Figure 3 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
This study investigates the barriers, adoption rates, performance impacts, and cost-benefit dynamics associated with sustainable IT implementation across various industries. The analysis highlights key barriers to sustainable IT adoption, including cultural resistance, regulatory requirements, lack of expertise, and cost of implementation. Cost, id...
Context in source publication
Context 1
... moderate rating, around 0.5, indicating that while sustainability efforts boost brand reputation, it may vary depending on the industry and customer awareness. In the case of regulatory compliance, rated around 0.6, showing that sustainable IT practices support compliance with environmental and data regulations, though it may depend on specific regulatory demands (Figure 3). Studies have shown that companies implementing sustainable IT often see increased customer satisfaction, as consumers increasingly value environmental responsibility. ...
Citations
... However, recent studies have begun to highlight the potential benefits of combining AI and blockchain to address complex business challenges. For instance, some researchers suggest that AI can improve the scalability and performance of blockchain networks by optimizing consensus mechanisms and reducing energy consumption (Aziz et al., 2023). Conversely, data can enhance the transparency and trustworthiness of AI systems, ensuring that AI models are secure, auditable, and transparent (Alasa et al., 2021; Alasa et al., 2020; Islam et al., 2023). ...
Artificial intelligence (AI), Machine Learning (ML), and blockchain technology are revolutionizing businesses by fostering creativity, efficiency, and security across a range of sectors. This paper explores the mutually beneficial relationship between these technologies using bibliometrics and content analysis, shedding light on their emerging applications and new research directions. We identify key industries like banking, supply chain management, and healthcare where blockchain and artificial intelligence are significantly influencing these disciplines by looking at a broad range of academic and commercial publications. Findings indicate that combining blockchain's decentralized security characteristics with AI-driven predictive analytics enhances automated decision-making, fraud detection, and transparent transactions. In a similar vein, supply chain management employs AI to forecast demand and maximize inventory, while smart contracts driven by blockchain technology streamline transportation. Blockchain and AI integration are used in healthcare applications to improve diagnosis, safeguard patient information, and facilitate interoperable medical records. Despite these advancements, problems with scalability, regulatory ambiguity, and technical complexity are impeding widespread adoption. Multidisciplinary collaboration, innovative policymaking, and advancements in blockchain and AI infrastructures are required to address these issues. By mapping the most significant papers, organizations, and academics that are affecting the field, this study offers valuable information for future research and business endeavors in this transformative sector.
... These technologies have demonstrated remarkable capabilities in areas such as medical imaging, predictive analytics, and clinical decision support. The application of ML to AS diagnosis promises significant improvements in accuracy, efficiency, and consistency, reducing the human error that can occur with traditional diagnostic methods (Aziz et al., 2023;Islam et al., 2023). ML models can learn from large datasets, identify complex patterns, and provide insights that might not be readily apparent to human clinicians. ...
Aortic Stenosis (AS) is a prevalent and potentially life-threatening cardiovascular condition that requires accurate diagnosis for optimal management. Traditional diagnostic methods, while effective, face limitations in terms of precision, timely detection, and clinician workload. The emergence of Machine Learning (ML) offers an innovative solution to these challenges, enhancing diagnostic accuracy and improving patient outcomes. This article explores how ML algorithms can be utilized to refine AS diagnosis, particularly through medical imaging and predictive modeling. In addition, the integration of ML in health information systems must be coupled with robust data security measures to protect sensitive patient information. We discuss the intersection of machine learning and healthcare IT security, focusing on innovative methods for safeguarding health data while improving diagnostic efficiency. The paper examines various ML techniques applied to AS, evaluates their impact on clinical workflows, and identifies the security protocols necessary to ensure compliance with privacy regulations. Finally, the study presents the potential challenges and future directions for integrating ML and health information security in clinical practice.
... Business models in various sectors are evolving rapidly, especially with the emergence of new technologies such as IoT and Blockchain. Traditional models are giving way to digital-first approaches that prioritize sustainability, transparency, and customer-centricity (Goyal et al., 2021;Chen et al., 2020;Islam et al., 2023;Aziz et al., 2023). IoT and Blockchain offer the ability to reimagine existing business processes to create more sustainable, efficient, and customer-focused models. ...
... IoT devices can collect patient health metrics, while blockchain ensures secure sharing of sensitive data across healthcare providers. MIS can analyze this data for early warning systems and personalized healthcare solutions, particularly in underserved regions (Goyal et al., 2021;Aziz et al., 2023). The other two objectives of the future with DeFi are reducing inequality and ensuring financial inclusion. ...
