International Journal of Information Management Data Insights最新文献

{"title":"Digital marketing strategies for luxury fashion brands: A systematic literature review","authors":"Fung Yi Tam,&nbsp;Jane Lung","doi":"10.1016/j.jjimei.2024.100309","DOIUrl":"10.1016/j.jjimei.2024.100309","url":null,"abstract":"<div><div>The main objective of this study is to examine how digital marketing strategies can be put into practice and integrated into the actual operation of luxury fashion brands. To do this, the study explores the implementation of digital marketing strategies that are adopted by the luxury fashion brands. It uses a systematic literature review (SLR) and the real cases of luxury fashion brands as the research methodology. After conducting the SLR, the 15 digital marketing strategies for luxury fashion brands mapping into six categories in terms of types of digital marketing media, and the level of company's control and communication are identified. As digitization has become part of our daily routines and consumers are spending more time online and using social media, this study proposes that digital marketing strategies emerge as a powerful force for luxury fashion brands to better succeed in the competitive global market.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning algorithm for personalized healthy and sustainable grocery product recommendations","authors":"Laura Z.H. Jansen ,&nbsp;Kwabena E. Bennin","doi":"10.1016/j.jjimei.2024.100303","DOIUrl":"10.1016/j.jjimei.2024.100303","url":null,"abstract":"<div><div>Nowadays, retailers try to optimize the shopping experience for consumers by offering personalized services. Recommending food options, i.e. providing consumers suggestions on what products to buy, is one of such services. Food recommender systems for grocery shopping are typically preference-based, using consumers' shopping history to determine what products they would like. These systems can predict well what a consumer would potentially like to buy, however, they do not stimulate consumers to buy healthier or more sustainable food options. In response to increasing global concerns about public health and sustainability, this paper aims to integrate healthiness and sustainability levels of food options in recommender systems to encourage consumers to buy better food options. To assess the impact of integrating healthiness and sustainability information of food choices in predicting an item to buy, we employ three food recommendation models: a Baseline popularity-based model, Restricted Boltzmann Machine (RBM), and Variational Bayesian Context-Aware Representation (VBCAR) based on (1) preferences, (2) preferences and health, (3) preferences and sustainability, and (4) all combined attributes. Models were trained and tested using two different datasets: Instacart and a Dutch supermarket dataset. The experimental results indicate improved performance for VBCAR compared to Baseline and RBM. Models that emphasize healthiness and/or sustainability of food choices do not significantly alter model performance compared to preference-based models. The results of the health and sustainability-based recommender systems demonstrate the potential of recommender systems to assist people in finding healthier and more sustainable products that are also suited to their preferences.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100303"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring semantic relatedness and revealing fairness and biases through trend tests","authors":"Jean-Rémi Bourguet ,&nbsp;Adama Sow","doi":"10.1016/j.jjimei.2024.100305","DOIUrl":"10.1016/j.jjimei.2024.100305","url":null,"abstract":"<div><div>An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100305"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How digital technologies and AI contribute to achieving the health-related SDGs","authors":"Philipp Koebe","doi":"10.1016/j.jjimei.2024.100298","DOIUrl":"10.1016/j.jjimei.2024.100298","url":null,"abstract":"<div><div>Enhancing global health stands as a pivotal objective within the United Nations' Sustainable Development Goals (SDGs). In the wake of the ongoing digital transformation across various spheres of life, the incorporation of new digital technologies and the utilization of artificial intelligence hold the potential to contribute significantly to the attainment of these objectives. Leveraging the scalability inherent in digital business models, coupled with the widespread adoption of smartphones, facilitates the broad dissemination of digital healthcare services, even within emerging and developing nations. This inquiry adopts a quantitative research methodology to examine the implications of this phenomenon. In 2023, a cohort of 103 experts within German-speaking countries participated in an online survey, offering their insights into the impact of digitalization on health-related sustainability goals. The survey encompassed an assessment of the influence of digital technologies and AI on 13 sub-goals within the health domain, as well as on six additional SDGs. The comprehensive evaluation revealed that all 19 sub-goals exhibit a discernible medium to high impact. The analysis underscores that domains such as education and early warning systems are particularly amenable to digital interventions. Conversely, endeavors targeting the reduction of tobacco consumption or drug abuse may benefit from complementary measures. Conclusively, this study not only presents a developmental perspective on modeling but also formulates ten actionable recommendations, elucidating potential avenues for advancing the integration of digital technologies and artificial intelligence to enhance health-related sustainability goals.