JournalTOCs

Journal of Information & Knowledge Management
Journal Prestige (SJR): 0.19

Citation Impact (citeScore): 1
Number of Followers: 216

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-6492 - ISSN (Online) 1793-6926
Published by World Scientific

[121 journals]

Wisdom and Veterans: Enhancing the Workforce with Self-Insight,
Experience, and Sound Judgement
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: T. Eaves, J. Allen, A. Rosellini, N. Bank
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Military veterans bring many unique and desirable traits to the workforce, including self-insight, experience, and sound judgment. Through their service in the American military branches, veterans have experience in heightened leadership roles in high stress and high stakes environments that shape their personalities, leadership skills, and behaviours. Because of this experience, veterans often demonstrate wisdom as they apply self-awareness and judgment through different ranks and roles in their service to the country. Hiring these experienced and seasoned workers to the benefit of businesses can be part of the solution in addressing the current labour shortage. Research is warranted in seeking to understand why veterans experience difficulties in their transition from the military into their business careers in higher numbers compared to the general population. The misinformation and misconceptions about hiring veterans are depriving businesses of a talented and unique population that can bring more wisdom to their workforce.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-04-28T07:00:00Z
  DOI: 10.1142/S0219649223500193
The Impact of Attitude and Subjective Norm on Knowledge-Sharing Behaviour
Among the Non-Academic Staff: Behavioural Intention as a Mediating
Variable
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Shorouq Eletter, Abdoulaye Kaba, Chennupati K. Ramaiah, Ghaleb A. El Refae
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The main purpose of this study is to understand the attitude of non-academic staff towards knowledge sharing. Additionally, this study investigates the potential factors that might affect knowledge sharing among the non-academic staff. This is a cross-sectional study of 467 non-academic staff from two universities in the United Arab Emirates (UAE) and India. The findings show that male participants are more involved in knowledge-sharing behaviour ([math]) than the female participants ([math]); participants aged 50 years and above recorded the highest knowledge-sharing behaviour ([math]); participants with non-managerial positions appeared to be more involved in knowledge-sharing behaviour ([math]) than those with managerial positions; and finally, the participants from Indian university reported more involvement in knowledge-sharing behaviour ([math]) than their UAE counterparts ([math]). The findings of the study revealed a significant relationship between attitude towards knowledge sharing, subjective norm and knowledge-sharing behaviour ([math]). In addition, the study explored the mediating role of behavioural intentions within the previous variables. The findings stressed that management in higher education institutions needs to foster a culture of knowledge sharing to encourage non-academic staff to share knowledge.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-04-10T07:00:00Z
  DOI: 10.1142/S0219649223500156
A New Information Management Model for Measuring the Institutional
Performance
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Zuhair A. Al-Hemyari, Abdullah M. Al-Sarmi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The purpose of this paper is to develop a Knowledge Management (KM) model in order to investigate and monitor the performance of HEIs in Oman. In this paper, a KM model of five KM components based on the relative performance factor and a critical set of 22 indicators from an original set of 180 indicators are proposed and outlined; in terms of the use of the KM categories and selected indicators, the performance of 30 Higher Education Institutions was measured and studied. In this paper, the literature and related approaches were studied. A KM model which is based on the five main components of KM, relative performance factor, indicators, weights, and data collection was proposed. Three types of data were collected from institutions to calculate the indicators/the relative performance factor, and several tests were carried out. In addition, the conceptualisation and operationalisation processes of the model are discussed. The proposed KM model was implemented in all the private institutions and the obtained results show the applicability and accuracy of the technique, and the proposed technique was compared with the classical approach. The results of the proposed approach have been tested and analysed comprehensively: these results of the statistical tests of the aforementioned proposed approach show a high level of accuracy of the results, and that the results of institutions differ significantly one from another, and also that the differences between institutions are not due to any random factor. The discussion of the numerical results shows that the performance of HEIs was placed in three categories. The findings of the paper highlight the performance of HEIs and they give significant evidence to individual HEIs and to the stakeholders so as to allow them to act on the findings and distinguish between them in terms of the actual performance.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-04-05T07:00:00Z
  DOI: 10.1142/S0219649223500119
GepH: Entity Predictor for Hindi News
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Prafulla B. Bafna
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In this era, news is not only generated continuously with high speed but also growing in its amount by different web sources like talent hunt, news agencies, and so on. To predict the exact class of news depending on its topic, GepH (Grouped entity predictor for Hindi) is proposed using entity extraction and grouping. Entity extraction is popular for English corpus. Hindi is a national language due to its resource scarceness not being explored so much by researchers. More than 1,270 news are processed to apply entity extraction, clustering, and classification using the vector space model for Hindi (VSMH), Synset vector space model for Hindi (SVSMH), and grouped entity document matrix for Hindi (GEDMH). Synset-based dimension reduction techniques are used to get improved accuracy. Evaluation of HAC using three matrices shows the best performance of GEDMH for varied datasets. Thus labelled corpus obtained after applying HAC (Hierarchical agglomerative clustering) to GEDMH is used as a training dataset and predictions are done using random forest and Naïve Bayes. The Naïve Bayes classifier implemented using the proposed GEDMH performs the best. GepH shows 0.8 purity, 0.4 entropy, and 0.3 as error rate for 1,273 Hindi news.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-31T07:00:00Z
  DOI: 10.1142/S0219649223500168
An Enhanced Grey Wolf Optimisation–Deterministic Convolutional Neural
Network (GWO–DCNN) Model-Based IDS in MANET
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: S. K. Prashanth, Hena Iqbal, Babu Illuri
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  At present, ensuring the security of MANET is a highly challenging chore due to the dynamic topology of the network. Hence, most of the existing Frameworks for Intrusion Detection Systems (IDS) seek to predict the attacks by utilising the clustering and classification mechanisms. Still, they face the major problems of reduced convergence speed, high error rate and increased complexity in the algorithm design. Therefore, this paper intends to utilise integrated optimisation and classification methods for accurately predicting the classified label. This framework comprises the working modules of preprocessing, feature extraction, optimisation and classification. Initially, the input datasets are preprocessed by filling the missing values, and normalising the redundant contents. After that, the Principal Component Analysis (PCA) technique is employed for selecting the set of features used for improving the classification performance. Consequently, the Grey Wolf Optimisation (GWO) technique is utilised for selecting the most optimal features based on the best fitness value, which reduces the overall complexity of IDS. Finally, the Deterministic Convolutional Neural Network (DCNN) technique is utilised for predicting whether the classified outcomes are normal or attacks. For validating the results, various performance metrics have been assessed during the analysis, and the obtained results are compared with the recent state-of-the-art models.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-31T07:00:00Z
  DOI: 10.1142/S0219649223500107
Tacit Knowledge Management in Engineering Industries: Systematic
Literature Review and Development of a Conceptual Model
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: N. Jayaram, N. M. K. Bhatta
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Knowledge of every organisation is the most important resource, often not fully leveraged by organisations. Moreover, the value of tacit knowledge of an individual is much less understood and not exploited to the desired levels in organisations. It is quite evident when an experienced employee retires from an organisation, he carries away a very valuable chunk of knowledge with him, which is a strategic resource of the organisation. The COVID-19 pandemic and the great resignation times created a sudden loss of experts which troubled many organisations and made the topic of tacit knowledge management very relevant to current times. While PRISMA framework was used for selection of articles among the vast literature using justifiable validation criteria, a combination of bibliometric and narrative methods is used for the process of analysis and summarisation. The relevant papers are obtained using keywords “Tacit knowledge”, “Tacit knowledge management”, “Knowledge sharing”. Overall, 50 relevant and quality papers are picked after systematic filtration and study. The important keywords from these papers are picked and analysed to arrive at six major themes under which all the papers can be categorised. The six themes are tacit knowledge concept evolution, barriers to knowledge sharing, tacit knowledge acquisition process — various methods, role of context in knowledge sharing, enablers for tacit knowledge sharing, and IT for explicit Knowledge capture and reuse. Each of these themes also has sub-themes under which the papers are categorised. The gist of papers under each of these themes/sub-themes is captured in detail, providing key insights under each theme. While literature review papers focus on a certain domain or industry, this work developed a conceptual model using these themes which represents the current literature in tacit knowledge management as a system model, thus making this work complementary to the current body of knowledge. This model is useful in placing any future literature under one of the themes. Future work also can focus on adding additional themes as well as making this model more insightful through continuous improvement. Each of the themes in the model created can be colour coded to represent high and least researched areas, which can be a valuable guide for the future researchers. Future research directions relevant for the current engineering industries are collated from the literature reviewed, detailed under the above themes in chronological order.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-31T07:00:00Z
  DOI: 10.1142/S0219649223500120
