This study aims to examine the intellectual capital and its effect on the financial performance of Ethiopian private commercial banks using the pulic model. Quantitative panel data from audited annual reports of Ethiopian private commercial banks from 2011 to 2019 are collected. The robust fixed effect regression model has been adopted to investigate the effect of IC and the financial performance measures of the banks. The study results show a positive relationship between the value added intellectual coefficient (VAIC) and the financial performance of private commercial banks in Ethiopia. The study also revealed that the components of VAIC (i.e., human capital efficiency, capital employed efficiency, and structural capital efficiency) have a positive and significant effect on the financial performance of banks measured by return on asset and return on equity over the study periods. Practically, the results of the study could be useful for shareholders to consider IC as a strategic resource and hence emphasise these intangibles, and to the bank managers to benchmark themselves against the best competitors based on the level of efficiency rankings.

The present study is a modest attempt to investigate the impact of gender diversity on firm performance of BSE 200 listed companies. The study is based on the secondary data collected from the EMIS database and the corporate governance reports for a period of eight years, i.e., from 2012 to 2019. Sample size of the present study is 174 Indian companies listed in the Bombay Stock Exchange. The study has employed multiple regression models by considering the endogeneity issue to empirically test the impact of gender diversity on firm performance in Indian context. Based on the multiple regression models, we find that the impact of gender diversity is positive and significant on the market-based measure of firm performance. However, the impact becomes negative significant when firm performance was measured by accounting based measure of firm performance.

Corporate social responsibility (CSR) and sustainability are gaining worldwide recognition. The question of whether CSR and sustainability programs benefit an organisation's financial success is still being debated. This study aims to verify this phenomenon by examining the current literature pattern on this relationship using bibliometric and systematic review analysis. It further provides a taxonomy for understanding this association. VOSviewer is used to obtain comprehensive dataset mapping and clustering in the field. The manuscript offers promising insights regarding academia by assessing the pattern of publication trends, the most influential author in the area, and analysing the methodological and theoretical underpinnings of CSR, sustainability and firm performance linkage. The outcome of this study provides exploratory insights into research gaps and avenues for future research.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

This study examines the relationships between ICTs (e.g., chatbots, virtual assistants, social media platforms, e-mail marketing, mobile marketing, data analytics, interactive voice response, big data analytics, push notifications, cloud computing, and augmented reality) and e-customer relationship management (e-CRM) from the banking industry of China. Similarly, this study unfolds the moderation interference of trust and risk between the association of ICTs and e-CRM, respectively. The study provided a positive nexus between ICTs and e-CRM. On the other side, a significant moderation of trust, as well as financial risk was observed between the correlation of ICTs and customer relationship management. This study endows with insights into ICTs which are critical for achieving e-CRM by streamlining interactions and enhancing their experience. Similarly, trust and financial risk were observed as potential forces that sway the association between ICTs and e-CRM.

In several manufacturing firms, inventory constitutes most of the current assets, and this underscores the importance of inventory management as a fundamental issue for the majority of the firms irrespective of their sizes. Therefore, the purpose of this research is to assess the factors that influence the effectiveness of inventory management of Malaysian SMEs in the manufacturing sector. The study employs PLS-SEM technique to test the hypotheses. The main findings show that documentation and records, inventory control system and qualified personnel have positive effects on effective inventory management of Malaysian SMEs in the manufacturing sector. The study also reveals that effective inventory management has a mediating effect on the relationship between documentation and records, inventory control system, qualified personnel and organisational performance. Therefore, the study recommends that Malaysian SMEs in the manufacturing sector should improve their approaches to embracing effective inventory management practices in order to enhance organisational performance.

This research investigates the correlation between risk management and supply chain performance (SCP) along with moderation of AI-embedded technologies such as big data analytics, Internet of Things (IoT), virtual reality, and blockchain technologies. To calculate the results, this study utilised 644 questionnaires through the structural equation modelling (SEM) method. It is revealed using SmartPls that financial risk management (FRM) is positively linked with SCP. Second, it was observed that AI significantly moderates the connection between FRM and SCP. In addition, the study presents certain insights into supply chain and AI-enabled technologies and how these capabilities can beneficially advance SCP. Besides, certain implications, both managerial and theoretical are described for the supply chain managers along with limitations for future scholars of the world.

The phenomenon of cloud computing and related innovations such as Big Data have given rise to many fundamental changes that are evident in information and data. Managing, measuring and developing business value from the plethora of this new data has significant impact on many corporate agendas, particularly in relation to the successful implementation of business intelligence and analytics (BI&A). However, although the influence of Big Data has fundamentally changed the IT application landscape, the metrics for measuring success and in particular, the quality of information, have not evolved. The measurement of information quality and the antecedent factors that influence information has also been identified as an area that has suffered from a lack of research in recent decades. Given the rapid increase in data volume and the growth and ubiquitous use of BI&A systems in organisations, there is an urgent need for accurate metrics to identify information quality.

Evaluation of research artefacts (such as models, frameworks and methodologies) is essential to determine their quality and demonstrate worth. However, in the information quality (IQ) research domain there is no existing standard set of criteria available for researchers to use to evaluate their IQ artefacts. This paper therefore describes our experience of selecting and synthesising a set of evaluation criteria used in three related research areas of information systems (IS), software products (SP) and conceptual models (CM), and analysing their relevance to different types of IQ research artefact. We selected and used a subset of these criteria in an actual evaluation of an IQ artefact to test whether they provide any benefit over a standard evaluation. The results show that at least a subset of the criteria from the other domains of IS, SP and CM are relevant for IQ artefact evaluations, and the resulting set of criteria, most importantly, enabled a more rigorous and systematic selection of what to evaluate.