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100298"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Metaverse's experience through optimization of players’ decisions","authors":"Stav Cohen ,&nbsp;Barak Fishbain","doi":"10.1016/j.jjimei.2024.100316","DOIUrl":"10.1016/j.jjimei.2024.100316","url":null,"abstract":"<div><div>The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the \"SunflowerLand\" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is software design gender biased? A study on software-design effect on task performance","authors":"Samaa Elnagar","doi":"10.1016/j.jjimei.2024.100312","DOIUrl":"10.1016/j.jjimei.2024.100312","url":null,"abstract":"<div><div>Software design is critical to the development of software. However, existing literature highlighted the presence of gender bias in software design, which might be causing differences in task performance between males and females. Supported by theories such as the cognitive load theory, emotional design theory, and the Aesthetic-Usability Effect, this research aims to explore the potential disparities in task performance between males and females. The study developed two tasks using two different software in terms of user friendliness. The study was performed on two groups that possessed comparable educational backgrounds and professional experiences. The investigation encompassed two tasks aimed at evaluating performance in both professional and domestic contexts. Through the application of structural equation modeling and a range of statistical analyses, the study identified disparities among females, including high perception of cognitive load and lack of emotional design. The study emphasizes on the importance of incorporating phycological cognitive differences in design and ensuring inclusive design personas in software design. Addressing the cognitive and emotional aspects of software design will reduce task performance discrepancies and shift the misbelief that task performance discrepancies are attributable to gender-based intellectual differences, rather than deficiencies in software design.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opening a career door!: The role of ChatGPT adoption in digital entrepreneurial opportunity recognition and exploitation","authors":"Cong Doanh Duong,&nbsp;Thi Thanh Hoa Phan,&nbsp;Bich Ngoc Nguyen,&nbsp;Thanh Van Pham,&nbsp;Ngoc Diep Do,&nbsp;Anh Trong Vu","doi":"10.1016/j.jjimei.2025.100326","DOIUrl":"10.1016/j.jjimei.2025.100326","url":null,"abstract":"<div><div>This study aims to extend the Stimulus-Organism-Responses (SOR) model by investigating the influence of AI-related stimulus, particularly ChatGPT adoption in entrepreneurship, on individuals’ cognitive organism (e.g., digital entrepreneurial identity aspiration) and subsequent behavioral responses (e.g., digital entrepreneurial opportunity exploration and exploitation) as well as testing the negative moderation of stressor (i.e., technology anxiety). Using a sample of 1326 MBA students in Vietnam with a stratified sampling approach, structural equation modeling (SEM) is employed to rigorously examine the proposed relationships and test the moderation effect. The findings of the study unravel the complex dynamics within the extended SOR model, showcasing positive relationships between ChatGPT adoption in entrepreneurship and digital entrepreneurial identity aspiration, digital entrepreneurial opportunity exploration, and exploitation. Additionally, the research sheds light on the mediating role of digital entrepreneurial identity aspiration and the moderating effect of technology anxiety, providing nuanced insights into how these factors interact in the context of digital entrepreneurship. This research advances the academic discourse by applying the SOR framework to the context of digital entrepreneurship, investigating how AI-driven stimuli influence cognitive and behavioral dimensions. The inclusion of technology anxiety as a stressor adds a novel dimension, addressing the often-overlooked psychological barriers associated with AI adoption in entrepreneurial contexts. Practitioners can draw practical insights from this research to strategically leverage AI technologies in fostering digital entrepreneurial identity, navigating digital opportunities, and overcoming challenges posed by technology anxiety. While this study offers valuable contributions, its reliance on self-reported data and a cross-sectional design may limit causal inferences and generalizability. Future research should consider longitudinal designs and diverse samples, such as established entrepreneurs or non-Asian populations, to explore ChatGPT adoption in broader contexts.