Citation: Iqbal, A.M.;

Kulathuramaiyer, N.; Khan, A.S.;

Abdullah, J.; Khan, M.A. Intellectual

Capital: A System Thinking Analysis

in Revamping the Exchanging

Information in University-Industry

Research Collaboration. Sustainability

2022, 14, 6404. https://doi.org/

10.3390/su14116404

Academic Editor: Fabrizio

D’Ascenzo

Received: 7 March 2022

Accepted: 20 April 2022

Published: 24 May 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional affil-

iations.

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

sustainability

Article

Intellectual Capital: A System Thinking Analysis inRevamping the Exchanging Information in University-IndustryResearch CollaborationAbeda Muhammad Iqbal 1,* , Narayanan Kulathuramaiyer 1, Adnan Shahid Khan 2 , Johari Abdullah 2

and Mussadiq Ali Khan 3

1 Institute of Social Informatics & Technological Innovation, Universiti Malaysia Sarawak,Kota Samarahan 94300, Malaysia; nara@unimas.my

2 Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak,Kota Samarahan 94300, Malaysia; skadnan@unimas.my (A.S.K.); ajohari@unimas.my (J.A.)

3 Faculty of Economics and Business, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;khanconnexion@yahoo.com

* Correspondence: miabeda@unimas.my

Abstract: University-industry research collaboration (UIRC) is a major source for research, inno-vations and sustainable economic growth. Despite the extensive evidence on the importance ofsuch collaboration in developed and developing countries, literature related to the strengthening ofthis collaboration, along with its innovation performance, is still scarce. Scholars believe that theimpact of exchanging information has a vigorous influence on researcher’s innovative activities aswell as research and innovations. Moreover, to flatten the flow of exchanging information betweenresearchers, it is mandatory to refurbish human capital in conjunction with intellectual capital, alongwith their reinforcing factors i.e., communication and networking, respectively. In this paper, weevaluate the influence of human capital and intellectual capital along with their correspondingreinforcing factors on exchanging information using the system thinking method. Evidence fromUIRC in Malaysia provides empirical corroboration that intellectual capital along with its reinforcingfactors has a significant influence on exchanging information. Thus, the findings of this researchsuggest that intensifying the capabilities of intellectual capital with a reinforcing effect can sustainthe circulation of exchanging information.

Keywords: university-industry research collaboration; human capital; intellectual capital; networking;communication; system thinking; national innovation system

1. Introduction

Collaborations amongst multidisciplinary organizations aims to have a sustainableinfluence on our social wellbeing [1]. University-Industry research collaboration (UIRC)is one of the profound examples of such collaboration to ensure sustainable knowledgeand skill flow and, consequently, sustainable national economic growth. Moreover, UIRCis one of the key components that delivers potential pathways to accelerate the economiesof a nation [2–8]. Regardless of the extensive significance of UIRC, the existing literaturesuggests that the rate of technological innovation from UIRC is not satisfactory in severaldeveloping countries [9–11]. Several studies were conducted to explore the factors thatcan enhance such a rate of technological innovation by minimizing the barriers to UIRC.Nevertheless, these are mostly focused on university-industry orientation related factors,for instance, conducting workshops and seminars and hiring educated, trained and skilledpersonnel [12–14], both of which usually act as a symptomatic way out of the problem [15].

Furthermore, universities and industries are the primary components of nationalinnovation systems (NIS), which directly perform technological innovation, while the

Sustainability 2022, 14, 6404. https://doi.org/10.3390/su14116404 https://www.mdpi.com/journal/sustainability

Sustainability 2022, 14, 6404 2 of 15

factors of NIS are the secondary components that influence the interactions within the maincomponent (UIRC) [16–19]. In this regard, the author of [3] has emphasized that if the aimis to foster effective innovation, it is advisable to investigate the influence of the factors ofNIS on the efficiency of UIRC. However, comprehensive studies of the factors related toNIS and their influences on UIRC are still scarce.

Secondly, the main limitation of the current literature is the usage of an analyticalthinking approach, which analyses the efficiency of specific parts or elements withinthe system from a linear perspective and thus provides limited predictability regardingthe outcomes [20–23]. Moreover, as universities and industries are elements of the NIS,they maintain their existence through the mutual interaction of their secondary parts,which leads to the construction of circular causality and demands a systemic approachfor its evaluation [24–26]. Thus, only sequential consideration allows the recognition offundamental weaknesses, which consequently provides a sequential cause of the problemand the methods to cover it, which is impossible to achieve when using the analytical orlinear model [27,28].

This study aims to investigate the influence of key factors of NIS on a secondaryfactor of UIRC to strengthen the technological innovation in UIRC. In addition, this studyproposes the usage of a system thinking approach instead of analytical thinking. A systemthinking approach not only focuses on the linear parts of the system but also focuseson their patterns and events and describes how they work together (circular causality).Furthermore, system thinking not only provides a sequential solution to the problembut also comes up with reinforcing factors that can reinforce the system [29]. Thus, byutilizing the system thinking approach, exchanging information (EI) is identified as themain constraint in UIRC. System thinking consequently provides the solution to diminishthis constraint by indicating the factors human capital (HC) and intellectual capital (IC)as the critical factors of NIS. Furthermore, communication (COM) and networking (NW)are identified as the reinforcing factors to maximize the technological innovation of UIRC.Extensive and exhaustive discussion is elaborated in Section 2.

This research includes five main contributions. First, it contributes to the growingdebate on UIRC and presents a theory of system thinking as a practical solution to enhancetheir rate of technological innovations capabilities. Second, the theory of system thinkinghas not previously been used in studies of UIRC. This research proves the efficacy of thetheory of system thinking in the same context. Thirdly, this research extends the literatureof UIRC with the influence of the critical factors of NIS by illustrating the applicability ofthe theory of system thinking. Fourth, this research provides the reinforcing factors that canreinforce the innovative capability of UIRC. Lastly, this research has practical implicationsfor policymakers, who can consider the theory of system thinking and the importance ofHC and IC on the level of NIS as significant factors to receive valuable outcomes from theircountry’s universities and industries in the shape of new research and innovations.

The remainder of this paper is organized as follows. In Section 2, a literature reviewand hypothesis development are given. A detailed methodology is presented in Section 3.The results and an analysis of the study are given in Section 4, followed by discussion andconclusions in Section 5.

2. Literature Review and Hypothesis Development

It is generally accepted that human capital (HC) is an essential part of any nationalinnovation system and a central element of economic growth theory. An innovation systemand economy with a larger total stock of human capital experiences faster growth [30,31].However, it is observed that when UIRC is formed, EI is one of the major constraintsbetween them [32]. Exchanging information refers to the exchange of knowledge, expertiseand advice among research organizations to resolve the issues and problems that ariseduring research and innovation processes [33]. Similarly, the author of [34] identifies that re-search organizations with extensive exchanging information interactions normally producemore productive results as compared to those with the least exchanging of information.

