Information and knowledge management in complex systems

16th IFIP WG8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2015

Nepotism and turnover intentions amongst knowledge workers in Saudi Arabia

This chapter discusses how international assignment was used as tool to expand knowledge within the organisation using the example of the Kingdom of Saudi Arabia, we focus in particularly on the case of repatriation and problems with subsequent staff turnover, among repatriates in Saudi Arabia’s private sector. Before doing so, the chapter provides a background to the Saudi labour market and the impact of Saudization policies that aimed to reduce relying on foreign labour. Following this, the chapter discusses the Saudi government attempt create a national knowledgeable labour force through international assignment. Finally, using the example of an organisation in Saudi Arabia, this chapter illustrates the possible role of Wasta - a prevalent form of nepotism that permeates organizational life in Saudi Arabia - in repatriates managers turnover intention. Our focus is on unravelling the impact of Wasta on HRM practices with a particular focus on the management of the repatriation process of Saudi employees upon their completion of international assignments.

Developing a knowledge management system using an ontological approach in global organisation

This paper introduces an ontology-based knowledge model for knowledge management. This model can facilitate knowledge discovery that provides users with insight for decision making. The users requiring the insight normally play different roles with different requirements in an organisation. To meet the requirements, insights are created by purposely aggregated transnational data. This involves a semantic data integration process. In this paper, we present a knowledge management system which is capable of representing knowledge requirements in a domain context and enabling the semantic data integration through ontology modeling. The knowledge domain context of United Bible Societies is used to illustrate the features of the knowledge management capabilities.

Case-based analysis in user requirements modelling for knowledge construction

Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.

Clinical and subclinical mastitis in smallholder dairy farms in Tanzania: Risk, intervention and knowledge transfer

In a cross-sectional study of 400 randomly selected smallholder dairy farms in the Tanga and Iringa regions of Tanzania, 14.2% (95% confidence interval (CI) = 11.6-17.3) of cows had developed clinical mastitis during the previous year. The point prevalence of subclinical mastitis, defined as a quarter positive by the California Mastitis Test (CMT) or by bacteriological culture, was 46.2% (95% Cl = 43.6-48.8) and 24.3% (95% Cl = 22.2-26.6), respectively. In a longitudinal disease study in Iringa, the incidence of clinical mastitis was 31.7 cases per 100 cow-years. A randomised intervention trial indicated that intramammary antibiotics significantly reduced the proportion of bacteriologically positive quarters in the short-term (14 days post-infusion) but teat dipping had no detectable effect on bacteriological infection and CMT positive quarters. Other risk and protective factors were identified from both the cross-sectional and longitudinal included animals with Boran breeding (odds ratio (OR) = 3,40, 95% CI = 1.00-11.57, P < 0.05 for clinical mastitis, and OR = 3.51, 95% CI = 1.299.55, P < 0.01 for a CMT positive quarter), while the practice of residual calf suckling was protective for a bacteriologically positive quarter (OR = 0.63, 95% Cl = 0.48-0.81, P <= 0.001) and for a CMT positive quarter (OR = 0.69, 95% Cl = 0.63-0.75, P < 0.001). A mastitis training course for farmers and extension officers was held, and the knowledge gained and use of different methods of dissemination were assessed over time. In a subsequent randomised controlled trial, there were strong associations between knowledge gained and both the individual question asked and the combination of dissemination methods (village meeting, video and handout) used. This study demonstrated that both clinical and subclinical mastitis is common in smallholder dairying in Tanzania, and that some of the risk and protective factors for mastitis can be addressed by practical management of dairy cows following effective knowledge transfer. (c) 2006 Elsevier B.V. All rights reserved.

Knowledge transfer: the facilitating attributes in supply-chain partnerships

This article examines the operational characteristics of supply-chain partnerships and identifies the relational attributes that cultivate knowledge transfer in such partnerships. A set of theoretical propositions are developed. A case study of a computer manufacturer's supply chain was conducted to examine their validity. The findings support the view that trust, commitment, interdependence, shared meaning, and balanced power facilitate knowledge transfer in supply-chain partnerships, and that knowledge transfer should be treated as a dynamic multistage process.

