Paper to be presented at DRUID19Copenhagen Business School, Copenhagen, Denmark

June 19-21, 2019

Looking for the red thread: a systematic literature review on proximity and innovation

Matteo DevigiliUniversity of Trento and University of Florence

Economicsmatteo.devigili@unitn.it

Tommaso PucciUniversity of Siena

Department of Business and Lawtommaso.pucci@unisi.it

Lorenzo ZanniUniversity of Siena

Department of Business and Lawlorenzo.zanni@unisi.it

AbstractThe literature on the impact of proximity dimensions on innovation has exponentially increased in thelast decade. The number of publications and the variety of disciplines involved have both enriched theacademic discussion and increased the level of ambiguity. To take the first steps towards re-conceptualizing the proximity framework, the main findings on proximity and innovation in theliterature are reviewed, by selecting and analysing 202 articles from 62 top-ranked journals publishedbetween 1990 and 2017. Our analysis identifies 17 proximity dimensions, with geographical proximitybeing the most investigated. Seven clusters that make up the intellectual core of proximity andinnovation studies are described through a bibliometric analysis, and linked to seven other clustersconcerning the intellectual sub-structure. As a second step, 152 further research indications werecollected from articles and conference papers published between 2016 and 2018, from which a call fora re-think of the proximity dimensions emerged.

Looking for the red thread:

A Systematic Literature Review on Proximity and Innovation

The literature on the impact of proximity dimensions on innovation has exponentially increased in the last decade. The number of publications and the variety of disciplines involved have both enriched the academic discussion and increased the level of ambiguity. To take the first steps towards re-conceptualizing the proximity framework, the main findings on proximity and innovation in the literature are reviewed, by

selecting and analysing 202 articles from 62 top-ranked journals published between 1990 and 2017. Our analysis identifies 17 proximity dimensions, with geographical proximity being the most investigated. Seven clusters that make up the intellectual core of proximity and innovation studies are described through a bibliometric analysis, and linked to seven other clusters concerning the intellectual sub-structure. As a second step, 152 further research indications were collected from articles and conference papers published between 2016 and 2018, from which a call for a re-think of the proximity dimensions emerged.

Keywords: Proximity; Innovation; Literature Review.

1. Introduction

Proximity can be interpreted as a pre-condition for innovation, as it fosters knowledge and

technology transfer among actors (Knoben and Oerlemans 2006; Gertler 1995). Many scholars have

examined the relationship among proximity and innovation at different spatial levels: clusters (e.g.:

Geldes et al. 2017; Ozer and Zhang 2015), regions (e.g.: Rodriguez-Pose and Crescenzi 2008; Vaz et

al. 2014), and nations (e.g.: Andersson and Lööf 2012; Luintel and Khan 2017). Since the renowned

contribution of Boschma (2005), the focus has shifted from solely spatial proximity to cognitive,

organizational, social, and institutional dimensions. Then, other dimensional specifications have

gradually been introduced (see, among others: Moodysson and Jonsson 2007; Cabrer-Borrás and

Serrano-Domingo 2007; Caniëls et al. 2014). Today, these themes continue to attract the attention of

scholars from several disciplines (e.g.: Capone and Lazzeretti 2018; Davids and Frenken 2018;

Divella 2017; Presutti et al. 2017).

Although the multidimensionality of proximity is widely accepted in the literature, few studies

investigate all of the aspects of proximity described by Boschma (see, among others: Fitjar et al.,

2016; Lazzeretti and Capone 2016). In addition, the interrelationships of the disciplines that consider

the concept of proximity have increased the ambiguity and the definitions of the nature of proximity

(Capone and Lazzeretti 2018). Moreover, empirical investigations on the role of proximity in the

innovation process provide divergent results (see, among others: Crescenzi et al. 2016; Geldes et al.

2017; Lazzeretti and Capone 2016; Marrocu et al. 2013; Molína-Morales et al. 2014). Furthermore,

new information and communication technologies mean that the role played by proximity in the

innovation process should be re-assessed (Baycan et al. 2017).

The aim of the present study is to assess the diverse literature on proximity and innovation,

and thus to investigate its (i) intellectual core in order to highlight the research themes developed

until now; (ii) intellectual sub-structure to figure out relationships and affiliations with previous

research streams; (iii) the future challenges of research. To the best of our knowledge, few reviews

or critical assessments have been conducted (e.g., Boschma 2005; Knoben and Oerlemans 2006)

and thus recent developments and changes of direction have not been considered.

The paper is structured as follow: section two provides an explanation of the methodology;

section three provides the research findings on the types of proximities, the intellectual core,

intellectual sub-structures, and future research topics; and section four provides conclusions and

limitations of this research.

2. Methodology

The methodology used in this research consists of four steps: (1) data collection and database

preparation, (2) bibliographic coupling analysis (BCA) and co-citation analysis (CCA), (3) a BCA-

CCA matrix, and (4) future research topic collection.

Data Collection and Database Preparation. We obtained relevant articles from the Scopus

database. We entered the words “proximity” AND “innovation” to be searched among “Article title,

Abstract, Keywords”, and limited the research to “articles” published until 2017. At this stage we

obtained 843 articles from 504 journals. To obtain the key contributions only, we then selected articles

from top ranked journals using the Academic Journal Guide 2018 (rank: 3, 4, 4*) or the Scimago

Journal and Country Rank 2017 (rank: Q1; journals subject areas: “Business, Management, and

Accounting” and “Economics, Econometrics and Finance”). After this screening we obtained 281

articles from 102 journals. We then conducted relevance and consistency checks by reading the

abstract of each paper and when necessary the whole article (c.f. Lazzeretti et al. 2013). After this

final screening we obtained 202 articles from 62 journals published from 1990 to 2017. Table 1 shows

the top 10 journals in terms of the number of relevant articles published.

Table 1. Top 10 sources of the 202 articles dataset

N Journal Documents Citations

1 Regional Studies 33 4175

2 Research Policy 26 1939

3 Economic Geography 11 1021

4 Industry and Innovation 10 308

5 Journal of Economic Geography 9 1304

6 The Journal of Technology Transfer 9 169

7 Technovation 9 388

8 Small Business Economics 7 381

9 Entrepreneurship and Regional Development 6 196

10 Journal of Business Research 5 67

The references of the selected articles were all checked so we could delete mistakes or merge

dissimilar formatting styles.

Table 2 shows the top 10 cited works in the 202 articles.

Table 2. Top 10 cited references by the 202 articles dataset (“citations” refers to intra-database citations)

N Cited Reference Citations

1 Boschma (2005) 82

2 Jaffe et al. (1993) 60

3 Audretsch and Feldman (1996) 59

4 Cohen and Levinthal (1990) 55

5 Saxenian (1994) 53

6 Bathelt et al. (2004) 41

7 Marshall (1920) 39

8 Jaffe (1989) 36

9 Porter (1990) 35

10 Torre and Rallet (2005) 33

Bibliographic Coupling Analysis (BCA) and Co-Citation Analysis (CCA). The visualization

of bibliometric networks allows researchers to analyse citation, co-authorship, and co-occurrence

relationships (Van Eck and Waltman 2014). Through the VOSviewer 1.6.8 software, we focused on

citation analyses that conduct both BCA and CCA, using a fractional counting methodology. By using

a weighting strategy, this methodology decreases the impact of both highly cited articles and

publications with long reference lists (such as review articles). BCA focuses on the similarities in the

reference lists, thus combining articles that share significant numbers of cited references (Appio et

al. 2017). Therefore, the bibliographic relation of two articles is greater with the increase of the

references shared (Van Eck and Waltman 2014). In our case, the 202 articles are grouped in 8 clusters.

However, the last cluster is made up of 5 articles published between 1990 and 2001, thus giving a

reference list composed mainly of old contributions and belonging to different thematic categories.

Therefore, we decided to exclude cluster 8 from our analysis of the results. CCA focuses on the

number of times two publications are cited together, thus revealing the intellectual sub-structures

(Appio et al. 2017; Van Eck and Waltman 2014). To show the evolution over time of the intellectual

sub-structures, we conducted the CCA with the lower-bound of citations of a cited reference set at 2,

giving 1602 articles meeting the threshold. To effectively interpret the content of these sub-structures,

we decided to set the threshold at 3, thus obtaining 740 references divided into 7 clusters.

BCA-CCA Matrix. To link the 7 clusters representing the intellectual sub-structure (CCA)

with the 7 characterizing the intellectual core (BCA), the following steps were conducted. First, we

exported the complete reference list of each cluster from the BCA to Excel 16.17 using VOSViewer.

Second, we assigned the corresponding CCA cluster number to each reference. Third, for each BCA

cluster, we added all citations received by each CCA cluster and weighted them over the total citations

received by the 7 CCA clusters in the BCA cluster under analysis. We then created a matrix of the

weighted links [0,1] among BCA-CCA. Last, we created two network maps using both UCINET

6.652 and VOSViewer.

Further Research Topics Collection. To create a database suitable to detect future streams of

research, we conducted a second round of research on Scopus. We entered the words “proximity”

AND “innovation” to be searched among “Article title, Abstract, Keywords”, limiting the research to

“articles”, “articles in press” and “conference paper” and to the years 2016-2018. At this stage we

obtained 74 Conference Papers and 263 Articles. We then included only articles in top ranked journals

by the Academic Journal Guide 2018 (rank: 3, 4, 4*) or by the Scimago Journal and Country Rank

2017 (rank: Q1; journals subject areas: “Business, Management, and Accounting” and “Economics,

Econometrics and Finance”). We obtained 89 suitable articles, which were further checked for

consistency and relevance, and excluded 12 articles. Similar consistency and relevance checks were

conducted for Conference Papers. The final database was composed of 77 Articles from 38 Journals

and 22 Conference Papers. Table 3 shows the most represented journals in this database:

Table 3. Most Represented Journals

N Journal Documents

1 Regional Studies 8

2 Research Policy 8

3 Industry and Innovation 7

4 Journal of Technology Transfer 7

5 Small Business Economics 5

6 Journal of Business Research 4

7 Journal of Cleaner Production 3

8 Urban Studies 2

9 Technological Forecasting and Social Change 2

10 Journal of Small Business Management 2

After the database creation, we downloaded all articles and read through them, and collected 152

“further research” indications.