Sustainability 2022, 14, 6404 3 of 15

In developed countries, the trend of exchanging information provision services be-tween universities and industries is widely promoted and practiced in different forms,such as research-driven exchanging interaction, commercializing-driven interaction andopportunity-driven interaction [35]. However, in developing countries, the rate of ex-changing information services between universities and industries remains at a minimumlevel [35]. The author of [32] highlights some causes of a lack of exchanging informationservices between universities and industries in which the perceptions of universities and in-dustry is a considerable factor in the causes of a lack of exchanging information interactionsbetween universities and industry. For instance, universities perceive that partnershipswith firms affect their pedagogic missions.

In other scenarios, for academicians, academics have an extra burden due to a smallernumber of staff, which is also a factor that inhibits university personnel in engaging in theexchanging of information services with industries. Thus, the lack of the exchanging ofinformation on technical issues always becomes a hindrance in university-industry researchcollaboration [36–41]. In this regard, human capital in national innovation systems (NIS) isa factor that provides a facility to interact with university-industry partners frequently andfacilitate the service of exchanging information between them. Human capital on a nationallevel is recognized as the largest and the most important asset of every organization, aswell as in research organizations.

According to [42], from the university-industry perspective, the term “human capital”has been defined as a key element in improving technological competency and increasingproductivity as well as sustaining a competitive advantage. Human capital in NIS isembodied in skilled and experienced personnel that provide professional training andskills via exchanging information and expertise, increasing the levels of technologicalabilities, which leads to the R&D partners’ satisfaction and performance and eventuallyUIRC innovation performance [43–45], the influence of human capital on exchanginginformation is well illustrated in Figure 1. In this regard, this research hypothesized that:

Sustainability 2022, 14, x FOR PEER REVIEW 4 of 15

Figure 1. Influence of Human Capital on Exchanging Information.

Similarly, [45], highlights that extant intellectual capital in the sectors of NIS can in-crease the organizational values, which can speed up the transfer of information and the development of new knowledge. Moreover, due to fierce competition in the marketplace, as well as globalization and explosions of technology over recent years, intellectual capital is considered as a necessity for every organization [46]. At the same time, to achieve mar-ket success and sustain a competitive advantage, businesses need to exploit new talents and intelligence, such as intellectual capital, which consists of integrative capabilities to make an organization more competitive by improving the knowledge of the human capi-tal [47,48]. The author of [49] defined intellectual capital as the total stock of the collective knowledge, information, experiences, learning, team communication and competence that are able to solve problems and create values for a firm.

Similarly, according to [50], intellectual capital refers to the behavior of using the brain and applying new knowledge. In developed countries, industries have gradually replaced the traditional style and have become prominent players in the field of research and innovations, engaging in a global competition through frequently hiring intellectual capital and improving their system of innovation [51–53]. Intellectual capital is becoming the most valuable asset for institutions and organizations, and it is widely accepted that an organization’s capability to innovate is closely tied to its intellectual capital, or its abil-ity to utilize its knowledge resources [54–56], the influence of intellectual capital on ex-changing information is well illustrated in Figure 2. Thus, this research hypothesized that:

Hypothesis 1 (H1b). Intellectual capital in NIS has a positive influence on exchanging infor-mation in UIRC.

Figure 1. Influence of Human Capital on Exchanging Information.

Hypothesis 1 (H1a). Human capital in NIS has a positive influence on exchanging informationin UIRC.

Sustainability 2022, 14, 6404 4 of 15

Similarly, [45], highlights that extant intellectual capital in the sectors of NIS canincrease the organizational values, which can speed up the transfer of information and thedevelopment of new knowledge. Moreover, due to fierce competition in the marketplace,as well as globalization and explosions of technology over recent years, intellectual capitalis considered as a necessity for every organization [46]. At the same time, to achieve marketsuccess and sustain a competitive advantage, businesses need to exploit new talents andintelligence, such as intellectual capital, which consists of integrative capabilities to make anorganization more competitive by improving the knowledge of the human capital [47,48].The author of [49] defined intellectual capital as the total stock of the collective knowledge,information, experiences, learning, team communication and competence that are able tosolve problems and create values for a firm.

Similarly, according to [50], intellectual capital refers to the behavior of using thebrain and applying new knowledge. In developed countries, industries have graduallyreplaced the traditional style and have become prominent players in the field of researchand innovations, engaging in a global competition through frequently hiring intellectualcapital and improving their system of innovation [51–53]. Intellectual capital is becomingthe most valuable asset for institutions and organizations, and it is widely accepted that anorganization’s capability to innovate is closely tied to its intellectual capital, or its ability toutilize its knowledge resources [54–56], the influence of intellectual capital on exchanginginformation is well illustrated in Figure 2. Thus, this research hypothesized that:

Sustainability 2022, 14, x FOR PEER REVIEW 5 of 15

Figure 2. Influence of Intellectual Capital on Exchanging Information.

Furthermore, according to the theory of system thinking, the system can generate its desired condition. Considering the actual condition of the system and its condition after corrective actions, by taking some reinforcing action, the desired condition can be achieved in a system. Thus, this research proposes some reinforcing factors to reinforce the HC and IC of UIRC, such as effective communication, which creates successful R&D collaboration due to the effective exchanging of information and ideas between team members [57]. Furthermore, according to [58], effective communication among the sectors of NIS is the best way to develop skills and technical competency in university-industry collaboration. Communication is defined as a process where information, concepts and ideas are exchanged in different sectors of innovation [59]. Moreover, the author of [60] explains that effective communication influences the process of innovation by enhancing the level of knowledge, training and technological competencies in research collaboration.

Developed countries that establish a successful innovational rank indicate that they do not have communication problems among the collaborating actors of NIS. On the other hand, the developing countries with unsuccessful research collaboration between univer-sities and industries indicate a communication gap among the sectors of NIS and between their collaborating partnerships as well [61]. Thus, to maximize the innovative perfor-mance of UIRC, communication as a reinforcing factor is induced at the HC and IC. This reinforcing factor boosts up the capabilities of HC and IC, which consequently positively influences the exchanging of information between university and partners and enhances the innovative capabilities of UIRC. The designing of accurate communication channels. which moves towards a constant transmitting of information and the interchange of con-cept and ideas within the HC entities, is the core of the success of the collaborating part-ners in research and development [62,63], the detailed theoretical frame using system thinking approach is well illustrated in Figure 3. Thus, this study hypothesizes that:

Hypothesis 2 (H2a). Communication as a reinforcing factor of human capital and intellectual capital has a positive influence on exchanging information in UIRC.

Similarly, to maximize the innovative performance of UIRC, this research proposes networking (NW) as a reinforcing factor. Prior studies generally suggest that innovative

Figure 2. Influence of Intellectual Capital on Exchanging Information.

Hypothesis 1 (H1b). Intellectual capital in NIS has a positive influence on exchanging informationin UIRC.

Furthermore, according to the theory of system thinking, the system can generateits desired condition. Considering the actual condition of the system and its conditionafter corrective actions, by taking some reinforcing action, the desired condition can beachieved in a system. Thus, this research proposes some reinforcing factors to reinforcethe HC and IC of UIRC, such as effective communication, which creates successful R&Dcollaboration due to the effective exchanging of information and ideas between teammembers [57]. Furthermore, according to [58], effective communication among the sectorsof NIS is the best way to develop skills and technical competency in university-industrycollaboration. Communication is defined as a process where information, concepts and

Sustainability 2022, 14, 6404 5 of 15

ideas are exchanged in different sectors of innovation [59]. Moreover, the author of [60]explains that effective communication influences the process of innovation by enhancingthe level of knowledge, training and technological competencies in research collaboration.

