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American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 4.6 

Volume. 9, Number 3; July-Sept, 2022; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadipub.com/Journals/index.php/aijbe 

 

 

10 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

THE IMPACT OF THE SPIRAL OF KNOWLEDGE FRAMEWORK 

ON INNOVATION QUALITY AND CROSS-REGIONAL 

INTEGRATION IN INTERNATIONAL R&D TEAMS 

 

 

 

Thompson N 

Department of Economics, University of Campania L. Vanvitelli 

Abstract: The role of knowledge in generating sustainable competitive advantage and innovation is well-

recognized in the literature. However, efficient innovation strategies require a balance between local and non-

local exploration for new knowledge. In this context, the transfer and integration of tacit and explicit 

knowledge play a fundamental role in the integration of knowledge across regions. This study proposes a 

dynamic approach that highlights the importance of the conversion process that expands tacit and explicit 

knowledge in both quality and quantity. Adopting a mixed design method of qualitative and quantitative 

research methodology, this study verifies the role of the spiral of knowledge in the internationalization of 

R&D teams, cross-regional integration, and the quality of innovation. The framework of knowledge 

management process adopted in this research is a revised form of the Nonaka and Takeuchi model supported 

by empirical verification. The empirical study focuses on U.S. manufacturing firms. Our research aims to 

expand empirical research related to the role of intellectual capital in the generation of innovation and 

identifies in detail the factors of the SECI model that positively influence the effectiveness and efficiency of 

innovation. 

Keywords: knowledge management, spiral of knowledge, innovation, internationalization, R&D teams, 

cross-regional integration, tacit knowledge, explicit knowledge, intellectual capital, SECI model. 

 

Introduction 

The ability to generate innovation and to protect and use intangible knowledge assets are critical factors for 

the superior performance of firms. Knowledge as a source of sustainable competitive advantage is well-

recognized in the literature. Innovation is not only generated endogenously by the enterprise but also derives 

from the combination of internal ideas with external ones. Therefore, an efficient innovation strategy must 

balance the exploitation of existing knowledge generated by local research with non-local exploration for new 

knowledge. Access to knowledge dispersed in a globalized world requires R&D teams. However, cross-

regional transfer of tacit knowledge is quite hard even within firm boundaries. The formal and informal 

mechanisms of transfer and integration of tacit and explicit knowledge within the company play a fundamental 

role in the integration of knowledge across regions. Strong interpersonal relationships between international 

R&D teams are an important mechanism that facilitates the flow of knowledge in companies. The ways in 

which these relationships must be developed in order to combine tacit and explicit knowledge involve the 



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knowledge management process. The objective of this research is to verify the role of the spiral of knowledge 

in the internationalization of R&D teams, cross-regional integration, and the quality of innovation. The 

framework of knowledge management process adopted in this research is a revised form of the Nonaka and 

Takeuchi model supported by empirical verification. This study proposes a dynamic approach that highlights 

the importance of the conversion process that expands tacit and explicit knowledge in both quality and 

quantity. 

2. LITERATURE REVIEW The knowledge-creation process   

Knowledge is an important intangible asset of an entity that includes know-how (functional knowledge), 

know-what (tactical knowledge), and know-why (hypothetical knowledge) (Campanella et al., 2014; 

Sanchez and Heene, 1997).  

Knowledge can be combined with the already available knowledge or transformed into new knowledge and 

improved capabilities (Chen and Huang, 2009). This process is known as knowledge management.  

Indeed, according to other authors, knowledge management is related to innovation (Dahiyat, 2015) 

because it can stimulate the creation of new intellectual capital (Du Plessis, 2007; Huang and Li, 2009). For 

this reason, knowledge and, mainly,  the capability to create and utilize knowledge are considered the most 

important source of a firm’s sustainable competitive advantage (Nonaka and Toyama,2013).  Furthermore, 

some scholars identified knowledge management as a process that turns tacit knowledge in explicit 

knowledge (Li & Gao, 2003).  

The process of transforming tacit knowledge into explicit has been described in the SECI model 

(Socialization, Externalization, Combination, Internalization) by Nonaka and Takeuchi (1995). In the 

Knowledge management literature this model is known as a "spiral of knowledge".  

Socialization is the process through which the tacit knowledge generates new tacit knowledge within 

physical social relations. This phase is essential to activate the externalization process.  