3. Findings

3.1 What Kind of Proximities?

We read through the 202 articles constituting our intellectual core to establish the

similarities/differences in the (i) definitions and (ii) operationalizations of proximity. As Table 4

shows, while geographic proximity has been used in almost all articles, other forms of proximity are

far less investigated.

Table 4. Percentage of usage of proximities

Proximities %

Geographic 93,1%

Organizational 16,8%

Cognitive 14,4%

Social 13,9%

Institutional 13,4%

Technological 9,9%

Other 16,8%

N = 202 articles

Table 5 summarizes the main findings regarding definitions and variable operationalizations. As the

most studied, geographical proximity shows a wide variety in terms of both definitions and

operationalizations. However, more recent or less investigated forms of proximities, such as personal,

vision or commercial proximity are only loosely outlined, both theoretically and empirically.

Table 5. Proximities definitions and operationalizations

Variable Definitions Operationalization

Geographical

Proximity

Geographic proximity “indicates the positioning of agents within a

predetermined spatial framework. This type of proximity must therefore

remain distinct from a physical proximity which would represent the outcome

of ‘natural’ constraints in that it is a social construction, built as much by the

installation and development of transportation and communication

infrastructure as by architectural aspects and technical imperatives" (Kiriat

and Lung 1999, 29)

Geographic proximity concerns "spatial separation between economic actors

(in reference to physical factors but also to social constructions such as

transport infrastructures or telecommunication technologies)" (Torre and

Gilly 2000, 178)

"It refers to the spatial or physical distance between economic actors, both in

its absolute and relative meaning" (Boschma 2005, 69)

Geographical proximity, “needs to be understood in more nuanced and

multidimensional terms...local social ties have to be actively constructed

rather than assumed to arise automatically, which means that geographical

proximity must necessarily include a relational dimension" (Healy and

Morgan 2012, 1047)

"...geographical proximity is not just a near-far dichotomy but involves

choices concerning real places to access knowledge...Geographical distance

is thus more accurately seen as a dynamic trade-off between effort, preference

and dependency" (Rutten 2017, 169)

Discontinuous Space:

• Inside/Outside cluster, region, nation (Broström 2010; De Fuentes and Dutrénit 2016; Molina-Morales and Martínez-Fernández 2010; Rosenkopf and Almeida 2003) or Co-Location in the same area (Agrawal et al. 2008)

• Within/without fixed km/miles boundaries (Gittelman 2007; Weterings and Boschma 2009) or overnight stay required or not (Hansen 2015)

• Core vs Periphery (Wershing 2007)

Continuous Space:

• Euclidean Distance (Marek et al. 2017) or Spherical Geometry (Guan and Yan 2016)

• Distance between capital cities (MacGarvie 2005), specific cities (Ter Wall 2014), centroid of two regions (Marrocu et al. 2013)

• Average distance between a firm and all other firms at time t, weighted on other firms’ knowledge to share (Funk 2014).

Time Space:

• Road Travel Distance (Drejer and Østergaard 2017) or fixed time threshold (Herrmann et al. 2012; Ponds et al. 2009)

• Distance decay function based on threshold travel time (Ahlfeldt and Wendland 2013)

Self-Assessed Space:

• Perceived Proximity or Distance (Hoegl and Proserpio 2004)

• Perceived proximity advantages (Fernandes and Ferreira 2013; Romijn and Albaladejo 2002)

Temporary

Geographical

Proximity

“(1) the needs for geographical proximity can be fulfilled temporarily through

travelling; and (2) they can be fulfilled without the interaction leading to the

permanent co-location of the partners. It is this mechanism that is called here

temporary geographical proximity (Torre and Rallet, 2005). It corresponds

to the possibility of satisfying certain needs for face-to-face contact between

actors by travelling to different locations.” (Torre, 2008, 881)

“Temporary geographical proximity in the form of business trips or meetings

in conferences may be sufficient to establish relationships that may be

maintained over longer distances afterwards” (Weterings and Ponds, 2009,

13)

Proxy:

• Average attendance to an exposition fair (Ramírez-Pasillas 2010)

• Duration of contact (Weterings and Ponds 2009)

Organizational

Proximity

“Organizational proximity consists of shared organizational principles, rules,

and codes, including a corporate identity and a corporate philosophy (Blanc

and Sierra 1999, 196), to promote a certain coherence within a firm and

compatibility among collaborating firms.” (Zeller, 2004, 88)

Organizational proximity “involves the rate of autonomy and the degree of

control that can be exerted in organizational arrangements” (Boschma, 2005,

65)

“organized proximity is relational in essence. By this, the author refers to the

ability of an organization to make its members interact” (…) “Two main

reasons explain this fact: Belonging to an organization translates into the

existence of interactions between its members that are inscribed in the genes

or routines of the organization” [logic of belonging] (…) “The members of

an organization are said to share a same system of representations, or set of

beliefs, and the same knowledge” [logic of similarity] (Torre, 2008, 877-878)

“Organizational proximity refers to the relations within the same group or

organization which influence the individual capacity to acquire new

knowledge coming from different agents” (…) “it provides an area of

definition of practices and strategies within a set of rules based on

organizational arrangements” (Marrocu et al., 2013, 1486)

Proxies:

• Similarity of legal forms of enterprises (Marek et al. 2017)

• Work(-ed) in the same organization (Crescenzi et al. 2017) or same legal entity (Hansen 2015)

• Years of collaboration experience (Lazzeretti and Capone 2016)

• R&D cooperation and external contributions to the innovation process (Oerlemans and Meeus 2005)

• Ratio of knowledge created through interaction among district members over the total knowledge created in the district (Dangelico et al. 2010)

Self-Assessed:

• Self-assessment on arrangements with partners (Fitjar et al. 2016)

• Construct composed by similarity on: organizational culture, organizational structure, inter-organizational relationships, technologies (Geldes et al. 2017)

Cognitive

Proximity

“With the notion of cognitive proximity, it is meant that people sharing the

same knowledge base and expertise may learn from each other” (Boschma,

2005, 63)

“[Cognitive] proximity can be associated to the similarity in the way that

actors perceive, interpret, understand, and evaluate the world (Wuyts et al.

2005). In our view, elements such as common culture, values, customs,

norms, routines, visions, goals and objectives determine the way the

environment is approached and known, and also organizational behavior

itself (Inkpen and Tsang 2005).” (Molina-Morales et al., 2014, 234)

“Cognitive proximity is associated with differences and similarities in

capabilities of economic agents. Capabilities at the firm level derive from

learning processes by which additional technical and non-technical skills are

acquired by individuals and through them by the organization.” (Hansen,

2015, 1674)

Proxies:

• Similar/dissimilar scientific domain (Lazzeretti and Capone 2016) or patents in similar/dissimilar technology field (Crescenzi et al. 2016)

• Number of employs working in a sector over total regional employee (Marek et al. 2017) or knowledge created through imitation or interaction inside the district over total knowledge created in the district (Dangelico et al. 2010)

• Correlation coefficient to measure structural equivalence among industries based on firm level data (Enkel and Heil 2014)

Self-Assessed:

• Similarity/dissimilarity in the educational background (Hansen 2015) or in the knowledge base and expertise (Fitjar et al. 2016) with partners

• Construct composed by similarity on: knowledge base, level of experience, language, educational level, and cultural level (Geldes et al. 2017)

Social

Proximity

“Social proximity is defined here in terms of socially embedded relations

between agents at the micro-level. Relations between actors are socially

embedded when they involve trust based on friendship, kinship and

experience” (Boschma 2005, 66)

“Social proximity comes about as a result of shared personality

characteristics, personal interaction and a sense of familiarity between

individual actors. In its mode of creating mutuality among actors, it is closely

related to institutional proximity, but takes place at the micro-level and occurs

in the form of friendship or kinship or also based on past interactions” (Mattes

2012, 1089)

“Social proximity refers to the strength of social ties between agents at the

micro-level resulting from friendship, family relations or previous work-

related interactions. Again, this proximity influences the risk of opportunism,

however here through mechanisms of trust” (Hansen 2015, 1674)

Proxies:

• Co-inventorship (Marrocu et al. 2013), worked for the same company in the same period, have a shared co-inventor (Crescenzi et al. 2017), sometimes assuming a time decay threshold (Crescenzi et al. 2016)

• Inverse of the path length between inventors in the co-invention network (Ter Wall 2014) or the opposite of the number of actor pairs at distance 2 (Lazzeretti and Capone 2016)

Self-Assessed:

• Perceived social interaction with partners (Fitjar et al. 2016)

• Construct composed by: friendship, confidence (trust), previously known, common experiences, reputation (Geldes et al. 2017)

Institutional

Proximity

“Institutional proximity, by contrast, implies a degree of congruence between,

and acceptance of the legitimacy of, the institutional infrastructure in which

agents operate. And, in turn, the impact the institutional framework has upon

the development of cognitive models” (Freel 2003, 754)

Institutional proximity “refers to the institutional framework in countries and

regions, such as legislative conditions, labor relations, business practices and

accounting rules, dominant workplace practices, and the training system,

which are all outcomes and elements of the evolution of political power

relations that contribute to a “cultural affinity” (Zeller 2004, 88)

“The notion of institutional proximity includes both the idea of economic

actors sharing the same institutional rules of the game, as well as a set of

cultural habits and values (Zukin and DiMaggio 1990)” (Boschma 2005, 68)

“Institutional proximity refers to the social and cultural norms that regulate

the business and non-business relationships in a specific context” (Ben

Letaifa and Rabeau 2013, 2072)

Proxies:

• Regions are from the same/different country/ies (Marrocu et al. 2013)

• Inventors belong to the same institution: university vs private sector (Crescenzi et al. 2017)

• Similar/Dissimilar actor typology: research center, public institution, small and large firms, and universities (Lazzeretti and Capone 2016)

Self-Assessed:

• Similarity/Dissimilarity with partners culture, in terms of norms, habit and values (Fitjar et al. 2016; Hansen 2015)

