Co-Operation and Development,2005). In addition, the OECD published a manual for the propertracking of research and development efforts, the Frascati Manual(Organisation for Economic Co-Operation and Development,2002).

The Oslo Manual and Frascati Manual have been used as guidesto develop several innovation studies conducted in OECDcountries (primarily in Europe); however, the techniques inthe manuals have also been used as a basis for studies inCanada, Australia, and several Asian countries. The techniqueshave not yet been applied widely in the United States. Basedon the structure of the study process outlined in thesemanuals, it seems logical to apply the techniques in theUnited States at a national, state, and regional level, whichwould allow for more direct comparisons of innovation effortsand results on a global scale (Organisation for Economic Co-Operation and Development, 2005).

Clustering

Innovation itself is not developed in a vacuum, it ishighly dependent on the available resources - includingcapital, professional skills, and environmental resources(Amin & Cohendet, 2005; Anthony, Johnson, Sinfield, & Altman,

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2008). If such resources are constrained, the process ofinnovation will be stifled. Therefore, proximity to resources,and the clustering of resources by specific industries in ageographic region, has long been considered an importantfactor in the promotion of both the volume and the qualityinnovation (Porter, 2001). The belief that close geographicproximity of key resources would reduce friction and speedaccess. The true value of clustering emerges when proximity ofkey resources fosters the spillover of knowledge within andacross industries, or the sharing of knowledge andtechnologies, among the members of the cluster (Christensen,1997; Porter, 2001).

Creative Commons

When industries cluster, the proximity allows for thesharing of tacit knowledge, contractors, services, andcommonly needed skills between organizations in the cluster.Pisano and Shih (2009) describe that just as similar inprinciple to the agrarian commons that develop in the US priorto the industrial revolution. In case of agriculture thecommons were tracks of land there, or other physicalresources, that were used by members of the community to thebetterment of all. In the case of an industrial cluster,industrial commons are the cumulative knowledge in the cluster,the “suppliers of advanced materials, tools, productionequipment, and components” (p. 116).

One of the most frightening prospects suggested by Pisanoand Shih (2009) is that current practice of off-shoring ofmanufacturing is destroying the industrial commons of the US.They site what has happened in consumer electronics andpersonal computers as an early warning to other industries.They point out that industrial process and innovation arehighly intertwined. “Once manufacturing is outsourced,process-engineering expertise can't be maintained, since itdepends on daily interaction with manufacturing” (p. 119).Therefore, off-shoring leads to the destruction of anindustry’s ability to innovation and eventually to theindustry itself. At the same time those countries that wereinitially only handling simple assembly tasks are nowdeveloping sophisticated design and manufacturingcapabilities.

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Virtual Presence and Virtual Proximity

Organizational infrastructure now includes resources thatcan be accessed through the internet and the web. In additionto physical presence or proximity, organizations should becomeaware of their own virtual presence and virtual proximity andthe virtual presence and virtual proximity of their partnersand potential innovation resources as part of a broad-basedorganizational infrastructure. The following arecharacteristics of a complete infrastructure: (a) physicalpresence or proximity that can be measured by the distance, time,and effort necessary for an innovation resource to be engagedby an innovator, (b) virtual presence that can be measured byexistence of the firm on the internet and the number of webpage hits or other virtual traffic metrics (thesecommunications can be unidirectional, such as a web page hit.Interactive communication is not required to create presence),and (c) virtual proximity that can be measured by how active a firmor organization is in engaging outside resources withinformation and communications technologies (ICT)collaboration tools (this is a measure of virtualcollaboration and connectedness).

