Decoding Trust: Blockchain Solutions for Transparency in Digital Advertising

This study seeks to identify respondents' attitudes toward online advertisements from Jordanian tourism companies by examining their exposure patterns, viewing motivations, and preferences for this medium. The study adopted a quantitative methodology to collect data from a sample of 450 participants in Jordan, using an online questionnaire designed to measure engagement with online advertisements and analyze the factors influencing individuals' responses. The results aim to provide a deeper understanding of Jordanian digital consumer behavior in the tourism sector and support companies in developing more effective marketing strategies. The study also seeks to shed light on the role of digital technology in shaping tourism consumer decisions. The results showed that the majority of individuals who interact with tourism companies’ online advertisements do so moderately at 75.4%, reflecting the prevalence of this advertising style and its impact on the target audience. It was also found that the main motivation for individuals to interact with these advertisements is to benefit from the promotional offers and discounts on the services provided, indicating that economic aspects play a crucial role in attracting the audience’s attention. In addition, the study revealed that one of the most prominent trends towards these advertisements is the belief that they are more effective than traditional advertising methods, which enhances their role in tourism promotion and contributes to improving the digital marketing strategies of Jordanian tourism companies. The study indicates that Jordanian tourism companies seek to develop their digital marketing strategies by offering attractive promotional offers and improving the quality of advertising content to enhance audience engagement and interest, as well as leveraging digital analytics tools to accurately and effectively target the appropriate audiences. The study highlights the need to invest in artificial intelligence and digital personalization technologies to enhance user experience and increase advertising response rates.

This article shows the impact of the war on small and medium-sized businesses in Ukraine during the full-scale invasion of the Russian Federation, which started on February 24, 2022, and ended in September 2023. The object of the study is small and medium-sized enterprises that carry out business activities in Ukraine during a full-scale invasion. The research objective is to determine the business strategies and Internet marketing strategies used by small and medium-sized enterprises after full-scale invasion. The purpose of this study is to identify the most effective Internet marketing strategies and Internet marketing tools that helped small and medium-sized businesses survive the full-scale invasion of Ukraine by the Russian Federation and continue to grow. The study was conducted through in-depth interviews with representatives of small and medium-sized businesses from different regions of Ukraine and from different fields of activity. Based on the categorical analysis of the responses, three periods of enterprise survival were summarized according to the chronology of military and political events in the country. The relevant Internet marketing strategies of small and medium-sized enterprises are identified. Business strategies and Internet marketing strategies that helped small and medium-sized enterprises increase their pre-war economic performance are also highlighted. Certain reasons for the deep crisis of small and medium-sized enterprises are outlined. The intensity and effectiveness of the use of Internet marketing tools by small and medium-sized businesses after a full-scale invasion are determined. The peculiarity of the results is the study of Internet marketing strategies and successful business strategies of small and medium-sized enterprises during the war. The scientific novelty of thisstudy is that it classifies the periods of survival and growth of small and medium-sized enterprises and identifies the most effective Internet marketing strategies for these periods. The efficiency and intensity of using Internet marketing tools were also systematized. The results obtained in the study will help small and medium-sized enterprises overcome crisis conditions and identify areas for the effective use of digital marketing. The findings are of practical value for small and medium-sized enterprises in other countries where there is a possibility of armed conflict.

In the current framework of human rights, data privacy is finding its home as an independent human right, separate from its historical home under the umbrella of general privacy. However, there is no consistent system or standard for defining this right, and different regions require substantially different levels of protection. This inconsistency has allowed for corporations, by way of their executive officers, to avoid or completely ignore the requirements imposed by many countries. Moreover, the penalties in many regions are not severe enough to incentivize corporations to change their behavior. The lack of a truly global system and standard for enforcing this right, and the specific lack of pressure on the officers that direct corporate policy, has allowed data privacy violations to go severely under-checked. This Note seeks to provide a novel solution for tackling corporate holdup in complying with data privacy laws. This Note examines the historical roots of data privacy as a human right, discusses its similarity to resources that have been considered public utilities, and provides examples of instances where the right to data privacy has been ignored by corporate officers. By modifying the United Nations’ existing sanction procedure and jurisdiction, this Note proposes that the United Nations would be able to target corporate officers individually for their roles in data privacy violations. By leveraging personal liability for noncompliance, the United Nations could generate a global sense of accountability to the modern, human right to privacy in one’s personal data.

The rapid advancement of digital services and online interactions has highlighted the need for secure, user-centric identity management systems. Traditional identity solutions, often centralized and dependent on trusted third parties, pose challenges related to privacy, security, and control over personal data. Distributed Ledger Technology (DLT), particularly blockchain, offers a promising solution for decentralized identity management by enabling self-sovereign identities (SSI). Through the use of decentralized identifiers (DIDs) and verifiable credentials (VCs), DLT allows individuals to maintain full control over their personal information, eliminating the need for intermediaries while ensuring data integrity and privacy. This paper explores the key principles of DLT-based decentralized identity management, discussing its potential to enhance privacy, security, and interoperability in digital ecosystems. We examine the various technical frameworks, challenges, and standards in the field, with a focus on the integration of DLT with emerging technologies such as zero-knowledge proofs (ZKPs) and secure multiparty computation (SMPC). Additionally, we evaluate real-world use cases, from financial services to healthcare, and the role of regulatory frameworks in shaping the future of decentralized identity systems. Ultimately, DLT presents a paradigm shift in identity management, offering scalable, transparent, and trusted solutions for the digital age.

