Journal of Information & Knowledge Management

Twitter is considered as one of the world’s largest social networking sites which allow users to customize their public profile, connect with others and interact with connected users. The proposed work introduces a distributed real-time twitter sentiment analysis and visualization framework by implementing novel algorithms for twitter sentiment analysis called Emotion-Polarity-SentiWordNet. The framework is applied to build an interactive web application called “TwitSenti” which can benefit companies and other organizations in knowing the people’s sentiment towards the aspects such as brands, current events, etc., which in turn helps in quick decision-making and planning marketing strategies. The algorithm is validated against three existing classifiers and hence proved that Emotion-Polarity-SentiWordNet provides highest accuracy value of 85%. Also, the framework showed best scalability results when evaluated through web app as four node clusters, proves to be fast and can scale well with massive data.

The purpose of the paper is to provide a detailed overview of the methods of indexing and categorizing data generated to solve problems in a complex and multifaceted field of knowledge related to the application of nanotechnology. Analysis of the capabilities and restrictions of various categorization methods are applied to the issues of the subject field, starting with simple classification schemes and up to high level ontologies. The content of integrating methods and approaches developed in many natural sciences is considered: life science, chemistry, material science, etc. The main restriction of the currently applicable ontologies and vocabularies has been identified — a primary focus on the tasks of bio- and medical informatics. It is shown that the way to overcome them includes the adoption of a new system for describing nanomaterials proposed in the CODATA-VAMAS international project. The overview shows how the extreme broadness and continuous evolution of the subject field are reflected in the means of data categorization. It is shown that the most developed of them can serve as a basis for building a knowledge base. The prospective tasks of nanoinformatics are stated required to be solved to cover fundamentally unlimited classes of materials, their properties and fields of application.

Studies that acknowledge the association between knowledge management (KM) and organisational performance (OP) are replete in extant literature. However, there are few of them providing empirical evidence of this relationship especially in academia. Multiple conjectures are made about this little-known relationship. This paper aims to explore the impacts of KM on OP in the public higher education institutions (HEIs) of Vietnam. Results of the studies were generated based on survey data collected in 30 universities located in three regions of Vietnam during 2017 by means of structured questionnaire. The hypothesised relationships between KM and OP are tested using structural equation modelling (SEM). Findings of the research showcase that KM does exert its impacts but not on all dimensions of OP in the public HEIs of Vietnam. Besides adding to the literature on performance management in academia by demonstrating the impacts of KM as a managerial tool for increasing OP, the paper also presents measurement dimensions with specific scale items measuring KM and OP of a HEI. Avenues for future research are also recommended.

Agility is a necessary condition, which is known as the main factor for the success of corporations to compete. In this sense, organisations have to determine their strategic priorities and accordingly develop and implement appropriate strategies in order to preserve their growth and survival. Meanwhile, managers play an important role in the process of strategic planning and making decisions related to their way of thinking. In addition, the existence of competitive intelligence plan for organisations has a striking role to optimally use the existing opportunities and using knowledge management (KM) in the organisation provides a platform to do so. This work aims to study the impact of using knowledge management on organisational agility as a case study in the ceramic and tile industry of the provinces of Yazd and Kerman. The data was collected using questionnaires. The structural equation modelling was used to analyse the research hypotheses using LISREL software. The results of the study showed that knowledge management application has a positive and significant effect on agility through using competitive intelligence and strategic thinking.

Institutional accounting functions are critical actors in the success of every organisation and emphasise distinct effectiveness compared to other practices. Meanwhile existing research has examined general knowledge management (KM) and its association with effectiveness at an overall institutional level. However, studies on the impact of KM capabilities on institutional accounting functional effectiveness (FE) is sparse. This study evaluates the impact of KM on institutional accounting FE based on resource-based theory (RBT) at various levels of analysis. Utilising empirical evidence, accounting knowledge management (AKM)-based effectiveness model was developed. Data were collected through a questionnaire survey of accounting officers in public research institutions in Malaysia. The study data were initially analysed using statistical package for social sciences (SPSS) version 23.0 and hypothesised relationship tested via partial least square structural equation modelling (PLS-SEM). Interestingly, the results provide support for the developed research AKM model underpinned by RBT viewpoint. AKM infrastructure (AKMI) and process capabilities impact institutional accounting FE significantly and AKM process capability partially mediates the influence of AKMI capability. The study offers new insight into the relationship between KM and institutional accounting practices. Thus, they create avenues for future research and directions for KM policy decisions in institutions.