Externalization is the process through which tacit knowledge becomes explicit through formalization in 

written documents and operational procedures. In this phase the individual is extracted from the social 

group and makes his knowledge available to everyone, using the most appropriate tools.  

Combination is the process through which knowledge is transformed from explicit to explicit. In this phase, 

explicit knowledge is combined with new contents becoming more complex. At this stage some tools 

facilitate the combination, such as indexing and storage software.  

Internalization is the phase in which knowledge is transformed from explicit to implicit. This phase is an 

individual process by which the individual enriches and broadens his tacit knowledge. The tool through 

which this process is called is defined "learning by doing" (Nelson, 1982).  

At this point, after the process of internalization, the individual re-socializes his knowledge and the process 

resumes from socialization.Thus,the movement through the four modes of knowledge conversion forms a 

spiral, not a circle, because the knowledge is constantly regenerating. This process takes place continuously, 

generating the spiral of knowledge.  

Distributed R&D and value of innovation   

Although the knowledge is generally intangible in nature, it is becoming widely accepted as a major 

corporate asset capable of generating sustainable competitive advantage  in a business (Barney, 1991). The 

knowledge and capabilities-based views (KBV) has emerged from the Resource Based View (Penrose, 

1959) by focusing on intangible resources, rather than on physical assets. In this perspective, knowledge is 

the most important resource in strategy underlying new value creation (Grant, 1996; Kogut & Zander, 

1992). In particular, Kogut and Zander (1996) define the firms as “a social community specializing in the 

speed and transfer of knowledge” (p. 503).  

In literature, there are some basic assumptions concerning knowledge and its role in production. Several 

studies have argued that novel innovations often derive from combination of accessible pieces of knowledge 



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base (Arora and Gambardella, 1990; Nonaka, 1994; Utterback, 1994; Hargadon and Sutton, 1997). Diverse 

knowledge provides more elements  useful for new innovative combinations, which gives the opportunity 

not only for important progress, but also for innovations that suffer from low evaluations as the 

combinations have unforeseen faults (Fleming, 1999).This  means that producing a good or service typically 

requires the combination of multiple types of knowledge (Kogut and Zander, 1993; Lin, Wu, Chang, Wang 

& Lee, 2012). But the main assumption concerning the knowledge is its limited transferability. Indeed, 

while explicit knowledge is easily communicated between individuals and organizations, tacit knowledge  

is manifest only in its practice. Key element to sharing the tacit knowledge are the willingness and capacity 

of individuals to share with others what they know and to use what they learn (Holste & Fields, 2010; H.F. 

Lin, 2007; Lee Endres, Endres, Chowdhury & Alam, 2007). Thus, its transfer is costly and slow.In order to 

overcome this problem, the knowledge integration process allows  individuals to apply their specialized 

knowledge to the production of goods and services (Demsetz, 1991). The importance of integrating 

knowledge, particularly technical knowledge is well established in the field of strategic management, in 

particular it appears as a unique source of value creation (Bartlett and Ghoshal, 1989; Cantwell and 

Piscitello, 2007; McEvily et al., 2004). In the contemporary business context, innovation represents, 

especially in some sector, a key source in order to achieve a sustainable position in markets. In this sense, 

organizational theories agreed  that businesses  have to develop both exploitative (incremental) and 

exploratory (radical) innovation (Duncan 1976; Gupta, Smith & Shalley, 2006; Tushman and 

O’Reilly,1996). According to March’s pioneering paper (1991), firms have to choose between structures 

that facilitate exploitation (the use of existing knowledge) and those that facilitate exploration (the search 

for new knowledge). This shows organizational ambidexterity from a trade-off perspective. Thus, some 

authors suggest that in order to achieve an effective strategy for innovation, a balance must be found 

between the exploration of new and  non-local knowledge and the exploitation of existing knowledge 

(Volberda,Baden-Fuller & Van den Bosch, 2001). Indeed, especially in the actual business context, which 

is characterized by globalization and international markets, in the management literature increased attention 

has been paid to involvement of both internal and external sources of knowledge within firm innovation 

processes to enhance innovation itself (Cassiman and Veugelers,2006; Enkel.,Gassmann, & Chesbrough 

2009; Rosenzweig,2016). Thus, novel innovations result not just from combining ideas within the firms but 

from their capacity to share, combine and create new knowledge outside the boundaries of the company 

(Teece,2007). In industries characterized by  regime of rapid technological development, the exploration 

of new, external and differentiated technologies constitutes an important component to have a competitive 

advantage.Trough exploitation mechanism, firms to access ideas, knowledge, skills and technologies within 

an external environment is commonly called as quadruple helix (Carayannis and Rakhmatullin, 2014). 

