International Journal of Information Management Data Insights最新文献

英文中文

Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials 将信任度和满意度纳入UTAUT 模型以预测聊天机器人的采用情况--Z 世代与千禧一代之间的比较

International Journal of Information Management Data Insights

Pub Date : 2025-03-11 DOI: 10.1016/j.jjimei.2025.100332

Himanshu Joshi

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.

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引用次数: 0

Assessing industry 5.0 readiness—Prototype of a holistic digital index to evaluate sustainability, resilience and human-centered factors 评估工业 5.0 准备情况--评估可持续性、复原力和以人为本因素的综合数字指数原型

International Journal of Information Management Data Insights

Pub Date : 2025-02-28 DOI: 10.1016/j.jjimei.2025.100329

Anja Brückner , Mandy Wölke , Franziska Hein-Pensel , Edgar Schero , Heiner Winkler , Iren Jabs

The European Commission introduced Industry 5.0, marking a paradigm shift in its strategic vision that differs from its predecessor in emphasizing social and sustainable factors. Consequently, a comprehensive reassessment of the social role of industry is inevitable. The European Commission has recognized the conceptual gap in the implementation of Industry 5.0. It recommends the development of technology roadmaps and new tools, including assessments, to guide organizations through this paradigm shift. The aim of this paper is threefold. First, characterizations of the new components of Industry 5.0 are provided to establish a baseline understanding. Second, an approach to measuring the maturity of Industry 5.0 is developed, considering the complexity of Industry 5.0. Third, the prototypical development of an innovative assessment, called the Digital Index, is presented. The assessment tool will offer an approach for companies to examine the requirements for Industry 5.0 and realize them with the use of practical recommendations.

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引用次数: 0

Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction

International Journal of Information Management Data Insights

Pub Date : 2025-02-27 DOI: 10.1016/j.jjimei.2025.100331

Payam Boozary , Sogand Sheykhan , Hamed GhorbanTanhaei , Cosimo Magazzino

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.

引用次数: 0

CovKG: A Covid-19 Knowledge Graph for enabling multidimensional analytics on Covid-19 epidemiological data considering spatiotemporal, environmental, health, and socioeconomic aspects

International Journal of Information Management Data Insights

Pub Date : 2025-02-27 DOI: 10.1016/j.jjimei.2025.100325

Rudra Pratap Deb Nath , S.M. Shafkat Raihan , Tonmoy Chandro Das , Torben Bach Pedersen , Debasish Ghose

The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive data on Covid-19 mortality and morbidity. Most Covid-19 data sources focus on spatiotemporal aspects, and existing research often overlook the combined impact of multiple interconnected factors. This study introduces a Covid-19 Knowledge Graph (CovKG) derived from 20 data sources, enabling multidimensional analysis of epidemiological data, including time, location, temperature, comorbidity, occupation, and others. CovKG is modeled using RDF, connected to 10,951 external resources, and semantically enriched with Data Cube (QB) and QB for OLAP (QB4OLAP) vocabularies to adhere to the FAIR principles and ensure OLAP compatibility. Finally, we perform a qualitative and comparative evaluation and extract statistical insights across multiple dimensions of Covid-19 epidemiology. When assessed, CovKG answers 100% of competency queries, outperforming other data stores that only answer 39%. CovKG and its analytical interface are available at https://bike-csecu.com/datasets/CovKG/.

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引用次数: 0

Customization of health insurance premiums using machine learning and explainable AI

International Journal of Information Management Data Insights

Pub Date : 2025-02-07 DOI: 10.1016/j.jjimei.2025.100328

Manohar Kapse , Vinod Sharma , Rutuj Vidhale , Varun Vellanki

This study presents an analysis of health insurance premiums across various customer segments. Specifically, it aims to identify the factors influencing the pricing of health insurance premiums, vis a vis their impact on different customer segments. Using a dataset from consumer surveys, coupled with multiple Machine Learning models, the study analyzed and predicted features of importance for premiums paid across various age groups, gender, health conditions, policy duration, and the number of members included in the policy. Finally, the explainable AI was used to predict the weightage of each variable in determining the price of the insurance policy for the individuals. The findings provide crucial insights into the factors such as demographic factors and lifestyle that effectively influence the pricing of health insurance premiums vis a vis their impact on various customer segments. The results of this study will assist prospective buyers and decision-makers in choosing the best health insurance plans.

引用次数: 0

Learning transactions representations for information management in banks: Mastering local, global, and external knowledge

International Journal of Information Management Data Insights

Pub Date : 2025-02-05 DOI: 10.1016/j.jjimei.2025.100323

Alexandra Bazarova , Maria Kovaleva , Ilya Kuleshov , Evgenia Romanenkova , Alexander Stepikin , Aleksandr Yugay , Dzhambulat Mollaev , Ivan Kireev , Andrey Savchenko , Alexey Zaytsev

In today’s world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categorized into two groups: (1) local ones, which focus on a client’s current state, such as transaction forecasting, and (2) global ones, which consider the general customer behaviour, e.g., predicting successful loan repayment. Unfortunately, maintaining separate models for each task is costly. Therefore, to better facilitate information management, we compared eight state-of-the-art unsupervised methods on 11 tasks in search for a one-size-fits-all solution. Contrastive self-supervised learning methods were demonstrated to excel at global problems, while generative techniques were superior at local tasks. We also introduced a novel approach, which enriches the client’s representation by incorporating external information gathered from other clients. Our method outperforms classical models, boosting accuracy by up to 20%.

