It’s not only what you know…. simulating research networks in the UK university sector
Professor Denise Jarratt
Dr Roderick Duncan
Senior Lecturer in Economics, School of Accounting and Finance
Charles Sturt University
School of Accounting and Finance
Building C2, Room 232
Panorama Avenue
Bathurst NSW 2795
Email:
Phone/Fax: 02 6338 4982
Professor Terry Bossomaier
Professor in Information Technology, Director of CRiCs, School of Computing and Mathematics
Charles Sturt University
School of Accounting and Finance
Building S15, Room 109
Panorama Avenue
Bathurst NSW 2795
Email:
Phone/Fax: 02 6338 4683
Professor Denise Jarratt
Dr Roderick Duncan BEc (Hons) ANU, LLB ANU, PhD Stanford
Dr. Duncan is a senior lecturer in economics and finance at Charles Sturt University. He has undergraduate degrees in economics and law from the Australian National University and a doctorate in economics from Stanford University. His research interests include international trade, development economics, resource and environmental economics and applied microeconomic analysis.
Recent publications:
Duncan, R. (2011). “Governance reform in the public sector in Pacific island countries: understanding how culture matters”. In R. Duncan (ed.) The Political Economy of Economic Reform in the Pacific, ISBN 9789290921752.
Sappey, J., Bone, Z. and Duncan, R. (2010). “The aged care industry in regional Australia: will it cope with the tsunami to come?". Australian Farm Business Management Journal, ISSN 1449-5937, 7(1): 21-28.
Chand, S. and Duncan, R. (2010). “Strengthening political parties in the Pacific”. Pacific Economic Bulletin, ISSN 0817-8038, 25(1): 35-45.
Duncan, R. (2008). “Solomon Islands and Vanuatu: an economic survey”. Pacific Economic Bulletin, ISSN 0817-8038, 23(3): 1-17.
Nair-Reichert, U. and Duncan, R. (2008). “Patent regimes, host country policies and the nature of MNE activities”. Review of International Economics, ISSN 0965-7576, 16(4): 783-797.
Duncan, R. (2008). “Agricultural productivity, the electoral cycle and ENSO effects in Papua New Guinea”. Pacific Economic Bulletin, ISSN 0817-8038, 23(1): 74-84.
Professor Terry Bossomaier BA Cantab, MSc PhD EAnglia, MACM
Professor Bossomaier is a professor in information technology at Charles Sturt University and Director of the Centre for Research in Complex Systems (CRiCs). He has an undergraduate business degree from the University of Cambridge and a masters and doctorate from the University of East Anglia. His research interests include parallel computing, complex systems, cellular automata, neural networks, vision, artificial intelligence, evolutionary computation and computer games technology.
Recent publications:
Bossomaier, T. (2012). “Introduction to the Senses: From Biology to Computer Science”, ISBN: 9780521812665
Carr, D. and Bossomaier, T. (2011). “Relativity in a rock field: A study of physics learning with a computer game”. Australasian Journal of Educational Technology, ISSN 1449-5554, 27(6): 1042–1067.
Harre, M. and Bossomaier, T. (2011). “The Development of Human Expertise in a Complex Environment”. Minds and Machines, ISSN 0924-6495, 21(3): 449 - 464.
Bossomaier, T., Standish, R. K. and Harre, M. (2011). “Simulation of trust in client-wealth Management Adviser relationships”. International Journal of Simulation and Process Modelling, ISSN 1740-2123, 6(1): 40 - 49.
Harre, M. and Bossomaier, T. (2010). “Equity trees and graphs via information theory”. European Physical Journal B, ISSN 1434-6028, 73(1): 59 - 68.
Harre, M., Bossomaier, T., Chu, R. and Snyder, A. (2010). “Strategic information in the game of Go”. Proceedings of the World Academy of Science, Engineering and Technology, ISSN 2010-376X, 41: 667-672.
It’s not only what you know…. simulating research networks in the UK university sector
Abstract:
Our research sought to validate and extend understanding of the Resource Advantage Theory of Competition (RATC) in a network context and to contribute to emerging work on network structure. Simulations were used to model the dynamics of the RATC in university science departments as they link with others in competing for national grants. RAE and research data was used to interpret the structural forms observed. Simulations revealed the network structures that form as a consequence of the increasing complexity of grant requirements and imperatives to improve RAE rankings. Our findings suggest that for those with limited resources, resource specialisation can be an effective strategy to link with others and build market position. This strategy has value for others competing in a complex and inimitable market, and therefore facilitates network formation. In this context it is the comparative advantage of the network that can create improved market position.
1. Introduction
Historically, the management discipline has been advanced through drawing on theory from a number of disciplines. Economic theory has been an important area for consultation about the behaviour of institutions and markets, however, traditional, “neoclassical, economic models of competitive markets although providing important concepts and insights, have been unable to explain the complexity that exists in competitive markets as they are experienced today” (Tay and Lusch, 2005: 1167).
The Resource-Advantage Theory of Competition (RATC) (Hunt and Morgan, 1995 and Hunt, 1997, 1999 and 2000) offers a knowledge-based theory of competition, in which continual refinement of resources through both proactive and reactive innovation creates market disequilibrium. Hunt (1999) argues that institutions achieve market positions of advantage through comparative advances in efficiency and/or effectiveness of their institutional competencies and capabilities, constantly challenging the stasis of markets. This theory offers explanations of institutions reinvesting in and reconfiguring resources to offset the changing resource configuration gains made by competing firms.
However, modern institutions often operate within networks to maximise their access to resources that are critical to achieve comparative advantage. In the case of universities, networks are formed through government, individual, and department strategic initiatives as a consequence of priorities to acquire national competitive grants (NCGs). But, “knowledge development and its significance is a ... complex affair involving networks of human and non-human acting in local contexts of contest and controversy and within shifting alliances and resistances” (Alferoff and Knights, 2009: 127).
A university’s competitive position is determined in the research economy in the UK by Research Assessment Exercise (RAE) rankings. Forming networks to facilitate the creation of new knowledge is particularly important for institutions with limited research resources seeking to enhance their competitive position. In the context of universities, collaborative behaviour of individuals as they form networks across institutions to access resources for the purpose of competing for NCG’s against other similarly formed networks are anticipated to lead to improved competitive advantage of institutions (Boschetti and Brede, 2009). Contrary to conclusions drawn from studying the healthcare industry that “two organizations will compete and cooperate simultaneously when each organization has complementary but distinctly different sets of resources and when the field of competition is distinctly separate from the field of cooperation” (Peng and Bourne, 2009: 377), collaboration within networks formed to conduct academic research co-exists with competition for NCG’s between other similar networks. Further, network members directly compete with each other as they strive to retain or enhance their RAE standing. Both the new knowledge (in the form of academic publications) created through drawing on the resources of the research network, and acquiring NCGs, are important outcomes that can enhance RAE standing of individual network members. Acquiring NCGs provides a) evidence of quality, innovative projects, b) the achievement of and potential for quality outputs through publications and c) the foundations for advancing RAE ranking.
As institutions adopt network forms, the structuring of those networks becomes central to resource exchange, innovation and performance. Seminal research by both Coleman (1988) and Burt (1992 and 2004) addressing connectivity and structural holes (connection gaps between network members who deliver overlapping information) has provided a foundation for understanding network structural properties and connectivity observed in practice. Network structures evolve as a consequence of institutions exiting, new institutions attaching at existing nodes and/or new linkages being forged between current network members as new objectives emerge.
Network theorists argue that network evolution is tied to dimensions of embeddedness (developing trust, knowledge exchange and repeated transactions) and prominence (demonstrating desirability, high levels of attachment and successful network history). Prominence signals attractiveness to non-attached members who are also prominent in other networks (Gulati and Gargiulo, 1999). However, Rosenkopf and Padula (2008: 670) argue that such centrally located firms “will also have little incentive to accept peripheral players”.
Drawing on work by Barbarassi et al. (1999), and Powell et al. (2005), Rosenkopf and Padula (2008: 671) also recognised that “graph theorists have also emphasized how firms develop ties with even more distant, unfamiliar firms—those that have not yet entered the network …. Indeed, recent studies have considered how patterns of entry may shape network structure. Particularly in a projectised context such as university research grants, where networks will dissolve and reform to meet changing complexity and subsequently, resource requirements, unattached firms may attractive to those firms driving network structure.
Competitive behaviour and performance is not just a function of competition between institutions, but also of competition between networks (Gulati et al., 2000 and Goerzen, 2007) and the structural properties of those networks. Peng and Bourke (2009: 382) also noted that: “different types of connectedness may have different influences on competition and cooperation between actors.” To date, no research has examined the structural properties of networks under conditions of differing complexity.
