The continuous transformation of the industrial society into a service and knowledge society is accompanied by profound change of demand: Customer requests will increasingly focus on individual products, shorter delivery times and appropriate prices. To encounter these challenges under the conditions of dynamic global markets and inter-regional competition, the CRC 457 “Non-Hierarchical Regional Production-Networks” at the Chemnitz University of Technology focuses on SME and investigates ways to implement customer-oriented, temporary networking of smallest, autonomous units (“competence cells”) in the region of South-West-Saxony. As a partial result of this research we are able to present a model of competence cell based networking distinguishing between three levels of networking, each with its own logic of reproduction (cp. Figure 1): 1) According to a most general concept, networks can be seen as given in any social context. Regarding a specific region, one has to assume that there is a broad array of infra-structural, mental and communicative relations; that as a whole we call the Regional Network. This level can be seen as basic level of competence cell based networking, specific elements (competence cells) of this network can be seen as necessary resources of the following level. 2) The second level of networking we call Competence Network. The first continuous problem of this network is to select competence cells according to relevant parameters that may be defined by customer orders or, in view of new market entry, be generated by the Competence Network itself. The second problem is to arrange these cells along a product specific value chain and, by this means, to create temporary production networks. Concerning both of the problems, the third is to find a non-hierarchical mode of negotiation. 3) On the third level temporary alliances of basically autonomous competence cells can be observed. After executing their order, these Production Networks dissolve and the constituent cells reenter the second or even the first level of networking. Generalising our observation we now assume that every phenomena labelled as regional or industrial network or district should be investigated in view of (these) different levels of networking, especially if we are interested in dealing more efficiently with the problems of network management. Hence, we suggest to first answer the question “Which (of these) networks are we to manage?” before asking how. Concerning the latter, a synopsis on the discourse on managing networks shows three major approaches: 1) Networks are assumed to be uncontrollable (cp. e.g. Castells 2001), as they are self-organised systems selecting external control impulses only by their own criterias of relevance, so we have either to adapt the logic of the target system (which implies self-adaption) or to take the risk of being totally ignored. With this in mind we can’t speak of control in terms of causal logic. 2) Networks are assumed to be cultivable: According to Wenger and Snyder (2000) for example networks are uncontrollable, but - like a gardener - we are able to set adequate general conditions for their “growth” and continuity. 3) Networks are assumed to be controllable: For authors like Sydow (2000) networks are to be seen as hybrid forms of coordination recombining aspects of market and hierarchy. Thus, recombining the classical means of control will finally lead to the ability to manage networks. It will be to show that each of these approaches focuses on specific aspects of networking, thus, each of them is creating a self-contained concept of networks: Castells refers to networks as a basal nexus of (global) interconnectedness that, like the Regional Network in our case, obviously can’t be managed. Wenger and Snyder are interested in network-organisations that (like Competence Networks) also can’t be controlled but can be implemented by and embedded in other social systems (cp. Grabher 1993). Sydow finally observes networks of more or less autonomous organisations corresponding to our Production Networks, which are in deed more or less controllable. Not least because of their different theoretical assumptions, all these concepts seemed to be not only incommensurable but incompatible so far. Nonetheless, during our work in CRC 457 we were to manage all these concepts while approaching to one single objective of research. Therefore, we had to ask, how many perspectives on network are we to manage? The answer is: at least three. Integrating these, we generated a holistic life-cycle model of network, which we like to present as starting point for further research in the field network management.

Presenteeism is problematic since it relates to lower health and productivity. Prior research examined many work and attitudinal variables relating to presenteeism at the individual level. Here, we conceptualize presenteeism as multilevel phenomenon also shaped by the overall attendance behavior (absenteeism and presenteeism) at the work unit. We surveyed employees at a manufacturing plant on presenteeism, health-related lost productive time (HLPT) and absenteeism (N = 911, 22 units) and collected preceding (past 12–7 and 6 months) objective absence data aggregating it at unit level. Considering the individual-level antecedents only higher physical demands predicted higher absence duration. Presenteeism related positively to physical demands, a burdensome social environment, and organizational identification and negatively to ease of replacement, and core self-evaluations. These relationships were similar for HLPT as outcome. Regarding unit-level factors, preceding unit-level absence frequency (but not duration) negatively related to presenteeism. The negative relationship between core self-evaluations and individual presenteeism decreased under a stronger presenteeism context supporting the hypothesized cross-level effect of unit-level presenteeism context strength. Moreover, individual and unit-level presenteeism correlated, as expected, more strongly with health complaints than absenteeism. Our study demonstrates the value of a contextual, multilevel approach for understanding antecedents and consequences of attendance behavior.

