Digital learning initiatives forging inclusive education in post-conflict nations

The transition to a post-conflict nation presents numerous challenges for higher education. This study explores the advantages and impediments of implementing digital learning in Afghan higher education institutions (HEIs). Digital learning in Afghan HEIs currently falls short of stakeholder expectations and requirements. Authorities and key stakeholders, however, have taken measures to address these issues and ensure seamless integration of digital learning into HEIs. Efforts have been made to promote digital learning and improve access to higher education for disadvantaged groups, which could expand educational opportunities and foster diversity, equity, and inclusion, particularly among girls in Afghanistan. Our research employed an interpretive field approach, conducting interviews with 102 participants across 12 Afghan universities—eight public and four privates—to examine the challenges and opportunities associated with digital learning initiatives and inclusive education in a post-conflict setting. We also recommended ways to improve digital learning and promote inclusive education in the country. Our findings provide recommendations for digital and policy strategies to enhance the digital learning experience and promote inclusive education in post-conflict nations. The experiences and lessons learned from Afghan HEIs offer valuable insights for developing more accessible, engaging, and effective online education models.</p

Exploring the Synergy Between Financial Technologies and Financial Inclusion: What We Know and Where We Should Be Heading?

Background: Innovative financial technologies (fintech) are gradually changing how financial transactions and processes are conducted. The adoption of fintech not only benefits the financial sector but can also have a broader impact on society. Due to their ability to provide customized services to a wide range of stakeholders, fintech is gaining traction and experiencing significant growth. Compared to traditional financial institutions, fintech companies can reach a wider audience and operate more efficiently. In addition to upending traditional financial services, fintech can also provide financial services to marginalized groups. We argue that fintech research and practice should focus on seizing opportunities and addressing challenges related to financial inclusion, especially in emerging markets. Method: We conducted a systematic literature review of 178 articles to understand the relationship between fintech and financial inclusion. Results: Our analysis highlights six fintech research themes: fintech and financial inclusion, fintech adoption and use, fintech and sectoral growth, fintech and lending, and technology shaping the fintech. We also present four future themes (basic, driving, niche, and emerging or declining research) that can accelerate financial inclusion. Conclusions: This study highlights the synergies between fintech and financial inclusion research. This study contributes to existing knowledge in three ways. First, the descriptive analysis maps existing research on fintech and financial inclusion. Second, the qualitative analysis provides a comprehensive overview of how fintech and financial inclusion topics is interconnected. Third, future research areas for fintech and financial inclusion were identified. In general, fintech democratizes financial inclusion for the unbanked and marginalized communities while reducing operating costs. Governments should promote financial inclusion among those most vulnerable and affected by global threats

A Quest for Context-Specific Stock Price Prediction:A Comparison Between Time Series, Machine Learning and Deep Learning Models

Understanding the complexities of buying, selling, and holding stocks is crucial for institutional and individual investors to make informed decisions. Despite their significance, many investors face challenges in this area. Accurate stock price forecasting is a vital tool that aids investors in making profitable decisions. This study evaluates stock trends and patterns with an in-depth analysis of the Bombay Stock Exchange (BSE) stock data. We utilized various techniques, including time-series analysis, machine learning, and deep-learning models. This investigation spanned two distinct datasets: one with and one without COVID-19 stock price data. By comparing the outcomes, we seek to identify the most effective model for stock price prediction. Our findings indicate that each model has its strengths and limitations. Time series models accurately forecast short-term stock prices, whereas machine learning models demonstrate superior generalization capabilities. Deep learning models, however, stand out for their ability to predict long-term stock prices more accurately. Understanding each model's performance nuances is crucial for institutional and individual investors and regulators to optimize their strategies and decision-making processes.</p

Forewarned is forearmed

Purpose Internet of Things (IoT) interconnects many heterogeneous devices to each other, collecting and processing large volumes of data for decision making without human intervention. However, the information security concern it brings has attracted quite a lot of attention, and, at this stage, the smart step would be to analyze the security issues of IoT platform and get to the state of readiness before embarking upon this attractive technology. The purpose of this paper is to address these issues. Design/methodology/approach IoT risk assessment through the application of the analytical hierarchy process (AHP), a favorite multi-criteria decision making technique, is proposed. The IoT risks are prioritized and ranked at different layers, before which a well-defined IoT risk taxonomy is defined comprising of 25 risks across six layers of the IoT model for developing control and mitigation plans for information security of IoT. Findings People and processes layer, network layer and applications layer are the top three critical layers with risks like the lack of awareness, malware injection, malicious code injection, denial of service and inefficient policies for IoT practice get the highest priority and rank. Pareto analysis of the overall risk factors revealed that the top ten factors contribute to 80 percent of the risks perceived by information security experts. Research limitations/implications The study focuses only on certain predefined constructs or layers of the IoT model traced from legacy studies. It is essential to re-look these constructs on a timely basis to prolong the results’ validity. The study’s empirical scope is confined only to the risk perception of select IoT experts and does not encompass a broader segment of the IoT ecosystem. Therefore, the risks assessment may not be sweeping to a bigger audience. Practical implications The study implications are two-fold: one it consolidates the earlier siloed works to intensify the need for risk assessment in the IoT domain, and second the study brings yet another contextual avenue of extending the application AHP and Pareto principle combination. The paper also draws specific critical organizational interventions about IoT risks. A comprehensive approach to prioritizing and ranking IoT risks are present in this research paper. Originality/value The contribution of this study to the benchmarking of IoT risk assessment is two-fold. One, a comprehensive risk assessment taxonomy is proposed, and two, the risks are prioritized and ranked to give a convincing reference for the organizations while making information security plans for IoT technology. </jats:sec

Prioritizing and Ranking the Taxonomy of Factors Critical to GDPR Compliance

Abstract This study aims to unfold the myriad factors essential in the viewpoint of GDPR implementation. Additionally, this study will prioritize and rank the critical factors which would ensure the successful enablement of GDPR in any organization.The approach is to prioritize and rank the underlying factors for GDPR compliance by implementing an efficient multi-criteria decision-making technique, specifically the analytical hierarchy process. A well-defined taxonomy of twenty factors has been defined that spreads across the four broad categories of critical factors essential for the enablement of GDPR in an organization. Governance and People related factors get the highest priority and rank. Factors such as issuing privacy policy on the public platform, recruitment of GDPR experts have the highest priority. Additionally, with the help of Pareto analysis, it was discovered that the top eight factors account for 80% of the total weight age out of the twenty factors. This study focuses only on the pre-established components for GDPR implementation identified from previous notable works. The scope of the study is constricted to the insight of select GDPR experts also does not incorporate a broader spectrum of the Data Privacy ecosystem. This study contributes to the prioritization of factors for GDPR compliance in a two-fold manner. One, a complete classification of underlying factors is proposed, and two, they are prioritized and ranked to serve as a rulebook for GDPR practitioners while working towards the enablement of GDPR in their organization.</jats:p