International Journal of Information Management Data Insights最新文献

{"title":"Driving user adoption of mobile apps through platform multihoming: The effects of multihoming delay and multihoming customization","authors":"Wondwesen Tafesse,&nbsp;Kathy Ning Shen","doi":"10.1016/j.jjimei.2024.100263","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100263","url":null,"abstract":"<div><p>Developers release their mobile apps through multiple app store ecosystems to extend their reach and reduce their dependency on a single distribution platform and its governance mechanisms. While this topic has piqued the interest of a growing number of studies, key multihoming decisions faced by developers including when to multihome mobile apps and whether and how much to customize them for the multihomed platform have not yet been jointly examined. In addition, the potential role that the user value of mobile apps (hedonic or utilitarian) plays in developers’ multihoming decisions has largely been overlooked. To address these gaps, the present study jointly investigates the effects of multihoming delay and multihoming customization on the user adoption of mobile apps on the multihomed platform. It further investigates the moderating effects of the user value of mobile apps. The study employed a large dataset of mobile apps that were initially released through Apple's App Store and subsequently multihomed into Google's Play Store (<em>N</em> = 24,906). By combining traditional count regression model with a machine learning approach, the study derives a range of theoretical and predictive insights that significantly contribute to the mobile app multihoming literature.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000521/pdfft?md5=d6eea4c2edd704bf72fa8dd663341666&pid=1-s2.0-S2667096824000521-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438944","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":"Causes, consequences, and strategies to deal with information overload: A scoping review","authors":"Leila Shahrzadi ,&nbsp;Ali Mansouri ,&nbsp;Mousa Alavi ,&nbsp;Ahmad Shabani","doi":"10.1016/j.jjimei.2024.100261","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100261","url":null,"abstract":"<div><p>The exponential growth of digital information has led to the pervasive problem of information overload, affecting decision-making, productivity, and well-being. This article reviews the existing literature on the various effects of information overload, its underlying causes, and strategies for managing it. A scoping review of English literature up until January 2023 was conducted using Scopus, Web of Science, PubMed, and Emerald. The findings reveal that information overload is caused by personal factors, information characteristics, task parameters, organizational parameters, and information technology parameters. The effects include poor decision-making, decreased productivity, and cognitive pressures. Strategies for managing information overload include learning multiple skills and using filtering, prioritizing, and technology tools. This article provides a foundation for future research and interventions in this area.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100261"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000508/pdfft?md5=1d90bc4bd7ae364fed6c402fd60129a8&pid=1-s2.0-S2667096824000508-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438943","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":"Privacy concerns in social media use: A fear appeal intervention","authors":"Joana Neves ,&nbsp;Ofir Turel ,&nbsp;Tiago Oliveira","doi":"10.1016/j.jjimei.2024.100260","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100260","url":null,"abstract":"<div><p>Privacy violations concern many social networking sites users. Here, we seek to understand how it might affect SNS use reduction. In Study 1, we untangle a mechanism through which this concern drives SNS use reduction. To do so, we leverage the stressor-strain-outcome framework (SSO) and examine whether privacy concerns can trigger fatigue, which in turn motivates intended SNS reduction. To extend this view, we theorize on how SNS addiction can moderate the abovementioned framework. This first study shows that privacy can indirectly lead to SNS use reduction and that addiction negatively impacts the adoption of healthy SNS use. In Study 2, we build on fear appeal theories to examine whether privacy fear appeals can drive actual SNS use reduction. It was demonstrated that privacy fear appeals effectively promote a limited and more controlled SNS use.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000491/pdfft?md5=315923719f91e467944f6456ad7c0aa6&pid=1-s2.0-S2667096824000491-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433884","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 sociotechnical perspective for explicit unfairness mitigation techniques for algorithm fairness","authors":"Nimisha Singh ,&nbsp;Amita Kapoor ,&nbsp;Neha Soni","doi":"10.1016/j.jjimei.2024.100259","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100259","url":null,"abstract":"<div><p>With the increasing use of artificial intelligence (AI) applications in decision making, there are heightened concerns about the fairness of such decisions. Initiatives like Responsible AI, Fair ML, Ethics in AI have provided guidelines for developing AI as an attempt to address these challenges. These approaches have been criticized for taking a top down approach by applying abstract principles to practice without taking into account the context and particularities of the algorithm development. Using the sociotechnical lens, we propose a framework for developing Fair algorithm. We apply this framework to mitigate unfairness in three distinct datasets: COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), Crimes and Community, and a synthetic dataset. Our methodology involves nonconvex optimization for regression with fairness constraints. The experimentation examines the correlation coefficient, Area Under the Curve (AUC), and Root Mean Square Error (RMSE) in relation to a fairness parameter, epsilon. Our findings suggest three objectively testable propositions namely, 1) Fairness Constraint and Predictive power, 2) Fairness Constraints and Discriminatory Ability, 3) Fairness Constraints and Prediction Accuracy.