Abstract:Sustainable solutions for complex societal problems, like poverty, require informing stakeholders about progress and changes needed as they collaborate. Yet, inter-organizational collaboration researchers highlight monumental challenges in measuring seemingly intangible factors during collective impact processes. We grapple with the question: How can decision-makers coherently conceptualize and measure seemingly intangible factors to sustain partnerships for the emergence of collective impact? We conducted an … Show more
“…Others showed that the role of geographical distance between research subjects in scientific cooperation networks has not weakened but has instead strengthened [42,43]. In conclusion, the relative importance of proximity of different dimensions in the process of scientific cooperation changes depending on the type of knowledge produced [44,45]. It is worth emphasizing that there is a large gap in the resources of higher education between Chinese cities, which is an important factor affecting the scientific cooperation of Chinese scientists.…”
Section: Literature Review and Hypothesesmentioning
The collaboration of scientists is important for promoting the scientific development and technological progress of a country, and even of the world. Based on the cooperation data of academicians of the Chinese Academy of Sciences (CAS) in the China National Knowledge Infrastructure (CNKI), we portray the scientific cooperation network of Chinese scientists using Pajek, Gephi, ArcGIS, and other software, and the complexity of the scientific cooperation network of Chinese scientists and its proximity mechanism are explored by combining complex network analysis, spatial statistical analysis, and negative binomial regression models. Our main conclusions are as follows: (1) In terms of network structure, the scientific cooperation network of Chinese scientists has a multi-triangular skeleton, with Beijing as its apex. The network has an obvious hierarchical structure. Beijing and Shanghai are located in the core area, and 16 cities are located in the semi-periphery of the network, while other cities are located at the periphery of the network. (2) In terms of spatial distribution, the regional imbalance of the scientific cooperation of Chinese scientists is obvious. Beijing–Tianjin–Hebei, the Yangtze River Delta, and the central-south region of Liaoning are hot spots for the scientific research activities of Chinese scientists. (3) The negative binomial regression model accurately explains the proximity mechanism of the scientific cooperation network of Chinese scientists. The geographical proximity positively affects the scientific cooperation of Chinese scientists under certain conditions. The educational proximity is the primary consideration for scientists to cooperate in scientific research. The closer the educational level of the cities, the greater the cooperation. Economic and social proximity can promote scientific cooperation among scientists, whereas institutional proximity negatively and significantly affects scientific cooperation.
“…Others showed that the role of geographical distance between research subjects in scientific cooperation networks has not weakened but has instead strengthened [42,43]. In conclusion, the relative importance of proximity of different dimensions in the process of scientific cooperation changes depending on the type of knowledge produced [44,45]. It is worth emphasizing that there is a large gap in the resources of higher education between Chinese cities, which is an important factor affecting the scientific cooperation of Chinese scientists.…”
Section: Literature Review and Hypothesesmentioning
The collaboration of scientists is important for promoting the scientific development and technological progress of a country, and even of the world. Based on the cooperation data of academicians of the Chinese Academy of Sciences (CAS) in the China National Knowledge Infrastructure (CNKI), we portray the scientific cooperation network of Chinese scientists using Pajek, Gephi, ArcGIS, and other software, and the complexity of the scientific cooperation network of Chinese scientists and its proximity mechanism are explored by combining complex network analysis, spatial statistical analysis, and negative binomial regression models. Our main conclusions are as follows: (1) In terms of network structure, the scientific cooperation network of Chinese scientists has a multi-triangular skeleton, with Beijing as its apex. The network has an obvious hierarchical structure. Beijing and Shanghai are located in the core area, and 16 cities are located in the semi-periphery of the network, while other cities are located at the periphery of the network. (2) In terms of spatial distribution, the regional imbalance of the scientific cooperation of Chinese scientists is obvious. Beijing–Tianjin–Hebei, the Yangtze River Delta, and the central-south region of Liaoning are hot spots for the scientific research activities of Chinese scientists. (3) The negative binomial regression model accurately explains the proximity mechanism of the scientific cooperation network of Chinese scientists. The geographical proximity positively affects the scientific cooperation of Chinese scientists under certain conditions. The educational proximity is the primary consideration for scientists to cooperate in scientific research. The closer the educational level of the cities, the greater the cooperation. Economic and social proximity can promote scientific cooperation among scientists, whereas institutional proximity negatively and significantly affects scientific cooperation.