Impacts of Market Orientation on Firm Performance in SMEs of Turkey: A
Serial Mediation Approach
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Burcu Özgül, Dilek Karaca, Cemal Zehir
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  No previous studies in the literature have investigated the serial mediation impact of innovation capability and product innovation on the influence of market orientation on firm performance. The focus in this research is on the role of the combination of innovation capability and product innovation in the influence of market orientation on firm performance. To that end, it is aimed to examine the direct and indirect impacts of market orientation on firm performance (through innovation capability and product innovation, respectively). The data collected from 739 small and medium-sized enterprises (SMEs) in Turkey by employing the online survey method were tested using the SmartPLS 3 analysis programme. Pieces of empirical evidence have revealed that both market orientation and innovation capability act as important antecedents of product innovation. Moreover, mediation analysis demonstrates that innovation capability and product innovation in turn mediate the connection between market orientation and firm performance. Consequently, this research provides pieces of evidence showing that SMEs can increase their firm performance with innovations they can make in their products by improving their innovation capability with a market-oriented approach so that they can open up to foreign markets in theory and practice.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-29T07:00:00Z
  DOI: 10.1142/S0219649223500132
Integration of Knowledge and Hybrid Institutional Logic in a Startup
Development Stage — An Online Collaboration Case
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Firmansyah David
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The study in this paper investigates the strategies used by collaborators in an effort to integrate knowledge in the context of a multi-institutional environment. By approaching a startup engaged in the digital marketplace, this study aims to provide empirical evidence on the adoption of virtual workplace in the context of competing institutional logics. The theoretical model is built using the perspective of institutional logic and knowledge approach. Our findings suggest that collaborators in an effort to integrate their colleagues’ knowledge use hybrid strategies — segmentation and combination. The level of skill in segmentation and combination strategies depends on the level of experience and knowledge of collaborators outside of their specialty. The study in this paper contributes in two directions. First, collaborative networks through online collaboration resulted in knowledge integration can be developed with hybrid actor roles and skills. Second, this paper provides empirical evidence on the vertical relationship between institutions, organisations, and individuals in institutional theory and the emphasis on the micro-institutional level.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-29T07:00:00Z
  DOI: 10.1142/S021964922350017X
The Organisation’s Size-Innovation Performance Relationship: The Role of
Human Resource Development Mechanisms
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ferry Koster
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Design: The paper relies on a quantitative analysis of 707 Dutch companies. Purpose: Prior research focussed on the positive relationship between organisation’s size and its innovation performance. This study investigates the role of human resource development mechanisms, in particular organisational learning and renewal of human resource management practices in this relationship. Findings: The analysis of survey data revealed that, taking into account several background variables, smaller organisations have lower innovation performance than larger ones. The differences between organisations disappear after organisational learning practices and renewal of human resource management are taken into account. These two human resource development mechanisms are, in turn, positively related to innovation performance. Originality: Prior research focussed on the direct relationship between organisations’ size and innovation performance. This paper examines this relationship more closely by focussing on intermediating mechanisms.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-24T07:00:00Z
  DOI: 10.1142/S0219649223500181
A Comparative Review of Sentimental Analysis Using Machine Learning and
Deep Learning Approaches
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Archana Nagelli, B. Saleena
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The sentiment data provides vital information about the feedback of the user’s opinion, attitude and emotions. The business of product development and digital marketing teams entirely depends upon the outcome of these sentiments and they apply various Data Mining techniques, Machine Learning and Deep Learning approaches to analyse the depth of the dataset. The Sentiment Analysis provides the automatic data mining of reviews, comments, opinions and suggestions, received from various input methods, including text, audio notes, images and emoticons, through Natural Language Processing. The analysis assists in the classification of reviewer feedback in terms of positive, negative and neutral categories. In this study, the opinions shared by individuals over various social networking sites in the case of any big event, the release of any new product or show and political events were analysed. Machine Learning and Deep Learning techniques are discussed and used dominantly to illustrate the outcome of opinions and events. The accurate analysis of vast information shared by individuals free of cost and without any influence can provide vital information for organisations and management authorities. This review analyses various techniques in the field of Aspect-Based Sentiment Analysis along with their features and research scopes and thus, it helps researchers to focus on more precise works in the future. Among the machine learning algorithms, Random Forest performed much better as compared to other methods, and among the Deep Learning approaches, Multichannel CNN outperformed with the highest accuracy of 96.23%. The paper includes the comparative study of multiple Machine Learning and Deep Learning techniques for the evaluation of sentiment data and concludes with the challenges and scope of Sentiment Analysis.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-18T07:00:00Z
  DOI: 10.1142/S021964922350003X
Vocational Education Information Technology Based on Cross-Attention
Fusion Knowledge Map Recommendation Algorithm
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Peng Jiang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-10T08:00:00Z
  DOI: 10.1142/S0219649223500077
Redesigning Knowledge Management Through Corporate Sustainability Strategy
in the Post-Pandemic Era
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Prabir Chandra Padhy, Remya Lathabhavan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This study investigates the role of Knowledge Management (KM) in integrating corporate sustainability practices in the post-pandemic context. It also examines the current literature on KM and sustainable development and develops a sustainable conceptual model. Based on a survey of contemporary literature and KM and corporate sustainability approach, this study proposes a conceptual framework with KM and corporate sustainability strategy as fundamental constructs to attain organisational excellence (OE) in the post-pandemic era. The research adds conceptual and situational elements such as the interaction between KM and sustainability strategy, creative approaches for developing a structural framework, and the right direction for boosting efficiency. The research is one of the first to present a comprehensive framework for achieving OE in the post-pandemic era. Furthermore, by focussing on COVID-19 and the post-pandemic environment, this research provides a new perspective on KM and corporate sustainability literature.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-10T08:00:00Z
  DOI: 10.1142/S0219649223500089
An Empirical Investigation of ERP System Self-Efficacy Beliefs: Examining
the Effects of ERP System Characteristics
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Bassam Hasan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Computer self-efficacy (CSE) is well recognised as a significant and reliable determinant of enterprise resource planning (ERP) adoption and utilisation. However, CSE is a multifaceted concept that can be applied at a general computing level or an application-specific level, most past studies of CSE in ERP settings failed to make this distinction or examine CSE towards ERP systems and focused predominantly on CSE as a general computing construct. Furthermore, past research has focused on investigating the consequences of CSE in ERP-related behaviours and little or no attention has been given to exploring factors that can influence CSE at the ERP level. This study attempts to address these two issues and fill this void in the literature. First, this study seeks to address and examine CSE at the ERP system level. Second, this study will focus on investigating external factors affecting ERP self-efficacy beliefs. The external factors examined in this study are the ERP system characteristics of user interface, complexity, and learnability on. The results provide strong support for the effects of ERP user interface, complexity, and learnability on ERP self-efficacy beliefs. ERP user interface also demonstrated a significant impact on perceived complexity and learnability of ERP systems. Several practical and research contributions can be drawn from the findings reported in this study.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-03-03T08:00:00Z
  DOI: 10.1142/S0219649223500016
Exploring the Factors of Online Social Networks (OSNs) on Individual
Investors’ Capital Market Investment Decision: An Integrated Approach
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Md. Ziaul Haque, Aimin Qian, Suraiea Akter Lucky
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Online social networks (OSNs) are a terrifically emerging platform for information dissemination around the world. Like other settings, acceptance and adoption of OSNs among the individual capital market investors are extensive. The study developed a conceptual model for behavioural finance integrating a technology acceptance model (TAM) and valence framework from the information systems and marketing disciplines, respectively. The integrated model added some persuasive constructs from social capital and diffusion innovation theory with a view to explore the key factors swaying investors’ intention to adopt and use the OSN’s services. By using an online and offline structured questionnaire, 510 data were collected from individual capital market investors in Bangladesh. Structural Equation Modelling (SEM) was used for data analysis. The study determined that the proposed integrated model with additional constructs outperformed other models. Perceived usefulness (PU), perceived enjoyment (PE), trust and personal innovativeness in IT (PIIT) had a substantial sway on the investor’s intention to use OSNs. Hedonic value is more robust predictor of intention to use OSNs than utilitarian value. Intention to use properly mediated the relationships and had strong significant impact on investor’s investment decision. But perceived ease of use (PEOU) and perceived risk had no direct significant effect on intention to use. PEOU had significant impact on intention to use through PU and PE. Gender moderated the relationships of different constructs with the intention to use OSNs for investment decisions in the capital market. It contributes knowledge by including the integration of different models in stock market perspectives and the inclusion of technological aspect in the behavioural finance literature. The findings of the study will also succor different firms and regulatory authorities to adopt OSNs as an information dissemination platform.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-23T08:00:00Z