Developed countries that establish a successful innovational rank indicate that theydo not have communication problems among the collaborating actors of NIS. On theother hand, the developing countries with unsuccessful research collaboration betweenuniversities and industries indicate a communication gap among the sectors of NIS andbetween their collaborating partnerships as well [61]. Thus, to maximize the innovativeperformance of UIRC, communication as a reinforcing factor is induced at the HC andIC. This reinforcing factor boosts up the capabilities of HC and IC, which consequentlypositively influences the exchanging of information between university and partners andenhances the innovative capabilities of UIRC. The designing of accurate communicationchannels. which moves towards a constant transmitting of information and the interchangeof concept and ideas within the HC entities, is the core of the success of the collaboratingpartners in research and development [62,63], the detailed theoretical frame using systemthinking approach is well illustrated in Figure 3. Thus, this study hypothesizes that:

Sustainability 2022, 14, x FOR PEER REVIEW 6 of 15

networking among the actors of national innovation systems enables research organiza-tions to access complementary knowledge, information, training, skills, resources [64,65] and complementary technologies [66,67], and enhance learning capabilities [68,69], thus boosting research and innovation performance. Networking emphasizes knowledge shar-ing, and knowledge sharing among the actors of a national innovation system enhances the integrative capabilities of IC [70,71], develops and strengthens internal competencies [72,73] and increases the likelihood of successful innovation activities on behalf of the re-searchers [73,74]. In this regard, this study hypothesizes that:

Hypothesis 2 (H2b). Networking as a reinforcing factor of human capital and intellectual capital has a positive influence on exchanging information in UIRC.

Figure 3. Theoretical Framework Using System Thinking.

3. Methodology In this study, a survey approach based on the positivism paradigm was utilized, in

which an open-ended questionnaire is used for data collection. In this paradigm, data, evidence and rational consideration first shape the knowledge, and later the hypothesis is tested with the help of statistical methods, after which claims are made [75,76]. Further-more, the theory of system thinking and the verified statistical software smart PLS and SPSS were utilized for the elaboration and proof of our hypothesis. As the study contained technological innovations, so the data for this study were obtained from all five research universities (RU) in Malaysia, which are known to be within the top 500 global QS rankings. From RUs, two departments were chosen, including the departments of electrical and chem-ical engineering.

From the webometric search, it has been found that both departments have greater numbers of research groups, industrial collaborations and numbers of ongoing research projects compared to other departments. Thus, these two departments and their collabo-rating industries were selected as respondents. In this study, top tier academic professors (universities) and top management from collaborating industries were identified as an individual unit of analysis to meet the requirements for answering the research questions. Usually, they have to answer the research questions based on their ongoing projects. They were given six months to send their responses electronically. The instruments utilized a five-point Likert scale level of measurement, where 1 is very low and 5 is very high. The

Figure 3. Theoretical Framework Using System Thinking.

Hypothesis 2 (H2a). Communication as a reinforcing factor of human capital and intellectualcapital has a positive influence on exchanging information in UIRC.

Similarly, to maximize the innovative performance of UIRC, this research proposesnetworking (NW) as a reinforcing factor. Prior studies generally suggest that innovativenetworking among the actors of national innovation systems enables research organiza-tions to access complementary knowledge, information, training, skills, resources [64,65]and complementary technologies [66,67], and enhance learning capabilities [68,69], thusboosting research and innovation performance. Networking emphasizes knowledge shar-ing, and knowledge sharing among the actors of a national innovation system enhancesthe integrative capabilities of IC [70,71], develops and strengthens internal competen-cies [72,73] and increases the likelihood of successful innovation activities on behalf of theresearchers [73,74]. In this regard, this study hypothesizes that:

Hypothesis 2 (H2b). Networking as a reinforcing factor of human capital and intellectual capitalhas a positive influence on exchanging information in UIRC.

Sustainability 2022, 14, 6404 6 of 15

3. Methodology

In this study, a survey approach based on the positivism paradigm was utilized, inwhich an open-ended questionnaire is used for data collection. In this paradigm, data,evidence and rational consideration first shape the knowledge, and later the hypothesis istested with the help of statistical methods, after which claims are made [75,76]. Furthermore,the theory of system thinking and the verified statistical software smart PLS and SPSSwere utilized for the elaboration and proof of our hypothesis. As the study containedtechnological innovations, so the data for this study were obtained from all five researchuniversities (RU) in Malaysia, which are known to be within the top 500 global QS rankings.From RUs, two departments were chosen, including the departments of electrical andchemical engineering.

From the webometric search, it has been found that both departments have greaternumbers of research groups, industrial collaborations and numbers of ongoing researchprojects compared to other departments. Thus, these two departments and their collabo-rating industries were selected as respondents. In this study, top tier academic professors(universities) and top management from collaborating industries were identified as anindividual unit of analysis to meet the requirements for answering the research questions.Usually, they have to answer the research questions based on their ongoing projects. Theywere given six months to send their responses electronically. The instruments utilized afive-point Likert scale level of measurement, where 1 is very low and 5 is very high. Thetotal population of both departments is approximately 500, which includes only professors,associate professors and their top managerial personnel in their corresponding collaborat-ing industries. Thus, according to the table of Krejcie & Morgan [77], in 500 populationswith a 95% confidence level, the required respondents are 210. However, in this research,evidence has been collected from 214 respondents to obtain more accurate results. Ourresearch instrument includes EI as a dependent variable, while HC, IC, COM and NW wereindependent and reinforcing variables, respectively. Valid variables were selected from theprevious studies and measured based upon the scope of the current study. Table 1 showsour detailed research instruments, which include dependent variables (DV), independentvariables (IDV) and reinforcing factors (RF), with their corresponding constructs and items.

Table 1. Variables, Constructs and Items of Research Instruments.

Variables Constructs N Items

DV

EI 1 Sharing information expertise andadvice on innovative organizations.

2 Experts’ mobility amongst researchorganizations.

3Exchanging intellectual ideas andmethodologies amongst research

organizations.

4 Conferences and seminars amongstcollaborative organizations.

IDV

HC 1 Number of educated people forresearch and innovation.

2 Number of skilled personals forresearch and innovation.

IC 1 Talents and intelligence ininnovations.

2 Utilizing knowledge and expertise ininnovations.

Sustainability 2022, 14, 6404 7 of 15

Table 1. Cont.

Variables Constructs N Items

RF

NW 1 Voluntary collaboration amongstactors of innovation.

2 Cooperative behavior amongst actorsof innovation.

3 Strategic research and social alliancesamongst the actors of innovation.

COM 1 Frequent communication among theactors of innovation

2 Exchanging information among theactors of innovation.

The pre-analysis of the quantitative data collected was analyzed using a statisticaltechnique available in the statistics package for social science SPSS software that has beenused by the researchers. In this regard, checking the data for missing data, outliers andnormality is essential before starting the data analysis. Preparation and screening the dataare the first stages of data analysis to address the possible issues of the frequencies of theresponses, missing values, outliers and normality. Thus, basic descriptive statistics suchas (mean value) were utilized to replace the missing data. Similarly, boxplots were usedto identify the outliers, and the indexes of skewness and kurtosis were used to check thenormality of the data. Data screening and cleaning, missing data, outliers and normality(skewness and kurtosis analysis) were conducted carefully to ensure the data was usablefor analysis.