“…The relational dimension of social capital that impacts inter-organizational learning is trust. Scholars highlight that trust "facilitates the openness for exchange of tacit knowledge, which is relatively difficult to communicate or trade in markets, and durability of relationships, which otherwise may collapse when problems arise between exchanging partners in pure market relationships" [183]. The literature highlights that the stronger the trust between partners, the stronger their ties and the more they can learn and innovate in a partnership [122,183,184].…”
Section: Relational Dimension Of Social Capital: Trustmentioning
confidence: 99%
“…Scholars define cognitive distance as differences of partner organizations with regards to their organizational frames, which are "interpretations used to make sense of the world." In other words, cognitive distance is to do with the "similarity in actors' frames of reference, and mental modes facilitate effective and efficient communication and transfer of knowledge, although some extent of differentiation is needed for new ideas, creativity, and innovation to emerge" [183]. On the other hand, institutional distance is referred to as field-level differences between organizations with regards to their institutional logics which are "taken-for-granted assumptions and practices that shape the behavior of organizations in specific societal sectors" [189].…”
Section: Cognitive Dimension Of Social Capital: Optimal Distancementioning
Sustainable development goals (SDGs) have become increasingly important for today’s firms as they build sustainability strategies that integrate SDGs into their core activities. Addressing these goals collaboratively, in line with SDG 17—partnerships for the goals, has gained momentum, hence the growing literature on sustainability-oriented partnerships. However, addressing SDGs through partnerships is not straightforward. For firms, contributing to SDGs through alliances and partnerships requires building environmental capabilities and embracing new value frames; in other words, going through the complex process of inter-organizational learning. This paper reviews the literature on sustainability-oriented partnerships with a focus on the inter-organizational learning process. As a result of the review, a model of inter-organizational sustainability learning is presented. This model captures the different levels and types of the inter-organizational learning process; partner and partnership characteristics that impact learning; the environmental conditions that set the conditions for learning to take place; the catalyst and inhibitors of learning; and finally outcomes of learning. This model expands and re-organizes the existing scholarly conversation about inter-organizational learning in the context of sustainability-oriented alliances and partnerships and offers a learning-based understanding of sustainability partnerships to practitioners. Based on the review, the paper proposes ideas for future research and contributes to the development of a future research agenda in the area of sustainability-oriented alliances and partnerships.
“…By virtue of its mandate, this convener needs to have an extensive network of ties to different actors across society. Beyond the existence of ties, the convening entity should be one that can draw attention to the problem and exhort action from actors tied to its causes or solutions in relation to their core resources and capabilities [44,63,64]. Finally, the convener also needs to have a reputation and credibility that can accord legitimacy to the initiative [20,44], bring more actors into the fold, and ensure all actors are informed and engaged [63].…”
Section: Modularizing Societal Problemsmentioning
confidence: 99%
“…Government organizations have been a natural choice to play this convening role since both economic growth and societal well-being fall within their mandate, and they have considerable authority over other societal actors [39,63]. They can employ a variety of tools ranging from persuasion and incentives to strictly enforced regulations to shape the behavior of others.…”
We develop a conceptual governance framework to guide creating and managing a modular interorganizational network to address complex social problems. Drawing on theoretical foundations in modularity and interorganizational networks, we propose that modularizing complex social problems is a dialectic, emergent process that blends a convener-led network formation with a consultative problem definition and solution design. We also posit that social systems are imperfectly modular and need purposefully designed interface governance to integrate the modules. Finally, we advance how leveraging modularity may simultaneously advance the interests of participating actors and deliver societal value. Together, the propositions advance a governance framework for a modular, multi-actor adaptive system suited to tackle the scale, diversity, and dynamics of complex social problems.
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