# University-Industry Relationships beyond Technology Transfer: the Role of Intangible Knowledge

Carlos Plata

► To cite this version:

Carlos Plata. University-Industry Relationships beyond Technology Transfer: the Role of Intangible Knowledge. Triple Helix, 2024, 11 (2), pp.135-169. 10.1163/21971927-bja10046. hal-05454253

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# University-Industry Relationships beyond Technology Transfer: the Role of Intangible Knowledge

Carlos Plata | ORCID: 0000-0002-0781-7393

TSM-Research, University of Toulouse Capitole, CNRS, Toulouse, France

carlos.plata@tsm-education.fr

Received 6 November 2023 | Accepted 4 July 2024 |

Published online 25 July 2024

## Abstract

This article explores the complexities of intangible knowledge transfer in university-industry collaborations, offering a comprehensive view of the current landscape and its future direction. Using a bibliometric and systematic literature review of 1496 articles and 20 selected studies, the research identifies key trends and patterns. Despite differences between academic and practical domains, the study finds that informal networking and personal interactions facilitate mutually beneficial partnerships, regardless of industry type, firm size, or academic specialization. The paper highlights the crucial role of intangible knowledge transfer platforms in fostering effective collaborations and advocates for streamlined protocols to enhance knowledge exchange between academia and businesses. Introducing the Unity Index, the paper provides a novel tool to analyze key authors and relationships between variables, offering new insights and opportunities for future research. This study emphasizes the critical importance of intangible knowledge transfer in university-industry relationships, addressing gaps often overlooked in traditional analyses.

## Keywords

bibliometric analysis – intangible knowledge – knowledge transfer – intangible knowledge – university-industry collaboration
# 1 Introduction

Social development and economic growth are grounded on the relations of three major actors: governments, society, and higher-education institutions. When they work in synergy, they can raise the standard of living and improve the overall well-being of their citizens (Corpuz, 2021). However, this synergy is not usual in modern society, as multiple agendas exist with different interests, which are not usually aligned (Rykun et al., 2015).

On the one hand, universities has as a first mission and focus on teaching future generations, task that is complemented by creating knowledge through research processes (Sam and Van der Sijde, 2014; Scott, 2006). Originally, this creation of knowledge does not directly seek direct financial benefits, as governments or universities themselves commonly fund these processes. Nonetheless, the practice of research has evolved and some universities have found different mechanisms to commercialize their results and innovations (Etzkowitz, 2004; Friedman and Silberman, 2003; Perkmann et al., 2011), to sustain their endeavors and improve research facilities and other necessary assets.

On the other hand, companies usually focus on profit maximization, and their innovation and research usually seek profitability and commercialization (Weersink and Fulton, 2020). Consequently, the business world has different time frames and objectives in terms of product launches, and research and development processes (Searls, 2009).

There has been a long-standing and active relationship between companies and universities, as the latter trains the current and future workforce, delivering the knowledge and skills needed by the former (Scott, 2006). This relationship has certainly evolved throughout the years. Initially, universities traded technologies and innovations resulting from hard sciences (Etzkowitz, 2004; Friedman and Silberman, 2003; Penan, 1996; Perkmann et al., 2011). Thereafter, universities started to create technological centers in charge of providing technical services and technology developed within institutions (Chilana et al., 2015). Likewise, technology transferring offices were created, to be in charge of this technology transfer business model, shaping, and pushing from within toward, a close relationship with industries (Baglieri et al., 2018).

The next step in this fast-changing era was the aspiration of universities to create new value for society (Chunoo and Osteen, 2016). One way of achieving this, developing further the industry-university relationship, was through the creation of university-supported ventures, generally labeled academic entrepreneurship (Crow et al., 2020). These start-ups were one step forward compared to business incubators, since the university shared part of the financial
risk and, in some cases, scholars started to be part of managerial or advisor boards (Neves and Brito, 2020).

The most recent development in the university-industry relationship is the knowledge-transferring process, where universities teach the next generation of workers (Good et al., 2019). In its most basic form, this knowledge-transferring process could be simply regarded as “teaching and preparing” the workforce (Scott, 2006). Nevertheless, it has evolved into a more complex and intricate relationship consisting of consultancy, joint research, co-publication, design of professional training or student placement, among others (Hagen, 2008). These relations have become critical, as they aid in the development and sustenance of the skills of managers within the company (Leischnig and Geigenmüller, 2020).

These knowledge-transferring processes are more aligned and, at their best, create a more synergic relationship, in accordance to the so-called four-helix model, where innovations come from the meeting point of needs and capabilities of governments, businesses, universities, and society in general (Cai and Lattu, 2022; Carayannis and Campbell, 2009).

It is important to review and evaluate the literature on university-industry relations regarding the exchange and transfer of intangible knowledge, as there is a lack of research for this specific kind of skill. The literature has typically focused on technology transfer (Sanchez and López, 2018), since it can be accurately measured and, when studying knowledge transfer, researchers tend to mix both intangible and tangible results (Agrawal, 2001). This mixture has resulted in intangible knowledge transferring to be mainly studied between companies or inside corporations (Anand et al., 2021; Minbaeva, 2007). However, the transfer of intangible knowledge is one of the most relevant relationships between universities and businesses. Thus, understanding these relations as substantially different from their hard skill counterparts can avoid misguided policies, improving them and bringing the business and academic worlds closer and answer the following research question “*How do intangible knowledge transfer mechanisms influence the development and sustainability of university-industry collaborations, and what are the key factors that facilitate or hinder these processes?*”

The systematic literature review and the bibliometric analysis pursue complementary objectives. First, the bibliometric analysis gives an overview of the field, by providing quantitative information on scientific production. The systematic literature review, on its part, seeks a deep understanding of the literature through a qualitative approach, i.e., a detailed analysis and synthesis of the existing research on university-industry relations related to intangible knowledge transferring.
2 Literature Review

Knowledge can be quite difficult to understand due to its non-tangible nature and its close relation with concepts like data and information. To clarify, data can be defined as the most basic form of events, while information is defined as the relationship between data points (Ackoff, 1989). Likewise, from an organizational standpoint, information represents the union between is systems and the people required to carry out any work activity (Braganza, 2004). Knowledge, on the other hand, is a broader and deeper concept, defined by Davenport and Prusak, (1998: 5) as “*a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information*”.