Validation and extension of theories drawn from other disciplines, such as the RATC, is an important challenge as scholars are already drawing on these theories to advance understanding of the dynamism of change in institutions and markets. However, traditional analytical approaches are unsuited to this purpose. Complexity theory provides a way forward. Our research addresses this validation and extension challenge within a network context through simulating the dynamics of the RATC in the complex world of university research departments linking with others to compete for scarce NCG resources. We will focus attention on the interactivity of relational and competitive market behaviour of these institutions using an agent-based modelling simulation.
Our research aims are twofold: a) to validate and extend understanding of the RATC in a network context and b) to contribute to the emerging body of work on network structure. Specifically, we seek to address the question: How do resource investment and efficiency adjustments impact on institution and network comparative advantage, and on network structure? We are particularly interested in examining the comparative advantage of institutions that is derived from these ongoing resource investment decisions both in terms of the institution and the network to which it is attached, as well as the structures emerging from network attachments under different complexity conditions.
2. Literature Review
Hunt and Morgan’s RATC shares affinities with the resource based view of the firm, evolutionary economics, institutional economics and economic sociology, i.e. theories that explain the strategic drive of institutions to achieve superior performance through achieving differential resource advantage. Institutional learning forms the foundation of comparative advantage, with those learning quickly performing the best (Dickson, 1992). It is the re-investment in resources through the development of new knowledge that enhances efficiency and effectiveness, and which consequently reinforces resource heterogeneity, and builds comparative advantage (Hunt, 2000). This process increases in complexity when the resources are accessed via a network of relationships, particularly where the technologies, skills and knowledge base required to create new knowledge are not located in one institution (Hendry and Brown, 2006).
Our definition of resources is consistent with that offered by Bingham and Eisenhardt (2008) which incorporates both tangible and intangible assets and organisational processes/capabilities to leverage those assets. The particular focus on resources in our study is on intangible knowledge assets and research services, physical assets of research infrastructure and relational resources (processes, skills and infrastructure) necessary to build network relationships, and access and leverage knowledge resources across the network. The selection of our research context of innovation networks across university institutions is consistent with the view of Gilbert et al.(2007: 100) who confirm that “firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms”.
Networks exhibit embedded structures through which network knowledge and innovation will emerge (Dyer and Nobeoka, 2000). Prior research has established that knowledge integration and innovation will emerge from directly connected, low density and/or co-located subgroups of network members rather than from subgroups reflecting dense, but also many indirect connections (Burt, 2004 and 2007). Further, sub-group inter-connectivity has been shown to evolve to manage the increased complexity of knowledge generation (Guimera et al., 2005). It is therefore anticipated that structures observed in a simulation of academic research networks will contain networks exhibiting subgroups and direct and indirect connectivity.
In turbulent and highly complex environments, institutions with complementary resources tend to group together and provide an institution with the capacity to develop resources critical for growth or survival, while retaining the benefits of traditional hierarchical architectures (Cravens et al, 1996, Powell et al., 1996, Shenkar and Li, 1999, Combs and Ketchen Jr, 1999, Lorenzoni and Lipparini, 1999, Tallman and Jenkins, 2002, and Goerzen, 2007). In academic research networks, we anticipate that both limited and extensive research networks will be observed, although more extensive networks are likely when new knowledge creation is complex.
Transaction cost economics theory advises that relationship development at a network node will derive cost efficiencies. However Jarratt and Katsikias (2008) and Narayandas and Rangan (2004) agree that cost efficiencies may not always accrue to the institution forging network relationships. In fact, Narayandas and Rangan found that to ‘gain permission’ to link at a specific network node, the relationship instigator was required to transfer potential financial benefits arising from the relationship to the institution targeted for relationship activity, and absorb costs arising from relationship activities. Consequently, a relational resource has been incorporated into the design of our simulation. Importantly, the ‘cost’ of relationship development is captured in our simulation through assigning a negative resource value to the relational resource.
Simulations have been described as an extension of deduction and induction approaches to scientific discovery. In his book on the Complexity of Cooperation Robert Axelrod (1997) explains how simulations, like deduction, are defined through a set of assumptions and, like induction, can be used to find patterns in the data. However, simulation cannot be used to prove consequences from those assumptions, and unlike induction, the data is generated from a set of rules. Axelrod argues that social scientists have found this approach extremely useful in the discovery of new knowledge. Agent-based modelling is a form of simulation used in the social sciences to build understanding of fundamental processes of complex phenomena characterised by interactions and emergent behaviour.