A few articles have a more holistic view in discussing network performance (Provan and Milward, 2001; Iansiti and Levien 2004b). The authors view interorganizational networks
as functionality. They view the network as a system and make normative statements such
as “it must become a viable organizational entity if it is to survive”, “to operate effectively member agencies must act as a network” and “to function effectively each domain in it that is critical to the delivery of a product or service should be healthy”. Vervest et al. (2004) described this ability of a business network as fault tolerance. We term this the systems lens for business network performance which focuses on the operation and endurance of networks. Its central concepts robustness, diversity, adaptation and survival are derived from population ecology (Hannan and Freeman, 1977) and systems theory (Wolfram, 1988).
Systems theory studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. A stream in the literature on complex systems discusses complex networks as diverse as the World Wide Web and protein-interactions and topological robustness which refers to the ability of a network to remain connected and retain functionality under errors or attacks (e.g. Albert et al. 2000; Barabasi and Bonabeau 2003). Even with the removal of a small fraction of nodes, these network structural properties such as the diameter of the network, remain the same. This is known as topological robustness. However, these networks are extremely vulnerable to targeted attacks, i.e. to the selection and removal of a few nodes that play the most important role in assuring the network’s connectivity.
Saavedra et al. (2008) study this phenomenon of topological robustness for a declining interfirm network of designers and contractors in the apparel industry and found, in spite of a substantial net loss of nodes, a relatively stable topology and functionality. Functionality is measured here as the fraction of bilateral transactions that include refunds per year (i.e. errors). However in the discussion, topological robustness itself is emphasized more as an outcome or performance of the network. While Saavedra et al. (2008) focus on one stage in the production process (dyads of designers and contractors), disruptions of entire supply chains have also been described where next to topological robustness, robustness is also related to the capabilities and characteristics of firms. For example, when a fire destroyed the plant of Toyota’s sole supplier for a brake-related part in 1997, there was network-wide effort by the suppliers of Toyota where temporary sites were set up by them to recover production. It allowed Toyota to resume full production of its cars in nine days instead of several weeks, thereby minimizing losses from this incident (Nishiguchi and Beaudet, 1998). The measures of robustness that come across from such cases (e.g. response times, predisaster levels of functioning) are described in the literature on disaster relief / emergency response management (e.g. Green and Kolesar, 2004; Kapucu, 2005). Such as the time that elapses before a disruption is being handled by one or more firms in the network or the time that it takes to restore system functionality as with such a fire.
Next to natural catastrophes, economic, political and health crises can also disrupt business networks, such as devaluations, civil wars or outbreaks of disease. Li and Fung (mentioned in section 1.1) operates a network of suppliers across Asia and is able to divert textile manufacturing from high-risk to low-risk countries in such cases (Fung et al., 2008). Summing the total of prevented losses from such disruptions can be an indicator of network performance. Measuring robustness of the networks to disruptions can be done ex post after disruptions have taken place but there are also strategies for enhancing robustness e.g. a flexible supply base, flexible transportation, strategic stock and postponement of product differentiation (Tang, 2006) or supply network design and scenario planning (Shukla et al., 2010). The execution of these strategies can lead to valid proxies for capturing the robustness of business networks ex ante.
There are shocks, such as the ones described above, that disrupt and threaten the current functioning of business networks but there are also shocks that have the potential to shift business network models and change their structure and composition. These disruptions are of a more long term nature and resilience or adaptation is the ability of a network to persist or adjust when these environmental changes occur. Networks can be permanently altered by technological developments but also through regulatory changes such as the deregulation of the insurance industry. Such shocks can radically change business networks and threaten firms’ positions within them or create new roles (Kambil and Short, 1994). Networks that are highly resilient are those where the relationships among network members are buffered against perturbations (Hamel and Välikangas, 2003; Iansiti and Levien, 2004ab). This concept and view on performance corresponds with population ecology theory of organizations (Hannan and Freeman, 1977). Population ecology studies the environment in which organizations compete and a process like natural selection occurs. The theory highlights the death of organizations (firm mortality), the birth of new organizations (organizational founding), organizational growth and change. Iansiti and Levien (2004b) offer metrics for resilience and adaptation such as survival rates of network members, persistence of network structure, predictability, limited obsolescence, continuity of use experience and use cases.
Based on the discussion above, we provide the following definition:
Definition 4: Network performance seen through the systems lens is the ability of the
network to handle internal and external shocks that threaten its functioning and continuity.
We now conceptualize network performance using the insights from previous discussions. This definition draws upon all three theoretical lenses:
Definition 5: Network performance is defined as the ability of the network to fulfill the
objectives of member organizations, provide benefits and recurring value for end customers under multiple conditions including internal and external shocks.
The development of the three lenses also leads to the following implication that Richard et al. (2007) proposed for organizational performance, that measuring network performance requires weighing the relevance of performance to focal stakeholders. We propose the following guideline for further theory development in interorganizational network performance research:
Guideline 2: Theory building regarding network performance in interorganizational
networks is enhanced by authors’ explicit specification and justification of the lens and performance type of interest: firm, customer or systems.