One of the main sources of knowledge are universities through their research process, which nowadays is usual among universities worldwide (Stich and Reeves, 2016). Nonetheless, research-focused institutions have existed since pre-industrial Germany in the 1800s, starting with the University of Berlin, with its primary goal of conducting strong innovative research, instead of simply teaching (Scott, 2006).

Universities are “factories” of knowledge (Heinzl et al., 2013), historically transferred only through teaching or training, also known as the first mission of the university, providing the next generation the skills necessary to perform and be an active part of society (Scott, 2006). However, this idea has evolved with the increase of the volume and complexity of the intellectual results universities can deliver. Today, the universities are embracing a third mission that emphasizes their contribution to societal and economic development (Compagnucci and Spigarelli, 2020). This mission includes activities such as technology transfer, innovation support, and community engagement, which enhance the impact of academic knowledge beyond the confines of the university. The products that are subject to transfer can be intangible or tangible. Some examples of the former are joint research, contract research, consulting, student mobility, and employee training (Hagen, 2008; Heinzl et al., 2013; Schartinger et al., 2002); while the latter consists of, e.g., patents, software, and industrial designs (Sadaghiani and Jamali, 2012).

Tangible asset transferring is easier to understand and track because the use of a tangible asset in a transaction gives both parties the possibility of accurately following its impact. This “hard science” transferring is commonly known as technology transfer and is defined by the (Competence Centre on Technology Transfer, 2022) of the European Commission as the process of transferring results of technological research, alongside complementary skills and procedures, to the marketplace and society at large.
On the other hand, intangible asset transfer is also known as knowledge transferring and is generally defined as a process where a body of knowledge used and organized in one setting is exchanged with other organizations (Bloedon and Stokes, 1994). This type of transferring process is harder to understand due to its very nature (no tangibility). Hence the resulting transaction is harder to follow (Sung and Gibson, 2000) and, in some cases, the accuracy when measuring its impact can be questioned.

Technology transfer usually comes with a financial benefit for the university (D'Este and Perkmann, 2011). This benefit undoubtedly fosters further research on similar or complementary topics/technologies, which could, in turn, change the research panorama and the internal structures of the university and its business partners (Pisano, 2006). As stated by Schaeffer et al., (2020) a linear model for understanding technology transfer is insufficient, as it reduces university objectives to the maximization of financial returns, based on the commercialization of property rights.

However, this reductionist conception ignores the relevance of universities to economic development. Indeed, one of its most reliable ways to contribute to economic development is by means of accompanying or coaching companies in their business path. This accompaniment usually comes in the form of consultancies, defined by Landry et al. (2010) as companion activities that are commissioned by industrial partners or governments to bring the research expertise into the field. It is important to note that Grimpe and Heusinger, (2013) found that both formal and informal consultancy are the most utilized form of transfer, through collaborative research, contract research, and licensing/technology acquisition.

The process of hard-skill transferring has been studied extensively, from its conception to the process of seeking funding, and the impact of a constant flow of financial resources on the infrastructure of the company and the university. Nevertheless, the study of the process of intangible knowledge transferring is still in its infancy. Consequently, this paper seeks to contribute to the discussion of the latter.

# 3 Methodology

## 3.1 *Bibliometric Analysis*

A bibliometric study, part of scientometrics, is known for using statistical methods to quantitatively analyze bibliographical data (Broadus, 1987; Naseer and Mahmood, 2009). It provides important criteria to select, evaluate, and monitor published research (Moed et al., 1995). While these methods are not
<figure><img src="image_3.png" /><figcaption>FIGURE 1 Bibliometric flow</figcaption></figure>

new, they have become popular among scholars with the rise of comprehen-
sive bibliographic databases such as Clarivate Analytics', WoS database and
Elsevier's Scopus database, and open software tools (Bengoa et al., 2021).

Bibliometric studies are divided into 2 major sections: information retrieval and analysis, as shown in Figure 1. First, the retrieval of information starts with choosing the information source. In this case, Elsevier's Scopus database was chosen due to its wide inclusion of journals and the detailed information it provides (Harari et al., 2020). A preliminary number of 85.705 hits was obtained after a search of "knowledge AND transfer" (using Boolean operators) in the
abstract, title, and keywords. To limit the search results, only articles inside the business, management, and accounting fields were considered, reducing the number of hits to 9.075.

Thereafter, to refine the search further, only journal articles written in English and in the final stage of their publication process were considered. This brought the number down to 5.954 hits. Likewise, to focus the search on knowledge transferring, all the papers with technology transferring as keywords were excluded, reducing the number of hits to 4.973. Finally, after limiting the search to an exact match of keywords and removing duplicates, the final data set has 1.496 papers,¹ is important to note that there were no limitations or considerations regarding the geographical scope.

In the phase of the bibliometric analysis, choosing the correct indicator is crucial (Ding et al., 2014). To achieve a broad understanding of the topic, while deep diving in the systematic literature review section of this paper, only five main analyses where made. In this regard, it is important to note that certain variables were added, such as H-Index, to break the usual linearity of the bibliometric studies. This brought some innovative ways to mix different variables when targeting the same issue and will help to bring to light different authors and journals, in order to make a more comprehensive analysis.

To perform the analysis, software tools as the bibliometrix pack (Aria and Cuccurullo, 2017) in the version running in R in the 4.2.2 version, and VOSViewer (van Eck and Waltman, 2010; Waltman et al., 2010) in the version 1.6.18 were used. Likewise, the matplotlib libraries in version 3.5.2 Python were used to correctly plot the information.