Enterprise Resource Planning (ERP) system is a very powerful solution to many academic and non-academic institutions in case it has been implemented and used effectively. Otherwise, the system will interrupt several business processes. This research paper investigates the impact of ERP system on academic performance at Sohar University. A survey questionnaire is distributed to several academic stakeholders to investigate the impact of ERP system on academic performance within the University context. A total of 110 questionnaires was received from the key academic stakeholders to examine the relationship between the three core ERP modules i.e. the students’ information module, the financial module, and human resource module. The research outcomes indicate that there is a relationship between the three modules and the academic performance. However, only students’ information module and financial management module demonstrate a significant impact on academic performance, though the human resource module shows no impact on the academic performance at Sohar University. This study is a single case study approach, which might limit the findings to be generalized on other education institutes, but it gives a chance to other researchers to do multiple case studies in other Universities in the region.

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.

Although technology acceptance and adoption have been intensively investigated using well-established theories called the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), this paper claims that Emerging Technology (ET) has particular characteristics that differentiate it from the adoption of traditional technology that has been used for a long time. Therefore, it argues that TAM and IDT are not sufficient to investigate the adoption of ET. Investigating the adoption of ET requires additional, unique, non-traditional factors (constructs). Therefore, this paper aims first to conceptually develop a model of ETs adoption (META). To achieve this objective, TAM and IDT will be reviewed. Then, this paper will use the characteristics of ET as the basis for developing the factors that influence the adoption of ET. Secondly, to validate the model, a case study of an ET (i.e. Virtual Reality) will be analysed in-depth to reveal the factors that influence on its adoption by applying META. A discussion of META applications and implications for future research are also provided.

Transformation in patterns of human interaction has shifted significantly in the use of the Internet, as these trends are rapidly evolving with the advent of online means for social interaction, such as social media, social networking, messaging, and so on. The transformation happens to give us challenges and opportunities to improve the quality of knowledge management undertaken by an organization. The most critical problem that in knowledge management issue is how to identify the state, ownership, and connectedness of critical knowledge in an organization visually. The studies to overcome these problems already done with a knowledge map approach, but in practice to do knowledge mapping is still done partially, either by the manual or automatic approach. Accordingly, this study aims to identify the critical components in building integrated knowledge maps from various electronic information sources that developed in the current Internet era. Through a literary study in an article that examines the map model of knowledge and by analysing smart terminology in several contexts from several kinds of literature. This study proposes a model of smart knowledge mapping conceptual model to be an initial model in developing a knowledge map that integrates various critical components.

The growth of data from scientific experiments is increasing nowadays. These data came from different experiments done through various laboratory instruments or machines. It became an issue to manage and analyse scientific experimental data because of the heterogeneous nature of data structure and format. This paper proposed a knowledge metadata representation model to standardise the scientific experimental data representation to make it a standard structure. We discussed the methodology of the proposed model and gives the analysis of results. The evaluation and validation of the knowledge metadata representation model, as well as the verification of the metadata elements extraction, show promising results.

Valuable technological knowledge attracts more imitations. In light of knowledge-level perspective, this study investigates how firms could generate rare and valuable knowledge that is also hard to imitate. By applying specialised complementary assets concept to the technological portfolio of a firm, we show that core and background technological knowledge from internal and external sources, respectively, are complementarily combined to create new technologies that delay inter-firm knowledge externalities and that generate significant intra-firm knowledge flows simultaneously. The results suggest that the combination of knowledge in certain technological categories would have significant appropriability benefits, allowing firms to generate valuable and hard to imitate technological knowledge. This finding contributes to knowledge management, patent economics, and appropriability literature. Managerial implications for knowledge management are also discussed.

Medical laboratories are complex facilities in which managing knowledge could impact patients’ lives. This paper presents a comprehensive and phased framework for knowledge management (KM) developed and applied within the Research and Medical Analysis Laboratory of the Gendarmerie Royale in Morocco. The model is built according to the PDCA wheel, and the four pillars of methodology are: leadership, knowledge core process, performance evaluation, and finally elements for its continuous improvement. This KM framework will help identify the knowledge needs and expectations of the medical laboratory’s relevant interested parties, support medical laboratory professionals with the decision-making process, and therefore enhance the quality of the services they provide.

An intelligent decision support system should based on a knowledge warehouse (KW). A KW gathers knowledge initially expressed in different formalisms and therefore heterogeneous. Consequently, the KW building process requires knowledge homogenisation. This paper deals with this main issue; it introduces a three-layer architecture for a KW; more precisely, it focuses on the first layer architecture called Knowledge Acquisition and Transformation. This layer aims to transform heterogeneous knowledge models into the MOT (Modeling with Object Types) semi-formal language [Paquette, G (2002). Knowledge and Skills Modeling: A Graphical Language for Designing and Learning. Sainte-Foy: University of Quebec Press (in French).] that we have selected as a pivot knowledge model. For this transformation step, first, we design four meta-models; one for MOT and one for each of the three explicit knowledge models, namely, decision tree, association rules and clustering. Secondly, we define 15 transformation rules that we formalise in ATL (Atlas Transformation Language). Finally, we exemplify the knowledge transformation in order to show its usefulness for the KW building process.