Multiple Perspectives on Networks: Conceptual Development, Application and Integration in an Entrepreneurial Context

Multiple Perspectives on Networks: Conceptual Development, Application and Integration in an Entrepreneurial Context. The purpose of this thesis is to enhance cross-fertilization between three different approaches to network research. The business network approach may contribute in terms of how relationships are created, developed and how tie content changes within ties, not only between them. The social network approach adds to the discussion by offering concepts of structural change on a network level. The network approach in entrepreneurship contributes by emphasizing network content, governance and structure as a way of understanding and capturing networks. This is discussed in the conceptual articles, Articles 2 and 3. The ultimate purpose of this thesis is to develop a theoretical and empirical understanding of network development processes. This is fulfilled by presenting a theoretical framework, which offers multiple views on process as a developmental outcome. The framework implies that change ought to be captured both within and among relationships over time in the firm as well as in the network. Consequently, changes in structure and interaction taking place simultaneously need to be included when doing research on network development. The connection between micro and macro levels is also stressed. Therefore, the entrepreneur or firm level needs to be implemented together with the network level. The surrounding environment impacts firm and network development and vice versa and hence needs to be integrated. Further, it is necessary to view network development not only as a way forward but to include both progression and regression as inevitable parts of the process. Finally, both stability and change should be taken into account as part of network development. Empirical results in Article 1 show support for a positive impact of networks on SME internationalization. Article 4 compares networks of novice, serial and portfolio entrepreneurs but the empirical results show little support for differences in the networks by type of entrepreneur. The results demonstrate that network interaction and structure is not directly impacted by type of entrepreneur involved. It indicates instead that network structure and interaction is more impacted by the development phase of the firm. This in turn is in line with the theoretical implications, stating that the development of the network and the firm impacts each other, as they co-evolve.

Multidimensionality of Actors in Business Networks: The Influence of Social Action in Pharmacy Networks in Finland

In the markets-as-networks approach business networks are conceived as dynamic actor structures, giving focus to exchange relationships and actors’ capabilities to control and co-ordinate activities and resources. Researchers have shared an understanding that actors’ actions are crucial for the development of business networks and for network dynamics. However, researchers have mainly studied firms as business actors and excluded individuals, although both firms and individuals can be seen as business actors. This focus on firms as business actors has resulted in a paucity of research on human action and the exchange of intangible resources in business networks, e.g. social exchange between individuals in social networks. Consequently, the current conception of business networks fails to appreciate the richness of business actors, the human character of business action and the import of social action in business networks. The central assumption in this study is that business actors are multidimensional and that their specific constitution in any given situation is determined by human interaction in social networks. Multidimensionality is presented as a concept for exploring how business actors act in different situations and how actors simultaneously manage multiple identities: individual, organisational, professional, business and network identities. The study presents a model that describes the multidimensionality of actors in business networks and conceptualises the connection between social exchange and human action in business networks. Empirically the study explores the change that has taken place in pharmaceutical retailing in Finland during recent years. The phenomenon of emerging pharmacy networks is highly contemporary in the Nordic countries, where the traditional license-based pharmacy business is changing. The study analyses the development of two Finnish pharmacy chains, one integrated and one voluntary chain, and the network structures and dynamics in them. Social Network Analysis is applied to explore the social structures within the pharmacy networks. The study shows that emerging pharmacy networks are multifaceted phenomena where political, economic, social, cultural, and historical elements together contribute to the observed changes. Individuals have always been strongly present in the pharmacy business and the development of pharmacy networks provides an interesting example of human actors’ influence in the development of business networks. The dynamics or forces driving the network development can be linked to actors’ own economic and social motives for developing the business. The study highlights the central role of individuals and social networks in the development of the two studied pharmacy networks. The relation between individuals and social networks is reciprocal. The social context of every individual enables multidimensional business actors. The mix of various identities, both individual and collective identities, is an important part of network dynamics. Social networks in pharmacy networks create a platform for exchange and social action, and social networks enable and support business network development.

The Most Probable Bayesian Network and Beyond

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem.

