Journal of Information & Knowledge Management

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.

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.

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.

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.

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 ($\mathrm{\text{average}}=3.65$) than the female participants ($\mathrm{\text{average}}=3.59$); participants aged 50 years and above recorded the highest knowledge-sharing behaviour ($\mathrm{\text{average}}=3.90$); participants with non-managerial positions appeared to be more involved in knowledge-sharing behaviour ($\mathrm{\text{average}}=3.63$) than those with managerial positions; and finally, the participants from Indian university reported more involvement in knowledge-sharing behaviour ($\mathrm{\text{average}}=3.81$) than their UAE counterparts ($\mathrm{\text{average}}=3.38$). The findings of the study revealed a significant relationship between attitude towards knowledge sharing, subjective norm and knowledge-sharing behaviour ($p<0.05$). 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.

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.

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.

The development of computer technology promotes the emergence of a large amount of data. How to mine the required information from the massive data has become a problem that needs to be considered by enterprise human resources. In view of the data-based requirements of enterprise human resource management in the era of data, this paper studies the use of decision tree technology for data mining of enterprise employees, and on this basis, uses the improved AdaBoost-c4.5 algorithm to carry out experiments on personnel recruitment in human resource departments. The experimental results show that the performance of the algorithm is relatively stable in five cycles, and reaches 83.27% in the fifth cycle. On this basis, the AdaBoost-c4.5 algorithm is compared with ID3 algorithm and C4.5 algorithm. The results show that the performance of AdaBoost-c4.5 algorithm is improved compared with the two algorithms, and in the specific application of enterprise employee recruitment, it can effectively improve the processing of incomplete data in the case of incomplete data, but when the number of iterations reaches more than 10 times, its accuracy will not be improved. This shows that the performance of this algorithm has reached a critical value. If the number of iterations can be reduced, better results can be obtained. At the same time, through comparison with SOC curve, it is found that AdaBoost-c4.5 algorithm has lower cost and is more operable, which has practical significance in actual personnel recruitment.

Spoken language identification is the process of recognising language in an audio segment and is the precursor for several technologies such as automatic call routing, language recognition, multilingual conversation, language parsing, and sentimental analysis. Language identification has become a challenging task for low-resource languages like Kashmiri and Ladakhi spoken in the UT’s of Jammu and Kashmir (JK) and Ladakh, India. This is mainly due to speaker variations like duration, moderator, and ambiance particularly when training and testing are done on different datasets whilst analysing the accuracy of language identification system in actual implementation, thus producing low accuracy results. In order to tackle this problem, we propose a hybrid convolutional bi-directional gated recurrent unit (Bi-GRU) utilising the effects of both static and dynamic behaviour of the audio signal in order to achieve better results as compared to state-of-the-art models. The audio signals are first converted into two-dimensional structures called Mel-spectrograms to represent the frequency distribution over time. To investigate the spectral behaviour of audio signals, we employ a convolutional neural network (CNN) that perceives Mel-spectrograms in multiple dimensions. The CNN-learned feature vector serves as input to the Bi-GRU that maintains the dynamic behaviour of the audio signal. Experiments are done on six spoken languages, i.e. Ladakhi, Kashmiri, Hindi, Urdu, English, and Dogri. The data corpora used for experimentation are the International Institute of Information Technology Hyderabad-Indian Language Speech Corpus (IIITH-ILSC) and the self-created data corpus for the Ladakhi language. The model is tested on two datasets, i.e. speaker-dependent and speaker-independent. Results show that when validating the efficiency of our proposed model on both speaker-dependent and speaker-independent datasets, we achieve optimal accuracies of 99% and 91%, respectively, thus achieving promising results in comparison to the state-of-the-art models available.

Economic and social development has made financial engineering an increasingly important research area, and more and more financial problems cannot be solved directly by analytical formulas. In view of this, algorithms that apply computer technology to financial engineering have emerged. In this study, the Backward Stochastic Differential Equation (BSDE) algorithm is used to investigate and analyse the problem of option pricing calculation in finance. In the research process, GBSDE-Theta parallel algorithm composed of BSDE-Theta algorithm and GPU algorithm uses the new algorithm to establish a computing model in the financial engineering field, which applies to the calculation of enterprise option pricing. The research results show that compared with the basic algorithm, the actual option values of the option pricing data obtained by using the GBSDE-Theta parallel algorithm are more closely matched. The computational model can achieve a speedup ratio of about 230 times of the serial version with the number of time steps $N=128$ and the number of simulated paths 80,000. About the relative error of the GBSDE-Theta algorithm, there are 80 points within 3% and only 16 points over 3.00%, which is a relatively small error. The above results show that the financial computing system obtained in this study is highly feasible and effective, and can provide a new research idea for the progress and development of other computations in the financial field.