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266709682400048X/pdfft?md5=8b9077e68824868011989c6c7cd49d83&pid=1-s2.0-S266709682400048X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291860","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 could entertainment content marketing affect intention to use the metaverse? Empirical findings","authors":"Malek Alsoud ,&nbsp;Ali Trawnih ,&nbsp;Hussam Yaseen ,&nbsp;Tha'er Majali ,&nbsp;Anas Ratib Alsoud ,&nbsp;Omar Abdel Jaber","doi":"10.1016/j.jjimei.2024.100258","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100258","url":null,"abstract":"<div><p>Despite the metaverse's potential to transform digital business, there remains a scarcity of research investigating on how content marketing affects users' intention to use the metaverse. This study aimed to identify if entertainment content marketing affects users to adapt the metaverse. by adapting the Spice framework, incorporating variables like continuity, sense of presence, interoperability, concurrence, and economic flow. Data was collected via an online survey of 454 online gamers from Jordan who were purposefully selected. Structural equation modelling using partial least squares analysis revealed that all five factors positively affected metaverse intention. Overall, the study contributes to the knowledge by examining the Spice Framework in the context of metaverse technologies. Beyond the academic realm, our findings carry practical significance for marketers, policymakers, and stakeholders shaping the dynamic landscape of digital interactions within the metaverse.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000478/pdfft?md5=9af7531af0893e41611c989bd4ea3d56&pid=1-s2.0-S2667096824000478-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291859","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":"Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models","authors":"Abed Mutemi,&nbsp;Fernando Bacao","doi":"10.1016/j.jjimei.2024.100256","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100256","url":null,"abstract":"<div><p>As online shopping expands rapidly, so does the prevalence of fraud, resulting in significant losses for retailers. According to the 2020 National Retail Federation (NRF) report, organized retail crime costs retailers nearly $800,000 per billion in sales, with an expected global annual increase of over fourteen percent. This paper introduces a text-based fraud detection framework to mitigate these losses efficiently. The framework comprises four key components: text preprocessing, representation, knowledge extraction via machine learning algorithms, and model evaluation. By integrating data augmentation techniques, the framework enhances classifier performance in detecting fraud. The proposed method, employing a combination of FastText and Random Forest classifiers, achieves an impressive F1 score of 0.833 and AUC score of 0.99 on an augmented dataset, surpassing conventional keyword-based models. Informed by best practices in fraud detection, this scalable framework promises a solution to combat the escalating fraud associated with the exponential growth of online shopping.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000454/pdfft?md5=f2982a76bf6932ab6df20d902d121a79&pid=1-s2.0-S2667096824000454-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291738","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":"Evaluating omni channel retailing in the emergence of industry 5.0: A perspective of South Asian generation Z","authors":"Vardhan Choubey ,&nbsp;Ayush Gautam","doi":"10.1016/j.jjimei.2024.100257","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100257","url":null,"abstract":"<div><p>This study investigates the influence of Omnichannel-Retailing (OCR) on customer-retention (CR) for Generation Z (Gen Z) in the nascent Industry 5.0 era. Employing a survey with South Asian respondents, the research explores their experiences with retailer-provided omnichannel services. A novel model examines the relationships between OCR, customer-satisfaction (CS), CR, and customer-engagement (CE). Additionally, the model investigates the moderating role of Man-Machine-Collaboration (MMC) on the OCR-CR association. Process macro analysis is employed to assess mediation and moderation effects. The findings reveal positive associations between OCR and both CR and CE. Interestingly, CS does not mediate the OCR-CR relationship, suggesting a more nuanced effect of OCR on retention. However, CE emerges as a significant mediator. Furthermore, the moderating role of MMC is confirmed. This research holds novelty by examining the early stages of Industry 5.0 and its potential disruption of marketing practices, particularly for CR strategies targeting Gen Z in South Asia.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000466/pdfft?md5=12c88b74c9979e14116304ad6f64448b&pid=1-s2.0-S2667096824000466-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240373","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":"Continuance usage intention of e-wallets: Insights from merchants","authors":"Mia Deanna Sara binti Mohd Reza ,&nbsp;Siow-Hooi Tan ,&nbsp;Lee-Lee Chong ,&nbsp;Hway-Boon Ong","doi":"10.1016/j.jjimei.2024.100254","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100254","url":null,"abstract":"<div><p>The aim of this study is to examine the factors that impact merchants’ inclination to persist in utilizing e-wallets as a payment system in Malaysia. The study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the Expectation Confirmation Model (ECM). Additionally, it includes three other constructs: awareness, online customer service, and network externalities. A total of 146 survey responses were collected and subsequently analyzed using PLS-SEM. The findings revealed that awareness and online customer service exert positive influences on performance expectancy and effort expectancy, respectively. It was determined that performance expectancy significantly and positively affects satisfaction. The study showed that effort expectancy, network externalities, and satisfaction positively affect merchants’ continuous intention to use the e-wallet system. Conversely, performance expectancy was not identified as a significant predictor of continuance usage intention of e-wallets.