Abstract

This study involved 51 Chinese universities from the Quacquarelli Symonds (QS) World University Ranking 2021. With based the resource-based view (RBV) as a framework, it aimed to identify the determinants of human resource capital that were related to universities’ production of MOOCs. Three determinants were detected—size, lifelong learning, and proximity to the political centre. Both size and proximity to the political centre proved to be significant. The findings provide timely implications for university managers and suggest that the variety of management spaces be expanded to increase the portfolio of high-quality Chinese universities that facilitate the production of MOOCs. In addition, universities should increase their proximity awareness to remedy the disadvantages of uneven resource allocation due to geographical proximity.

Keywords: MOOCs, resource-based view, knowledge management, proximity

Addressing the Resource-Based View: Determinants That Drive Chinese Universities to Offer MOOCs

MOOCs (massive open online courses) are viewed as a means to promote and publicize universities, given increasing demand from students for online learning options coupled with the advanced information and communication technology (ICT; Wang et al., 2022). MOOCs have offered universities the opportunity to connect with students from different countries, which expands their profiles and increases their potential competitive advantages, given the influence of higher education marketisation (Howarth et al., 2022). Additionally, Chinese students have perceived the usefulness of MOOCs and continue seeking for appropriate MOOCs as an alternative way to improve themselves (Wang, 2023).

In particular, universities have been challenged to connect and incorporate diverse roles and facets of value-added management processes (Chatterton & Goddard, 2000). It is essential to ascertain the forces driving organizational success and competition regarding MOOCs. Therefore, understanding the determinants that affect Chinese universities in producing MOOCs is of the utmost importance considering the rapid expansion of online education; this is especially relevant given China’s unique educational landscape and potential impact on global online learning trends.

This study aimed to identify and interpret the determinants affecting Chinese universities to produce MOOCs. Following the sample selection method from Zakharova (2019) and considering the impact MOOCs caused on university internationalization (Chuang & Ho, 2016), this study considered only the universities from the Quacquarelli Symonds (QS) World University Ranking 2021 (see Appendix, Table A1), given that leading universities initiated MOOCs before other universities with various online courses. The resource-based view (RBV) is an organizational framework that focused on the resources an organization can use to achieve its sustainable and competitive advantage (Barney et al., 2001). Therefore, this paper considered RBV as the theoretical framework to detect the determinants affecting Chinese university to produce MOOCs.

To begin, exploratory factor analysis (EFA) was used to identify the underlying factors among the data set. Ordinary least squares (OLS) and Tobit regression were adopted to further verify the relationship between the factors and the production of MOOCs, which could help us understand the determinants driving MOOCs’ operation in universities.

Literature Review and Hypotheses

MOOCs in China

MOOCs are defined as an instructional approach that uses technologies to provide learners opportunities to access online courses freely worldwide (Wang et al., 2022). Zheng and Yang (2017) considered that MOOCs have changed the relation of supply and demand in terms of knowledge acquisition in China. Compared with traditional education, advances in ICT make MOOCs possible in practice. They also argued that universities should bear the support and service roles for producing and developing MOOCs. Hence, 14 Chinese MOOC platforms, established by enterprise and university respectively or jointly, have appeared since 2012 (Zheng et al., 2018). Most published literature has focused on six categories (Moreno-Marcos et al. 2018): (a) dropout, (b) scores prediction, (c) forum posts classification, (d) students’ motivation, (e) relevance of content, and (f) students’ and teachers’ behaviour. Little literature has considered the university side and sought to identify the determinants that influence universities to produce MOOCs. This study served to bridge the research gap.

Resource-Based View

RBV is an organizational framework used in strategic management (Khanra et al., 2022); it focuses on the interaction of an organization’s internal resources—which are variable, rare, inimitable, and non-substitutable—to determine the strategic resources an organization may use to achieve its sustainable and competitive advantage (Barney et al., 2001). The resources in RBV include tangible as well as intangible assets. Intangible resources hinder competitors’ efforts to imitate and substitute in the short term, due to the inherent complexity and specificity of their accumulation process, including financial and capital assets, reputation, human capital, management skills, organizational processes, and an organization’s information and knowledge (Battisti et al., 2022).