  DOI: 10.1142/S0219649223500028
A Joint Model for Target-Oriented Opinion Words Extraction and Sentiment
Classification
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Chenyang Dai, Bo Shen, Fengxiao Yan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Target-oriented opinion word extraction and aspect-level sentiment classification are two highly relevant tasks in aspect-based sentiment analysis. Previous studies tend to separate them and focus on one of the tasks, which ignore the connection between opinion word extraction and sentiment classification, and result in the waste of useful connection information. In this paper, we propose a co-extraction model, in which the two tasks are formulated as a sequence labeling problem. The model involves two stacked Bi-LSTM modules and an information interaction component to generate all opinion-polarity pairs of the input sentences simultaneously. The experimental results show that our model achieves advanced results in target opinion word-polarity co-extraction. The performance of both tasks is stronger than the baseline, and the model is of low complexity and high operational efficiency.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-18T08:00:00Z
  DOI: 10.1142/S0219649222500976
Load Balancing Control Algorithm of Internet of Things Link Based on
Non-parametric Regression Model
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Xinyan Yu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In order to solve the problems of poor channel balance control ability and unable to effectively reduce the output bit error rate in the traditional Internet of things link load balance control methods, a new Internet of things (IoT) link load balance control algorithm based on non-parametric regression model is proposed in this paper. The transmission model of IoT link channel is constructed, and the sparse random cluster analysis method is used to extract the load characteristics of IoT link. According to the load feature extraction results, through the estimated regression function of known data features, a non-parametric regression model is constructed, and the fuzzy cyclic iterative control is used to realize the load balancing control of the Internet of things link. The experimental results show that this method has strong channel balance control ability, low output bit error rate, the maximum average link utilisation can reach 1, and the maximum output bit error rate is only 102, which improves the stability of the Internet of things.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-15T08:00:00Z
  DOI: 10.1142/S0219649223500041
Intelligent Techniques for Predicting Stock Market Prices: A Critical
Survey
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Esra’a Alshabeeb, Malak Aljabri, Rami Mustafa A. Mohammad, Fatemah S. Alqarqoosh, Aseel A. Alqahtani, Zainab T. Alibrahim, Najd Y. Alawad, Mashael A. Alzeer
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The stock market is an exciting field of interest to many people regardless of their occupational background. It is a market where individuals with adequate knowledge can join and earn an additional income. Nowadays, life expenses have increased. Hence, the number of people investing in stocks is increasing dramatically. Anyone may indeed start participating in the stock market at any time, yet it is not ensured that they will profit from this investment. The stock market is a risky field of investment, given that it is unknown whether the stock will rise or fall. Stock market prediction using Artificial Intelligence techniques is a possible way to help people anticipate stock market directions. Current research showed that many factors aid in changing the stock market value in general and specifically in the Saudi stock market. To our knowledge, most research studies only consider historical data in predicting stock market trends. However, this research aims to enhance the accuracy of the daily closing price for three Saudi stock market sectors by considering historical and sentimental data. Several intelligent algorithms are considered, and their performance indicators are discussed and contrasted against each other. This research concluded that more accurate stock market prediction models could be produced by employing historical and sentimental data.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-08T08:00:00Z
  DOI: 10.1142/S021964922250099X
Knowledge Sharing in Health Community of Practice (CoP) and Online Health
Communities (OHCs): A Bibliometric Analysis
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Muhammad Ashraf Fauzi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This study aims to provide a scientific mapping based on a bibliographic database of knowledge sharing in the health community of practice (CoP) and online health communities (OHCs). These online platforms have provided an efficient means for members to share best practices, expertise, and information on health-related matters and acquire knowledge in the fast-moving health and medical field. Applying a bibliometric method, this study contributes to the body of knowledge concerning health CoP function, capacity, and contribution. This study tends to uncover past themes (citation analysis), current themes (co-citation analysis), and predict trends (co-word analysis) on health CoP and OHCs perspectives. Findings indicate the imperative role of CoP in the field, mainly due to the rapid development of sophisticated tacit knowledge on complicated health issues and unpredictable diseases affecting public health. Implications on theory and managerial aspect to strengthen the function of CoP in providing optimum health service to the public is discussed.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-08T08:00:00Z
  DOI: 10.1142/S0219649223500065
Deep Data Mining of the Characteristics of Enterprise’s Technology
Development Trend
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Changliang Wang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This paper studies a deep-seated data mining method for the development trend of enterprise technology. Technical distance, technical personnel and R & D investment are selected as the enterprise’s technical characteristics mined by the deep data mining method. The deep mining of enterprise’s technical characteristics is realised by defining mining objectives, data sampling, data exploration, data preprocessing, pattern discovery and prediction modelling of restricted Boltzmann machine. The mining results are used to analyse the impact of enterprise’s technical characteristics on the development trend. Ten science and technology enterprises are selected as the empirical analysis object. The empirical research results show that the three enterprise’s technical characteristics of technical distance, technicians and R & D investment have a great impact on the enterprise development trend. The results show that the method in this paper has certain practical application significance, and also provides a theoretical basis for enterprises to use technological innovation to occupy the market.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-02-06T08:00:00Z
  DOI: 10.1142/S0219649223500090
Explainable Recommendation Based on Weighted Knowledge Graphs and Graph
Convolutional Networks
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Rima Boughareb, Hassina Seridi, Samia Beldjoudi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Knowledge Graphs (KGs) have been shown to have great potential to provide rich and highly defined structured data about Recommender Systems (RSs) items. This paper introduces Explain- KGCN, an Explainable RS based on KGs and Graph Convolutional Networks (GCNs). The system emphasises the importance of semantic information characterisation and high-order connectivity of message passing to explore potential user preferences. Thus, based on a relation-specific neighbourhood aggregation function, it aims to generate for each given item a set of relation-specific embeddings that depend on each semantic relation in the KG. Specifically, the relation-specific aggregator discriminates neighbours based on their relationship with the target node, allowing the system to model the semantics of various relationships explicitly. Experiments conducted on two real-world datasets for the top-[math] recommendation task demonstrate the state-of-the-art performance of the system proposed. Besides improving predictive performance in terms of precision and recall, Explain-KGCN fully exploits wealthy structured information provided by KGs to offer recommendation explanation.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2023-01-30T08:00:00Z
  DOI: 10.1142/S0219649222500988
Spread Dynamics of Tourism-related Messages within Social Networks
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Dan Luo, Bojian Xiong, Yu Cao
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Tourism-related messages can alter the images of tourism destinations. In the new media time, messages from individual perception of the destination can spread among the social networks. Here, based on three basic assumptions, we developed a model to investigate the spread dynamics of tourism-related messages. In the model, two variables of individual behaviour, representing the probabilities of sharing or forgetting the messages, respectively, and a variable to represent the message’s importance were integrated. Within the simulated small-world networks, we observed two distinct patterns in the spread dynamics. The patterns were determined by individuals’ willingness to share messages and the message’s importance. If a majority of people choose not to send a message that they have received, the informed population will eventually become negligible; whereas, while they are inclined to spread, the informed population will remain constant over time. These patterns were influenced by neither the density of network connections nor the message sources. The message sources only determine the speed and the scale of diffusion. In summary, our model revealed the patterns of the spread of tourism-related messages.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-12-15T08:00:00Z
  DOI: 10.1142/S0219649222500964
Effect of Knowledge Management on Employee Performance
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Samuel Partogi Hasudungan Sinaga, Erna Maulina, Pratami Wulan Tresna, Iwan Sukoco, Margo Purnomo, Nenden Kostini
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This study aims to determine the effect of knowledge management (KM) where the variables consist of people, processes and technology on employee performance at Pupuk Sriwidjaja Inc. The object of this research is associated with the development of the world of fertiliser industry, while the scope of research aimed at Pupuk Sriwidjaja Inc as one of the largest fertiliser companies in Indonesia. The type of research is descriptive with explanatory research. Sampling is conducted by proportional stratified random sampling method. Data analysis uses multiple linear regression analysis. The results show KM where the variables consist of people, process, and technology simultaneously influence on the employee performance.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-12-07T08:00:00Z