The first analysis consisted of the evolution of scientific production. To achieve this, the number of publications and the average citation per year were compared. In addition, a thematic evolution map was created to observe the evolution in terms of central topics, and not only in terms of publications. The next two analyses related to the relevant journals and relevant articles included in the study. These were chosen to have a more complete view of the production. This method is in line with the Leiden Manifesto (Hicks and Wouters, 2015), as it not only considers the number of publications (Merigó and Yang, 2017), but also adds the H-Index (Hirsch, 2005) and the number of citations, to attain a more holistic understanding of the subjects and the journal's performance.

Additionally, to evaluate the authors, the Unity Index was used (Plata, 2022). This new index is a three-part point-based system, as shown in equation 1, where the points are assigned by granting 1 point to the author with the most published articles and increasing the number of points assigned by 1 to all
subsequent authors. The same system is conducted in relation to the remaining 2 indicators: acceptance -citations- and impact -h-index- (Plata, 2022).

$$ \uparrow \textit{Unity Index} = \downarrow \textit{PointsPublication} + \downarrow \textit{PointsCitations} \downarrow \textit{PointsH}_{\textit{index}} $$

### Eq 1: Unity Index

Moreover, the Unity Index serves as an additional form of observing scientific production from a more comprehensive approach, while still using traditional scientometrics. This helps more impactful authors, though not top producers, to share the spotlight with top producers in different bibliometric studies. As an example, from the pool of 3,156 authors, Linda Argote, top author on both the number of publications and H-Index, is placed 91st in number of citations. As a result, she is in 5th place in the Unity index and shared the spotlight with Andrea Caputo (placed 9th in the Unity Index) placed 40th in publications, 79th in H-Index and 7th in citations. Using traditional linear ways of comparing scientific works can lessen the prominence of authors with a high impact, but not a substantial number of publications. A more complex form of this index could consider assigning different weights to the variables. However, considering the interdependency between the variables, this was not considered for this study.

To understand the use of keywords and their relations, a keyword interaction network was created using a co-occurrence analysis and generating clusters of affinity. Finally, to understand the affiliation interaction, a 3-field map was generated, which included authors, their affiliations, and the targeted journals.

## 3.2 Systematic Literature Review

To review the empirical literature regarding the process of transferring intangible knowledge and their possible impact on actors, the first step of the proposal, in line with the best practices of systematic literature reviews (Hiebl, 2021), is to conduct a comprehensive search on the databases available through the University's bibliothecarial services.

The preliminary databases chosen were EBSCO, with the market name Business Source Ultimate, and ScienceDirect, based on their comprehensive-ness across journals and disciplines. The preliminary keywords proposed for this search are “Knowledge transfer” and “Process”. It is essential to note that the search does not have any time restrictions but may be limited to articles that had both of the keywords in their abstract, keywords, or title in the case of ScienceDirect. Likewise, since there is no equivalent option regarding Business
Source, and following the advice of a librarian from the University Toulouse I,
the search is limited to the abstract only.

Every search is limited to academic journals and peer-reviewed publications,
excluding dissertations, books, book chapters, conferences, or any other "grey"
literature study (Harari et al., 2020), no limitation on the geographical scope
was took into consideration. Additionally, since the ScienceDirect database
has multiple disciplines outside the focus of the study, the search is expected
to include only articles from the following subjects: economics, econometrics
and finance, psychology, business management and accounting, and social sci-
ences. On the other hand, for the case of Business Source Ultimate, as there is
no discipline filter, the area was slightly delimited by excluding those topics
that are evidently not related to the subjects of the study, for example, urban
ecology.

The first number of hits in both databases was screened using the techno-
logical platform COVIDENCE, tool that addresses the pertinence of the title and
the abstract and decides to either include or exclude it of the full-text reading
and categorization phase. Only articles in English researching the intangible
knowledge transfer between the HEI and the companies were considered.

Figure 2, based on Page et al. (2021), details the methodology process and
the workflow until the coding process.

The coding process will begin with the full-text reading of the final included
articles and the information recording was done with Microsoft Excel. In
addition to this extraction and organization of the information related to
knowledge-transferring processes, descriptive information from the paper,
such as sample, year, journal, country, and sector, was also recorded. To assess
the different fields of journals, this study used the Chartered Association of
Business Schools Academic Journal Guide (AJG guide). Finally, after gathering
all the information, it was analyzed and presented, to conclude with recom-
mendations and a research agenda.

4 Results and Discussion

4.1 *Bibliometric Analysis*

4.1.1 Evolution of the Scientific Production

The number of scientific publications, often in peer-reviewed journals, are a
tangible measurement of the scientific output and the interest of the commu-
nity in a specific field or topic. The 1,496 articles published during over more
than 30 years in Figure 3 reflect a clear trend in this regard. The first article
was published in 1990 and, since then, the field of knowledge transferring
Identification of studies via databases and registers

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Prisma</figcaption></figure>

has continued in an upward trend. This trend is certainly consistent with the research interest in the sister topic of technology transferring (Bengoa et al., 2021). When analyzing the publication distribution per decade, there is an initial phase from 1990 to 2000 with an average of 2 publications per year, followed by an expansion phase between 2001 and 2010 with an average of 46 publications per year, and a final phase between the 2011 and 2020 with an average of 89 publications per year. The most rapid growth period was between 2005 and 2009 and, in the following years, there was an average of 83 publications per year. 2014, 2015, 2019, and 2020, on their part, were above the average.

In addition to the number of publications, and their evolution, an analysis of the number of average citations per article per year was conducted. It is important to note that this study did not include the graphical representation
<figure><img src="image_5.png" /><figcaption>FIGURE 3 Comparison of the evolution of scientific production and citations</figcaption></figure>

of this evolution as presented by Szulanski, (1996), which has 4915 citations. The average citation per article per year was 58, and the years 1999, 2000, 2003, and 2004 were at least 3 times above the average. In contrast, during the last decade, no year surpassed this average, with the years 2013 to 2016 having the best citations rate.