Indeed, in these ever-changing sectors  the exploitation of new knowledge and skills is  necessary to put in 

place a true competitive strategy (Amburgey, Dacin & Singh 1996; Brockhoff, 1992; Calabrese,Baum & 

Silverman 2000). Thus, biotechnology sector no single firm has internally existing capabilities necessary 

for innovation success (Baum, Calabrese & Silverman; 2000; Gemser , Leenders, M. A., & Wijnberg; 1996; 

Powell 1996; Koput & Smith-Doerr; Shan and Song; 1997). According to DeBresson, and  Amesse (1991), 

more significant innovation resources reside in a network and not in the firm alone; and thus firm 

collaboration for innovation has taken on a global imperative in order to achieve competitive advantage in 

international markets and manage some of the more complex aspects of innovation projects (Hoegl & 

Proserpio, 2004; Shan et al., 1994). Håkansson and Snehota (2002) highlighted the importance of firm 

collaborations. The authors noted that while a company is a clearly defined within clear boundaries from 

an organizational point of view, from a resource and activity point of view it is different. An important 

body of research argued that most significant innovations  are not created in isolation, but developed within 

of a broader context of a network of interdependent relationships (Bower, 1993; Snehota and Håkansson, 



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1995; Du Chatenier, Verstegen, Biemans, Mulder & Omta,2009).Consequently, a company should not be 

seen as an island, but as a part of a mainland-a network.   

It is self-evident that technology is becoming increasingly globalized, and  this is also evident in the related 

literature (Clark and Slotta., 2000; Herstad, Aslesen & Ebersberger, 2014; Sørensen and Sorenson, 

2003).Indeed, firms are more likely to increase their reliance on external knowledge in order to achieve  

innovations for different factors, such as vertical disintegration pressures (Langlois, 2003), the difficult 

appropriation of investments in intangibles (Chesbrough, 2003), and also the growth of specialised 

technology markets (Arora, Fosfuri & Gambardella; 2001).Alongside the increasing technology 

globalization, R&D is currently undergoing a process of globalization (Singh,2005) although progress does 

vary considerably across different sectors and more or less developed countries (Asakawa and Som, 2008; 

Chen,2003). Within such a process, firms have placed increasing focus on establishing networks, leveraging 

and aligning both their internal and external R&D units around the world (Demirbag & Glaister, 2010; 

Feinberg and Gupta, 2003; Perks,2006; Watanabe, Tsuji & Griffy-Brown,2001). Thus, internationalization 

of R&D has become important in recent years in response to the increase in technological sophistication. 

In the internationalization of R&D process, firms reach outside of their boundaries to gain access to 

knowledge and capabilities that are geographically bound in a foreign location. So, the acquisition of skills 

is subordinated on the underlying technological capabilities that foster the acquisition of external 

technologies. Thus, only undertaking international R&D activities is not sufficient to achieve increased 

innovative outcomes. Because in R&D process increments to an existing stock of knowledge are facilitated 

by possessing high levels of existing knowledge stock (Ma & Lee, 2008), firms with significant amounts 

of basic R&D may generate greater innovative output through external collaborations. Many previous 

studies have shown the importance of basic R&D when firms expand overseas, acquiring new external 

knowledge (Zahra, Ireland, & Hitt; 2000). In other words, to be successful, firms must possess existing 

research capabilities that are complementary capabilities that are to ones they seek in foreign nations and 

they do own (Teece, 1987). According to Dyer & Singh (1998), complementary resources are “distinctive 

resources of alliance partners that collectively generate greater rents than the sum of those obtained from 

the individual endowments of each partner” (pp. 666-667).  

In the knowledge creation process, it is important being in a network. To date, the literature concerning the 

question of what factors facilitate or impede the integration of knowledge in firms with global technology 

strategies is growing (Gupta and Govindarajan, 2000; Håkanson and Nobel, 2001, Singh, 2008), but the 

findings of empirical studies are still controversial (Chen, Huang & Lin, 2012; Penner-Hahn & Shave, 

2005; Selmi 2013; Singh 2008; Song,2011;Thompson,2006).   

