Journal of Information & Knowledge Management

This study uses bibliometric analysis to integrate, synthesise, and expand the knowledge regarding the relationship between automation and the labour market. In this paper, the authors examined the Web of Science (WoS) core collection database for articles published between 2002 and 2022. The co-citation, co-occurrence, and publication patterns were analysed using VOSviewer 1.6.19. The study comprised 287 papers, with the United States having the highest percentage of research publications, followed by Germany, China, and the United Kingdom. The institutional study shows that the Massachusetts Institute of Technology, Boston University, National Bureau of Economic Research, Harvard University, and the University of London are all leading institutions in this field of study and have more than 100 links. The co-occurrence of keywords revealed “automation”, “employment”, “growth”, and “jobs” as the most discussed terms. The paper concludes by identifying gaps in the literature and proposing possibilities for future studies.

Salespersons are a source of invaluable market knowledge that can help change the course of organisational strategy. However, research on extracting this knowledge is scarce. Therefore, this research aims to develop and empirically test a model that explains the tacit knowledge exchange process between salespersons and marketing. Data were collected from 224 randomly selected business-to-business and business-to-consumer salespersons (boundary spanners) of commercial banks in Pakistan. Structural equation modelling (SEM) via maximum likelihood estimation (MLE) was conducted using AMOS 24. Except for inter-functional communication quality and inter-functional conflict, all antecedents significantly influence tacit knowledge exchange. Tacit knowledge exchange significantly affects relative efficiency, marketing program innovation and relative effectiveness. Training programs that bring together the sales and marketing employees to increase trust, increase socialisation opportunities and increase task conflict may strengthen the tacit knowledge exchange process in the banking sector.

The current employment environment is becoming increasingly complex, with many job seekers competing with each other in more concentrated and narrower fields, worsening the job market as well as inhibiting the career potential of job seekers. There is a need to provide better employment guidance and employment quality assessment for slash youth. This study attempts to design a job recommendation model for slash youths by combining an improved collaborative filtering algorithm and a dynamic bilateral matching algorithm (BMA). The test results show that the precision rate of the BMA is always the largest with the increase of the number of clusters, with the highest value reaching 90.04%; the average ranking inverse curve of bilateral matching has the fastest growth rate, with the maximum value of 62.04%, which is 34.26% and 10.06% higher than the other two maximum values, and the optimal number of clusters is set to 24. The highest precision rate of the algorithm is 82.17% when the number of recommendations is 10. The algorithm also performed better in terms of recommendation diversity, with a maximum value of around 0.28. The recommendation success rate and satisfaction value reached 87.72% and 47.86%, respectively. The recommendation precision of the model designed in this study is high. It is conducive to solving problems such as difficulty in recruiting and finding jobs, and promotes the healthy development of the recruitment market.

Analysing wound tissue is a crucial research field for assessing the progression of wound healing. Wounds exhibit certain attributes concerning colour and texture, although these features can vary among different wound images. Research in this field serves multiple purposes, including confirming the presence of chronic wounds, identifying infected wounds, determining the origin of the wound and addressing other factors that classify and characterise various types of wounds. Wounds pose a substantial health concern. Currently, clinicians and nurses mainly evaluate the healing status of wounds based on visual examination. This paper presents an outline of digital image processing and traditional machine learning methods for the tissue analysis of chronic wound images. Here, we propose a novel wound tissue analysis system that consists of wound image pre-processing, wound area segmentation and wound analysis by tissue segmentation. The wound area is extracted using a simple U-Net segmentation model. Granulation, slough and necrotic tissues are the three primary forms of wound tissues. The $k$-means clustering technique is employed to assign labels to tissues. Within the wound boundary, the tissue classification is performed by applying the Random Forest classification algorithm. Both segmentation (U-Net) and classification (Random Forest) models are trained, and the segmentation gives 99% accuracy, and the classification model gives 99.21% accuracy.