Basic virtual presence is the fact that an individual, anorganization, or a resource exists in or participates on theinternet, web, or other virtual technology platform. But virtualpresence is not enough to encourage innovation. Innovationhappened when the innovator has a feeling of accessibility, orwhen the physic distance (Beckerman, 1956) between the innovatorand the resource is small. When virtual technology is used toreduce the physical distance between resources and theinnovator we begin to create virtual proximity. Virtual proximity couldbe further defined as, the combinations of accessibility, andthe possibility of casual interaction between the organizationand its innovation resources. It is important in the creationof proximity that the communications could lead toserendipitous discoveries or collaborative work. Virtual proximitycan be measured in terms of access and connectedness to anorganizational resource through the use of internet and webtechnologies. Metrics such as the number of channels of two-way communications, especially those that show persistence(such as instant messaging or social networks), and the number

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of touch points per resource could be an indicator ofproximity.

Discovering the Nature of Virtual Proximity

Within Southwestern Connecticut the most economicallysignificant region is the Bridgeport-Stamford-Norwalk CoreBased Statistical Area (CBSA). This region has a population ofapproximately 900,000 that is part of the larger New York Citymetropolitan area. Using the fourth version of the CommunityInnovation Survey (CIS4) as a base, the author (a) measuredthe existence of innovation activity in the Bridgeport-Stamford-Norwalk CBSA as measured by the CIS4 surveyinstrument, (b) measured the level of virtual proximity in theregion, and (c) correlated the existence of innovation withthe level of virtual proximity in the organizations involved.

RESEARCH QUESTION

The purpose of the study was to examine at the effects ofvirtual proximity on innovation. There is an overwhelming bodyof evidence that innovation will be a primary driver ofeconomic prosperity in the future and that innovation hashistorically been affected by the geographic proximity ofinnovation resources (Council on Competitiveness, 2005;Fagerberg, Mowery, & Nelson, 2005; Florida, 2005a, 2005b,2006; UK Department for Business Enterprise & RegulatoryReform, 2004). The researcher believes a firm’s virtualproximity to its innovation resources can also enhance theability of the firm to innovate. Using a proven instrumentdeveloped by the OECD, the CIS4, the level of innovation forfirms in the Bridgeport-Stamford-Norwalk CBSA was established.The addition of a question to the OECD instrument enabled thenecessary data to be collected to develop a virtual proximityindex (VPI). The index, coupled with other indexes developedby the researcher using the OECD questions, allowed theresearcher to analyze whether virtual proximity exists,whether virtual proximity varies by industry, and whetherinnovation varies significantly across the region.

Innovation is extremely important in regions likesouthwestern Connecticut, which contains highly skilled white-

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collar industries where the value of local economic output maybe driven by the uniqueness of the highly skilled localinputs. As the sophistication of the global economy expands,it will become increasingly important to expand the range andinteractivity and of the creative commons, and includeresources and intellectual perspectives from outside theregion to continue to maintain the region’s growth rate andcompetitive standing (Ghemawat, 2007a, 2007b; Pasano & WillyShih, 2009). Understanding the relationship between virtualproximity and innovation might provide information that isadvantageous in the development of innovation developmentstrategies.

Several issues relative to the level of innovationoccurring in the Bridgeport-Stamford-Norwalk CBSA were thenanalyzed:

1. Is there a significant difference in the level ofinnovation present in organizations within the Bridgeport-Stamford-Norwalk CBSA by city? 2. Is there a significant relationship between the level ofinnovation and the organization’s virtual proximity? 3. Is the level of virtual proximity significantly differentamong the surveyed top North American IndustrialClassification System (NAICS) industry categories?.

HYPOTHESES

Because the majority of the literature about proximitycurrently focuses on the concept that proximity is based on ageographical factor with respect to proximity as a factor ininnovation, the null hypotheses were as follows:

H01: There is no current statistically significant correlation of virtual proximity toinnovation resources on the level of the innovation output of an organization. H02: There is currently no statistically significant difference in virtual proximitybetween NAICS industry categories. 

Because a reasonable expectation exists for a connectionbetween virtual proximity to innovation and the potential ofan organization to be innovative or produce innovation

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effected by industry, the alternative hypotheses were asfollows:

Ha1: There is a statistically significant correlation of virtual proximity to innovationresources on the level of the innovation output of an organization.Ha2: There is a statistically significant difference in virtual proximity between NAICSindustry categories. 

