International Journal of Information Management Data Insights最新文献

{"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}

{"title":"Forecasting cryptocurrency returns using classical statistical and deep learning techniques","authors":"Nehal N. AlMadany ,&nbsp;Omar Hujran ,&nbsp;Ghazi Al Naymat ,&nbsp;Aktham Maghyereh","doi":"10.1016/j.jjimei.2024.100251","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100251","url":null,"abstract":"<div><p>The emergence of cryptocurrencies has generated enthusiasm and concern in the modern global economy. However, their high volatility, erratic price fluctuations, and tendency to exhibit price bubbles have made investors cautious about investing in them. Consequently, it is essential to develop methods and models to forecast cryptocurrency returns to benefit investors, traders, and the scientific community. Despite the considerable volume of research on Bitcoin price forecasting, other cryptocurrencies have received little attention in academic literature. Additionally, the current body of literature on predicting cryptocurrency prices or returns emphasizes the use of in-sample methodologies. However, this method is susceptible to overfitting. To address these gaps in the literature, this study employs autoregressive moving average (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH), and long short-term memory (LSTM) deep learning neural networks to forecast returns for the ten most actively traded digital currencies: Bitcoin, Ethereum, Ripple, Chainlink, Litecoin, Cardano, Ethereum Classic, Bitcoin Cash, Tether, and Binance Coin. To assess the accuracy of the two models, this study utilizes an out-of-sample method with data gathered sequentially from November 9, 2017, to September 18, 2022. The results indicate that all models exhibit high accuracy, as evidenced by their low root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE) values. Meanwhile, the hybrid EGARCH-LSTM or GARCH-LSTM models demonstrate slightly better accuracy compared with the other models. The findings are valuable for investors, traders, and researchers involved in cryptocurrency forecasting.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000405/pdfft?md5=0f01b5aca6e5ef34121a507ba9a98cd9&pid=1-s2.0-S2667096824000405-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141083767","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":"Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach","authors":"Vikas Sharma ,&nbsp;Kshitiz Jangir ,&nbsp;Munish Gupta ,&nbsp;Ramona Rupeika-Apoga","doi":"10.1016/j.jjimei.2024.100252","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100252","url":null,"abstract":"<div><p>As the number of FinTech start-ups continues to rise globally, the utilization of these services by users becomes increasingly crucial, especially considering potential risks. Various factors affect users' utilization of financial technology, with the quality of services offered by FinTech providers standing out as a significant consideration. The aim of this study is to investigate the relationship between the quality of services offered by FinTech payment platforms and the utilization of FinTech services. We develop a novel conceptual model integrating elements from SERVQUAL and TAM (the Technology Acceptance Model) to investigate these dynamics. To gain a comprehensive understanding, we employed a mixed methods research approach. This approach included a quantitative survey analyzed using a partial least squares structural equation model (PLS-SEM) to examine the proposed framework and the relationships between its constructs. Following the survey, a follow-up focus group discussion with industry experts and academics was conducted to delve deeper into the findings and explore the \"why\" behind the statistical relationships. The findings reveal a significant impact of the quality of services offered by FinTech payment service providers on the utilization of such services. It demonstrates that in the FinTech sector, perceived usefulness does not always dominate perceived ease of use. Moreover, it confirms the profound influence of perceived usefulness in shaping attitudes and subsequent behaviour related to technology use. These insights contribute to an enhanced understanding of the factors driving the utilization of FinTech services.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000417/pdfft?md5=ee3c88fe482fc94dfd68ebbc6a19e91d&pid=1-s2.0-S2667096824000417-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078186","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":"From self-descriptions (SD) to self-recommendations (SR): Evolving Gaia-X for the future European economy","authors":"Vincent Geilenberg ,&nbsp;Wolfgang H. Schulz ,&nbsp;Jonathan Mize ,&nbsp;Henrik Kleis","doi":"10.1016/j.jjimei.2024.100249","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100249","url":null,"abstract":"<div><p>The European decentralized-organized data-infrastructure ecosystem Gaia-X faces the challenge of initiating, maintaining, and intensifying data exchange between data providers and data consumers in all its Gaia-X data exchange domains to contribute significantly to the future viability of the European economy. The overall success of Gaia-X would enhance the innovative power and competitiveness of European companies and reduce their dependence on American and Chinese platform companies. This paper analyzes how the Big Data era affects the design of centralized-organized and decentralized-organized platforms and applies the findings to Gaia-X. It proposes to extend the Self-Descriptions (SD) already firmly embedded in Gaia-X with so-called Self-Recommendations (SR) because, in this way, the decentralized-organized data-infrastructure ecosystem Gaia-X can better express its advantages over centralized-organized platform companies.