Organizational Network Analysis

Auditing Intangible Resources

Anna Ujwary-Gil

• Editorial: Routledge
• Año: 2019
• ISBN:
  9781000730425
• Edición: 1
• Categoría: Business
• Categoría: Organisational Behaviour

The integrated meta-model for organizational resource audit is a consistent and comprehensive instrument for auditing intangible resources and their relations and associations from the network perspective. This book undertakes a critically important problem of management sciences, poorly recognized in literature although determining the current and future competitiveness of enterprises, sectors and economies. The author notes the need to introduce a theoretical input, which is manifested by the meta-model. An expression of this treatment is the inclusion of the network as a structure of activities, further knowledge as an activity, and intangible assets as intellectual capital characterized by a structure of connections. The case study presented is an illustration of the use of network analysis tools and other instruments to identify not only the most important resources, tasks or actors, as well as their effectiveness, but also to connect the identified networks with each other. The author opens the field for applying her methodology, revealing the structural and dynamic features of the intangible resources of the organization. The novelty of the proposed meta-model shows the way to in-depth applications of network analysis techniques in an intra-organizational environment.

Organizational Network Analysis makes a significant contribution to the development of management sciences, in terms of strategic management and more strictly resource approach to the company through structural definition of knowledge; application of the concept of improvement-oriented audit abandoning a narrow understanding of this technique in terms of compliance; reliable presentation of audits available in the literature; rigorous reasoning leading to the development of a meta-model; close linking of knowledge and resources with the strategy at the design stage of the developed audit model, including the analysis of link dynamics and networks together with an extensive metrics proposal; an interesting illustration of the application with the use of metrics, tables and charts. It will be of value to researchers, academics, managers, and students in the fields of strategic management, organizational studies, social network analysis in management, knowledge management, and auditing knowledge resources in organizations.

1 Theoretical Foundations of Networks and Resource-Based Approaches in Organization Management

At present, looking at organizations from the point of view of a network of relations and connections has become an attractive, yet still poorly examined field of management research, in particular in terms of identifying and analyzing intangible resources and the dynamics of their connections. Until now, the system approach to organizations dominated, emphasizing the human factor. It concentrates on the inside of the organization as a socio-technical system and strongly emphasizes the relations between its components (sociogram and technogram). Chapter 1 focuses on the theoretical framework for the intangible resource audit model discussed here, which uses the network-based view for identifying and analyzing those resources. It is crucial to present theoretical approaches to networks in the organizational context, including the emerging network theory as one of the disciplines in network science. In network science, network theory, including social network theory, intertwines with social network analysis techniques, algebra, and statistics, which become the basic tools for formulating and testing network theories.

Defining basic terms related to networks and organizations is of prime importance in the debate. Since network theory (including social network theory) concerns mainly human actors, the reader must be familiar with the concepts of social capital and social network. However, the concept of intangible resource audit goes beyond the dominant aspect of social network actors. Therefore it is important to look more broadly at networks and to include both nonhuman actors (actor–network theory) and human actors’ activities (activity theory), which interact with one another to create an organization, as an organization does not solely include social actors. It is in an ongoing process of creating various relations between people, resources, and activities performed by individuals in the workplace, depending on their knowledge and skills. Actor–network theory and activity theory seem to be among the most interesting approaches to organizations from the network perspective, which “animates” the system we know so well through various translations, flows, relations, connections, or ties.

The basic research tools of the network-based view of intangible resources are social, organizational, and dynamic network analysis (SNA, ONA, DNA) techniques. In organizational network analysis, the multimode view of the network was highlighted, where the network comprises both human (animate) and nonhuman (inanimate) actors, creating a network of information, knowledge, tasks, and resources. Through a comprehensive approach to these three techniques, the SODNA model (a combination of SNA, ONA, and DNA) was presented, as a tool for managing intangible resources in the organization.

In order to include the concept of intangible resources and their audit using the aforementioned techniques, the well-known resource-based, knowledge-based, and intellectual capital–based views were used. Therefore, this chapter looks at the organization and intangible resources from the point of view of selected theoretical approaches and social, organizational, and dynamic networks.