Focusing on higher education institutions (HEIs), researchers have expanded the RBV in researching global alliances (Sanders &Wong, 2021), competitive strategy making (Valaei et al., 2022), and information technology (IT) adoption (Karim et al., 2022). Institutional resources are essential for universities to achieve objectives (Williams, 2014). It is crucial for universities to manage the tangible and intangible resources that can be bundled to construct organizational capabilities to produce MOOCs as innovative educational products of HEIs. Therefore, this study used RVB as a research framework to identify the underlying determinants behind MOOCs and further interpret how these determinants, integrated as complementary capabilities, help MOOCs be successful in terms of higher education strategic management.

Research Model and Hypotheses

Empirical studies of higher education management have included measures of institutional resources as independent variables or control variables. This study considered the variables confirmed by Lowry (2004), Muscio et al. (2013), Sav (2013), Schlesselman and Coleman, (2013), and Ospina-Delgado et al. (2016). Several hypotheses were generated and are outlined below.

Wernerfelt (1989) considered an organization’s age to be an intangible resource that impacted performance. In addition, Schlesselman and Coleman (2013) and Ospina-Delgado et al. (2016) determined the year in which a college was established to be significantly correlated with the performance of HEIs. Therefore, we proposed our first hypothesis:

H1: The age of a university positively influences it to produce MOOCs.

In terms of resource-related measures, Huang and Lee (2012) indicated that human resources were one of a university’s essential internal resources, and they considered the number of teaching faculty to be an input variable (Sav, 2013). In this sense, we proposed a second hypothesis:

H2: The number of teachers positively influences a university to produce MOOCs.

Student enrollment has been viewed as a resource acquired by the university, which is a crucial indicator of institutional characteristics and a university’s ability to achieve economies of scale (Worthington & Higgs, 2011). Therefore, we proposed a third hypothesis:

H3: The number of students positively influences a university to produce MOOCs.

Rothschild et al. (1991) indicated that the degree program portfolios offered by a university were a crucial resource factor for competing against rivals in the marketplace. Loukkola et al. (2020) stated that the number of degrees obtained at each of the bachelor, master, and doctoral levels were crucial indicators in funding mechanisms. Thus, we proposed our fourth, fifth, and sixth hypotheses:

H4: The number of bachelor programs positively influences a university to produce MOOCs.

H5: The number of master programs positively influences a university to produce MOOCs.

H6: The number of doctoral programs positively influences a university to produce MOOCs.

The location of university can be defined as geographical proximity, which has been well documented as being related to organizational outcomes such as innovation and knowledge creation (Catalini, 2018). Besides, the political environment has the capacity to influence education policies, school curricula, and investment in education and research (Boschma, 2005; Jowett & O’Donnell, 2014). For this reason, we proposed a seventh hypothesis:

H7: Proximity to the political centre (Beijing) positively influences a university to produce MOOCs.

Internationalization as a concept and strategic agenda is a diverse phenomenon in tertiary education (De Wit & Altbach, 2021). The international activities in universities have expanded dramatically, ranging from traditional study-abroad programs to foreign language programs. Loukkola et al. (2020) suggested that international students be part of statistics related to measuring internationalization. Therefore, we proposed our eighth hypothesis:

H8: The number of international students positively influences a university to produce MOOCs.

The number of post-doctoral positions played a crucial role in research productivity (Scaffidi & Berman, 2011). According to Chen et al. (2015) post-doctoral positions can be viewed as a university’s research resource. Thus, we proposed a ninth hypothesis:

H9: The number of post-doctoral programs positively influences a university to produce MOOCs.

Hence, following the hypotheses above, we proposed the research model below:

Testable model: NMC = α + β1Factor1 + β2Factor2 +··· +β9Factor9 + ε

Where, NMC = the number of MOOCs; α = the intercept of the regression equation; β = coefficients of independent variables; and ε = error term.

