# Identifying the Dimensions of University–Industry Research Partnerships in Saudi Arabia

## Abstract

The research seems to help determine the latent dimensions and predictors of research collaboration between universities and the industrial sector in the Kingdom of Saudi Arabia. A survey using a structured questionnaire was conducted based on a census-style sample of 253 academic staff from six Saudi universities. A seven-point Likert scale (0-6) was used to gauge responses and capture delicate perceptions. However, exploratory factor analysis has identified four main dimensions of partnership: economic, academic, technological, and strategic alignment. Thereafter, the multiple linear regression analysis revealed that these dimensions, in conjunction with the type of university, accounted for 39.2% of the variability in the perceived levels of research partnership. The most potent dimensions were academic and economic, whereas strategic alignment produced the least significant results. The type of university was a significant factor in engagement, with established universities having higher partnership rates. The results have a considerable impact on policymakers and academic leaders who are keen on operationalising the Vision 2030 through an increase in the innovation ecosystem. The strategy has been aligned with the requirements to enable the effect and sustainability of university-industry collaboration and match the institutional policies that are personalised.

## Body

Introduction:

Universities are not only the fundamental pillars of education in the knowledge industry but also act as the pioneers of research and development in modern societies. Lopes and Lussuamo (2021) highlight that they contribute to scientific research, enriching human knowledge and the intellectual, cultural, social, and economic development of society. Nevertheless, the university is regarded as a scientific, educational, and research institution that assumes a prominent developmental role in all aspects of social, cultural, and economic life (Filippetti & Savona, 2017). The ongoing scientific revolution worldwide poses new challenges for universities (Mannak et al., 2019). These relate to the methods of utilising universities’ scientific research to fulfil society’s needs, as analysed by Parmentola Ferretti and Panetti (2021), with an enhanced approach that activates the relationship between the university, economic, and social development institutions.

Numerous studies validate the various stages of development observed through scientific research. According to Alunurm et al. (2020) and Figueiredo and Ferreira (2022), the evolution of the scientific research concept in the twentieth century no longer relies on the individual successes of scientists such as Edison and his peers in the scientific community, as was the custom in the nineteenth century. The focus has now shifted towards the research programs adopted by the government, where scientific research has evolved into a collective and cooperative effort that emphasises research partnerships for mutual benefits (Dobrzanski et al., 2021). According to Francisco (2011) and Ribeiro and Nagano (2021), in the current era, the focus of joint research is based on a holistic view of societal issues and the employment of various disciplines in solving these prevailing issues.

It is significant to note that educational partnerships are wideranging, as universities engage in research partnerships with various developmental sectors, including industrial sector institutions (Bodas Freitas et al., 2013). The research partnership involves advancing the relationship and communication between universities and the industrial sector, where universities act as consulting firms through consultations, and the industrial sector is the beneficiary of the research (Canhoto et al., 2016). The research partnership falls within the framework of an organisational institutional relationship (Adamovský, 2024; Halásková & Bednář, 2023). Notably, the university and the industrial sector are not in a competitive rivalry situation and need to collaborate through an integrated system to attain common goals (Mascarenhas, Ferreira & Marques, 2018).

While universities focus on generating and disseminating knowledge, companies aim to apply knowledge to solve their issues and gain their clients' trust (Chryssou, 2020; Mirza et al., 2021; Nsanzumuhire, 2021). Additionally, they may engage in knowledge creation, either individually or in collaboration with universities and research centres (Fuentes & Dutrénit, 2012; He et al., 2021; Ghauri & Rosendo-Rios, 2016). Particularly, to realise and amplify the effectiveness of the partnership between universities and companies (Czerwińska-Lubszczyk et al., 2020; Figueiredo & Fernandes, 2020). A transformation has occurred in the universities ‘approach and policies, as analysed by Attia (2015) and Calvo et al. (2019); this is related to their need to interconnect with their communities through channels of effective participation in the productive and service activities of institutions.

Despite the growing significance of the topic, many developing countries have not prioritised establishing an effective partnership between universities and the industrial sector (Gerbin & Drnovsek, 2016). Most research projects at universities have neither been transformed into practical applications nor achieved the desired goal of serving and developing society (Galán-Muros & Plewa, 2016). The role of Saudi universities in the development of the industrial sector is considered weak. Moreover, an imperfect understanding and coordination between universities and industrial companies, as analysed by Gattringer, Hutterer, and Strehl (2014), and the relationships between universities and industrial companies are concise (Nsanzumuhire & Groot, 2020). The paper can contribute to the existing body of knowledge on innovation systems in countries (NIS) by providing evidence from a developing economy. The study offers context-sensitive findings regarding Saudi Arabia, as a rapidly changing nation under the Vision 2030. In contrast, the study can significantly offer more context-specific concepts that build upon existing frameworks of NIS, which have been developed largely in Western contexts.

#### 1.1. Research Aim

The study aims to identify the dimensions of university-industry research partnerships in Saudi Arabia.