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Diese Dissertation beschäftigt sich mit Methoden zur Behandlung von nicht-ignorierbaren fehlenden Daten und Stichprobenverzerrungen – zwei häufig auftretenden Problemen bei der Analyse von Umfragedaten. Beide Datenprobleme können die Qualität der Analyseergebnisse erheblich beeinträchtigen und zu irreführenden Inferenzen über die Population führen. Daher behandle ich innerhalb von drei verschiedenen Forschungsartikeln, Methoden, die eine Durchführung von sogenannten Sensitivitätsanalysen in Bezug auf Missing- und Selektionsmechanismen ermöglichen und dabei auf typische Survey-Daten angewandt werden können. Im Rahmen des ersten und zweiten Artikels entwickele ich Verfahren zur multiplen Imputation von binären und ordinal Mehrebenen-Daten, welche es zulassen, einen potenziellen Missing Not at Random (MNAR) Mechanismus zu berücksichtigen. In unterschiedlichen Simulationsstudien konnte bestätigt werden, dass die neuen Imputationsmethoden in der Lage sind, in allen betrachteten Szenarien unverzerrte sowie effiziente Schätzungen zuliefern. Zudem konnte ihre Anwendbarkeit auf empirische Daten aufgezeigt werden. Im dritten Artikel untersuche ich ein Maß zur Quantifizierung und Adjustierung von nicht ignorierbaren Stichprobenverzerrungen in Anteilswerten, die auf der Basis von nicht-probabilistischen Daten geschätzt wurden. Es handelt sich hierbei um die erste Anwendung des Index auf eine echte nicht-probabilistische Stichprobe abseits der Forschergruppe, die das Maß entwickelt hat. Zudem leite ich einen allgemeinen Leitfaden für die Verwendung des Index in der Praxis ab und validiere die Fähigkeit des Maßes vorhandene Stichprobenverzerrungen korrekt zu erkennen. Die drei vorgestellten Artikel zeigen, wie wichtig es ist, vorhandene Schätzer auf ihre Robustheit hinsichtlich unterschiedlicher Annahmen über den Missing- und Selektionsmechanismus zu untersuchen, wenn es Hinweise darauf gibt, dass die Ignorierbarkeitsannahme verletzt sein könnte und stellen erste Lösungen zur Umsetzung bereit. / This thesis deals with methods for the appropriate handling of non-ignorable missing data and sample selection, which are two common challenges of survey data analysis. Both issues can dramatically affect the quality of analysis results and lead to misleading inferences about the population. Therefore, in three different research articles, I treat methods for the performance of so-called sensitivity analyses with regards to the missing data and selection mechanism that are usable with typical survey data. In the first and second article, I provide novel procedures for the multiple imputation of binary and ordinal multilevel data that are supposed to be Missing not At Random (MNAR). The methods’ suitability to produce unbiased and efficient estimates could be demonstrated in various simulation studies considering different data scenarios. Moreover, I could show their applicability to empirical data. In the third article, I investigate a measure to quantify and adjust non-ignorable selection bias in proportions estimated based on non-probabilistic data. In doing so, I provide the first application of the suggested index to a real non-probability sample outside its original research group. In addition, I derive general guidelines for its usage in practice, and validate the measure’s performance in properly detecting selection bias. The three presented articles highlight the necessity to assess the sensitivity of estimates towards different assumptions about the missing-data and selection mechanism if it seems realistic that the ignorability assumption might be violated, and provide first solutions to enable such robustness checks for specific data situations.