Intellectual Capital: A System Thinking Analysis in Revamping the Exchanging Information in University-Industry Research Collaboration

Abstract

University-industry research collaboration (UIRC) is a major source for research, innovations and sustainable economic growth. Despite the extensive evidence on the importance of such collaboration in developed and developing countries, literature related to the strengthening of this collaboration, along with its innovation performance, is still scarce. Scholars believe that the impact of exchanging information has a vigorous influence on researcher’s innovative activities as well as research and innovations. Moreover, to flatten the flow of exchanging information between researchers, it is mandatory to refurbish human capital in conjunction with intellectual capital, along with their reinforcing factors i.e., communication and networking, respectively. In this paper, we evaluate the influence of human capital and intellectual capital along with their corresponding reinforcing factors on exchanging information using the system thinking method. Evidence from UIRC in Malaysia provides empirical corroboration that intellectual capital along with its reinforcing factors has a significant influence on exchanging information. Thus, the findings of this research suggest that intensifying the capabilities of intellectual capital with a reinforcing effect can sustain the circulation of exchanging information.

1. Introduction

Collaborations amongst multidisciplinary organizations aims to have a sustainable influence on our social wellbeing [1]. University-Industry research collaboration (UIRC) is one of the profound examples of such collaboration to ensure sustainable knowledge and skill flow and, consequently, sustainable national economic growth. Moreover, UIRC is one of the key components that delivers potential pathways to accelerate the economies of a nation [2,3,4,5,6,7,8]. Regardless of the extensive significance of UIRC, the existing literature suggests that the rate of technological innovation from UIRC is not satisfactory in several developing countries [9,10,11]. Several studies were conducted to explore the factors that can 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 skilled personnel [12,13,14], both of which usually act as a symptomatic way out of the problem [15].

Furthermore, universities and industries are the primary components of national innovation systems (NIS), which directly perform technological innovation, while the factors of NIS are the secondary components that influence the interactions within the main component (UIRC) [16,17,18,19]. In this regard, the author of [3] has emphasized that if the aim is to foster effective innovation, it is advisable to investigate the influence of the factors of NIS on the efficiency of UIRC. However, comprehensive studies of the factors related to NIS and their influences on UIRC are still scarce.

Secondly, the main limitation of the current literature is the usage of an analytical thinking approach, which analyses the efficiency of specific parts or elements within the system from a linear perspective and thus provides limited predictability regarding the outcomes [20,21,22,23]. 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 approach for its evaluation [24,25,26]. Thus, only sequential consideration allows the recognition of fundamental weaknesses, which consequently provides a sequential cause of the problem and the methods to cover it, which is impossible to achieve when using the analytical or linear model [27,28].

This study aims to investigate the influence of key factors of NIS on a secondary factor of UIRC to strengthen the technological innovation in UIRC. In addition, this study proposes the usage of a system thinking approach instead of analytical thinking. A system thinking approach not only focuses on the linear parts of the system but also focuses on their patterns and events and describes how they work together (circular causality). Furthermore, system thinking not only provides a sequential solution to the problem but also comes up with reinforcing factors that can reinforce the system [29]. Thus, by utilizing the system thinking approach, exchanging information (EI) is identified as the main constraint in UIRC. System thinking consequently provides the solution to diminish this 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 growing debate on UIRC and presents a theory of system thinking as a practical solution to enhance their rate of technological innovations capabilities. Second, the theory of system thinking has not previously been used in studies of UIRC. This research proves the efficacy of the theory of system thinking in the same context. Thirdly, this research extends the literature of UIRC with the influence of the critical factors of NIS by illustrating the applicability of the theory of system thinking. Fourth, this research provides the reinforcing factors that can reinforce the innovative capability of UIRC. Lastly, this research has practical implications for policymakers, who can consider the theory of system thinking and the importance of HC and IC on the level of NIS as significant factors to receive valuable outcomes from their country’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 review and 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 and conclusions in Section 5.

2. Literature Review and Hypothesis Development

It is generally accepted that human capital (HC) is an essential part of any national innovation system and a central element of economic growth theory. An innovation system and 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 constraints between them [32]. Exchanging information refers to the exchange of knowledge, expertise and advice among research organizations to resolve the issues and problems that arise during research and innovation processes [33]. Similarly, the author of [34] identifies that research organizations with extensive exchanging information interactions normally produce more productive results as compared to those with the least exchanging of information.