• Construct composed by similarity on: laws and regulations, cultural norms, values, habit and routines (Geldes et al. 2017)

Technological

Proximity

“Technological proximity is based on shared technological experiences,

bases, and platforms. It facilitates shared perceptions, as well as the

anticipation of technological developments.” (Zeller 2004, 88)

“interactions can be based on similarities related to the way in which actors

perceive, interpret, understand, and evaluate the world (Wuyts et al. 2005),

as emphasized by cognitive proximity. This relational attribute refers to how

actors interact, whereas technological proximity – which refers to shared

technological experiences and knowledge – is based on what they exchange

as well as the potential value of these exchanges (Knoben 2008, 55)” (Cantù

2010, 888)

Technological proximity “is based on shared technological experiences and

knowledge bases, and increases the effectiveness of external learning

processes” (Enkel and Heil 2014, 244)

Proxies:

• Differences on technological classes of patents (Rosenkopf and Almeida 2003)

• Distance as the shortest path on the technological circle (Wersching 2007)

• Same/Different industry on different levels of Sic-code aggregation (Isaksson et al. 2016)

• Knowledge spillovers and research overlap (Fung 2003)

Personal

Proximity

“personal distance felt between individuals in an organization can prevent

knowledge transfer from occurring. Person-related distance can give rise to

faultlines in an organization (Bezrukova et al. 2009)” (Dolfsma and van der

Eijk 2016, 273)

“Caniëls, Kronenberg, and Werker (2014) use the concept of personal

proximity” (…) that “encompasses the degree of similarity in partners’

personal features, characteristics and behaviours.” (Capone and Lazzeretti

2018, 900) 1

Self-Assessed:

• Strong/weak working relationship with alters (Dolfsma and van der Eijk 2016, 279)

Functional

Proximity

“Functional proximity refers to physical distance affected by mobility. An

alternative conception associated with functional proximity is therefore

accessibility. It is hence not only bare Euclidean physical distance, but also

includes time and cost dimensions” (Moodysson and Jonsson 2007, 118)

“Functional distance (Maggioni and Uberti 2007) refers to differences

between regions in innovation performance. Maggioni and Uberti (2007)

showed that knowledge does not flow easily between areas if they differ

strongly in their innovation capacity. Consequently, a strong asymmetry in

performance and capability (that is, too much functional distance) will limit

the opportunities for mutual advantages of integration” (Lundquist and Trippl

2013, 453)

Proxy:

• Path overlap: overlap between a dyad’s functional zones (Kabo et al. 2014)

“To develop a truer measure of functional proximity, we must incorporate a

sense of how human behavior interacts with spatial layout to produce

proximity.” (Kabo et al. 2014, 1471)

Cultural

Proximity

“Cultural proximity is interrelated with institutional proximity and is

expressed by a common cultural background, which facilitates the

understanding of information and the establishment of norms of behavior

between innovative actors and researchers (Lundvall 1988, 355)” (Zeller

2004, 88)

“Cultural–ethnic (whether co-patenting inventors share the same national,

cultural, and/or ethnic background)” (Crescenzi et al. 2016, 178)

Proxy:

• Belonging or not to the same cultural, ethnic, or linguistic subgroup (Crescenzi et al. 2016)

• National score on the six cultural dimensions of Hofstede (Guann and Yann 2016)

Commercial

Proximity

“In an industrial context, the innovative contiguity between productive

sectors, wij, is often set equal to 1 if the intensity in their commercial

relationships is higher than the average. If we follow this idea, we can define

the proximity between regions from a commercial perspective. In this case

we can use the intensity of bilateral trade flows as the bilateral weights, wij,

to approximate the intensity of regional interdependences” (Cabrer-Borrás

and Serrano-Domingo,2007, 1363)

Proxy:

• Intensity of bilateral trade (Cabrer-Borrás and Serrano-Domingo 2007)

Relational

Proximity2

“Relational proximity is expressed by informal structures that reinforce or counteract the effects of the formal organization.

Knowledge, especially knowledge produced outside the firm, cannot be acquired, transferred, and transformed without continuing

personal relationships (Sierra 1997, 25). An innovative firm must participate in the localized social capital” (Zeller 2004, 88)

“The notion of relational proximity could be used as an umbrella term for a number of non-tangible dimensions discussed in the

literature” (…) and it “is associated with the structures, relations and processes that originate, for instance, from the social dynamics,

governance structures, regulation and cultural identities that together comprise the embeddedness of social action (Granovetter,

1985)” (Lundquist and Trippl 2013, 453)

Temporary

Relational

Proximity

“firms that engage in partnerships share a temporary relational proximity.

When two firms launch a partnership, they establish a non-disclosure

agreement for a specific period of time (Bathelt et al. 2004). When the

specific partnership is terminated, social strands are built between actors in

the firms” (Ramírez-Pasillas 2010, 160)

Proxy:

• Firms engaging in partnership during an exposition fair (Ramírez-Pasillas 2010)

1 For completeness, we added the contribution of Capone and Lazzeretti (2018) even though it is part of the future research topics and does not belong

to intellectual core articles;

2 For these proximities we have not displayed the column “operationalization”, since we haven’t found operationalizations in our intellectual core

articles

3.2 BCA – The Intellectual Core

This section gives the results of the BCA, and provides a short description for each of the seven

clusters. Figure 2 shows the coupling results:

Figure 2. Bibliographic Coupling of the 202 articles database (fractional, no lower limits, 8 clusters)

Virtual

Proximity2

“cyberspace is not a paraspace, a separate realm to geographic space, but forms part of an experiential continuum in people's lives'

(Dodge and Kitchin 2001). Virtual proximity may well be a surrogate for physical proximity in the context of standardized

transactions, but not in the context of transactions which are high in complexity, ambiguity and tacitness” (Morgan 2004, 5)

“Virtual proximity can be produced by using communication and information technologies. An MNC can create virtual proxmity

to substitute partially for spatial proximity for a period of time on the condition that it disposes of organizational, cultural, and

relational proximity among the members of its network, to allow real communication to be established (cf. Howells 1995)” (Zeller

2004, 88-89)

Network

Proximity2

“Spatial proximity describes positions in space and changes whenever actors move in space” (…) “Network proximity describes

the degree of separation and structural embedding between network positions. It changes when actors, even third actors, connect to

new nodes” (Menzel 2015, 1899)

Vision

Proximity2

“Moreover a deeper development of long-term relationships is influenced by the shared vision and gradual convergence of

objectives characterizing vision proximity. Meanwhile, an aggregation of organizations” (…) “cannot be limited by geographic

proximity, but rather requires technological and cognitive proximities as well as proximity of vision. The latter allows for long-

term relationships” (Cantù 2010, 896)

Socioeconomic

Proximity2

“Bouba-Olga and Grossetti (2008) suggest another typology based on more recent developments of sociological economy” (…)

“They further divide socioeconomic proximity in two sub-categories:(1) Resource-base proximity based on both material (objects,

tools . . .) and immaterial resources (information, knowledge, rules, norms . . .); (2) Coordination proximity which includes relational

proximity (social network dimension i.e. the closeness of actors) and mediation proximity (institutional dimension as previously

emphasized by Boschma, 2005)” (Crespin-Mazet et al. 2013, 1704)

“Beyond Geographical Proximity” (red) is the first cluster emerging from the BCA analysis

and it is composed of 47 articles. The main topic of this cluster concerns the determinants of

knowledge networks, thus changing the focus from basic co-location to other forms of proximity.

Giuliani (2007) challenged the view of a pervasive and random diffusion of knowledge solely through

spatial proximity, thus showing that knowledge networks do not randomly involve every industrial

cluster member, but only those firms with capabilities to transfer and absorb knowledge. Similarly,

Moodysson underlined that interactive knowledge creation arises from strategic and planned actions

guided by ‘practical issues like time economy and flexibility’ (2008, 460) and not only by local

embeddedness. Amin and Roberts (2008, 365) highlighted that ‘situated knowledge’ cannot be

reduced to a simple co-location, but it is the result of a ‘tangled assemblage’ of heterogeneous forms

of proximity. By viewing geographic proximity as a facilitator, the literature on social capital

underlined the impact of social interactions and networking on knowledge diffusion (Hauser et al.,

2007; Huggins and Johnston 2007; Molina-Morales and Martínez-Fernández 2010). Likewise, Mattes

(2012) highlighted both geographical and social proximity as auxiliary mechanisms, reinforcing the

effect of other forms of proximity. While being aware that permanent co-location is not necessary,

Torre (2008) introduced the concept of ‘temporary geographic proximity’, thus claiming that

short/medium term visits may be sufficient for sharing the required information.

The “Scouts” cluster (green) is composed of 45 articles that first attempted to conceptualize

the relationship among proximity dimensions and innovation. The shift from competition on price to

competition on knowledge creation (Maskell and Malmberg 1999), the increasing importance of the

institutional and productive environments of firms (Torre and Gilly 2000), and the paradoxical

significance of geography despite the rising of digitalization and globalization (Morgan 2004), are

the factors influencing the focus on closeness in the literature. ‘Because information diffuses rapidly

across organizational and territorial borders, it wrongly assumes that understanding does too’

(Morgan 2004, 3). Therefore, closeness should be intended in a broader sense, and thus should include

not only physical and socio-cultural factors (Maskell and Malberg 1999), but also work practices and

training cultures (Gertler 1995), the logics of belonging and similarity (Torre and Gilly 2000), and

institutional contexts (Kirat and Lung 1999). The work of Boschma (2005) represents a

comprehensive combination of all the attempts made to grasp the multidimensionality of proximity,

thus highlighting both positive and negative effects.

The “Knowledge Spillovers” cluster (blue) is composed of 37 articles examining the effect of

proximities on knowledge spillovers. The literature has extensively identified geographical proximity

as a channel able to foster knowledge spillovers (Ponds et al. 2009; Rodrìguez-Pose and Crescenzi

2008) and maintaining its positive effect over time (Sonn and Storper 2008). Moreover, spatial

proximity to research institutions and to industrial innovative activities, together with technological

capabilities, is able to determine the location choices of firms (Alcácer and Chung 2007). These

consider spillover gains that can be obtained by inward spillovers, net of the cost related to outward

spillovers. Social proximity can also act as a substitute for geographical proximity, and may enhance

spillovers among social or professional networks (Agrawal et al., 2008). Last, technological

proximity has been found to outperform geographical proximity in fostering knowledge spillovers

(MacGarvie 2004; Marrocu et al. 2013).

