Diversification and its reflections on profitability - strategic change in pulp and paper industry in the years 1996 and 2006

The purpose of the thesis was to analyze diversification in pulp and paper industry (PPI), which is an industry facing enormous strategic challenges as many of the basic parameters of its operational environment are rapidly changing. The objective was to explore, how PPI companies have reacted to these changes by adjusting their strategies in terms of diversification and how the adjustments have affected their profitability. The study was statistical in nature. The results indicate that PPI companies in deed had reduced the degree of unrelated and related diversification , but the positive performance implications of the changes were debatable. In light of the data used in the study lower level of diversification did not lead to better profitability, in fact, the companies with the highest level of diversification had the best profitability. By contrast, reducing the level of unrelated diversification improved the profitability development; whereas reducing the level of related diversification deteriorated the profitability of the company. The results were not statistically significant and they cannot be generalized outside the data of the study.

Pharmacy Industry in Russia and in the Baltic States

The aim of this study is to describe the structure of the pharmacy industry in four post-Soviet states on the Baltic Sea: Russia, Estonia, Latvia, and Lithuania. In addition to this, the opportunities that these markets have to offer for international pharmaceutical companies are explored. After the Soviet Union collapsed at the beginning of 1990s, the pharmacy sector has gone through tremendous changes. The pharmacy market shifted from a centrally controlled, one supplier system to an industry in which multiple distributors are competing in importing, wholesaling, and retailing of medicinal products. In the Baltic States, the number of pharmacies has not increased during the last years and companies have been growing mainly by acquisitions. Especially in Estonia the market is saturated and price competition is fierce. Similarly, in Latvia and Lithuania, markets are consolidating and wholesalers are growingly taking part in retailing by acquiring smaller chains. In Russia, the market is still fragmented and only one national pharmacy chain can be named, pharmacy chain “36.6”. Pharmacy chains are growing mostly through organic growth and competition between the biggest players is relatively low. The Russian market clearly offers many opportunities for international pharmaceutical operators. The ageing population, growing level of income, and changing living habits are creating new and growing needs for modern healthcare services and products.

Impact of Investment Strategies on Performance in Pulp and Paper Industry

The main objective of this study was to examine, what kind of investment strategies the leading European and North American pulp and paper industry companies (PPI) used in 1991-2003, and how the selected strategies affected their performance. The investment strategies were categorised in three classes including mergers and acquisitions, investments in new capacity and investments in existing capacity. The results showed that mergers and acquisitions represented the largest share of total investments in 1991-2003 followed by investments in existing capacity. PPI companies changed investment strategies over time by increasing the share of mergers and acquisitions, which decreased investments in new capacity especially among North American companies. According to the results, good asset quality and investments in new and existing capacity provided better profitability than often expensive acquisitions. Also the capacity decreases had a positive impact on profitability. Average asset quality and profitability were higher among European companies. The study concluded that in the long term the available value creating investment opportunities should limit capital expenditure levels, not the relation of capital expenditure to depreciation.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Beyond pure public and pure private management models: Partial privatization in the European airport industry

The use of private funding and management is enjoying an increasing trend in airports. The literature has not paid enough attention to the mixed management models in this industry, although many European airports take the form of mixed public-private companies, where ownership is shared between public and private sectors. We examine the determinants of the degree of private participation in the European airport sector. Drawing on a sample of the 100 largest European airports, we estimate a multivariate equation in order to determine the role of airport characteristics, fiscal variables, and political factors on the extent of private involvement. Our results confirm the alignment between public and private interests in partially privatized airports. Fiscal constraints and market attractiveness promote private participation. Integrated governance models and the share of network carriers prevent the presence of private ownership, while the degree of private participation appears to be pragmatic rather than ideological.

Challenges in Supplier-Buyer Relationships in Electronics Industry

This thesis examines the supplier-buyer relationships in the Finnish electronics industry. The aim of the study was to increase understanding on the challenges that suppliers face in their relationship with the buyer. The research was conducted using qualitative methods because they allow more perspective for the research problem than quantitative methods would have. Choosing qualitative method also affected the selection of a research technique. Analysis of secondary data from written documents was chosen to give more perspective to a broad problem. The main findings of this research are that the relationships between supplier and buyer in electronics industry are challenging because supplier must understand and face three types of challenges. The challenges are: understanding the environment, choosing and implementing correct strategy and managing relationships. For the supplier it is important to understand the environment so it can adjust own strategy to fit to the environment. The supplier should also be careful not to be too dependent on the buyer.

Fiber flows in global paper and pulp industry. A panel data approach.

The purpose of this thesis is to study factors that explain the bilateral fiber trade flows. This is done by analyzing bilateral trade flows during 1990-2006. It will be studied also, whether there are differences between fiber types. This thesis uses a gravity model approach to study the trade flows. Gravity model is mostly used to study the aggregate data between trading countries. In this thesis the gravity model is applied to single fibers. This model is then applied to panel data set. Results from the regression show clearly that there are benefits in studying different fibers in separate. The effects differ considerably from each other. Furthermore, this thesis speaks for the existence of Linder’s effect in certain fiber types.