#### 1.2. Research Objectives

The research objectives of this study are;

• To discover the major dimensions (economic, academic, technological, and strategic alignment) of the universityindustry research cooperation in Saudi Arabia by using exploratory factor analysis

• To discover the major dimensions (economic, academic, technological, and strategic alignment) of the universityindustry research cooperation in Saudi Arabia by using exploratory factor analysis

• To measure the degree to which these variables can predict the variation in the effectiveness of university-industry partnerships

• To provide recommendations to enhance research cooperation between universities and industry to support Saudi Vision 2030 based on evidence

#### 2. Theoretical Background

#### 2.1. Importance and Role of Research Partnership

Cooperation between universities and industry is crucial to national systems of innovation, economic competitiveness, and technological advancement. These partnerships facilitate the crossflow of knowledge, facilities, and expertise between universities and industries.

#### 2.2. Key Benefits of University-Industry Collaboration

• Economic Advancement - Research partnerships lead to productivity and economic growth due to innovation, the transfer of technology, and the commercialisation of knowledge (Adamovsky, 2024).

• Knowledge Transfer - Universities are knowledge centres where industry can access the latest scientific and professional requirements, facilities, and research (Haláskova & Bednara, 2023).

• Knowledge Transfer - Universities are knowledge centres where industry can access the latest scientific and professional requirements, facilities, and research (Haláskova & Bednara, 2023).

• Regional Development - Collaboration between universities and industry facilitates regional innovation and localised development (Amaral et al., 2011).

• Public Policy Support - Governments worldwide are supporting research through funding, tax advantages, innovation centres, and science parks (Bastos et al., 2021).

However, Bruneel et al. (2010) identify that research collaborations in Saudi Arabia are underdeveloped. Ultimately, considerable investment has been made in higher education and innovation by the government through incentives, as analysed by Chryssou (2020), Canhoto et al. (2016), and Alunurm et al. (2020), including Vision 2030. Bruneel et al. (2010) and Dobrzanski et al. (2021) highlight that there is usually little industrial integration in universities, which often operate in silos, according to academics. Nonetheless, weak coordination mechanisms and the absence of commercialisation of academic outputs are highlighted (Alexander et al., 2020; Figueiredo & Ferreira, 2022). This leaves much to be desired, particularly given national agendas in diversification of the economy and knowledge-intensive industries. The policy comparison can similarly be drawn to several projects based in the Gulf, including the 'Mohammed Bin Rashid Innovation Fund' in the UAE and the Science and Technology Park in Qatar (Chryssou, 2020). In these cases, despite different scales of economies, the coordinated government activity can effectively drive the process of breaking through the academia-industry synergy in the Gulf region.

#### 2.3. Dimensions of Research Partnership between Universities and the Industrial Sector

Research partnerships between universities and industries involve a variety of activities (Alibekova et al., 2019). They may be categorised into five broad areas to make them simpler and to bring clarity:

#### 2.3.1. Human Capital Exchange

• Engagement of university graduates into industrial jobs (Mirza et al., 2021; Nsanzumuhire, 2021)

• The thesis of post-graduates is read jointly (Dobrzanski, 2021)

• Industrial leave to academic staff (Canhoto et al., 2016)

• Instructors at the universities provided by the company are professionals

Despite this, institutions such as KAUST have initiated talent exchange programs; however, these exchanges remain minimal in most public universities in the Kingdom.

#### 2.3.2. Collaborative Research and Knowledge Sharing

• Collaborative authorship and publications (Coudounaris, 2016)

• Informal academia-industry communication and consultations (Alunurm et al., 2021)

• Summarisation and joint research centres and programs (Attia, 2015; Calvo et al., 2019)

Current partnerships tend to be disjointed and faculty-centred as opposed to being institution-level and institutional partnerships; the wider frames of engagement are required.

#### 2.3.3. Infrastructure and Resource Utilisation

• Industry partners shared access to university laboratories (Antonioli et al., 2017)

• Establishment of research and technology parks (AttaOwusu et al., 2021)

• The university hosted business incubators (Galán-Muros & Plewa, 2016)

• Although some initiatives, such as Riyadh Techno Valley, aim to bridge the infrastructure gap, most universities lack policies or physical structures to consistently engage with industries.

#### 2.4. Commercialisation and Innovation

• Intellectual property transfer and license patenting (Daniel & Alves, 2020)

• Creating startups and spin-offs based on academic research (Dutta et al., 2020)

• Patents and IP management support documentation (Bastos et al., 2014)

Most universities have failed to establish a mature technology transfer office or an effective IP commercialisation strategy, and as a result, they are not maximising their contribution to industrial innovation.

#### 2.5. Training and Capacity Building

• University-led industry-based training (Chryssou, 2020; Ashraf et al., 2018)

• Upskilling and continuous education of the corporate personnel As a nation strives to enhance the competitiveness of its workforce, one major group that remains untapped is its university institutions, which can play an active role in lifelong learning and education that aligns with industry needs (Czerwińska-Lubszczyk et al., 2020; Figueiredo & Fernandes, 2020). Through the classification and contextualization of various types of collaboration despite the useful ideologies provided by global frameworks, a partnership exists. Consequently, Saudi Arabia needs to enhance its policy modalities, institutional capabilities, and cultural alignment between the university and industry to achieve the maximum benefits of such collaborations.

#### 3. Methodology

This study employed a descriptive survey method, accumulating information and data about the phenomenon under investigation to identify the study's approach, determine its status, and assess its advantages and disadvantages, while being mindful of the extent to which the situation is viable or the need for partial or comprehensive changes. A questionnaire survey was utilised as a tool for data collection (Punch, 2013; De Vaus, 2014). An electronic link to the questionnaire survey was sent to the participants. The questionnaire statements were derived from the procedural meaning of research partnerships between universities and the industrial sector. Additionally, the statements were measured on a seven-point Likert scale from zero to six. Hence, this scale exhibited higher variance, as the study extrapolated the dimensions of research partnerships between universities and industry. Subsequently, multiple linear regression was performed to predict the impact of these dimensions on the level of research partnership between universities and the industrial sector in the Kingdom of Saudi Arabia (KSA).

#### 3.1. Data Collection and Data Analysis

The Target Population included employees of the deanships of scientific research in five Saudi universities: King Abdulaziz University, King Khalid University, Taibah University, Imam Muhammad bin Saud Islamic University, Imam Abdul Rahman bin Faisal University, and the University of Tabuk. The universities have been carefully chosen to represent all provinces and diversity in terms of institutional maturity. This can enable the investigation to consider variances, which might exist because of geographical and administrative differences that can result in the need to increase findings in the generalisability within the whole country. This target population is estimated by the Ministry of Education's statistics (2022) to be 253 individuals. These universities were chosen to combine the prestigious (old-established) universities: King Abdulaziz University and Imam Muhammad bin Saud Islamic University, and the emerging universities: Taibah University, Tabuk University, King Khalid University, Imam Abdul Rahman bin Faisal University, in addition to their different geographical representation. A comprehensive inventory method was used for this group due to their limitations, in other words, known as census-style sampling. The study tool was administered to all participants, and 197 individuals responded, representing 78.2% of the total sample size in this study. The return was after excluding questionnaires with standardised answers (choosing one scale score for all statements); it is 192 questionnaires after excluding five questionnaires.

#### 3.2. Instrumentation and Scale

A designed, self-administered questionnaire was administered to gather data. Nevertheless, the questionnaire items were developed by referring to the conceptual dimensions of university-industry partnerships. A seven-point Likert scale was used, with values measured by clearly stating the question, ranging from 0 (strongly disagree) to 6 (strongly agree). According to Holmes-Smith (2012) and Treiblmaier and Filzmoser (2010), the scaling of 0-6 was chosen to achieve higher variability and sensitivity in responses, allowing for more detailed measurement of perception.

#### 3.3. Ethical Considerations

During the research process, adherence was strictly maintained. Although Xu et al. (2020) state that the parties were informed of the study's purpose and their informed consent was obtained before data collection, the sample was collected anonymously and voluntarily, and participants were assured that they could withdraw at any point without penalty. Moreover, steps were taken to ensure anonymity and confidentiality, and data management was conducted by the institution's protocol for data safety. Furthermore, the study design and the study tools were approved by the Institutional Review Board (IRB) of the principal research institution, as noted by Balon et al. (2019); hence, the study conformed to the ethical standards of research involving human subjects.

#### 4. Results and Analysis

#### 4.1. Exploratory Factor Analysis (EFA)

Using the online questionnaire reduced the risk of missing statements, which is the central dilemma when analysing (HolmesSmith, 2012). However, five questionnaire items were excluded due to being answered with a single score. A Common Factor Analysis (CFA) was then conducted using the Principal Axis Factoring (PAF) method as an indicator of the construct validity of the questionnaire and a step in refining it, which would later be required for multiple linear regression. Since the common variance has been isolated, the idiosyncratic variance and the error variance explain the latent factors from which the questionnaire statements branch out and which account for the highest common variance. Therefore, the PAF method was chosen due to the difficulty of achieving distribution equivalence (Holmes-Smith, 2012; Treiblmaier & Filzmoser, 2010). Horn's (1965) model was also adopted. This is one of the oldest and most stable models for determining the number of factors. After excluding two phrases with saturations of Communality less than 0.3, as in Table 1.

Table 1: Statements Excluded from Factor Analysis
Statement | Mean | Variance | Communalities
--- | --- | --- | ---
Item 19: Research partnerships between universities and industry contribute to competitive advantage. | 3.24 | 0.516 | 0.165
Item 20: Reduced the effectiveness of teamwork spirit | 1.37 | 0.391 | 0.219

Table 1 depicts the statements excluded from the analysis. These were excluded not due to their lack of importance in the study subject, but due to their lack of variability, which made it difficult to evaluate their impact using statistical techniques, as respondents assessed the utilisation of the statements similarly. Although the variance is almost zero, these statements therefore have low averages that are considered neutral. Hence, the validity criteria in the factor analysis were established.

