**1. Introduction**

In the last decades, there has been an increasing trend toward scientific collaboration (SC) [1, 2]. Getting more insights about trends in scientific collaboration (SC) is important because SC is assumed to enhance the quality of the research for a number of stemming benefits largely discussed in the literature [3–5]. It brings together complementary knowledge and expertise. The presence of co-authors often implies a higher internal quality control than

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

single-authored papers; learning, social networks creation, knowledge diffusion, and crossfertilization across individuals and/or disciplines are enhanced. From an economic viewpoint, SC also provides benefits including access to a wide variety of resources and new foundations or instruments. These benefits, together with the well-known role of knowledge creation and diffusion as the main sources for sustainable economic growth in the long run [6, 7], have shaped the European policy. The European government initiative aimed to convert Europe into the "the most competitive and dynamic knowledge-based economy" [8] giving priority to invest more in knowledge and innovation and to give Europe a new "fifth liberty," the free circulation of knowledge in order to construct a European research area [9].

(e.g. [4, 17, 18]). Despite some authors claimed the death of distance due to ICT development, Hoekman et al. [18] found that physical distance still impedes research collaboration,

Patterns of Academic Scientific Collaboration at a Distance: Evidence from Southern European…

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• *Cognitive proximity*, that is, the degree of the shared knowledge base of organizations, facilitates knowledge transfer by contributing to building absorptive capacity that enables actors to identify, acquire, understand, and exploit knowledge available from others [19]. Nevertheless, recent studies have shown a certain degree of cognitive distance as a potential source of complementarities in order to improve knowledge base [20, 21]. Thus, the challenge is to collaborate with actors that provide access to heterogeneous sources of knowledge to generate sufficiently diverse complementarities, while ensuring the absorp-

• *Institutional proximity* is defined by the degree of similarity in formal institutions, such as laws and rules, and informal institutions, like culture norms and habits, may enable knowledge flows by facilitating trust and reducing uncertainty and risks [15, 22]. Hoekman et al. [18] found that SC is more likely to occur within the same sub-national region, within the same country, and within the same linguistic area. Hennemann et al. [23] look in detail at the spatial structures of scientific activity (epistemic communities) showing that intra-country collaboration is more likely to occur than international collaboration.

• *Social proximity*, that is, socially embedded relations based on friendship, kindship and past experience between agents at the micro-level, is expected to stimulate interactive learning due to the trust and commitment [15]. It is commonly accepted to measure social proximity

• *Organizational proximity* can be understood as a variable capturing organization that share the same or similar regulation and routines at a micro-level. In that sense, a certain degree of organizational proximity is desirable to reduce uncertainty and opportunism in knowledge creation within and between organizations. In research collaboration literature, this dimension has been often included by a variable capturing whether partners to the same institutional arrange, for example, by belonging to the same corporation [27]. In this research, difficulties to consider organizational proximity in Boschma's sense, arises due to the absence of hierarchical relations among universities. However, they cannot be considered homogenous organizations because research institutions differ in their norms, structure, size, and strategy [28, 29]. • *Economic distance* (differences in economic resources among geographic areas) may determine the spatial patterns in SC, as derived from the center-periphery hypothesis applied to research collaboration [10, 11]. According to this literature, scientists in peripheral countries are willing to collaborate with core countries to gain access to resources, while core areas seek for complementarities [16]. However, empirical evidence provided by Acosta et al. [10] using data on a sample of co-authored papers among regions in EU-15 showed that differences in per capita income do not affect collaboration, while having similar levels of resources devoted to R&D play a positive role. They argue that having access to greater resources increase opportunities for mobility and attendance to international conferences, which enables establishing and reinforcing personal contacts

based on prior collaborations or previous research experiences [24–26].

with no evidence of a declining effect in the period 2000–2007.

tion capacity enabled by the shared knowledge base.

for future collaborations.

The contribution of this research is twofold. First, we provide a comprehensive analysis of the evolution of geographical, cognitive, institutional, social, and organizational proximity on scientific collaboration. Apart from these, we also add economic distance as suggested in the recent literature [10, 11]. Second, we provide a joint analysis of trends in SC in all disciplines included in the Science Citation Index (SCI) of the Web of Science (WoS), and a separated analysis for *Chemistry & Chemical*, *Life Sciences* and *Physics and Astronomy* in order to examine whether there are differences across disciplines. We have chosen these disciplines because, jointly with *Medicine & Biomedicine*, they have the highest publication and collaboration share1 . For our purpose, we use an original dataset containing information on 152,140 collaborations in publications in Science and Engineering (excluding social sciences) indexed in the Science Citation Index (SCI) provided by WoS and co-authored among academics from different universities. Our analysis includes 175 public universities from peripheral countries in Southern Europe: Spain, Greece, Italy, and Portugal. Focusing on peripheral countries is relevant because they usually include universities and regions far from core centers of knowledge with the lower level of resources and fewer opportunities to integrate in collaboration networks.

The remainder of the chapter is organized as follows. In Section 2, we review the relevant literature. Section 3 describes the data and explains the methodology. Section 4 provides the results. The main conclusions and policy implications are obtained at the end of the paper.