Methodology

Data Collection

The data considered for this study were all secondary data from university Web pages, accessed from September to December, 2021. This period was selected because September was the first month of a new academic year for Chinese universities and all the data would be updated and presented through the university Web pages. Furthermore, based on the extant literature and discussion with professors who were experts in the field of online learning and higher education management, this study followed previous empirical methods for selecting variables. The number of MOOCs was the dependent variable, with the following independent variables: (a) age of the university (Year); (b) number of teachers (NT); (c) number of students (NS); (d) number of bachelor programs (NBP); (e) number of master programs (NMP); (f) number of doctoral programs (NDP); (g) number of post-doctoral programs (NPDP); (h) distance to Beijing (political centre of China; DB); and (i) number of international students (NIS).

Examining Variance Inflation Factors (VIF)

Table 1 presents the results of examining VIF for the independent variables and indicates that the variables were highly correlated and the multicollinearity issue exists. Therefore, factor analysis was used to elicit the data.

Table 1

VIF Values

Exploratory Factor Analysis

This study adopted exploratory factor analysis to elicit information regarding interrelationships among the variables. Conducting exploratory factor analysis considered three stages, namely to assess the suitability of data, extract factors, and rotate factors.

Regarding the adequacy of a sample size, the consensus is that the larger the sample size, the better. Later studies have confirmed the adequacy of a small sample size, less than 50, for evaluation research (Costello & Osborne, 2005; Mundfrom et al., 2005). Therefore, a sample size of 51 was considered adequate for this study. In terms of the interrelationship among the variables, the correlation matrix approach has been recommended to look for coefficient values, the more there are coefficient values higher than 0.3, the more acceptable the sample size (Ogunsanya et al., 2019). We used the approaches of correlation matrix, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s test of sphericity to confirm the conditions above. KMO statistics vary from 0 to 1, with values greater than 0.5 considered acceptable, values between 0.5 and 0.7 mediocre, values between 0.7 and 0.8 good, and values between 0.8 and 0.9 considered superior (Kaisier, 1974).

This study adopted principal component factor analysis to extract factors, and the varimax approach for factor rotation. In terms of factor loadings, greater than 0.3 are considered significant, greater than 0.4 are considered more critical, and 0.5 or higher are considered very significant (Hair et al., 2003).

OLS Regression and Tobit Regression

OLS is a type of linear least square method for examining the unknown parameters in a linear regression model based on the assumption of independent observations (Kashki et al., 2021). OLS selects the parameters of a linear function of a set of explanatory variables according to the principle of least squares, which minimizes the sum of squares of the difference between the observed dependent variable and the linear function prediction in a given dataset (Ahmad et al., 2021). In this study, OLS was considered one of the methods to model a dependent variable regarding its relationship with a set of independent variables. In comparison, the Tobit model (Tobin, 1985) was designed for estimating linear relationships among variables when the dependent variable is either left-censored or right-censored (Kumari et al., 2021). Thus, following the methods of Schlup and Brunner (2018), Tobit regression analysis was further considered as a robust test for validating the estimating results.

Results

Data Description

Table 2 shows the general variable description of the number of MOOCs.

Table 2

Data Description

The Results of Exploratory Factor Analysis

Regarding the suitability of data, (see Appendix, Table A2) shows correlation coefficients and proves the inter-correlation strength among the variables in this study. Table A3 (see Appendix) indicates the data are appropriate for performing factor analysis with the KMO value of 0.703 and a significant P-value of 0,000. The values of communality (Table A4, see Appendix) further demonstrate the adequacy of a small sample size for this study since the average value is 0.729 and all the values are higher than 0.5.

Table 3 shows three values extracted with an eigenvalue of greater than 1, which explained 72.855% of the total variance, and the eigenvalue of the fourth factor is far from the reference value of eigenvalue of 1.

Table 3

Total Variance Explained

Table 4 shows that three factors were generated, each impacting Chinese universities to produce MOOCs. Six variables were represented in the first factor, namely the (a) number of post-doctoral programs, (b) number of teachers, (c) years of existence, (d) number of students, (e) number of bachelor programs, and (f) number of international students. Given that the role of post-doctoral study is mainly for academic research, teachers were also required to conduct academic research. Size was the label we gave to this first factor. The second factor was represented by two variables—the number of master programs and the number of doctoral programs. We named this factor lifelong learning, given that the master programs (professional and academic) and doctoral programs were aimed at training people for pursuing careers (McCorkle et al., 2023). The third and final factor was represented by one variable, namely the distance to Beijing. Thus, we named this factor proximity to the political centre.