Purpose: Management Information Systems (MIS) function as the foundation for the integration of IoT and blockchain technology, facilitating data-driven decision-making and optimal resource allocation. This collaboration enables enterprises to innovate while tackling significant global issues. The United Nations' Sustainable Development Goals (SDGs) serve as a global framework aimed at addressing some of the most pressing issues facing humanity, such as poverty, inequality, and environmental degradation. Material and Methods: In response, businesses worldwide are increasingly aligning their strategies to these goals, recognizing that sustainable practices can offer both social and financial value. Among the transformative technologies driving these efforts are the Internet of Things (IoT) and Blockchain. IoT, with its ability to gather real-time data from connected devices, and Blockchain, with its focus on creating secure, transparent, and tamper-proof records, present unique opportunities for businesses to contribute meaningfully to the SDGs. Findings: This study explores how these technologies can be harnessed by businesses to advance the SDGs, examine their individual contributions, synergies, and case studies of successful implementation. Implications to Theory, Practice and Policy: By delivering real-time information and promoting cooperation, MIS improves the execution of sustainable initiatives in accordance with the United Nations' Sustainable Development Goals.
The Learners Information System (LIS) was introduced by the Department of Education (DepEd) in the Philippines to streamline administrative processes and enhance educational governance. However, its effectiveness in rural settings remains underexplored. This study evaluates the implementation of LIS in a rural Philippine high school using the E-Government and Digital Inclusion frameworks. A mixed-methods explanatory sequential design was employed, combining surveys and focus group discussions with 69 participants, including teachers, LIS coordinators, and the school principal. Findings reveal that LIS improves record-keeping and reduces administrative workload, but faces significant challenges, including inadequate IT infrastructure, technical failures, high maintenance costs, and limited inclusivity. These barriers hinder its potential to support Sustainable Development Goal 4 (SDG 4) and DepEd’s digital transformation goals. The study recommends targeted investments in IT infrastructure, offline functionalities, stakeholder training, and accessibility features to enhance the efficiency and inclusivity of LIS. Addressing these issues is critical for ensuring equitable digital transformation in rural education.
The cybersecurity threat landscape is constantly actively making it imperative to develop sound frameworks to protect the IT structures. Based on this introduction, this paper aims to discuss the application of cybersecurity frameworks into the IT security with focus placed on the role of such frameworks in addressing the changing nature of cybersecurity threats. It explores widely used models, including the NIST Cybersecurity Framework, Zero Trust Architecture, and the ISO/IEC 27001, and how they apply to industries including finance, healthcare and government. The discussion also singles out such technologies as Artificial Intelligence (AI) and Machine Learning (ML) as the core for real-time threat detection and response mechanisms. As these integration challenges demonstrate, the study provides tangible and proven approaches to tackle framework implementation issues such as legitimate security issues, limited availability of funds and resources, and compliance with legal requirements. By capturing current trends and exposures, the findings promote strong, portfolio-based and risk-appropriate security approaches adjusted for organizational goals and capable to prevent advanced cyber threats.
Globally, approximately one-third of the total food produced is wasted. The environmental effect of such wastage is the contribution to greenhouse gas emissions, and economically, money that could have been reinvested in the economy is lost. This research showcases the ability of IoT devices to gather data on wastage and management as they happen, as well as big data analytics in facilitating data acquisition for enhanced decision-making. The research proposal seeks to show how the use of these technologies can support waste minimization, efficiently utilize resources, and promote sustainability in the agro-food chain. Thus, a literature survey will be applied to outline the meth-odologies currently in use, the holes in current practice, and new ideas on how to incorporate IoT and big data in waste management. Furthermore, several case studies 'success stories' are highlighted to explain and quantify an actual application of these technologies in different sectors of the agro-food chain with tangible results in terms of reduction of the number of wastes and gains in efficiency. The application of Internet of Things (IoT) technologies and big data analytics for real-time handling of agro-food waste is a major problem in the agro-food sector. The conclusions highlight the positive effect of IoT and big data in tackling agro-food waste; at the same time, they give practical suggestions for the key players throughout the supply chain. Thus, our study gives insights into a shift in the management of agro-food waste through efficient technology and rallying policymakers, industry experts, and researchers to fully capture technological improvements for development.
The globalization of the retail industry exposes its supply chain to risks that affect operation, increase cost, and reduce customer satisfaction. This research focuses on exploring how generative AI can be implemented to improve the supply chain supply chain agility in the retail context. In this case, Generative AI will be functional in creating different supply chain schedules and analyzing these scenarios to predict disruptions and create responses appropriately. The supplier, logistics, and demand forecasting systems provide real-time information to formulate risk management strategies effectively. A detailed case within the retail industry for example shows that supply chain effectiveness increases of up to forty percent during disruption. The evidence points to Generative AI’s central function in the creation of durable supply chains that can effectively and continuously provide service and maximize retailers’ competitiveness.
Businesses use Generative Artificial Intelligence (AI) to support their marketing practices more often now. This paper proposes a new kind of Generative AI solution that would help with content generation, both copy and visuals, and the determination of customers’ segments. Based on the analysis of the past campaigns and the market, the data sets for the AI model create marketing content more appealing and effective for interaction and advertising revenues. A survey carried out in a retail grocery chain revealed an enhanced thirty percent in the relative effectiveness of the given promotion campaign and a significant decrease of overall marketing expenditures. The results presented have confirmed the ability of Generative AI to shift the Retail Marketing paradigm towards relying on data to make marketing decisions with little to no human influence. Empirically, this study expands the knowledge of AI applications in the retail context and provides applied recommendations for marketing managers wanting to implement sophisticated technological tools.
