A capability-based view of organisational renewal : Combining opportunity- and advantage-seeking growth in large, established European and North American wood-industry companies

The purpose of this study was to extend understanding of how large firms pursuing sustained and profitable growth manage organisational renewal. A multiple-case study was conducted in 27 North American and European wood-industry companies, of which 11 were chosen for closer study. The study combined the organisational-capabilities approach to strategic management with corporate-entrepreneurship thinking. It charted the further development of an identification and classification system for capabilities comprising three dimensions: (i) the dynamism between firm-specific and industry-significant capabilities, (ii) hierarchies of capabilities and capability portfolios, and (iii) their internal structure. Capability building was analysed in the context of the organisational design, the technological systems and the type of resource-bundling process (creating new vs. entrenching existing capabilities). The thesis describes the current capability portfolios and the organisational changes in the case companies. It also clarifies the mechanisms through which companies can influence the balance between knowledge search and the efficiency of knowledge transfer and integration in their daily business activities, and consequently the diversity of their capability portfolio and the breadth and novelty of their product/service range. The largest wood-industry companies of today must develop a seemingly dual strategic focus: they have to combine leading-edge, innovative solutions with cost-efficient, large-scale production. The use of modern technology in production was no longer a primary source of competitiveness in the case companies, but rather belonged to the portfolio of basic capabilities. Knowledge and information management had become an industry imperative, on a par with cost effectiveness. Yet, during the period of this research, the case companies were better in supporting growth in volume of the existing activity than growth through new economic activities. Customer-driven, incremental innovation was preferred over firm-driven innovation through experimentation. The three main constraints on organisational renewal were the lack of slack resources, the aim for lean, centralised designs, and the inward-bound communication climate.

Bayesian Inference for Retrospective Population Genetics Models Using Markov Chain Monte Carlo Methods

Genetics, the science of heredity and variation in living organisms, has a central role in medicine, in breeding crops and livestock, and in studying fundamental topics of biological sciences such as evolution and cell functioning. Currently the field of genetics is under a rapid development because of the recent advances in technologies by which molecular data can be obtained from living organisms. In order that most information from such data can be extracted, the analyses need to be carried out using statistical models that are tailored to take account of the particular genetic processes. In this thesis we formulate and analyze Bayesian models for genetic marker data of contemporary individuals. The major focus is on the modeling of the unobserved recent ancestry of the sampled individuals (say, for tens of generations or so), which is carried out by using explicit probabilistic reconstructions of the pedigree structures accompanied by the gene flows at the marker loci. For such a recent history, the recombination process is the major genetic force that shapes the genomes of the individuals, and it is included in the model by assuming that the recombination fractions between the adjacent markers are known. The posterior distribution of the unobserved history of the individuals is studied conditionally on the observed marker data by using a Markov chain Monte Carlo algorithm (MCMC). The example analyses consider estimation of the population structure, relatedness structure (both at the level of whole genomes as well as at each marker separately), and haplotype configurations. For situations where the pedigree structure is partially known, an algorithm to create an initial state for the MCMC algorithm is given. Furthermore, the thesis includes an extension of the model for the recent genetic history to situations where also a quantitative phenotype has been measured from the contemporary individuals. In that case the goal is to identify positions on the genome that affect the observed phenotypic values. This task is carried out within the Bayesian framework, where the number and the relative effects of the quantitative trait loci are treated as random variables whose posterior distribution is studied conditionally on the observed genetic and phenotypic data. In addition, the thesis contains an extension of a widely-used haplotyping method, the PHASE algorithm, to settings where genetic material from several individuals has been pooled together, and the allele frequencies of each pool are determined in a single genotyping.