Table 4

Rotation Factors and Sums of Squared Loadings

Results of OLS and Tobit Regression

Tables 5 and 6 both support the hypothesis of this study and show that the goodness of fit was considered. The two factors of size and proximity to the political centre indicated that the hypotheses as proposed were significantly supported except the number of master programs (H4) and the number of doctoral programs (H5). The positive correlation coefficients between the number of MOOCs and size indicated that the size of universities would increase the number of MOOCs produced. However, proximity to the political centre was found to be negatively correlated with the number of MOOCs, idicating that the closer universities were to the political centre, the fewer MOOCs they produced.

Table 5

OLS Regression Analysis Results

Note. Prob > F = 0.0000; R² = 0.4492; Adj-R² = 0.4117.

*p < 0.1. **p < 0.05. ***p < 0.01.

Table 6

Tobit Regression Analysis Results

Note. Prob > Chi² = 0.0000; pseudo R² = 0.2102.

***p < 0.01.

Discussion and Conclusions

Size

In this study, the determinant of size (i.e., NPDP, NT, NS, Year, NBP, NIS) was considered the university’s intangible resource within the RBV framework. The role of size should be stressed due to its impact on supply chain integration and sustainable performance. Studies have accentuated that size is an essential determinant for an organization, impacting the level of implementation for sustainability-oriented strategies and practices (Gallo & Christensen, 2011). Besides, many operational or strategic resources are associated with size, which could significantly impact the organization’s ability to deliver projects (Carr and Pearson, 1999; Hong et al., 2019).

In China, MOOCs as the educational products of higher education involve high resource consumption, because MOOCs not only need to satisfy the diverse needs of students looking for alternative means to acquire new knowledge and skills, but also must strengthen universities’ competitive advantage in the higher education industry. Size is an important factor analyzed in higher education management (Martínez, 2013) and is critical for promoting sustainability management and producing new projects (Hörisch et al., 2015). According to Williams (2014) size was considered one of the essential resources for universities, viewed as a proxy for the institution’s operational and marketing capabilities. Therefore, big universities had an advantage in producing MOOCs to further implement their operational and marketing capabilities to meet the demands of students, as well as to reinforce their competence among HEIs. This was consistent with Ospina-Delgado et al. (2016) who found that size played an essential role in the production of MOOCs.

Lifelong Learning

The interface between lifelong learning and higher education has become increasingly important for updating professional skills. With the expansion of higher education, an increased proportion of the labour force is comprised of graduates (Brooks & Everett, 2008). Lifelong learning emphasizes the learning process wherein people can formally or informally engage in learning activities related to knowledge and skills necessary for personal, social, and employment-related demands (Taşçi & Titrek, 2019).

MOOCs could be seen as an opportunity to redesign dynamic environments with current learning styles, thus contributing to improving learning and lifelong learning (Ospina-Delgado et al., 2016). However, this determinants off NMP and NDP were confirmed as insignificant in impacting HEIs to produce MOOCs. As the existing literature has demonstrated, lifelong learning participants are mainly graduates rather than other groups of people (Brooks & Everett, 2008). Furthermore, graduate programs are aimed at training people for pursuing academic careers (McCorkle et al., 2023), which have been traditionally offered through face-to-face instruction rather than virtual. Additionally, academic procrastination has been one of the barriers for lifelong learning in HEIs (Barnová & Krásna, 2018).

Proximity to Political Centre

Proximity is often interpreted as geographical proximity, defined as the spatial distance between individuals or organizations, and considered as an external variable that stimulated the formation and evolution of institutions (Boschma, 2005; Christensen & Pedersen, 2018). There is a causal relationship between proximity to the political centre, and managerial and innovative operations (Funk, 2014). The political geography has a pervasive effect on investment in organizations, and those located in areas with strong control by the ruling party could experience greater opportunities, and more risk as well (Kim et al., 2012).

Our study has confirmed that proximity was a determinant that significantly impacted HEIs to produce MOOCs. This was consistent with the literature that organizational outcomes such as communication, social ties, innovation, and knowledge creation are positively associated with proximity to the political centre (Boschma 2005; Catalini, 2018). Furthermore, this study found that the coefficient of proximity to the political centre was negative—HEIs closer to the political centre produced fewer MOOCs than those farther away. This finding did not concur with previous studies, in which a positive relationship was observed between political proximity and the rate of investment.