The KMO reached an appreciable level (0.891), indicating the adequacy of the sample based on the criterion of Sharma (1996). Additionally, Bartlett's Test of Sphericity was statistically significant (χ² = 6086.164, p < 0.001), indicating that the correlation matrix is not random. Unit. The Kaiser rule (Latent Root > 1 Kaiser rule: Eigenvalue > 1) identified four factors that explained 58.042% of the variance in the data, leaving the remaining items. This is an encouraging percentage in the diversity of educational science issues (Sharma, 1996). The percentage of the total variance explained by each factor was: 15.59%, 15.4%, 13.9%, and 12.45%, respectively. This indicates convergence and gradation in the factors sharing the variance that can be explained (58.04%).

Completing the factor analysis, which investigates the variance trends of the statements, reveals the fundamental factors that shape the dimensions of the partnership between universities and the industrial sector, based on the variance of the data. The following tables clearly show the factor matrix using the Varimax orthogonal analysis method. Table 2 parts consist of the factor matrix after orthogonal rotation, which is split into each dimension:

Table 2a: Factor Loadings – Economic Dimension
Statement | Factor Loading
--- | ---
Item 5: Knowledge broker offices | 0.79
Item 7: Link research to national plans | 0.73
Item 2: Product transformation | 0.67
Item 1: Connect researchers with industry | 0.64
Item 3: Base research on industry needs | 0.57

Table 2b: Factor Loadings – Academic Dimension
Statement | Factor Loading
--- | ---
Item 8: Integrated research programs | 0.84
Item 4: Marketing centres with standards | 0.77
Item 15: International partnerships | 0.74
Item 12: Support startup formation | 0.57

Table 2c: Factor Loadings – Technological Dimension
Statement | Factor Loading
--- | ---
Item 16: E-admin systems for research marketing | 0.75
Item 18: Modern labs with technology | 0.75
Item 17: Integrated electronic services | 0.71
Item 14: Digital governance difficulties | 0.66

Table 2d: Factor Loadings – Strategic Alignment Dimension
Statement | Factor Loading
--- | ---
Item 13: Innovation management centres | 0.74
Item 10: Business incubators | 0.65
Item 9: Patent documentation | 0.64
Item 6: Research parks | 0.54
Item 11: Commercialise inventions | 0.53

Table 2 parts represent the statements' order according to their interpenetration ratio in the factor. Notably, the interpenetration of statements is greater in their factors compared to other factors. When examining the statements that constitute each factor, we observe that the four-factor statistical solution is enhanced by the apparent interdependence of meaning and theoretical significance, which is a decisive factor in extracting factors (Treiblmaier & Filzmoser, 2010). The five statements in the first factor represent the economic dimensions, the four statements in the second factor represent the academic dimension, the four statements in the third factor represent the technological dimension, and the fourth and final factor represents the five statements of the strategic alignment dimension of the partnership between universities and the private sector.

#### 4.2. Confirmatory Factor Analysis (CFA)

Confirmatory Factor Analysis was used to evaluate the model, to understand how the variables relate to the questionnaire items shown in Figure 1.

Fig 1: CFA model for extrapolating the dimensions of research partnership between universities and the industrial sector

Fig 1: CFA model for extrapolating the dimensions of research partnership between universities and the industrial sector

Table 3: Goodness of Fit Indicators for Confirmatory Factor Analysis (CFA)
Goodness-of-Fit Indices for the Study Model | Acceptable Fit Value (Hair et al., 2010)
--- | ---
X²/df = 2.165 | X²/df < 3
GFI = 0.910 | GFI > 0.90
CFI = 0.909 | CFI > 0.90
TLI = 0.900 | TLI > 0.90
RMSEA = 0.051 | RMSEA < 0.08

The results in Table 3 indicate that the goodness-of-fit indicators for the study model (X²/df = 2.165, GFI = 0.910, CFI = 0.909, TLI = 0.900, RMSEA = 0.051) meet the acceptable criteria. Therefore, the results confirm that the model is a perfect fit for the current study data.

#### 4.3. Reliability

After extracting the standard weight to saturate each statement in Table 3 with its factor (field), McDonald's Omega Reliability scale was adopted to measure the internal reliability of each factor. This coefficient was chosen because it considers the weight of each statement in the domain, hence avoiding the occurrence of the statistical error during the application of Cronbach’s alpha (HolmesSmith, 2012; Treiblmaier & Filzmoser, 2010).