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000387/pdfft?md5=93a626e5a29d4fdfc128d6be09324706&pid=1-s2.0-S2667096824000387-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951330","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":"Artificial intelligence technology readiness for social sustainability and business ethics: Evidence from MSMEs in developing nations","authors":"Apoorva Vikrant Kulkarni ,&nbsp;Shaji Joseph ,&nbsp;Kanchan Pranay Patil","doi":"10.1016/j.jjimei.2024.100250","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100250","url":null,"abstract":"<div><p>Social, economic, and environmental development together contributes to sustainable development. Social sustainability (SS) is essential to create just and inclusive societies where people's basic needs are satisfied, human rights are protected, and social cohesion and fairness exist. To achieve holistic, sustainable development, policymakers and management must consider SS and environmental and economic considerations. Employees' social and behavioral interactions impact SS and business ethics. Micro, small, and medium enterprises (MSMEs) can learn from artificial intelligence (AI) for better decision-making, operational optimization, increased employability, and employee empowerment. Therefore, this research aims to assess how artificial intelligence technology affects SS and business ethics in the MSMEs. We studied the artificial intelligence (AI) readiness among MSME companies in developing nations. We analyzed AI readiness using the theory of technology-organization-environment (TOE). Further, we also studied AI readiness and its influence on business ethics and SS, which we measured through skill development, work conditions, environment, and safety among MSMEs. We collected the data from 236 MSME employees. We used Structural Equation Modeling using SmartPLS software for data analysis. The research findings indicated that AI readiness directly impacts SS. We also found that the findings directly impact employees' social and ethical behavior. We also observed that business ethics significantly affects SS, indicating partial mediation. This study has substantial theoretical and managerial implications as policymakers and MSME leadership need to consider SS an essential component of sustainable development.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000399/pdfft?md5=d8f0f5b1aebabeeedcc5dbaf8fecc2f8&pid=1-s2.0-S2667096824000399-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951329","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":"Textual similarity for legal precedents discovery: Assessing the performance of machine learning techniques in an administrative court","authors":"Hugo Mentzingen ,&nbsp;Nuno António ,&nbsp;Fernando Bacao ,&nbsp;Marcio Cunha","doi":"10.1016/j.jjimei.2024.100247","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100247","url":null,"abstract":"<div><p>The importance of legal precedents in ensuring consistent jurisprudence is undisputed. Particularly in jurisdictions following the Common law, but even in Civil law systems, uniformity in case law requires adherence to precedents. However, with the growing volume of cases, manual identification becomes a bottleneck, prompting the need for automation. Leveraging the capabilities of natural language processing (NLP) and machine learning (ML), our study delves into the potential of automation in identifying similar cases indicative of precedents. Drawing from a unique, substantial dataset of legal cases from an administrative court in Brazil, we extensively evaluated over one hundred combinations of document representations and text vectorizations. Contrary to earlier studies that relied on minimal validation samples, ours employed a statistically significant sample vetted by legal experts. Our findings reveal that models focusing on granular text representations perform optimally, especially when extracting concepts and relations. Notably, while intricate models may not always guarantee superior outcomes, the importance of refining textual features cannot be understated. These findings pave the way for creating efficient decision support systems in judicial contexts and set a direction for future research aiming to integrate technology in legal decision-making.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000363/pdfft?md5=27abd719154af3d76e4033b1afbe7e3d&pid=1-s2.0-S2667096824000363-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946871","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":"An empirical evidence on the impact of social customer relationship management on the small and medium enterprises performance","authors":"Fathey Mohammed ,&nbsp;Rahayu Binti Ahmad ,&nbsp;Syahida Binti Hassan ,&nbsp;Yousef Fazea ,&nbsp;Ahmed Ibrahim Alzahrani","doi":"10.1016/j.jjimei.2024.100248","DOIUrl":"https://doi.org/10.1016/j.jjimei.2024.100248","url":null,"abstract":"<div><p>Social media has swiftly established itself as a primary source of products’ information for customers. Nowadays, Small and Medium-size Enterprises (SMEs) can use social media to develop Customer Relationship Management platforms (social CRM). Firms, particularly SMEs in developing countries need to understand the factors affecting their performance by implementing social CRM. However, there is a dearth of awareness on the impact of social CRM on the performance of SMEs. This study proposes an integrated model that aims to investigate the effects of social CRM on SMEs’ performance. The model is constructed by incorporating three dominant theoretical frameworks: the Fit-Viability Model (FVM), Network Externalities, and the Resource-Based View (RBV). A cross-sectional survey was used to gather data from 149 SMEs managerial staff. Findings revealed that almost 50% of the variability in the performance of SMEs is explained by the fitness and viability of social CRM. In addition, network externalities of social media significantly impact the social CRM fitness in the context of SMEs with path coefficient 0.617. Furthermore, the internal financial resources factor makes sCRM viable for SMEs as the results show significant relationship between the internal financial resources and the sCRM viability with 0.536 path coefficient. Manager innovativeness, IT knowledge, top management support, and government assistance, on the other hand, do not contribute significantly to the viability of social CRM for SMEs. The model aids SMEs in making well-informed decisions regarding the adoption of social CRM by evaluating both the suitability of social media for CRM tasks and the enterprise preparedness to implement social CRM, leading to enhanced performance.</p></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667096824000375/pdfft?md5=eee9061522d353839a0fdc0a4b7c1383&pid=1-s2.0-S2667096824000375-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905587","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}