More than a half billion people use the internet in China, and the environment in which these users work, study, and play using the internet is a rapidly changing one. User perceptions of the quality of information accessed through the internet and through more traditional sources of information may shift over time as the underlying social, cultural, and political environment changes. This study reports the results of a longitudinal survey study of perceptions of information quality of young adults using the internet in China. Results suggest that perceptions of the information quality of internet-based information have shifted more from 2007 to 2012 than perceptions of traditional text sources of information. Implications of the findings for researchers, educators, and information providers are discussed.

Today, income management is one of the attractive and controversial issues in accounting investigation areas from both investigations and regulatory view. Managers do manage income either to distort information or to defer and report the information related to future incomes. This investigation aims at examining the effect of taxable income management on the information content of taxable income of firms. Tests of research hypotheses were performed with an empirical method based on econometric and using multivariate regression analysis, t-test, Wilcoxon total scores, and specifically by using the panel data model across 147 firms listed on the Tehran Stock Exchange between 2002 and 2011. Findings show that taxable income management reduces the information content of taxable income. In addition, firms manage accounting income to defer information.

With the rapid development of computer information technology, the work of accountants has gradually evolved into an electronic trend and the management of accounting files has also undergone great changes. Combining with the current development trend of informatisation, this paper discusses the electronic management mode of enterprise accounting files under the condition of informatisation. Combined with the latest information technology, an enterprise electronic accounting file system is established and the research and development system is compared with the traditional paper accounting file management. The results have shown that the retrieval and query time of traditional paper accounting files is close to 2 hours. After the implementation of the electronic accounting file system, the retrieval and query time of files can be completed in only 2 minutes, and the query efficiency of files has been increased by nearly 60 times.

With the rapid development of Artificial Intelligence (AI) technology, significant breakthroughs have been made in its application in many fields. Especially, in the field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application of AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. In general, the application of AI can improve the efficiency and accuracy of enterprise human resource management, and provide new possibilities for employee performance evaluation. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

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.

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.

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.

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.

The EU 'General Data Protection Regulation' (GDPR) marked the most important step towards reforming data privacy regulation in recent years, as it has brought about significant changes in data process in various sectors, ranging from healthcare to banking and beyond. Various concerns have been raised, and as a consequence of these, certain parts of the text of the GDPR itself have already started to become questionable due to rapid technological progress, including, for example, the use of information technology, automatisation processes and advanced algorithms in individual decision-making activities. The road to GDPR compliance by all European Union members may prove to be a long one and it is clear that only time will tell how GDPR matters will evolve and unfold. In this paper, we aim to offer a review of the practical, ethical and constitutional aspects of the new regulation and examine all the controversies that the new technology has given rise to in the course of the regulation's application.

The European Union Rights Management Directive 2014/26/EU, provides regulatory oversight of European Union (EU) Collective Management Organizations (CMOs). However, the Directive has no provision indicating how members of EU CMOs may conduct non-financial audits of their CMO income and reporting. This paper addresses the problem of a lack of an audit method through a case study of the five writer members of the music group Duran Duran, who have been members of the UK's CMO for performing rights - the Performing Rights Society (PRS) for over 35 years. The paper argues a new audit CMO member method that can address the lacunae regarding the absence of CMO member right to audit a CMO and an applicable CMO audit method.

The present paper offers a critique of the General Data Protection Regulation in the realm of access to information. Even though the GDPR supports the constitutionally obvious position that the right to data protection does not outweigh other equally important rights, the enhanced protection of the right to the protection of personal data leads to the potential neglect of other constitutional rights, such as that of access to information. Data protection and access to information authorities should be established both on an EU, as well as at national level as a single authority. Scientific research must be facilitated through access to a multitude of information. The present article explores the question of data ownership and aims to propose a new system that will enhance access to information. A key tool of our research will be the comparative overview of existing legislative systems and a review of the different approaches in the case-law of independent authorities.

This paper presents a review of the literature on the challenges of national information and communication technology (ICT) policies in the context of African countries. National ICT policies have been aligned with socio-development agendas of African countries. However, the policies have not delivered the expected outcomes due to many challenges. Studies have been conducted in isolation to highlight the challenges in the policy process. The study used grounded theory informed literature review to holistically analyse the problems in the context of African countries. The results were categorised in the typology of the policy process to understand the challenges from a broad perspective. The problems were categorised into agenda setting, policy formulation, legal frameworks, implementation and evaluation. In addition, there were constraints related to policy monitoring in the policy phases and imbalance of power among the policy stakeholders. The review suggests areas of further research.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

International universities are changing with their style of operation, mode of teaching and learning operations. This change is noticeable rapidly in India and also in international contexts due to healthy and innovative methods, educational strategies, and nomenclature throughout the world. Technologies are changing rapidly, including ICT. Different subjects are developed in the fields of IT and computing with the interaction or applications to other fields, viz. health informatics, bio informatics, agriculture informatics, and so on. Agricultural informatics is an interdisciplinary subject dedicated to combining information technology and information science utilisation in agricultural sciences. The digital agriculture is powered by agriculture informatics practice. For teaching, research and development of any subject educational methods is considered as important and various educational programs are there in this regard viz. Bachelor of Education, Master of Education, PhD in Education, etc. Degrees are also available to deal with the subjects and agricultural informatics should not be an exception of this. In this context, Doctor of Education (EdD or DEd) is an emerging degree having features of skill sets, courses and research work. This paper proposed on EdD program with agricultural informatics specialisation for improving healthy agriculture system. Here, a proposed model core curriculum is also presented.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.