Corporate strategies – the institutional approach

The present paper introduces a model of corporate strategies, based on institutional theories of the firm and formalized with the concepts of the theory of games. Corporate strategies are balanced outcomes of four social games: capital market, corporate governance, product market and social responsibility. Two empirical applications of the model are then introduced: a qualitative one, consisting in comparative study of strategies deployed by Royal Dutch Shell and Israel Corporation, then a quantitative one, presenting a study of capital accumulation and operational efficiency in 79 companies listed in the Warsaw Stock Exchange.institutional economics, strategy, corporation

A Process Approach to Corporate Coherence

We address the notion of 'corporate coherence', recently made prominent by Teece, Rumelt, Dosi and Winter (1994). We argue that the literature is confused on the meaning of the notion (and similar notions) in a number of dimensions. Drawing on insights from market-process theories, we put forward a dynamic understanding of corporate coherence as involving the corporate capacity to strike a favorable balance between the production and the exploitation of new knowledge. This argument is elaborated drawing on Austrian, evolutionary and post- Marshallian economics.Corporate coherence, knowledge, competences

New perspectives on Web search engine research

Purpose–The purpose of this chapter is to give an overview of the context of Web search and search engine-related research, as well as to introduce the reader to the sections and chapters of the book. Methodology/approach–We review literature dealing with various aspects of search engines, with special emphasis on emerging areas of Web searching, search engine evaluation going beyond traditional methods, and new perspectives on Webs earching. Findings–The approaches to studying Web search engines are manifold. Given the importance of Web search engines for knowledge acquisition, research from different perspectives needs to be integrated into a more cohesive perspective. Researchlimitations/implications–The chapter suggests a basis for research in the field and also introduces further research directions. Originality/valueofpaper–The chapter gives a concise overview of the topics dealt with in the book and also shows directions for researchers interested in Web search engines

REVEALING GHOST BUSINESS-TO-BUSINESS CLIENTS THROUGH GOSH’S MODEL. A CHALLENGE FOR EU LEISURE AND ENTERTAINMENT ACTIVITIES

The concept of intermediate market trade contacts (ITC) have been developed as transactional capital resource measured by business-to-business purchases or sales. In the framework of the resource-based view, only valuable, rare, inimitable and non-substitutable firm's resources might be considered as a source of sustained competitive advantage. Supplydriven Ghosh's model has been applied to Eurostat symmetric input-output tables of 23 EU countries. A strong and direct relationship between market horizontal multiplier for productive use and indirect ITC frequency has been found for recreational, cultural and sporting activities. Consequently, valuable and distinct hidden-client purchases could be used as a resource favoring firm development, innovation and territorial integration strategies

Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced