Template-Type: ReDIF-Article 1.0 Author-Name: Oluseye Olugboyega Author-X-Name-First: Oluseye Author-X-Name-Last: Olugboyega Author-Name: Godwin Ehis Oseghale Author-X-Name-First: Godwin Ehis Author-X-Name-Last: Oseghale Author-Name: Clinton O. Aigbavboa Author-X-Name-First: Clinton O. Author-X-Name-Last: Aigbavboa Title: If you cannot fly, then run: a model of BIM implementation taxonomies and thresholds Abstract: The barriers to BIM adoption are various and overpowering. These barriers should be continuously defeated through a recursive BIM implementation strategy and evaluation. The point of this paper is to recognise the key reduction indicators for tracking BIM adoption barriers and lay out whether the key reduction indicators will give a model of BIM implementation taxonomies and thresholds for assessing BIM implementation performance. Meta-analysis methodology was utilised to synthesise the diverse findings. These key reduction indicators were sorted into three BIM implementation thresholds: <i>BIM advanced industry</i>, <i>BIM emerging industry</i>, and <i>BIM frontier industry</i>. It was observed that BIM implementation taxonomies have various levels of the implementation plan, levels of market adequacy, and levels of goals. The study inferred that the proposed model would assist with smoothing out the necessities and instruct on the BIM implementation needs concerning different construction industries, most especially the developing construction industries. Journal: Int. J. of Information Technology and Management Pages: 64-88 Issue: 1 Volume: 23 Year: 2024 Keywords: building information modelling; BIM; BIM implementation; BIM adoption; BIM adaptation; BIM application; BIM utilisation; BIM adoption barriers; BIM implementation taxonomies; BIM implementation thresholds. File-URL: http://www.inderscience.com/link.php?id=136194 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:64-88 Template-Type: ReDIF-Article 1.0 Author-Name: Grigori Feigin Author-X-Name-First: Grigori Author-X-Name-Last: Feigin Author-Name: Anna Hayduk Author-X-Name-First: Anna Author-X-Name-Last: Hayduk Title: Global information technology outsourcing: issues of attractiveness of some regions in Eastern Europe Abstract: The article deals with the problem of the development of global information technology outsourcing. Based on the summary of main theoretical approaches used to explain the phenomenon of outsourcing, the conditions for making decisions on information technology outsourcing (ITO) nationally and globally are identified. The authors' overview of the chances (positive effects for companies) arising from the ITO application specific to the national and global outsourcing is provided. The causes of the ITO insufficient spread at the present stage are indicated. A model classifying the factors that influence the attractiveness of the national economies of some countries for ITO is proposed. The attractiveness of the countries of the Eastern European region for ITO is assessed based on the example of a comparative analysis of Russia and Ukraine. Journal: Int. J. of Information Technology and Management Pages: 89-118 Issue: 2 Volume: 23 Year: 2024 Keywords: information technology outsourcing; ITO; supplier company; client company; chances and risks; factors of attractiveness of regions; Eastern Europe; comparative analysis; Russia; Ukraine. File-URL: http://www.inderscience.com/link.php?id=137758 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:2:p:89-118 Template-Type: ReDIF-Article 1.0 Author-Name: Yachun Tang Author-X-Name-First: Yachun Author-X-Name-Last: Tang Title: A prediction and analysis model of complex system based on extension neural network - taking the prediction and analysis of terrorist events in the big data environment as an example Abstract: In predictive analysis of terrorist incidents there are often problems such as large data volumes, large data types, large redundancies, and difficulty dealing with multiple constraints, making it difficult to obtain effective prediction results for terrorist event prediction. Therefore, the methods of analysing and predicting terrorist events in big data environment are analysed, and a big data prediction analysis model for terrorist events based on improved extension neural network is presented. Firstly, a meta-element model for predicting and analysing terrorist events is established, the preliminary cluster analysis of the terrorist event prediction analysis based on the extension transformation is performed, and a terror event prediction extension principal component analysis model is established. Then, based on the prediction of the extension principal component analysis in the terrorist incident, an improved extension neural network structure is constructed, and the extension neurons based on different extensions are established. Furthermore, the predictive analysis model of the terrorist network extension neural network is formed, and the corresponding model and algorithm implementation steps are