Table 4: McDonald's ω Reliability Coefficient Values for Each of the Four Extracted Factors
Factor (Latent Root) | Proposed Name (Theoretical Significance) | Number of Statements | McDonald's ω
--- | --- | --- | ---
First | Economic Dimensions | 5 | 0.827
Second | Academic Dimensions | 4 | 0.816
Third | Technological Dimensions | 4 | 0.909
Fourth | Strategic Compatibility | 5 | 0.851

It is clear from Table 4 that the values of McDonald's ω coefficient (greater than .8) for all factors, even for factors with few terms. This indicates that these factors (latent roots) have high internal consistency. Moreover, it will have a positive impact when applied to multiple linear regression.

#### 4.4. Multiple Regression

Multiple regression was adopted for five independent variables, four dimensions of partnership between universities and the industrial sector as detected from factor analysis, in addition to the variable type of university in terms of establishment: King Abdulaziz University and King Imam Muhammad bin Saud Islamic University, which are ancient universities, and Taibah University, University of King Khalid, the University of Tabuk, and Abdul Rahman bin Faisal University are among the emerging universities. The dependent variable (Y) is a 100- point scale on which the respondent evaluates the level of partnership between universities and the industrial sector at his university. According to the following equation:

Y = aa + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + e

Whereas,

- Y - Perceived level of research partnership (0–100 scale)
- a - Constant term
- X1, X2, X3, X4, X5 - Factor Matrix after orthogonal rotation as per each dimension
- b1, b2, b3, b4, b5 - Regression Coefficients
- e - Error term (the dependent variable)

The Coefficient of Multiple Determination (R²) reached 0.392, indicating that 39.2% of the variation in the level of research partnership between universities and the industrial sector can be explained by this model. Although it is worth confirming that the mathematical model is characterized by goodness of fit, it is also important to examine the standard error of the estimate, which represents the standard deviation of the variance in the level of partnership that the model did not explain, and it reached 19.69 degrees. This means that more than two-thirds of the study sample (68%) have a deviation from the value expected by the regression line of less than one-fifth of the scale range (100). Note that the lowest value is zero, and the highest is 100, with an average of 68.16 and a standard deviation of 18.25.

Table 5 shows the results of multiple linear regression for the variable of the level of research partnership between universities and the industrial sector at his university. Table 5 shows the regression coefficients, the standard error associated with each variable, the level of statistical significance (P-values), and the standard regression coefficients, which are the regression coefficients for the same variables when converted to standard scores (Z-scores).

Table 5: Multiple Regression Model for the Level of Research Partnership Between Universities and the Industrial Sector
Factors | Regression Coefficient | Standard Error | Regression Coefficient | Standard Indication | Confidence Range for the Regression Coefficient (95%)
--- | --- | --- | --- | --- | ---
Constant: m (level apart from all independent factors) | 14.923 | 4.227 | 0 | ***.000 | [6.621, 23.224]
Old-established university = 1, Emerging university = 0 | 8.475 | 1.628 | 0.168 | ***.000 | [5.277, 11.672]
Economic Dimensions | 5.790 | 0.515 | 0.288 | ***.000 | [4.635, 7.306]
Academic Dimensions | 5.971 | 0.680 | 0.364 | ***.000 | [4.778, 6.801]
Technological Dimensions | 2.760 | 0.704 | 0.129 | ***.000 | [1.376, 4.143]
Strategic Compatibility | 1.831 | 0.691 | 0.083 | **.008 | [0.473, 3.188]
Sample number = 628; R square (R²) = 0.392; Standard error of expectation (SEE) = 19.69; Total degrees of freedom (df) = 627 P ** < 0.01; *** P < 0.001

Noted from the previous table that the regression equation constant is 14.92, which is statistically significant at the zerosignificance level (p=.000). Regardless of all influencing factors, the level of research partnership is this amount. This is a relatively low amount, suggesting that other factors may play a more significant role in shaping the level of research partnership between universities and the industrial sector. All five factors were statistically significant, as shown in Table 5 above.

#### 5. Discussion

The research findings confirm that academic and economic aspects are the most potent factors in determining the effectiveness of university-industry collaboration in Saudi Arabia. The most significant standardized coefficient (beta = 0.364) was found under the academic dimension, which directly corresponds to the core of scholarly interaction, collaborative research programs, and the generation of knowledge, all of which have been the most significant in establishing collaborative relationships. Additionally, it aligns with the conclusions drawn by Galan-Muro & Plewa (2016) and Bodas Freitas et al. (2013), who emphasize the importance of academic contributions in fostering fruitful industry partnerships.

Next came the economic dimension, as the economic return of the research is an important feature that must be translated into the economic dimension, encompassing commercialization, consultancy, and matching the research with national development plans. These findings reinforce the literature, as described by Adamovsky (2024) and Figueiredo and Ferreira (2022), which found that mutual economic-related motives are essential to drive industry-academia interaction. Notably, the technological aspect influenced the total of the responses moderately (r = 0.129). This can be a sign of the deficiency in the existing abilities of Saudi universities to offer advanced technological infrastructure or the acceptability and adoption of academic developments by the business world. It corresponds to the discoveries of Gattringer et al. (2014) and Mannak et al. (2019), which indicate a non-alignment between expectations and the level of preparation in meeting innovation, as expected to sabotage technological collaboration.