4. Results and Analysis

For the data analysis, SPSS and partial least square analysis (PLS) were utilized. Hereit is important to mention that Section 2 clearly shows that all variables of this study areformative. In this regard, for the evaluation of the formative path model, assessment of themeasurement and structure model must be carried out sequentially [78,79].

4.1. Assessment of Measurement Model

This model describes how the latent constructs are measured in terms of their mea-surement properties. In this regard, the measurement model is assessed by measuring thevalidity of the constructs and their indicators.

4.1.1. Assessment of Constructs Validity

At the construct level, it is suggested that there should not be redundancy between theconstructs. For this purpose, multicollinearity is deduced for each of the constructs. Multi-collinearity occurs when there is a high correlation between two or more variables in themodel. Estimates of a regression coefficient become unreliable if there is multicollinearitybetween the variables. The present study has five variables; thus, sufficient efforts weremade to operationalize those variables properly.

For construct validity, the variance of inflation factors (VIF) was tested to evaluatethe possibility of multicollinearity issues. Based on [80], formative construct VIF must notbe greater than 5 and tolerance should be higher than 0.20. Table 2 shows the VIF test byrunning the stepwise regression analysis for each construct. The result indicated that allthe VIFs were less than 5 and all the tolerance values were above 0.20; consequently, nosign of multicollinearity was found.

Sustainability 2022, 14, 6404 8 of 15

Table 2. Assessment of Constructs Validity.

Collinearity Statistics

Constructs Indicators Tolerance VIF

ExchangingInformation

EI_1 0.332 3.012

EI_2 0.820 1.220

EI_3 0.757 1.320

EI_4 0.315 3.171

Human CapitalHC_1 0.831 1.203

HC_2 0.831 1.203

Intellectual CapitalIC_1 0.307 3.262

1C_2 0.307 3.262

Networking

NW_1 0.874 1.145

NW_2 0.920 1.087

NW_3 0.830 1.205

CommunicationCOM_1 0.410 2.440

COM_2 0.410 2.440

4.1.2. Assessment of Indicators Validity

At the indicator level, the question arises as to whether each indicator delivers acontribution to the construct by carrying the intended meaning. It is suggested that thereshould be strong relevancy between the indicator and the construct. To check the relevancyof the indicators with their construct, the weight of each indicator is assessed [81,82].Furthermore, PLS estimates the indicators’ weight (p < 1/

√n), measuring the contribution

of each indicator to the constructs. Here, it is mentioned that in this research a minimum of2 and a maximum of 4 indicators have been used for each of the constructs, so p-values are2, 3, and 4 indicators are 0.709, 0.578 and 0.5, respectively, as shown in Table 3.

Table 3. Assessment of Indicators Validity.

Constructs Indicators Indicators Weight(t-V)

Indicators Loading(t-V)

Exchanging Information

EI_1 0.3450 0.8027

EI_2 0.2550 0.6018

EI_3 0.4054 0.7338

EI_4 0.3272 0.8316

Human CapitalHC_1 0.9319 0.9906

HC_2 0.1445 0.5318

Intellectual CapitalIC_1 −0.0476 0.8178

1C_2 0.0393 0.9997

Networking

COL_1 0.6093 0.5138

COL_2 −0.3969 0.7512

COL_3 0.6450 0.5268

CommunicationCOM_1 −0.4262 0.7131

COM_2 0.2895 0.9966

Table 3 shows the indicators’ weight of all the related constructs. The significant itemweight indicates that all the indicators explain a significant portion of the variance of their

Sustainability 2022, 14, 6404 9 of 15

constructs. Although 3 indicators, “IC (IC_1)”, “COL (COL_2)” and “COM (COM_1)”,based on their formulaic value, have somehow fluctuated frequency, in this regard, ac-cording to Hair et. al., (2012), item loadings are also countable when indicator weights arenot significant at (p < 1/

√n). Thus, the item loadings of all the constructs are significant

(p > 0.50) and show the absolute importance and relevancy for their respective constructs.After having a valid measurement model for this study, PLS analysis was conducted toassess the structural model in the next step phase.

4.2. Assessment of Structural Model

The hypothesized relationships in the structural model, including three main relation-ships (H1a, H1b) and two reinforcing effects (H2a, H2b), were examined. The structuralmodel was tested in terms of paths coefficients and R2 values.

Results of Hypothesis

The results of the hypothesis have been illustrated with the help of the research model.In this study, the research model has been illustrated in two phases that include an initialstructural model and the final structural model. In more detail, Figure 4 shows the effectof the factors of NIS (HC) and (IC) on the constraint (EI) of UIRC. In this regard, Pathcoefficient (β) values indicate the effect of HC and IC, and the R2 values explain thevariances on the EI of UIRC. For instance, the β value of HC (−0.201) does not show thesignificant effects on EI of UIRC. Similarly, the R2 values of EI 0.40% also do not show thesignificant variance from HC to EI, while the β value of IC (0.505), showing the significanteffects on EI and R2 values of EI 25.5%, shows the significant variance from IC to EI,respectively. Hence, Figure 4 proves that IC is the critical factor of NIS that can enhance theEI in UIRC.

Sustainability 2022, 14, x FOR PEER REVIEW 10 of 15

HC EI0.040

HC_1

HC_2

EI_1

EI_2

EI_3

EI_4

-0.201

Figure 4. Effect of Human Capital on Exchanging Information.

Furthermore, Figure 5 showed support for the reinforcing role of the factors of NIS and consequently on the constraints of UIRC. Inducing reinforcing factor COM and NW increased the path coefficients of HC (−0.201 to 0.175) and IC (0.505 to 0.628) to EI and simultaneously increased the variances (R2) from HC to EI 0.040 (0.40%) to 0.304 (30.4%) and from IC to EI 0.255 (25.5%) to 0.644 (64.4%), respectively as shown in Figure 6. Figure 5 shows that COM and NW are the considerable reinforcing factors in enriching the effi-ciencies of HC and IC and consequently enhancing the EI of UIRC. Additionally, t-Statis-tics was also examined to investigate the accuracy of each path.

IC EI0.255

IC_1

IC_2

EI_1

EI_2

EI_3

EI_4

0.505

Figure 5. Effect of Intellectual Capital on Exchanging Information.

COM

NW

HC EI0.304

COM_2

COM_1

HC_1

HC_2

NW_1

NW_2

NW_3

EI_1

EI_2

EI_3

EI_4

0.175

Figure 6. Effect of Human Capital on Exchanging Information.

Figure 4. Effect of Human Capital on Exchanging Information.

Furthermore, Figure 5 showed support for the reinforcing role of the factors of NISand consequently on the constraints of UIRC. Inducing reinforcing factor COM and NWincreased the path coefficients of HC (−0.201 to 0.175) and IC (0.505 to 0.628) to EI andsimultaneously increased the variances (R2) from HC to EI 0.040 (0.40%) to 0.304 (30.4%)and from IC to EI 0.255 (25.5%) to 0.644 (64.4%), respectively as shown in Figure 6. Figure 5shows that COM and NW are the considerable reinforcing factors in enriching the efficien-cies of HC and IC and consequently enhancing the EI of UIRC. Additionally, t-Statisticswas also examined to investigate the accuracy of each path.