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引用次数: 0

Opening a career door!: The role of ChatGPT adoption in digital entrepreneurial opportunity recognition and exploitation

International Journal of Information Management Data Insights

Pub Date : 2025-02-03 DOI: 10.1016/j.jjimei.2025.100326

Cong Doanh Duong, Thi Thanh Hoa Phan, Bich Ngoc Nguyen, Thanh Van Pham, Ngoc Diep Do, Anh Trong Vu

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.

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引用次数: 0

Enhancing DataOps practices through innovative collaborative models: A systematic review

International Journal of Information Management Data Insights

Pub Date : 2025-02-02 DOI: 10.1016/j.jjimei.2025.100321

Aymen Fannouch, Jihane Gharib, Youssef Gahi

The rapidly evolving field of Data Operations (DataOps) is essential for enhancing data management within large-scale enterprises. However, persistent challenges, such as inefficiencies in data integration, delivery, and governance, limit its potential impact. These obstacles hamper the seamless implementation of DataOps strategies, slowing down operational processes and affecting organizational performance in data-driven environments. To address these issues, this research employs a systematic literature review, analyzing contributions from 2004 to 2024, to identify relevant solutions and innovations. The study highlights the value of frameworks, methodologies, and advanced technologies—such as automation, cloud platforms, and continuous delivery pipelines—that have reshaped the DataOps landscape. These contributions guide enterprises toward best practices in data strategy and foster improved collaboration across business and IT teams. Building on this analysis, our research also proposes a personal framework designed to offer a comprehensive approach to DataOps strategy. This framework integrates key insights from existing research and provides practical recommendations and best practices to streamline workflows, enhance data governance, and align IT operations with business goals. The enhanced DataOps practices derived from our framework demonstrate significant potential to boost operational efficiency, accelerate decision-making processes, and unlock new growth opportunities. Furthermore, the implementation of such practices sets the foundation for future innovations in data management and offers a path forward for organizations seeking sustainable, long-term value.

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引用次数: 0

Exploring the drivers of digital technology adoption for enhancing domestic tax mobilization in Ghana

International Journal of Information Management Data Insights

Pub Date : 2025-02-01 DOI: 10.1016/j.jjimei.2025.100327

Alexander Asmah, Kingsley Ofosu Ampong, Dzifa Bibi, Wihlemina Ofori

Purpose

This study investigates the determinants of tax compliance through the lens of performance expectancy, effort expectancy, social influence, facilitating conditions and hedonic motivation.

Design/methodology/approach

The study adopted both quantitative and qualitative research methods to gather data on the adoption of tax technologies. Based on the five determinants, a conceptual framework was developed consisting of seven proposed hypotheses tested through a structural equation model. Interviews were conducted to gain further insight into the drivers of the taxpayers’ portal in Ghana.

Findings

The study finds that performance expectancy and effort expectancy are the most significant factors predicting tax compliance intentions, indicating that taxpayers consider the portal as a useful tool in managing their taxes and very easy to use. It reduces their exposure to corrupt government officials and lessens their cost of paying taxes. It is also very convenient and serves as a useful way to avoid the long queues they experience at the tax offices. The study recommends that the Ghana Revenue Authority (GRA) and the Ministry of Finance (MoF) should promote more revenue collection technologies and create more awareness among taxpayers to utilise the portal.

Originality/value

The taxpayers’ portal in Ghana was recently introduced to enhance revenue mobilisation. No empirical research has been conducted to identify the adoption factors which will aid its smooth implementation. This paper thus provides significant value to both literature and practice.

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引用次数: 0

Machine learning in banking risk management: Mapping a decade of evolution

International Journal of Information Management Data Insights

Pub Date : 2025-01-30 DOI: 10.1016/j.jjimei.2025.100324

Valentin Lennart Heß, Bruno Damásio

The techniques used in banks' risk management are evolving as opposed to the process of risk management. It is necessary to respond to these market- and technology-driven changes appropriately. Innovative approaches are needed to overcome the limitations of traditional methods. Machine learning (ML) algorithms are suitable for dealing with the various risk types banks face. Academic literature focuses on applying ML in credit risk management. This article addresses market, operational, liquidity, and other risk types, with the objective to examine how ML algorithms predict, assess, and mitigate these risks and identify both their advantages and challenges. This article systematically reviews 46 recent studies and highlights the expanding role of ML in enhancing risk management strategies. The article has revealed that ML is adequately covered in the context of market and operational risk. The learning ability and predictive capabilities of artificial neural networks and other algorithms are promising for risk management. Our findings offer a concise overview of current ML applications for multiple risk types in banking, identifying research gaps, highlighting opportunities and challenges and providing actionable directions for further studies. By providing a focused overview of the expanding role of ML in banking risk management, we underscore the potential to strengthen the robustness of banks’ strategies and practices.

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引用次数: 0

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全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

International Journal of Information Management Data Insights

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