Journal of Information & Knowledge Management

This research aims at understanding the open collaboration involved in producing Wikipedia Good Articles (GA). To achieve this goal, it is necessary to analyse who contributes to the collaborative creation of GA and how they are involved in the collaboration process. We propose an approach that first employs factor analysis to identify editing abilities and then uses these editing abilities scores to distinguish editors. Then, we generate sequence of editors participating in the work process to analyse the patterns of collaboration. Without loss of generality, we use GA of three Wikipedia categories covering two general topics and a science topic to demonstrate our approach. The result shows that we can successfully generate editor abilities and identify different types of editors. Then we observe the sequence of different editor involved in the creation process. For the three GA categories examined, we found that GA exhibited the characteristic of highly scored content-shaping ability editors involved in the later stage of the collaboration process. The result demonstrates that our approach provides a clearer understanding of how Wikipedia GA are created through open collaboration.

A partially mixed-methods case study in a Fortune 50 technology company was conducted to delineate the interaction between organisational culture (OC) types (competitive, bureaucratic and clan) and intra-organisational knowledge sharing (KS). This study provided empirical evidence that show differences in KS horizontally (peer-to-peer) and vertically (between direct-report and manager) within an organisation. By focussing on “socialization” adopted from the organisational knowledge creation theory, the iceberg theory and the competing values framework, we addressed an unexamined area within the body of knowledge. Survey data of 82 employees and interview data of 23 employees were analysed. Multivariate analysis of covariance (MANCOVA) was used to analyse the quantitative survey data. The qualitative interview data were analysed through content analysis. A triangulation design was then followed to merge the data through an equivalent status ($\mathrm{\text{QUAN}}+\mathrm{\text{QUAL}}$) interpretation to derive meta-inferences. MANCOVA displayed a statistically significant interaction between OC and KS via socialisation. The triangulated results showed that OC types distinctly impacted KS via socialisation with differences between seeking, contributing and the direction of knowledge flow (vertical and horizontal). The empirical evidence shows that organisations must consider the direction of knowledge flow (vertical or horizontal) when enforcing cultural values to drive KS via socialisation. Similarly, researchers should not ignore the directional knowledge sharing paradigm, nor the organisational knowledge creation theory, when examining intra-organisational KS.

Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.

The knowledge spectrum provides information about pragmatic knowledge types, which are placed on a spectrum based on their potential cognitive force. Based on the relationship between insight and Dretske’s three knowledge types, a conceptual framework for the equations is provided. These equations provide a shorthand method for determining the cognitive force of a specific pragmatic knowledge type. Together, the graphical and mathematical techniques of knowledge microanalysis, which is based on the knowledge chemistry approach, will be calibrated and refined. Two tools will be used to estimate cognitive force. These tools will enable organisations to develop models of their existing and proposed knowledge-intensive processes for analysis and refinement.

The purpose of this paper is to evaluate the effects of certain motivational factors on driving the continuance usage intention of mobile government-to-employees services (MG2ES). These services have been frequently overlooked by the extant IT adoption literature in determining the predictors that drive the user’s continuance intention to adopt them. To respond to this lack, an integrated model incorporating factors from several IT adoption theories was developed. These factors were divided into two categories, namely, m-service-centric and user-centric ones. Both categories were specified as direct antecedents of the MG2ES continuance intention. A structured questionnaire-based survey was carried out to empirically examine the hypothesised relationships between the model constructs. The target population of this survey was employees of Saudi’s public sector. The analysis of the collected data (i.e. 194 valid responses) was conducted using the structural equation modelling (SEM) approach. The results demonstrated that only two m-service-centric factors (i.e. m-service strength and effort expectancy) and one user-centric factor (i.e. attitude towards the MG2ES usage) are having positive impacts on the continuance intention to use MG2ES. These findings provide valuable insights and clarifications to the key MG2ES stakeholders about the aspects that motivate such intention, including augmenting the MG2ES strength, implementing effective design mechanisms to reduce the MG2ES usage efforts, delivering more compatible services, and acquiring effective tools for improving information sharing.

This study examines the effect of network externalities and flow on continual usage of e-banking services. A sample of 400 e-banking users was conveniently engaged using a structured questionnaire. The method of analysis used included Spearman’s correlation analysis, confirmatory factor analysis and structural equation modelling analysis. The findings indicate that referent network size does not significantly influence continuance intention of e-banking users. However, flow positively influences continuance intention of e-banking users. Stakeholders in the financial institutions will understand the driving factors behind continual usage of e-banking services. Some researchers have explored continual usage of e-banking but such studies are rare in the African context. This study will contribute to extant literature by adding a new dimension, intrinsic and extrinsic factors, of e-banking continual usage.