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100326"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare data governance assessment based on hospital management perspectives","authors":"Shinta Oktaviana R ,&nbsp;Putu Wuri Handayani ,&nbsp;Achmad Nizar Hidayanto ,&nbsp;Bambang Budi Siswanto","doi":"10.1016/j.jjimei.2025.100342","DOIUrl":"10.1016/j.jjimei.2025.100342","url":null,"abstract":"<div><div>Hospitals are the most prominent health data producers of clinical data and administrative data. Reusing health data from hospitals has problems regarding data quality and integrity within the institution. These problems raise healthcare expenses, cause errors in patient treatment, and consume a lot of hospital IT personnel's work time for data extraction and synchronization. Implementing health data governance helps hospitals improve patient safety, increase research in the health sector, establish policy, protect data and information assets, and determine accountabilities and processes for managing data and information. However, only a few countries can implement health data governance in hospitals. In developing countries, research related to health data governance is rare. We conducted this research to determine the requirement for health data governance for hospitals in developing countries. This study is qualitative research with a single case study. The data was taken from the National Cardiovascular Center in Jakarta from June 2022 until December 2023. We used semi-structured interviews with IT and hospital management. There are 18 people as our interviewees. We used thematic analysis. Data governance domain areas are used in our initial code, and we continue to open coding to find the meaning of the interviewee's statement. The axial code was performed to get critical problem-related health data in the hospital. We got six themes as our problem identification. The themes were incomplete data, incorrect data, data redundancy, lack of documentation, data discoverability issues, and no ethical consideration in data access. After that, we mapped the problem to the domain area of data governance. This research found that the domain areas most required for health data governance were data quality management, metadata management, and data security management.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction","authors":"Payam Boozary ,&nbsp;Sogand Sheykhan ,&nbsp;Hamed GhorbanTanhaei ,&nbsp;Cosimo Magazzino","doi":"10.1016/j.jjimei.2025.100331","DOIUrl":"10.1016/j.jjimei.2025.100331","url":null,"abstract":"<div><div>This paper investigates the use of machine learning models for customer churn prediction, focusing on the comparative effectiveness of ensemble approaches such as XGBoost and Random Forest with classical classifiers. The study evaluates the benefits and shortcomings of each strategy in dealing with complicated datasets by analyzing confusion matrices and Receiver Operating Characteristic (ROC) curves in detail. Ensemble models outperformed on key criteria such as accuracy, precision, recall, and F1 scores, yielding excellent results. These results demonstrate the effectiveness of ensemble approaches in producing accurate and trustworthy forecasts, making them suitable for client retention efforts. The report offers practical insights for firms looking to use sophisticated machine learning approaches to make better strategic decisions and retain more customers.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100331"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials","authors":"Himanshu Joshi","doi":"10.1016/j.jjimei.2025.100332","DOIUrl":"10.1016/j.jjimei.2025.100332","url":null,"abstract":"<div><div>This paper examines the key determinants of behavioral intention, user satisfaction, and chatbot adoption among urban, college-educated student populations within Generation Z and Millennials in India. While Millennials grew up with the Internet, Gen Z was born into the era dominated by social media and smartphones, making them inherently tech-savvy and drawn to chatbots for information access. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating technological elements with trust and satisfaction to propose a conceptual model. Using a mixed-method approach, data were collected through a cross-sectional online survey of 487 chatbot users from urban educational institutions in India. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test 11 hypothesized direct relationships. The results suggest that users' willingness to adopt chatbots is significantly influenced by performance expectancy, social influence, trust, and satisfaction. Regarding user satisfaction, both facilitating conditions and trust played substantial roles. Additionally, this study found meaningful associations between facilitating conditions, satisfaction, intention, and adoption. Multi-group analyses revealed notable differences in chatbot adoption factors between Gen Z and Millennials within the study's sampled population. Given the importance of trust in chatbot adoption, the paper highlights that reducing perceived risks can strengthen trust, enhance user satisfaction, and drive chatbot intention and adoption. The above findings offer context-specific insights for chatbot providers in devising strategies to improve user trust, satisfaction, and adoption within similar demographics.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100332"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}