Despite these differences,prior studies argued that integration of scientific knowledge across sources in 

multiple locations (Leiponen & Helfat,2010) requires the firm’ ability and willingness to assimilate diverse 

knowledge and skills associated with dispersed R&D.   

The literature on knowledge integration stated it is possible to define the correlation between the elements 

concerning the "effectiveness and efficiency of innovation". In fact, while the cross regional and the 

distribution refer to the efficiency of innovation, the value of innovation refers to the effectiveness of 

innovation. According to the literature, innovation efficiency is a measure of innovation performance, and 

it is determined by the cost and the time involved in the innovation project (Brown and Eisenhardt, 1995; 

Wheelwright and Clark, 1992;Valle and Avella, 2003). Instead, the effectiveness of innovation is related to 

the organizational and managerial characteristics or factors that allow company to grow and innovate 

(JerezGómez, Céspedes-Lorente & Valle-Cabrera, 2005).   

Instead, firms that remain confined to a single location should have an disadvantage respect to firms that  

use multiple R&D locations, accessing more and more diverse knowledge sources (Tzabbar and 



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Vestal,2015). Frost and Zhou (2005) stated that mechanisms useful to the integration of knowledge may 

increases levels of absorptive capacity among participating units.   

Drawing on the knowledge-based view of the firm, the aim of this research is to find evidence for the 

following research hypotheses:  

Hypothesis H.1. The factors of the spiral of knowledge positively and significantly influence the quality 

of innovation  

Hypothesis H.2. The factors of the spiral of knowledge positively and significantly influence the 

geographic distribution of R&D  

Hypothesis H.3. The factors of the spiral of knowledge positively and significantly influence the 

crossregional integration  

3. SAMPLE AND METHODS  

Our analysis is based upon successful patents applied for during between 2016 and 2019.  

Our empirical study focuses on 432 U.S. manufacturing firms.. The sample of patents was obtained from 

USPTO and NBER dataset.  

Therefore, according to the literature review, three dependent variables (Y) have been identified: Value of 

innovation (Y1), Geographic distribution of R&D (Y2), and Cross regional knowledge integration (Y3). 

These variables measure the value of innovation, the R&D distribution and knowledge integration of firms. 

To investigate the research hypotheses, the relationships between these three variables and 15 independent 

variables (X) were analyzed by measuring the spiral of knowledge which was proposed by Nonaka and 

Takeuchi. The 15 independent variables are factors that affect the knowledge-conversion process in the 

banking system and can be grouped into the four modes of knowledge conversion using the following 

classification:  

Socialization: Promotion of Periodic Brainstorming (X1), Periodic Promotion of Internal Conferences on 

Specific Financial Issues (X2), Information Networking (X3), Awards as a Means of Stimulating 

Knowledge Sharing (X4), Community of Practice (X5), and Knowledge Sharing Fair (X6).   

Externalization: Existence of an Enterprise Content Management System (X7), Existence of a Business 

Process Management System (X8), Knowledge Mapping (X9), and Publishing and Describing Information 

Through Metadata (X10).  

Combination: Indexing (X11), Digital Storage (X12), and Skills Management (X13).  

Internalization: Internal Staff Training System (X14) and Storytelling Management (X15).  

Finally, the following two control variables that represent the size of team and R&D expenses are: Team 

size (X16), R&D intensity(X17).  

The definition of each variable is provided in Annex 1.  

Hypotheses 1, 2 and 3 are tested by the following models: Value of innovation (Y1) = f (Socialization; 

Externalization; Combination; Internalization; Control Variables)  

Geographic distribution of R&D (Y2) = f (Socialization; Externalization; Combination; Internalization; 

Control Variables)  

Cross regional knowledge integration (Y3) = f (Socialization; Externalization; Combination; 

Internalization; Control Variables).  

The values of the variables were obtained from the following sources: 1) U.S. Patents and Trademarks 

Office (USPTO) with 2) additional data fields made available in a National Bureau of Economic Research 

(NBER) database described by Jaffe and Trajtenberg (2002) and also used by Singh (2008), and 3) 

questionnaires directly administered to the firms through a computer-aided telephone interviewing (CATI) 

system.  



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The questionnaires were submitted to the same sample of 432 staff directors each year from 2016 to 2019. 

The use of the CATI system enabled a large amount of information on a significant sample of firms to be 

collected over a four-year period.   