Harmful algae in the planktonic food web of the Baltic Sea

This study deals with algal species occurring commonly in the Baltic Sea: haptophyte Prymnesium parvum, dinoflagellates Dinophysis acuminata, D. norvegica and D. rotundata, and cyanobacterium Nodularia spumigena. The hypotheses are connected to the toxicity of the species, to the factors determining toxicity, to the consequences of toxicity and to the transfer of toxins in the aquatic food web. Since the Baltic Sea is severely eutrophicated, the fast-growing haptophytes have potential in causing toxic blooms. In our studies, the toxicity (as haemolytic activity) of the haptophyte P. parvum was highest under phosphorus-limited conditions, but the cells were toxic also under nitrogen limitation and under nutrient-balanced growth conditions. The cellular nutrient ratios were tightly related to the toxicity. The stoichiometric flexibility for cellular phosphorus quota was higher than for nitrogen, and nitrogen limitation led to decreased biomass. Negative allelopathic effects on another algae (Rhodomonas salina) could be observed already at low P. parvum cell densities, whereas immediate lysis of R. salina cells occurred at P. parvum cell densities corresponding to natural blooms. Release of dissolved organic carbon from the R. salina cells was measured within 30 minutes, and an increase in bacterial number and biomass was measured within 23 h. Because of the allelopathic effect, formation of a P. parvum bloom may accelerate after a critical cell density is reached and the competing species are eliminated. A P. parvum bloom indirectly stimulates bacterial growth, and alters the functioning of the planktonic food web by increasing the carbon transfer through the microbial loop. Our results were the first reports on DSP toxins in Dinophysis cells in the Gulf of Finland and on PTX-2 in the Baltic Sea. Cellular toxin contents in Dinophysis spp. ranged from 0.2 to 149 pg DTX-1 cell-1 and from 1.6 to 19.9 pg PTX-2 cell-1 in the Gulf of Finland. D. norvegica was found mainly around the thermocline (max. 200 cells L-1), whereas D. acuminata was found in the whole mixed layer (max. 7 280 cells L-1). Toxins in the sediment trap corresponded to 1 % of DTX-1 and 0.01 % PTX-2 of the DSP pool in the suspended matter. This indicates that the majority of the DSP toxins does not enter the benthic community, but is either decomposed in the water column, or transferred to higher trophic levels in the planktonic food chain. We found that nodularin, produced by Nodularia spumigena, was transferred to the copepod Eurytemora affinis through three pathways: by grazing on filaments of small Nodularia, directly from the dissolved pool, and through the microbial food web by copepods grazing on ciliates, dinoflagellates and heterotrophic nanoflagellates. The estimated proportion of the microbial food web in nodularin transfer was 22-45 % and 71-76 % in our two experiments, respectively. This highlights the potential role of the microbial food web in the transfer of toxins in the planktonic food web.

Interactions between harmful algae and calanoid copepods in the Baltic Sea

The aim of the studies reported in this thesis was to examine the feeding interactions between calanoid copepods and toxic algae in the Baltic Sea. The central questions in this research concerned the feeding, survival and egg production of copepods exposed to toxic algae. Furthermore, the importance of copepods as vectors in toxin transfer was examined. The haptophyte Prymnesium parvum, which produces extracellular toxins, was the only studied species that directly harmed copepods. Beside this, it had allelopathic effects (cell lysis) on non-toxic Rhodomonas salina. Copepods that were exposed to P. parvum filtrates died or became severely impaired, although filtrates were not haemolytic (indicative of toxicity in this study). Monospecific Prymnesium cell suspensions, in turn, were haemolytic and copepods in these treatments became inactive, although no clear effect on mortality was detected. These results suggest that haemolytic activity may not be a good proxy of the harmful effects of P. parvum. In addition, P. parvum deterred feeding, and low egestion and suppressed egg production were consequently observed in monospecific suspensions of Prymnesium. Similarly, ingestion and faecal pellet production rates were suppressed in high concentration P. parvum filtrates and in mixtures of P. parvum and R. salina. These results indicate that the allelopathic effects of P. parvum on other algal species together with lowered viability as well as suppressed production of copepods may contribute to bloom formation and persistence. Furthermore, the availability of food for planktivorous animals may be affected due to reduced copepod productivity. Nodularin produced by Nodularia spumigena was transferred to Eurytemora affinis via grazing on filaments of small N. spumigena and by direct uptake from the dissolved pool. Copepods also acquired nodularin in fractions where N. spumigena filaments were absent. Thus, the importance of microbial food webs in nodularin transfer should be considered. Copepods were able to remove particulate nodularin from the system, but at the same time a large proportion of the nodularin disappeared. This indicates that copepods may possess effective mechanisms to remove toxins from their tissues. The importance of microorganisms, such as bacteria, in the degradation of cyanobacterial toxins could also be substantial. Our results were the first reports of the accumulation of diarrhetic shellfish toxins (DSTs) produced by Dinophysis spp. in copepods. The PTX2 content in copepods after feeding experiments corresponded to the ingestion of <100 Dinophysis spp. cells. However, no DSTs were recorded from field-collected copepods. Dinophysis spp. was not selected by the copepods and consumption remained low. It seems thus likely that copepods are an unimportant link in the transfer of DSTs in the northern Baltic Sea.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