  DOI: 10.1142/S0219649221500210
KMD Quest-SW: A Diagnosis of Knowledge Management Addressed to Software
Industry SMEs
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Danieli Pinto, Nelson Tenório, Flávio Bortolozzi, Cláudia Herrero Martins Menegassi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The software industry depends intensively on its actor’s knowledge to develop its products. This knowledge is crucial to leverage innovation and market sustainability within the software industry companies. Knowledge Management (KM) processes are accomplished in the small- and medium-sized software industry companies daily, however, sometimes not formally. This paper proposes a questionnaire aimed to diagnose KM in small- and medium-sized enterprises (SME) of the software industry, namely, KMD Quest-SW. The KMD Quest-SW was designed to fill up the gap of KM diagnosis in SME in the software industry. The KMD Quest-SW has 46 statements distributed in six dimensions: so-called creation process, registration process, knowledge sharing, knowledge use, innovation process, and knowledge in the software industry. From the software industry perspective, our proposal appears as a promising tool to diagnose and map the knowledge flow in SMEs. From a scientific perspective, the questionnaire breaks new grounds for KM theoretically and practitioners to be adapted for other SME companies interested in KM.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-28T08:00:00Z
  DOI: 10.1142/S0219649222500800
Research on the Implementation Path of Enterprise Strategic Property
Management from the Perspective of Technological Innovation
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Shuo Wu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The aim of this paper is to improve the innovation level and innovation performance of enterprise strategic property management technology. Also, this paper studies the implementation path of enterprise strategic property management from the perspective of technological innovation by analysing the opportunities and challenges faced by the enterprise’s intellectual property management, building an enterprise strategic property rights management system with the company’s intellectual property organisation management, intellectual property value management, and intellectual property strategic management as the core; establishing SC’s intellectual property management system by stages, adopt a phased approach to build SC’s intellectual property management system and by increasing corporate profitability through intellectual property transfer, licensing and other activities. Through intellectual property management, it provides a strong guarantee for enterprises to make profits. Interviews and investigations were conducted with KT company in the field of temperature control, QR company in the field of energy saving and environmental protection, and JZ company in the field of biomedicine. Only by basing itself on its own reality and market demand, gradually improving the level of technological innovation, and planning and implementing the strategy of independent intellectual property rights, can the performance of technological innovation be effectively improved.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-24T08:00:00Z
  DOI: 10.1142/S0219649222500915
Using Two Theories in Exploration of the Health Information Diffusion on
Social Media During a Global Health Crisis
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Hanan Alasmari, Oksana L. Zavalina
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  As the possibility of sharing inaccurate information on social media increases markedly during the health crisis, there is a need to develop an understanding of social media users’ motivations for online sharing of information related to major public health challenges such as COVID-19. This study utilised an online survey based on Theory of Planned Behaviour and Diffusion of Innovation Theory to examine how the behavioural intention to share COVID-19-related content on social media is impacted and to develop a model of health information sharing. Results indicate that opinion leadership, beliefs held towards the source of the information, and peers’ influence serve as determinants of the intention to share COVID-19-related information on social media, while the opinion-seeking attitude does not, which could be explained by opinion seekers’ inherent tendency to seek more sources to verify new information obtained. The study contributes to the Information Science field by addressing the previously under-researched area and proposing a new model that explains the impact of the factors on behavioural intention to share health-related information during the health crisis in the online network environment.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-24T08:00:00Z
  DOI: 10.1142/S0219649222500952
Knowledge Creation in International Non-Profit Organisations
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Quang Ngoc Le, Kulthida Tuamsuk
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This study empirically investigates knowledge creation (KC) in the context of the not-for-profit environment and seeks an understanding of how the dynamic of KC occurs in non-profit organisations (NPOs) in developing countries. The study, qualitative in nature, was applied in international non-profit organisations (INPOs), which is based on the 31 interviews taken at various such organisations in Thailand. The research illustrates the context of interaction dynamic that figures out the exampled NPOs, that is the creation of four types of ba, which enhances the KC process and identifies the specific knowledge types that are managed in these organisations. The investigation offers a holistic approach of the processes of organisational knowledge-creating in the NPOs. Furthermore, it is intended that the evidence presented a critical attitude regarding knowledge management (KM)-NPOs domains, especially in the KC process in the academic community. For NPO practitioners, this paper allows us to discover the organisational practices on the process-creating to enhance KM activities in their organisation.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-19T08:00:00Z
  DOI: 10.1142/S021964922250085X
A Novel Hybrid Forecasting Approach for Customers Churn in Banking
Industry
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Saeed Rouhani, Ali Mohammadi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Competitive markets and customers’ changing needs in the bank industry necessitate accurately predicting customers who may leave the firm in the near future. Consequently, creating an approach to predict precisely and identify churn-leading causes is a part of retention strategies in customer relationship management. The approach that has been utilized in this research to predict customer churn combines decision tree (DT) and multinomial regression (MR) to classify customers with no limitation of binary classification in the churn prediction context. A customer club dataset of a commercial bank case as a real churn problem is used in this study to benchmark the hybrid forecasting approach against its building blocks. The results showed that the hybrid forecasting approach outperformed DT and MR with an average accuracy of 87.66%, 90.74% micro-average, and 90.44% macro-average of AUC. Further analysis of the model performance per class indicated that the hybrid approach’s misclassification error for the churn class decreased significantly, which is the most costly error in churn problems. Moreover, due to the structure of hybrid forecasting approach, more interoperability is obtained by assessing the impact of features in different segments, resulting in transforming them into actionable insights. The proposed approach is applied to the banking industry to prevent financial loss by detecting leading churn causes. Accordingly, after predicting the risk of customer churn, marketers and managers can determine appropriate actions that will have the most significant retention impact on each customer by applying proactive retention marketing.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-19T08:00:00Z
  DOI: 10.1142/S0219649222500897
A Wide Scale Survey on Weather Prediction Using Machine Learning
Techniques
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Shabnam Kumari, P. Muthulakshmi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Several losses had been witnessed due to many natural calamities like earth quakes, storms, cyclones, etc. These natural calamities have direct or indirect effects on the lives of billions of people across the world. The prediction of environmental impact due to the changes in weather had been a critically challenging task. In countries like India, where agriculture is the livelihood of many people (49.5%) and rainfall is very essential for the cultivation of crops, rainfall is very much needed to all forms of lives. Extreme rainfall has its effects on the economy of any country. Heavy loss of lives and properties had been encountered due to havoc of flood in varying degrees. In this research work, the rainfall forecasting is highly focussed and it discusses on several models of weather prediction. Note that in the previous decades, many researchers have made some serious attempts to reach out with forecasting systems for weather prediction (which include statistical and analytical models for rainfall prediction) but maximum models proposed by the researchers are found to be unfit in terms of less accuracy, when these proposed prediction models are applied on a large scale. The research work presents the reviews of works that are proposed by many pioneers, who had taken lots of efforts arrive at a good prediction system. In this work, it is also found that that there had been a big gap between the prediction reports/weather news and the actually happening. This paper considers most of the features belonging to the models found from scientific articles published across the globe to find the factors that are widening the gap between the forecast data and the actual phenomenon.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-18T08:00:00Z
  DOI: 10.1142/S0219649222500939
The Integration of Hadoop and a Smart Utility Scoring with a Case-Based
Reasoning System for Managing Large and Complex Medical Case Base
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Seema Sharma, Deepti Mehrotra, Narjès Bellamine Ben Saoud
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In case-based reasoning structure, the quality and the complexity of the case data play a significant role in searching, retrieving, updating, and holding distinct case data at or from distinct databases for various purposes. However, managing the very complex and large case-data sets is not easy. Furthermore, the complex algorithm for maintaining case-based reasoning (CBR) structure makes it more critical and increases the time complexity considerably when the dataset is large and complex. Consequently, distinct available techniques related to the maintenance of the CBR system concentrate on deleting the less important and very complex case data to reduce the number of cases. At the same time, it reduces the efficiency and effectiveness of handling the CBR system. Hence, this research employs an integrated platform with the combination of the Hadoop parallel platform and an intelligent utility scoring mechanism. The smart indexing system and the parallel processing capacity of Hadoop reduce the time complexity for processing large and complex datasets. The distributed storage capability effectively manages the data repository and retrieval system for enormous case datasets without compromising case data. The intelligent utility scoring system employs supervised learning and a unique CBR system to quickly and efficiently retrieve the effective solution for each particular case. Furthermore, Hadoop offers a distributed structure that helps the user to access the case data and update the database through the network. This research uses distinct healthcare text-based datasets for testing the performances of the proposed integrated technique in different aspects. The experimental results show the superiorities in producing a higher percentage of accuracy with consuming less Retrieval time over other related techniques. It further shows its time efficiency by offering higher throughput and lower Cyclomatic Complexity with lower read and write Latency Times. Finally, the performances of the proposed technique in these various aspects have been compared with distinct existing techniques and the performances of various databases in managing large and complex medical data to establish its superiority over them.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-16T08:00:00Z