Contemporary supply chains operate within increasingly complex environments where traditional enterprise resource planning architectures struggle to deliver comprehensive traceability and data immutability across geographically dispersed networks. This article investigates the integration of blockchain technology with SAP Business Network to address persistent challenges in supply chain transparency, fraud prevention, and regulatory compliance. The integration framework explores architectural patterns connecting SAP Business Technology Platform with enterprise blockchain frameworks, including Hyperledger Fabric, examining how distributed ledger technology complements existing SAP capabilities through immutable transaction recording, smart contract automation, and cryptographic verification mechanisms. Empirical applications across pharmaceutical, food and agriculture, aerospace, and luxury goods sectors demonstrate measurable improvements in counterfeit detection, recall efficiency, authentication accuracy, and consumer trust. The article offers technical integration models such as middleware-based event translation, hybrid visibility designs coupling SAP Global Track and Trace with blockchain authentication, and in-app blockchain capability in SAP Fiori applications. Implementation issues touching on data privacy, scalability constraints, interoperability needs, and consortium governance are given a thorough evaluation together with countermeasures. The model offers prescriptive advice for organizations considering blockchain adoption in SAP environments, covering both technical requirements and organizational readiness drivers necessary for effective deployment.

The integration of blockchain technology and hybrid deep learning techniques in healthcare systems is a ground-breaking approach that aims to improve scalability, security, and patient care. Blockchain provides a decentralized, transparent framework for secure data storage, sharing, and access control, ensuring data integrity, privacy, and interoperability. Hybrid deep learning, combining deep learning algorithms with traditional machine learning approaches, enables accurate and efficient processing of complex healthcare data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, ensuring only authorized entities can access and modify sensitive health information. The framework also enables real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning offers numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision-making. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient data management, personalized patient care, and advancements in medical research.

The paper explores how blockchain technology can transform data management, especially when it comes to handling privacy and security issues. Blockchain's immutability, transparency, and decentralization make it a potentially useful tool for addressing the expanding problems associated with data security and integrity. However, the slow adoption of blockchain technology is influenced by a complex interplay of developing privacy concerns, legislative uncertainty, and technological subtleties. The current study aims to identify key data privacy, protection, and security challenges associated with blockchain adoption in various domains, assess the adequacy of the current data privacy, protection, and security laws to address these challenges, and propose potential solutions and areas for further research to mitigate data privacy, protection, and security risks in blockchain applications. Additionally, the research has examined the adaptability and dependability of the two main kinds of blockchain—public and private—as well as the variations in their reach and visibility.

This research aims to investigate the impact of advertising on Instagram on consumer purchase intentions using the Scopus database, covering the time from 2015 to 2024. Furthermore, this research provides recommendations for additional potential domains of investigation and advances in this discipline. Utilizing VOSviewer software, 50 articles underwent bibliometric analysis to visualize patterns such as keyword occurrences. The research underscores the dominance of quantitative methodologies, comprising 84% of the research approaches, while qualitative methods remain underexplored. The findings highlight that consumer purchase intentions are influenced by complex factors, including consumer behavior, brand strategy, social media, advertising, and product involvement, and several trend issues were obtained, including time risk, brand awareness, Gen Z, attitude, online advertising, brand strategy, and consumer behavior. Understanding these dynamics is crucial for marketers to design effective strategies that attract and fulfill consumer needs, ultimately driving profitable purchasing decisions. The study suggests a critical need for future research to integrate qualitative approaches and deepen exploration into the impact of Instagram advertising on consumer behavior, providing insights for more effective marketing strategies and scientific advancements in this field.

This paper examines the impact of different internet advertising strategies on the effectiveness of skincare product promotion. As online shopping has become an integral part of modern consumer habits, understanding which advertising methods yield the most effective results is crucial for optimizing marketing strategies. This study analyzes various case examples of online skincare advertisements to identify patterns of effectiveness based on factors such as platform type, engagement style, and target audience. For instance, different channels like social media ads, search engine promotions, and content marketing often have their unique advantages in attracting potential customers. Moreover, the study will focus on how different engagement styles—such as interactive content, short videos, and image-rich advertisements—affect audience engagement and purchase intentions. The findings of this research will offer practical recommendations for future campaigns, helping businesses to design more effective marketing strategies that enhance advertising reach and sales conversion. These insights will not only benefit the skincare industry but also provide valuable guidance for digital marketing in other sectors.

