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This paper presents a new mixed methods approach which allows researchers to scan industry sectors for institutional change periods and to locate periods of significant institutional change agency.

The approach is grounded on the institutional logics perspective and on institutional entrepreneurship theory and combines an automated quantitative content analysis with a cognitive mapping exercise.

The paper describes the development of this approach and its application to the urban water management sector of Australia. Three periods of significant institutional change agency are identified, described and discussed.

The paper puts forward a new methodological approach that enables a robust and objective identification of actor-driven institutional change periods which can be used as a precursor for more targeted qualitative inquiries into institutional change research.

The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool.

There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing.

This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system.

The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge.

The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.

A web site usually contains a large number of concept entities, each consisting of one or more web pages connected by hyperlinks. In order to discover these concept entities for more expressive web site queries and other applications, the web unit mining problem has been proposed. Web unit mining aims to determine web pages that constitute a concept entity and classify concept entities into categories. Nevertheless, the performance of an existing web unit mining algorithm, iWUM, suffers as it may create more than one web unit (incomplete web units) from a single concept entity. This paper presents two methods to solve this problem. The first method introduces a more effective web fragment construction method so as reduce later classification errors. The second method incorporates site‐specific knowledge to discover and handle incomplete web units. Experiments show that incomplete web units can be removed and overall accuracy has been significantly improved, especially on the precision and F1 measures.

Mission statements are usually related to strategic management and elements related to the organization's identity. Catholic higher education organizations (CHEOs) identity is based on the Charisma of the founder of the Catholic order or congregation. If in contradiction, it puts their organizational legitimacy at risk. If organizations deviate from their identity, it means a mission drift. Even more severe is when mission statements are misaligned with the identity. In this study, the authors seek better understand the mission drift by the misalignment between the mission statement and the organizational identity of the CHEOs.

The authors examine the mission statements of 112 Catholic CHEOs in Brazil. They used lexical analysis based on descending hierarchical classification and post-factorial analysis. They analyzed the vocabularies of each class extracted from the descending hierarchical classification and determine the presence or absence of the Charisma.

The results indicate that aspects of Catholic identity through the Charisma are manifested in the organizational mission but are not predominant. There is a variation of the mission statements relative to the Charisma of the orders and congregations. A significant part manifests generically. They respond in a similar and isomorphic way or to internal institutional pressures of CHEOs.

The authors empirically identified a mission drift, considering the mismatch between the mission statement and the Charisma. The authors emphasize that for organizational identity to manifest, it should consider the identity that emerges from the founder's Charisma. This influence must appear in central elements of the organizational identity, such as the mission statements.

The aim of this study is to explore and understand corporate governance patterns in family firms across Latin America. This is in response to several calls in the academic literature urging for more empirical studies in corporate governance in developing regions.

Following a configurative perspective, a hierarchical cluster analysis is applied to a sample of the 155 largest Latin American family firms.

The authors identify three main corporate governance configurations across Latin American countries. First, the exported governance model resembles many characteristics of Anglo-American and Continental Europe governance patterns of public listed control, having independence from the board of directors, and mainly hiring non-family management. Second, the super-familial governance model describes private ownership where one or multiple families control both the board of directors and the top-management team. Finally, the hybrid governance model is the largest cluster identified in the sample and combines governance characteristics of both of the foregoing configurations. This configuration exhibits ownership structured through public offerings of shares combined with leadership of the board of directors by a family member as well as moderate family influence on the board and management.

This is the first study to investigate corporate governance in the largest listed and privately-owned family firms in Latin America. The article extends the conversation on family firm heterogeneity and contributes to the configurative approach in the family business field by offering a cross-country perspective and identifying meaningful taxonomies that are applicable beyond national boundaries.

Automatic classification of Web pages is an effective way to organise the vast amount of information and to assist in retrieving relevant information from the Internet. Although many automatic classification systems have been proposed, most of them ignore the conflict between the fixed number of categories and the growing number of Web pages being added into the systems. They also require searching through all existing categories to make any classification. This article proposes a dynamic and hierarchical classification system that is capable of adding new categories as required, organising the Web pages into a tree structure, and classifying Web pages by searching through only one path of the tree. The proposed single‐path search technique reduces the search complexity from θ(n) to θ(log(n)). Test results show that the system improves the accuracy of classification by 6 percent in comparison to related systems. The dynamic‐category expansion technique also achieves satisfying results for adding new categories into the system as required.