METHODLOGY

Data Collection

The data collection was done by online survey.Participants were invited to participate through email,postcard, and telephone solicitation. The net result of thesolicitation effort was 7,004 unique contacts, which resultedin 128 responses, of which 74 were substantially completed.Once the data were imputed, the researcher was able to workwith a dataset of 114 records.

Research Instrument

A proven instrument was used to quantify the existenceand level of innovation in the study area: the CIS4 developedby the OECD. In addition, the study followed all the standardprotocols for the implementation of the CIS4 as outlined bythe OECD and the Oslo Manual (Organisation for Economic Co-Operation and Development, 2005). The version of the studyimplemented by the OECD in the United Kingdom acted as a base,and the language of the study was localized for clarity wherenecessary (UK Department for Business Enterprise & RegulatoryReform, 2004, p. 2).

Measurements

In order to apply the methods outlined in the Oslo manualthe OECD developed a Community Innovation Survey (CIS) whichwas designed to measure innovation across a broad spectrum andinclude innovations not captured by studies of regulatoryinstruments. The CIS asks a series of questions about (a) thesize of the company, (b) the number of products the companyintroduced, (c) the source of the product innovation, (d)

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company research and development efforts, (e) levels ofpartnership, and (f) the financial success of the productsover a 3-year period. Contributory factors such as educationor availability of resources, which also have an effect oninnovation both inside and outside the firm, were alsostudied. Beginning in 2006, the OECD planned to have membersparticipate every 2 years, alternating between a simplifiedand full version of the survey. As with past procedures, theprocedures are equally applicable to U.S. organizations andorganizational infrastructures (Organisation for Economic Co-operation and Development, 2006).

Virtual Proximity

Virtual proximity means a feeling of connectedness existsbetween two or more parties accomplished by using internettechnology. Therefore, to accomplish virtual proximity theremust be some sort of ongoing interaction, transaction, orcollaboration on the internet. Unlike virtual presence, it ispossible to have different levels of virtual proximity. Toestablish the level of virtual proximity, the researchercreated an index that correlated the level to a scale based onthe positive answers to a series of questions related to anorganization’s virtual proximity and virtual collaborationactivity. In addition, virtual and physical presence/proximityare not mutually exclusive. When resources have some level ofboth, they become more valuable to the innovator.

Data Analysis

The initial data were downloaded from the commercialsurvey hosting service as a Microsoft Excel worksheet. Becausethe data were arranged in 130 fields, and due the attributesof the data, it was important to consolidate the data for moreeffective analysis. Therefore, a number of indexes werecreated that would help make manipulation and analysis of thedata far more intuitive and hopefully more accurate. Theindexes included: Innovation Index (II), Virtual ProximityIndex (VPI), Virtual Proximity Index HML (VPI-HML),Cooperation Index (CI), Intellectual Capital Protection Index(ICPI), and Organizational Change Index (OCI).

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Statistical Analysis Process

After the indexes had been built, the data were importedinto a statistical analysis package from SAS Institute, Inc..The package was used to generate a series of reports including(a) Pearson correlation coefficients, (b) ANOVA analysis, (c)chi-square analysis, and (d) cross-tabulations of the data.The initial data were loaded into SAS and the statisticalanalysis was run. Following this process, the data wereimputed and the statistical analysis rerun on the newlyimputed data.

FINDINGS OF STUDY

After the data were imputed, all the statisticalcalculations were run. Included in Tables 1 to 4 are thepostimputation Pearson correlation coefficients,postimputation chi square analysis, and postimputation ANOVAanalysis. These testes were used as the primary basis foranalysis. The rational for the use of each test is the same aswith the nonimputed data; however, the imputed data should bemore representative of the actual levels of innovation,virtual proximity, cooperation, intellectual capitalprotection, and organizational change.

