Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

A Project of Developing a Knowledge Management System

Knowledge is an essential element of modern business and increasing attention is given to its acquisition, distribution and exploitation in everyday business activities. Therefore, KONČAR launched the development of a knowledge management system for its own demands and initiated a collaboration with the academic community for scientific research purposes and potential broader social significance of the project. With regard to the multidisciplinary nature of knowledge management, an agreement was reached with the University of Zagreb, the Faculty of Humanities and Social Sciences and the Faculty of Electrical Engineering and Computing. The knowledge management system will enable an effective management of all segments of intellectual capital of an organization, resulting in increase in productivity and higher market competitiveness, as well as an increased capability for generating new values for all parties to the agreement

Valuable Business Knowledge Asset Discovery by Processing Unstructured Data.

Modern organizations are challenged to enact a digital transformation and improve their competitiveness while contributing to the ninth Sustainable Development Goal (SGD), “Build resilient infrastructure, promote sustainable industrialization and foster innovation”. The discovery of hidden process data’s knowledge assets may help to digitalize processes. Working on a valuable knowledge asset discovery process, we found a major challenge in that organizational data and knowledge are likely to be unstructured and undigitized, constraining the power of today’s process mining methodologies (PMM). Whereas it has been proved in digitally mature companies, the scope of PMM becomes wider with the complement proposed in this paper, embracing organizations in the process of improving their digital maturity based on available data. We propose the C4PM method, which integrates agile principles, systems thinking and natural language processing techniques to analyze the behavioral patterns of organizational semi-structured or unstructured data from a holistic perspective to discover valuable hidden information and uncover the related knowledge assets aligned with the organization strategic or business goals. Those assets are the key to pointing out potential processes susceptible to be handled using PMM, empowering a sustainable organizational digital transformation. A case study analysis from a dataset containing information on employees’ emails in a multinational company was conducted.post-print5352 K

How does big data affect GDP? Theory and evidence for the UK

We present an economic approach to measuring the impact of Big Data on GDP and GDP growth. We define data, information, ideas and knowledge. We present a conceptual framework to understand and measure the production of “Big Data”, which we classify as transformed data and data-based knowledge. We use this framework to understand how current official datasets and concepts used by Statistics Offices might already measure Big Data in GDP, or might miss it. We also set out how unofficial data sources might be used to measure the contribution of data to GDP and present estimates on its contributions to growth. Using new estimates of employment and investment in Big Data as set out in Chebli, Goodridge et al. (2015) and Goodridge and Haskel (2015a) and treating transformed data and data-based knowledge as capital assets, we estimate that for the UK: (a) in 2012, “Big Data” assets add £1.6bn to market sector GVA; (b) in 2005-2012, account for 0.02% of growth in market sector value-added; (c) much Big Data activity is already captured in the official data on software – 76% of investment in Big Data is already included in official software investment, and 76% of the contribution of Big Data to GDP growth is also already in the software contribution; and (d) in the coming decade, data-based assets may contribute around 0.07% to 0.23% pa of annual growth on average

Web competitive intelligence methodology

Master’s Degree DissertationThe present dissertation covers academic concerns in disruptive change that causes value displacements in today’s competitive economic environment. To enhance survival capabilities organizations are increasing efforts in more untraditional business value assets such intellectual capital and competitive intelligence. Dynamic capabilities, a recent strategy theory states that companies have to develop adaptive capabilities to survive disruptive change and increase competitive advantage in incremental change phases. Taking advantage of the large amount of information in the World Wide Web it is propose a methodology to develop applications to gather, filter and analyze web data and turn it into usable intelligence (WeCIM). In order to enhance information search and management quality it is proposed the use of ontologies that allow computers to “understand” particular knowledge domains. Two case studies were conducted with satisfactory results. Two software prototypes were developed according to the proposed methodology. It is suggested that even a bigger step can be made. Not only the success of the methodology was proved but also common software architecture elements are present which suggests that a solid base can be design for different field applications based on web competitive intelligence tools

An Investigation of the Effects of Intellectual Capital on Innovations in the Egyptian Banks: The Mediating Role of Organisational Capital

