International Journal of Information Management Data Insights最新文献_第2页

Smart dairy: Unleashing emerging ICT-enabled lean dairy supply chains through data-driven decision-making 智能乳品：通过数据驱动决策，释放新兴信息和通信技术带来的精益乳品供应链

International Journal of Information Management Data Insights Pub Date : 2024-10-17 DOI: 10.1016/j.jjimei.2024.100297

Upendra Kumar , Ravi Shankar

引用次数: 0

The influence of AI competency and design thinking skills on innovative entrepreneurial competency: The role of strategic intelligence amongst new age entrepreneurs in Thailand 人工智能能力和设计思维能力对创新创业能力的影响：泰国新时代企业家中战略情报的作用

International Journal of Information Management Data Insights Pub Date : 2024-10-11 DOI: 10.1016/j.jjimei.2024.100301

Narinthon Imjai , Chawapong Nui-Suk , Berto Usman , Phiphop Somwethee , Somnuk Aujirapongpan

{"title":"The influence of AI competency and design thinking skills on innovative entrepreneurial competency: The role of strategic intelligence amongst new age entrepreneurs in Thailand","authors":"Narinthon Imjai , Chawapong Nui-Suk , Berto Usman , Phiphop Somwethee , Somnuk Aujirapongpan","doi":"10.1016/j.jjimei.2024.100301","DOIUrl":"10.1016/j.jjimei.2024.100301","url":null,"abstract":"<div><div>This study investigates the impact of Artificial Intelligence (AI) competency and design thinking skills on the innovative capacities of new-age entrepreneurs in Thailand, based on a sample of 187 students enrolled in business management and entrepreneurship programs. Utilizing Structural Equation Modeling (SEM) and factor analysis, the study evaluates how these competencies influence entrepreneurial innovation. The findings reveal that both AI competencies and design thinking skills significantly enhance the innovation capacity of entrepreneurs. The study underscores the importance of cultivating these skills to improve competitiveness and adaptability in the digital age. Moreover, it presents policy recommendations and necessary training initiatives to effectively integrate AI and design thinking into the entrepreneurial processes of new age entrepreneurs in Thailand. These strategic directions aim to equip them with the requisite skills to navigate evolving challenges within the business sector, thus preparing them for successful entrepreneurial endeavors in increasingly digital market environments.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100301"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418311","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}

引用次数: 0

The tale of two sides in the 2019 anti-CAA protest—An analytical framework 2019 年反《反垄断法》抗议活动中的双方故事--一个分析框架

International Journal of Information Management Data Insights Pub Date : 2024-10-11 DOI: 10.1016/j.jjimei.2024.100300

Bhaskarjyoti Das , Krithika Ragothaman , Raghav T. Kesari , Sudarshan T.S.B.

{"title":"The tale of two sides in the 2019 anti-CAA protest—An analytical framework","authors":"Bhaskarjyoti Das , Krithika Ragothaman , Raghav T. Kesari , Sudarshan T.S.B.","doi":"10.1016/j.jjimei.2024.100300","DOIUrl":"10.1016/j.jjimei.2024.100300","url":null,"abstract":"<div><div>The 2019 anti-CAA protest in India witnessed massive Twitter participation from people on both sides. It was unique compared to most online social movements that showcase people’s movements against authority. The article offers a framework for a big data-driven outside-in analysis of such online social movements. Unlike most existing research focusing on a particular aspect of such a movement, the framework presented examines mobilization and counter-mobilization from various angles. The work systematically juxtaposes the proponents and opponents using statistical analysis, text mining, and graph analysis techniques. Different aspects such as users, content, themes and focus of the conversations, conversational patterns, instrumentation of virality, leadership styles, emotions, and toxicity of the discourse have been considered. The study also examines them as types of frame alignment effort as per Frame Alignment Theory. The framework proposed by this work can be successfully employed to understand any future online social movement and any inductive research using user-generated Big Data.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418310","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}

引用次数: 0

Customers' sentiment on food delivery services: An Arabic text mining approach 顾客对送餐服务的看法：阿拉伯语文本挖掘方法

International Journal of Information Management Data Insights Pub Date : 2024-10-10 DOI: 10.1016/j.jjimei.2024.100299