In the current educational context in China, although the government is committed to improving education, there are considerable flaws in current educational processes; these affect educational equality, cost, and educational resources (Tang & Carr-Chellman, 2016). The universities located in Beijing can benefit from more opportunities and fundings brought by political geography, which highlights the issue of educational equality. However, after the COVID-19 pandemic, there has been a significant increase in the number of MOOCs and students have become accustomed to online learning. MOOCs, as an educational innovation, bridge the gap in educational equality. They offer hope to HEIs that are far from political centre without adequate educational resources to garner educational information and resources, address the inequality in HEIs, and further decrease educational cost. This represents the line regarding producing MOOCs that proximity for Chinese universities far away from political centre cannot be only viewed as the geographical disadvantage, but the opportunity to increase competence among the demand side of students and universities.

Conclusions

Following the COVID-19 pandemic, there was a significant increase in the number of MOOCs worldwide. In the educational industry, the sustainability and competitiveness benefits of delivering MOOCs are known, but among Chinese universities, the determinants that successfully impact MOOC production are diverse because different factors are responsible. Therefore, this study focused on the question of what determinants drive universities to produce MOOCs and aimed to identify and interpret the determinants affecting Chinese universities to produce MOOCs based on the RBV framework. In terms of results, except for lifelong learning, size and proximity to political centre were confirmed as significant determinants driving Chinese university to produce MOOCs.

Regarding the two perspectives of students’ demand side and the competence among universities, the findings lent support to and aligned with studies in strategical management regarding higher education management. Therefore, it is recommended that efforts to increase the size of universities should be intensified; bigger universities perform better generating new competing projects such as producing MOOCs to satisfy the student’s needs and strengthen their competitive capability. Awareness of proximity should also be intensified by those universities far away from the political centre to remedy the disadvantages of uneven resource allocation due to lack of geographic proximity.

Implications, Limitations, and Future Research Directions

This study was the first to explore and summarize the essential determinants behind MOOCs’ operation in Chinese universities. In terms of theoretical implications, this study addressed the research gap of HEIs strategic management in the MOOC context and provided considerable variables for HEIs with respect to MOOC production.

This study further expanded the research scope of RBV in strategic management of HEIs by providing empirical evidence. As well, this study has extracted three new variables for future studies to explain the performance and strategy of universities regarding MOOCs. Focusing on managerial implications, size was proven to be a crucial determinant driving universities to produce MOOCs, indicating institutional resources are essential to operate new objectives for HEIs. Thus, this study suggested the universities should invest in fundamental resources and leverage their size to improve comprehensive competitive advantages and capabilities. Furthermore, proximity was also confirmed as a significant determinant influencing Chinese university to operate MOOCs. Thus, universities that are far away from the political centre with fewer educational resources, especially the universities located in the northern and western parts of China (e.g., Xinjiang Uygur Autonomous Region and Tibet Autonomous Region) could consider MOOCs to be a way to expand their profile regarding students’ demands and marketing competence. Additionally, in light of the findings related to Chinese universities, universities outside China could further leverage their profile worldwide through MOOCs; the indicator of international students was positively involved in the determinant of size.

Addressing the limitations of this study, first, the sample size may have been a limitation, since we considered only Chinese universities from the QS ranking 2021, in line with Chuang and Ho (2016) and Zakharova (2019). Therefore, the future research could involve more Chinese universities from outside the QS ranking. The second limitation relates to data collection. University Web pages could not provide us enough detailed information such as the types of MOOCs these universities provided. The third limitation concerns the variables selected for this study. The literature has demonstrated both the lack of empirical evidence and the operationalisation of variables regarding MOOCs. The variables selected for measuring MOOC operations would differ, depending on country contexts and history due to cultural differences and parameters adopted for measuring success, which has also been advocated by a range of scholars (Welter, 2011). Thus, in future research, it would be interesting to establish a standard measurement for universities or HEIs, even if they have different contexts and are located in other latitudes. Given the increase in MOOCs, it is worth knowing the impact of these at regional, national, or international levels.

