International Journal of Information Management Data Insights最新文献

In both the developed and developing world, cyber-violence presents a problem that affects most families and societies. However, there is a lack of diagnostic studies dedicated to this phenomenon, especially its factors and the relevant models. Consequently, the aim of this study was to extract the factors that bear upon cyber-violence by conducting a systematic literature review. This was anticipated to lead to a better understanding of those factors with their models and effects, as explored in previous research. This systematic literature review resulted in a comprehensive and rigorous examination of the relevant literature, which included numerous articles of interest. The findings indicate that most of the past research on cyber-violence has primarily looked at the factors affecting the behavioral intention to engage in the phenomenon, while very few studies have looked at its effects, especially in developing countries. The period from which the primary studies were collected was 2010-2024, using the ScienceDirect, IEEE Explorer, Springer, SAGE, Taylor and Francis, MDPI and Emerald Insight databases. Inclusion and exclusion criteria were applied, resulting in 91 studies to be systematically reviewed. It emerged from this review that engagement in cyber-violence was predominantly due to a lack of cyber-violence models and a failure to consider the factors that potentially contribute to engagement in cyber-violence. Hence, these factors are identified in this paper, looking at the academic, economic and social impact of cyber-violence. The outcomes of this review could assist future researchers by providing a roadmap of institutional repositories and guidelines for recognizing the models and factors of cyber-violence, together with its effects.

The research delves into the intricacies of green service consumption within the retail sector, concentrating specifically on delineating customer expectations and elucidating the transformative technological interface. The study elucidates the intricate dynamics of customer expectations and technological transformations to enhance the purchase experience in retail stores, focusing on the evolving paradigm in the retail landscape from a product-centric to a service-oriented economy and unveiling the pivotal role of societal influences, technology and customer expectations. The central theme revolves around understanding green service consumption within the retail sector, emphasizing the interplay between societal values, technological innovations and the dynamism of customer expectations. The research design employed is qualitative, leveraging a multidisciplinary approach and gleaning insights from sociotechnical perspectives, environmental sustainability, AI and consumer behavior. The sampling design involved experts in the field who participated in interviews and were purposefully selected based on their expertise in green service consumption and retail practices. The analysis utilized the fuzzy DEMANTEL method, involving a rudimentary exploration of dimensions. The research leverages extensive and in-depth deliberations involving theoretical explorations, connections among influencing factors and methodological insights from fuzzy logic and DEMATEL to dissect the complexities of green service consumption. The study elucidates the pivotal transition in the retail landscape, unveiling the changing nature of customer expectations, the surging significance of green service consumption and the influence of societal values and technological advancements. The discourse signifies the paramount role of societal values, particularly the increasing awareness of sustainability, ethical sourcing and eco-friendly strategies, in shaping retail strategies. The research presents a multifaceted view of green service consumption, thereby contributing to an enriched theoretical landscape of green service-focused retail experiences.

Customer Lifetime Value (CLV) represents the total worth of a customer to a company over time, aiding businesses in resource allocation and tailored marketing for profitability. This literature review fills a research gap by examining how customer risk factors are integrated into CLV calculations. We conducted a systematic literature review across databases, adhering to strict criteria for relevance and quality. The review analyzed CLV methodologies and outcomes, highlighting the use of mean–variance analysis to optimize customer portfolios, with customer income fluctuations identified as a major risk factor. The study also explores the evolution of CLV research, particularly in the application of Machine Learning (ML) for risk-adjusted CLV. Our findings offer a comprehensive overview, laying the groundwork for future research and helping businesses refine risk management strategies, identify high-risk customers, and enhance customer value through more dynamic, data-driven models.

The new normal has transformed IT company employees' working styles and patterns to accommodate changing business demands. It brings back the attention required for self-health, and the pandemic has demonstrated the importance of Health. The prominence of Gamification and its adaptation is growing in the education and learning industries. This research paper aims to study, analyze, and understand users' intention to use gamified health monitoring tools or applications with emerging health disruptions in the post-pandemic era. The study gathered data from Indian users as the primary source, where the participants' backgrounds were from Information Technology (IT) companies, and the survey gathered 185 responses online from Gen Z women. This study shows a significant association between the gamified engagement approach and user intention to engage. Considering the ongoing business uncertainty and growing work-from-home hours for IT employees, disruption may persist while creating the need for gamified health engagement.

