IOR Lectures PDF

LECTURE 1 EXPLORING THE FIELD Setting the stage Draw your own ego-network Course introduction Learning Goals Positioning Network as a social structure “A network is defined as a set of nodes and the set of ties representing some relationship, or lack of relationship, betwee...

LECTURE 1 EXPLORING THE FIELD Setting the stage Draw your own ego-network Course introduction Learning Goals Positioning Network as a social structure “A network is defined as a set of nodes and the set of ties representing some relationship, or lack of relationship, between the nodes. Nodes can be individuals, teams, units, organizations…” (Borgatti et al. 2009) Example of a network as social structure Network of organizations in R&D Consortia Serendipitous vs engineered IONs Serendipitous Networks: social system on the basis of dyadic or triadic interactions between organizations but without having necessarily a common goal, a joint identity or even conscious knowledge of each other beyond the direct contacts designed/ engineered (purpose- oriented) networks: inter-organizational networks that are consciously created often by a lead organization or more bottom up by professionals in different organizations to achieve network level goals that none of the organizations could achieve on their own What is an inter-organizational Relationship?  Relationship between more or less autonomous organizations to achieve their goals more efficiently and more effectively Organizations can be competitors, suppliers, customers, service providers, knowledge institutes,... Organizations can cooperate or collaborate horizontally or vertically with regard to the value chain Why do organizations collaborate with other organizations? Strengthening their own power position (SNT) Gaining and managing legitimacy (inst. theory) Reduce uncertainty (RDT) Increasing efficiency (TCE) Access to knowledge and resources (RBV) Enhancing effectiveness Managerial functions with regard to IOR Selection and deselection of partners allocation and reallocation of tasks, resources and responsibilities between partners Regulation: negotiation and renegotiation of rules for collaboration Evaluation of exchange relationships Network as perspective and empirical tool: Core assumptions and ideas Heading towards a society of networks? The Study of Social Networks Kilduff and Brass (2010): Organizational Social Network Research: Core Key Ideas Social relations ○ In contrast to attribute-oriented approaches Embeddedness  ○ Economic transactions occur in context of social relationships Structural patterning Utility of network connections ○ opportunities and constraints that affect outcomes important to actors Assumptions Key assumption: relations are at least as important for the explanation of social, political, and organizational phenomena as attributes Actors and their actions are viewed as interdependent rather than independent, autonomous units Individual’s position determines opportunities and constraints → Social network analysis is therefore not a collection of “neutral” statistical procedures, but an analytical toolbox resting on very specific sociological asumptions Example of Network Analysis → it’s j Network Data Sources and research traditions: Survey research → questionnaires Ethnographic research → observations Documentary research → texts and documents, statistical databases Experimental research → experiments, simulations Critique on IOR/N Research Kilduff & Brass (2010) Where is the agency? Actor characteristics are neglected (self-monitoring capacity, strategic choice) Cognition: accurate knowledge of third ties Boundary specification Relations are dynamic Are we perhaps guilty of anthropomorphization, or do we attribute human behaviour and outcomes to non-human entities? → YES. (...) influenced inter-organizational researchers to attribute individually oriented behaviours (such as communication or learning to organizations” (Gosh & Rosenkopf, 2015) In the context of innovation and knowledge flows: → we overlooked the issue of friction: defined as the resistance that one surface or object encounters when moving over another 4 sources of friction (Gosh & Rosenkopf) Friction in dyads: ○ Sender and receiver need to be motivated and reliable Knowledge eomplexity: ○ E.g. degree of tacitness Network embeddedness ○ Of dyads increases friction Friction varies with type of tie: ○ e.g. interlock vs alliance Network as a form of governance Governance can be defined as rhe use of institutions, structures of authority and even collaboration to allocate resources and coordinate ot control in society ot the economy In general terms, governance occurs in three broad ways: 1. Top-down methods 2. Use of market mechanisms 3. Networks of collaboration Why does network as form of governance exist? 1. Functional necessities 2. Reduction of transaction costs 3. Institutional expectations 4. Reduction of uncertainty and dependency.  -. What is actually a network theory? Network theories Conditions for networks to develop Network should be considered as a distinctive form of organizing Network (and not relations) should be the unit of analysis Network should be considered as a variable Relational theories Explain the formation, functioning, and termination of ties Balance theory Homophily theory Transaction cost theory Resource dependence theory Network of tie information (network is independent variable), see Kenis/Oerlemans 2008 How can this be applied in practice and what are potential levers for intervention? Raise awareness among decision-makers: what are the relational features my organization has to pay attention to? Personal vs. organizational relationships (how can we develop and stabilize organizational relations beyond personal ones?) Partner selection Dyadic relationships (what are our most important partners? How can we get to stable and reliable relationships?) Being part of serendipitous networks, what’s our position? Being part of goal directed networks, what do we want to achieve with and in that network? What are our roles and tasks? Potential levers/interventions: Make, buy, ally or join? Choosing the right level(s) to intervene (individual, organizational, inter-organizational, network) Managing IORs Determining and improving your position in a network Network design Management in and of networks  LECTURE 2 SOCIAL NETWORK ANALYSIS What is a network? Case: Safety Houses (Crime Prevention Networks) Network of law enforcement and social service organizations Operational goal: joint treatment plan for multi-problem families Societal goal: reduction of recidivism and criminality History of social network analysis Conceptual roots lie in the works of late nineteenth and early twentieth century social thinkers as for example Georg Simmel: ○ Interaction as basis of any society 1930s: development of the sociometric approach in social psychology (Moreno and Jennings) and introduction of sociogram First Harvar Thrust, 1920s: Roethlisberger, Mayo and others use network perspective and sociometry in developing Human Relations approach 1970s: “Harvrd breakthrough”. Use of computer capabilities to calculate network perspective since then is in itself a network process over several generations of researchers 1990s: Development of personal computers and user friendly programs lead to wide application in the social sciences Now: established field that is regarded as one of the major innovations in the social sciences in the last 30 years ➔ Grounded in systematic empirical data, drawing heavily on graphic imagery, relying on the use of mathematical and/or computational models  ➔ International association INSNA that provides resources for researchers in many disciplines around the world The study of social networks Key assumption: relations are at least as important for the explanation of social, political and organizational phenomena as attributes Actors viewed as interdependent rather than independent, autonomous units Individual’s position determines opportunities and constraints Network: set of objectives (often also calles nodes, positions, vertices or actors) and a set of relations (also calles edges, ties or links) ○ Nodes ○ Linkages ○ Present and absent ties ○ Boundaries: Examples of boundaries Meeting-based (solid) Contract-based (dashed) Network boundary specification Delimiting the graph (who is in and who is out?) Approaches for the specification of functional or institutional boundaries (Laumann/Knoke 1987) ○ Nominalist → determined by criterion defined by the researcher ○ Realist → network as recognized by its actors Name generators vs lists of actors/ organizations Level of analysis Actor Dyad (inter-organizational relation) Triad Ego(centric)- netowrk Complete network (inter-organizational network) ➔ Actors from ties, and ties link up to form networks Types of relational data Attribute data: properties of research objects → “analysis of variables”  Relational data: links between research objects → network analysis. Represented by graphs and matrices: Relational content (different types of tie) Relational form, relational strength Directed vs undirected Symmetric vs asymmetric Binary vs values Multiples ties Sources of relational data and data collection Sources and research traditions: Survey research → questionnaires Ethnographic research → observations Documentary research → texts and documents, statistical databases Experimental research → experiments, simulations Surveys and questionnaires Row-based: each actor is asked about relations going to toher actors (e.g. to give advice) Row and column based: each actor is asked not only whom they give advice, but from whom they receive it - If relations are not confimed, need for coding criteria: minimum, maximum, average method Consensus method: each actor is asked to indicate the relationships among every pair of actors - Since there are always many estimation (n= number of experts) about existing relations, there is need for coding criteria Observation Principle: observer in room or area records all interactions and relations that exist or take place over a continuous period in front of the observer ○ Example: Hawthorne studies. Records categorical (quality, physical examinations, mental tests) and relational (friendship, conflicts, support, etc.) Observation of randomly chosen time intervals Principle: used in ethnology. Observer shows up at random times and records who is doing what to whom over a short interval. Goal is to have large number of these snap-shots for individuals at random hours collected over a longer period ○ Example: Hames, R. (1994) Ye'kwana Time Allocation Data Base. Behavioural data on a traditional community in the Amazon. 