References

Ahmad, I., Dar, M. A., Fenta, A., Halefom, A., Nega, H., Andualem, T. G., & Teshome, A. (2021). Spatial configuration of groundwater potential zones using OLS regression method. Journal of African Earth Sciences, 177, 104147. http://doi:10.1016/j.jafrearsci.2021.104147

Barney, J., Wright, M., & Ketchen, D. J., Jr. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625-641. https://doi.org/10.1177/014920630102700601

Barnová, S., & Krásna, S. (2018). Academic procrastination—One of the barriers in lifelong learning. R&E-SOURCE. https://www.researchgate.net/profile/Silvia-Barnova-2/publication/330345583_Academic_Procrastination_-_One_of_the_Barriers_in_Lifelong_Learning/links/5c3a12ed458515a4c720ce27/Academic-Procrastination-One-of-the-Barriers-in-Lifelong-Learning.pdf

Battisti, E., Nirino, N., Leonidou, E., & Thrassou, A. (2022). Corporate venture capital and CSR performance: An extended resource based view’s perspective. Journal of Business Research, 139, 1058-1066. https://doi.org/10.1016/j.jbusres.2021.10.054

Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61-74. https://doi.org/10.1080/0034340052000320887

Brooks, R., & Everett, G. (2008). The impact of higher education on lifelong learning. International Journal of Lifelong Education, 27(3), 239-254. https://doi.org/10.1080/02601370802047759

Carr, A. S., & Pearson, J. N. (1999). Strategically managed buyer-supplier relationships and performance outcomes. Journal of Operations Management, 17(5), 497-519. https://doi.org/10.1016/S0272-6963(99)00007-8

Chatterton, P., & Goddard, J. (2000). The response of higher education institutions to regional needs. European Journal of Education, 35(4), 475-496. https://doi.org/10.1111/1467-3435.00041

Chen, S., McAlpine, L., & Amundsen, C. (2015). Postdoctoral positions as preparation for desired careers: A narrative approach to understanding postdoctoral experience. Higher Education Research & Development, 34(6), 1083-1096. https://doi.org/10.1080/07294360.2015.1024633

Christensen, P. H., & Pedersen, T. (2018). The dual influences of proximity on knowledge sharing. Journal of Knowledge Management, 22(8), 1782-1802. https://doi.org/10.1108/JKM-03-2018-0211

Chuang, I., & Ho, A. (2016). HarvardX and MITx: Four years of open online courses (fall 2012 to summer 2016). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2889436

Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1), 7. https://doi.org/10.7275/jyj1-4868

De Wit, H., & Altbach, P. G. (2021). Internationalization in higher education: Global trends and recommendations for its future. Policy Reviews in Higher Education, 5(1), 28-46. https://doi.org/10.1080/23322969.2020.1820898

Funk, R. J. (2014). Making the most of where you are: Geography, networks, and innovation in organizations. Academy of Management Journal, 57(1), 193-222. https://www.jstor.org/stable/43589253

Gallo, P. J., & Christensen, L. J. (2011). Firm size matters: An empirical investigation of organizational size and ownership on sustainability-related behaviors. Business & Society, 50(2), 315-349. https://doi.org/10.1177/0007650311398784

Hair, J. J., Jr., Tatham, R. J., Anderson, R. E., & Black, W. (2003). Multivariate data analysis (5th ed.). Pearson.

Hong, Y., Hammad, A. W., & Akbarnezhad, A. (2019). Impact of organization size and project type on BIM adoption in the Chinese construction market. Construction Management and Economics, 37(11), 675-691. https://doi.org/10.1080/01446193.2019.1575515

Hörisch, J., Johnson, M. P., & Schaltegger, S. (2015). Implementation of sustainability management and company size: A knowledge‐based view. Business Strategy and the Environment, 24(8), 765-779. https://doi.org/10.1002/bse.1844

Howarth, J., D’Alessandro, S., Johnson, L., & White, L. (2022). Massive open online courses and consumer goals. International Journal of Consumer Studies, 46(3), 994-1015. https://doi.org/10.1111/ijcs.12742

Huang, H. I., & Lee, C. F. (2012). Strategic management for competitive advantage: A case study of higher technical and vocational education in Taiwan. Journal of Higher Education Policy and Management, 34(6), 611-628. https://doi.org/10.1080/1360080X.2012.727635

Jowett, G. S., & O’Donnell, V. (2014). Propaganda and persuasion examined. In Propaganda and persuasion (pp. 179-230). Sage.