The strategic alignment dimension has the shortest effect (significance level value of 0.083), which means that the area of policies and efforts to align them is not optimised or potentially visible to stakeholders. This can be attributed to the fact that strategic plans are often generic or at high levels; they cannot necessarily be translated into specific practices or incentives at the interface between the university and the firm. Besides, a low scale variance of its strategic alignment (as reported during factor analysis) means that strategic alignment is highly responsive to uniform agreement, which will weaken the predictive value of strategic alignment in the regression model. They agree with Attia (2015), who reported that the lack of a practical implementation framework implies that strategic objectives must be characterised more as a symbol than a transformation. The poor ranking of the scale across all the years in the dimension of strategic alignment illustrates institutional goals and the agenda of the national innovation regarding a disconnection. This area of inconsistency highlights the importance of performance-based funding patterns and connection with the offices at strategic levels in universities in converting policies at a macro-level to operational techniques.

In comparison to the neighboring countries, such as the UAE and Malaysia, the interaction between universities and industries is in an infant stage in Saudi Arabia. The South Korean researchers, scholars, and projects, in which institutional arrangements in the form of the 'BK21 Program' stimulated the academic-industrial merger. Mascarenhas et al. (2018) resulted in the finding that long-term government-sponsored projects and special liaison offices can meaningfully increase alignment. Saudi Arabia can follow in the footsteps of the policy instruments used and speed up the process of commercialisation of knowledge.

Consequently, the type of university variable was considered to be a key predictor (0.168); older and more mature universities had a higher likelihood of participating in effective partnerships with industry. This can be a direct result of better institutional networks, developed infrastructure, and increased reputational capital; hence, industrial cooperation is drawn towards it. Therefore, for policymakers, this indicates that more investment is needed to build the capacity of new universities, enabling them to gain inclusion in national innovation agendas. The institutional readiness challenges can be addressed to promote a more balanced Saudi Arabian knowledge economy, in line with Vision 2030.

#### 5.1. Self-Reported Limitations

The most frequent limitation in surveys is social desirability or the inability to provide all necessary information on the level of institutional collaboration, which makes participants overstate this aspect (Chryssou, 2020). Although the study has convincing empirical findings, the use of self-reporting measures can bring in bias in the responses. In future research, survey data must be evaluated against objective performance figures such as patent applications and coauthored articles.

#### 6. Conclusion

The exploratory factor analysis utilised to extrapolate the dimensions of the university-industry partnership for the universities revealed the following four dimensions: economic, academic, technological, and strategic alignment. The multiple regression model utilised in the study explained 39.2% of the variance in the level of university-industry partnership based on the research partnership dimensions and the university type in terms of establishment. Hence, to reinforce the applied significance of these results, Saudi universities should adopt focused plans to streamline each dimension of the partnership, especially strategic alignment, which was reported to have the least impact, despite its significance.

Based on the current study, future research can focus on the following directions

• Conduct comparative studies to benefit from the experiences of global universities and their research partnerships with the private sector.

• Focus on psychometric studies to build metrics for the partnership level.

• Conduct surveys on the best practices in university-private sector partnerships that add value.

Additionally, future studies should examine the institutional challenges that prevent new universities from establishing productive partnerships and evaluate policy-level interventions that can fill the existing gaps.

## References

Adamovský, P. (2024). Quadruple helix in reality: An empirical inquiry into innovation cooperation triggering competitiveness in central and northeastern Europe. Journal of Competitiveness, 16(1), 46–61. https://doi.org/10.7441/joc.2024.01.03.

Alexander, A., Martin, D. P., Manolchev, C., & Miller, K. (2020). University-industry collaboration: using meta-rules to overcome barriers to knowledge transfer. The Journal of Technology Transfer, 45(2), 371–392. https://doi.org/10.1007/s10961-018-9685-1.

Alibekova, G., Tleppayev, A., Medeni, T. D., & Ruzanov, R. (2019). Determinants of the technology commercialization ecosystem for universities in Kazakhstan. The Journal of Asian Finance, Economics and Business, 6(4), 271–279. https://doi.org/10.13106/jafeb.2019.vol6.no4.271.

Alunurm, R., Rõigas, K., & Varblane, U. (2020). The relative significance of barriers to higher education-industry cooperation for different firms. Industry and Higher Education, 34(6), 377–390. https://doi.org/10.1177/0950422220909737.

Amaral, M., Ferreira, A., & Teodoro, P. (2011). Building an entrepreneurial university in Brazil: the role and potential of university-industry linkages in promoting regional economic development. Industry and Higher Education, 25(5), 383–395. https://doi.org/10.5367/ihe.2011.0061.