To qualitatively understand the evolution of the topic, a thematic evolution representation using the bibliometrix tool (Aria and Cuccurullo, 2017) was plotted and presented in Figure 4. The inputs for this analysis are the abstracts of the selected papers, segmented into three-time slices in 2009, 2014, and 2018. The purpose of this map is to illustrate how different themes have evolved over time and how they have influenced each other.

Figure 4 reveals several key insights into the thematic progression of knowledge transfer. During the first period, from 1990 to 2009, the concept of knowledge transfer was prominently associated with themes such as knowledge flows, information systems, and organizational learning. This could show that during this period, research in knowledge transfer was foundational, focusing on the mechanisms and processes of how knowledge moves within and between organizations.

In the second period, from 2010 to 2014, knowledge transfer continued to be a central theme, but the focus shifted towards competitive advantage and multinational corporations. This suggests that during this time, research emphasized the strategic importance of knowledge transfer in helping organizations
<figure><img src="image_6.png" /><figcaption>FIGURE 4 Thematic evolution</figcaption></figure>

gain and maintain a competitive advantage. Additionally, themes like product development and knowledge sharing became prominent, indicating a growing interest in the practical applications and benefits of knowledge transfer in innovation and collaboration.

The third period, from 2015 to 2018, shows knowledge transfer influencing fields such as knowledge management, product development, and absorptive capacities. This shift highlights an integration of knowledge transfer into organizational practices that drive innovation and efficiency. Knowledge management, as a discipline, benefited from insights into how knowledge can be effectively transferred and utilized across different parts of an organization. Similarly, the role of knowledge transfer in product development highlighted its importance in fostering innovation and bringing new products to market. The concept of absorptive capacity, which refers to an organization's ability to recognize, assimilate, and apply external knowledge, also gained prominence, highlighting the evolving understanding of how organizations can better harness external knowledge to drive internal improvements.

In the final period from 2019 to 2022, knowledge transfer continued to maintain its relevance and began to influence more specialized topics such as limited trading and operational research. The persistence of knowledge management as a theme indicates its enduring importance in organizational strategy and operations. The mention of limited trading suggests an emerging interest in how knowledge transfer can affect market dynamics and trading practices, reflecting a more nuanced view of how knowledge flows impact economic and market behaviors. Additionally, themes such as research limitations and implications, semi-structured interviews, and positive effects indicate a growing sophistication in research methodologies and a deeper understanding of the broader impacts of knowledge transfer.

Additionally, a thematic map, using the bibliometrix (Aria and Cuccurullo, 2017) and *Keywords Plus* parameter, was plotted and presented in Figure 5. This map aims to understand each theme and its classification. Basic themes are
<figure><img src="image_7.png" /><figcaption>FIGURE 5 Thematic map</figcaption></figure>

part of the usual mainstream research, while motor themes foster the most production. Additionally, emerging themes are the ones rising in importance in the fields and have the potential to become motors in the future.

The analysis found that knowledge transfer, along with knowledge and information management, are among the most persistent topics, positioned between basic and motor themes. This indicates their foundational importance and their role in driving further developments within the field. Knowledge innovation is identified as a motor theme, supporting its critical role in advancing the field, as also evidenced by the topic evolution analysis. These results highlight that knowledge transfer and management are pivotal topics, consistently influencing and shaping research over time.

Knowledge engineering, categorized as an emerging theme, represents the natural evolution of knowledge management and acquisition. This theme suggests a more structured and systematic approach to handling knowledge, applying engineering principles to solve common problems, and generate knowledge more effectively. Despite its emerging status, knowledge engineering holds significant promise for future research and application.

The themes of project management and information systems also appear in the emerging category, indicating areas that are gaining traction but are still developing. On the other hand, topics like education, economics, and social effects, positioned as niche themes, reflect specialized interests that, while relevant, do not have the same central influence as motor or basic themes.
4.1.2 Most Relevant Journals

The relevance of sources and to what extent they influence the knowledge transfer field initially was measured by the function that correlated the number of articles published, the number of citations and the H-index. Figure 6 shows the 25 most relevant journals in knowledge transfer. In the list, we can find journals such as the Journal of Technology Transfer, Research Policy, and Technovation journals traditionally linked to the technology transfer field, showing the interest of traditional journals in adopting a new stream of research.

In terms of citations, the *Strategic Management Journal* leads with over 3,500 citations, followed by *Organization Science*, *Research Policy*, and the *Journal of International Business Studies*, each with more than 2,000 citations. The *Journal of Knowledge Management* and *Research Policy* has the highest H-Index at 39 and 30, respectively, with the *Strategic Management Journal*, *Journal of Business Research*, and *Journal of Technology Transfer* close behind at around 20.

Although most of these journals do not focus exclusively on knowledge transfer, their high citation counts indicate a rising interest in the topic, especially within technology transfer and strategic management. Figure 7 shows the relationship between citations, publications, and H-Index, divided into four quadrants to categorize the top 25 journals.

The top 25 journals in terms of number of H-Index are presented in the graph above. As observed, the resulting quadrants are not balanced, which may imply that most journals are quite similar, but some are stronger on the subject in question. Quadrant I highlights top journals like the *Journal of Knowledge Management*, the *Strategic Management Journal*, and *Research Policy*, excelling in publications, citations, and H-Index, likely due to their focus on knowledge transfer and management. Quadrant II includes journals such as *Organization Science*, *Management Science*, and the *Journal of International Business Studies*, which have fewer publications but high citations, indicating well-received limited output.

Quadrant III features journals like the *Journal of Business Research* and the *International Business Review*, which have average numbers of publications and citations, suggesting potential for greater impact. Quadrant IV includes *Industry and Higher Education*, *Knowledge Management Research and Practice*, and the *Journal of Technology Transfer*, with average publications but fewer citations, possibly due to their broader focus.