Building legitimacy by criticising the pharmaceutical industry: a qualitative study among prescribers and local opinion leaders.

PRINCIPLES: The literature has described opinion leaders not only as marketing tools of the pharmaceutical industry, but also as educators promoting good clinical practice. This qualitative study addresses the distinction between the opinion-leader-as-marketing-tool and the opinion-leader-as-educator, as it is revealed in the discourses of physicians and experts, focusing on the prescription of antidepressants. We explore the relational dynamic between physicians, opinion leaders and the pharmaceutical industry in an area of French-speaking Switzerland. METHODS: Qualitative content analysis of 24 semistructured interviews with physicians and local experts in psychopharmacology, complemented by direct observation of educational events led by the experts, which were all sponsored by various pharmaceutical companies. RESULTS: Both physicians and experts were critical of the pharmaceutical industry and its use of opinion leaders. Local experts, in contrast, were perceived by the physicians as critical of the industry and, therefore, as a legitimate source of information. Local experts did not consider themselves opinion leaders and argued that they remained intellectually independent from the industry. Field observations confirmed that local experts criticised the industry at continuing medical education events. CONCLUSIONS: Local experts were vocal critics of the industry, which nevertheless sponsor their continuing education. This critical attitude enhanced their credibility in the eyes of the prescribing physicians. We discuss how the experts, despite their critical attitude, might still be beneficial to the industry's interests.

Growth Strategies in Pulp and Paper Industry: An Empirical Investigation on Profitability Effects

This study investigates whether there are differences in profitability of PPI companies based on the growth strategy they have chosen to follow. It is examined whether those companies following organic growth strategy are more profitable than those companies following acquisitive growth strategy. It is also investigated are ones larger than the others, or are ones growing faster than the others. Also, the factors affecting the profitability of acquisitive companies are further examined. The results showed that there actually are differences between the two groups. Organically grown companies were found to be more profitable, smaller and growing slower than acquisitive companies. When it comes to examining only acquisitive companies there could be found factors that better or worsen the profitability of companies. For example targets that the company has bought in developing markets were making them more profitable.

Clustering and international competitiveness of information technology industry in the Saint Petersburg area

The main objective of this study is to assess the potential of the information technology industry in the Saint Petersburg area to become one of the new key industries in the Russian economy. To achieve this objective, the study analyzes especially the international competitiveness of the industry and the conditions for clustering. Russia is currently heavily dependent on its natural resources, which are the main source of its recent economic growth. In order to achieve good long-term economic performance, Russia needs diversification in its well-performing industries in addition to the ones operating in the field of natural resources. The Russian government has acknowledged this and started special initiatives to promote such other industries as information technology and nanotechnology. An interesting industry that is basically less than 20 years old and fast growing in Russia, is information technology. Information technology activities and markets are mainly concentrated in Russia’s two biggest cities, Moscow and Saint Petersburg, and areas around them. The information technology industry in the Saint Petersburg area, although smaller than Moscow, is especially dynamic and is gaining increasing foreign company presence. However, the industry is not yet internationally competitive as it lacks substantial and sustainable competitive advantages. The industry is also merely a potential global information technology cluster, as it lacks the competitive edge and a wide supplier and manufacturing base and other related parts of the whole information technology value system. Alone, the industry will not become a key industry in Russia, but it will, on the other hand, have an important supporting role for the development of other industries. The information technology market in the Saint Petersburg area is already large and if more tightly integrated to Moscow, they will together form a huge and still growing market sufficient for most companies operating in Russia currently and in the future. Therefore, the potential of information technology inside Russia is immense.

Beyond pure public and pure private management models: Mixed firms in the European Airport Industry

The use of private funding and management enjoys an increasing trend in airports. The literature has not paid enough attention to the mixed management models in this industry, although many European airports take the form of mixed firms or Institutional PPP, where ownership is shared between public and private sectors. We examine the determinants of the degree of private participation in the European airport sector. Drawing on a sample of the 100 largest European airports we estimate a multivariate equation in order to determine the role of airport characteristics, fiscal variables and political factors on the extent of private involvement. Our results confirm the alignment between public and private interests in PPPs. Fiscal constraints and market attractiveness promote private participation. Integrated governance models and the share of network carriers prevent the presence of private ownership, while the degree of private participation appears to be pragmatic rather than ideological.

Recovery Risk and Labor Costs in Public-Private Partnerships : Contractual Choice in the US Water industry

We use an ordered logistic model to empirically examine the factors that explain varying degrees of private involvement in the U.S. water sector through public-private partnerships. Our estimates suggest that a variety of factors help explain greater private participation in this sector. We find that the risk to private participants regarding cost recovery is an important driver of private participation. The relative cost of labor is also a key factor in determining the degree of private involvement in the contract choice. When public wages are high relative to private wages, private participation is viewed as a source of cost savings. We thus find two main drivers of greater private involvement: one encouraging private participation by reducing risk, and another encouraging government to seek out private participation in lowering costs.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.