The imperative need of this hour is to measure the influence of high-performance work system (HPWS) research and comprehend its patterns as HPWS became more prevalent in service- oriented businesses. Underscoring this, authors aim to shed light on the publication trends and set the future research schema in the arena of HPWS with special reference to the service sector context. The study adopts a bibliometric approach as authors intend to identify and analyse the research breadth of HPWS research in the service context across the world, so that a statistical and analytical comprehensive overview of the research and suggestions for future directions can be provided. A portfolio of 262 articles was extracted from the Scopus database and various bibliometric techniques were used to analyse the collected research articles using “R” programming with biblioshiny web interface. The bibliometric results revealed the dynamics of research trends in HPWS service context studies, the most influential publications, authors, sources, the most productive countries, and affiliations. The citation analysis revealed the most cited scientific publications, and the countries from which most citations were received. Likewise, the thematic analysis revealed the underlying themes and patterns of HPWS service context studies that emerged with time. In such ways, this study contributes to the literature by depicting the intellectual landscape of HPWS research in the service context, that will be useful to the researchers, academicians, practitioners, policy makers and funding agencies.

Science and technology are highly inheritable undertakings, and any scientific and technological worker can make good progress without the experience and achievements of predecessors or others. In the face of an ever-expanding pool of literature, the ability to efficiently and accurately search for similar works is a major challenge in current research. This paper uses Latent Dirichlet Allocation (LDA) topic model to construct feature vectors for the title and abstract, and the bag-of-words model to construct feature vectors for publication type. The similarity between the feature vectors is measured by calculating the cosine values. The experiment demonstrated that the precision, recall and WSS95 scores of the algorithm proposed in the study were 90.55%, 98.74% and 52.45% under the literature title element, and 91.78%, 99.58% and 62.47% under the literature abstract element, respectively. Under the literature publication type element, the precision, recall and WSS95 scores of the proposed algorithm were 90.77%, 98.05% and 40.14%, respectively. Under the combination of literature title, abstract and publication type elements, the WSS95 score of the proposed algorithm was 79.03%. In summary, the study proposes a robust performance of the literature screening (LS) algorithm based on the LDA topic model, and a similar LS system designed on this basis can effectively improve the efficiency of LS.

Fake news is a growing problem in modern society. With the rise of social media and ever- increasing internet accessibility, news spreads like wildfire to millions of users in a very short time. The spread of fake news can have disastrous consequences, from decreased trust in news outlets to overturned elections. Such concerns call for automated tools to detect fake news articles. This study proposes a predictive model that can check the authenticity of a news article. The model is constructed using two different techniques to construct our model: (1) linguistic features and (2) feature extraction. We employed some widely used traditional (e.g. K-nearest neighbour (KNN) and support vector machine (SVM)) as well as state-of-the-art (e.g. bidirectional encoder representations from transformers (BERT) and extreme machine learning (ELM)) machine learning algorithms using feature extraction methods and linguistic features. After generating the models, performance metrics (e.g. accuracy and precision) are used to compare their performance. The model generated via logistic regression using feature hashing vectorisation emerged as the best model, with 99% accuracy. To the best of our knowledge, no extant studies have compared the traditional and contemporary methods in this context and demonstrated the traditional ones to be better performers. The fake news detection model can help curb the spread of fake news by acting as a tool for news organisations to check the authenticity of a news article.

