Hannu Vanharanta

Tacit knowledge acquisition and sharing in a project work context

Abstract In this article we address the question of what kind of social engagements provide the proper project work context for tacit knowledge acquisition and sharing to take place. In pursuit of this objective two epistemological assumptions are presented, and the analytical tool for understanding the behaviour of project team members, the Holistic Concept of Man, is illustrated and discussed. Project as a context of tacit knowledge utilisation is discussed, and different factors and situations that affect acquisition and sharing of tacit knowledge in project work, are analysed. The results of the study suggest that the situations, where the members of a project team can interact face-to-face with each other, reinforces tacit knowledge sharing. Also used language, mutual trust and proximity are factors which affect the grade of tacit knowledge utilisation in project work.

The role of tacit knowledge in innovation processes of small technology companies

Abstract This paper reports on a conceptual analysis of the role of tacit knowledge in innovation processes. The presentation will focus on foundations of tacit knowledge, how tacit knowledge is acquired and transferred, and how it is utilised in the innovation functions of small technology companies. The study hints that tacit knowledge can play an important role in the initial stages of the innovation processes of small technology enterprises.

Combining Data and Text Mining Techniques for Analysing Financial Reports

There is a vast amount of financial information on companies’ financial performance available to investors today. While automatic analysis of financial figures is common, it has been difficult to automatically extract meaning from the textual part of financial reports. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data mining methods for analyzing quantitative and qualitative data from financial reports, in order to see if the textual part of the report contains some indication about future financial performance. The quantitative analysis has been performed using selforganizing maps, and the qualitative analysis using prototype-matching text clustering. The analysis is performed on the quarterly reports of three leading companies in the telecommunications sector.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking:

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

Comparing numerical data and text information from annual reports using self-organizing maps

Abstract More and more companies provide their accounting information in electronic form today. The accounting information in electronic form can be found in large commercial databases or on the web. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and management. For the stakeholders it is important to be able to extract both quantitative and qualitative information concerning the companies they are interested in. The annual reports contain information both in numerical and symbolic form. So far, only the numerical information has been analyzed with help of computers. However, technology has evolved and in particular neural networks in the form of self-organizing maps (SOMs) provide a new tool for analyzing also text information. In this paper, we compare results on quantitative data with results on qualitative data from annual reports. We use smart encoding, SOMs, and document histograms for comparing the performance of forest companies worldwide. Firstly, we cluster the companies according to, on the one hand, quantitative information, and on the other hand, qualitative information. Secondly, we compare the results produced by the clustering methods. Our results of the comparison show that there is a difference between the results.

The language of quarterly reports as an indicator of change in the company's financial status

This paper adopts a multi-methodological approach to information systems research in order to produce new information through data mining. This approach is particularly suitable for mining material that consists of both qualitative and quantitative information. The contents of quarterly reports from three telecommunications companies were compared. The study focused on the years 2000-2001, a period of economic decline for many IT companies. The central quantitative data, reflected by seven financial ratios, were visualised using self-organising maps. The qualitative data, consisting of the textual contents of the reports, were visualised using collocational networks; these showed the relationships between the central concepts in the texts. As the visualisations of the contents were compared, certain patterns could be found. The results seemed to suggest that changes in the networks indicated future changes in the self-organising maps. In the cases studied, a change in the textual data usually indicated a change in the financial data in the following quarter. This may be a consequence of the fact that the texts reflected the plans and future expectations of management, whereas the financial ratios reflected the current financial situation of the company.

The potential for achieving mass customization in primary production supply chains via a unified taxonomy

Develops a theoretical taxonomy that can be used by management to strategically assess their current capabilities and identify areas of change to move towards a mass customization environment. Although many of the components have been previously published, this paper brings them together as a unified whole. The classification is applied to case study supply chains with a focus upon the primary producer. These are illustrated through process maps. By adopting a mass customization approach, these companies could generate competitive advantages. However, this is difficult for them to achieve in the dynamic production environment often advocated for mass customization. A more stable, supply-chain-based approach is needed. With this in mind, we use vendor-managed inventory to demonstrate the application of the taxonomy. This provides greater flexibility in the logistics system to deliver mass customization. The paper concludes that only by using a unified taxonomy can management get a full understanding of the challenges faced in implementing mass customization and the solution does not necessarily require purely a production-based response.

Visualizing sequences of texts using collocational networks

This paper presents the collocational network, a method originating in corpus linguistics, as a tool for visualizing sequences of texts. A collocational network is a two-dimensional picture of the most central words in a text and the connections between them. When collocational networks are created out of sequences of documents, they offer the user the possibility to quickly discover the most significant differences between the documents. As a case study, a sequence of financial reports is turned into collocational networks for visual comparison.

Industry‐specific cycles and companies' financial performance comparison using self‐organizing maps

Multilevel environment analysis is important for companies operating on the global market. Previous studies have in general focused on one level at a time, but the need to perform multilevel environment analysis has also been stressed. Multilevel analysis can partly explain the benchmarking gap between companies, as changing conditions in the upper environment levels affect lower levels. In todays information‐rich era, it is difficult to conduct multilevel analysis without suitable computational tools. This paper illustrates how the self‐organizing map can be used for the simultaneous comparison of industry‐level changes and financial performance of pulp and paper companies. The study shows the importance of simultaneous analysis, as some simultaneous changes were found at both industry and corporate levels. Also found were some industry‐specific explanatory factors for good (Scandinavian companies) and poor (Japanese companies) financial performance. The results indicate that the self‐organizing map could be a suitable tool when the purpose is to visualize large masses of multilevel data from high‐dimensional databases.

Validation of Text Clustering Based on Document Contents

In this paper some results of a new text clustering methodology are presented. A prototype is an interesting document or a part of an extracted, interesting text. The given prototype is matched with the existing document database or the monitored document flow. Our claim is that the new methodology is capable of automatic content-based clustering using the information of the document. To verify this hypothesis an experiment was designed with the Bible. Four different translations, one Greek, one Latin, and two Finnish translations from years 1933/38 and 1992 were selected as test text material. Validation experiments were performed with a designed prototype version of the software application.