Sustainability 2022, 14, x FOR PEER REVIEW 10 of 15

HC EI0.040

HC_1

HC_2

EI_1

EI_2

EI_3

EI_4

-0.201

Figure 4. Effect of Human Capital on Exchanging Information.

Furthermore, Figure 5 showed support for the reinforcing role of the factors of NIS and consequently on the constraints of UIRC. Inducing reinforcing factor COM and NW increased the path coefficients of HC (−0.201 to 0.175) and IC (0.505 to 0.628) to EI and simultaneously increased the variances (R2) from HC to EI 0.040 (0.40%) to 0.304 (30.4%) and from IC to EI 0.255 (25.5%) to 0.644 (64.4%), respectively as shown in Figure 6. Figure 5 shows that COM and NW are the considerable reinforcing factors in enriching the effi-ciencies of HC and IC and consequently enhancing the EI of UIRC. Additionally, t-Statis-tics was also examined to investigate the accuracy of each path.

IC EI0.255

IC_1

IC_2

EI_1

EI_2

EI_3

EI_4

0.505

Figure 5. Effect of Intellectual Capital on Exchanging Information.

COM

NW

HC EI0.304

COM_2

COM_1

HC_1

HC_2

NW_1

NW_2

NW_3

EI_1

EI_2

EI_3

EI_4

0.175

Figure 6. Effect of Human Capital on Exchanging Information.

Figure 5. Effect of Intellectual Capital on Exchanging Information.

Sustainability 2022, 14, 6404 10 of 15

Sustainability 2022, 14, x FOR PEER REVIEW 10 of 15

HC EI0.040

HC_1

HC_2

EI_1

EI_2

EI_3

EI_4

-0.201

Figure 4. Effect of Human Capital on Exchanging Information.

Furthermore, Figure 5 showed support for the reinforcing role of the factors of NIS and consequently on the constraints of UIRC. Inducing reinforcing factor COM and NW increased the path coefficients of HC (−0.201 to 0.175) and IC (0.505 to 0.628) to EI and simultaneously increased the variances (R2) from HC to EI 0.040 (0.40%) to 0.304 (30.4%) and from IC to EI 0.255 (25.5%) to 0.644 (64.4%), respectively as shown in Figure 6. Figure 5 shows that COM and NW are the considerable reinforcing factors in enriching the effi-ciencies of HC and IC and consequently enhancing the EI of UIRC. Additionally, t-Statis-tics was also examined to investigate the accuracy of each path.

IC EI0.255

IC_1

IC_2

EI_1

EI_2

EI_3

EI_4

0.505

Figure 5. Effect of Intellectual Capital on Exchanging Information.

COM

NW

HC EI0.304

COM_2

COM_1

HC_1

HC_2

NW_1

NW_2

NW_3

EI_1

EI_2

EI_3

EI_4

0.175

Figure 6. Effect of Human Capital on Exchanging Information. Figure 6. Effect of Human Capital on Exchanging Information.

Table 4 shows the results of t-Statistics values, specifically the t-statistics of {H1a andH2a and H1b and H2b)}. According to the table, the t-statistics of the HC (t = −3.55) is notsignificant at (p > 1.96) from their path estimates, while the t-Statistics of the IC (13.08) issignificant from its path estimate. Simultaneously, the t-statistics of the reinforcing factorsillustrates that the COM (t = 3.977), (t = 5.872) is more significant as compared to networking(t = 3.155), (t = 1.723). However, COM and NW are both reinforcing factors with significantinfluence on both NIS factors HC and IC and consequently on EI as shown in Figure 7.

Table 4. Path Coefficient and t-Statistics.

N Hypothesis Path Coefficient StandardError t-Statistics

H1 HC→ ET −0.201 0.052 −3.55

H1a COM→ EI 0.455 0.037 3.977

H2a NW→ EI 0.127 0.042 3.155

H2 IC→ EI 0.505 0.028 13.08

H1b COM→ EI 0.286 0.045 5.872

H2b NW→ EI 0.096 0.171 1.723

Sustainability 2022, 14, 6404 11 of 15

Sustainability 2022, 14, x FOR PEER REVIEW 11 of 15

Table 4 shows the results of t-Statistics values, specifically the t-statistics of {H1a and H2a and H1b and H2b)}. According to the table, the t-statistics of the HC (t = −3.55) is not significant at (p > 1.96) from their path estimates, while the t-Statistics of the IC (13.08) is significant from its path estimate. Simultaneously, the t-statistics of the reinforcing factors illustrates that the COM (t = 3.977), (t = 5.872) is more significant as compared to network-ing (t = 3.155), (t = 1.723). However, COM and NW are both reinforcing factors with sig-nificant influence on both NIS factors HC and IC and consequently on EI as shown in Figure 7.

COM

NW

IC EI0.644

COM_2

COM_1

IC_1

IC_2

NW_1

NW_2

NW_3

EI_1

EI_2

EI_3

EI_4

0.628

0.09

6

Figure 7. Effect of Intellectual Capital on Exchanging Information.

Table 4. Path Coefficient and t-Statistics.

N Hypothesis Path Coefficient Standard Error t-Statistics

H1 HC → ET −0.201 0.052 −3.55 H1a COM → EI 0.455 0.037 3.977 H2a NW → EI 0.127 0.042 3.155 H2 IC → EI 0.505 0.028 13.08

H1b COM → EI 0.286 0.045 5.872 H2b NW → EI 0.096 0.171 1.723

5. Discussion and Conclusions 5.1. Discussion

Based on the analysis, this research proves that the factor of NIS (IC) is the critical successful factor to enhance the innovative capabilities of UIRC. It is generally accepted that human capital is the factor that provides a facility to facilitate the interaction of uni-versity-industry partners and develop a program of exchanging information between them and consequently improve the technological competencies of both parties. Thus, based on the previous literature, this research hypothesized that human capital in inno-vation systems has a positive influence on exchanging information in UIRC. However, anti-reciprocally of the previous literature, the result of this research does not show the significant influence of human capital on exchanging information (B = −0.201 t = −3.55).

Figure 7. Effect of Intellectual Capital on Exchanging Information.

5. Discussion and Conclusions5.1. Discussion

Based on the analysis, this research proves that the factor of NIS (IC) is the criticalsuccessful factor to enhance the innovative capabilities of UIRC. It is generally accepted thathuman capital is the factor that provides a facility to facilitate the interaction of university-industry partners and develop a program of exchanging information between them andconsequently improve the technological competencies of both parties. Thus, based on theprevious literature, this research hypothesized that human capital in innovation systemshas a positive influence on exchanging information in UIRC. However, anti-reciprocally ofthe previous literature, the result of this research does not show the significant influence ofhuman capital on exchanging information (B =−0.201 t =−3.55). Thus, in an unlikely result,from the previous considerations and perception about human capital and its relationshipwith the exchanging information of UIRC, the result of the present analysis showed thatintellectual capital has a positive influence on exchanging information (B = 0.505, t = 13.08).Thus, from the findings of this research, it can be concluded that as compared to humancapital, intellectual capital is the most preferable factor in NIS to enhance the outcome ofUIRC. Intellectual capital is the most valuable prerequisite requirement of UIRC to enhanceknowledge and ideas for the development of research and innovations.

