Factors affecting web links between European higher education institutions

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Abstract

We examine the extent to which the presence and number of web links between higher education institutions can be predicted from a set of structural factors like country, subject mix, physical distance, academic reputation, and size. We combine two datasets on a large sample of European higher education institutions (HEIs) containing information on inter-university web links, and organizational characteristics, respectively. Descriptive and inferential analyses provide strong support for our hypotheses: we identify factors predicting the connectivity between HEIs, and the number of web links existing between them. We conclude that, while the presence of a web link cannot be directly related to its underlying motivation and the type of relationship between HEIs, patterns of network ties between HEIs present interesting statistical properties which reveal new insights on the function and structure of the inter organizational networks in which HEIs are embedded.

Highlights

► Predict web links between European HEIs from structural factors. ► Combine large datasets on inter HEI web links and HEI characteristics. ► Tests on the presence and intensity of connection. ► Antecedents significantly predict the existence and intensity of connection.

Introduction

During the last decade increasing attention has been dedicated to the study of connections between higher education institutions (HEIs) through their web domains (Bar-Ilan, 2009). Web sites are important coordination devices that may be used to support a wide range of inter organizational communications (Thelwall & Zuccala, 2008). A number of studies are available that investigate the motivation behind their creation (Bar-Ilan, 2004, Vaughan et al., 2007), as well as the structure of interlinking within and between European countries (Ortega et al., 2008, Thelwall, 2002b).

Against this background, in the paper we want to identify the major factors influencing the probability of the creation of web links between two HEIs. Potential antecedents include institutional factors defined, for example, in terms of national and linguistic boundaries, the distance between HEIs and organizational factors such as size and research quality. While previous research agrees that these factors affect the presence and number of web links (Thelwall, 2002a), their relative strength has never been investigated on a sample large enough to draw robust conclusions and to generalize results beyond national situations.

The analysis is based on a sample of 1181 HEIs in 28 European countries obtained by matching interlinking data provided by the Cybermetrics lab (Ortega et al., 2008) with structural characteristics of HEIs derived from the EUMIDA dataset (Bonaccorsi et al., 2010). The matching of the two datasets represents and important innovation, which allows a better understanding of the relationship between web links, the characteristics of individual HEIs and their relative position in the institutional and physical space of European higher education.

We organize the paper as follows. In the next section we introduce our approach to modeling weblinks, while in Section 3 we present the dataset and the measures of antecedents. In Section 4 we provide a descriptive analysis of web links, while in Section 5 we report the results of inferential analyses of the antecedents of network ties between HEIs and of their strength. We conclude the paper by discussing the methodological and substantive implications of these results for the study of network relations between HEIs.

Section snippets

Background and theoretical framework

The conceptual framework of this paper can be outlined as follows: HEIs are connected through a web of relationships related to a diverse set of activities and motivations. Social network theory predicts that their presence and strength is influenced by a set of factors through assortative and proximity mechanisms (Rivera, Soderstrom, & Uzzi, 2010). Previous research shows that Web links provide a synthetic indicator for relationships and, accordingly, we expect their presence to depend on

Data sources and methods

A major element of innovation in our work derives from matching the number of weblinks connections between HEIs with a data set containing information on their individual characteristics. Previous studies either worked on a single national context (Bar-Ilan, 2004, Vaughan et al., 2007), or relied on webometrics data only (Ortega et al., 2008).

Interlinking data. The interlinking data were obtained from commercial public search engines following the methodology described in Aguillo, Granadino,

Descriptive statistics

The sample includes 1181 HEIs: 731 of them award PhD certificates, and 182 are in the Leiden ranking, 937 are public, 154 private and 90 are government-dependent private, i.e. private institutions that receive most of their funding from the government. The largest country in terms of representation in the sample are Germany (292), UK (145), Poland (85) and Italy (80). German is the predominant language (379) followed by English (166 – Malta not considered) (Table 2).

Dyadic QAP correlations2

Testing antecedents of web links

Table 5 presents the results of negative binomial regressions on the full sample for three models. The country model is superior to the null model, but the complete model is largely superior to both. The third model represents the optimal balance between fit and the number of variables included, as adding English, Legal Status, Language, or cross-terms does not improve the statistical performance meaningfully. The function ‘hurdle’ separately predicts estimates for the zero values and for the

Discussion

Before discussing the implications of our results, it is important to acknowledge the limitations of the study. The literature on web links among HEI supports the claim that they reflect underlying inter-organizational relations, but involve a variety of motivations. Accordingly, one needs to be careful in interpreting tie strengths, as different numbers of web links might be generated by different types of relationships. Our data provide a single time window; moreover, one has to take in mind

Acknowledgments

We thank Francesca Pallotti for comments on preliminary versions of the article. We thank Marco Calderisi for his help with the statistical tests and Michele Seeber for the support to the Python scripts. A preliminary version of this paper was presented to the ENID/STI conference, Rome, September 2011. We also thank two anonymous referees for their useful comments.

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