Antonioli, D., Marzucchi, A., & Savona, M. (2017). Pain shared, pain halved? Cooperation as a coping strategy for overcoming innovation barriers. The Journal of Technology Transfer, 42(4), 841–864. https://doi.org/10.1007/s10961-016-9545-9.

Ashraf, R. U., Hou, F., Kirmani, S. A. A., Ilyas, M., Zaidi, S. A. H., & Ashraf, M. S. (2018). Collaborative university-industry linkages in Pakistan. Human Systems Management, 37(2), 207–218. https://doi.org/10.3233/HSM-17236.

Atta-Owusu, K., Fitjar, R. D., & Rodríguez-Pose, A. (2021). What drives university-industry collaboration? Research excellence or firm collaboration strategy? Technological Forecasting and Social Change, 173, 121084. https://doi.org/10.1016/j.techfore.2021.121084.

Attia, A. M. (2015). National Innovation Systems in Developing Countries: Barriers to University–Industry Collaboration in Egypt. International Journal of Technology Management & Sustainable Development, 14(2), 113–124. https://doi.org/10.1386/tmsd.14.2.113_1.

Balon, R., Guerrero, A. P., Coverdale, J. H., Brenner, A. M., Louie, A. K., Beresin, E. V., & Roberts, L. W. (2019). Institutional review board approval as an educational tool. Academic Psychiatry, 43(3), 285–289. https://doi.org/10.1007/s40596-019-01027-9.

Bastos, A. P., Serra, M., Almeida, L., & Diniz, M. J. (2014). University-enterprise partnerships in the Brazilian Amazon: obstacles, dilemmas, and challenges. Transnational Corporations Review, 6(1), 71–85. https://doi.org/10.1080/19186444.2014.11658382.

Bastos, E. C., Sengik, A. R., & Tello-Gamarra, J. (2021). Fifty years of university-industry collaboration: a global bibliometrics overview. Science and Public Policy, 48(2), 177–199. https://doi.org/10.1093/scipol/scaa077.

Bodas Freitas, I. M., Geuna, A., & Rossi, F. (2013). Finding the right partners: institutional and personal modes of governance of university-industry interactions. Research Policy, 42(1), 50–62. https://doi.org/10.1016/j.respol.2012.06.007.

Bruneel, J., D’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers to university-industry collaboration. Research Policy, 39(7), 858–868. https://doi.org/10.1016/j.respol.2010.03.006.

Calvo, N., Fernández-López, S., Rodeiro-Pazos, D., & Rodríguez-Gulías, M. J. (2019). Analysis of the researcher’s motivators to collaborate with firms as drivers of the triple helix dynamics. Global Business and Economics Review, 21(3–4), 304–329. https://doi.org/10.1504/gber.2019.099389.

Canhoto, A. I., Quinton, S., Jackson, P., & Dibb, S. (2016). The co-production of value in digital, university-industry R&D collaborative projects. Industrial Marketing Management, 56, 86–96. https://doi.org/10.1016/j.indmarman.2016.03.010.

Chryssou, C. E. (2020). University-industry interactions in the Sultanate of Oman: challenges and opportunities. Industry and Higher Education, 34(5), 342–357. https://doi.org/10.1177/0950422219896748.

Coudounaris, D. N. (2016). Moderating factors and effects: different perceptions of university researchers in Sweden. Journal of Promotion Management, 22(4), 556–572. https://doi.org/10.1080/10496491.2016.1190550.

Czerwińska-Lubszczyk, A., Grebski, M., & Jagoda-Sobalak, D. (2020). Cooperation between universities and businesses in Poland and the USA—A perspective from the scientific environment. Management Systems in Production Engineering, 28(1), 40–46. https://doi.org/10.2478/mspe-2020-0007.

Daniel, A. D., & Alves, L. (2020). University-industry technology transfer: the commercialization of the university's patents. Knowledge Management Research & Practice, 18(3), 276–296. https://doi.org/10.1080/14778238.2019.1638741.

De Vaus, D. (2014). Surveys in social research (6th ed.). Crows Nest, NSW: Allan & Unwin.

Dobrzanski, P., Bobowski, S., Chrysostome, E., Velinov, E., & Strouhal, J. (2021). Toward innovation-driven competitiveness across African countries: An analysis of efficiency of R&D expenditures. Journal of Competitiveness, 13(1), 5–22. https://doi.org/10.7441/joc.2021.01.01.

Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (Eds.). (2020). Global innovation index 2020: Who will finance innovation? (13th ed.). Cornell University, INSEAD, and World Intellectual Property Organization (WIPO); Ithaca, Fontainebleau, and Geneva.

Figueiredo, N., & Fernandes, C. (2020). Cooperation university-industry: a systematic literature review. International Journal of Innovation and Technology Management, 17(8), 2130001. https://doi.org/10.1142/S0219877021300019.

Figueiredo, N. L., & Ferreira, J. J. M. (2022). More than meets the partner: a systematic review and agenda for university-industry cooperation. Management Review Quarterly, 72, 231–273. https://doi.org/10.1007/s11301-020-00209-2.