The “Proximity vs. Distance” (yellow) cluster consists of 27 articles that focus on the trade-

off between proximity and distance, and not only spatial, but also cognitive, organizational, and

technological. Whittington et al. (2009) showed that being central in a regional knowledge network

enhances firms’ access to tacit knowledge, while centrality in the global network avoids stacking

obsolete knowledge, thus facilitating both access to vital resources and pursuit of novelty. Similarly,

Guan and Yan (2016) showed that knowledge homogenization due to an excessive technological

proximity may hinder re-combinative innovation. Rosenkopf and Almeida (2003) focused on the

lock-in effect and showed that inventors’ mobility is an extremely useful mechanism for gaining new

knowledge from a distant context, and particularly at a high level of technological distance. However,

in cases of high technological distance among actors, geographic proximity may enable knowledge

transfer and consequently the spread of breakthrough innovation (Phene et al. 2006). For Ter Wal

(2014), geographical proximity is crucial in an early industrial stage, while in an established industrial

stage inventor collaboration is important, both at local and distant levels. However, to gain the

advantage from distant collaboration, actors should invest in the potential absorptive capacity, to

decrease excessive inter-organizational cognitive distance that otherwise would hinder knowledge

absorption (Enkell and Heil 2014).

The “Knowledge cycle” (purple) cluster consists of 14 articles that describe the drivers,

determinants, mechanisms, and barriers of knowledge creation, acquisition, transfer, and diffusion.

Across both internal (senior managers, satisfaction surveys, etc.) and external (suppliers, competitors,

etc.) sources, firms can obtain tacit and explicit knowledge (Weidenfeld et al. 2010), which can be

consequently adopted by using organizational resources and capabilities (Florida et al. 2001). Once

implemented, knowledge can be transferred to other actors through several mechanisms reinforced

by spatial proximity, such as executive mobility and labor mobility (Still and Strang 2009;

Weidenfeld et al. 2010); temporary face-to face interactions (Weidenfeld 2013); close interpersonal

relations and inductive learning (Gaba and Meyer 2008); and learning by imitation (Weidenfeld et al.

2010). From an evolutionary perspective, Maskell and Malmberg described the process of ‘social or

technical innovation, selection, and retention’ (2007, 613) leading to the establishment of routines

that, on one hand, may ‘economize on fact-finding and information processing’, but on the other hand

may generate ‘functional and/or spatial myopia’ (2007, 614). This cycle may lead to barriers to

knowledge creation and acquisition, thus bringing inertia and the decline of spatial clusters.

The “University” (sky blue) cluster consists of 14 articles that concern determinants and

effects of University-Industry collaboration. Although geographical proximity to a university has

been found to enhance firms’ decisions to collaborate, university research quality remains the most

crucial collaboration determinant (Laursen et al. 2011) for sustaining regional innovation (Fritsch and

Slavtchev 2007). Indeed, distance to the nearest university has no effect on collaboration decisions

(Hewitt-Dundas 2013), and firms with higher absorptive capacity were found to collaborate with

universities regardless of location (De Fuentes and Dutrénit 2016). Nevertheless, proximity to

universities may also affect firm creation (Bonaccorsi et al., 2013), product and process innovation

(Maietta 2015), the technological performance of firms (Leten et al. 2014), and it may foster tacit

knowledge flow (De Fuentes and Dutrénit 2016). Last, Steinmo and Rasmussen (2016) highlighted

that while collaboration among engineering-based firms and universities is nurtured by social and

geographic proximity, collaboration among science-based firms and universities relies on cognitive

and organizational proximity.

The “Face-to-Face” (orange) cluster consists of 11 articles. The main topic concerns how

spatial proximity among users/customers and providers/producers may affect innovation, and 6 out

of 11 articles based their empirical investigation on knowledge intensive business services (KIBS).

García-Quevedo and Mas-Verdú (2008) and Fernandes and Ferreira (2013) highlighted how

geographic proximity may foster collaboration among actors, especially with KIBS. Bindroo et al.

(2012) and Fernandes and Ferreira (2013) concluded that proximity among users and producers may

foster both collaboration and innovation. However, other authors have questioned whether spatial

proximity can foster innovation, and conclude that neither face-to-face interactions nor knowledge

spillovers are keystones of the geography of innovation (Doloreaux and Shearmur 2012). Indeed, the

crucial advantage is of the access that a location provides to both local and distant innovation input,

and the ease of maintaining the other types of proximity (Shearmur 2011). Weterings and Boshma

came to a similar conclusion: ‘spatial proximity to customers does not strengthen the effect of regular

face-to-face interactions on the innovative performance of software firms’ (2009, 753).

The results of BCA are summarized in Table 6.

Table 6. Main topics analyzed by BCA clusters

Cluster Name Short Description Years

Range

Journals (n of documents) Average

Citations

Beyond

Geographical

Proximity

Looking for determinants of

knowledge networks beyond

co-location

2005-2017

o Regional Studies (14) o Economic Geography (4) o Journal of Business

Research (4)

59

Scouts

Seeking to conceptualize the

Proximity and Innovation

relationship

1994-2014 o Regional Studies (9) o Research Policy (6) o Technovation (6)

156

Knowledge

Spillovers

Trying to capture the effect

of proximities on knowledge

spillovers

2000-2017 o Research Policy (7) o Journal of Economic

Geography (4) o Regional Studies (3)

43

Proximity vs

Distance

Investigating the trade-off

between proximity and

distance, not only spatial

2003-2017

o Research Policy (5) o Industry and Innovation

(3) o Strategic Management

Journal (2) o Technovation (2)

59

Knowledge cycle

Describing factors enabling

or not knowledge creation,

acquisition, transfer and

diffusion

2001-2016 o Journal of Industrial

Ecology (2) o Journal of Tourism

Research (2)

48

University

Describing determinants and

effects of University-

Industry collaboration

2006-2017 o Research Policy (3) o The Journal of

Technology Transfer (3)

30

Face-to-Face

Investigating how spatial

proximity among

users/customers and

providers/producers affect

innovation

1999-2015 o Research Policy (2) o Regional Studies (2)

32

3.3 CCA – The Intellectual Sub-Structure

This section displays the results of both CCA and BCA-CCA matrix. Figure 3 shows the intellectual

sub-structure evolution from 1990 to 2017:

Figure 3a. 1990-2001 (lower-bound 2 citations)

Figure 3b. 1990-2006 (lower-bound 2 citations)

Figure 3c. 1990-2011 (lower-bound 2 citations)

Figure 3d. 1990-2017 (lower-bound 2 citations)

The time analysis shows how CCA clusters were formed and the evolution of the intellectual sub-

structure. In line with Appio et al. (2017), we chose four time periods: 1990-2001, 1990-2006, 1990-

2011, and 1990-2017. Each area has a color that identifies the density of contributions, ranging from

blue (low-) to red (high-density), the font size concerns inter-database article citations, and the

proximity of articles reveals their similarity. Figure 3 shows that the study of Saxenian (1994)

emerges as a crucial contribution in all four phases. Other central contributions are those of Jaffe

(1989), Jaffe et al. (1993) and Audretsch and Feldman (1996), which increased their impact over the

four phases. Notably, Boschma’s (2005) work has the higher citation increase between 2011 and

2017. By comparing 1990-2001 to 1990-2017, it is clear that the relevance of several works have

diminished in terms of the definition of the intellectual sub-structure. Thus, to obtain a reliable and

effective understanding of this sub-structure, we decided to rely only on the core density area of 1990-

2017, thus leaving out the most peripheral contributions. Hence, by setting the lower-bound of CCA

to 3 citations we obtained 7 clusters, as shown in Figure 4.

Figure 4. 1990-2017, co-citation analysis (7 clusters, lower-bound 3 citations)

The “Locus of Innovation” (red) cluster. A firm alone cannot achieve the knowledge needed

to innovate, thus innovation should be sought across networks of learning (Powell et al. 1996).

However, a firm can learn from, assimilate, and apply new knowledge only if it has enough innovation

capability (absorptive capacity), which depends on its knowledge base (Cohen and Levinthal 1990):

the development of absorptive capacity is not only history- but also path-dependent. The flow of

information is also influenced by geographical propinquity, organizational form, embeddedness, and

weak ties (Granovetter 1973; Owen-Smith and Powell 2004; Uzzi 1997).

The “Seminal” (green) cluster includes fundamental contributions regarding innovation and

competitive advantage (Cohen and Levinthal 1989; Nelson and Winter 1982; Porter 1990; Von

Hippel 1988). Among these, several works investigate the relationships concerning innovation and

the spatial dimension of innovation (Camagni 1991; Lundvall 1992; Saxenian 1994), and in particular

this cluster is characterized by research highlighting positive correlations among the spatial

agglomeration of firms (at local, regional, and national levels) and their innovation capacity

(Audretsch 1998; Baptista and Swann 1998; Cooke et al. 1997).

The “Not only Local” (blue) cluster. Geographical proximity cannot be considered as the sole

dimension fostering learning and innovation (Boshma 2005). Indeed, tacit and codified knowledge

may be exchanged at both local and global levels (Bathelth et al. 2004). Therefore, this cluster

explored not only the new proximity dimensions that can affect inter-organizational learning (Boshma

2005; Knoben and Oerlemans 2006; Malberg and Maskell 2002; Ponds et al. 2007; Torre and Gilly

2000) but also firm-specific characteristics (Giuliani 2007).

The “Public Research & Knowledge Spillovers” (yellow) cluster. Here, innovation spills over

from universities to influence business actors and the creation of commercial innovation (Anselin and

Varga 1997; Jaffe 1989; Zucker et al. 1998). Geographic proximity can foster university-industry

collaboration (Audretsch and Stephan, 1996), so universities and businesses located in the same place

may share tacit knowledge, thus enhancing local economic development (Zucker et al. 1998).