Furthermore, this research contributes to the literature by proposing COM and NW asthe reinforcing factors, although, from the analysis of the research it is coherent that theIC at a national level not only has a capability to reduce the constraints of EI, but COMand NW as a reinforcing factor enhances the efficiencies of HC and IC as well as provide aleading assistance to the IC to be more efficient for the provision of knowledge and skills toresearch and innovative organizations.

5.2. Conclusions and Recommendations

As university-industry research collaboration (UIRC) has a strong and direct impact onthe economic growth of a country, in this regard understanding and identifying the factorsthat have important roles in enhancing the innovative capability of UIRC is mandatory.The present study was undertaken to gain a better understanding of the developmentof the innovative capability of UIRC. To improve the innovative capabilities of UIRC,

Sustainability 2022, 14, 6404 12 of 15

policymakers need to have a comprehensive understanding of the influence of nationalsystems of innovation (NIS). A review of the literature, measurement items and analysesand the theory of the study present a framework to enhance the innovative capabilities ofUIRC [83–87]. Previous studies have evaluated research collaboration between universitiesand industries from the perspective of analytical thinking, which is mostly related to theinternal efficiencies and effectiveness of UIRC.

Finally, from the perceptions of the practical implications, the researcher suggestedthat although, this framework has been developed for enhancing the innovative capabilityof UIRC in Malaysia, it can be implemented generally in any country by simply followingthe procedure of the developed framework. Secondly, system thinking can help policy-makers by having an extensive and comprehensive knowledge of the influence of (NIS) on(UIRC) [88]. In terms of practical implications, this study tried to develop a framework tostrengthen the innovative capability of UIRC. In other words, the findings of the currentstudy provide intuition to policymakers to understand the relationship between a strongsystem of innovation and the innovative capabilities of UIRC.

This research identifies future research directions that will help in overcoming thelimitations of this research. Using Malaysia as the scope of the study, this research proposescomparative works conducted across other developed and developing countries. Further-more, replicating the study by comparing other countries could be valuable to identify themajor differences in terms of enhancing the innovative capability of UIRC.

Author Contributions: Conceptualization, A.M.I. and A.S.K.; methodology, A.M.I., N.K., J.A.; soft-ware, A.M.I. and J.A.; validation, A.S.K., J.A., M.A.K. and N.K.; investigation, A.M.I.; writing—originaldraft preparation, A.M.I. and A.S.K.; writing—review and editing, J.A. and M.A.K.; visualization,N.K.; supervision, N.K.; project administration, N.K. and J.A.; funding acquisition, A.M.I. and N.K.All authors have read and agreed to the published version of the manuscript.

Funding: This research was fully funded by Universiti Malaysia Sarawak.

Conflicts of Interest: The authors declare no conflict of interest.

References1. Andres, B.; Poler, R.; Guzman, E. The Influence of Collaboration on Enterprises Internationalization Process. Sustainability 2022,

14, 2843. [CrossRef]2. Lin, J.Y.; Yang, C.H. Heterogeneity in industry–university R&D collaboration and firm innovative performance. Scientometrics

2020, 124, 1–25.3. Messeni Petruzzelli, A.; Murgia, G. University—Industry collaborations and international knowledge spillovers: A joint-patent

investigation. J. Technol. Transf. 2020, 45, 958–983. [CrossRef]4. Jones, S.E.; Coates, N. A micro-level view on knowledge co-creation through university-industry collaboration in a multi-national

corporation. J. Manag. Dev. 2020, 39, 723–738. [CrossRef]5. Ting, S.H.; Yahya, S.; Tan, C.L. Importance-Performance Matrix Analysis of the Researcher’s Competence in the Formation of

University-Industry Collaboration Using Smart PLS. Public Organ. Rev. 2020, 20, 249–275. [CrossRef]6. Iqbal, A.M.; Khan, A.S.; Abdullah, J.; Kulathuramaiye, N.; Senin, A.A. Blended system thinking approach to strengthen the

education and training in university-industry research collaboration. Technol. Anal. Strateg. Manag. 2021, 34, 447–460. [CrossRef]7. Iqbal, A.M. Influence of National Innovation System on University-Industry Research Collaboration. Ph.D. Thesis, Universiti

Teknologi Malaysia, Skudai, Malaysia, 2018.8. Iqbal, A.M.; Khan, A.S.; Bashir, F.; Senin, A.A. Evaluating national innovation system of malaysia based on university-industry

research collaboration: A system thinking approach. Asian Soc. Sci. 2015, 11, 45.9. Parmentola, A.; Ferretti, M.; Panetti, E. Exploring the university-industry cooperation in a low innovative region. What differences

between low tech and high tech industries? Int. Entrep. Manag. J. 2021, 17, 1469–1496. [CrossRef]10. Tseng, F.C.; Huang, M.H.; Chen, D.Z. Factors of university–industry collaboration affecting university innovation performance.

J. Technol. Transf. 2020, 45, 560–577. [CrossRef]11. Bohin, P. Effectiveness of innovative policies to enhance university- industry collaboration in developing countries. Towards

technical university-industry links in Ghana. Br. J. Educ. 2018, 6, 54–70.12. Chen, K.; Lu, W.; Wang, J. University–industry collaboration for BIM education: Lessons learned from a case study. Ind. High.

Educ. 2020, 34, 401–409. [CrossRef]13. Dooley, L.; Gubbins, C. Inter-organisational knowledge networks: Synthesising dialectic tensions of university-industry knowl-

edge discovery. J. Knowl. Manag. 2019, 23, 2113–2134. [CrossRef]

Sustainability 2022, 14, 6404 13 of 15

14. Brazile, T.; Hostetter, S.G.; Donough, C.M.; Citters, D.W. Promoting innovation: Enhancing transdisciplinary opportunities formedical and engineering students. Med. Teach. 2018, 40, 1264–1274. [CrossRef] [PubMed]

15. Iqbal, A.M.; Khan, A.S.; Senin, A.A. Reinforcing the National Innovation System of Malaysia Based on University-IndustryResearch Collaboration: A System Thinking Approach. Int. J. Manag. Sci. Bus. Res. 2015, 4, 6–15.

16. Iqbal, A.M.; Khan, A.S.; Iqbal, S.; Senin, A.A. Designing of success criteria-based evaluation model for assessing the researchcollaboration between university and industry. Int. J. Bus. Res. Manag. 2011, 2, 59–73.

17. Iqbal, A.M.; Khan, A.S.; Senin, A.A. Determination of high impact evaluation metrics for evaluating the University-industrytechnological linkage. Int. J. Phys. Soc. Sci. 2012, 2, 111–122.

18. Iqbal, S.; Iqbal, A.M.; Shahid, A.; Senin, A.A. A Modern Strategy for the Development of Academic Staff Based on University-Industry Knowledge Transfer Effectiveness& Collaborative Research. Sains Hum. 2013, 64. [CrossRef]

19. Chen, Y.; Han, J.; Xuan, Z.; Gao, W. Higher education’s role in Chinese national innovation system: A perspective of university-industry linkages. In Proceedings of the 17th International Conference on Scientometrics and Informetrics, ISSI 2019, Rome, Italy,2–5 September 2019; Volume 1, pp. 573–583.