Data discretisation focuses on converting continuous attribute values to discrete ones which are closer to a knowledge-level representation that is easier to understand, use, and explain than continuous values. On the other hand, instance selection aims at filtering out noisy or unrepresentative data samples from a given training dataset before constructing a learning model. In practice, some domain datasets may require processing with both discretisation and instance selection at the same time. In such cases, the order in which discretisation and instance selection are combined will result in differences in the processed datasets. For example, discretisation can be performed first based on the original dataset, after which the instance selection algorithm is used to evaluate the discrete type of data for selection, whereas the alternative is to perform instance selection first based on the continuous type of data, then using the discretiser to transfer the attribute type of values of a reduced dataset. However, this issue has not been investigated before. The aim of this paper is to compare the performance of a classifier trained and tested over datasets processed by these combination orders. Specifically, the minimum description length principle (MDLP) and ChiMerge are used for discretisation, and IB3, DROP3 and GA for instance selection. The experimental results obtained using ten different domain datasets show that executing instance selection first and discretisation second perform the best, which can be used as the guideline for the datasets that require performing both steps. In particular, combining DROP3 and MDLP can provide classification accuracy of 0.85 and AUC of 0.8, which can be regarded as the representative baseline for future related researches.

The core of human resources is the division of labour of enterprise employees. A human resource allocation method based on the E-CARGO model was designed to improve the development potential of enterprises and balance the preferences of managers and the task execution of employees in human resource allocation. This method was used to analyse the preferences of enterprise managers for employees and describe their preferences using the E-CARGO model. Then the OCAT method was used to mine the relationship between the team execution and the preferences of managers to find the balance between the two. The results showed that the experimental Scheme found the balance between the team execution and the preferences of managers. The original experimental scheme found three balance points, the improved scheme found three balance points, and the improved scheme found one balance point and one balance interval. Among the three experimental schemes, the improvement Scheme 1 achieved the highest execution ability and the shortest time. The research successfully analyses the relationship between the team execution and the preferences of managers in enterprise human resource assignment and puts forward a human resource assignment scheme that takes both into account.

Aiming at the low success rate of incubation investment of China’s technology-based start-ups, how to scientifically evaluate technology-based start-ups has become an important issue that needs to be faced. Considering the expert consultation characteristics of the Delphi method is extremely suitable for decision-making problems in the field of uncertainty, as well as the strong nonlinear mapping ability and learning ability of the backpropagation neural network (BPNN). According to the characteristics of the evaluation object and following the principle of index selection, the study uses the Delphi method to determine the evaluation index system suitable for technology-based entrepreneurial enterprises in the current environment and obtain the scores of each index. Based on the established evaluation index system, the BPNN evaluation model is further constructed, and its parameters are optimised to improve its performance. Aiming at the problem that it is easy to fall into a local optimal, a genetic algorithm (GA) is used to optimise it, and a GA-BPNN model is constructed. The comprehensive capability of GA-BPNN is evaluated by using the excellent nonlinear characteristic analysis ability of GA-BPNN to provide a reference for important decisions such as investment. Using BPNN simulation, it was concluded that the correct rate of evaluation of qualified enterprises was between 23.32% and 89.99%, with an average correct rate of 58.32%. The average correct rate was 80.99%. The evaluation accuracy rate was unstable and the average accuracy rate was low. The optimised GA-BPNN model had an average evaluation accuracy rate of 80.32% for qualified enterprises and 93.66% for unqualified enterprises, and the average evaluation accuracy rate increased by 21.99% and 12.66%, respectively. The effectiveness of the model and algorithm was verified. It shows that the GA-BPNN model can be used as an effective tool for the evaluation and screening of technology-based entrepreneurial enterprises. The evaluation system of technology-based entrepreneurial enterprises established by research is scientific and can be applied in practice.

Engineering projects must meet quality and schedule requirements during construction. This is a typical multi-objective problem and a difficult point in the management of engineering enterprises. To address these issues, a research study proposes an intelligent multi-objective optimisation technique. First, analyse the optimisation objectives of the enterprise in the context of digitalisation. Then, construct a multi-objective cost optimisation model for engineering enterprises. Second, the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is introduced to solve multi- objective problems. To improve the multi-objective optimisation effect of the model, the inertia weight parameters and particle learning behaviour are optimised and adjusted, as the model is prone to getting stuck in local optima. In the performance test of the algorithm model, the optimised MOPSO model can accurately search for the minimum value of 0 at the position (0, 0) under the Rastrig in function, and at the same time, the number of iteration convergence is the least. The GA, ACOM, and traditional MOPSO models have more iterative convergence times, and the optimisation results are 0.10, 0.15, and 0.14, respectively. It can be seen that the performance of the optimised MOPSO model is better. In the specific example analysis, using the optimised MOPSO solution, the project cost was reduced from 31 million yuan in the contract to 30.52 million yuan, and the construction period was shortened from 588 days to 540 days, and met the environmental protection and quality requirements. The research content can provide important decision support for engineering project managers.

