Journal of Information & Knowledge Management

Whilst the world is witnessing the impact of technology proliferation on human lives and livelihoods, the personal safety of women, though paramount, is still technologically under-addressed. This study empirically investigated the perception of Indian women ($N=$210) towards personal safety apps and their intention to accept them to ensure personal safety. This study uniquely blended the Fogg behaviour model, which comprises motives, abilities and triggers, with the Technology Acceptance Model (TAM), which comprises perceived usefulness, ease of use and behavioural intentions. Structural equation modelling using SmartPLS 4 was used to analyse the model. Some exciting outcomes emerged from this study. The motives namely subjective norms, facilitating conditions and perceived trust, significantly impacted women’s perceived usefulness of personal safety apps, while the perceived risk was insignificant. The significant impactors of women’s perceived ease-of-use of personal safety apps include the abilities of self-efficacy and technology stress, but exclude perceived behavioural control. With regard to the trigger, only response efficacy impacted women’s behavioural intentions to use personal safety apps, while the magnitude of noxiousness and exposure expectancy did not. Women’s perception of the usefulness and ease of use of personal safety apps significantly impacted their behavioural intentions, ultimately impacting their perception of personal safety. Further, this study presented implications to theory and practice before concluding by stating research limitations and future directions.

Abstract.This study aimed to assess the connection between HEXACO personality traits (i.e. honesty–humility, emotionality, extraversion, agreeableness, conscientiousness, and openness to experience) and knowledge sharing behaviour (KSB) of university academic staff. Moreover, based on the theory of planned behaviour (TPB), this study aimed to examine the underlying process through which HEXACO personality traits contribute to academics’ KSB by examining mediating role of affective commitment. With a structured questionnaire survey, the data was conveniently collected from 234 full-time university faculty members employed in 20 private universities located in Bangladesh. Structural equation modelling partial least square (SEM-PLS) was utilised to analyse the research model. The results revealed that, with the exception of emotionality, all HEXACO personality traits (i.e. honesty– humility, extraversion, agreeableness, conscientiousness, and openness to experience) have positive direct effects on academics’ KSB. The study further showed that the effects of honesty–humility, extraversion, agreeableness and conscientiousness on KSB were significantly mediated by affective commitment. Thus, universities should focus on academics’ personality profiles to improve knowledge sharing practices within the institutions. In particular, personality test should be introduced in the selection process of university teachers to identify the candidates with desirable personality traits. The findings offer a new understanding of how HEXACO personality traits result in academics’ KSB directly as well as via affective commitment.

Knowledge sharing can be considered an important activity to improve the performance among various entities of a supply chain. The purpose of this study is to identify and evaluate a set of critical knowledge-sharing enablers that might aid in successfully managing a circular supply chain (CSC) in the context of the Indian manufacturing sector. The knowledge-sharing enablers were determined through a review of the extant literature, coupled with discussion with subject matter experts (SMEs). The quantitative technique of interpretive structural modelling (ISM) was used to analyse the identified knowledge-sharing enablers. The findings of this study revealed that the knowledge-sharing capabilities of an organisation, organisation structure and support from the top management formed the most significant enablers for Indian manufacturing organisations. This study has significant managerial and academic contributions. While supply chain managers can use the findings of this study to gain a better understanding of the role of knowledge sharing in managing CSC in the Indian manufacturing context, policymakers can use these findings to formulate strategies for effectively managing the CSC, as well as improving its operational effectiveness. The findings can also aid academic researchers to further analyse the role that knowledge sharing might play in successfully managing CSC, including other industries (for example, service industries), as well as other geographical regions.

This paper highlights the significance of centralising innovation within organisations to gain a competitive edge in a rapidly changing world. It emphasises the pivotal role of ISO 56002, a standardised innovation management system, in fostering innovation capacity through diffusion, certification and development. By establishing innovation standards, organisations can consistently generate successful innovations, leveraging their resources and knowledge capital. However, technology transfer faces trust and inter-organisational cooperation barriers. ISO 56002 provides comprehensive guidelines to overcome these challenges and bridge standardisation and innovation perspectives. The proposed framework integrates technology transfer with the ISO 56002 system, consisting of five phases: engagement in innovation processes, certification of technology transferors, overcoming barriers, technology transfer agreements and analysing effectiveness. ISO 56002 facilitates collaboration, resource allocation, strategic planning and adopts a customer-centric approach. This framework fills a literature gap, offering organisations a competitive advantage in technology transfer. The study explores the relationship between ISO 56002 guidelines and technology transfer, emphasising collaboration, leadership, planning, support, operation, performance evaluation and improvement. ISO 56002 aligns with various aspects of technology transfer, emphasising sustainability, connectivity and competitiveness. Implementing ISO 56002 drives organisational growth and successful technology transfer.