Issues in language and development in multilingual settings: a case-study of knowledge exchange and teacher education in South Africa

The quality of a country’s human-resource base can be said to determine its level of success in social and economic development. This study focuses on some␣of the major human-resource development issues that surround the implementation of South Africa’s policy of multilingualism in education. It begins by discussing the relationship between knowledge, language, and human-resource, social and economic development within the global cultural economy. It then considers the situation in South Africa and, in particular, the implications of that country’s colonial and neo-colonial past for attempts to implement the new policy. Drawing on the linguistic-diversity-in-education debate in the United Kingdom of the past three decades, it assesses the first phase of an in-service teacher-education programme that was carried out at the Project for Alternative Education in South Africa (PRAESA) based at the University of Cape Town. The authors identify key short- and long-term issues related to knowledge exchange in education in multilingual societies, especially concerning the use of African languages as mediums for teaching and learning.

How Twitter is studied in the medical professions: a classification of Twitter papers indexed in PubMed

Background: Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. // Objective: This paper aims to identify published work relating to Twitter indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Limiting the study to papers indexed by PubMed ensures the work provides a reproducible benchmark. // Methods: Papers, indexed by PubMed, on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain and aspect. // Results: As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focussed on Twitter (the others referring to it tangentially). The early Twitter focussed papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. // Conclusions: This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used five dimensions to categorise published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research relating to Twitter in the area of medicine and beyond, can position and ground their work.

Price discovery of credit spreads in tranquil and crisis periods

In this paper we investigate the price discovery process in single-name credit spreads obtained from bond, credit default swap (CDS), equity and equity option prices. We analyse short term price discovery by modelling daily changes in credit spreads in the four markets with a vector autoregressive model (VAR). We also look at price discovery in the long run with a vector error correction model (VECM). We find that in the short term the option market clearly leads the other markets in the sub-prime crisis (2007-2009). During the less severe sovereign debt crisis (2009-2012) and the pre-crisis period, options are still important but CDSs become more prominent. In the long run, deviations from the equilibrium relationship with the option market still lead to adjustments in the credit spreads observed or implied from other markets. However, options no longer dominate price discovery in any of the periods considered. Our findings have implications for traders, credit risk managers and financial regulators.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Assumption of knowledge and the Chinese Room in Turing test interrogation

Whilst common sense knowledge has been well researched in terms of intelligence and (in particular) artificial intelligence, specific, factual knowledge also plays a critical part in practice. When it comes to testing for intelligence, testing for factual knowledge is, in every-day life, frequently used as a front line tool. This paper presents new results which were the outcome of a series of practical Turing tests held on 23rd June 2012 at Bletchley Park, England. The focus of this paper is on the employment of specific knowledge testing by interrogators. Of interest are prejudiced assumptions made by interrogators as to what they believe should be widely known and subsequently the conclusions drawn if an entity does or does not appear to know a particular fact known to the interrogator. The paper is not at all about the performance of machines or hidden humans but rather the strategies based on assumptions of Turing test interrogators. Full, unedited transcripts from the tests are shown for the reader as working examples. As a result, it might be possible to draw critical conclusions with regard to the nature of human concepts of intelligence, in terms of the role played by specific, factual knowledge in our understanding of intelligence, whether this is exhibited by a human or a machine. This is specifically intended as a position paper, firstly by claiming that practicalising Turing's test is a useful exercise throwing light on how we humans think, and secondly, by taking a potentially controversial stance, because some interrogators adopt a solipsist questioning style of hidden entities with a view that it is a thinking intelligent human if it thinks like them and knows what they know. The paper is aimed at opening discussion with regard to the different aspects considered.

Hierarchical clustering based web service discovery

This paper presents a hierarchical clustering method for semantic Web service discovery. This method aims to improve the accuracy and efficiency of the traditional service discovery using vector space model. The Web service is converted into a standard vector format through the Web service description document. With the help of WordNet, a semantic analysis is conducted to reduce the dimension of the term vector and to make semantic expansion to meet the user’s service request. The process and algorithm of hierarchical clustering based semantic Web service discovery is discussed. Validation is carried out on the dataset.