Journal of Information & Knowledge Management

This study aims to analyse the correlation among enterprise performance, knowledge management, technological capability and innovation. The sample of the study comprised 96 textile enterprises among the 500 largest companies in Turkey. Among those 96 companies, 45 of them responded to the research proposal and 225 questionnaires were assessed. The data acquired was analysed with factor analysis, correlation analysis, regression analysis and descriptive analysis. As a result of the research, a high level and significant correlation among the variables was established and the positive effect of knowledge management and technological capability on innovation was detected. Moreover, innovation was found to have a positive effect on performance as well.

This empirical study examines the intellectual capital (IC) performance of Hong Kong companies and its association with business performance. Data were collected from constituent companies of the Hang Seng Index listed on the Hong Kong Stock Exchange (2005–2008). An IC measurement, Value Added Intellectual Coefficient (VAIC^TM), was utilised to evaluate the IC investment of the companies.

Four accounting ratios: market-to-book value (MB), return on assets (ROA), asset turnover (ATO) and return on equity (ROE) were used as the indicators of business performance. Regression analyses were conducted to test the ability of IC and its components in order to explain the variance in business performance measures.

No conclusive evidence was found to support the associations between VAIC^TM as an aggregate measure and the four financial indicators. However, components of VAIC^TM were found to predict a substantial variance in business performance. Capital Employed Efficiency (CEE) was found to be a key factor in predicting business financial performance. Structural Capital Efficiency (SCE) was found to have a significant effect on businesses' market valuation, as measured by MB, and on profitability, as measured by ROE. Negative correlations were found between Human Capital Efficiency (HCE) and the financial indicators. The findings indicate a gap between the traditional accounting perspective and the value creation perspective, which is central to the VAIC^TM methodology in measuring IC.

It is believed that the findings of this research provide insights for business stakeholders of Hong Kong companies in utilising IC, particularly the noted impact of structural capital. While our findings indicate the importance of IC for corporations, as shown by the significant effect of SCE on ROE, physical and financial assets may still be considered as the key resources in delivering business success.

Ontologies are a basic interest in various computer science disciplines such as semantic web, information retrieval, database design, etc. They aim at providing a formal, explicit and shared conceptualisation and understanding of common domains between different communities. In addition, they allow for concepts and their constraints of a specific domain to be explicitly defined. However, the distributed nature of ontology development and the differences in viewpoints of ontology engineers have resulted in the so-called "semantic heterogeneity" between ontologies. Semantic heterogeneity constitutes the major obstacle against achieving interoperability between ontologies. To overcome this obstacle, we present a multipurpose framework which exploits the WordNet generic knowledge base for the following purposes: (i) discovering and correcting the incorrect semantic relations between the concepts of the ontology in a specific domain, this step is a primary step of ontology merging; (ii) merging domain-specific ontologies through computing semantic relations between their concepts; (iii) handling the issue of missing concepts in WordNet through the acquisition of statistical information on the Web; (iv) enriching WordNet with these missing concepts. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic- and semantic-based systems validate our proposal.

The dynamics of knowledge management (KM) have evolved from the information-processing view of KM to the sense-making view of KM. The information-processing view of KM has been prevalent in the slow-paced, predicable environment. The sense-making view of KM, on the other hand, emerges in the environment where the fast, unpredictable pace of change is salient. Despite its significant and growing importance, there is a limited and fragmented understanding of the sense-making view of KM. This study addresses the issue of how the sense-making view of KM can be embodied by presenting and testing a research model. Data collected from 208 interdisciplinary teams were used to examine the research model by structural equation modeling. Results indicate that cognitive empowerment has a significant impact on perspective taking. Trust and media richness are critical determinants of cognitive empowerment. This study provides important insights of the role of cognition and behaviour in building the sense-making view of KM.

Social networking, through media such as Twitter and Facebook, is changing influence streams on consumer (customer) attitudes and behaviours. The direct and readily available consumer-to-consumer communication, made accessible through social networking, provides a repository of information from one's referent group, as well as an organisation's customer-facing facade. Therefore as connections among customers increase, the customers assume a higher degree of brand control at the expense of corporate marketing efforts to establish a specific brand image. This paper presents the results of interviews with four organisations that have created interactive marketing strategies built upon social networking. These companies are using social media and social networks to create online communities where they can leverage peer-to-peer network influence and use this influence to reinforce or increase a positive brand image.

The objective of this paper is to investigate the similarities and dissimilarities in Knowledge Management (KM) practices in the supply chain (SC) among different selected sectors of Indian manufacturing organisations, through formulation and testing of some hypotheses. A questionnaire-based survey was conducted on four sectors, namely, automobile, engineering, process and fast moving consumer goods (FMCG). It is observed from this study that selected sectors do not significantly differ on types of SC knowledge and KM use in various SC performance indicators. It is also observed that selected sectors differ on use of KM in different SC functional areas and various supplier measurement criteria. The analysis reveals that selected sectors have fundamental similarities and dissimilarities in their operations and functioning. This might be the reason for the similarities and dissimilarities found in their KM practices in SC.

Ontology represents a data source at a higher level of abstraction. Extracting metadata from an autonomous data source and transforming it into source ontology is a tedious and error prone task because the metadata are either incomplete or not available. The essential metadata of a source can be extracted from its data. Our proposed methodology extracts the essential metadata from the data through reverse engineering. In addition, it comprises a set of transformation rules that transform extracted metadata into ontology. The transformation system R2O has been implemented. The evaluation of the proposed transformation is based on two factors, namely (a) correct identification and transformation of metadata and (b) preservation of information capacity. The research has been evaluated through experimental results and mathematical proof. The evaluation shows that the transformation is total and injective, and it preserves information capacity.

This paper introduces three new text classification methods: Clustering-Based Generalised Instances Set (CB-GIS), Multilevel Clustering-Based Generalised Instances Set (MLC_GIS) and Multilevel Clustering-Based, k Nearest Neighbours (MLC-kNN). These new methods aim to unify the strengths and overcome the drawbacks of the three similarity-based text classification methods, namely, kNN, centroid-based and GIS. The new methods utilise a clustering technique called spherical K-means to represent each class by a representative set of generalised instances to be used later in the classification. The CB-GIS method applies a flat clustering method while MLC-GIS and MLC-kNN apply multilevel clustering. Extensive experiments have been conducted to evaluate the new methods and compare them with kNN, centroid-based and GIS classifiers on the Reuters-21578(10) benchmark dataset. The evaluation has been performed in terms of the classification performance and the classification efficiency. The experimental results show that the top-performing classification method is the MLC-kNN classifier, followed by the MLC-GIS and CB-GIS classifiers. According to the best micro-averaged F1 scores, the new methods (CB-GIS, MLC-CIS, MLC-kNN) have improvements of 4.48%, 4.65% and 4.76% over kNN, 1.84%, 1.92% and 2.12% over the centroid-based and 5.26%, 5.34% and 5.45% over GIS respectively. With respect to the best macro-averaged F1 scores, the new methods (CB-GIS, MLC-CIS, MLC-kNN) have improvements of 10.29%, 10.19% and 10.45% over kNN, respectively, 0.1%, 0.03% and 0.29% over the centroid-based and 3.75%, 3.68% and 3.94% over GIS respectively.