In this paper, we theoretically discuss and empirically investigate the impact of digital transformation on the ambidextrous innovation of enterprises. We also examine the transmission mechanism of knowledge flows between digital transformation and ambidextrous innovation in enterprises. Based on the data of Shanghai and Shenzhen-listed companies from 2012 to 2020, using a two-way fixed-effect panel regression model, the results show that: (1) Digital transformation has a significant positive effect on both enterprise ambidextrous innovation, and its effect on exploratory innovation is greater than that on exploitative innovation. (2) Digital transformation promotes exploratory and exploitative innovation by accelerating knowledge flow. (3) The impact of digital transformation on exploratory innovation in enterprises is heterogeneous, while the impact on exploitative innovation is not heterogeneous. That is, the positive effect of digital transformation on exploratory innovation is significant in enterprises in high-tech industries, but not significant in enterprises in nonhigh-tech industries. Overall, our findings provide a deeper understanding for enterprises to grasp digital opportunities and carry out digital transformation in a timely and appropriate manner, as well as an essential policy implication for the government to strengthen the construction of digital infrastructure and improve the policies related to digital transformation.

One of the most dangerous threats to computer networks is the use of botnets, which can seriously harm systems and steal private data. They are remote-controlled networks of compromised computers that an individual or group of individuals is using for malicious purposes. These infected computers are frequently called “bots” or “zombies”. A wide variety of malicious activities, including the distribution of malware and credential theft, can be carried out using botnets. The CTU-13 dataset is a collection of network traffic information that includes examples of various botnet types. Using this, our study compares the abilities of decision trees, random forests, 1D convolutional neural networks, and a proposed system based on long short-term memory and residual neural networks to detect botnets. According to our findings, the suggested system performs better than every other algorithm, achieving a higher accuracy rate. Our suggested system has the ability to precisely identify botnet traffic patterns, which can assist organisations in proactively preventing botnet attacks.

This research aims to analyse the factors that contribute to strategic management knowledge acquisition in open organizations using importance-performance analysis (IPA). To conduct this research, an educational research institution with open system characteristics was selected as the case study. A qualitative questionnaire was developed to gather data on strategic management knowledge acquisition factors. Based on the analysis of the data collected, 48 main factors of strategic management knowledge acquisition in open organizations were identified. These factors were categorised into four strategic ranges: “focus,” “continue,” “low priority” and “waste”. The findings provide valuable insights into the factors that contribute to strategic management knowledge acquisition in open organizations. By understanding the importance and performance of these factors, organizations can prioritise their efforts and allocate resources effectively to improve their knowledge acquisition processes. Additionally, 20 executive proposals were presented as recommendations for open organizations seeking to enhance their knowledge acquisition practices.

Due to the rapid growth of network technology, huge volume and distinct data sent via networks is expanding constantly. The situation shows how complex and dense cyber attacks and hazards are developing. Due to the rapid advancement in network density, cyber security specialists find it difficult to monitor all network activity. Due to frequent and sophisticated cyber attacks, it is becoming more challenging to detect and identify abnormalities in network events. The use of deep learning provides a variety of tools and strategies for automated cyber-attack detection as well as quick attack-type prediction as well as evaluation. This work introduces a novel cyber-attack detection and mitigation process under the following phases including preprocessing, feature extraction via the Map Reduce framework that handles the big data, feature selection, attack detection and mitigation. The Improved Normalisation process is achieved on the preprocessing phase. The work is examined from a big data perspective; hence Map Reduce framework is utilised for this. As a result, the framework will manage the feature extraction process, where features including statistical features, raw features, improved correlation-based features, and info gain-based features will be extracted. Following feature extraction, the Improved Recursive Feature Elimination procedure is processed that selects the relevant features. The hybrid detection model, which combines Recurrent Neural Networks (RNN) Deep and Belief Networks (DBN) is used to detect the attacks. Once an attack has been detected, the attacker must be mitigated. To accomplish this, an improved BAIT-based mitigation procedure is used. The two datasets used in this work are, namely, Intrusion Detection Systems (IDS) 2018 Intrusion CSVs (CSE-CIC-IDS2018) and UNSW_NB15. Finally, the suggested model and the alternative methods are contrasted using a variety of measures such as accuracy, sensitivity, specificity, precision, FDR, FNR and FPR.