In recent years, location check-in on mobile components is a trending topic over social media. At the same time, hackers grasp the geographical position (geo-position) data that destruct the security of users. Hence, it is crucial to detect the originality of geo-position. A plethora of methods have been developed for geo-position spoofing identification that depends on geo-position data. Nonetheless, such techniques are incapable in terms of missing prior data or insufficient of large samples. To counterpart this issue, an effective model is invented to detect spoofing activity by Adaptive Honey Badger Spider Monkey Optimization_Deep residual Network (AHBSMO-based DRN). Here, neuro camera footprint refining is performed using Neuro Fuzzy filter and extracted footprint image obtained while considering the input and spoofed image are fused using Pearson correlation coefficient. Meanwhile, geo-tagged value of input image and spoofed image is also fused based on same Pearson coefficient. Finally, fusion is performed and then, spoofing detection is accomplished by comparing the Discrete Cosine Transform (DCT) foot print of two images to find if the input image is spoofed or not. Moreover, AHBSMO-based DRN model has gained outstanding outcomes in regard of accuracy of 0.921, True Positive Rate (TPR) 0 of 0.911, and False Positive Rate (FPR) of 0.136.

The state’s economy depends significantly on the work of fishermen. The purpose of the Development of Fisher folk is to create a community of fishermen that is fully developed in all areas, including education, health, social standing, and economic development. The many Fisher community’s development programs are designed to aid all men, women, youth, and children who work in fishing activities and reside in coastal areas by enhancing their access to education, healthcare, culture, and employment possibilities. The paper examines the factors influencing the inclusive development of fisher folk in southerly districts of Tamil Nadu. Through field surveys, fundamental knowledge is gathered. To compile a thorough profile of the socioeconomic circumstances of the households of the fishermen, an interview schedule has been created. By using convenience sampling, 200 respondents from Tamil Nadu’s southern districts, were included in the final sample set. Therefore, the results revealed that climatic change and inadequate technology significantly impact the Fisher communities’ inclusive development. The outcome of the results also shows the insignificant relation between inadequate facilities for storing the catch impact and inclusive development of fishing communities. Factors influencing inclusive development include socio-demographic characteristics, climate change, lack of occupational return, inadequate storage facilities, lack of knowledge, technology, and financial institutions. Age, marital status, education, and involvement do not significantly impact development. The findings also show that the development of the fishing community is unaffected by a lack of financial institutions, expertise, or awareness. The results confirmed that state assistance was inefficient in reaching the targeted society and emphasised the need for further planned government intervention. By creating additional capacity structure initiatives that maintain and provide continuous social defence and engage the coastal community through an innovative alertness campaign, the authorities may demonstrate their commitment to the full development of aquatic fishermen.

When loaded with difficulties in fulfilling daily requirements, a lot of people in today’s world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre- processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional Variational Autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are optimised using the Adaptive Egret Swarm Optimisation Algorithm (AESOA) so that the weighted fused features are obtained from these two sets of extracted features. Then, a Cascaded Deep Temporal Convolution Network with Attention Mechanism (CDTCN-AM) is used to recognise stress and emotion. The validation of the results from the developed stress and emotion recognition approach is carried out against traditional models in order to showcase the effectiveness of the suggested approach.