Additionally, university research can nurture both new R&D projects and also the completion of

existing ones (Cohen at al. 2002).

The “Patent & Knowledge Spillovers” (purple) cluster. The main focus of this cluster is on

patents (Acs et al. 2002; Glaeser et al. 1992; Griliches, 1990; Jaffe, 1986; Jaffe et al. 1993) and R&D

(Audrestch and Feldman, 1996; Griliches, 1979). Knowledge externalities are fundamental in

industries where knowledge plays a crucial role, thus increasing spatial clustering (Audrestch and

Feldman, 1996). Indeed, knowledge spillovers are localized (Jaffe et al. 1993), but co-location alone

is not sufficient: to benefit from other firms’ R&D, actors should themselves invest in R&D (Jaffe,

1986).

The “Interactions and Knowledge” (sky blue) cluster. Face-to-face interactions, proximity,

and mobility are crucial factors in enabling knowledge flow (Torre and Rallet 2005). Indeed, (i) face-

to-face contact provides efficient communication, solves incentive problems, enhances socialization,

and provides psychological motivation (Storper and Venables 2004), and (ii) as the need for

geographical proximity is not permanent, mobility can enhance the share of information necessary

for cooperation (Torre and Rallet 2005). Interactions should not only be within an industrial cluster,

but also between clusters, fostering both related and unrelated knowledge flow and thus specialization

and diversification (Feldman and Audretsch 1999; Frenken et al. 2007; Maskell 2001). Hence, the

acquired synthetic and/or analytic knowledge base of firms influences the type of innovation

achievable, either incremental or radical (Asheim and Coenen 2005).

The “Structure, Actors and Innovation” (orange) cluster. The Triple Helix model underlines

the importance of university-industry-government relationships to foster the evolution of innovation

systems (Etzkowitz and Leydesdorff 2000). However, this should not be considered as a static model,

as the replacement of old resources or obsolete structures and the renewal of institutions are necessary

to achieve sustainable competitiveness (Maskell and Malmberg 1999). At a regional level, Morgan

(1997) highlighted the interdependence between public and private power, which can nurture or

hinder innovativeness. At a cluster level, Giuliani and Bell (2005) show that firms may play different

roles in cluster knowledge networks (gatekeepers, isolated, etc.), interacting differently with

institutions and public research actors, and influencing the cluster innovativeness and competitiveness

as a whole. Co-location with other innovative firms can enhance a firm’s likelihood for innovation

(Beaudry and Breschi 2003). At the sector level, the technological trajectories are dominated by three

different firms’ categories: suppliers, production intensive or science based (Pavit 1984).

As shown in Figure 5, the BCA-CCA matrix displays the relationship among BCA and CCA

clusters. In Figure 5 (up), the sky-blue arrows show the most cited CCA cluster (purple square) from

the BCA cluster (green circle), thus highlighting the intellectual sub-structure of each “proximity”

and “innovation” intellectual core cluster. For example, the “seminal” cluster represents the

theoretical foundation of the “scout” cluster. In Figure 4 (down), while the majority of BCA and CCA

clusters tend to fall in pairs, “Behind Geographical Proximity”, “Proximity vs. Distance”,

“Knowledge Cycle” (BCA), “Not only Local” and “Locus of Innovation” (CCA) are clustered

together (red), thus highlighting their complementarity in the debate regarding the space of

innovation.

Figure 5. BCA/CCA network representation

3.4 Further Research – The Future

The future research questions collected were categorized into four groups.

First Group refers to questions concerning essential aspects of proximity and innovation. The

multidisciplinary literature has expanded the boundaries of proximity research, thus diminishing both

clarity and unanimity regarding the nature of the numerous proximity dimensions (Capone and

Lazzeretti 2018). Therefore, future research should investigate the conceptual development of

proximity, thus enhancing the definitions that can capture the true meaning of each dimension

(Lazzeretti and Capone 2016; Steinmo and Rasmussen 2016). Furthermore, as personal bonds can

influence strategic, research and business collaborations, the concept of personal proximity must now

be considered in the proximity framework (Ooms et al. 2018). In addition, the synergic effect between

different types of proximity is still controversial (Marek et al. 2017), thus requiring further

investigation into the interactions among proximities (Fitjar et al. 2016; Presutti et al. 2017; Steinmo

and Rasmussen 2016). From the innovation perspective, many authors have underlined the necessity

to differentiate both the sources and types of innovation, to achieve a better understanding (Li et al.

2018; Geldes et al. 2017; Presutti et al. 2017; Pucci et al. 2017). In terms of the relationship between

proximity and innovation, the impact of actors’ technological distance on collaboration and

innovation should be assessed (Kudic at al. 2016; Pucci et al. 2017; Rodríguez et al. 2018). Moreover,

the ‘perspective that argues that contemporary information and communication technologies (ICTs)

provide a way to leapfrog the dominant role of proximity in innovation processes, creating a

complimentary rather than a substitution effect for more remote and peripheral places’, merits further

investigation’ (Baycan et al. 2017, 962). Similarly, future research should investigate the ability of

digital technology to foster knowledge flows (Forman and van Zeebroek 2018). Last, ‘more research

is needed to understand how innovation processes and collaborative dynamics unfold at the

intersection between virtual and physical spaces’ (Aslesen et al. 2018, 10).

Second Group refers to those questions concerning actors and relationships. In terms of actors,

recent interest has increased for multinational enterprises and their commitment to local innovation

dynamics (le Duc and Lindeque 2018). Similarly, researchers are questioning the role that anchor

firms may play in local level innovation productivity (Hamidi and Zandiatashbar 2018). For what

concerns actors’ characteristics, diversity on knowledge base and on contingency factors (such as

absorptive capacity) and their relationship with proximity and innovation should be further

investigated (Presutti et al. 2017; Rodríguez et al. 2018). Moreover, both organizational structures

and firms’ boundaries should be considered as factors influencing innovative collaboration and

knowledge diffusion (Crescenzi et al. 2016). On terms of relationships, there is a necessity to reveal,

deepen and identify knowledge-sharing instruments (Rodríguez et al. 2018), formal mechanism for

gathering knowledge spillovers from market stakeholders (Li et al. 2018), and patterns of knowledge

exchange between public and private research (Rizzo et al. 2018). The differential effect of intentional

and serendipitous knowledge flow relying on vertical and horizontal cognitive ties, has been recently

identified, thus calling for further research (Teixeira and Oliveira 2017). Additionally, both formal

and informal relationships should be investigated and compared to understand the relational dynamics

of networks (Capone and Lazzeretti 2018). Last, further research on the role of trust and of other

social capital dimensions in local environments is required (Nestle et al., 2018; Nugroho, 2018).

Third Group refers to various challenging and promising questions. Further research should

(i) include the nature, scope and scale of collaborative agreements (Drejer and Østergaard 2017), (ii)

understand the reason why actors cooperate (Crescenzi et al. 2016), and (iii) analyze partner selection

decisions (Gattringer et al. 2017). For researchers to take multifaceted and dynamic perspectives, they

should examine the influence power constellations have on the impact of proximity dimensions on

collaborations and innovation (Hansen and Mattes 2017). Additionally, investigations into spatial

ambidexterity, which is the technological exploration and exploitation pursued by firms in local

environments, should be further extended to include non-technological activities (Geerts et al. 2018).

From a managerial perspective, the combination of management of proximities and knowledge bases

may generate a new organizational capability, which is particularly valuable in a multi-locational

company (Davids and Frenken 2018).

Fourth Group concerns technical or methodological issues examined in the literature. Many

authors have questioned the suitability of using patents to measure technological capabilities or

innovation (Aharonson and Schilling 2016; Liang and Liu 2018). Furthermore, both network and

longitudinal analysis are required to understand the evolution of relationships (Leszczyńska and

Khachlouf 2018; Romero 2018). Qualitative research can also address the challenge of understanding

the reasons behind knowledge exchange and inter-firm collaborations (Jespersen et al. 2017; Teirlink

2018).

4. Conclusion and Limitations

This research demonstrates an increase in the proximity literature over the last decade, both in terms

of numbers of contributions and issues covered. The contribution of Boschma (2005) was found to

be fundamental to analytically summarizing previous research intuitions and findings, thus providing

the literature with a structured framework comprising the pros and cons of each highlighted proximity

dimension. The literature then entered a second phase of exploration towards new dimensional

conceptualizations. However, as previous research has found (e.g.: Capone and Lazzeretti 2018;

Lazzeretti and Capone 2016), this development increases the ambiguity and divergence in the

definitions of the nature of proximities, and the empirical results are often conflicting. As the 17

dimensions described in Table 5 show, the path of the concept of proximity has become dispersed.

Additionally, as previously noted by Knoben and Oerlemans (2006), several proximity definitions

overlap (e.g.: cognitive-technological, institutional-cultural, institutional-organizational, vision-

organizational, etc.). This ambiguity and the fragmentation of proximity dimensions may become

detrimental. Indeed, if we break up the concept of proximity into many subdimensions, we risk

considering proximity as a discontinuous phenomenon, thus losing the aggregate dimension. We are

thus in danger of creating an ineffective concept of proximity. Thus, as future research suggests, it is

necessary to reach an agreed conceptualization of proximity. This work represents a first contribution

towards this goal, thus offering the following inputs to those readers looking for the red thread linking

proximity to innovation:

1. The description of the intellectual core clusters allows to detect not only themes, but

those prominent journals to monitor, thus decreasing the complexity faced by old and new researchers

dealing with proximity and innovation.

2. The explanation of the sub-structure and the BCA-CCA matrix enables us to place

each intellectual core cluster in a broader literature picture, thus suggesting from where, how and why

these themes have been originated.

3. The detection of recent literature progress offers an up to date list of modern

challenges guided by the strengthening of globalization forces, new technological paradigms, new

sources of competitive advantage, and new ways in which actors interact.