Among text-oriented applications, Natural Language Processing (NLP) plays a significant role in managing and identifying a particular text data. NLP is broadly used in text mining domains as well. In general, text mining is the process of combining diverse techniques to characterise and transform the text. Hence, the syntactic information and semantic information are utilised together in the NLP model to assist the process of analysing, or extracting the text. On the other hand, the text mining model is examined by different standard measurements, which also vary concerning text objectives or applications. Due to the advent of machine and deep learning models, text mining has become the hot research area used in various domains like classification, recognition, sentiment analysis, and speech-related topics. Though the models are not time-consuming and effective, certain factors are considered for further enhancement of these models. Thus, this survey paper elucidates the evaluation metrics used in text mining approaches by deploying standard algorithms. It explores the literature work on the formerly implemented text mining approaches for analysing the evaluation metrics in text mining. In addition to this, the proposed model to perform text mining in every survey paper is analysed. Further, it provides the algorithmic categorisation of existing research works, discussion on different datasets used with the consideration of various evaluation metrics, and finally categorises the metrics used for analysing the performance of such text mining approaches. This survey work also illustrates the merits and demerits of the existing text-mining approaches. Finally, the research gaps and challenging issues are given to direct future work.

This study aimed to review the effective factors, tools and methods of knowledge sharing used by nurses during the COVID-19 pandemic. This mix-methods study was conducted in 2023 and included two stages. In the first stage, a systematic review of relevant literature was conducted. In the second stage, a panel of 10 experts justified and appended the findings of the previous stage. The main source of knowledge during the COVID-19 epidemic was social networks followed by official authorities, online training courses, media and colleagues. Different factors affecting knowledge sharing among nurses categorised into process-oriented factors, Human-oriented factors, Technology-oriented factors and organisation-oriented factors. Various known and innovative methods have been used by nurses to share knowledge during the COVID-19 pandemic including utilising social networks, Telementoring, team approaches for new staff and continuous training in line with the latest scientific findings. Nurses used both formal and informal sources to explore evidence supporting evolving clinical practices and social networks were a prominent source of knowledge during the pandemic. By recognising the significance of affecting factors, healthcare organisations can enhance nurses’ ability to effectively share knowledge, ultimately improving patient outcomes and overall healthcare response during similar crises in the future.

Abstract.Cloud computing is quickly expanding, with applications in practically every industry, including education. E-learning systems often necessitate a large number of hardware and software resources. Many educational institutions cannot afford such investments, thus cloud computing is the best solution. Here, the Matrix Factorisation-based maximum rate recommendation system (MatFac-Maxirate RS) is utilised to recommend the courses for students to choose their career. According to user access, the e-learning application server is acquired from the E-Khool dataset which is subjected to learner or course agglomerative matrix calculation. The E-learning application server is executed based on Minkowski and Kumar Hasebrook’s distance to retrieve learner preference items. The recommended course having the maximum rating is considered which is forecasted with matrix factorisation considering the course ID and learner ID. The MatFac-Maxirate RS generated the finest efficacy with the best precision of 88.9%, recall of 88.2% and F-measure of 87.5%.