The questionnaire consisted of 15 closed-ended questions and only two yes / no responses. The simplicity 

of the electronic questionnaire allowed for a high response rate and the collection of homogenous remarks 

over the study period. The questions relate to the following information: 1) the periodic promotion by the 

management of brainstorming among employees, 2) the periodic promotion of internal conferences on 

specific financial issues, 3) the existence of formal informational networking, 4) the existence of premiums 

for bank employees with the best innovative ideas, 5) the existence of incentives for bank employees for 

the creation of a community of practice, 6) the existence of knowledge sharing fairs for bank employees, 

7) the existence of an enterprise content management system, 8) the existence of a business process 

management system, 9) the existence of software for the bank's knowledge mapping, 10) the use of 

publishing and describing information through metadata, 11) the use of indexing for information created 

by employees, 12) the use of digital information storage, 13) the existence of a systematic evaluation system 

and planning of individual members' skills within an organization, 14) the existence of staff training offices, 

and 15) the use of the storytelling technique for disseminating knowledge in the bank.  

The model includes a number of binary variables, aimed at taking into account factors that have not been 

measured by the other variables.   

The statistical models generally applied for estimating equations where the underlying dependent variable 

has a non-negligible probability of zero and has a discrete nature are applications and generalizations of the 

Poisson distribution (Hausman et al., 1984).   

With regard to the methodology, hypothesis demonstration was carried out using a fixed negative binomial 

regression on a set of variables, which is desirable given overdispersion of data. Indeed, our data show extra 

variation that is greater than the mean. The negative binomial model is an generalization of the Poisson 

model that allows the variance of the distribution to grow faster than the mean. In addition, the negative 

binomial model generates correct standard errors for count data that is overdispersed (Cameron and Trivedi, 

1986). The same methodological approach is used by other authors in similar works (Gittelman and  

Kogut,2003; Singh,2008) in order to exploit the longitudinal nature of the data. To analyze data we used 

Stata 15.   

RESULTS  

In order to investigate the relationship between the variables under investigation and to address the research 

hypotheses, a negative binomial analysis was performed (Table 1). The next section contains the discussion 

about the empirical results, with theoretical and practical implications.  

Table 1. Negative binomial regression models (fixed effects)  

 Y1  Y2  Y3  

𝑋!  0.096 (8.37)***  0.001 (0.02) ***  0.017 (1.53) ***  

𝑋"  -0.062 (-5.35) ***  0.014 (1.05)  0.047 (4.61) ***  

𝑋#  0.036 (3.13) **  0.018 (1.37)  0.015 (1.51)  

𝑋$  -0.006 (-0.41)  -0.019(-1.39)  0.007 (0.64)  

𝑋%  -0.036 (-0.81)  0.033(2.51) ***  0.017 (1.68) ***  

𝑋&  0.016(1.31)  -0.029 (-2.24) ***  -0.026(-2.77) ***  

𝑋’  0.140(11.78) ***  -0.029 (-2.17) ***  -0.096 (-9.48) ***  

𝑋(  0.128 (11.5)***  0.028 (2.06) ***  0.043 (4.33) ***  

𝑋)  -0.005 (-0.07)  0.050(3.61) *  0.080 (7.85) ***  

𝑋!*  0.035(0.002) ***  -0.085 (-6.42) **  -0.021 (-2.10) **  



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𝑋!!  -0.003 (-0.11)  0.047(4.61) ***  0.018(1.78) *  

𝑋!"  0.121(10.23) ***  -0.009 (-0.68)  -0.009 (-0.95)  

𝑋!#  -0.008(-0.40)  0.028(2.06) **  0.097(9.71) ***  

𝑋!$  -0.009 (-0.46)  0.021(1.54)  0.001(0.01)  

𝑋!%  -0.004 (-0.14)  0.006(0.61)  0.013(0.71)  

𝑋!&  0.002(0.11)  0.006(0.34)  -0.000 (-0.04)  

𝑋!’  -0.008 (-0.44)  -0.003 (-0.19)  0.012(0.71)  

Log likelihood  -4,055  -3,736  -5,113  

***Correlation is significant at the 0.01 level 

**Correlation is significant at the 0.05 level  

*Correlation is significant at the 0.10 level  

4. DISCUSSION AND CONCLUSION  

Considering these results, it seems that Nonaka and Takeuchi’s spiral of knowledge has a positive influence 

on the quality of innovation, the geographic distribution of R&D and the cross-regional integration in the 

business context, but there is some criticality about the internalization process. Not all 15 selected variables 

have a positive influence on the three dependent variables of innovation (partially confirmed hypotheses).  