Fake news plays a major role by broadcasting misinformation, which influences people’s knowledge or perceptions and distorts their decision-making and awareness. Online forums and social media have stimulated the broadcast of fake news by embedding it with truthful information. Thus, fake news has evolved into the main challenge of better impact in the information-driven community for intense fakesters. The detection of fake news articles that is generally found by considering the quality of the information in their news feeds under uncertain authenticity calls for automated tools. However, designing such tools is a major problem because of the multiple faces of fakesters. This paper offers a new text-analytics-driven method for detecting fake news to reduce the risks impacted by the consumption of fake news. The methodology for improved fake news detection focusses on four phases: (a) pre-processing, (b) feature extraction, (c) optimal feature selection and (d) classification. The pre-processing of the text data will be initially done by stop word removal, blank space removal and stemming. Further, the feature extraction is performed by term frequency-inverse document frequency, and grammatical analysis is done using mean, Q25, Q50, Q75, Max, Min and standard deviation. Then, the optimal feature selection is developed, which minimises the number of input variables. It is intended to reduce the number of input variables to improve the model’s performance by minimising the computational cost of modelling. An improved meta-heuristic algorithm called successive position-based barnacles mating optimisation is used for optimal feature selection and classification. As the main contribution, the influence of deep learning is employed, which employs optimised long short-term memory. Finally, the result shows the superiority in terms of different significant measures by the proposed model over other methods for fake news detection experimentally done on a publicly available benchmark dataset.

Voice quality enhancement is a significant method for any speech communication model. Speech Enhancement (SE) and noise reduction approaches can significantly improve the perceptual voice quality of a hands-free communication system and increase the recognition rates of automatic speech recognition systems. Speech communications in real-world cases require high-performance enhancement techniques for addressing the distortions, which can corrupt the intelligibility and quality of the speech signal. Recent portable devices generally incorporate several microphones that can be easily used for improving signal quality. This paper plans to present a novel dual-channel SE model using the coherence function and heuristic concepts. The adaptive coherence function relates to the dual-microphone SE approach suitable for smartphones with primary and reference microphones. With this improved signal, the enhancement is performed by optimising denoising using Discrete Wavelet Transform (DWT) by Adaptive wind speed-based Deer Hunting Optimization Algorithm (AWS-DHOA). The considered objective function depends on the quality measure called Perceptual Evaluation of Speech Quality (PESQ) score. From the results, the RMSE of the proposed model using AWS-DHOA is 39.8%, 45.5%, 53.8% and 45.5% minimised than GWO-CFD, WOA-CFD, CSA-CFD, and RDA-CFD, respectively, on considering the babble noise. Finally, the comparative analysis confirmed that the proposed method improves speech quality and intelligibility by comparing diverse algorithms when different noise types corrupt the speech.

Information-seeking behaviour is the process of acquiring information from sources in response to a need. The acquired information can then be transferred into knowledge for use, archiving, or sharing. This study investigates the information-seeking behaviour and knowledge transfer among food industry business (fbiz) owners in Kuwait, and how these differ across generations of fbiz owners. Eighteen interviews with 10 large fbiz owners from different generations were carried out. The study found that the information needs of fbiz owners changed at different stages of their business. For information seeking, fbiz members consult a variety of information sources, with the newer generation relying more on online sources than word-of-mouth. Both paper-and-computer-based archiving is used, but information loss remains a threat. Effective knowledge transfer requires better communication between generations and appropriate archiving systems. Each fbiz in Kuwait, irrespective of generation, is required to follow Islamic law. This study brings together the related but differing fields of information behaviour and knowledge management. Most importantly, it contributes two models—a family business information behaviour model and a family business knowledge retention and transfer model that would be useful to the research and practice of information behaviour and knowledge management in family businesses.

Artificial intelligence-based investment services (robo-advisors) are becoming increasingly commercialized. Robo-advisors are expected to expand further due to the enhancement of accessibility to investment for general investors through customized portfolio selection and automated transactions established upon the artificial intelligence-based algorithm. This study comprehensively investigates factors that influence acceptance intention of and resistance to robo-advisors using a combined model of technology acceptance model and innovation resistance model. The model was examined through conducting a choice-based conjoint analysis of 158 users with investment experience and age ranging from 20s to 60s. The independent variables of the research for robo-advisors are transparency, customization, social presence, and user control. The effects of the independent variables on acceptance intention and innovation resistance are analyzed, respectively, through mediator variables of perceived usefulness, perceived complexity, and perceived safety. This study indicates the fundamental factors for the promotion of the domestic robo-advisor market based on the analysis of further advanced overseas robo-advisor markets. The significance of this study derives from providing implications on the direction of development for companies or financial institutions in the sphere of robo-advisors.

Energy industries are the pioneers in exploiting the knowledge management (KM) for meeting the challenges. Renewable energy industries are emerging to meet the energy security and climate changes and challenges. Therefore, it was of interest to study how the Indian renewable energy (RE) industries are able to exploit the KM practices to boost their organisation performance. Pilot study was undertaken to study the prevalence of the knowledge management (KM) practices in Indian renewable energy industries through the questionnaire and the measurement of Knowledge Management Performance Index (KMPI) value. The questionnaire was modified based on the outcomes of pilot study. The same qualitative analysis and quantitative analysis were done for the pilot study, and the KMPI value was also determined. The relation of all KM concepts, viz. KM creation, KM storage, KM transfer, KM exploitation and KM dissemination was the construct. This study provides one of first insights of KM performance in promoting new and renewable energy technologies. The clarity on the knowledge and technological gap, process of extracting and disseminating information, difficulty in accessing skilled labour, lack of collaborative R and D and research activities and storage of knowledge were found to be major issues in the exploitation of KM in RE industries in India.