Analyzing journal quality via the Chartered Association of Business Schools (AJG) ranking (Laing et al., 2018), 41% of the 1,198 articles in ranked journals are in the upper tiers, showing significant quality and innovation. In the (AJG),²
<figure><img src="image_8.png" /><figcaption>FIGURE 6 Most relevant sources</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 7 Multivariate analysis most relevant journals</figcaption></figure>
ranking, 50% of articles are published in *Organizational Studies, Innovation, Information Systems*, and *International Business*, highlighting knowledge transfer's interdisciplinary nature (Wagner et al., 2011). Additionally, there are notable publications in *Human Resource Management, Education, Management and Employment Studies*, and *Operations Research and Management Science*. The 50 most cited articles are listed in Supplemental Material A for further reference.

### 4.1.3 Most Relevant Authors

The analysis of the most relevant authors, based on the number of articles published, local citations, and H-index, reveals a high tendency toward co-authorship with 3,156 authors involved. The most prominent author by publications is Linda Argote with 10 articles, followed by Robert J. Jensen, Gabriel Szulanski, Yang Liu, and Sajjad Jasimuddin, each with 7 articles.

For local citations, Ivo Tafkov, Kristy Towry, and Flora Hailan Zhou stand out with a single article receiving 323 citations. In terms of the H-Index, the most impactful authors are Linda Argote, Gabriel Szulanski, Robert J. Jensen, Suprateek Sarker, and Daniel Z. Levin, all with an H-Index above 5. Figure 8 illustrates the top authors based on the unity index, which combines local citations, publications, and the H-Index. The x-axis represents the number of publications, the y-axis shows local citations, and the bubble size indicates the H-Index. The quadrants help categorize the authors.

In quadrant I, no authors are present, indicating that no one exceeds the average in both citations and publications. Quadrant II includes authors like Caputo A, Zhang Y, and Aggarwal V, who have few publications but high citations, suggesting that their work is highly influential. Quadrant III features authors such as Krabel S and Muscio A, who rank high in the Unity Index but are average in publications and citations, indicating significant contributions in specific areas. In quadrant IV, Linda Argote has an above-average number of publications and H-Index but below-average citations, implying selective influence. Deflorin P also shows a good acceptance of publications.

A follow-up analysis, displayed in Figure 9, shows how different authors shift places in the “ranking” when their place in the Unity index and their publications are compared.

Comparing the first 25 authors with the best Unity Index shows that, on average, each author scaled higher by 11 places. 10 authors were placed lower, while 15 authors scaled higher. Moreover, Zhang Y, Mollaoglu S, Zhao S, Caputo A, and Aggarwal V were the authors who scaled the most: 80, 31, 31, 28, and 25 places higher, respectively. These shifts can come from an over- or under-indexation. The over-indexed authors that scaled places were Zhang Y,
<figure><img src="image_10.png" /><figcaption>FIGURE 8 Multivariate analysis most relevant authors</figcaption></figure>

Caputo A, or Aggarwal v, thanks to the high number of citations per paper. Additionally, Kapoor M or He Q scaled one place higher based on their high H-Index.

Subsequently, the under-indexed authors were Liu Y or Franco M, placed lower, due to a strong number of publications but a lower citation number compared to other authors from the data set.

### 4.1.4 Popular Keywords

In Figure 10, a network map of keywords is presented. This network map was made with the VOSviewer software (van Eck and Waltman, 2010; Waltman et al., 2010) and was generated by fully counting the co-occurrences, using every keyword as a unit. The minimum occurrence was set to 25 and every keyword that met the threshold was graphed.

Figure 10 presents 5 clusters, the first cluster – in blue – has as central node “knowledge transfer”, the stronger node of the network. This cluster includes words such as absorptive capacity, organizational learning, multinational companies, and social capital. This cluster shows the importance of knowledge inside organizations, its effect in their internal relationships and in their social tissue.

A second cluster – in red –, with “knowledge management” as a central node, includes words as knowledge sharing, knowledge-based systems, knowledge
<figure><img src="image_11.png" /><figcaption>FIGURE 9 Raise in rank authors</figcaption></figure>

engineering, knowledge sharing, and knowledge acquisitions. This cluster presents the importance of knowledge management inside institutions and for future endeavors.

A third cluster – in green –, with “innovation” as the central node, shows a relationship with universities, research, and learning. It presents how knowledge can be created inside universities through research and innovation processes.

A fourth cluster – in yellow –, with “knowledge creation and trust” as a central node, shows that the relevance of trust is in the process of knowledge creation and of transferring tacit knowledge. In the last cluster – in purple –, the
<figure><img src="image_12.png" /><figcaption>FIGURE 10 Keyword network</figcaption></figure>

relation between human resource management and project management can be found.

When holistically analyzing the network, one can find a relation in knowledge-transferring processes between its generation (universities, innovation, and research), management (knowledge-based systems, knowledge engineering), its impact on the processes inside the company (product development, absorptive capacities) and how institutions achieve successful transferring (tacit knowledge, organizational learning). This shows the central and important role of a proper and structured transfer of knowledge.

### 4.1.5 Contributing Organizations

In order to understand the ways authors work when researching and their affiliations, a 3-field map was generated using bibliometrix (Aria and Cuccurullo, 2017), as presented in Figure 11. This 3-field map has in its center the author, accompanied by the journal on the left and the affiliation of the authors on the right. Likewise, the maximum number of iterations for each field was set to 25. This 3-field plot shows the affiliation the author declares at the time of the publication, and the journal where the work was published. This is important to find the affiliations that most support research in the topic, and the journals that most authors target.

In terms of most relevant affiliations, the Copenhagen Business School was at the top, with 3 main authors declaring it as their affiliation at a point in time.
<figure><img src="image_13.png" /><figcaption>FIGURE 11 3-field map</figcaption></figure>

Every other institution has a maximum of 2 authors affiliated. Following previous results, Robert J. Jensen and Linda Argote are central authors with 3 and 4 papers, and 2 different affiliations, respectively. Additionally, the most receiving journals were the Journal of Knowledge Management with 9 authors publishing, or aiming to publish, in it, and Organizational Science, on its part, with 6. This shows that authors contribute with each other and that is common for a scholar to change institutions. Moreover, it shows that authors target multiple journals, which can be the result of aiming to find the journal that better fits their research and influences the community the most.