Blockchain has applications in a broad range of settings, including healthcare (e.g., patient data management, health supply chain management, financial and insurance claims, and preventing counterfeit medications). There are, however, challenges associated with implementing such solutions, for example project failure rate, and the need for various parties to function together. Currently, there is a lack of quantitative and comprehensive models in the literature that researchers and practitioners can use to assess healthcare organizations’ readiness for blockchain adoption, particularly from multiple perspectives and using expert judgements. Thus, the objective of this article is to develop a scoring model to evaluate healthcare organization's readiness to adopt blockchain in the context of electronic health records system management. Specifically, in this article, a literature review and expert feedback were used to identify the most important factors influencing blockchain adoption. The outcomes of the article present an identification and prioritization of the blockchain adoption impacting factors. The article identified 17 factors. These factors are grouped into five perspective: financial, social, technical, organizational, and regulations and legal. The results of the article highlight that regulation compliance, regulation uncertainty, budget availability, management support, security and privacy, and financial risk and uncertainty play an important role in the organization's readiness for blockchain adoption. The readiness scoring model then developed and evaluated through two real-world case studies at two healthcare organizations. Two case studies were conducted to demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions in the healthcare organization's capabilities. The goal is to prevent any possible issues, before the project starts, in order to increase chances of a successful blockchain adoption.

The rapid evolution of wireless communication systems has driven the development of advanced antenna technologies like massive MIMO, beamforming, and millimeter-wave antennas. These innovations are crucial for 5G networks and beyond, offering enhanced performance and capabilities. However, their successful adoption relies on effective marketing strategies that educate stakeholders and demonstrate value. This chapter explores integrated marketing communications (IMC) approaches for promoting advanced antenna technologies across aerospace and telecommunications industries. It outlines key IMC principles, addresses challenges in communicating technical benefits, and recommends diverse marketing tactics, including thought leadership content, case studies, industry events, digital advertising, and influencer partnerships. The chapter emphasizes the importance of audience segmentation, data-driven optimization, and cross-functional collaboration between marketing, sales, and technical teams.

The rapid evolution of artificial intelligence (AI) has significantly reshaped the digital advertising landscape, offering new opportunities for businesses to enhance efficiency, personalization, and customer engagement. This dissertation explores the transformative role of AI in digital advertising strategies, focusing on its impact on targeting precision, content creation, campaign automation, and real-time analytics. Through a combination of secondary data analysis and case studies, the study highlights how AI technologies—such as machine learning, natural language processing, and predictive analytics—enable marketers to understand consumer behavior better and deliver hyper-personalized experiences. The research also examines the ethical challenges and limitations of AI in advertising, including data privacy concerns and algorithmic bias. The findings underscore the importance of strategic AI integration to maintain competitiveness in the digital marketing arena. This study contributes to a deeper understanding of how businesses can leverage AI to revolutionize their advertising approaches and improve overall marketing performance. KEYWORDS: Artificial Intelligence, Digital Advertising, Personalization, Targeting Precision, Content Creation, Campaign Automation, Real-time Analytics, Machine Learning, Natural Language Processing, Predictive Analytics, Consumer Behaviour, Data Privacy, Algorithmic Bias, Marketing Performance

Simple SummaryAs Australians increasingly purchase their companion dogs online, Australian state and territory authorities are faced with the challenge of ensuring online sales adhere to local regulations. Using webscraping techniques, we analysed 1735 unique advertisements for dogs and puppies from Gumtree—one of Australia’s most popular trading platforms—and benchmarked levels of microchipping, desexing and breeder identification numbers in each state and territory. We found an increased likelihood of microchipping in states requiring microchipping prior to sale and inclusion of chip numbers in advertisements. Older animals who were microchipped were more likely to be desexed, and advertisements placed by breeders who were selling vaccinated puppies were more likely to include a breeder registration number in their ad than sellers who identified themselves as owners. We recommend regulatory bodies use this data to make evidence-based decisions on future regulation and use this benchmark to monitor effectiveness of any changes.In Australia, each state and territory authority implements and enforces regulations regarding dog management—including the breeding and sale of dogs online—which is increasingly becoming the most popular method of obtaining pets. The aims for this study included: 1. Benchmarking regulatory compliance in online dog advertisements in Australia, and, 2. Understanding factors associated with regulatory compliance in online advertisements. We collected advertisements for dogs and puppies from Gumtree—one of Australia’s most popular online trading platforms—on two separate days, two weeks apart (25 March and 8 April 2019). A total of 1735 unique advertisements were included in the dataset. Chi-squared tests and multivariable logistic regression models were used to identify risk factors for microchipping, desexing and breeder identification number, and compliance levels. State laws requiring animals to be microchipped prior to sale and the inclusion of chip numbers in advertisements were found to be the biggest factor in increasing likelihood of microchipped animals in Gumtree advertisements, while desexing was more common in microchipped and older animals. The online ad was more likely to include a breeder ID if the dog was young, vaccinated, and advertised by a breeder rather than an owner. The findings from this study will assist regulators to make evidence-based decisions on managing online advertisements for companion animals. In the future, the benchmarking this study has presented will allow future analysis of the effectiveness of regulation changes.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

Data privacy and cybersecurity challenges in the digital transformation of the banking sector