  DOI: 10.1142/S0219649222500812
The Influence of Personality, Entrepreneurship Curriculum,
Entrepreneurship Knowledge, and Attitude on Entrepreneurship Intention
with Social Support as Mediation
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Sri Setyo Iriani
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The purpose of this research was to analyse the influence of student’s personality, entrepreneurship curriculum, entrepreneurship knowledge, and student’s attitude on entrepreneurship intentions of FE Unesa management department students with social support as mediator. This research was descriptive quantitative using structural equation model (SEM). This research was conducted in Faculty of Economics, State University of Surabaya (Unesa). The approach and design in this study examined the variables of personality, entrepreneurship curriculum, entrepreneurship knowledge, attitude, social support and entrepreneurship intention in the students of class of 2012 in the Department of Management of Faculty of Economics, Unesa. The population in this study was all students who were active in the Department of Management of Faculty of Economics of Unesa, in the class of 2012. Partially, personality, entrepreneurship curriculum, and entrepreneurship knowledge had influence to social support, and entrepreneurship intention to the students of class of 2012 in the Department of Management of Faculty of Economics of Unesa. This research was a small part of previous studies on entrepreneurial intentions in students. The entrepreneurship intention of a student while they were still a student and after graduation had been studied previously, influenced by the personality variable. Other studies also showed the entrepreneurial intentions of a student influenced the role of entrepreneurial learning on campus, both formally and in the form of courses. The originality of this research is the use of social support as the mediation variable.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-16T08:00:00Z
  DOI: 10.1142/S0219649222500848
Analysing Knowledge-Sharing Practices Using Activity Theory in the SME
Organisation
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: S. M. F. D. Syed Mustapha
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  An activity theory method is used to analyse the knowledge-sharing practices. The activity theory emphasises the necessity of analysing the SME organisation as a whole. In the context of knowledge-sharing practices, activity theory is used to collect interconnected parts of SME practices. A cross-sectional design was used to study the relationship among relationship commitment, knowledge-sharing practices, employee development, team performance, and a moderating role of social identification. The majority of the SMEs were established 3–5 years ago (46.3%), and 84.4% were private, with an employee range of less than 50 (73.1%). Furthermore, 82.1% of the SMEs in this study were in the growth stage. Knowledge-sharing practices have a significant positive effect on team performance (0.278, [math]), with a moderating impact of role and behaviour on knowledge-sharing practices and team performance (0.178, [math]). The findings have confirmed the significant and positive effects of knowledge-sharing practices on the mediation of employee development (0.045, [math]). The activity theory models for knowledge-sharing practices emphasise the contextual nature of knowledge sharing and ensure systematic evaluation.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-16T08:00:00Z
  DOI: 10.1142/S0219649222500940
Roadmap to Precision Agriculture Under Circular Economy Constraints
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Mohammed Yaqot, Brenno C. Menezes, Tareq Al-Ansari
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Today’s smart and sustainable agricultural enterprises are founded on the intercorrelation between Industry 4.0 (I4) technologies and circular economy (CE) fundaments. At the industrial level, it is mandatory to constrain the design, operation, and control of the next generation of manufacturing, logistics, and supply chain systems that respect economic, environmental, and social aspects. At the individual level, behaviours and preferences require re-do (as recycling) and a re-do-not (as rethinking) actions to reduce human inefficiencies and excesses in the economy and impacts on the environment. Both industrial and individual levels are responsible for the growth of a harmonious industry and society into a new I4–CE mandate intertwined with sustainable development concerns. Particularly, this paper reveals how the state-of-the-art I4 technologies could promote CE initiatives in the agriculture industry by addressing such technologies as a foundation for the so-called precision agriculture (PA). The outcomes of this paper are as follows: (a) address the mutually advantageous linkage between PA and CE; (b) detailed knowledge of the potential contributions of PA technologies to the regenerate, share, optimise, loop, virtualise, and exchange (ReSOLVE) as a CE model; (c) propose an action plan for future study integrating smart PA and CE principles based on green supply chain management theories.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-14T08:00:00Z
  DOI: 10.1142/S0219649222500927
An Experimental Study on Evaluating Alzheimer’s Disease Features
using Data Mining Techniques
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Hadeel Albalawi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Alzheimer’s disease (AD) predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Predicting AD and mild cognitive impairment (MCI) can lengthen the lifespan of patients and help them to access necessary medical resources. One potential approach to achieve an early diagnosis of AD is to use data mining techniques which explore various characteristic traits related to MCI, cognitively normal (CN), and AD subjects to build classifiers that reveal important contributors to the disease. These classifiers are used by physicians during the AD diagnostic process in a clinical evaluation. In this research, we compare between different data mining algorithms through empirical data approach to deal with the AD diagnosis. Experimental evaluation, using attribute selection methods, and classifiers from rule induction and other classification techniques have been conducted on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-MERGE). The results illustrate the good classification performance of classifiers with rules in predicting AD.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-11T08:00:00Z
  DOI: 10.1142/S0219649222500782
How Professional Service Firms Derive Triple Value Bottomline: An IC
Perspective
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Junaid Rehman, Igor Hawryszkiewycz, Osama Sohaib, Fatuma Namisango, Abdul Samad Dahri
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of Information & Communication Technologies (ICTs) globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, these tools & technologies would only be meaningful if these are appropriately utilized by a knowledgeable workforce. In other words, this knowledge age has changed the success mantra of business competitiveness by re-shifting the focus from ICT-based transformations to knowledge-based transformations, though the availability of ICT systems has further augmented the organizational capabilities. Moreover, truly capitalizing on these warrants a knowledge-enabled work culture and recognizing as such the strategic significance of in-house Intellectual Capital (IC) that serves as a prime mover of achieving a sustainable competitive advantage. However, the maximum potential of IC for deriving multi-stakeholder value has not been fully achieved. Therefore, by administering 12 face-to-face semi-structured interviews at Australian Professional Service Firms (PSFs), this research offers a novel perspective on IC valuation by presenting the concept of ‘Triple Value Bottomline’ coupled with ‘IC Best Practices for PSFs’. These collectively offer IC evaluation, measurement and management mechanisms. Overall, the findings reveal immense potential of IC for achieving diverse value outcomes for multi-stakeholders in PSFs.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-11T08:00:00Z
  DOI: 10.1142/S0219649222500873
Modelling and Stability Analysis of a Rumour Propagation Model with
Sentiments as Microscopic Observation
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Greeshma N. Gopal, Binsu C. Kovoor
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  A mathematical model (SEPNS) for rumour spreading on social media is proposed here with the required differential equations. Microscopic observations are considered here to obtain the compartments in this epidemiological model. The predictions based on this model can help social media analysts provide valuable and specific suggestions in business and politics. The equilibrium points are obtained for this model. Later, the stability analysis based on basic reproduction number [math] is done for both the rumour free equilibrium and the endemic equilibrium. Finally, numerical simulation of the model is done to understand the influence of different parameters during rumour spread.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-10T08:00:00Z
  DOI: 10.1142/S0219649222500861
What Can Cluster Analysis Offer Stock Investors' Evidence from the
China’s Energy Industry
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Luxing Liu, Yufeng Cai, Yalu Wei, Hong Jin, Yin Pei Teng
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  China is one of the world’s major producers and consumers of energy. The investment value of China’s energy industry has attracted the attention of investors at home and abroad. Few studies, however, have specifically investigated investment ratings in China’s traditional energy industry. This study, therefore, uses scientific analysis methods to help investors measure the investment value and returns of China’s energy industry. From the perspectives of market performance and earnings management, we select factors that influence stock value evaluation indicators and undertake an empirical analysis using financial statement data for 2020 from the Wind database. Based on a factor analysis of the main financial indicators (e.g. amplitude, turnover rate, gross profit margin of sales, growth rate of operating revenue), we obtain five main factors: stock market performance, trading heat, profit quality, profit scale, and profit potential. The [math]-means algorithm in Python is then used to analyse 56 stocks in China’s energy industry, and we divide their investment ratings into six grades: risk stocks, prudent holding, undetermined class, hold rating, ordinary rating, and buy rating. By identifying the group characteristics of different types of stocks, this study can provide a decision-making basis for investors while also having reference value for research institutions, financial departments, and government departments.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-09T08:00:00Z