## 4.2 Systematic Literature Review

### 4.2.1 Descriptive Information

The descriptive results from the articles included in the review show a spike in publications in the past decade, with 60% of the publications made between 2012 and 2019. In this period, the year with most publications is 2012, and there are between 1 or 2 publications for each of the other years. In Figure 12(a), detailed information regarding this evolution can be found.

As shown in Figure 12(b), and considering the type of study –in the outer ring –, 50% (n=10) of the articles have a quantitative method, with only cross-sectional research designs (Andersen and Rossi, 2011; Arvanitis and Woerter, 2009; Berbegal-Mirabent et al., 2012; Bodas Freitas et al., 2013; Govender et al., 2018; Handoko et al., 2014; Hewitt-Dundas, 2012; Hughes and Kitson, 2012; Martinelli et al., 2008; Olmos-Peñuela et al., 2014). The remaining 50% (n=10) presented a qualitative design. Indeed, these articles presented a
<figure><img src="image_14.png" /><figcaption>FIGURE 12 (a) Publication per year – (b) Methods</figcaption></figure>

<figure><img src="image_15.png" /></figure>

more varied methodology, with 5 publications using interviews (Becerra et al., 2019; Canonico et al., 2020; Dee and Leisyte, 2017; Siegel et al., 2003; Thomas, 2012), 2 publications using action research (Jones and Coates, 2020; White et al., 2019) and 1 publication using grounded theory (Elliot, 2013), longitudinal studies (Miller et al., 2016) and exploratory approaches (Schaeffer et al., 2021).

According to the fields listed in the AJG guide (Laing et al., 2018), and as shown in Figure 13(b), the subject with more publications is innovation with 6. Moreover, there are 3 publications in the field of Economics, Econometrics, and Statistics. Additionally, 2 publications were found in the field of General Management, Ethics, Gender; and Social Responsibility, Management Development, and Education and Social Sciences, respectively. Finally, the fields of International Business and Area Studies, Information Systems, Operations Research, Management Science, Organizational Studies and Sports, Leisure, Tourism, and Sector Studies only have 1 publication each.

Additionally, Figure 13(a) shows the rank of the journal, with four papers published in rank 4 journals, followed by 6 papers in rank 3 journals, and 4 papers in rank 2 journals. Likewise, in ranked 1 journals, 6 papers were published. It is important to note that there are no publications in rank 4* journals. This statistic may be explained by the limited number of papers targeting academic and non-academic participants in the knowledge transfer process. It is important to note that the lack of interest in high-ranked journals seems an unlikely explanation, given the data coming from the aforementioned bibliometric analysis.

The geographical scope of the reviewed articles spans a diverse range of regions, reflecting the global nature of knowledge transfer and university-industry collaborations. The United Kingdom is specifically well addressed with seven articles (Andersen and Rossi, 2011; Hewitt-Dundas, 2012; Hughes and Kitson, 2012; Jones and Coates, 2020; Martinelli et al., 2008; Thomas, 2012; White et al., 2019), showing the compromise of the social actors to generate
<figure><img src="image_16.png" /><figcaption>FIGURE 13 (a) Publication by rank – (b) Publication by fields</figcaption></figure>

university-collaborations. Europe is featured in five articles, highlighting the region's active engagement in knowledge transfer initiatives (Arvanitis and Woerter, 2009; Berbegal-Mirabent et al., 2012; Bodas Freitas et al., 2013; Canonico et al., 2020; Olmos-Peñuela et al., 2014). North America is represented by three articles (Canonico et al., 2020; Dee and Leisyte, 2017; Siegel et al., 2003). Furthermore, emerging economies are present in five articles, two articles focus on Asia (Govender et al., 2018; Handoko et al., 2014), and two articles feature Latin America (Becerra et al., 2019; Schaeffer et al., 2021) and one in Australia (Elliot, 2013) which examines the unique dynamics of knowledge transfer in these rapidly developing regions. This comprehensive coverage underscores the widespread importance and diverse implementation of knowledge transfer practices across different geographical contexts.

### 4.2.2 Relations between Academic Sectors and Non-Academic Sectors

Unexpectedly, the active sectors in intangible knowledge transferring are diverse, and not necessarily linked to services. There are companies related to hard-sciences products, such as gas separation and compressed air (Jones and Coates, 2020), computer science (Elliot, 2013), manufacturing (Bodas Freitas et al., 2013) and automobiles (Canonico et al., 2020). Additionally, there are studies that include multiple companies from different sectors (Andersen and Rossi, 2011; Berbegal-Mirabent et al., 2012; Hewitt-Dundas, 2012; Hughes and Kitson, 2012; Martinelli et al., 2008). This suggests that all types of business can engage with, and use, knowledge-transferring platforms. Consequently, scholars could choose a niche sector that could better use their knowledge and approach. However, this may lead to an excessive specialization of academic-industry relations and may be an inflection point in terms of innovative research.

In terms of disciplines and fields, there were also knowledge-transferring processes in projects related to corporate social responsibility and environmental sustainability (Elliot, 2013), marketing (Jones and Coates, 2020), tourism
management (Thomas, 2012), business management (Miller et al., 2016), prod-
uct management (Canonico et al., 2020) and Human ressources management
(Govender et al., 2018). Additionally, there were some mixed publications
where the study was within the field of social sciences and, specifically, man-
agement (Hewitt-Dundas, 2012; Hughes and Kitson, 2012; Olmos-Peñuela et al.,
2014). This diversity of disciplines suggests that multiple scholars from mul-
tiple disciplines are willing to engage in knowledge transferring, which may
serve as a means to reduce the gap between management practitioners and
scholars.

