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The purpose of this study is to develop a meta-model for organisational change based on a literature review across organisational theories, specific theories about organisational change and systems theories related to theories of organisational change.

The research is based on an extensive literature review for the period 1972–2012 which covers organisational theories, systems theories relating to organisational change theories, and specific theories of organisational change. It used the Social Sciences Citation Index using as search items change, transformation, organization[al] model, theory, systems, and combinations of these terms. The research is based on an extensive literature review for the period 1972–2012 which covers organisational theories, systems theories relating to organisational change theories, and specific theories of organisational change. It used the Social Sciences Citation Index using as search items change, transformation, organization[al] model, theory, systems, and combinations of these terms.

The meta-model is constructed as a complex systems model including the four discourses and their process elements. As each discourse provides specific and different insights into how organisational change occurs, we can widen our field of view on change by switching between different discourses. This also allows a holistic rather than the reductionist methods of other approaches.

The meta-model makes it possible to look at organisational change from a variety of angles. Structural, cultural, behavioral and strategic change can be looked at from four different dimensions. It allows for insights from the different discourses to be drawn upon, as each of which have their merits but also their own limitations. By going beyond the normative discourse, it provides for a model of organisational change that better reflects the complexity of change in real life settings and captures the complexity of the research literature.

The paper seeks to demonstrate that a systems model of change is better able to capture the complex nature of change than are linear models. Synthesizing this literature has been undertaken previously but this has usually been done with linear models of change which have produced limited results.

The paper considers the application of neural networks to model driver decisions to change lane on a dual carriageway road. The lane changing process is treated as consisting of two decisions, namely motivation and opportunity. Separate backpropagation neural networks are applied to represent each of the two decisions. The trained motivation and opportunity neural network models are linked to produce a layered network which represents the complete lane changing process. Separate models are developed to represent the nearside to offside lane changing decision, and the offside to nearside lane changing decision. This paper describes the development of the model of the nearside to offside lane changing decision.

For model development, data were collected from several subject vehicle drivers. The results are presented and the implications considered. Selected data were applied to train the neural networks and then an independent subset of data were used to assess performance. When the complete nearside lane changing neural network model was presented with the unseen test examples, 93.3% of the examples were correctly predicted as a lane change or no lane change. These results are shown to be a considerable improvement on those obtained previously.

The study here examines how business actors adapt to changes in networks by analyzing their perceptions or their network pictures. The study is exploratory or iterative in the sense that revisions occur to the research question, method, theory, and context as an integral part of the research process.

Changes within networks receive less research attention, although considerable research exists on explaining business network structures in different research traditions. This study analyzes changes in networks in terms of the industrial network approach. This approach sees networks as connected relationships between actors, where interdependent companies interact based on their sensemaking of their relevant network environment. The study develops a concept of network change as well as an operationalization for comparing perceptions of change, where the study introduces a template model of dottograms to systematically analyze differences in perceptions. The study then applies the model to analyze findings from a case study of Norwegian/Japanese seafood distribution, and the chapter provides a rich description of a complex system facing considerable pressure to change. In-depth personal interviews and cognitive mapping techniques are the main research tools applied, in addition to tracer studies and personal observation.

The dottogram method represents a valuable contribution to case study research as it enables systematic within-case and across-case analyses. A further theoretical contribution of the study is the suggestion that network change is about actors seeking to change their network position to gain access to resources. Thereby, the study also implies a close relationship between the concepts network position and the network change that has not been discussed within the network approach in great detail.

Another major contribution of the study is the analysis of the role that network pictures play in actors' efforts to change their network position. The study develops seven propositions in an attempt to describe the role of network pictures in network change. So far, the relevant literature discusses network pictures mainly as a theoretical concept. Finally, the chapter concludes with important implications for management practice.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.