This paper discusses a knowledge based information retrieval model with hierarchical thesaurus. The model computes the conceptual distance between a query and an object and both are indexed with weighted terms from a hierarchical thesaurus. The hierarchical thesaurus is represented by a hierarchical‐concept graph (HCG) in which nodes represent concepts and directed edges represent generalisation relationships. Rada et al. have developed a similar model. However, their model considered only a binary indexing scheme and revealed some counter‐intuitive results. Our proposed model extends theirs by allowing the index term and the edge of the HCG to be weighted. A new concept mapping method is devised to overcome Rada's counter‐intuitive results. In addition, a scheme for allowing Boolean operators in user queries is provided with a formula for computing conceptual distance from negated index terms. Experimental results have shown that our model simulates human performance more closely than Rada's model.

To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such.

A range of works dealing with automated classification of full‐text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages.

Provides major similarities and differences between the three approaches: document pre‐processing and utilization of web‐specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized.

The paper does not attempt to provide an exhaustive bibliography of related resources.

As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities.

To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.

With the significant growth in electronic education materials such as syllabus documents and lecture notes, available on the internet and intranets, there is a need for robust central repositories of such materials to allow both educators and learners to conveniently share, search and access them. The purpose of this paper is to report on the work to develop a national repository for course syllabi in Ireland.

The paper describes a prototype syllabus repository system for higher education in Ireland, which has been developed by utilising a number of information extraction and document classification techniques, including a new fully unsupervised document classification method that uses a web search engine for automatic collection of training set for the classification algorithm.

Preliminary experimental results for evaluating the performance of the system and its various units, particularly the information extractor and the classifier, are presented and discussed.

In this paper, three major obstacles associated with creating a large‐scale syllabus repository are identified, and a comprehensive review of published research work related to addressing these problems is provided. Two different types of syllabus documents are identified and describe a rule‐based information extraction system capable of extracting structured information from unstructured syllabus documents is described. Finally, the importance of classifying resources in a syllabus digital library is highlighted, a number of standard education classification schemes are introduced, and the unsupervised automated document classification system, which classifies syllabus documents based on an extended version of the International Standard Classification of Education, is described.

Nowadays, the event detection is so important in gathering news from social media. Indeed, it is widely employed by journalists to generate early alerts of reported stories. In order to incorporate available data on social media into a news story, journalists must manually process, compile and verify the news content within a very short time span. Despite its utility and importance, this process is time-consuming and labor-intensive for media organizations. Because of the afore-mentioned reason and as social media provides an essential source of data used as a support for professional journalists, the purpose of this paper is to propose the citizen clustering technique which allows the community of journalists and media professionals to document news during crises.

The authors develop, in this study, an approach for natural hazard events news detection and danger citizen’ groups clustering based on three major steps. In the first stage, the authors present a pipeline of several natural language processing tasks: event trigger detection, applied to recuperate potential event triggers; named entity recognition, used for the detection and recognition of event participants related to the extracted event triggers; and, ultimately, a dependency analysis between all the extracted data. Analyzing the ambiguity and the vagueness of similarity of news plays a key role in event detection. This issue was ignored in traditional event detection techniques. To this end, in the second step of our approach, the authors apply fuzzy sets techniques on these extracted events to enhance the clustering quality and remove the vagueness of the extracted information. Then, the defined degree of citizens’ danger is injected as input to the introduced citizens clustering method in order to detect citizens’ communities with close disaster degrees.

Empirical results indicate that homogeneous and compact citizen’ clusters can be detected using the suggested event detection method. It can also be observed that event news can be analyzed efficiently using the fuzzy theory. In addition, the proposed visualization process plays a crucial role in data journalism, as it is used to analyze event news, as well as in the final presentation of detected danger citizens’ clusters.

The introduced citizens clustering method is profitable for journalists and editors to better judge the veracity of social media content, navigate the overwhelming, identify eyewitnesses and contextualize the event. The empirical analysis results illustrate the efficiency of the developed method for both real and artificial networks.