During the last 30 years, a proliferation of Data Management Maturity Models has been observed. Most of this proliferation was driven by consulting companies that used existing models and tried to differentiate their offerings by renaming the concepts. This has created confusion that led organisations to have difficulty selecting a Maturity Model and applying it. This paper proposes a simpler and more Integrative Framework to help the organisation assess and sustainably enhance its data management. To do so, in-depth academic papers and professional documents have been gathered following a structured approach. The outcome of the paper is an Integrative Framework and a Data Maturity Evaluation and Enhancement Process that tries to simplify Data Management Maturity Models.

Knowledge management is an important topic for different types of organisations, since one of the results of applying knowledge can be an increase in work performance. However, the process of knowledge creation and acquisition is complex and depends on several factors to make an impact on the performance of activities. This study aims to verify the relationships between the perception of knowledge value, acquisition and the impact on work performance in public organisations. The relationship between knowledge creation, organisational learning and work performance was also observed. An empirical study was carried out through a survey, using an electronic questionnaire as a data collection instrument, which obtained a sample of 49.3% of 136 Master’s graduates from a Professional Master’s Program focussed on public organisations. These graduates had gained knowledge that can be applied to work, addressing the use of the knowledge obtained in the Program and the relationship with their work performance. After analysing the research results using the PLS–SEM (Partial Least Squares–Structural Equation Modelling) technique, the proposed model was assessed and validated. The results support that when knowledge is understood as having more value, there is greater potential for acquiring this knowledge. Acquisition positively impacts knowledge creation, and this, in turn, has a positive relationship with organisational learning. Both knowledge creation and organisational learning positively impact work performance. Additionally, it was observed, through a multigroup analysis, that the relationships between knowledge creation and organisational learning, and between perceived value of knowledge and knowledge acquisition are greater for employees who had worked for less time in the institution and who were younger, identifying greater knowledge application, perception of knowledge creation impact and organisational learning by people who have worked for less than 10 years in the same institution.

Competitive advantage and sustainability of business organizations can only be achieved when an organization identifies, boosts, and directs its resources toward building its capabilities. The literature identified knowledge management (KM) resources and capabilities as the most critical enablers of organizational performance and innovations. This systematic review identified KM resources (enablers) and their effects on KM capabilities. The review also went into the theories used in the empirical research on the problem. To achieve those goals, the authors summarized 27 quantitative, peer-reviewed studies found in well-known databases and published in the last 5 years between January 2014 and April 2019. The review revealed that the primary organizational KM resources are culture, people, leadership, organization structure, resources, capabilities, strategy and technology. To these, the review added some factors, which are social factors, organizational knowledge and organizational characteristics. The study is significant in finding the most common variables or factors in business KM and their implication of them for enhancing knowledge capability and organizational performance.

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.

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.

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.

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, $p<0.001$), with a moderating impact of role and behaviour on knowledge-sharing practices and team performance (0.178, $p<0.05$). The findings have confirmed the significant and positive effects of knowledge-sharing practices on the mediation of employee development (0.045, $p<0.05$). The activity theory models for knowledge-sharing practices emphasise the contextual nature of knowledge sharing and ensure systematic evaluation.

Mobile edge computing (MEC)-oriented solutions are essential for various 5G wireless communication systems. It is considered a key technology for future communication systems because of its capability for fulfilling a broad variety of necessities of the developing wireless terminals in the form of Intelligent Vehicles, augmented reality and virtual reality devices like huge computation, low latency and high data rate. Further, the resource package has made the research on mobile data offloading. One probable novel spectrum in the subsequent generation networks is the millimetre wave (mmWave) communication systems. It attains important attention because of its high rate. This research work, this area focuses on the delay of minimisation strategy in mmWave MEC by jointly optimising the hybrid beam-forming and also resource allocation. Here, the utilisation of a well-performing Red Deer Algorithm (RDA) is the ultimate aim of the suggested model that intends to optimise the analogue beam-forming vectors at the users, the analogue and digital beam-forming matrices at the Base Station (BS), the computation task offloading ratios and resource allocation at the MEC server. Here, the minimisation of the delay or latency is attained. The comparative analysis of the proposed model over the other models demonstrates the superiority of the proposed algorithm in assisting the mmWave MEC system.

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.