Therefore, by excluding factors that have a negative influence or are not significant, it is possible to formulate 

an empirical model for the relationship between the spiral of knowledge and the innovation’ effectiveness and 

efficiency, as defined in the related literature (Alegre and Chiva, 2013).  

Figure 1 – Empirical model  

 
This empirical model shows the relevance of  knowledge sharing, knowledge externalization and 

knowledge combination in order to improve the efficacy and effectiveness of innovation. Although the 

literature about knowledge and innovation is copious, the relationship between Nonaka and Takeuchi’s 

spiral of knowledge and the efficacy and effectiveness of innovation has not been  examined systematically. 

Numerous factors in our empirical model measuring knowledge sharing, knowledge externalization and 

knowledge combination in companies positively influence the dependent variables that measure efficacy 

and effectiveness of innovation.   

Particularly, our findings provide new evidence regarding the importance of intellectual capital on 

innovation, showing what factors positively impact on the efficiency (represented by the cross regional and 

the distribution R&D) and the effectiveness of innovation (namely value of innovation). As shown in Figure 

1, some element referring to knowledge socialization (information networking,brainstorming, community 

of practice, internal conferences), to the knowledge externalization (existence of an Enterprise Content 

Management  System, publishing and describing information  through metadata, existence of a Business 

Process Management System; knowledge mapping) and to the knowledge combination (indexing, skills 

                 

Knowledge 

socialization 
Information

 networking Brainstorming

  Community of

 Practice 

Knowledge

  externalization 

BPM

 SysteKnowledge

  Mapping 

Knowledge

  combination 

Indexing  

Skills

  Management 

Businesses 

Value

 innovation 

Distribution

 ofR&D 
Cross regional

  integration 

Positively  

affect  



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management) generate a new innovation spiral that promotes and encourages efficacy and effectiveness of 

innovation with a spiral movement.  

More generally, this empirical research shows that the transformation of tacit knowledge into explicit 

knowledge and the diffusion of knowledge are not to be underestimated in the firms. Indeed, some factors 

of the spiral of knowledge have a key role in the internationalization of R&D teams, in cross-regional 

integration and in the quality of innovation.  

This process should be included in the new best operative practices of businesses. Indeed, introducing our 

empirical model in the managerial best practices, firms may improve innovation quality, as well as 

crossregional knowledge integration and R&D teams for innovation quality. In this way and according to 

the literature (Barney, 1991; Kogut and Zander, 1992), intellectual capital management may allow firms to 

grow and develop, gaining a competitive advantage in markets and manage some of the more complex 

aspects of innovation projects (Hoegl and Proserpio, 2004; Shan, Walker & Kogut, 1994).  We believe our 

proposed model will enhance scholars' ability to study the relationship between intellectual capital 

management and innovation (1) encouraging new theorizing about the causes, effects, mechanisms of SECI 

model; (2) providing a new empirical model that can be used ex ante for new research designs, as well as 

post hoc for re-interpretations of previous research.   

This empirical research not only confirms some statements made in the existing literature on the role of 

intellectual capital in the enhancement of innovation (Cooke, and Wills,1999) but also addresses a gap in 

the existing empirical research.  

Indeed, compared to the existing literature, this research proposes an operational framework supported by 

an empirical verification using a large (432 firms) and geographically diverse (24 OECD countries) sample. 

Moreover, compared to other studies that are limited to investigating the relationship between knowledge 

and innovation, this research is based on the broader concepts of Nonaka and Takeuchi's spiral of 

knowledge.  

Therefore, this research proposes a "dynamic approach" that highlights the importance of the  conversion 

process that expands tacit and explicit knowledge in both quality and quantity. Concluding this study 

proposes an innovative approach for the business sector, particularly for works related to the creation of 

innovation.  

ANNEX 1  

Y1 = Value of innovation = This variable measures the number of citations received by a patent. Numerous 

studies have shown that the number of citations of a patent is an efficient proxy for the value of innovation 

(Abraham and Moitra, 2001; Ahuja and Lampert, 2001; Argyres and Silverman, 2004; Lee, Yoon & Park, 

2009; Rosenkopf and Almeida, 2003). Similarly to Singh (2008) the measurement of citations includes both 

self-citations and external citations.   