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575

Karim, M. S., Nahar, S., & Demirbag, M. (2022). Resource-based perspective on ICT use and firm performance: A meta-analysis investigating the moderating role of cross-country ICT development status. Technological Forecasting and Social Change, 179, 121626. https://doi.org/10.1016/j.techfore.2022.121626

Kashki, A., Karami, M., Zandi, R., & Roki, Z. (2021). Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR: A case study Shiraz City, Iran. Urban Climate, 37, 100832. https://doi.org/10.1016/j.uclim.2021.100832

Khanra, S., Kaur, P., Joseph, R. P., Malik, A., & Dhir, A. (2022). A resource‐based view of green innovation as a strategic firm resource: Present status and future directions. Business Strategy and the Environment, 31(4), 1395-1413. https://doi.org/10.1002/bse.2961

Kim, C. F., Pantzalis, C., & Park, J. C. (2012). Political geography and stock returns: The value and risk implications of proximity to political power. Journal of Financial Economics, 106(1), 196-228. https://doi.org/10.1016/j.jfineco.2012.05.007

Kumari, P. B., Singh, Y. K., Mandal, J., Shambhavi, S., Sadhu, S. K., Kumar, R., Ghosh, M., Raj, M., & Singh, M. (2021). Determination of safe limit for arsenic contaminated irrigation water using solubility free ion activity model (FIAM) and Tobit Regression Model. Chemosphere, 270, 128630. https://doi.org/10.1016/j.chemosphere.2020.128630

Loukkola, T., Peterbauer, H., & Gover, A. (2020). Exploring higher education indicators. European University Association, 38(6), 1278-1300. https://www.modip.uoc.gr/sites/default/files/files/2020%20EUA_Exploring%20HE%20Indicators.pdf

Lowry, R. C. (2004). Markets, governance, and university priorities: Evidence on undergraduate education and research. Economics of Governance, 5(1), 29-51. https://doi.org/10.1007/s10101-002-0056-9

Martínez, T. L. (2013). La actividad investigadora de la universidad española en la primera década del siglo XXI: la importancia del tamaño de la Universidad [The research activity of Spanish universities in the first decade of the 21st century: the importance of university size]. Revista Española de Documentación Científica, 36(4), 36-15. https://doi.org/10.3989/redc.2013.4.1046

McCorkle, L. S., Vestal, A., & Diamond, L. L. (2023). Preparing doctoral students in special education: What do we really know? Teacher Education and Special Education, 46(3), 185-203. https://doi.org/10.1177/08884064221134815

Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., & Kloos, C. D. (2018). Prediction in MOOCs: A review and future research directions. IEEE Transactions on Learning Technologies, 12(3), 384-401. https://doi.org/10.1109/TLT.2018.2856808

Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159-168. https://doi.org/10.1207/s15327574ijt0502_4

Muscio, A., Quaglione, D., & Vallanti, G. (2013). Does government funding complement or substitute private research funding to universities? Research Policy, 42(1), 63-75. https://doi.org/10.1016/j.respol.2012.04.010

Ogunsanya, O. A., Aigbavboa, C. O., Thwala, D. W., & Edwards, D. J. (2019). Barriers to sustainable procurement in the Nigerian construction industry: An exploratory factor analysis. International Journal of Construction Management, 22(5),1-12. https://doi.org/10.1080/15623599.2019.1658697

Ospina-Delgado, J. E., Zorio-Grima, A., & García-Benau, M. A. (2016). Massive open online courses in higher education: A data analysis of the MOOC supply. Intangible Capital, 12(5), 1401-1450. http://dx.doi.org/10.3926/ic.798

Rothschild, M., & White, L. J. (1991). The university in the marketplace: Some insights and some puzzles (NBER Working Paper No. w3853). https://ssrn.com/abstract=473983

Sanders, J. S., & Wong, T. (2021). International partner selection among higher education institutions in Hong Kong, Singapore and Japan: A resource-based view. Journal of Higher Education Policy and Management, 43(2), 214-229. https://doi.org/10.1080/1360080X.2020.1774097