A Pearson correlation executed between the CooperationIndex and the Innovation Index. The purpose of the test is todiscover if there is a significant difference between thelevel of correlation of the Innovation Index to the VirtualProximity Index and Innovation Index to the Cooperation Index.If there is a significant difference, it can be assumed thatVirtual Proximity is more than just a form of cooperation.

Table 1

 

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The results of the Pearson correlation clearly shows thatthere is a statistically significant relationship between thelevel of innovation that exists within the firm and the firm'slevel of virtual proximity to its innovation resources.Although there is a relationship between virtual proximity andsimple cooperation with outside resources, as is demonstratedby the cooperation index to virtual proximity index, virtual proximitygoes far beyond simple cooperation. Virtual proximity has thepower to help to reduce the psychic distance and increase thenumber and variety of available resource. The results of thesePearson Correlations validate the hypotheses in that it showsthat it is possible that the level of innovation is affectedby virtual proximity, and that the author’s assertion that itexpands the creative commons is likely.

In Table 2 are the results of a chi square showing theexpected verses actual number of firms who had met the OECDinnovation threshold of at least one new significant productor service in the past three years, and how many of thosefirms rated as low, medium or high on the virtual proximity index. Ap-value of less than .05 would be an indicator of arelationship between the level virtual proximity andinnovation. Therefore the resulting p-value of .0001 is anindicator of a very strong relationship between the level ofvirtual proximity and the level of innovation. By using bothparametric and nonparametric tests, and receiving confirmingresults on both, would seem to indicate that virtual proximityis real and it has an effect on the level of innovation withinan organization.

OECD ThresholdYes No Total

HighObserved 23   23

VIP HMLExpected

17.50 5.50 23.00

Medium

Observed 49 13 62 Expecte 47.1 14.81 62.00

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d 9

LowObserved 14 14 28 Expected

21.31 6.69 28.00

TotalObserved 86 27 113 Expected

86.00 27.00 113.00

18.01 chi-square2 df

.0001 p-value

Table 2. Chi square of postimputed data of firms that rated asinnovation under the OECD Innovation Threshold grouped by thefirm’s Level of Virtual Proximity HML.

In the results of the ANOVA test on the imputed data isdisplayed in Table 3. This test was unable to show asignificant difference in the level of innovation between thecities understudy. However, the sample did not have largepopulations in each city, in most cases the sample size waswell under 30 participants per city.

Source        DF Sum of Squares Mean Square FValue

Pr > F

Model 9 46.0385630 5.1153959 1.05 0.4048

Error 92 446.8459686 4.8570214    

Correctedtotal

101 492.8845316      

 

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R-Square Coeff Var Root MSE INNOVATION INDEX Mean

0.093406 73.30247 2.203865 3.006536

 

Source DF Anova SS Mean Square F Value Pr > F

City Code 9 46.03856298 5.11539589 1.05 0.4048

Table 3. ANOVA of original imputed data by Innovation Index andCity Code

Correlation of Virtual Proximity to Innovation

The concept of Ba, as presented by Amin & Cohendet(2005), suggests the creation of proximity is based on avariety of factors including but not limited to culturalfactors, organizational factors, group membership, andgeography; therefore, it is reasonable to assume that therecould be a virtual component to proximity. The assumptionswere that virtual proximity would open the firms that embraceit to new sets of resources; such as design and creativetalent, professional skills, capital, industry knowledge,etc.. If Toffler (1970) is correct, the electroniccommunications infrastructure that has developed since the1990s will provide access to these resources and provide thefirms greater opportunity to be innovative. The first set ofhypotheses related to the correlation of virtual proximity tothe production of innovation. The same issues are relevant tothe second research question. The hypotheses and questionaddressed first are as follows:

H01: There is currently no statistically significant correlation of virtualproximity to innovation resources on the level of the innovation output of anorganization.

Ha1: There is a statistically significant correlation of virtual proximity toinnovation resources on the level of the innovation output of an organization.