given. Finally, the algorithm and the model are illustrated and compared by specific case analysis, which shows the validity and feasibility of the model and algorithm. Journal: Int. J. of Information Technology and Management Pages: 119-136 Issue: 2 Volume: 23 Year: 2024 Keywords: predictive analysis model; extension neural network; extension theory; big data; terrorist incidents. File-URL: http://www.inderscience.com/link.php?id=137762 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:2:p:119-136 Template-Type: ReDIF-Article 1.0 Author-Name: Dongwei Shi Author-X-Name-First: Dongwei Author-X-Name-Last: Shi Author-Name: Yanyin Li Author-X-Name-First: Yanyin Author-X-Name-Last: Li Author-Name: Xing Yu Author-X-Name-First: Xing Author-X-Name-Last: Yu Title: Dynamic options hedging model under mark-to-market risk Abstract: In this article, a model for options hedging under the budget and margin calls restrictions for buying put options and selling call options, respectively, is proposed. The proposed models are then solved by using the innovative interior point approach. This study analyses the effectiveness of Chinese SSE 50ETF options for hedging with and without the addition of margin calls. We find that the hedging approach of net buying put options or net selling call options is less profitable than the hedging strategy of buying call options while simultaneously selling put options. To assess the validity of the study's conclusions, we examine the hedging efficiencies of several approaches when the underlying price has an upward tendency and find that the options hedging suggested in this research is still the best alternative. We advise investors to use call options and put options with lower strike prices for hedging in the sensitivity analysis. Journal: Int. J. of Information Technology and Management Pages: 137-155 Issue: 2 Volume: 23 Year: 2024 Keywords: options hedging; mark-to-market risk; margin calls; dual interior point algorithm. File-URL: http://www.inderscience.com/link.php?id=137769 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:2:p:137-155 Template-Type: ReDIF-Article 1.0 Author-Name: Xin Liu Author-X-Name-First: Xin Author-X-Name-Last: Liu Title: Study on marketing strategy innovation of mobile payment service under internet environment Abstract: In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business. Journal: Int. J. of Information Technology and Management Pages: 175-192 Issue: 3/4 Volume: 23 Year: 2024 Keywords: questionnaire survey; marketing strategy; user portrait; mobile payment service; user classification. File-URL: http://www.inderscience.com/link.php?id=139567 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:175-192 Template-Type: ReDIF-Article 1.0 Author-Name: Xiangyuan Liu Author-X-Name-First: Xiangyuan Author-X-Name-Last: Liu Title: A data mining method based on label mapping for long-term and short-term browsing behaviour of network users Abstract: In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high. Journal: Int. J. of Information Technology and Management Pages: 219-231 Issue: 3/4 Volume: 23 Year: 2024 Keywords: long-term and short-term browsing behaviour; data mining; lifting small transformation method; data mapping; label mapping; SVM secondary classification. File-URL: http://www.inderscience.com/link.php?id=139568 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:219-231 Template-Type: ReDIF-Article 1.0 Author-Name: Yan Liu Author-X-Name-First: Yan Author-X-Name-Last: Liu Title: An evaluation of customer trust in e-commerce market based on entropy weight analytic hierarchy process Abstract: In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1. Journal: Int. J. of Information Technology and Management Pages: 193-205 Issue: 3/4 Volume: 23 Year: 2024 Keywords: e-commerce market; customer trust; trust evaluation; entropy weight method; analytic hierarchy process; index weight; degree of membership; commentary. File-URL: http://www.inderscience.com/link.php?id=139569 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:193-205 Template-Type: ReDIF-Article 1.0 Author-Name: Guojuan Li Author-X-Name-First: Guojuan Author-X-Name-Last: Li Title: Student's classroom behaviour recognition method based on abstract hidden Markov model Abstract: In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably. Journal: Int. J. of Information Technology and Management Pages: 232-243 Issue: 3/4 Volume: 23 Year: 2024 Keywords: classroom behaviour; hidden Markov model; HMM; abstract space; behaviour recognition; probability density; behaviour category. File-URL: http://www.inderscience.com/link.php?id=139570 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:232-243 Template-Type: ReDIF-Article 1.0 Author-Name: Quan Zhang Author-X-Name-First: Quan Author-X-Name-Last: Zhang Author-Name: Bohan Liu Author-X-Name-First: Bohan Author-X-Name-Last: Liu Title: Research on evaluation method of e-commerce platform customer relationship based on decision tree algorithm Abstract: In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes. Journal: Int. J. of Information Technology and Management Pages: 291-303 Issue: 3/4 Volume: 23 Year: 2024 Keywords: decision tree algorithm; electronic commerce; customer relationship; multi-period excess income method; weighted sampling. File-URL: http://www.inderscience.com/link.php?id=139571 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:291-303 Template-Type: ReDIF-Article 1.0 Author-Name: Jinxin Wang Author-X-Name-First: Jinxin Author-X-Name-Last: Wang Author-Name: Mingli Sun Author-X-Name-First: Mingli Author-X-Name-Last: Sun Title: Evaluation method of cross-border e-commerce supply chain innovation mode based on blockchain technology Abstract: In view of the low evaluation accuracy of the effectiveness of cross-border e-commerce supply chain innovation model and the low correlation coefficient of innovation model influencing factors, the evaluation method of cross-border e-commerce supply chain innovation model based on blockchain technology is studied. First, analyse the operation mode of cross-border e-commerce supply chain, and determine the key factors affecting the innovation mode; Then, the comprehensive integration weighting method is used to analyse the factors affecting innovation and calculate the weight value; Finally, the blockchain technology is introduced to build an evaluation model for the supply chain innovation model and realise the evaluation of the cross-border e-commerce supply chain innovation model. The experimental results show that the evaluation accuracy of the proposed method is high, and the highest correlation coefficient of the influencing factors of innovation mode is about 0.99, which is feasible. Journal: Int. J. of Information Technology and Management Pages: 261-277 Issue: 3/4 Volume: 23 Year: 2024 Keywords: blockchain; cross border e-commerce; supply chain; innovation mode; inverse function; trust relationship. File-URL: http://www.inderscience.com/link.php?id=139572 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:261-277 Template-Type: ReDIF-Article 1.0 Author-Name: Hua Wang Author-X-Name-First: Hua Author-X-Name-Last: Wang Title: Quantitative evaluation method of ideological and political teaching achievements based on collaborative filtering algorithm Abstract: In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect. Journal: Int. J. of Information Technology and Management Pages: 330-344 Issue: 3/4 Volume: 23 Year: 2024 Keywords: collaborative filtering; ideological and political education; quantitative analysis; Jaccard coefficient; neighbour set. File-URL: http://www.inderscience.com/link.php?id=139573 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:330-344 Template-Type: ReDIF-Article 1.0 Author-Name: Jianlei Zhang Author-X-Name-First: Jianlei Author-X-Name-Last: Zhang Title: The performance evaluation of teaching reform based on hierarchical multi-task deep learning Abstract: The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields. Journal: Int. J. of Information Technology and Management Pages: 318-329 Issue: 3/4 Volume: 23 Year: 2024 Keywords: hierarchical multi-task deep learning; reform in education; performance evaluation; analytic hierarchy process; loss function. File-URL: http://www.inderscience.com/link.php?id=139574 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:318-329 Template-Type: ReDIF-Article 1.0 Author-Name: Yan Shi Author-X-Name-First: Yan Author-X-Name-Last: Shi Title: A risk identification method for abnormal accounting data based on weighted random forest Abstract: In order to improve the identification accuracy, accuracy and time-consuming of traditional financial risk identification methods, this paper proposes a risk identification method for financial abnormal data based on weighted random forest. Firstly, SMOTE algorithm is used to collect abnormal financial data; secondly, the original accounting data is decomposed into features, and the features of abnormal data are extracted through random forests; then, the index weight is calculated according to the entropy weight method; finally, the negative gradient fitting is used to determine the loss function, and the weighted random forest method is used to solve the loss function value, and the recognition result is obtained. The results show that the identification accuracy of this method can reach 99.9%, the accuracy rate can reach 96.06%, and the time consumption is only 6.8 seconds, indicating that the risk identification effect of this method is good. Journal: Int. J. of Information Technology and Management Pages: 304-317 Issue: 3/4 Volume: 23 Year: 2024 Keywords: SMOTE algorithm; weighted random forest; loss function; negative gradient fitting. File-URL: http://www.inderscience.com/link.php?id=139575 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:304-317 Template-Type: ReDIF-Article 1.0 Author-Name: Fulei Chen Author-X-Name-First: Fulei Author-X-Name-Last: Chen Author-Name: Mingzhu Sun Author-X-Name-First: Mingzhu Author-X-Name-Last: Sun Author-Name: Lei