This research aims to analyse the direct and indirect effects of human capital, social capital and customer capital on the different types of innovations via organisational capital in the service sector. It also examines the interaction among the different types of innovations including product, process and organisational innovations and tests the role of human capital, social capital and customer capital in supporting organisational capital. This research employs the first stage of Actor Network Theory named problematisation to justify the research model. This study adopts a positivism philosophy, a deduction approach and a quantitative method as the research methodology. Hence, a questionnaire was used to gather data from 198 managers in the Egyptian banks (54% response rate). Structural Equation Modelling by Partial Least Square (warp PLS 3.0) was applied to test the research hypotheses. The research findings indicate that product, process and organisational innovation are positively associated with organisational capital. It is found that social capital and human capital have direct and indirect positive effects on both product and organisational innovation via organisational capital. It appears that social capital and human capital do not have a direct influence on process innovation whereas organisational capital fully mediates the relationship between social capital, human capital and process innovation. The study explores the direct and indirect positive effects of customer capital on three types of innovation through organisational capital. Additionally, organisational innovation has a positive relation with process and product innovation, which is significantly associated with process innovation. The most significant influence of intellectual capital is on product innovation, followed by organisational innovation, whereas the least significant influence is on process innovation. Moreover, the results also show that there are no significant differences between the public and private banks in terms of the path coefficients. The effect size of organisational capital on product and process innovation in the private banks is substantially larger than it is in the public banks. In the same way, the private banks have relatively larger effect sizes for human capital on product and process innovation via organisational capital than those in the public banks. Unexpectedly, in the public banks, the positive effect size of customer capital on product and process innovation via organisational capital is larger than it is in the private banks. This study has contributed to intellectual capital, innovation and service sector literature. It explores many benefits for the managers of the banks. It suggests that they should view intellectual capital as a catalyst for the different types of innovations. For example, banks should maintain and promote social connections amongst their employees to support innovation and to foster the cohesion of informal organisation.The sponsor is Egyptian Government

Applying text timing in corporate spin-off disclosure statement analysis: understanding the main concerns and recommendation of appropriate term weights

Text mining helps in extracting knowledge and useful information from unstructured data. It detects and extracts information from mountains of documents and allowing in selecting data related to a particular data. In this study, text mining is applied to the 10-12b filings done by the companies during Corporate Spin-off. The main purposes are (1) To investigate potential and/or major concerns found from these financial statements filed for corporate spin-off and (2) To identify appropriate methods in text mining which can be used to reveal these major concerns. 10-12b filings from thirty-four companies were taken and only the Risk Factors category was taken for analysis. Term weights such as Entropy, IDF, GF-IDF, Normal and None were applied on the input data and out of them Entropy and GF-IDF were found to be the appropriate term weights which provided acceptable results. These accepted term weights gave the results which was acceptable to human expert\u27s expectations. The document distribution from these term weights created a pattern which reflected the mood or focus of the input documents. In addition to the analysis, this study also provides a pilot study for future work in predictive text mining for the analysis of similar financial documents. For example, the descriptive terms found from this study provide a set of start word list which eliminates the try and error method of framing an initial start list --Abstract, page iii

Exploratory Content Analysis Using Text Data Mining: Corporate Citizenship Reports of Seven US Companes from 2004 to 2012

This study demonstrates the use of Text Data Mining (TDM) for exploring the content of a collection of Corporate Citizenship(CC) reports. The collection analyzed comprises CC reports produced by seven Dow Jones companies (Citi, Coca-Cola, ExxonMobil, General Motors, Intel, McDonalds and Microsoft) in2004, 2008 and 2012.Exploratory con-tent analysis using TDM enables insights for CC professionals and analysts, in less time using fewer resources, which in turn could help them explore collaboration opportunities around supply chains, re-training programs, and alternative risk mitigation strategies in terms of governance and compliance. In addition, TDM, using supervised machine learning on the whole collection (or corpus) as well as unsupervised machine learning on document collections by year, suggests the integration of CC considerations related to environmental sustain-ability in CC report components discussing the core business of some firms. This method has been used in many contexts in which a collection of documents needs to be categorized and/or analyzed to uncover new patterns and relationships