Correlation Research Question: Is there a significantrelationship between the level of innovation and theorganization’s virtual proximity?

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There is a clear correlation between the existence ofvirtual proximity and the level of innovation in theBridgeport-Stamford-Norwalk CBSA. Two sets of statistics, oneparametric and one nonparametric, illustrated the connection.First, the Pearson correlation coefficient between the VirtualProximity Index and the Innovation Index for all versions ofthe data showed a medium to high correlation (nonimputed data0.518498, imputed data 0.44542, and 10 or more employeesimputed 0.39865). Second, the chi square analysis of the OECDThreshold for Innovation and VPI-HML showed a p-value of .001,indicating a statistically significant level of correlation.Because a correlation occurs in both parametric andnonparametric statistics, it is reasonable to conclude thereis a correlation here

Industry and Virtual Proximity

The literature clearly shows that there is a culturalcomponent to innovation and that and nature of industriestended to create an environment where some industries havegreater virtual proximity than others (Anfuso, 1999; Cameron &Quinn, 2006; Lester & Piore, 2004). The second set ofhypotheses questioned how virtual proximity correlates to theprimary industry. The same theme was carried in the thirdresearch question as well. The hypotheses and researchquestion are as follows:

H02: There is currently no statistically significant difference in virtual proximitybetween NAICS industry categories.

Ha2: There is a statistically significant difference in virtual proximity betweenNAICS industry categories.

Industry Research Question: Is the level of virtualproximity significantly different among the top NAICS industrycategories that respond to the survey?

There are differences in culture, communications,educations, and protectiveness of intellectual capital(Anfuso, 1999; Cameron & Quinn, 2006; Lester & Piore, 2004).Therefore, there should be differences in the level of virtualproximity and innovation. Again, the results are clear. In allcases, the data showed a clear difference in the correlation

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level of innovation and virtual proximity by industry. Forexample, in the imputed dataset in those industries with 6 ormore respondents, the Pearson correlation coefficient ofinnovation to virtual proximity index varied as follows:0.61347, 0.33772, 0.63750, 0.45658, 0.40327, and 0.24772.

City and Innovation Variation

The only remaining research question has to do with thedifference in innovation per city in the Bridgeport-Stamford-Norwalk CBSA. Both Florida (2002, 2005a, 2005b, 2006)andPorter (2005, 2003, 2006) point to regional culture as animportant factor in the support of an innovation culturewithin an organization. It is therefore reasonable to assumeby their nature each city would have a culture and that eventhe slightest difference in culture would attract a more orless innovative firm. The research question was as follows:

City and Innovation Variation Question: Is there a significantdifference in the level of innovation present in organizationswithin the Bridgeport-Stamford-Norwalk CBSA by city?

The data failed to reject the null. To answer thisquestion, an ANOVA comparing the mean innovation index of allfirms grouped by city was performed. The results of the ANOVAshowed F = 1.41. It would seem clear there was no statisticaldifference between the mean innovation by city within theBridgeport-Stamford-Norwalk CBSA.

CONCLUSIONS

The length of the survey made it particularlychallenging. The survey contained 36 questions. Manyrespondents found there were too many questions to answersince after only the 7th question, 40 of the 128 respondentsfailed to continue. It is assumed that by the 7th question theparticipants realized they were only on the 3rd page of a 12-page survey and felt this was far more work than they hadsigned up for. In Europe, the CIS surveys are run bygovernmental or quasi-governmental agencies. In some casesthey are even included in tax filings. Therefore, there is a

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much more compelling reason for participation on the part ofthe firms under study (Organisation for Economic Co-operationand Development, 2006). In the future, the researcher woulddramatically reduce the number of questions and wouldcarefully evaluate the complexity of the questions asked.