  DOI: 10.1142/S0219649222500769
A Double Metan-Semantic Search Model Based on Ontology and Semantic
Similarity: Asthma Disease
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Mourad Belabed, Abdeslem Dennai
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Metan-search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Metan-Semantic Search Model (2[math]-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Metan indicates that the search is done in depth ([math]-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-09T08:00:00Z
  DOI: 10.1142/S0219649222500824
Digital Skill Transformation and Knowledge Management Challenge in a
Global IT Service Firm: An Empirical Study
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Bijoy Talukder
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This paper aims to discover key performance indicators (KPIs) influencing digital skill transformation in global IT service firms, reveal its unique features, and assert the effect of these KPIs on firms’ digital skill transformation and knowledge management initiatives. This research is mainly based on primary data. The researcher started data collection by conducting a Focused Group Discussion (FGD) with subject matter experts (SMEs), followed by in-depth personal interviews with the key organisational individuals. Then, a primary survey is carried out using a qualitative questionnaire across all the existing employees of the largest business unit of a global IT service firm. Findings suggest that Total IT Experience (EXP), Reading Time (RT), Effective Mentoring (EM), and Training Effectiveness (TE) primarily impact employees’ digital skill transformation. Besides, the technical capability and understanding of existing employees’ supervisors or managers directly correlate with the project environment, which in turn impact employees’ effectiveness during their digital skill transformation journey.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-09T08:00:00Z
  DOI: 10.1142/S0219649222500903
A Proposal of a New Chaotic Map for Application in the Image Encryption
Domain
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Fadi Abu-Amara, Jawad Ahmad
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Several chaos-based image encryption schemes have been proposed in the last decade. Each encryption scheme has pros and cons regarding its speed, complexity, and security. This paper proposes a new chaotic map called Power-Chaotic Map (PCM). Characteristics of the proposed PCM, such as chaotic behaviour, randomness, sensitivity, and s-unimodality, are investigated. As an application of the proposed chaotic map, an image encryption scheme is proposed to encrypt greyscale and text images. The proposed three-phase image encryption scheme performs a series of substitution and permutation operations. The Pixel-Level phase utilises the PCM’s generated keystreams to perform the substitution operation of image pixels. The Row-Level phase permutates, via a proposed pseudorandom number generator, pixel locations of each row and then shuffles row locations. Finally, the Column-Level phase performs a substitution operation on pixels of each column. Performance of the proposed PCM-based image encryption scheme is investigated through histogram analysis, statistical correlation analysis, key sensitivity, encryption performance of text images, and permutation and substitution properties. Experimental results indicate that the PCM has a wider range of chaotic behaviour than well-known one-dimensional maps, meets the s-unimodality property, has high sensitivity, and generates keystreams with random-like behaviour. Furthermore, results indicate that the PCM-based image encryption scheme provides high encryption security for text images, high key sensitivity, immunity against brute-force attacks, strong statistical correlation results, strong encryption performance, and low computational complexity.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-07T08:00:00Z
  DOI: 10.1142/S0219649222500885
An Ensemble-Based Machine Learning Model for Emotion and Mental Health
Detection
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Annapurna Jonnalagadda, Manan Rajvir, Shovan Singh, S Chandramouliswaran, Joshua George, Firuz Kamalov
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Recent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person’s emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person’s emotions. However, these solutions often rely on techniques that invade people’s privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people’s emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user’s typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user’s emotions. The proposed solution has various applications in today’s world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-03T07:00:00Z
  DOI: 10.1142/S0219649222500757
Study on Evaluation of Development of Guilin E-Government Based on
E-Government Development Index
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Libin Xiao
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  E-government has become an important direction for the development of our government. Measuring national and regional e-government development indicators can help government agencies better understand the actual situation of e-government. This paper is based on E-government Development Index, including the Online Services Index (OSI), Telecommunications Infrastructure Index (TII) and Human Capital Index (HCI). The development of e-government in Guilin is evaluated by using the methods of index change analysis and comparative analysis. The relevant data of this paper come from Guangxi Statistical Yearbook, Guilin Economic and Social Statistical Yearbook and the official website of Guilin Bureau of Statistics. From the study of Guilin e-government data, we judged that Guilin e-government is in the trend of rapid development and comprehensive and coordinated development. Through its development trend, we can judge that in the future, Guilin e-government will continue to develop, but at the same time, if we do not deal with the relationship between development speed and development content, it will also bring great trouble to Guilin e-government. Therefore, we put forward the following development suggestions: (1) Guilin e-government should strengthen technological innovation and development, and create an intelligent e-government platform. (2) Guilin e-government will make government services more targeted and build a service platform for special groups. (3) Guilin e-government should formulate e-government policies and improve institutional innovation.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-03T07:00:00Z
  DOI: 10.1142/S0219649222500770
Comprehensive Analysis of Various Big Data Classification Techniques: A
Challenging Overview
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Hemn Barzan Abdalla, Belal Abuhaija
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Data over the internet has been increasing everyday, and automatic mining of essential information from an enormous amount of data has become a challenging task today for an organisation with a huge dataset. In recent years, the prominent technology in the domain of Information Technology (IT) is big data, which is unstructured data that solves the computational complexity of classical database systems. The data is fast and big and typically derived from multiple and independent sources. The three main challenges are data accessing, semantics, and domain knowledge for various big data utilisations and complexities raised by big data volumes. One of the major limitations is the classification of big data. This paper introduces well-defined classification methodologies employed for big data classification. This paper reviews 50 research papers based on classification methods of big data, and such methodologies are primarily categorised into six different categories, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fuzzy-based method, Bayesian-based method, Random Forest, and Decision Tree. In addition, detailed analysis and discussion are carried out by considering classification techniques, dataset utilised, evaluation metrics, semantic similarity measures, and publication year. In addition, research gaps and issues for several traditional big data classification techniques are explained to expand investigators’ works to provide effective big data management.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-11-02T07:00:00Z
  DOI: 10.1142/S0219649222500836
Influencing Factors Analysis and Prediction Model Development of Stroke:
The Machine Learning Approach
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Juhua Wu, Qide Zhang, Lei Tao, Xiaoyun Lu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Prediction is an important way to analyse stroke risk management. This study explored the critical influencing factors of stroke, used the classical multilayer perception (MLP) and radial basis function (RBF) machine learning (ML) algorithms to develop the model for stroke prediction. The two models were trained with Bagging and Boosting ensemble learning algorithms. The performances of the prediction models were also compared with other classical ML algorithms. The result showed that (1) total cholesterol (TC) and other nine factors were selected as principal factors for the stroke prediction; (2) the MLP model outperformed RBF model in terms of accuracy, generalization and inter-rater reliability; (3) ensemble algorithm was superior to single algorithms for high-dimension dataset in this study. It may come to the conclusion that this study improved the stroke prediction methods and contributed much to the prevention of stroke.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-10-22T07:00:00Z
  DOI: 10.1142/S0219649222500794
Machine Intelligence for Language Translation from Kashmiri to English
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Nawaz Ali Lone, Kaiser J. Giri, Rumaan Bashir
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Machine translation (MT) is an emerging research as well as application area in the contemporary world. It is receiving significant attention from academia, industry and corporate houses. A wide range of translation techniques are being applied either individually or in combination for machine translation of different languages across the globe. However, there are still many languages that are either completely missing or poorly visible on the machine translation map. The Kashmiri language is one such language where very little or negligible work has been done related to its machine translation. This paper aims to present a Kashmiri-to-English Machine Translation System and highlight various features of the Kashmiri language. The system is based on machine intelligence having the ability to learn various translation rules from the translated set of input sentences, using Long Short-term Memory (LSTM) architecture for deep sequence learning. The paper also discusses various challenges related to machine translation from Kashmiri to English or other languages. The work presented in this paper is the first of its nature and can serve as a bedrock for research community interested to work on machine translation of Kashmiri language.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-08-27T07:00:00Z