In contrast to the sectors and fields, the platforms through which knowl-
edge transferring occurs are not as varied. The most used platform by schol-
ars and practitioners to share different expertise is consultancy (Becerra et al.,
2019; Hewitt-Dundas, 2012; Olmos-Peñuela et al., 2014). This may be because,
although consultancy is a formal transferring platform, it opens the door for a
substantial number of informal encounters. Hence, it may be an avenue to bal-
ance the transfer of tacit knowledge. Another platform used is action research
(Jones and Coates, 2020; White et al., 2019). In contrast to a more standard
consultancy process, in this platform, research has a more active part in the
process and is a more “hands-on” initiative.

There are other platforms used for processes of industry-university knowl-
edge transferring such as joint research (Elliot, 2013; Olmos-Peñuela et al.,
2014) and collaborative projects (Berbegal-Mirabent et al., 2012). These types
of relations are more formal and, in general, less flexible in terms of the out-
puts and the objectives.

4.2.3 Impact of Knowledge-Transferring Relations

Knowledge-transferring processes affect both the offering (universities) and
the receiving parties (companies). This impact, regardless of the platform, may
be positive in terms of external credibility, which can accentuate even more
in universities with a high research intensity (Hewitt-Dundas, 2012; Jones and
Coates, 2020). Also, knowledge transfer can be a route for companies to bring a
new vision when understanding certain problems, and to generate more com-
plex and accurate models (Elliot, 2013), in order to more effectively invest their
resources into solutions.

Financial benefits are also part of the transactional results of the use of
transferring platforms. The spillover of financial resources in this exchange
can bring funding to the university, to support additional research endeavors
that do not necessarily end with a product that can be commercialized. Hence,
it can be a tool to support a wider research agenda (Berbegal-Mirabent et al.,
2012). However, this commercialization of knowledge may have disadvantages,
specially where there is an imbalance of power within different stakeholders (Miller et al., 2016). In these cases, university governing bodies would tend to support only research projects that could be commercialized. Thus, the diversity and the emergence of new topics of research could be jeopardized (Jones and Coates, 2020).

Additionally, university-industry relations can serve as an avenue to increase and improve innovation processes and the economic performance of firms (Arvanitis and Woerter, 2009). This can be achieved by coming up with new meanings and solutions to problems during the transferring process (Canonico et al., 2020), and by including innovations and expertise that the company lacks and that would be expensive to acquire (Jones and Coates, 2020).

The generation of knowledge transfer relations is crucial to avoid the perception of academic research as tangential, and unnecessarily complex for practitioners (Thomas, 2012). A way of generating long-lasting relationships would be the creation of knowledge transfer offices, centralized bodies in charge of ensuring a proper flow of information. These offices would certainly influence the university, as they would be a new body with a new hierarchy and governance structures. Thus, they would need to fit in the university structure and, in some cases, these offices would become a priority within strategic plans (Becerra et al., 2019).

Likewise, the impact of knowledge transferring on universities is not only financial, in terms of additional funding for new research and incentives towards engaging in more knowledge transfer contracts (Berbegal-Mirabent et al., 2012). It can also help in their main missions of education and research. Regarding education, industry knowledge fed to universities can help guide the creation and adaptation of curriculums for better educated future generations (Jones and Coates, 2020). In terms of research quality, knowledge-transferring processes and working closely with the industry would increase the likelihood of producing better and more impactful research (Hewitt-Dundas, 2012), through different points of view and new data.

### 4.2.4 Knowledge Transfer and Small Companies SME

The idea of generating knowledge transfer relations with universities as something exclusive to large companies is untrue. There are relations between universities and SMES, which often do not rely on any institutionalized infrastructures, as there is a stronger presence of personal relations and interactions between researchers and practitioners (Bodas Freitas et al., 2013). This is also the case for entrepreneurs and technology managers, which are more used to informal relationships (Siegel et al., 2003). These relations can positively impact the receiving party, as this type of company frequently encourages the
design of more innovative programs and platforms, in order to achieve a sus-
tainable advantage in a highly competitive scenario (Handoko et al., 2014).

Regarding structural or organizational changes inside SMES, knowledge transfer partnerships can serve a particular role and have been successful in delivering significant and sustainable changes (White et al., 2019). In addition to the important economic contribution that SMES can bring to a region where they are established, they can create opportunities to graduates of local universities, providing them with a space to stay and contribute with their skills in less developed regions (Hewitt-Dundas, 2012).

4.2.5 Creation and Maintenance of Relationships

The creation of knowledge transfer relations with the industry is usually initi-
ated through a personal contact between the researcher and people from the
industry. This personal contact may be created by to past experiences such as
participating in business events, professional networks, advisory boards, con-
ferences organized by non-academic external organizations, professional fairs,
lectures or providing employee training (Hughes and Kitson, 2012; Schaeffer
et al., 2021; Thomas, 2012).

These contacts can allow businesses to gain the necessary trust to build communication channels, to set up a well-supported relationship (Jones and Coates, 2020; Thomas, 2012). This is especially critical, as most of the knowledge transferred from the social sciences to companies is not protected through intellectual property mechanisms such as patents. Hence, there may be a lack of transparency in the knowledge-transferring process and contract negotiations may be hard, due to certain difficulties when assessing the economic value of the contributions (Andersen and Rossi, 2011).

Even though most of knowledge-transferring processes seem positive for companies, some practitioners see academic research as tangential, unnecessarily complex, and inaccessible. Thus, sometimes they are unwilling to engage in long-lasting relationships with universities (Thomas, 2012). Scholars and universities must certainly correct these issues through proper science communication. Additionally, to achieve better results, it is important to engage with knowledge-transferring processes that have more than one transferor, for instance, the government. Hence, the impact of the transfer can be more significant (Handoko et al., 2014) and overcome some of the usual bureaucratic barriers such as legislations, licenses, and permits. Moreover, the use of intermediaries for multiple stakeholders can aid further in the exploitation and commercialization of the knowledge (Miller et al., 2016).