Shen Author-X-Name-First: Lei Author-X-Name-Last: Shen Title: Risk assessment method of power grid construction project investment based on grey relational analysis Abstract: In view of the problems of low accuracy, long time consuming and low efficiency of the existing engineering investment risk assessment method; this paper puts forward the investment risk assessment method of power grid construction project based on grey correlation analysis. Firstly, classify the risks of power grid construction project; secondly, determine the primary index and secondary index of investment risk assessment of power grid construction project; then construct the correlation coefficient matrix of power grid project investment risk to calculate the correlation degree and weight of investment risk index; finally, adopt the grey correlation analysis method to construct investment risk assessment function to realise investment risk assessment. The experimental results show that the average accuracy of evaluating the investment risk of power grid construction projects using the method is 95.08%, and the maximum time consuming is 49 s, which proves that the method has high accuracy, short time consuming and high evaluation efficiency. Journal: Int. J. of Information Technology and Management Pages: 244-260 Issue: 3/4 Volume: 23 Year: 2024 Keywords: relevance; grey correlation analysis; forward backward algorithm; correlation matrix; weight calculation. File-URL: http://www.inderscience.com/link.php?id=139576 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:244-260 Template-Type: ReDIF-Article 1.0 Author-Name: Mingli Sun Author-X-Name-First: Mingli Author-X-Name-Last: Sun Author-Name: Jinxin Wang Author-X-Name-First: Jinxin Author-X-Name-Last: Wang Title: Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm Abstract: In order to improve the classification accuracy and online allocation accuracy of teaching resources and shorten the allocation time, this paper proposes a new online allocation method of college ideological and political curriculum teaching resources based on differential search algorithm. Firstly, the feedback parameter model of teaching resources cleaning is constructed to complete the cleaning of teaching resources. Secondly, according to the results of anti-interference consideration, the linear feature extraction of ideological and political curriculum teaching resources is carried out. Finally, the online allocation objective function of teaching resources for ideological and political courses is constructed, and the differential search algorithm is used to optimise the objective function to complete the online allocation of resources. The experimental results show that this method can accurately classify the teaching resources of ideological and political courses, and can shorten the allocation time, with the highest allocation accuracy of 97%. Journal: Int. J. of Information Technology and Management Pages: 278-290 Issue: 3/4 Volume: 23 Year: 2024 Keywords: differential search algorithm; college ideological and political course; teaching resources; online allocation. File-URL: http://www.inderscience.com/link.php?id=139577 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:278-290 Template-Type: ReDIF-Article 1.0 Author-Name: Qilei Wang Author-X-Name-First: Qilei Author-X-Name-Last: Wang Title: Research on construction of police online teaching platform based on blockchain and IPFS technology Abstract: Under the current framework of police online teaching, in order to effectively solve the lack of high-quality resources of the traditional platform, backward sharing technology, poor performance of the digital platform and the privacy problems faced by each subject in sharing. This paper designs and implements the online teaching platform based on blockchain and interplanetary file system (IPFS). Based on the architecture of a 'decentralised' police online teaching platform, the platform uses blockchain to store hashes of encrypted private information and user-set access control policies, while the real private information is stored in IPFS after encryption. In the realisation of privacy information security storage at the same time to ensure the effective control of the user's own information. In order to achieve flexible rights management, the system classifies private information. In addition, the difficulties of police online teaching and training reform in the era of big data are solved one by one from the aspects of communication mode, storage facilities, incentive mechanism and confidentiality system, which effectively improves the stability and security of police online teaching. Journal: Int. J. of Information Technology and Management Pages: 345-356 Issue: 3/4 Volume: 23 Year: 2024 Keywords: blockchain; IPFS technology; police; online teaching. File-URL: http://www.inderscience.com/link.php?id=139583 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:345-356 Template-Type: ReDIF-Article 1.0 Author-Name: Xueqi Liu Author-X-Name-First: Xueqi Author-X-Name-Last: Liu Title: An evaluation of English distance information teaching quality based on decision tree classification algorithm Abstract: In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds. Journal: Int. J. of Information Technology and Management Pages: 357-371 Issue: 3/4 Volume: 23 Year: 2024 Keywords: decision tree classification; information gain theory; rough set theory; index weight; membership matrix. File-URL: http://www.inderscience.com/link.php?id=139586 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:357-371 Template-Type: ReDIF-Article 1.0 Author-Name: Yinghui Liu Author-X-Name-First: Yinghui Author-X-Name-Last: Liu Author-Name: Xiaosi Xu Author-X-Name-First: Xiaosi Author-X-Name-Last: Xu Author-Name: Qixing Yin Author-X-Name-First: Qixing Author-X-Name-Last: Yin Title: Research on fast mining of enterprise marketing investment databased on improved association rules Abstract: Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment. Journal: Int. J. of Information Technology and Management Pages: 206-218 Issue: 3/4 Volume: 23 Year: 2024 Keywords: improve association rules; enterprise marketing investment; Crawler framework; correlation degree; Apriori algorithm. File-URL: http://www.inderscience.com/link.php?id=139592 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:3/4:p:206-218 Template-Type: ReDIF-Article 1.0 Author-Name: GrĂ©gory Jemine Author-X-Name-First: GrĂ©gory Author-X-Name-Last: Jemine Title: Beyond the storm: an exploratory survey on HR managers' representations of epidemic-induced telework Abstract: This paper sets out to provide insights into how HR managers experienced epidemic-induced telework, understood as an unprecedented form of telework due to its scale and effects on organisations and individuals. So far, scholars have mostly studied epidemic-induced telework through surveys and interviews conducted with teleworkers themselves. By contrast, the present paper investigates HR managers' representations of epidemic-induced telework. It is argued that the question is both timely and significant, since HR managers usually play important decision-making roles in the design of teleworking policies. Following an exploratory survey addressed to HR managers of Belgian firms conducted between April and May 2021, four ideal types of managerial reactions to epidemic-induced telework are developed: entrepreneurs, preservers, adapters, and questioners. These ideal types make it possible to better characterise the wide heterogeneity of HR managers' experiences of the pandemic and attitudes towards epidemic-induced telework. Journal: Int. J. of Information Technology and Management Pages: 156-174 Issue: 2 Volume: 23 Year: 2024 Keywords: telework; epidemic-induced telework; managerial representations; constrained telework; human resource managers; remote working; homeworking; exploratory survey. File-URL: http://www.inderscience.com/link.php?id=137865 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:2:p:156-174 Template-Type: ReDIF-Article 1.0 Author-Name: Faouzi Mechraoui Author-X-Name-First: Faouzi Author-X-Name-Last: Mechraoui Author-Name: Pedro Martins Author-X-Name-First: Pedro Author-X-Name-Last: Martins Author-Name: Filipe Caldeira Author-X-Name-First: Filipe Author-X-Name-Last: Caldeira Title: OpenStack: a virtualisation overview Abstract: The major cloud computing software companies offer a new concept, on which resources are virtualised to provide these as a service on the internet. Currently, there are multiple service providers, and additional options to virtualise services on-premises. OpenStack is an open-source alternative to create virtual local or cloud setups, which supports petabytes of data, unlimited scale, and configurable networking. These features make this tool suitable for large scale virtualisation, reducing maintenance costs and optimising hardware resource utilisation (e.g., schools, government). This paper presents an overview of the study of the OpenStack software, oriented to build a scalable hosting architecture suitable for an educational setup. Functional and architectural details are discussed to implement unique cloud computing to fit virtualisation purposes. An experimental virtualisation setup is described in the scope of an educational scenario. Finally, a guideline to configure OpenStack is given. Journal: Int. J. of Information Technology and Management Pages: 1-12 Issue: 1 Volume: 23 Year: 2024 Keywords: OpenStack; infrastructure as a service; IaaS; virtualisation; cloud computing; open source. File-URL: http://www.inderscience.com/link.php?id=136181 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:1-12 Template-Type: ReDIF-Article 1.0 Author-Name: Asare Yaw Obeng Author-X-Name-First: Asare Yaw Author-X-Name-Last: Obeng Author-Name: Alfred Coleman Author-X-Name-First: Alfred Author-X-Name-Last: Coleman Title: Consequential effects of leading technology-driven offensive strategy in a universal bank Abstract: Globally, banking industry is saddled with