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000430/pdfft?md5=91e628d8dff07ca120f5ace744832476&pid=1-s2.0-S2667096824000430-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164066","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":"Genie: Enhancing information management in the restaurant industry through AI-powered chatbot","authors":"Megha Gupta ,&nbsp;Venkatasai Dheekonda ,&nbsp;Mohammad Masum","doi":"10.1016/j.jjimei.2024.100255","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100255","url":null,"abstract":"<div><p>In the dynamic restaurant industry, we introduce \"Genie,\" an AI-powered chatbot, represents an advancement in customer service efficiency through technological innovation. Designed to enhance restaurant operations including order processing, reservations, and FAQs management, Genie leverages advanced Natural Language Processing (NLP) techniques. By converting input queries into word embeddings and applying a sophisticated tag classification system, Genie precisely interprets customer intents and generates accurate responses, thereby markedly improving the dining experience. Our thorough examination of various word embeddings and classifiers—Word2Vec, Glove, BERT, Gaussian Naive Bayes, XGB, Artificial Neural Networks (ANN), and Recurrent Neural Networks—revealed that the combination of Word2Vec and ANN is the most effective, achieving an impressive accuracy rate of 88.9 %. This discovery highlights Genie's capability to not only streamline restaurant operations but also enhance customer satisfaction by minimizing wait times and facilitating contactless service options. Additionally, this study enriches the understanding of AI's application in service industries and explores the potential future impact of generative AI technologies on chatbot interactions. As AI technology advances, its integration is essential for Genie to deliver increasingly personalized and dynamic customer experiences, aligning with the evolving demands of the digital era. This research emphasizes the transformative impact of AI in the restaurant industry, providing valuable insights into its practical applications and future prospects for automated customer service solutions.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000442/pdfft?md5=19720530e4f4a3ad085d6cf05bf7d1b8&pid=1-s2.0-S2667096824000442-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097721","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":"Automating attendance management in human resources: A design science approach using computer vision and facial recognition","authors":"Bao-Thien Nguyen-Tat ,&nbsp;Minh-Quoc Bui ,&nbsp;Vuong M. Ngo","doi":"10.1016/j.jjimei.2024.100253","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100253","url":null,"abstract":"<div><p>Haar Cascade is a cost-effective and user-friendly machine learning-based algorithm for detecting objects in images and videos. Unlike Deep Learning algorithms, which typically require significant resources and expensive computing costs, it uses simple image processing techniques like edge detection and Haar features that are easy to comprehend and implement. By combining Haar Cascade with OpenCV2 on an embedded computer like the NVIDIA Jetson Nano, this system can accurately detect and match faces in a database for attendance tracking. This system aims to achieve several specific objectives that set it apart from existing solutions. It leverages Haar Cascade, enriched with carefully selected Haar features, such as Haar-like wavelets, and employs advanced edge detection techniques. These techniques enable precise face detection and matching in both images and videos, contributing to high accuracy and robust performance. By doing so, it minimizes manual intervention and reduces errors, thereby strengthening accountability. Additionally, the integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing efficiency, making it suitable for resource-constrained environments. This system caters to a diverse range of educational institutions, including schools, colleges, vocational training centers, and various workplace settings such as small businesses, offices, and factories. Its adaptability to distinct organizational requirements ensures its relevance and effectiveness across a broad spectrum of users. One of the distinguishing features of this system is its robust integration with databases. It enables efficient storage of attendance records and supports customizable report generation. This comprehensive data management capability ensures that attendance data is readily accessible for monitoring and analysis purposes, contributing to improved decision-making processes. Implementing this Haar Cascade-based attendance management system offers several significant benefits. It not only reduces the manual workload associated with attendance tracking but also minimizes errors, enhancing overall accuracy. The system's affordability and efficiency democratize attendance management technology, making it accessible to a broader audience. Consequently, it has the potential to transform attendance tracking and management practices, ultimately leading to heightened productivity and accountability. In conclusion, this system represents a groundbreaking approach to attendance tracking and management. By combining Haar Cascade, OpenCV2, and the NVIDIA Jetson Nano, it addresses the specific needs of educational institutions and workplaces, offering a cost-effective, efficient, and adaptable solution that has the potential to revolutionize attendance management practices.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100253"},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000429/pdfft?md5=25a278c07f5815441c224a8bdcbcef1a&pid=1-s2.0-S2667096824000429-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141083822","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}