20. Kafouros, M.; Wang, C.; Piperopoulos, P.; Zhang, M. Academic collaborations and firm innovation performance in China: Therole of region-specific institutions. Res. Policy 2015, 44, 803–817. [CrossRef]

21. Fuentes, D.C.; Dutrénit, G. Best channels of academia-industry interaction for long-term benefit. Res. Policy 2012, 41, 1666–1682.[CrossRef]

22. Iqbal, A.M.; Khan, A.S.; Parveen, S.; Senin, A.A. An efficient evaluation model for the assessment of university-industry researchcollaboration in Malaysia. Res. J. Appl. Sci. Eng. Technol. 2015, 10, 298–306. [CrossRef]

23. Freitas, I.M.B.; Geuna, A.; Rossi, F. Finding the right partners: Institutional and personal modes of governance of university–industry interactions. Res. Policy 2013, 42, 50–62. [CrossRef]

24. Razorenov, Y.I.; Vodenko, K.V. Innovative development of the national university system in Russia: Trends and key elements. Int.J. Sociol. Soc. Policy 2021, 41, 253–262. [CrossRef]

25. Melamed-Varela, E.; Navarro-Vargas, L.; Blanco-Ariza, A.B.; Olivero-Vega, E. University-Industry-Government linkage topromote innovation at regional systems: Documentary research. Rev. Estud. Reg. 2019, 114, 147–169.

26. Wilson, L.E.; Gahan, M.E.; Lennard, C.; Robertson, J. Why do we need a systems thinking approach to military forensic science inthe contemporary world? Aust. J. Forensic Sci. 2020, 52, 323–336. [CrossRef]

27. Allender, S.; Brown, A.D.; Bolton, K.A.; Fraser, P.; Lowe, J.; Hovmand, P. Translating systems thinking into practice for communityaction on childhood obesity. Obes. Rev. 2019, 20, 179–184. [CrossRef] [PubMed]

28. Befus, D.R.; Lich, K.H.; Kneipp, S.M.; Bettger, J.P.; Coeytaux, R.R.; Humphreys, J.C. A qualitative, systems thinking approach tostudy self-management in women with migraine. Nurs. Res. 2018, 67, 395–403. [CrossRef]

29. Sarriot, E.; Morrow, M.; Langston, A.; Weiss, J.; Landegger, J.; Tsuma, L. A causal loop analysis of the sustainability of integratedcommunity case management in Rwanda. Soc. Sci. Med. 2015, 131, 147–155. [CrossRef]

30. Santos-rodrigues, H.; Dorrego, P.F.; Jardon, C.F. The influence of human capital on the innovativeness of firms. Int. Bus. Econ. Res.J. 2010, 9, 53–63. [CrossRef]

31. Gössling, T.; Rutten, R. Innovation in Regions. European Planning Studies; Taylor & Francis Group: Tilburg, ND, USA, 2007; Volume15, pp. 253–270.

32. Esham, M. Strategies to develop university-industry linkages in Sri Lanka. In Research Studies on Tertiary Education Sector, StudySeries 4; National Education Commission: Colombo, Sri Lanka, 2008.

33. Cohen, W.M.; Nelson, R.R.; Walsh, J.P. Links and impacts: The influence of public research on industrial R&D. Manag. Sci. 2002,48, 1–23.

34. Rebne, D. Faculty consulting and scientific knowledge—A traditional university–industry linkage. Educ. Adm. Q. 1989, 25,338–357. [CrossRef]

35. Perkmann, M.; Walsh, K. How firms source knowledge from universities: Partnerships versus contracting (forthcoming). InCreating Wealth from Knowledge: Meeting the Innovation Challenge; Bessant, J., Venables, T., Eds.; Edward Elgar: Cheltenham,UK, 2007.

36. Saleh, Z. Reforming Public Sector Accounting: Meeting the Challenges of the Knowledge Economy: Exploring Globalization in AccountingEducation and Financial Accounting and Reporting; KPMG: Kuala Lumpur, Malaysia, 2008; pp. 14–36.

37. Dodgson, M.; Mathews, J.; Kastelle, T.; Hu, M.-C. The evolving nature of Taiwan’s national innovation system: The case ofbiotechnology innovation networks. Res. Policy 2008, 37, 430–445. [CrossRef]

38. Kaymaz, K.; Eryigit, K.Y. Determining factors hindering university-industry collaboration: An analysis from the perspective ofacademicians in the context of Entrepreneurial Science Paradigm. Int. J. Soc. Inq. 2011, 4, 185–213.

39. Iqbal, A.M.; Iqbal, S.; Khan, A.S.; Senin, A.A. A novel cost efficient evaluation model for assessing research-based technologytransfer between university and industry. J. Teknol. 2013, 64. [CrossRef]

40. Iqbal, A.M.; Aslan, A.S.; Khan, A.S.; Iqbal, S. Research Collaboration Agreements: A Major Risk Factor Between University-Industry Collaboration. An Analysis Approach. In Proceedings of the 3rd International Graduate Conference on Engineering,Science and Humanities (IGCESH2010), Johor Bahru, Malaysia, 2–4 November 2010.

41. Iqbal, A.M.; Aslan, A.S.; Khan, A.S. Innovation Oriented Constraints between University-industry Technological Collaboration.Proc. ICPE-4 2010, 4, 24.

Sustainability 2022, 14, 6404 14 of 15

42. Schultz, T.W. The economic importance of human capital in modernization. Educ. Econ. 1993, 1, 13–19. [CrossRef]43. Marimuthu, M.; Arokiasamy, L.; Ismail, M. Human Capital Development and Its Impact on Firm Performance: Evidence from

Development Economics. J. Int. Soc. Res. 2009, 2, 265–272.44. Iqbal, A.M. Evaluation of Research Collaboration between University-Industry. Master’s Thesis, Universiti Teknologi Malaysia,

Skudai, Malaysia, 2012.45. Mallana, M.F.B.A.; Iqbal, A.M.; Iqbal, S.; Khan, A.S.; Senin, A.A. The critical factors for the successful transformation of technology

from developed to developing countries. J. Teknol. 2013, 64. [CrossRef]46. Edvinsson, L.; Sullivan, P. Developing a model for managing intellectual capital. Eur. Manag. J. 1996, 14, 356–364. [CrossRef]47. Roose, G.; Pike, S.; Fernstorm, L. Managing Intellectual Capital in Practice; Elsvier Butterworth-Heinemann: Burlington, MA,

USA, 2005.48. Subramanian, M.; Youndt, M.A. The influence of intellectual capital on the type of innovative capabilities. Acad. Manag. J. 2005,

48, 450–463.49. Stewart, T.A. Intellectual Capital: The New Wealth of Organizations; Bantam Double Leady Dell Publishing Group Inc.: New York,

NY, USA, 1997.50. Galbraith, J.K. The Affluent Society; Houghton Mifflin Company: London, UK, 1969; Volume 2.51. Hu, M.-C.; Mathews, J.A. National innovative capacity in East Asia. Res. Policy 2005, 34, 1322–1349. [CrossRef]52. Nasierowski, W.; Arcelus, F.J. On the efficiency of national innovation systems. Socio-Econ. Plan. Sci. 2003, 37, 215–234. [CrossRef]53. Pan, T.-W.; Hung, S.-W.; Lu, W.-M. DEA performance measurement of the national innovation system in Asia and Europe.