Learning organisations facilitate the acquisition of new knowledge and skills for both organisations and their members, enabling the application of the latest insights in a dynamic environment. This study addresses a research gap by constructing a model identifying the factors influencing learning organisations and their subsequent impact. The research delves into uncovering the catalysts behind learning organisations concerning the career advancement of information technology (IT) employees and their intent to remain in their roles. The study, conducted in a developing country, India, employs an explanatory research design to explore the interrelationship between learning organisations, career progression and employee retention. Primary and secondary data have been used for this study. The primary data has been collected from 389 IT sector employees at various employment positions in Chennai through a structured and standard questionnaire based on the Dimensions of Learning Organizations Questionnaire (DLOQ) by Watkins, KE and Marsick, VJ (2023) [Rethinking workplace learning and development catalyzed by complexity. Human Resource Development Review, 22(3), 333-344. doi:10.1177/15344843231186629], demonstrating a high reliability at 96.4%.

Fintech solutions offer innovative tools and platforms that streamline financial operations, enhance convenience, and improve efficiency. The purpose of this research is to examine the impact of fintech on administrative efficiency among universities in Bangladesh. To fulfil this purpose, this research assesses the current level of fintech adoption among university students in Bangladesh and seeks to gain insights into students’ perceptions and attitudes towards fintech, particularly in relation to its impact on administrative tasks within their respective universities. Finally, this research identifies the challenges and opportunities associated with the integration of fintech in administrative processes and explores potential strategies to address them effectively. This research used both quantitative and qualitative strategies to achieve these goals. A survey was conducted to collect quantitative data from the university students of Bangladesh. Findings show that the adoption and usage of fintech have a statistically significant positive impact on administrative efficiency, resource allocation and utilisation, enhanced communication, and a sustainable and eco-friendly administrative environment.

Although this research has made significant progress in image generation models, they still face issues such as insufficient diversity of generated images, poor quality of high-resolution images, and the need for a large amount of training data for model optimization. This paper studies poster design based on deep learning automatic image generation algorithm, using a recursive supervised image generation algorithm framework of generative adversarial networks for multi-view image generation and super-resolution generation tasks of small sample digital poster images. Various improvements have been proposed to enhance the performance of the GAN network model for poster design image generation tasks. Based on experimental research, this paper’s model uses generative adversarial networks to distinguish randomly cropped low resolution and high-resolution poster blocks, ensuring that high-resolution posters maintain their original resolution canvas texture and brush strokes, effectively improving the automatic generation effect of poster images. The evaluation results show that the quantitative evaluation of the proposed algorithm model in knowledge management is distributed in a reasonable range, which indicates that the proposed algorithm model has good performance in knowledge management. The poster design model based on deep learning automatic image generation algorithm proposed in this paper has certain effects. In subsequent practice, the automatic image generation algorithm can be combined with practical needs to improve the efficiency and design effect of poster design.

In the current traditional tourism recommendation systems, significant amounts of manpower and resources are required to manually identify the characteristics of resources, resulting in extremely poor economic benefits. To address this issue, this study proposes a smart tourism model based on deep learning and attention mechanisms. It uses a deep learning model to extract semantic information and improves it with the attention mechanism. This is to enable the model to take into account the complete meaning of the text and the association between individual words, thereby achieving a more comprehensive extraction of tourism resource features. The experiment showcases that the $F$₁-value of the algorithm proposed by us reached 0.961, the Recall value reached 0.958, the accuracy reached 0.980 and the area under the receiver operating characteristic curve reached 0.956. All parameters are superior to the comparison algorithm, and in practical application testing, its fitting degree reached 0.981. The above results indicate that the smart tourism proposed by us based on deep learning and attention mechanism has excellent performance in the field of tourism resource recommendation, which can effectively extract hidden features from the resources. This can also accurately push the tourism resources that users are interested in, which can effectively promote the integration and development of the tourism industry and the Internet, and has strong positive significance for economic development.