In developed countries, the trend of exchanging information provision services between universities and industries is widely promoted and practiced in different forms, such as research-driven exchanging interaction, commercializing-driven interaction and opportunity-driven interaction [35]. However, in developing countries, the rate of exchanging information services between universities and industries remains at a minimum level [35]. The author of [32] highlights some causes of a lack of exchanging information services between universities and industries in which the perceptions of universities and industry is a considerable factor in the causes of a lack of exchanging information interactions between universities and industry. For instance, universities perceive that partnerships with firms affect their pedagogic missions.

In other scenarios, for academicians, academics have an extra burden due to a smaller number of staff, which is also a factor that inhibits university personnel in engaging in the exchanging of information services with industries. Thus, the lack of the exchanging of information on technical issues always becomes a hindrance in university-industry research collaboration [36,37,38,39,40,41]. In this regard, human capital in national innovation systems (NIS) is a factor that provides a facility to interact with university-industry partners frequently and facilitate the service of exchanging information between them. Human capital on a national level is recognized as the largest and the most important asset of every organization, as well 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 increasing productivity as well as sustaining a competitive advantage. Human capital in NIS is embodied in skilled and experienced personnel that provide professional training and skills via exchanging information and expertise, increasing the levels of technological abilities, which leads to the R&D partners’ satisfaction and performance and eventually UIRC innovation performance [43,44,45], the influence of human capital on exchanging information is well illustrated in Figure 1. In this regard, this research hypothesized that:

Hypothesis 1 (H1a).

Human capital in NIS has a positive influence on exchanging information in UIRC.

Similarly, [45], highlights that extant intellectual capital in the sectors of NIS can increase 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 market 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 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 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 [51,52,53]. 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 ability to utilize its knowledge resources [54,55,56], the influence of intellectual capital on exchanging 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 information in UIRC.

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 universities and industries indicate a communication gap among the sectors of NIS and between their collaborating partnerships as well [61]. Thus, to maximize the innovative performance 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 concept and ideas within the HC entities, is the core of the success of the collaborating partners 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 networking among the actors of national innovation systems enables research organizations 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 sharing, 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 researchers [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.

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]. Furthermore, 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 chemical 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 collaborating 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 total population of both departments is approximately 500, which includes only professors, associate professors and their top managerial personnel in their corresponding collaborating industries. Thus, according to the table of Krejcie & Morgan [77], in 500 populations with 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. Our research instrument includes EI as a dependent variable, while HC, IC, COM and NW were independent and reinforcing variables, respectively. Valid variables were selected from the previous studies and measured based upon the scope of the current study.

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

Variables	Constructs	N	Items
DV	EI	1	Sharing information expertise and advice on innovative organizations.
		2	Experts’ mobility amongst research organizations.
		3	Exchanging intellectual ideas and methodologies amongst research organizations.
		4	Conferences and seminars amongst collaborative organizations.
IDV	HC	1	Number of educated people for research and innovation.
		2	Number of skilled personals for research and innovation.
	IC	1	Talents and intelligence in innovations.
		2	Utilizing knowledge and expertise in innovations.
RF	NW	1	Voluntary collaboration amongst actors of innovation.
		2	Cooperative behavior amongst actors of innovation.
		3	Strategic research and social alliances amongst the actors of innovation.
	COM	1	Frequent communication among the actors of innovation
		2	Exchanging information among the actors of innovation.

shows our detailed research instruments, which include dependent variables (DV), independent variables (IDV) and reinforcing factors (RF), with their corresponding constructs and items.

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

4. Results and Analysis

For the data analysis, SPSS and partial least square analysis (PLS) were utilized. Here it is important to mention that Section 2 clearly shows that all variables of this study are formative. In this regard, for the evaluation of the formative path model, assessment of the measurement 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 measurement properties. In this regard, the measurement model is assessed by measuring the validity 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 the constructs. For this purpose, multicollinearity is deduced for each of the constructs. Multicollinearity occurs when there is a high correlation between two or more variables in the model. Estimates of a regression coefficient become unreliable if there is multicollinearity between the variables. The present study has five variables; thus, sufficient efforts were made to operationalize those variables properly.

For construct validity, the variance of inflation factors (VIF) was tested to evaluate the possibility of multicollinearity issues. Based on [80], formative construct VIF must not be greater than 5 and tolerance should be higher than 0.20.