Asia-Pac. J. Oper. Res. 2010, 27, 369–392. [CrossRef]54. Bontis, N. Intellectual capital: An exploratory study that develops measures and models. Manag. Decis. 1998, 36, 63–76. [CrossRef]55. Rehman, W.U.; Rehman, C.A.; Rehman, H.U.; Zahid, A. Intellectual Capital Performance and Its Impact on Corporate Performance:

An Empirical Evidence from Modaraba Sector of Pakistan. Aust. J. Bus. Manag. Res. 2011, 1, 8–16. [CrossRef]56. Bin Ahmad, S.; Mushraf, A.M. The Relationship between Intellectual capital and Business Performance: An empirical study in

Iraqi industry. In International Conference on Management and Artificial Intelligence; IACSIT Press: Bali, Indonesia, 2011; pp. 104–109.57. Chin, C.M.M.; Yap, E.H.; Spowage, A.C. Project management methodology for university-industry collaborative projects. Rev. Int.

Comp. Manag. 2011, 12, 901–918.58. Hamdan, H.; Yusof, F.; Omar, D.; Abdullah, F.; Nasrudin, N.; Abullah, I.C. University Industrial Linkages: Relationship Towards

Economic Growth and Development in Malaysia. Int. J. Econ. Manag. Eng. 2011, 5, 1284–1291.59. Mora-Valentin, E.; Montoro-Sanches, A.; Guerras-Martin, L. Determining Factors in the Success of R&D Cooperative Agreements

between Firms and Research Organizations. Res. Policy 2004, 33, 17–40.60. Lee, K.; Ohta, T.; Kakehi, K. Formal boundary spanning by industry liaison offices and the changing pattern of university–industry

cooperative research: The case of the University of Tokyo. Technol. Anal. Strateg. Manag. 2010, 22, 189–206. [CrossRef]61. Butcher, J.; Jeffrey, P. A view from the coal face: UK research student perceptions of successful and unsuccessful collaborative

projects. Res. Policy 2007, 36, 1239–1250. [CrossRef]62. Ceci, F.; Iubatti, D. Personal relationships and innovation diffusion in SME networks: A content analysis approach. Res. Policy

2012, 41, 565–579. [CrossRef]63. Elmuti, D.; Abebe, M.; Nicolosi, M. An overview of strategic alliances between universities and corporations. Albuquerque:

University of New Mexico, Department of Communication and Journalism, Report to the Mitsubishi International Corporation.J. Workplace Learn. 2005, 17, 115–129. [CrossRef]

64. Teece, D. Profiting from technological innovation: Implications for integration collaboration, licensing and public policy. Res.Policy 1986, 15, 285–305. [CrossRef]

65. Baum, J.A.C.; Calabrese, T.; Silverman, B.S. Don’t go it alone: Alliance network composition and startups’ performance incanadian biotechnology. Strateg. Manag. J. 2000, 21, 267–294. [CrossRef]

66. Miotti, L.; Sachwald, F. Co-operative R&D: Why and with whom? Res. Policy 2003, 32, 1481–1499.67. Mohnen, P.; Hoareau, C. What type of enterprise forges close links with universities and government labs? Evidence from CIS 2.

Manag. Decis. Econ. 2003, 24, 133–145. [CrossRef]68. Kleinknecht, A.; Reijnen, J.O.N. Why do firms cooperate on R&D? An empirical study. Res. Policy 1992, 21, 347–360.69. Becker, W.; Dietz, J. R&D cooperation and innovation activities of firms—Evidence for the German manufacturing industry. Res.

Policy 2004, 33, 209–223.70. Hagedoorn, J.; Schakenraad, J. The Effect of Strategic Technology Alliances on Company Performance. Strateg. Manag. J. 1994, 15,

291–309. [CrossRef]71. Powell, W.W.; Koput, K.W.; Smith-Doerr, L. Interorganizational collaboration and the locus of innovation: Networks of learning

in biotechnology. Adm. Sci. Q. 1996, 41, 116–145. [CrossRef]72. Mitchell, W.; Singh, K. Survival of businesses using collaborative relationships to commercialize complex goods. Strateg. Manag.

J. 1996, 17, 169–195. [CrossRef]73. Ahuja, G. The Duality of Collaboration: Inducements and Opportunities in the Formation of Interfirm Linkages. Strateg. Manag. J.

2000, 21, 317–343. [CrossRef]74. Uzzi, B. Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Adm. Sci. Q. 1997, 42, 35–67.

[CrossRef]

Sustainability 2022, 14, 6404 15 of 15

75. Phillips, D.C.; Burbules, N.C. Postpositivism and Educational Research; Rowman & Littlefield: New York, NY, USA, 2000.76. Park, Y.S.; Konge, L.; Artino, A.R. The Positivism Paradigm of Research. Acad. Med. 2020, 95, 690–694. [CrossRef] [PubMed]77. Krejcie, R.V.; Morgan, D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970, 30, 607–610. [CrossRef]78. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modeling, 3rd ed.;

Sage: Thousand Oaks, CA, USA, 2018.79. Merrill, W. Models and Applications in the Decisions Science: Best Papers from the 2015 Annual Conference; Decision Science Institute:

Atlanta, GA, USA, 1995.80. Hair, J.F.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An

emerging tool in business research. Eur. Bus. Rev. 2014, 26, 106–121. [CrossRef]81. Henseler, J.; Chin, W.W. A Comparison of Approaches for the Analysis of Interaction Effects Between Latent Variables Using

Partial Least Squares Path Modeling. Struct. Equ. Modeling 2009, 17, 82–109. [CrossRef]82. Tenenhaus, M.; Esposito Vinzi, V. PLS regression, PLS path modeling and generalized procrustean analysis: A combined approach

for multiblock analysis. J. Chemom. 2005, 19, 145–153. [CrossRef]83. Brimble, P.; Doner, R.F. University-Industry Linkages and Economy Development: The case of Thailand. World Dev. 2007, 35,

1021–1036. [CrossRef]84. Xu, D. Research on Improving the Technological Innovation Capability of SMEs by University-Industry Collaboration. J. Eng. Sci.

Technol. Rev. 2013, 6, 100–104. [CrossRef]85. Li, X. Sources of external technology, absorptive capacity, and innovation capability in Chinese state-owned high-tech enterprises.

World Dev. 2011, 39, 1240–1248. [CrossRef]86. Azizan, S.A. Strengthening Malaysia’s Scientific and Technological Development through Human Capital Development. Procedia

Soc. Behav. Sci. 2013, 91, 648–653. [CrossRef]87. Alexander, C.; Yuriy, H. Problems and Perspectives of Performance of Higher education institutions in the Development of

Russian Innovative System (regional aspect). Procedia-Soc. Behav. Sci. 2018, 166, 497–504. [CrossRef]88. Richmond, B. System Dynamics Review. Summer-Fall 1994, 10, 140.