The personalised learning is growing rapidly with the help of mobile and online technology. The e-learning recommendation scheme provides the suggestion concerning the courses to the students from numerous countries without past information of the courses online. The accuracy is an important issue in the e-learning course recommendation method. Hence, in this paper, Fuzzy-c-means clustering (FCM) and collaborative filtering are applied in the E-Khool user log data for effective e-learning recommendation system. The training phase and testing phase are the two phases of the devised method. During training, the relationship among the data in clustering is determined using the weighted cosine similarity and the data clustering is carried out with the help of FCM. During testing, the rating of the course is calculated using collaborative filtering. At last, the deep RNN classifier is used to evaluate prediction measure of the course recommendation. The devised e-learning recommendation method based on FCM and collaborative filtering offered a higher accuracy of 0.97 and less mean square error of 0.00115, respectively.

Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.

This work shows how we have designed and implemented a digital platform for Smart Villages, which can serve as a knowledge management and decision support system for rural stakeholders, including planners, administrative staff, and decision-makers. Our platform is intended to help pilot a smooth transition into a sustainable administration, help to boost rural services through digital innovation, and, in general, reduce the gap concerning the city’s world. Although the platform has arisen around a community belonging to the European Alpine Space, the lessons learned can be easily transferred to other scenarios in which determining and improving the degree of smartness maturity of the rural communities is a critical challenge that can facilitate undertaking public or private investments. Our goal is to provide the necessary tools for collaboration and sharing experiences and data collection and analysis to enable the best possible informed decision-making concerning the promotion of smarter villages.

The purpose of this research was to further the understanding of knowledge exchange within organisations by examining how the dyadic relationships between individuals, in terms of the channels of communication used (structural capital), knowledge awareness (cognitive capital), and the quality of their relationships (relational capital), influence opportunities for knowledge exchange (access to advice), and ultimately individual performance. data were analysed using social network analysis to determine individual network centralities, and structural equation modelling was used to test the hypotheses at the individual level. The findings suggest (1) face-to-face channels with trusted sources are the most preferred method for exchanging sensitive knowledge, (2) knowing where expertise resides and source availability is key to research knowledge exchange, and (3) centrality in knowledge network does not result in uniform increases in individual performance. While technology has the potential to increase the efficiency of knowledge exchange by removing the barriers to same-time, same-place interactions, computer-mediated communication may actually inhibit the exchange of tacit knowledge and advice because of the lean medium of the exchange, negatively impacting performance. Using a network perspective, this study adds to the literature on intra-organisational learning networks by examining how an individual’s use of different communication channels to share knowledge is related to centrality in knowledge networks, and how this impacts individual performance.

Goal/purpose: This study focused on information professionals working in the GLAM (galleries, libraries, archives and museums) sector, and how information was sought and used by them for community engagement and to attain wiser outcomes. The primary purpose was to investigate the information collection, use, reflection and values of professionals in the GLAM sector to determine if wise actions occur that may potentially benefit the community.

Methodology: A qualitative approach was used to conduct this research using the wise action model’s (WAM) wisdom characteristics. Data were collected from information professionals working in managerial positions in the GLAM sector using in-depth interviews. Thematic analysis was used to analyse the data.

Results: The findings indicate that while most participants exhibit some elements of wisdom, there are gaps that need to be addressed before wise functioning is deemed applicable in their roles. While knowledgeable information acquisition and community engagement were very visible, more emphasis on values and stakeholder well-being is recommended for wiser considerations.

Originality/Value: Study of wisdom certainly deserves more attention in knowledge management research as previous studies have indicated. With increasing stresses in the lives of professionals, it is now more important than ever to gain an understanding of how much wisdom prevails in organisational functioning to improve the works of individuals and consequently improve the well-being of impacted communities.

The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.

As the subject of pollutant emissions, enterprises play an important role in environmental governance and should consciously fulfil their environmental responsibility. So, can the enterprise perform environmental responsibility to increase their investment in research and development (R&D)? Based on the data from China’s Shanghai and Shenzhen A-share listed enterprises in 2011–2019, this paper explores the relationship between corporate environmental responsibility and R&D investment and examines the dual moderating effects of public attention and intellectual property protection. It is found that the implementation of corporate environmental responsibility can promote R&D investment. Public attention moderates the positive relationship between corporate environmental responsibility and R&D investment; it seeks that the positive relationship becomes higher with public attention. With higher regional intellectual property protection levels, the moderating effect of public attention on corporate environmental responsibility and R&D investment will be more obviously higher. This research provides theoretical support for enterprises to fulfil their environmental responsibilities and a new perspective for revealing the factors of enterprises’ R&D investment, which can offer a foundation for future research and practices.