This work presents some limitations. As in any bibliometric analysis, our results are influenced by

the online abstract and citation database selected, the key words searched, the subject areas selected,

and the journal rankings used for the detection of the most influential journals. The description of the

bibliometric findings is also the result of the subjective understanding of authors, and thus liable to

bounded rationality. Additionally, future research indications are collected from articles and

conference proceedings published in the last three years. Nevertheless, we recognize that some major

future research findings may have emerged in previous years, and thus we hope future research will

accomplish a more inclusive investigation in this direction. Furthermore, we have not included

contributions from journals that mainly deal with geography, so we hope that this gap will be filled

by further contributions in this area (e.g.: Yu and Jun 2018).

References

Acs, Z.J., Anselin, L., Varga, A., 2002. Patents and innovation counts as measures of regional

production of new knowledge. Research Policy 31(7), 1069–1085.

Agrawal, A., Kapur, D., McHale, J., 2008. How do spatial and social proximity influence

knowledge flows? Evidence from patent data. Journal of Urban Economics 64(2), 258–269.

Ahlfeldt, G.M., Wendland, N., 2013. How polycentric is a monocentric city? Centers,

spillovers and hysteresis. Journal of Economic Geography 13, 53–83.

Aharonson, B.S., Schilling, M.A., 2016. Mapping the technological landscape: Measuring

technology distance, technological footprints, and technology evolution. Research Policy 45(1), 81–

96.

Alcácer, J., Chung, W., 2007. Location Strategies and Knowledge Spillovers. Management

Science 53(5), 760–776.

Amin, A., Roberts, J., 2008. Knowing in action: Beyond communities of practice. Research

Policy 37, 353–369.

Andersson, M., Lööf, H., 2012. Small business innovation: Firm level evidence from Sweden.

The Journal of Technology Transfer 37, 732–754.

Anselin, L., Varga, A., Acs, Z., 1997. Local Geographic Spillovers between University

Research and High Technology Innovations. Journal of Urban Economics 42(3), 422–448.

Appio, F.P., Martini, A., Massa, S., Testa, S., 2017. Collaborative network of firms:

antecedents and state-of-the-art properties. International Journal of Production Research 55(7), 2121-

2134.

Asheim, B.T., Coenen, L., 2005. Knowledge bases and regional innovation systems:

Comparing Nordic clusters. Research Policy 34(8), 1173–1190.

Aslesen, H.W., Martin, R., Sardo, S., 2018. The virtual is reality! On physical and virtual

space in software firms’ knowledge formation. Entrepreneurship and Regional Development.

Audretsch, D. (1998). Agglomeration and the location of innovative activity. Oxford Review

of Economic Policy 14(2), 18-29.

Audretsch, D.B., Feldman, M.P. 1996. Innovative clusters and the industry life cycle. Review

of Industrial Organization 11(2), 253–273.

Audretsch, D.B., Stephan, P.E., 1996. Company-scientist locational links: the case of

biotechnology. American Economic Review 86(3), 641-652.

Baptista, R., Swann, P., 1998. Do firms in clusters innovate more? Research Policy 27(5),

525-540.

Bathelt, H., Malmberg, A., Maskell, P., 2004. Clusters and knowledge: Local buzz, global

pipelines and the process of knowledge creation. Progress in Human Geography 28(1), 31–56.

Baycan, T., Nijkamp, P., Stough, R., 2017. Spatial Spillovers Revisited: Innovation, Human

Capital and Local Dynamics. International Journal of Urban Regional Research 41, 962–975.

Beaudry, C., Breschi, S., 2003. Are firms in clusters really more innovative? Economics of

Innovation and New Technology 12(4), 325–342.

Ben Letaifa, S., Rabeau, Y., 2013. Too close to collaborate? How geographic proximity could

impede entrepreneurship and innovation. Journal of Business Research 66(10), 2071–2078.

Bezrukova, K., Jehn, K.A., Zanutto, E.L., Thatcher, S.M.B., 2009. Do workgroup faultlines

help or hurt? Organization Science 20(1), 35–50.

Bindroo, V., Mariadoss, B.J., Pillai, R.G., 2012. Customer Clusters as Sources of Innovation-

Based Competitive Advantage. Journal of International Marketing 20(3), 17–33.

Blanc, H., Sierra, C., 1999. The internationalisation of R&D by multinationals: A trade-off

between external and internal proximity. Cambridge Journal of Economics 23(2), 187–206.

Bonaccorsi, A., Colombo, M.G., Guerini, M., Rossi-Lamastra, C., 2013. University

specialization and new firm creation across industries. Small Business Economics 41(4), 837–863.

Boschma, R., 2005. Proximity and Innovation: A Critical Assessment. Regional Studies

39(1), 61-74.

Bouba-Olga, O., Grossetti, M., 2008. Socio-economie de proximite. Revue d’Economie

Regionale et Urbaine 3, 311-328.

Broström, A., 2010. Working with distant researchers - Distance and content in university-

industry interaction. Research Policy 39(10), 1311–1320.

Cabrer-Borrás, B., Serrano-Domingo, G., 2007. Innovation and R&D spillover effects in

Spanish regions: A spatial approach. Research Policy 36, 1357–1371.

Camagni, R., 1991. Innovation networks: spatial perspectives. Belhaven Press, London And

New York.

Caniëls, M. C. J., Kronenberg, K., Werker, C. (2014). Conceptualizing Proximity in Research

Collaborations between Universities and Firms, In: Rutten, R, Benneworth, P., Irawati, D., Boekema,

F. (Eds), The social dynamics of innovation networks. Routledge, pp. 221–238.

Cantù, C., 2010. Exploring the role of spatial relationships to transform knowledge in a

business idea - Beyond a geographic proximity. Industrial Marketing Management 39(6), 887–897.

Capone, F., Lazzeretti, L., 2018. The different roles of proximity in multiple informal network

relationships: evidence from the cluster of high technology applied to cultural goods in Tuscany.

Industry and Innovation 25(9), 897-917.

Cohen, W.M., Levinthal, D.A., 1989. Innovation and learning: the two faces of R&D.

Economic Journal 99, 569-596.

Cohen, W.M., Levinthal, D., 1990. Absorptive Capacity: A New Perspective on Learning and

Innovation. Administrative Science Quarterly, 35(1), 128-152.

Cohen, W.M., Nelson, R.R., Walsh, J.P., 2002. Links and impacts: the influence of public

research on industrial R&D. Management Science 48(1), 1–23.

Cooke, P., Uranga, M.G., Etxebarria, G., 1997. Regional innovation systems: institutional and

organisational dimensions. Research Policy 26(4-5), 475-491.

Crescenzi, R., Filippetti, A., Iammarino, S., 2017. Academic inventors: collaboration and

proximity with industry. The Journal of Technology Transfer 42(4), 730–762.

Crescenzi, R., Nathan, M., Rodríguez-Pose, A., 2016. Do inventors talk to strangers? on

proximity and collaborative knowledge creation. Research Policy 45, 177–194.

Crespin-Mazet, F., Goglio-Primard, K., François, S., 2013. Open innovation processes within

clusters – the role of tertius iugens. Management Decision 5(8), 1701-1715,

Dangelico, R.M., Garavelli, A.C., Petruzzelli, A.M., 2010. A system dynamics model to

analyze technology districts’ evolution in a knowledge-based perspective. Technovation 30, 142–

153.

Davids, M., Frenken, K., 2018. Proximity, knowledge base and the innovation process:

towards an integrated framework. Regional Studies 52(1), 23–34.

De Fuentes, C., Dutrénit, G., 2016. Geographic proximity and university–industry interaction:

the case of Mexico. The Journal of Technology Transfer 41(2), 329–348.

Divella, M., 2017. Cooperation linkages and technological capabilities development across

firms. Regional Studies 51, 1494–1506.

Dodge, M., Kitchin, R., 2001. Mapping Cyberspace. Routledge, London.

Dolfsma, W., van der Eijk, R., 2016. Distances in Organizations: Innovation in an R and D

Lab. British Journal of Management 27(2), 271–286.

Doloreux, D., Shearmur, R., 2012. Collaboration, information and the geography of

innovation in knowledge intensive business services. Journal of Economic Geography 12(1), 79–105.

Drejer, I., Østergaard, C.R., 2017. Exploring determinants of firms’ collaboration with

specific universities: employee-driven relations and geographical proximity. Regional Studies 51(8),

1192–1205.

Enkel, E., Heil, S., 2014. Preparing for distant collaboration: Antecedents to potential

absorptive capacity in cross-industry innovation. Technovation 34(4), 242–260.

Etzkowitz, H., Leydesdorff, L., 2000. The dynamics of innovation: from national systems and

‘‘mode 2’’ to a triple helix of university–industry–government relations. Research Policy 29(2), 109-

123.

Feldman, M.P., Audretsch, D., 1999. Innovation in cities: Science-based diversity,

specialization and localized competition. European Economic Review 43(2), 409–429.

Fernandes, C.I., Ferreira, J.J.M., 2013. Knowledge spillovers: Cooperation between

universities and KIBS. R&D Management 43(5), 461–472.

Fitjar, R.D., Huber, F., Rodríguez-Pose, A., 2016. Not too close, not too far: testing the

Goldilocks principle of ‘optimal’ distance in innovation networks. Industry and Innovation 23(6),

465–487.

Florida, R., Atlas, M., Cline, M., 2001. What Makes Companies Green? Organizational and

Geographic Factors in the Adoption of Environmental Practices. Economic Geography 77(3), 209-

224.

Forman, C., van Zeebroeck, N., 2018. Digital technology adoption and knowledge flows

within firms: Can the Internet overcome geographic and technological distance? Research Policy, In

Press.

Freel, M.S., 2003. Sectoral patterns of small firm innovation, networking and proximity.

Research Policy 32(5), 751-770.

Frenken, K., van Oort, F., Verburg, T. N., 2007. Related variety, unrelated variety and

regional economic growth. Regional Studies 41(5), 685-697.

Fritsch, M., Slavtchev, V., 2007. Universities and innovation in space. Industry and

Innovation 14(2), 201–218.

Fung, M.K., 2003. Technological proximity and co-movements of stock returns. Economic

Letters 79(1), 131–136.

Funk, R.J., 2014. Making the most of where you are: Geography, networks, and innovation

in organizations. Academy of Management Journal 57(1), 193–222.