Table 2. Assessment of Constructs Validity.

		Collinearity Statistics
Constructs	Indicators	Tolerance	VIF
Exchanging Information	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 Capital	HC_1	0.831	1.203
	HC_2	0.831	1.203
Intellectual Capital	IC_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
Communication	COM_1	0.410	2.440
	COM_2	0.410	2.440

shows the VIF test by running the stepwise regression analysis for each construct. The result indicated that all the VIFs were less than 5 and all the tolerance values were above 0.20; consequently, no sign of multicollinearity was found.

4.1.2. Assessment of Indicators Validity

At the indicator level, the question arises as to whether each indicator delivers a contribution to the construct by carrying the intended meaning. It is suggested that there should be strong relevancy between the indicator and the construct. To check the relevancy of 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 of 2 and a maximum of 4 indicators have been used for each of the constructs, so p-values are 2, 3, and 4 indicators are 0.709, 0.578 and 0.5, respectively, as shown in

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 Capital	HC_1	0.9319	0.9906
	HC_2	0.1445	0.5318
Intellectual Capital	IC_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
Communication	COM_1	−0.4262	0.7131
	COM_2	0.2895	0.9966

.

Table 3 shows the indicators’ weight of all the related constructs. The significant item weight indicates that all the indicators explain a significant portion of the variance of their 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, according to Hair et. al., (2012), item loadings are also countable when indicator weights are not 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 to assess the structural model in the next step phase.

4.2. Assessment of Structural Model

The hypothesized relationships in the structural model, including three main relationships (H1a, H1b) and two reinforcing effects (H2a, H2b), were examined. The structural model 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 initial structural model and the final structural model. In more detail, Figure 4 shows the effect of the factors of NIS (HC) and (IC) on the constraint (EI) of UIRC. In this regard, Path coefficient (β) values indicate the effect of HC and IC, and the R2 values explain the variances on the EI of UIRC. For instance, the β value of HC (−0.201) does not show the significant effects on EI of UIRC. Similarly, the R2 values of EI 0.40% also do not show the significant variance from HC to EI, while the β value of IC (0.505), showing the significant effects 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 the EI in UIRC.

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 efficiencies of HC and IC and consequently enhancing the EI of UIRC. Additionally, t-Statistics was also examined to investigate the accuracy of each path.

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

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 networking (t = 3.155), (t = 1.723). However, COM and NW are both reinforcing factors with significant influence on both NIS factors HC and IC and consequently on EI as shown in Figure 7.

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 university-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 innovation 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). Thus, in an unlikely result, from the previous considerations and perception about human capital and its relationship with the exchanging information of UIRC, the result of the present analysis showed that intellectual 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 human capital, intellectual capital is the most preferable factor in NIS to enhance the outcome of UIRC. Intellectual capital is the most valuable prerequisite requirement of UIRC to enhance knowledge and ideas for the development of research and innovations.

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

5.2. Conclusions and Recommendations

As university-industry research collaboration (UIRC) has a strong and direct impact on the economic growth of a country, in this regard understanding and identifying the factors that have important roles in enhancing the innovative capability of UIRC is mandatory. The present study was undertaken to gain a better understanding of the development of the innovative capability of UIRC. To improve the innovative capabilities of UIRC, policymakers need to have a comprehensive understanding of the influence of national systems of innovation (NIS). A review of the literature, measurement items and analyses and the theory of the study present a framework to enhance the innovative capabilities of UIRC [83,84,85,86,87]. Previous studies have evaluated research collaboration between universities and industries from the perspective of analytical thinking, which is mostly related to the internal efficiencies and effectiveness of UIRC.

Finally, from the perceptions of the practical implications, the researcher suggested that although, this framework has been developed for enhancing the innovative capability of UIRC in Malaysia, it can be implemented generally in any country by simply following the procedure of the developed framework. Secondly, system thinking can help policymakers 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 to strengthen the innovative capability of UIRC. In other words, the findings of the current study provide intuition to policymakers to understand the relationship between a strong system of innovation and the innovative capabilities of UIRC.

This research identifies future research directions that will help in overcoming the limitations of this research. Using Malaysia as the scope of the study, this research proposes comparative works conducted across other developed and developing countries. Furthermore, replicating the study by comparing other countries could be valuable to identify the major differences in terms of enhancing the innovative capability of UIRC.