intense competition. Management of banking firms are required to formulate effective strategies to drive their capabilities to compete, discover and defend their positions in a competitive industry. İn Ghana, universal banks are leveraging the capabilities of information technology to devise technology-driven offensive strategies. The core of these strategies is embarking on innovative activities to enhance performance and respond to business challenges. A qualitative analysis was performed using primary data from 17 participants. Using NVivo, consequential effects of IS-technological innovation (ISTI) on business challenges, innovation performance, operational performance; and specific moderating factors of ISTI were assessed. Strategic-IS Project impacts strong (<i>r</i> = 0.634601) and positive on ISTI than the other moderators. ISTI impacts strongly (<i>r</i> = 0.644951) and positively on operational performance. With <i>r</i> = 0.7422, innovation performance positively and strongly influences operational performance. ISTI impacts positively on business challenges. Journal: Int. J. of Information Technology and Management Pages: 13-32 Issue: 1 Volume: 23 Year: 2024 Keywords: bank; business challenges; Ghana; information systems; moderating factors; offensive strategy; qualitative analysis; technological innovation. File-URL: http://www.inderscience.com/link.php?id=136182 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:13-32 Template-Type: ReDIF-Article 1.0 Author-Name: Zeng Saifeng Author-X-Name-First: Zeng Author-X-Name-Last: Saifeng Title: AQINM: an adaptive QoS management framework based on intelligent negotiation and monitoring in cloud Abstract: More and more federated cloud platforms have been constructed to deal with non-trivial large-scale applications, which typically require certain level of quality-of-service (QoS) guarantee. However, most of existing cloud-oriented QoS solutions are likely to introduce extra overheads on either resource allocation or task execution, which is especially true in federated cloud environments. In this paper, we design and implement a QoS-enhancing framework, namely adaptive QoS management based on intelligent negotiation and monitoring (AQINM), which provides three QoS-enhancing services including policy management, service level agreement (SLA) negotiation, and SLA monitoring. Unlike the conventional QoS-enable middleware, these services in the AQINM framework introduce several novel mechanisms to offer more cost-effective and efficient solutions to enforcing the QoS management in federated clouds. The implementation of our AQINM framework are tested in a campus federated cloud platform by using different applications as experimental benchmarks, and its performance are compared with other similar solutions. The experimental results show that the proposed AQINM is capable of reducing the costs of SLA negotiation and monitoring for large-scale cloud application that deployed in federated cloud environments. Journal: Int. J. of Information Technology and Management Pages: 33-47 Issue: 1 Volume: 23 Year: 2024 Keywords: cloud computing; quality-of-service; QoS; service level agreement; SLA; resource virtualisation. File-URL: http://www.inderscience.com/link.php?id=136183 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:33-47 Template-Type: ReDIF-Article 1.0 Author-Name: Shardul Shankar Author-X-Name-First: Shardul Author-X-Name-Last: Shankar Author-Name: Ranjana Vyas Author-X-Name-First: Ranjana Author-X-Name-Last: Vyas Author-Name: Vijayshri Tewari Author-X-Name-First: Vijayshri Author-X-Name-Last: Tewari Title: Applying machine learning algorithms to determine and predict the reasons and models for employee turnover Abstract: In recent years, organisations have struggled with the turnover of employees, which has become one of the biggest issues that not only has inadvertent consequences for an organisation's growth, productivity, and performance but also has negative implications for the intrinsic cost associated with it. To cater to this problem, one such method is the use of machine learning algorithms. But one of the biggest issues in HR information system (HRIS) analysis is the presence of noise in data, leading to inaccurate predictions. This paper tries to examine the efficiency of six such algorithms, to determine the robustness, accuracy in real-time analysis of data, and then use that company's historical data to predict employee turnover for the present year. The dataset was mined from the HRIS database of a global organisation in the USA and Canada in the span of ten years to compare these algorithms to examine voluntary turnover, using Python and RStudio analytical tools. Journal: Int. J. of Information Technology and Management Pages: 48-63 Issue: 1 Volume: 23 Year: 2024 Keywords: employee turnover; machine learning; predictive algorithms; classification; voluntary turnover. File-URL: http://www.inderscience.com/link.php?id=136187 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:48-63