18,000 records of 88 residents of the village of Toki (1974-1976). Most individuals were recorded more than 300 times at random hours of the day over a period of many months. Documents/ texts / databases Principle: relations are coded on the basis of written/ stored documents and/or databases ○ Examples: Interlocking directorates Joint ventures, alliances among companies Organizational correspondence/ records Historical marriage records among 15th century Italian families Intracampus mail, memos, e-mail Archival records, letters, documents, minutes, etc. Experiments Principle: ○ Relations between research objects are triggered/induced by measures of experimental control ○ Computer simulations Examples: ○ Planting rumors in schools or colleges and observe the spread over time ○ Small world experiments: Stanley Milgram: examination of how many links are required to connect any two randomly chosen persons in the US Case Safety House Data entry/ data representation Matrix Adjacency (squared): 1-mode: “companies (m x m) slide 23 Affiliation or incidence (rectangular): for example 2-mode: “companies” and “directors” (m x n) example below Sociogram: importance of visualisation 2-mode network From affiliation matrix (2-mode) to adjacency matrix (1-mode) ○ Company by company ○ Director by director For example: director C sits on the board of company 2&4 → link between companies For example: director A,D and E are together in the board of company 1 → link between A&D, A&E, and D&E Exercise 7 organizations (A-G) and 7 research projects (1-7) ○ A & B anticipate in project 1 ○ A also participates in project 4 together with C ○ In project 2, B & D & E participate ○ Project 3 has two participating organizations: B & C ○ D & E work also together in project 5 ○ In project 6, B & F collaborate ○ G participates in project 7 Make the incidence matrix  Make a sociogram of this matrix Basic concepts and measures Component Path length/ geodesic Bridge and broker Prominence (centrality and status) Centralization, density Liques, n-clan ➔ Concepta and indices based on mathematical graph theory Example of network analysis Which actor is the most central in the network presented? Degree centrality Intuition: a node is central, if it has the most incoming (indegree) or outgoing (outdegree) linkages (local centrality measure) Closeness centrality Intuition: a node is central, if it is close (on average) to all other nodes in a network (global centrality measure) → direct connections Between centrality Intuition: a node is central, if it os between many pairs of other nodes (global centrality) → bridges between nodes Status Intuition: the status of a node is the total weighted number of paths reaching that node (where the contribution of path is decreasing exponentially with its length) Structural equivalence Being connected to the same third actors ○ Connecting in the same way → same role ○ Allows for identification of functionality equivalent actors Cohesive subgroups: are subsets of actors among whom there are relatively strong, direct, intense, frequent, or positive ties A clique in a graph is a maximal complete subgraph of three or more nodes Statistical models for social networks Problem: assumption of “independence of observations” is violated Aim: addressing network dependencies (e.g. reciprocity, transitivity) 1. Incorporating network structure through covariates 2. Controlling for network structure (QAP, clustering SE) 3. Modeling network structure (e.g. ERGM, SAOM, REM) Overview 1. The study of social networks 2. History of social network analysis 3. Types of relational data 4. Sources of relational data and data collection 5. Data entry and data representation  6. Basic concepts and measures 7. Statistical models for social networks But how? Potential levers/ interventions Relational awareness: what (types of) networks are we in? Strategies for generating management information Structured analysis: determining (your) position(s) in a network Potential for improving your position in a network? Sociological assumptions IOR- SNA: discussion statements 1. Documentary research (secondary data) is more suitable for studying inter-organizational networks than survey research (e.g. name generator/actos list) 2. The firm’s network position is more important for explaining its innovation outcomes than firm attributes such as the size of its R&D department 3. Research on inter-organizational networks should always include the individual level LECTURE 3 SOCIAL CAPITAL What is social capital? Social capital is defined as the advantage created by actor’s location in a structure of relationships. It explains how some actors gain more success in a particular setting through their superior connections to other actors Social capital is productive. It leads to achievement of ends  Social capital comes about through changes in the relations among persons that facilitate action Social capital is specific to certain activities Social capital is the last tangible of all forms of