Using text mining to analyse digital transformation impact on people

Digital transformation is changing people's lives in many ways, creating competition between people and machines. All aspects of people’s lives are being influenced with global impacts for society. In this context, many problems have emerged for which there is still no clear ideas of their effects on people's lives. To study these problems, new tools and methodologies are needed in order to compare large volumes of data. The analysis of texts, using Text Mining, has been gaining prominence, among researchers, as one of the most relevant methodologies. However, methodologies using Text Mining are not robust enough to allow researchers to compare data from different sources, such as report data and text data. The main objective of this paper is to propose an innovative Text Mining methodology that allows to compare different texts. This study is exploratory, and it is supported by quantitative methodologies. Using Text Mining to explore ECIAIR 2019 proceedings and other European reputed reports about digital transformation, and comparing the opinions expressed by researchers with those manifested by other people, it is intended to understand if there are coincidences in the language used by researchers and on the reports in what concerns what people feel about the impacts of digital transformation on their lives. This paper belongs to an ongoing research aiming to develop text mining tools that consider corpora as variables with specific values, treating those variables as statistic variables, contributing to the enrichment of the statistical methodologies used to study digital transformation impacts. The results show that there is a gap between the language of the investigators and the one used on the reports. At the same time, there are also overlaps in some topics analysed in the documents. These results indicate that there are topics that concern both the scientific community and the international organisations responsible for the preparation of public policy guiding reports.info:eu-repo/semantics/acceptedVersio

Trends of intangibles and intellectual capital: State of art and research

Conference proceedings about intellectual capital and knowledge management are important sources of current ideas about intellectual capital, intangibles, knowledge management, authors, institutions, trends and how these are related. Since those meetings are periodic concentrations of the main sources - the papers' authors - of innovative ideas about those subjects, it is believed that an adequate analysis and synthesis of those documents can be useful to identify emerging concepts, topics, trends, directions and relations involving those concepts, their creators and the places where they were presented. The purpose of this paper is to provide, using as a data source the texts of conference proceedings, a comprehensive knowledge about the state of art of the research on intangibles and intellectual capital overthe last decade and to identify the trends on those issues for future research. This study consists of a review of abstracts, titles, authors' names, emails and institutions, keywords and main texts of all the papers in the Proceedings of the European Conference on Intellectual Capital, presented between 2009 and 2017. The study also involves the identification and characterization of patterns, such as the main topics subjacent to such texts, including associations of concepts, and the trends of such associations, involving concepts of intellectual capital and intangibles, throughout that period and conference locations. The innovative methodology used in this study is text mining, based on the classic bag-of-words model and in more recent natural language processing approaches, incorporated in R or Python packages. This work also highlights some needs not covered by the present packages and presents directions for future researches and software development. The paper can be classified as a pilot study to support the construction of new computational and knowledge management methodologies in this area.info:eu-repo/semantics/acceptedVersio

Relating organizational knowledge with ISO 9001: 2015: An empirical approach

In a business market environment highly marked by competition, standardisation has proven distinctive in answering continuous challenges. Thus, organisations have been investing in obtaining ISO certifications, being "ISO 9001 – Quality Management System" among the most popular. The present empirical study aims to discover what is, in the opinion of managers expressed by their answers to the questionnaire, the relationship between the implementation of Knowledge Management practices, the Quality System and Organizational Performance while verifying if the introduction of Knowledge Management principles in the Standard ISO 9001 has affected the company's overall organisational performance. The proposed analysis methodology is supported by applying a questionnaire to 36 Portuguese Small and Medium Enterprises sample. The investigation results allowed us to infer that, in the managers' opinion expressed by the answer to the questionnaire, Knowledge Management acts as a mediator between the Quality System and performance while not directly influencing organisational performance. Within the companies studied, holders of the certification ISO 9001, through the application of Knowledge Management practices, show improvements in the management of the Quality System, with a consequent increase in organisational performance.info:eu-repo/semantics/publishedVersio

Framework for classroom student grading with open-ended questions: A text-mining approach

The purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as a learning and evaluation instrument with increasing frequency. Given the time-consuming grading process for those questions, their large-scale use is only possible when computational tools can help the teacher. This work assumes that the grading decision is entirely a teacher’s task responsibility, not the result of an automatic grading process. In this context, the teacher is the author of questions to be included in the tests, administration and results assessment, the entire cycle for this process being noticeably short: a few days at most. An attempt is made to address this problem. The method is entirely exploratory, descriptive and data-driven, the only data assumed as inputs being the texts of essays and compositions created by the students when answering OEQ for a single test on a specific occasion. Typically, the process involves exceedingly small data volumes measured by the power of current home computers, but big data when compared with human capabilities. The general idea is to use software to extract useful features from texts, perform lengthy and complex statistical analyses and present the results to the teacher, who, it is believed, will combine this information with his or her knowledge and experience to make decisions on mark allocation. A generic path model is formulated to represent that specific context and the kind of decisions and tasks a teacher should perform, the estimated results being synthesised using graphic displays. The method is illustrated by analysing three corpora of 126 texts originating in three different real learning contexts, time periods, educational levels and disciplines.info:eu-repo/semantics/publishedVersio