The level of virtual proximity would seem to be moreimportant than the level cooperation to innovation. In allcases, the Virtual Proximity Index showed a higher level ofcorrelation to the Innovation index (II) than did thecooperation index (CI). This would seem to suggest thatefficiency of cooperation is more important than the volume ofcooperation to the production of innovation. In addition, asevidenced by Figure 16, in which the chi-square analysis wascompared to local partnering with OECD Innovation Threshold,there seems to be no significant connection between how localthe resource was in the Bridgeport-Stamford-Norwalk CBSA andthe firm’s level of innovation.

IMPLICATIONS

Innovation is clearly affected by virtual proximity andindustry. It would seem that it increases the portfolio ofresources, which increases the number of opportunities to beinnovative. Therefore, managers should carefully consider thesort of resources their industry needs to be innovative andlook at how those needs could be served by increasing thenumber of communication channels and types of resourcesthrough the use of electronic media. In addition, this link toculture may enable some organizational and cultural changes.Contributory to the lattice organizational structure at Goreis the culture of open communications (Harrington, 2003). Itis possible that increased virtual proximity could helpencourage organizations to allow for more self directed teamsand organizational freedom.

Contrary to the findings of Pöyhönen & Smedlund (2004)the study did not show differences on the micro-geographicscale of a city within the selected CBSA; however, this doesnot indicate that the difference between cities in a singleCBSA can be generalized. An examination of a wide variety ofcompanies in a broad set of industries indicates organizationsthat are highly innovative tend to pull their virtualresources disproportionally from other highly innovative parts

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of the globe (Florida, 2002; 2005a; 2005b; 2006) supportedthis view.

The literature review in chapter 2 clearly indicated thatinnovation is not an option (Chesbrough, 2006; Christensen &Raynor, 2003; Kanter, 2005)To be competitive, organizationswill need to innovate on a variety of different levels.Organizations who become comfortable with virtual resourcesare more like to find what they need to compete and survive.According to Rogers (2003), it is likely that those industriesare just the leading edge of change. As virtual tools becomemore common, even many of the lagging industries will have nochoice but to include virtual resources in their portfolio tocompete.

RECOMMENDATIONS

Virtual proximity is not one thing. For this reason, amatrix was chosen in the current study to measure virtualproximity. Due to its nature, virtual proximity is temporal.Technologies change, markets change, and cultures change;therefore, the need for specific components of virtualproximity change in importance over time. However, the conceptof virtual proximity is likely to continue to be a presencefor the foreseeable future. It is the nature of virtualproximity that requires those who wish to leverage it to beconstantly vigilant of their relative level of virtualproximity. In a sense, virtual proximity is not digital, butanalog. That is, it is a series of flows rather thansomething that is turned on and off. Each technology that afirm leverages has a lifespan and an associated flow ofvirtual proximity to others who leverage that technology.Maintaining a level of virtual proximity requires constantlychecking the life-cycle position of each technology, thecommunities that could or should be leveraged, and whichtechnologies on the horizon are candidates for an investmentof time and capital to leverage the potential universe ofresources.

FURTHER STUDY

The study was a good first step, but there is far moreresearch needed to explore virtual proximity. At a minimum,

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virtual proximity should be tested at a state level, if not anational and international level. It is also recommended thatfuture studies be conducted in locations outside of the largeglobal megalopolises described by Florida (Florida, 2006).Even though the study failed to prove a connection existsbetween locations, the study was far from complete in thatarea.

The connection between virtual proximity, cooperation,and innovation needs further study. There is a clearconnection between virtual proximity and cooperation. Withoutcooperation, virtual proximity would not result in innovation.However, the cooperation index developed in the current studyonly measures the volume of cooperation along one metric.Cooperation is multidimensional, and the quality ofcooperation can have a tremendous effect on the results.

Virtual proximity should also be explored for its team-building and leadership aspects. As managers begin to expandthe use of self-directed teams and other management stylesthat rely on individuals to take on high levels of personalresponsibility, the use of virtual proximity tools could leadto more freedom of movement and the ability to balancemultiple virtual team connections. It is possible thatresearch in this area could lead to more effective matrix andlattice organizations.