Gaba, V., Meyer, A.D., 2008. Crossing the Organizational Species Barrier: How Venture

Capital Practices Infiltrated the Information Technology Sector. Academy of Management Journal

51(5), 976-998.

Gattringer, R., Wiener, M., Strehl, F., 2017. The challenge of partner selection in collaborative

foresight projects. Technological Forecasting and Social Change 120, 298–310.

García-Quevedo, J., Mas-Verdú, F., 2008. Does only size matter in the use of knowledge

intensive services? Small Business Economics 31(2), 137–146.

Geerts, A., Leten, B., Belderbos, R., Van Looy, B., 2018. Does spatial ambidexterity pay off?

On the benefits of geographic proximity between technology exploitation and exploration. Journal of

Product Innovation and Management 35(2), 151-163.

Geldes, C., Heredia, J., Felzensztein, C., Mora, M., 2017. Proximity as determinant of

business cooperation for technological and non-technological innovations: a study of an agribusiness

cluster. Journal of Business & Industrial Marketing 32(1), 167-178.

Gertler, M.S., 1995. “Being There”: Proximity, organization, and culture in the development

and adoption of advanced manufacturing technologies. Economic Geography 71(1), 1-26.

Giuliani, E., 2007. The selective nature of knowledge networks in clusters: Evidence from the

wine industry. Journal of Economic Geography 7(2), 139–168.

Giuliani, E., Bell, M., 2005. The micro-determinants of meso-level learning and innovation:

Evidence from a Chilean wine cluster. Research Policy 34(1), 47–68.

Gittelman, M., 2007. Does Geography Matter for Science-Based Firms? Epistemic

Communities and the Geography of Research and Patenting in Biotechnology. Organization Science

18(4), 724-741.

Glaeser, E., Kallal, H., Scheinkman, J., Shleifer, A.,1992. Growth in Cities. Journal of

Political Economy 100(6), 1126-1152.

Granovetter, M., 1973. The Strength of Weak Ties. American Journal of Sociology 78(6),

1360-1380.

Granovetter, M., 1985. Economic action and social structure: the problem of embeddedness.

American Journal of Sociology 91, 481–510.

Griliches, Z., 1979. Issues in Assessing the Contribution of Research and Development to

Productivity Growth. Bell Journal of Economics 10(1), 92-116.

Griliches, Z., 1990. Patent Statistics as Economic Indicators: A Survey. Journal of Economic

Literature 28(4), 1661-1707.

Guan, J.C., Yan, Y., 2016. Technological proximity and recombinative innovation in the

alternative energy field. Research Policy 45(7), 1460–1473.

Hamidi, S., Zandiatashbar, A., 2018. Does urban form matter for innovation productivity? A

national multi-level study of the association between neighbourhood innovation capacity and urban

sprawl. Urban Studies.

Hansen, T., 2015. Substitution or Overlap? The Relations between Geographical and Non-

spatial Proximity Dimensions in Collaborative Innovation Projects. Regional Studies 49(10), 1672-

1684.

Hansen, T., Mattes, J. 2017. Proximity and power in collaborative innovation projects,

Regional Studies 52(1), 35-46.

Hauser, C., Tappeiner, G., Walde, J. (2007). The Learning Region: Impact of Social Capital

and Weak Ties on Innovation. Regional Studies 41(1), 75-88.

Healy, A., Morgan, K., 2012. Spaces of Innovation: Learning, Proximity and the Ecological

Turn. Regional Studies 46(8), 1041–1053.

Herrmann, A.M., Taks, J.L., Moors, E., 2012. Beyond Regional Clusters: On the Importance

of Geographical Proximity for R&D Collaborations in a Global Economy-the Case of the Flemish

Biotech Sector. Industry and Innovation 19(6), 499–516.

Hewitt-Dundas, N., 2013. The role of proximity in university-business cooperation for

innovation. The Journal of Technology Transfer 38(2), 93–115.

Hoegl, M., Proserpio, L., 2004. Team member proximity and teamwork in innovative projects.

Research Policy 33(8), 1153–1165.

Howells, J. R., 1995. Going global: The use of ICT networks in research and development.

Research Policy 24(2), 169–84.

Huggins, R., Johnston, A., 2010. Knowledge flow and inter-firm networks: The influence of

network resources, spatial proximity and firm size. Entrepreneurship and Regional Development

22(5), 457–484.

Inkpen, A., Tsang, E., 2005. Social capital, networks, and knowledge transfer. Academy of

Management Review 30(1), 146–165.

Isaksson, O.H.D., Simeth, M., Seifert, R.W., 2016. Knowledge spillovers in the supply chain:

Evidence from the high tech sectors. Research Policy 45(3), 699–706.

Jaffe, A., 1986. Technological Opportunity and Spillovers of R & D: Evidence from Firms'

Patents, Profits, and Market Value. American Economic Review 76(5), 984-1001.

Jaffe, A.B., 1989. Real Effects of Academic Research. American Economic Review 79(5),

957-970.

Jaffe, A.B., Trajtenberg, M., Henderson, R., 1993. Geographic localisation of knowledge

spillovers as evidenced by patent citations. Quarterly Journal of Economics 108(3), 577-598.

Jespersen, K., Rigamonti, D., Jensen, M.B., Bysted, R., 2017. Analysis of SMEs partner

proximity preferences for process innovation. Small Business Economics 51(4), 879-904.

Kabo, F.W., Cotton-Nessler, N., Hwang, Y., Levenstein, M.C., Owen-Smith, J., 2014.

Proximity effects on the dynamics and outcomes of scientific collaborations. Research Policy 43(9),

1469–1485.

Kirat, T., Lung, Y., 1999. Innovation and proximity. Territories as loci of collective learning

processes. European Urban and Regional Studies 6 (1), 27–38.

Knoben, J., 2008. Firm mobility and organizational networks. Edward Elgar, Cheltenham.

Knoben, J., Oerlemans, L. A. G., 2006. Proximity and inter-organizational collaboration: A

literature review. International Journal of Management Reviews 8(2), 71-89.

Kudic, M., Pyka, A., Sunder, M., 2016. The formation of R&D cooperation ties: An event

history analysis for German laser source manufacturers. Industrial and Corporate Change 25(4), 649–

670.

Laursen, K., Reichstein, T., Salter, A., 2011. Exploring the Effect of Geographical Proximity

and University Quality on University–Industry Collaboration in the United Kingdom. Regional

Studies 45(4), 507–523.

Lazzeretti, L., Capone, F., 2016. How proximity matters in innovation networks dynamics

along the cluster evolution. A study of the high technology applied to cultural goods. Journal of

Business Research 69, 5855–5865.

Lazzeretti, L., Sedita, S.R., Caloffi, A., 2013. Founders and disseminators of cluster research.

Journal of Economic Geography 14(1), 21-43.

le Duc, N., Lindeque, J., 2018. Proximity and multinational enterprise co-location in clusters:

a multiple case study of Dutch science parks. Industry and Innovation 25(3), 282–307.

Leszczyńska, D., Khachlouf, N., 2018. How proximity matters in interactive learning and

innovation: a study of the Venetian glass industry. Industry and Innovation 25(9), 874-896.

Leten, B., Landoni, P., Van Looy, B., 2014. Science or graduates: How do firms benefit from

the proximity of universities? Research Policy 43(8), 1398–1412.

Li, J., Xia, J., Zajac, E.J., 2017. On the duality of political and economic stakeholder influence

on firm innovation performance: theory and evidence from Chinese firms. Strategic Management

Journal 39, 193-216.

Liang, X., Liu, A.M.M., 2018. The evolution of government sponsored collaboration network

and its impact on innovation: A bibliometric analysis in the Chinese solar PV sector. Research Policy

47, 1295–1308.

Luintel, K.B., Khan, M., 2017. Ideas production and international knowledge spillovers:

Digging deeper into emerging countries. Research Policy 46, 1738–1754.

Lundquist, K.J., Trippl, M., 2013. Distance, Proximity and Types of Cross-border Innovation

Systems: A Conceptual Analysis. Regional Studies 47(3), 450-460.

Lundvall, B.-Å, (1988). Innovation as an interactive process: From user-producer interaction

to the national system of innovation, in: Dosi,G., Freeman, C., Nelson, R., Silverberg, G., Soete, L.

(Eds.), Technical change and economic theory. Pinter, London, pp. 349–369.

Lundvall, B.-Å., 1992. National systems of innovation: towards a theory of innovation and

interactive learning. Pinter, London.

MacGarvie, M., 2005. The determinants of international knowledge diffusion as measured by

patent citations. Economic Letters 87(1), 121–126.

Maggioni, M., Uberti, E., (2007). Inter-regional knowledge flows in Europe: an econometric

analysis, in: Frenken, K. (Ed.), Applied Evolutionary Economics and Economic Geography. Edward

Elgar, Cheltenham, pp. 230–255.

Maietta, O.W., 2015. Determinants of university-firm R&D collaboration and its impact on

innovation: A perspective from a low-tech industry. Research Policy 44(7), 1341–1359.

Malmberg, A., Maskell, P., 2002. The elusive concept of localization economies: Towards a

knowledge-based theory of spatial clustering. Environment and Planning A: Economy and Space

34(3), 429–449.

Marek, P., Titze, M., Fuhrmeister, C., Blum, U., 2017. R&D collaborations and the role of

proximity. Regional Studies 51(12), 1761–1773.

Marrocu, E., Paci, R., Usai, S., 2013. Proximity, networking and knowledge production in

Europe: What lessons for innovation policy? Technological Forecasting and Social Change 80(8),

1484-1498.

Marshall, A., (1920) Principles of economics. Macmillan, London.

Maskell, P., 2001. Geographical Cluster. Industrial and Corporate Change 10(1), 921–943.

Maskell, P., Malmberg, A., 1999. Localised learning and industrial competitiveness.

Cambridge Journal of Economics 23, 167–185.

Maskell, P., Malmberg, A., 2007. Myopia, knowledge development and cluster evolution.

Journal of Economic Geography 7(5), 603–618.