Filippetti, A., & Savona, M. (2017). University-industry linkages and academic engagements: Individual behaviors and firm barriers. Introduction to the special section. The Journal of Technology Transfer, 42(4), 719–729.

Francisco, E. R. (2011). RAE-eletrônica: exploração do acervo à luz da bibliometria, geoanálise e redes sociais. Revista de Administração de Empresas, 51(3), 280–306. https://doi.org/10.1590/S0034-75902011000300008.

Fuentes, C., & Dutrénit, G. (2012). Best channels of academia-industry interaction for long-term benefit. Research Policy, 41(9), 1666–1682. https://doi.org/10.1016/j.respol.2012.03.026.

Galán-Muros, V., & Plewa, C. (2016). What drives and inhibits university-business cooperation in Europe? A comprehensive assessment. R&D Management, 46(2), 369–382. https://doi.org/10.1111/radm.12180.

Gattringer, R., Hutterer, P., & Strehl, F. (2014). Network-structured university-industry-collaboration: values for the stakeholders. European Journal of Innovation Management, 17(3), 272–291. https://doi.org/10.1108/EJIM-01-2013-0008.

Gerbin, A., & Drnovsek, M. (2016). Determinants and public policy implications of academic-industry knowledge transfer in life sciences: a review and a conceptual framework. The Journal of Technology Transfer, 41(5), 979–1076. https://doi.org/10.1007/s10961-015-9457-0.

Ghauri, P., & Rosendo-Rios, V. (2016). Organizational cross-cultural differences in the context of innovation-oriented partnerships. Cross-Cultural & Strategic Management, 23(1), 128–157. https://doi.org/10.1108/CCSM-06-2014-0059.

Halásková, M., & Bednář, P. (2023). Effect of public and private sector R&D indicators on innovation performance and competitiveness: A case study of Western European NUTS 2 regions. Journal of Competitiveness, 15(3), 104–121. https://doi.org/10.7441/joc.2023.03.06.

Holmes-Smith, P. (2012). Structural equation modeling (using AMOS): From the fundamentals to advanced topics. Melbourne: SREAMS.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185.

Lopes, J., & Lussuamo, J. (2021). Barriers to university-industry cooperation in a developing region. Journal of the Knowledge Economy, 12(3), 1019–1035. https://doi.org/10.1007/s13132-020-00646-0.

Mannak, R. S., Meeus, M. T. H., Raab, J., & Smit, A. C. (2019). A temporal tesspective on repeated ties across university-industry R&D consortia. Research Policy, 48(9), 103829. thamref>https://doi.org/10.1016/j.respol.2019.103829.

Mascarenhas, C., Ferreira, J. J., & Marques, C. (2018). University-industry cooperation: a systematic literature review and research agenda. Science and Public Policy, 45(5), 708–718. https://doi.org/10.1093/scipol/scy003.

Mirza, H., Sinawi, H. A., Al-Balushi, N., Al-Alawi, M., & Panchatcharam, S. M. (2020). Barriers to university-industry collaboration in an academic university department in London, United Kingdom. International Journal of Pharmaceutical and Healthcare Marketing, 14(3), 445–460. https://doi.org/10.1108/IJPHM-11-2016-0062.

Nsanzumuhire, S. U., Groot, W., Cabus, S. J., & Bizimana, B. (2021). Understanding the extent and nature of academia-industry interactions in Rwanda. Technological Forecasting and Social Change, 170, 120913. https://doi.org/10.1016/j.techfore.2021.120913.

Parmentola, A., Ferretti, M., & Panetti, E. (2021). Exploring the university-industry cooperation in a low-innovation region. What differences between low-tech and high-tech industries? The International Entrepreneurship and Management Journal, 17(3), 1469–1496. https://doi.org/10.1007/s11365-020-00671-0.

Punch, K. (2013). Introduction to Social Research: quantitative and qualitative approaches (3rd ed.). London: SAGE Publications Ltd.

Ribeiro, S. X., & Nagano, M. S. (2021). On the relation between knowledge management and university-industry-government collaboration in Brazilian National Institutes of Science and Technology. VINE Journal of Information and Knowledge Management Systems, 53(4), 1–22. https://doi.org/10.1108/VJIKMS-01-2020-0002.

Sharma, S. (1996). Applied multivariate techniques. Hoboken, NJ: John Wiley and Sons.

Treiblmaier, H., & Filzmoser, P. (2010). Exploratory Factor Analysis Revisited: How Robust Methods Support the Detection of Hidden Multivariate Data Structures in IS Research. Information and Management Journal, 47(4), 197–207.

Xu, A., Baysari, M. T., Stocker, S. L., Leow, L. J., Day, R. O., & Carland, J. E. (2020). Researchers’ views on, and experiences with, the requirement to obtain informed consent in research involving human participants: A qualitative study. BMC Medical Ethics, 21(1), 93. https://doi.org/10.1186/s12910-020-00538-7.