1.1 Theoretical Approaches to Networks in the Organizational Context

1.1.1 Network Theory

Network theory¹ is one of the disciplines of network science that developed significantly in the 21st century and has become one of the most prolific fields of interdisciplinary research promoting the idea that “networks are everywhere” (Barabási, 2016; Christakis & Fowler, 2011; Newman, 2010). Network science, network theory, social network theory, and social network analysis stem from social psychology, sociology, and anthropology (Brandes, Robins, McCranie, & Wasserman, 2013), where the network perspective allows one to pose research questions concerning human, biological, and economic systems. Network research is also deeply rooted in graph theory, as well as matrix algebra (Luce & Perry, 1949; Shimbel, 1951) and network statistics. Both matrix algebra and network statistics are important tools used in social network analysis (Figure 1.1).

It is believed that network science was born in 1736 when the physicist Leonhard Euler used a graph to formulate the “Seven Bridges of Königsberg” problem—how to plan a walk through the city that would cross each of those bridges once and only once (Wilson, 2012). According to Lewis (2009), the history of network science can be divided into three periods: the years 1736–1966, strongly related to the mathematical graph theory; the years 1967–1997, when it focused on applying network concepts derived from literature research; and the years from 1998 onward, when the significance of networks in the real world and their universal character have been observed.

A common concept in network science is identifying and studying complex structures and behaviors occurring between individuals (entities) who are the subjects of network research. Usually, these are people or other entities (e.g., organizations) called “social networks.” What makes them different from other networks is the fact that their actors’ actions are intentional. Complex structures of relationships between the examined objects (broadly understood actors²) and the achieved results based on the structures of relations are what distinguishes network science from other approaches (Robins, 2015). For example, reductionism divided complex systems in a way that enabled studying separated, individual nodes and their connections. Network theory, one of the disciplines in network science, returns to the study of the entirety and the consequences of mutual relationships. Wellman (1997) and Parkhe, Wasserman, and Ralston (2006) observe that network theory shifts the balance from an atomistic explanation of phenomena and independent cases to relations between the interdependent components of the system. This is a different approach than the search for the smallest, most fundamental component of society, that is, according to Weber (1978), the social actor (acting individual) and its actions.

Figure 1.1The roots of network theory and its position within network science

Two important terms recur throughout the book, “structure” and “network.” The Oxford Dictionary defines structure (http://oxforddictionaries.com/definition/english/structure) as “the arrangement of and relations between the parts or elements of something complex.” Network (http://oxforddictionaries.com/definition/english/network) is defined as “a group or system of interconnected people or things based on specific types of relations between them.”

Networks are omnipresent in science, technology, and business. In order to understand the complexity of a system, understood as a complex whole composed of things (elements) working together as parts of a mechanism or an interconnecting network, one must deeply penetrate the network of relations and connections comprising a given system (Barabási, 2016). The complexity of the system itself (for example, a socio-technical system) depends on the diversity of its components, their dynamics (inclusion and exclusion), and evolution. Intangible resource management is embedded in a dynamic and complex socio-technical system. At the macro level, the system includes social and technical elements. At the micro level, numerous factors are involved in every part. Technical elements include, for example, the processes, tasks, techniques, knowledge, and resources used for value creation and proposition. Social elements include people, their behaviors, attitudes, organizational norms, principles, and culture.

Network science can be attributed significance as a new emerging paradigm, understood as per Kuhn (1962), namely as a set of terms and theories creating the foundation of science. According to the author, science undergoes periodical paradigm shifts. Today we witness such a shift—the emergence of network science as an academic discipline (Lewis, 2009) and the network paradigm, inspired mainly by empirical studies on networks in the real world. These include: technological networks (e.g., Balthrop, Forrest, Newman, & Williamson, 2004; Gemünden & Heydebreck, 1995), biological networks (e.g., Li, Liakata & Rebholz-Schuhmann, 2014), information networks (e.g., Wellman, 2001), and social networks (e.g., Scott, 2012; Wasserman & Faust, 1994), as well as the discovery of common rules that govern them. Thus, network science examines complex networks related to various fields of research. An engineer, a sociologist, an entrepreneur—each of them will be interested in different kinds of networks, from communications networks, to networks of influence in social organizations, to informal networks that enable organizations to function.

A network is described through its structure (nodes and relations), dynamics, and behaviors. The study of structural and dynamic properties of the above-mentioned network representations is called network science (Watts, 2004). Furthermore, it is also a science of network (relationship) data collection, management, analysis, interpretation, and presentation, as well as an examination of network models (Brandes et al., 2013). Network models constitute representations of a given phenomenon within the network concept, with the help of network data and observations, which are mutually dependent.

It should be assumed that a networ...