Purpose: This study aims to enhance the efficiency and effectiveness of Chinese Clinical Named Entity Recognition by improving the Bert-BiLSTM-CRF model through the adoption of the RoBERTa pre-training model. Design/methodology/approach: A deep learning approach is employed, combining the RoBERTa pre-training model, Bi-directional Long Short-Term Memory (BiLSTM) network, and Conditional Random Field (CRF) model to form a Named Entity Recognition (NER) model. The model takes the pre-training model trained by the deep network model as input, mitigates the scarcity of annotated datasets, leverages the strong advantage of BiLSTM in learning the context information of words, and combines the CRF model to infer the ability of labels through global information. Findings: The RoBERTa-BiLSTM-CRF model has shown satisfactory results in the experiment. It enhances the reasoning ability between characters, allows the model to fully learn the feature information of the text, and improves the model performance to a certain extent. Originality/value: This paper proposes a RoBERTa medical named entity recognition model for the scarcity of annotated data in medical named entity recognition tasks and BERT’s inability to obtain word-level information. The model is not limited to medical entity recognition tasks and shows potential for other medical natural language processing tasks, considering data enhancement, data optimization, and domain transfer on the model to improve model performance and generalization capabilities.

Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.

As blockchain technology and smart contracts develop, computer technology is constantly integrating with smart chemical plants. Due to the continuous development of intelligent chemical plants, their systems have gradually become large and dispersed, posing a threat to safety management. In order to improve the performance of intelligent security management systems, the study first explores the principles of blockchain and smart contract technology, and then combined with the requirements of intelligent chemical plant security management systems, designs an intelligent security management system based on blockchain and smart contract technology. The experimental results showed that compared to systems without smart contract support, the communication success rate between nodes was lower. The error rates of blockchain-based encryption systems, deep learning-based encryption systems and improved data encryption systems proposed in the study were 0.22, 0.07 and 0.09, respectively. The packet loss rates were 0.13, 0.04 and 0.05, respectively. The lower the bit error rate and packet loss rate of the encryption system, the clearer the illegal eavesdropping information. The experimental results indicate that the intelligent security management system designed in this study has good encryption performance and a higher communication success rate. The results have certain reference value in security management application in intelligent chemical plants.

Advancements in tracking technologies like GPS, RFID and mobile devices have made trajectory data collection widespread. This surge in tracking device usage and location-based services popularity has greatly increased moving object trajectory data availability. The ontological modelling of this kind of data is of paramount importance in understanding and utilising such data effectively. By incorporating maximum semantic data into this model, a variety of essential elements related to mobile object trajectories can be captured. An ontology model rich in semantics not only accurately represents trajectory characteristics but also links them to other relevant elements such as spatial and temporal contexts, movement types and mobile object behaviours. This semantic richness grants the model great adaptability, allowing it to be reused in various contexts related to object mobility and making it generic. Moreover, by integrating this semantic data, the process of analysis and decision-making experiences significant improvement, as it relies on more comprehensive and well-structured information, thereby facilitating informed conclusions and effective strategy implementation. Our objective is to propose a generic ontological model for trajectory data that is rich in semantics and considers the various aspects of moving objects, their movements, their trajectories and their interactions with their environment, aiming to fill the gap identified in other models proposed in the literature.

Literature abounds with various statistical and machine learning techniques for stock market forecasting. However, Reinforcement Learning (RL) is conspicuous by its absence in this field and is little explored despite its potential to address the dynamic and uncertain nature of the stock market. In a first-of-its-kind study, this research precisely bridges this gap, by forecasting stock prices using RL, in the static as well as streaming contexts using deep RL techniques. In the static context, we employed three deep RL algorithms for forecasting the stock prices: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO) and Recurrent Deterministic Policy Gradient (RDPG) and compared their performance with Multi-Layer Perceptron (MLP), Support Vector Regression (SVR) and General Regression Neural Network (GRNN). In addition, we proposed a generic streaming analytics-based forecasting approach leveraging the real-time processing capabilities of Spark streaming for all six methods. This approach employs a sliding window technique for real-time forecasting or nowcasting using the above-mentioned algorithms. We demonstrated the effectiveness of the proposed approach on the daily closing prices of four different financial time series dataset as well as the Mackey–Glass time series, a benchmark chaotic time series dataset. We evaluated the performance of these methods using three metrics: Symmetric Mean Absolute Percentage (SMAPE), Directional Symmetry statistic (DS) and Theil’s U Coefficient. The results are promising for DDPG in the static context and GRNN turned out to be the best in streaming context. We performed the Diebold–Mariano (DM) test to assess the statistical significance of the best-performing models.