Mattes, J., 2012. Dimensions of Proximity and Knowledge Bases: Innovation between Spatial

and Non-spatial Factors. Regional Studies 46(8), 1085–1099.

Menzel, M.P., 2015. Interrelating Dynamic Proximities by Bridging, Reducing and Producing

Distances. Regional Studies 49(11), 1892–1907.

Molina-Morales, F.X., García-Villaverde, P.M., Parra-Requena, G., 2014. Geographical and

cognitive proximity effects on innovation performance in SMEs: A way through knowledge

acquisition. International Entrepreneurship and Management Journal 10(2), 231–251.

Molina-Morales, F.X., Martínez-Fernández, M.T., 2010. Social Networks: Effects of Social

Capital on Firm Innovation. Journal of Small Business Management 48(2), 258–279.

Moodysson, J., 2008. Principles and Practices of Knowledge Creation: On the Organization

of “Buzz” and “Pipelines” in Life Science Communities. Economic Geography 84(4), 449-469.

Moodysson, J., Jonsson, O., 2007. Knowledge collaboration and proximity: the spatial

organization of biotech innovation projects. European Urban and Regional Studies 14(2), 115-131.

Morgan, K., 1997. The learning region: institutions, innovation and regional renewal.

Regional Studies 31(5), 491-503.

Morgan, K., 2004. The exaggerated death of geography: Learning, proximity and territorial

innovation systems. Journal of Economic Geography 4(1), 3-21.

Nelson, R.R., Winter, S.G., 1982. An evolutionary theory of economic change. Harvard

University Press, Cambridge, Mass.

Nestle, V., Täube, F.A., Heidenreich, S., Bogers, M., 2018. Establishing open innovation

culture in cluster initiatives: The role of trust and information asymmetry. Technology Forecasting

and Social Change, In Press.

Nugroho, P., 2018. A Socio-spatial Dimension of Local Creative Industry Development in

Semarang and Kudus Batik Clusters. IOP Conference Series: Earth and Environmental Science.

Oerlemans, L.A., Meeus, M., 2005. Do organizational and spatial proximity impact on firm

performance? Regional Studies 39(1), 89–104.

Ooms, W., Werker, C., Caniëls, M., 2018. Personal and social proximity empowering

collaborations: the glue of knowledge networks. Industry and Innovation 25(9), 833–840.

Owen-Smith, J., Powell, W.W., 2004. Knowledge Networks as Channels and Conduits: The

Effects of Spillovers in the Boston Biotechnology Community. Organzational Science 15(1), 5–21.

Ozer, M., Zhang, W., 2015. The effects of geographic and network ties on exploitative and

exploratory product innovation. Strategic Management Journal 36, 1105-1114.

Pavitt, K., 1984. Sectoral patterns of technical change: towards a taxonomy and a theory.

Research Policy 13(6). 343-373.

Phene, A., Fladmoe-Lindquist, K., Marsh, L., 2006. Breakthrough innovations in the U.S.

biotechnology industry: The effects of technological space and geographic origin. Strategic

Management Journal 27(4), 369–388.

Ponds, R., van Oort, F., Frenken, K., 2009. Innovation, spillovers and university–industry

collaboration: an extended knowledge production function approach. Journal of Economic

Geography 10(2), 231-255.

Porter, M.E., 1990. The competitive advantage of nations. Macmillan, London.

Powell, W., Koput, K., Smith-Doerr, L., 1996. Interorganizational Collaboration and the

Locus of Innovation: Networks of Learning in Biotechnology. Administrative Science Quarterly,

41(1), 116-145.

Presutti, M., Boari, C., Majocchi, A., Molina-Morales, X., 2017. Distance to Customers,

Absorptive Capacity, and Innovation in High-Tech Firms: The Dark Face of Geographical Proximity.

Journal of Small Business Management.

Pucci, T., Brumana, M., Minola, T., Zanni, L., 2017. Social capital and innovation in a life

science cluster: the role of proximity and family involvement. The Journal of Technology Transfer,

1–23.

Ramírez-Pasillas, M., 2010. International trade fairs as amplifiers of permanent and temporary

proximities in clusters. Entrepreneurship and Regional Development 22(2), 155-187.

Rizzo, U., Barbieri, N., Ramaciotti, L., Iannantuono, D., 2018. The division of labour between

academia and industry for the generation of radical inventions. The Journal of Technology Transfer,

1-21.

Rodríguez, A., Nieto, M.J., Santamaría, L., 2018. International collaboration and innovation

in professional and technological knowledge-intensive services, Industry and Innovation, 25(4), 408-

431.

Rodríguez-Pose, A., Crescenzi, R., 2008. Research and development, spillovers, innovation

systems, and the genesis of regional growth in Europe. Regional Studies 42(1), 51-67.

Romero, C.C., 2018. Personal and business networks within Chilean biotech. Industry and

Innovation 25(9), 841-873.

Romijn, H., Albaladejo, M., 2002. Determinants of innovation capability in small electronics

and software firms in southeast England. Research Policy 31(7), 1053–1067.

Rosenkopf, L., Almeida, P., 2003. Overcoming Local Search Through Alliances and

Mobility. Management Science 49(6), 751–766.

Rutten, R., 2017. Beyond proximities: The socio-spatial dynamics of knowledge creation.

Progress in Humam Geography 41 (2), 159–177.

Saxenian, A., (1994) Regional advantage: culture and competition in Silicon Valley and route

128, Harvard University Press, Cambridge, Ma.

Shearmur, R., 2011. Innovation, Regions and Proximity: From Neo-Regionalism to Spatial

Analysis. Regional Studies 45(9), 1225–1243.

Sierra, C., 1997. Proximité(s), interactions technologiques et territoriales: Une revue. Revue

d’Économie Industrielle 82, 7–38.

Sonn, J.W., Storper, M., 2008. The increasing importance of geographical proximity in

knowledge production: An analysis of US patent citations, 1975-1997. Environt and Planning A:

Economy and Space 40(5), 1020–1039.

Steinmo, M., Rasmussen, E., 2016. How firms collaborate with public research organizations:

The evolution of proximity dimensions in successful innovation projects. Journal of Business

Research 69(3), 1250–1259.

Still, M., Strang, D., 2009. Who Does an Elite Organization Emulate? Administrative Science

Quarterly 54(1), 58-89.

Storper, M., Venables, A.J., 2004. Buzz: Face-to-face contact and the urban economy. Journal

of Economic Geography 4(1), 351–370.

Ter Wal, A.L.J., 2014. The dynamics of the inventor network in German biotechnology:

Geographic proximity versus triadic closure. Journal of Economic Geography 14(3), 589–620.

Teixeira E., Oliveira, M., 2017. The cognitive structure of clusters: Conceptualizing the

knowledge-based cluster theory. Proceedings of the European Conference on Knowledge

Management, ECKM.

Teirlinck, P., 2018. Pathways for knowledge exchange in SMEs in software-driven

knowledge-intensive business services. R&D Management 48(3), 343–353.

Torre, A., 2008. On the role played by temporary geographical proximity in knowledge

transmission. Regional Studies 42(6), 869–889.

Torre, A., Gilly, J.P., 2000. On the analytical dimension of proximity dynamics. Regional

Studies 34(2), 169-180.

Torre, A., Rallet, A., 2005. Proximity and localization. Regional Studies 39, 47–59.

Uzzi, B., 1997. Social Structure and Competition in Interfirm Networks: The Paradox of

Embeddedness. Administrative Science Quarterly 42(1), 35-67.

Van Eck, N.J., Waltman, L., 2014. Visualizing bibliometric networks. In: Ding, Y., Rousseau,

R., Wolfram, D. (Eds.), Measuring scholarly impact: Methods and practice. Springer, pp. 285–320.

Vaz, E., de Noronha Vaz, T., Galindo, P.V., Nijkamp, P., 2014. Modelling innovation support

systems for regional development-analysis of cluster structures in innovation in Portugal.

Entrepreneurship and Regional Development 26(1-2), pp. 23-46.

Von Hippel, E., 1988. The sources of innovation. Oxford University Press, New York.

Weidenfeld, A., 2013. Tourism and cross border regional innovation systems. Annals of

Tourism Research 42, 191–213.

Weidenfeld, A., Williams, A.M., Butler, R.W., 2010. Knowledge transfer and innovation

among attractions. Annals of Tourism Research 37(3), 604–626.

Wersching, K., 2007. Agglomeration in an innovative and differentiated industry with

heterogeneous knowledge spillovers. Journal of Economic Interaction and Coordination 2(1), 1–25.

Weterings, A., Boschma, R., 2009. Does spatial proximity to customers matter for innovative

performance?: Evidence from the Dutch software sector. Research Policy 38(5), 746–755.

Weterings, A., Ponds, R., 2009. Do regional and non-regional knowledge flows differ? An

empirical study on clustered firms in the Dutch life sciences and computing services industry.

Industry and Innovation 16(1), 11–31.

Whittington, K., Owen-Smith, J., Powell, W., 2009. Networks, Propinquity, and Innovation

in Knowledge-Intensive Industries. Administrative Science Quarterly 54(1), 90-122.

Wuyts, S., Colomb, M. G., Dutta, S., Nooteboom, B., 2005. Empirical tests of optimal

cognitive distance. Journal of Economic Behavior and Organization 58, 277–302.

Yu, Z., Jun, Z., 2018. Visualization Analysis of Regional Innovation Network from the

Innovative Geographical Perspective. ACM International Conference Proceeding Series, Guilin,

China 19 – 20 May 2018.

Zeller, C., 2004. North Atlantic innovative relations of Swiss pharmaceuticals and the

proximities with regional biotech arenas. Economic Geography 80(1), 83–111.

Zucker, L.G., Darby, M.R., Armstrong, J., 1998. Geographically localized knowledge:

spillovers or markets? Economic Inquiry 36(1), 65-86.

Zucker, L.G., Darby, M.R., Brewer, M.B., 1998. Intellectual human capital and the birth of

u.s. biotechnology enterprises. American Economic Review 88(1), 290-306.

Zukin, S., DiMaggio, P. (Eds), 1990. The Social Organization of the Economy. Cambridge University

Press, Cambridge.