Blockchain technology can enhance sustainable food security because of its distinct characteristics such as traceability, decentralized and unchangeable databases, and smart contract protocols. Nevertheless, blockchain technology in agricultural applications is still in the early stages of development. Therefore, this study aims to ascertain the efficiency and cognitive framework of blockchain technology for attaining long-term food security. This study used a review-based approach to ascertain the intellectual framework. A literature search was conducted using the "Scopus" database to locate research articles published between 2017 and 2023. A “systematic literature review” was performed using the PRISMA framework on the 52 eligible publications. The study results indicated that traceability, real-time information availability, and immutably distributed databases were the most influential factors. The results showed that blockchain technology has benefits beyond facilitating reliable data dissemination and establishing intimate bonds between manufacturers and clients. Furthermore, blockchain technology may pave the way for less food waste, improved supply chains and agricultural working environments, and more environmentally responsible eating practices. This study is the first of its kind to assess the intellectual structure of food security and augment the cognition of the in-depth examination of the benefits of blockchain technology that might ultimately provide a way to achieve zero-hunger goals.

The study offers a thorough investigation on the strategies necessary to attract and retain environmentally conscious customers in the E-Retail market in Southeast Asia. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) have been deployed to analyze the intricate relationships among the sustainable business practices, consumer engagement strategies, technological interventions for sustainability, integration of a triple bottom line approach, and the balance between sustainability and profitability. Hypotheses testing on the empirical data have revealed that there is no significant relationship between sustainable business practices and attracting and retaining environmentally conscious customers. However, there are positive correlations between consumer engagement strategies, technological interventions for Sustainability, and the integration of triple bottom line approach with client attraction and retention. The work highlights the importance of a thorough sustainability plan, in-line with consumer expectations. Practical consequences emphasize the importance of tailoring marketing efforts, ensuring effective communication, and implementing technological advancements to improve the eco-friendly shopping experience. Difficulty in establishing a balance between sustainability and profitability have been highlighted as a prominent challenge for the e-retailers.

Industry 5.0 supply chains face critical challenges in effectively managing the rapidly growing volume, variety, velocity, and veracity of big data while simultaneously ensuring sustainability, privacy, and ethical practices. The complex and interconnected nature of modern supply networks and the swift adoption of advanced technologies have created an urgent need for innovative frameworks to navigate these multifaceted challenges. Existing approaches often fail to adequately address the unique demands of Industry 5.0, lacking the ability to process data in real time, uncover deep insights, and enable dynamic, risk-informed decision-making. Moreover, there is a pressing need for frameworks that emphasize interdisciplinary collaboration and proactively address the potential negative impacts of emerging technologies. This paper introduces a novel, multidisciplinary framework that integrates cutting-edge techniques to tackle these challenges head-on, paving the way for more resilient, intelligent, and adaptable supply chains in the Industry 5.0 era.

This study tackles the complex task of measuring investor sentiment, a latent variable often measured through various proxies. The focus here is on textual sentiment extracted from online sources, specifically news media and social media sentiment. The central inquiry is whether these proxies are equivalent indicators of investor sentiment. Employing firm-level daily sentiment scores for DJIA stocks and leveraging Granger causality and transfer entropy, the research investigates the dynamics of information flow between these proxies. The findings show a prevailing pattern: information predominantly flows from social media to news for the majority of stocks while a reverse relationship is established for some stocks. The variations across stocks suggest that these proxies do not uniformly capture the same underlying phenomena. The study shows the significant role of social media in shaping news media sentiment and prompts considerations about regulating social media platforms in the context of their impact on financial markets.

Industry 5.0 ecosystems focus on a human-centric approach to operations and supply chain management by integrating stakeholders, advanced technologies, and processes. While incorporating social media (SM) information into demand forecasting can significantly improve accuracy, it also brings about several challenges. This paper proposes an approach to leverage Big Data originating from SM networks combined with human judgment to build demand forecasts for new products. The structured methodology is demonstrated to improve forecast accuracy in a real case of a F&B company while providing several insights into the challenges and opportunities of integrating advanced information technology into the demand forecasting process. The main challenges include effectively categorising the impact factors of SM on demand forecasting, translating insights from SM into actionable decisions, and ensuring the accuracy and reliability of the data obtained from SM networks. Future studies should involve collaborative expert input and validating the approach across various companies and industries.

Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.