The role of phosphorus as a regulator of bloom-forming diazotrophic cyanobacteria in the Baltic Sea

Eutrophication favours harmful algal blooms worldwide. The blooms cause toxic outbreaks and deteriorated recreational and aesthetic values, causing both economic loss and illness or death of humans and animals. The Baltic Sea is the world s only large brackish water habitat with recurrent blooms of toxic cyanobacteria capable of biological fixation of atmospheric nitrogen gas. Phosphorus is assumed to be the main limiting factor, along with temperature and light, for the growth of these cyanobacteria. This thesis evaluated the role of phosphorus nutrition as a regulating factor for the occurrence of nitrogen-fixing cyanobacteria blooms in the Baltic Sea, utilising experimental laboratory and field studies and surveys on varying spatial scales. Cellular phosphorus sources were found to be able to support substantial growth of the two main bloom forming species Aphanizomenon sp. and Nodularia spumigena. However, N. spumigena growth seemed independent of phosphorus source, whereas, Aphanizomenon sp. grew best in a phosphate enriched environment. Apparent discrepancies with field observations and experiments are explained by the typical seasonal temperature dependent development of Aphanizomenon sp. and N. spumigena biomass allowing the two species to store ambient pre-bloom excess phosphorus in different ways. Field experiments revealed natural cyanobacteria bloom communities to be predominantly phosphorus deficient during blooms. Phosphate additions were found to increase the accumulation of phosphorus relatively most in the planktonic size fraction dominated by the nitrogen-fixing cyanobacteria. Aphanizomenon sp. responded to phosphate additions whereas the phosphorus nutritive status of N. spumigena seemed independent of phosphate addition. The seasonal development of phosphorus deficiency is different for the two species with N. spumigena showing indications of phosphorus deficiency during a longer time period in the open sea. Coastal upwelling introduces phosphorus to the surface layer during nutrient deficient conditions in summer. The species-specific ability of Aphanizomenon sp. and N. spumigena to utilise phosphate enrichment of the surface layer caused by coastal upwelling was clarified. Typical bloom time vertical distributions of biomass maxima were found to render N. spumigena more susceptible to advection by surface currents caused by coastal upwellings. Aphanizomenon sp. populations residing in the seasonal thermocline were observed to be able to utilise the phosphate enrichment and a bloom was produced with a two to three week time lag subsequent to the relaxation of upwelling. Consistent high concentrations of dissolved inorganic phosphorus, caused by persistent internal loading of phosphorus, was found to be the main source of phosphorus for large-scale pelagic blooms. External loads were estimated to contribute with only a fraction of available phosphorus for open sea blooms. Remineralization of organic forms of phosphorus along with vertical mixing to the permanent halocline during winter set the level of available phosphorus for the next growth season. Events such as upwelling are important in replenishing phosphate concentrations during the nutrient deplete growth season. Autecological characteristics of the two main bloom forming species favour Aphanizomenon sp. populations in utilising the abundant excess phosphate concentrations and phosphate pulses mediated through upwelling. Whilst, N. spumigena displays predominant phosphorus limited growth mode and relies on more scarce cellular phosphorus stores and presumably dissolved organic phosphorus compounds for growth. The Baltic Sea is hypothesised to be in an inhibited state of recovery due to the extensive historical external nutrient loading, extensive internal phosphorus loading and the substantial nitrogen load caused by cyanobacteria nitrogen fixation. This state of the sea is characterised as a vicious circle .

Communicative and Anticipatory Decision-making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.