capital Coleman: Introduction Sociologist approach: “(...) the actor as socialized and action as governed by social norms, rules and obligations.” Economist approach: “(...) the actor as having goals independently arrived at, as acting independently, and as wholly self-interested. ○ Defect: ignores social context Exchange theory: rational action in social system (earlier) defects: 1. Limited to microsocial relations 2. Ad hoc introduction of principles Social capital as resource: “(...) the structure of relations between actors and among actors” that facilitates certain actions Coleman: forms of social capital Obligations, expectations, and trustworthiness of structures  ○ The higher the outstanding obligation the higher the social capital of individuals and the collectivity Information channels ○ Social relations can constitute a form of social capital that provides information that facilitates action Norms and effective sanctions ○ Effective norms (fostering collective action) can constitute a powerful form of social capital Coleman: Closure Closure provides social capital Closure of the social structure is important dor the existence of effective norms Closure creates trustworthiness in a social structure Closure secures information flows Burt (2005): Brokerage Advantages brokerage: Access to wider diversity of information (non-redundant) Early access to that information Control over information diffusion Network constraints Too few contacts Contacts too interconnected → redundancy Contacts too connected indirectly through a central person  Exploration: “(...) breaking with an existing dominant design ans a shift away from existing rules, norms, routines and activities, in search of novel combinations…” Dual task: 1. Novelty value; 2. Absorptive capacity Technological distance: “(...) the extent that firms differ in their technological knowledge and expertise.” Explorative innovation performance at mean level of network density Explorative innovation performance at mean level of technological distance → firms in alliance-networks that hve Conclusions The network perspective enhances our understanding of social phenomena. The locus of innovation resides in inter-organizational networks combining clusters of organizations with transitory shortcuts (bridging ties) between the clusters. “Brokerage is about coordinating people between whom it would be valuable, but risky, to trust. Closure is about making it safe to trust. The key to creating value is to put the two together, building closure around valuable bridge relations.” (Burt, 2005) But how? Potential levers/ interventions: Awareness about different network positions: brokerage/ closure Strategy about aspired network position Evaluation of current network position(s) Actions: ○ Closure (re)enforcing actions (e.g. social events) ○ Strengthening brokerage position (e.g. through weak ties) ○ Tertius Gaudens: taking advantage of brokerage position ○ Tertius Lungens: using position to connect alters Disucssion Statements 1. Organizations should always try to get in a brokerage position  2. Third-party safeguards against opportunism only hold if the organizations involved are of similar size 3. Policy makers responsible for innovation programs should prevent overlap between inter-organizational project teams in order to safeguard intellectual property rights of the participants LECTURE 4 NETWORK PRIMITIVES Introduction: ego network dynamics (local) Network primitives 1. Nodes 2. Ties 3. Structure → tie changes, also bring network changes! With whom to partner? Agency of most organization is constrained to their ego-network (own partner portfolio) Selection criteria Knowledge Resources Trustworthiness position/ social capital Search strategies to find these partners? relational/structural/positional embeddedness technological/geographical/ cultural proximity Partnering events (2-mode form of structural embeddedness)  Strategic interdependence: to get access to essential resources and capabilities under control of other organizations Social embeddedness in the inter-organizational network 1. Relational embeddedness (prior direct ties) 2. Structural embeddedness (indirect ties via common partners) 3. Positional embeddedness (joint centrality in the network) Structural differentiation (SD centrality = network centralization) Granovetter (1985): “actors do not behave or decide as atoms outside a social context, nor do they adhere slavishly to a script written for them by the particular intersection of social categories that they happen to occupy. Their attempts at purposive action are instead embedded in concrete, ongoing systems of social relations.” Mannak, van Zelst & Oerlemans (draft) “Fantastic partners and where to find them: a meta-analysis on the influence of embeddedness and proximity on the inter-organizational tie formation” Proximity: geographical, technical, cultural Combined model (embeddedness + proximity) needed, because: 1. Strong overlap in arguments: ○ Embeddedness: partner information, uncertainty reduction, congruence ○ Proximity: opportunity cost reduction, uncertainty reduction, congruence 2. Overestimation explanatory power in isolated models (up to 75%) 3. Explanatory power most predictors very low (