Finally, in addition to the expected barriers when finding a non-academic partner and a correct platform of knowledge transferring, inside universities,
scholars also find barriers to knowledge transferring, such as lack of flexibility of boards, insufficient resources devoted to external relations, and deficient incentives for researchers (Hughes and Kitson, 2012; Siegel et al., 2003).

### 4.2.6 Discussion

University-industry relationships are a major opportunity to transfer knowledge generated inside research processes of higher education institutions directly to the business world. This process has been widely used in the technology transfer field, where the relationship between scholars and practitioners has been tight and fruitful for years. This is not the case when it comes to intangible knowledge/social sciences (management) transferring. This could be explained by the fact that business problems have no unique answer, and this diversity opens the door for trial and error on behalf of practitioners, without the need of including an academic expert as mentor or consultant. In addition, when the opportunity arises to solve a business problem, the tendency has been to choose other business practitioners or companies to help.

It would be unfair to unload the responsibility of the lack of this knowledge transferring solely onto the practitioners. While positive in terms of scientific rigor, the fact that scholars choose to publicize their knowledge in journal publications or academic conferences limits the possibility of implementing their results. This creates a sort of bubble where scholars and practitioners follow different paths, with a growing disconnection between them.

After understanding the existing disconnections between both worlds, it is time to start a new chapter in the university-industry organizations. This has certainly begun with some scholar and practitioners. Knowledge-transferring processes can be an opportunity to gain experience, both in terms of academia and practice. On the one hand, businesses could change the trial-and-error culture to a knowledgeable culture, where decisions are based, not only on experiences and material conditions, but also on theoretical knowledge coming from schools. It is important to note that not all managerial decisions need to include a theoretical understanding of the matter, but key strategic decisions could profit more from them.

On the other hand, the academia can profit from this type of engagement to change the idea of companies as data machine generators toward conceiving them as an important stakeholder. This can lead to the generation of more impactful and useful research projects for companies. In short, knowledge-transferring processes have a double impact, where both the giving and the receiving party can profit from the exchange and generate expertise and experience, which will help in future endeavors.

Furthermore, in terms of knowledge-transferring platforms, choosing the correct platform is a key factor for success, as the correct implementation of
the platform will greatly affect the accomplishment of the endeavor. In this
sense, there is no "best platform" and is up to the parties to choose the most
convenient, based on the type of project and the expected results.

Moreover, it is important to highlight that these relations must be profit-
able for the companies to be interested in them. As with any other process,
profitability cannot be ensured. This can affect the willingness of companies
to engage in knowledge-transferring activities. Therefore, it is important to
reduce uncertainty. A way to encourage companies to engage in this type of
exchange is by the government, venture capitalists, or non-governmental orga-
nizations creating incentives. These incentives can initiate these kinds of rela-
tions by distributing the risk between actors. Thereafter, when the incentives
are over, the role of the university and the company would be to generate more
relationships, from which they can profit.

Understanding the common benefits that a fluent university-industry
relationship can bring may be an incentive for scholars to open more com-
munication spaces to a non-academic public, and for companies to approach
universities with business problems. These common spaces will serve as an
avenue for the alignment of both worlds. With it, the bubble of scholars could
be burst and, simultaneously, the trial-and-error culture could come into
question. Moreover, additional profits for both parties could aid universities
in their commercial endeavors in a way that does not force them to engage
in enterprises not related to their core objectives. Rather, they can do busi-
ness with what they know best: transferring knowledge through teaching and
researching.

Finally, as a suggested research agenda, it is important to generate additional
research projects that include both sides of the exchange, to understand from
both perspectives how to address these projects and their different motiva-
tions. Additionally, from an individual standpoint, it is relevant to understand
the different barriers that particular scholars/practitioners can encounter
when trying to engage with such types of processes.

5 Conclusion

The study examined the complexities of intangible knowledge transfer within university-industry relationships, providing a comprehensive understanding of the current landscape and its future trajectory. This research identifies significant trends and patterns in the field by performing a bibliometric analysis with 1,496 and a systematic literature review encompassing 20 selected studies. Despite the apparent discrepancies between academic and practical domains, the study reveals the emergence of mutually beneficial partnerships
facilitated by informal networking and personal interactions, rather than
being constrained by industry typology, firm scale, or academic specialization.

The findings also show the crucial role of the different platforms when fostering effective university-industry collaborations. These platforms are essential for developing and maintaining dynamic partnerships that go further than the traditional boundaries. Overall, this study emphasizes in recognizing and nurturing intangible knowledge transfer processes to bridge the gap between academia and industry. By doing so, both sectors can benefit from a more integrated approach to innovation and development, ensuring that knowledge generated within universities is effectively applied in practical, real-world scenarios.

Additionally, the use of the innovative Unity Index provides a valuable analytical tool that highlights key authors and elucidates additional relationships between variables. This index, combined with comprehensive analysis, highlights the importance of adopting protocols and procedures to enhance the efficiency of intangible knowledge exchange. Future research could delve deeper into these dynamics by quantifying the impact on both sides of the relationship and examining the motivations and adaptations of individuals involved in these partnerships.

Statements and Declarations

The author certifies that he has no affiliations with or involvement in any organ-
ization or entity with any financial interest or non-financial interest in the
subject matter.

Acknowledgments

I would like to thank my supervisor Professor Eric Jolivet for his guidance in
the development of this paper. Also, I would like to thank my colleague Lynn
Chahine from the Université Toulouse Capitole for her comments and sugges-
tions during the development of this paper. and to Alejandro Casallas, a Marie
Curie post-doctoral fellow at the Institute of Science And Technology Austria,
for his insightful comments and his help in the generation of the scripts.
A special thanks to the NumPy, Matplotlib, Bibliometrix, VOSViewer, Pandas
developers' teams, and Ricardo Sanchez. Also, I would like to thank Ricardo
Arenas from Universidad Externado de Colombia.
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