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About the Author:

Tom Coughlan is currently a College Campus Chair for the JohnSperling School of Business at the University of Phoenix Fairfield CountyCampus, and holds adjunct positions at both the European School ofEconomics New York City Camps and the Manhattan Institute of Management.His fields of study include the effects of virtual proximity and socialnetworking on innovation, organizational structure, and markets. Inaddition, Tom has over 25 years of professional experience in thetechnology, real estate, and professional services industries. Tom holds aDBA from the University of Phoenix.

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APPENDEX

Innovation Index

The Innovation Index was intended to create a summaryindex that includes the introduction of new products, thepercentage of new products in the product portfolio, newprocesses, and processes new to the industries. The new-to-the-industry products, services, and processes were given moreweight in the index, as were firms with 50% or more of theirproduct portfolios introduced in the past 3 years.

.Virtual Proximity Index

Question 25 of the survey was a 7 x 4 matrix.Participants were asked to rank the importance of thefollowing electronic tools in the firm’s innovation process:1. E-mail with partners and collaborators2. Collaboration software (i.e., Lotus Notes, MicrosoftSharePoint, Wiki’s)3. Web conferencing applications (i.e., Webex, GoToMyPC, LotusSametime)4. Instant messaging (i.e., AIM, MSN, Yahoo, ICQ, Google Talk)5. Social networking (i.e., LinkedIn, Plaxo, Facebook,MySpace) 6. Video conferencing (i.e., Skype, Polycom, HP Halo) 7. Online talent markets or ideagoras (i.e., Elance,Innocentive, Yet2)

The tools were ranked as being of the followingimportance, and the survey tool coded the answers with thenumbers as they appear 1 through 4: High

1. Medium 2. Low3. Not applicableThe coding of the responses 1 through 4 did not properly

reflect the weight the answers should hold in the analysis.The researcher wanted to have an index that showed animprovement in virtual proximity as the value of the indeximproved.

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Virtual Proximity Index HLM

The Virtual Proximity Index HLM is a nonparametric indexbased on the virtual proximity index described above. Theindex takes and separated the results of the above index intothree separate domains: (a) high, (b) medium, and (c) low.

Cooperation IndexQuestion 26 was a 6 x 5 matrix. In this matrix,

participants were asked to describe the types of collaborationresources their firms used and where those resources werelocated. The rows in the matrix were as follows:

1. Other businesses or enterprise groups2. Suppliers of equipment, materials, services, or

software3. Clients or customers4. Competitors or other businesses in your industry5. Consultants, commercial labs, or private research and

development institutes6. Universities or other institutions of higher educationThe columns in the matrix indicated where the resource

are located, and multiple answers were allowed in each row.The columns were as follows:

1. Bridgeport-Stamford-Norwalk CBSA2. Northeastern U.S.3. U.S. national 4. Outside the U.S.5. Not applicable In the dataset, each cell in the matrix could hold a null

or a number unique to the column.

Intellectual Capital Protection IndexQuestion 29 of the survey was a 3 x 4 matrix that tracked

how actively the participant’s firm made use of variousinstruments that protect intellectual capital. The firm wasasked to rank the following rows:

1. Registration of design2. Trademark3. Patents4. Confidentiality agreements5. Copyright

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6. Secrecy7. Complexity of design8. Lead-time advantage on competitorsThe rows were ranked in level of importance in the

following manner:1. High 2. Medium 3. Low4. Not usedThe intent was to have an index that increased with the

importance of intellectual capital protection instruments

Organizational Change IndexQuestion 30 is a 4 x 2 matrix that tracked the level of

management innovation within the participating firms. It looksat the change, existence, or lack of the following managementstructures, tools, or techniques in the past three years:

1. Corporate strategy change2. Advanced management techniques implemented3. Major changes to organizational structure 4. Major changes in marketing concepts or strategies

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