  DOI: 10.1142/S0219649222500745
Multimodal Medical Image Fusion with Improved Multi-Objective
Meta-Heuristic Algorithm with Fuzzy Entropy
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: N. Nagaraja Kumar, T. Jayachandra Prasad, K. Satya Prasad
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Medical image fusion enhances the significant and the valuable information such as exact abnormality localisation of the multimodal medical images. In the field of clinical environment, medical imaging acts as an important role in helping the doctors/radiologists. The information available in the images is important during the diagnosis. This can be enhanced using the multimodal medical image fusion technique through the integration of the information from several imaging modalities. Nowadays, several methodologies are proposed for fusing the medical images. Yet, the multimodal medical image fusion remains as a challenging task owing to the deprivation of medical images at the phase of acquisition. To handle this problem, this paper plans to develop the enhanced multi-objective medical image fusion model. Before initiating the fusion process, both the images to be fused are split into high-frequency sub-bands and low-frequency sub-bands by the improved Fast Discrete Curvelet Transform (FDCuT). Here, the fusion of low-frequency sub-images is accomplished by the averaging method, and high-frequency sub-images are fused by the optimised Type-2 fuzzy entropy. Both the FDCuT and Type-2 fuzzy entropy are enhanced by the multi-objective meta-heuristic algorithm by Adaptive Electric fish optimisation (A-EFO). The multi-objective function focuses on the “Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM), Feature SIMilarity (FSIM)”. The comparison of the developed methodology over the traditional approaches observes enhanced performance with respect to visual quality measures.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-08-10T07:00:00Z
  DOI: 10.1142/S0219649222500630
Deep Learning-Based Extraction of Concepts: A Comparative Study and
Application on Medical Data
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Sana Ben Abdallah Ben Lamine, Mohamed Aziz Dachraoui, Hajer Baazaoui-Zghal
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  With the exponential increase of data on the web, the manual acquisition of ontology has become a time-consuming and tedious task. Thus, switching to ontology learning enables the ontologies’ acquisition automation. In this paper, we deal with the phase of concepts’ extraction. Our motivation is to capitalise on the confirmed advantages of deep learning (DL) models and word embedding techniques to automatically extract relevant concepts from large amounts of textual data. A four phases approach is proposed where different models and techniques are applied and a comparative study is achieved: the preprocessing phase, the classification phase, based on DL models, the terms filtering phase, where we experimented and compared three methods to extract the relevant terms, and the semantic enrichment phase experimenting and comparing word embedding techniques to semantically enrich the discovered concepts. The approach is implemented and evaluated on different medical datasets. The obtained results proved the suitability of the experimented models and techniques for the concepts’ extraction.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-08-03T07:00:00Z
  DOI: 10.1142/S0219649222500721
Enhancement of Selected Knowledge Management Methods in ITSM
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Michal Dostál
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  ITSM, namely one of its most essential parts, the Service Desk, is a vital communication platform between users and the service provider. Good IT service management ensures the smooth run of the company operations and provides stable support for its employees in all technical matters. Companies also must not forget the knowledge management part of the ITSM process. Knowledge must be available at the right time to the right people to ensure quality service. There are several tools, methods and techniques used in knowledge management. In this paper, we describe and propose an enhancement of selected tools and processes, namely call ticketing system and on-the-job training, as those are essential parts of the ITSM practice.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-08-03T07:00:00Z
  DOI: 10.1142/S0219649222500733
Development of Novel Incremental Question Answering System Using Optimised
Deep Belief Network
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: M. Therasa, G. Mathivanan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Question answering system is a more eminent research area because of its vast usage in recent years, which can be modelled to solve the deep learning-related limitations. More number of research works have been presented in this question answering field, where most of the systems adopt deep learning as the major contribution. Question answering system focusses on satisfying the users in getting relevant answers regarding a certain question in natural language. This paper presents the incremental question answering system using optimised deep learning. The proposed model covers two-step feature extraction, feature dimension reduction, and deep learning-based classification. From the benchmark dataset collected from a public source, the initial process is to extract the features using word-to-vector. Further, Principle Component Analysis (PCA) is adopted for reducing the dimension of the feature vector. These dimension-reduced features are used for incremental question answering systems by the Optimised Deep Neural Network (O-DNN). Here, the testing weight of the DNN is updated by the Modified Deer Hunting Optimisation Algorithm (M-DHOA) for handling the incremental data. Various implementation details in the algorithms produce better results, which shows the superior performance of the proposed method over existing systems.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-29T07:00:00Z
  DOI: 10.1142/S0219649222500629
The Impact of IT Capability on Corporate Green Technological Innovation:
Evidence from Manufacturing Companies in China
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yulian Peng, Jianqing Zhou, Miaoxin Lin, Dawei Feng
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In this paper, we theoretically discuss and empirically show how IT capability spurs companies towards greener strategies. Based on the data of listed Chinese manufacturing companies from 2008 to 2018, using a panel regression model, the results show that: (1) corporate IT capability can promote green technological innovation. (2) Compared with state-owned enterprises (SOEs), the IT effect is more significant for non-state-owned enterprises (non-SOEs); compared with regions with weak environmental regulation, the IT effect is more significant in regions with strong environmental regulation. (3) Additionally, we found that the promotion of IT software capability is stronger than IT hardware capability, and they have a synergistic effect on green technological innovation. Overall, our findings offer a new point view for a deeper understanding of green technological innovation, and provide microscopic evidence for the objective evaluation of corporate IT capability.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-29T07:00:00Z
  DOI: 10.1142/S021964922250068X
The Role of Technology in Predicting Business Analytics Adoption in SMEs
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Mislina Atan, Rosli Mahmood
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Research shows that data-driven decision making using business analytics can create competitive advantages for organisations. However, this can only happen if the organisations successfully accept and use the business analytics effectively. Many studies reported business analytics implementation in large organisations, and fewer studies focus on Small and Medium Enterprises (SMEs). Furthermore, SMEs are scoring lower scores in technology absorption. Therefore, it is essential to examine the business analytics adoption among SMEs. Previous research has reported that relative advantage and compatibility were the most highlighted factors under the technology dimension in adopting innovative technologies. However, the literature reported inconsistent findings on the significance of relative advantage and compatibility in adopting various technologies. Therefore, this research conducted a quantitative survey-based study to examine the significance of relative advantage and compatibility in predicting business analytics adoption among SMEs. The sample was selected using systematic random sampling from a Malaysian national entrepreneurs database. There were 241 SMEs that responded to the online survey sent by email. The analysis using the partial least squares structural equation modelling (PLS-SEM) informed that relative advantage was significantly related to business analytics adoption; however, compatibility did not influence the business analytics adoption by SMEs in Malaysia. This finding shows that the better the relative advantage of business analytics SMEs know, the higher the possibility of adoption. In addition, less compatibility of the SMEs in Malaysia hindered the business analytics adoption. This study contributes to the theoretical aspect, which statistically informed the finding out of inconsistent gaps in technology adoption. Furthermore, this study also contributes to the practical aspect, in which managers, owners, vendors, and policy-makers can use these findings to spur and facilitate business analytics adoption among SMEs in developing countries.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-29T07:00:00Z
  DOI: 10.1142/S0219649222500708
Design and Development of Multi-Objective Hybrid Clustering Framework for
Smart City in India Using Internet of Things
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Rakesh Roshan, Om Prakash Rishi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The drastic growth of smart city has considerably gained attention around the world in the international policies and systematic literature. Numerous specialists should include diverse opinions owing to the hurdles to the design of smart cities in India. Thus, these experts have also offered their opinions regarding public, agriculture, industry and academia-fields, which help in developing the smart cities. Generally, more limitations have to be faced with offering energy optimisation and superior performance in Internet of Things (IoT)-enabled smart cities. In wireless sensor networks (WSNs) and IoT, the sensors or IoT devices or nodes are often grouped into clusters that result in selecting the cluster head, which gathers information from the entire nodes in cluster and plainly transmits with the base station. This paper makes an attempt on the development of smart cities in India using the hybrid meta-heuristic-based multi-objective cluster head selection model. The proposed model focusses on the design and development of new smart city model applicable for India by considering a multi-objective function using the constraints like distance, delay, energy, load and temperature of the IoT devices. The optimisation of these variables during the smart city development model by IoT is accomplished by a new hybrid Deer Hunting-Tunicate Swarm Optimisation (DH-TSO) algorithm. The performance of the proposed model is verified through a comparative analysis using various state-of-the-art optimisation models by concerning the number of alive nodes, and normalised energy, and thus ensures the overall lifetime of the network.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-28T07:00:00Z