Y2 = R&D dispersion = This variable is measured by adopting the R & D dispersion index. This index is 

defined as one minus the Herfindahl of geographic concentration of the firm’s (Singh, 2008). Using the 

definition of Singh (2008), dispersion index is calculated as follows: where: n is the number of patents that 

the firm has successfully applied for in the recent 4 years, and nk refers to the subset of patent developed 

by the first inventor in geographic “region” k.  

Y3 = Cross-regional knowledge integration = this variable is a dummy that has value 1 if the focal patent 

makes a backward citation to a patent originating in another geographic unit of the same firm (Jaffe and 

Trajtenberg, 2002; Singh, 2008); otherwise the variable has a value of 0. This variable has been used to 

capture within-firm knowledge flow.   

X1 = Promotion of Periodic Brainstorming. This variable has the value of one if the firm periodically 

promotes brainstorming aimed at exchanging and creating new knowledge to generate new financial 

products; otherwise, the variable has a value of 0.  



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X2 = Periodic Promotion of Internal Conferences on Specific Issues. This variable has the value of one if 

the bank promotes periodic employee conferences to enhance their skills related to specific issues; 

otherwise, the variable has a value of 0.  

X3 = Information Networking. This variable has the value of one if the firm adopts informal dissemination 

systems by promoting informal meetings in the company or on leisure time; otherwise, the variable has a 

value of 0. This variable is a proxy for the firm's ability to promote an informal community of knowledge 

sharing.  

X4 = Awards as a Means of Stimulating Knowledge Sharing. This variable has the value of one if the firm 

promotes premium competitions for the best innovative ideas for introducing product or process 

innovations; otherwise, the variable has a value as 0.   

X5 = Community of Practice. This variable has the value of one if the firm promotes the emergence of 

informal communities where work practices are shared; otherwise, the variable has a value of 0.   

X6 = Knowledge Sharing Fair. This variable has the value of one if the firm promotes internal fairs (even 

on-line events) for sharing knowledge. Otherwise, the variable has a value of 0.   

X7 = Existence of an Enterprise Content Management System. This variable has the value of one if the firm 

uses software to control and verify the integrity of the acquired information; otherwise, the variable has a 

value of 0.   

X8 = Existence of a Business Process Management System. This variable has the value of one if the firm 

uses information technology systems that allow managers to use analytics and change either technology or 

the organization based on the acquired information; otherwise, the variable has a value of 0.   

X9 = Knowledge Mapping. This variable has the value of one if the firm conducts knowledge mapping to 

develop encoded knowledge that is accessible to everyone; otherwise, the variable has a value of 0.   

X10 = Publishing and Describing Information Through Metadata. This variable has the value of one if the 

firm has a system that transforms tacit knowledge into explicit information by publishing it; otherwise, the 

variable has the value of 0. This variable is a proxy of the level of information encoding.  

X11 = Indexing. This variable has the value of one if the firm uses software that can briefly describe the 

content of the information, making it easier for employees to search for and combine explicit knowledge. 

Otherwise, the variable has a value of 0. This variable is a proxy for the level of information availability.  

X12 = Digital Storage. This variable has the value of one if the firm uses software that can quickly store and 

combine the content of information; otherwise, the variable has the value of 0. This variable is a proxy of 

the firm's ability to combine explicit knowledge.  

X13 = Skills Management. This variable has the value of 1 if the firm periodically performs a systematic 

assessment and assesses the competences of staff members. This variable is a proxy of the bank’s ability to 

combine explicit knowledge.   

X14 = Internal Staff Training System. This variable has the value of one if the firm has a staff training 

system in place; otherwise, the variable has the value of 0. This variable is a proxy for the firm's ability to 

increase the cultural level of employees to increase the potential for knowledge generation.  

X15 = Storytelling Management. This variable has the value of one if the firm applies the principles of a 

pedagogic narrative in the enterprise as a means to transform explicit knowledge into tacit knowledge; 

otherwise, the variable has the value of 0.   

X16 = Control variable for the team size = This variable measures the effect of the size of the team on the 

econometric model. This variable is measured by the natural logarithm of the the number of researchers in 

the innovating team for the focal patent.  

X17 = Control variable for R&D intensity = This variable is the ratio of R&D to sales for the firm.   



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