Sav, G. T. (2013). Four-stage DEA efficiency evaluations: Financial reforms in public university funding. International Journal of Economics and Finance, 5(1), 24. http://dx.doi.org/10.5539/ijef.v5n1p24

Scaffidi, A. K., & Berman, J. E. (2011). A positive postdoctoral experience is related to quality supervision and career mentoring, collaborations, networking and a nurturing research environment. Higher Education, 62(6), 685-698. https://doi.org/10.1007/s10734-011-9407-1

Schlesselman, L., & Coleman, C. I. (2013). College and school of pharmacy characteristics associated with US News and World Report rankings. American Journal of Pharmaceutical Education, 77(3). https://doi.org/10.5688/ajpe77355

Schlup, Y., & Brunner, T. (2018). Prospects for insects as food in Switzerland: A Tobit regression. Food Quality and Preference, 64, 37-46. https://doi.org/10.1016/j.foodqual.2017.10.010

Tang, H., & Carr-Chellman, A. (2016). Massive open online courses and educational equality in China: A qualitative inquiry. Journal of Educational Technology Development and Exchange, 9(1), 4. https://doi.org/10.18785/jetde.0901.04

Taşçı, G., & Titrek, O. (2019). Evaluation of lifelong learning centers in higher education: A sustainable leadership perspective. Sustainability, 12(1), 22. https://doi.org/10.3390/su12010022

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 26(1), 24-36. https://doi.org/10.2307/1907382

Valaei, N., Rezaei, S., Bressolles, G., & Dent, M. M. (2022). Indispensable components of creativity, innovation, and FMCG companies’ competitive performance: A resource-based view (RBV) of the firm. Asia-Pacific Journal of Business Administration, 14(1), 1-26. https://doi.org/10.1108/APJBA-11-2020-0420

Wang, K. (2023). The perception and behavioral intention toward MOOCs: Undergraduates in China. The International Review of Research in Open and Distributed Learning, 24(1), 22-46. https://doi.org/10.19173/irrodl.v24i1.6677

Wang, K., van Hemmen, S. F., & Criado, J. R. (2022). The behavioural intention to use MOOCs by undergraduate students: Incorporating TAM with TPB. International Journal of Educational Management, 36(7), 1321-1342. https://doi.org/10.1108/IJEM-11-2021-0446

Welter, F. (2011). Contextualizing entrepreneurship: Conceptual challenges and ways forward. Entrepreneurship Theory and Practice, 35(1), 165-184. https://doi.org/10.1111/j.1540-6520.2010.00427.x

Wernerfelt, B. (1989). From critical resources to corporate strategy. Journal of General Management, 14(3), 4-12. https://doi.org/10.1177/030630708901400301

Williams, S. D. (2014). A strategic resource-based view of higher education institutions’ resources. International Journal of Business and Social Science, 5(12). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=0232ba7c2d67ce04cb86b9c833ccad2115884bd7

Worthington, A. C., & Higgs, H. (2011). Economies of scale and scope in Australian higher education. Higher Education, 61(4), 387-414. https://www.jstor.org/stable/41477803

Zakharova, U. S. (2019). Online course production and university internationalization: Correlation analysis. In European MOOCs Stakeholders Summit (pp. 102-107). Springer. https://doi.org/10.1007/978-3-030-19875-6_12

Zheng, Q., Chen, L., & Burgos, D. (2018). The development of MOOCs in China. Springer.

Zheng, Y., & Yang, R. Y. (2017). The rise of MOOCs: The literature review of research progress and hot spots of MOOCs education in mainland China. Eurasia Journal of Mathematics, Science and Technology Education, 13(9), 6165-6174. https://doi.org/10.12973/eurasia.2017.01056a

Appendices

Table A1

Study Sample

Table A2

Correlation Matrix

Note. * means variables related.

Table A3

KMO and Bartlett’s Test

Table A4

Communalities Values

Addressing the Resource-Based View: Determinants That Drive Chinese Universities to Offer MOOCs by Kai Wang, Josep Rialp Criado, and Stefan Felix van Hemmen is licensed under a Creative Commons Attribution 4.0 International License.