  DOI: 10.1142/S0219649222500642
Impact of Real-Time Information for Travellers: A Systematic Review
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ammar Ashraf Narul Akhla, Thong Chee Ling, Abdul Samad Shibghatullah, Chit Su Mon, Aswani Kumar Cherukuri, Chaw Lee Yen, Lee Chiw Yi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Real-time information (RTI) is defined as any up-to-date information collected which is immediately made available for the users. RTI is often used in transportation such as location tracking and navigation purposes which can affect the travel experience of travellers. The main objective of this study is to conduct a systematic review of published literature as an evidence regarding the impact of RTI on travellers. To date, there is a lack of comprehensive and structured study in reviewing the impacts of RTI for travellers. Three main research questions drive this review study: (i) what are the purpose, methodology and findings mentioned in literature that involved the impact of RTI' (ii) what elements are impacted by the availability of RTI' and (iii) does RTI help to improve transportation experience' Thirty-one relevant articles are included in the systematic review through a comprehensive literature search strategy which discards irrelevant literature. The 31 articles are significant to the study as they present the general situation of RTI which possesses impact elements for travellers. Based on the review results, three main elements were identified: traveller’s behaviour, traveller’s waiting time and traveller’s path and route choice. Most of the findings from the literature consistently revealed the positive impact of RTI to travellers. This opens to various possibilities and opportunities for the development and improvement of RTI, especially in the transportation field of the future. This study contributes both practically and theoretically for the future research in the utilisation and availability of RTI in the transportation field.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-28T07:00:00Z
  DOI: 10.1142/S0219649222500654
Quadratic Particle Swarm Optimisation Algorithm for Task Scheduling Based
on Cloud Computing Server
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Guanghui Wei
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The task scheduling is one of the core problems of cloud computing and aims to assign tasks reasonably, realise the optimal scheduling strategy and improve the operating efficiency of overall cloud computing system. For the shortcomings of traditional particle swarm optimisation (PSO) algorithm in total completion time and average completion time, a quadratic particle swarm optimisation (QPSO) algorithm is proposed. Using the proposed algorithm, people can find a scheduling result with the short total completion time of task and also ensuring the short average completion time of task. Finally, the research made a simulation experiment with Cloud Sim. Experiment results show that in the same condition setting, the algorithm proposed is superior to the traditional PSO algorithm. When the number of tasks increases, the comprehensive scheduling performance of QPSO is more than 20% higher than that of PSO.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-27T07:00:00Z
  DOI: 10.1142/S0219649222500678
The Effect of Corporate Social Responsibility on Stock Price
Volatility — Evidence from Chinese Listed Companies
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Xiaotong Song
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This paper studies the relationship between corporate social responsibility (CSR) and stock price volatility based on data of Chinese A-share listed companies from 2010 to 2018, and further analyses the path and underlying mechanism of CSR that affects stock price volatility. With China’s gradual transition to a sustainable development model, both public and the government are paying increasing attention to CSR performance. At the same time, the Chinese government takes a more serious attitude towards systemic financial risks, emphasising the importance of controlling systemic risks such as an abnormal stock price on many public occasions. In this context, CSR and stock price volatility have received unprecedented attention, and it’s of great value for both industry and the government to explore the impact of CSR on stock price volatility.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-27T07:00:00Z
  DOI: 10.1142/S0219649222500691
A Hybrid Detection System for DDoS Attacks Based on Deep Sparse
Autoencoder and Light Gradient Boost Machine
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Raj Kumar Batchu, Hari Seetha
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-27T07:00:00Z
  DOI: 10.1142/S021964922250071X
An Investigation on the Impact of Technological Enablement on the Success
of Entrepreneurial Adoption Among Higher Education Students: A Comparative
Study
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Kay Hooi Keoy, Chee Ling Thong, Aswani Kumar Cherukuri, Yung Jing Koh, Su Mon Chit, Luqman Lee, Japos Genaro, Choon Ling Kwek
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Technology greatly supports people’s daily lives such as education, business, medical, and many other aspects. It can be noted that the higher education institutions’ students rely on technological support and university assistance for their studies during the COVID-19 pandemic. Technological enablement is the primary determinant for entrepreneurial initiation that received attention from scholars. The focus areas include how governmental support, entrepreneurial intention, entrepreneurial education and technological enablement (mediator factor) can influence the entrepreneurial initiation. Empirical studies showed the direct and indirect impacts of the contributing factors in a particular area. However, is it the same effect of the factors for different countries' This study conducted a self-administered questionnaire to collect topic-related information from higher education institutions in Malaysia and the Philippines. A formative-reflective model, PLS-MGA, was used to analyse the direct and indirect impacts alongside the mediating factor, technological enablement. The results showed that entrepreneurial competencies, Entrepreneurial Education System, Entrepreneurial Education Mechanism, and Entrepreneurial Intention positively and significantly impact entrepreneurial success in both regions. However, the result also demonstrated that the impact of technological enablement on entrepreneurial success is more significant in Malaysia than in the Philippines. With such findings, policymakers and institutions in both countries can understand the insight and importance of technological enablement in stimulating entrepreneurship and its perceived success. Hence, they can implement supportive strategies and necessary policies to ensure technology adoption, success in shaping students’ entrepreneurial mindset and achieving the perceived outcome.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-20T07:00:00Z
  DOI: 10.1142/S0219649222500605
Visual Saliency Modeling with Deep Learning: A Comprehensive Review
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Shilpa Elsa Abraham, Binsu C. Kovoor
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Visual saliency models mimic the human visual system to gaze towards fixed pixel positions and capture the most conspicuous regions in the scene. They have proved their efficacy in several computer vision applications. This paper provides a comprehensive review of the recent advances in eye fixation prediction and salient object detection, harnessing deep learning. It also provides an overview on multi-modal saliency prediction that considers audio in dynamic scenes. The underlying network structure and loss function for each model are explored to realise how saliency models work. The survey also investigates the inclusion of specific low-level priors in deep learning-based saliency models. The public datasets and evaluation metrics are succinctly introduced. The paper also makes a discussion on the key issues in saliency modeling along with some open problems and growing research directions in the field.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-07-20T07:00:00Z
  DOI: 10.1142/S0219649222500666
Knowledge Retention for Enhanced Organisational Growth in Higher Education
Institutions
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Rexwhite Tega Enakrire, Hanlie Smuts
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Knowledge retention (KR) is when ideas developed over time in the human brain are retained, for enhanced efficiency and effectiveness of job performance. KR is fundamental in every organisation. KR implies the ways through which the organisations grow, thus resulting in having a competitive advantage other their competitors. Therefore, retaining the individuals that carry diverse expertise in the organisation is important, because it helps to transform the knowledge economy. However, the issues of improper organisation of tasks, loss of experienced employees, the influx of young employees, thus resulting to transfer problem from one department/unit to another, low productivity causing a delay in operational excellence and achievement of timeous job specification, non-viability of the organisation, has made many staff members resign from their present organisation to join other institutions or organisations due to lack of KR. This study investigates KR for enhanced organisational growth in higher education institutions (HEIs). The qualitative research approach made use of the interpretive content analysis. The qualitative survey design made use of an unstructured monkey survey questionnaire in collecting data from respondents across different HEIs in Africa. The purposive and convenient sampling technique selected HEIs across Africa. The rationale behind selecting HEIs across Africa was due to the nature of activities that surrounds KR in transformative organisational growth and the ability to have a quick respondent’s response under the study being investigated. Results indicate that the understanding of KR was not uniform among respondents due to different contexts, fields of expertise, and the nature of work performed. Findings further indicate that KR has helped respondents to create new knowledge, strive to perform tasks in workplace learning, fostered and equipped individuals in their career pursuit, self-development, and deepen research drive. Different mechanism of memorising and keeping short notes, attending different courses, and helping others to solve their problem gives someone the experiences to always remember, and the tools of desktop computers, laptop, tablets, CD-ROM, emails, social media, flash drive, and YouTube are prevalent in support of KR among individuals. Diverse sets of print to electronic sources of information were used to support KR among respondents. Factors such as virus, lack of structures, no specific projects, lack of affirming organisational policy, environmental factors, electricity power supply, and lack of good reading facilities affected the individuals/staff members in their attempt to retain knowledge across sample HEIs. The study recommends attractive income, suitable provision of structure, favourable working environment, self-development opportunities, non-discriminatory treatment to staff, and opened organisational culture, which will enforce staff members to stay and be willing to retain their knowledge/potentials for the organisational growth in HEIs.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-23T07:00:00Z
  DOI: 10.1142/S021964922250054X