Dheya Mustafa , Safaa M. Khabour , Ahmed S. Shatnawi

{"title":"Customers' sentiment on food delivery services: An Arabic text mining approach","authors":"Dheya Mustafa , Safaa M. Khabour , Ahmed S. Shatnawi","doi":"10.1016/j.jjimei.2024.100299","DOIUrl":"10.1016/j.jjimei.2024.100299","url":null,"abstract":"<div><div>The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual Information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418309","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}

引用次数: 0

Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning 利用机器学习和深度学习分析电子商务领域客户满意度的预测模型

International Journal of Information Management Data Insights Pub Date : 2024-10-07 DOI: 10.1016/j.jjimei.2024.100295

Hoanh-Su Le , Thao-Vy Huynh Do , Minh Hoang Nguyen , Hoang-Anh Tran , Thanh-Thuy Thi Pham , Nhung Thi Nguyen , Van-Ho Nguyen

{"title":"Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning","authors":"Hoanh-Su Le , Thao-Vy Huynh Do , Minh Hoang Nguyen , Hoang-Anh Tran , Thanh-Thuy Thi Pham , Nhung Thi Nguyen , Van-Ho Nguyen","doi":"10.1016/j.jjimei.2024.100295","DOIUrl":"10.1016/j.jjimei.2024.100295","url":null,"abstract":"<div><div>In Vietnam's rapidly expanding e-commerce landscape, there is a critical need for advanced tools that can effectively analyze customer feedback to boost satisfaction and loyalty. This paper introduces a two-step predictive framework merging deep learning and traditional machine learning to analyze Vietnamese e-commerce reviews. Utilizing a dataset of 10,021 reviews on Tiki, Shopee, Sendo, and Hasaki between 2015 and 2023, the framework first employs fine-tuned deep learning models like BERT and Bi-GRU to extract aspect-based sentiments from reviews, tailored for the nuances of the Vietnamese language. Subsequently, machine learning algorithms like XGBoost predict customer satisfaction by integrating sentiment analysis with e-commerce data such as product prices. Results show BERT and Bi-GRU yield over 70% sentiment accuracy, while XGBoost achieves 80%+ satisfaction prediction accuracy. This framework offers a potent solution for discerning customer sentiments and enhancing satisfaction in Vietnam's dynamic e-commerce landscape.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418308","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}

引用次数: 0

Deciphering the evolution of metaverse - A techno-functional perspective in digital marketing 解读元宇宙的演变--数字营销中的技术功能视角

International Journal of Information Management Data Insights Pub Date : 2024-10-03 DOI: 10.1016/j.jjimei.2024.100296

Mohammad Wasiq , Abu Bashar , Brighton Nyagadza , Amar Johri

{"title":"Deciphering the evolution of metaverse - A techno-functional perspective in digital marketing","authors":"Mohammad Wasiq , Abu Bashar , Brighton Nyagadza , Amar Johri","doi":"10.1016/j.jjimei.2024.100296","DOIUrl":"10.1016/j.jjimei.2024.100296","url":null,"abstract":"<div><div>The metaverse has disrupted the traditional marketing practices and it has potential to transform entire world of marketing activities with thrilling immersive experiences. This study provides an analysis of evolving field of metaverse marketing in the context of information systems using state of the art bibliometric and scientometric tools coupled with machine learning algorithms. Utilizing 257 documents from Scopus database that published between 1996 and 2024, this research maps and unveils the development of metaverse marketing from its inception and the role of information systems in its evolution. The analysis of literature resulted in five main emerging themes of the role of information systems in metaverse marketing research as User Experience, Customer engagement, Convergence of metaverse Technology, Design of virtual goods & experience and Global Social Interaction. The major sub-themes of the study are User Behaviors and Preferences, Branding on virtual environment, Virtual reality, Virtual wearables and Virtual Socialization. This study also reveals the emerging trends and gaps in literature that pave the ways for future research expansion in the information systems and metaverse marketing. Few of the important future research areas identified are understanding user experience, design of immersive customer engagement strategies, customer virtual presence and Security & privacy concerns of the users on metaverse platform.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418307","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}

引用次数: 0

Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda 人工智能在可持续供应链管理中的信息管理应用：系统回顾与未来研究议程

International Journal of Information Management Data Insights Pub Date : 2024-09-30 DOI: 10.1016/j.jjimei.2024.100292

Alok Yadav, Rajiv Kumar Garg, Anish Sachdeva

{"title":"Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda","authors":"Alok Yadav, Rajiv Kumar Garg, Anish Sachdeva","doi":"10.1016/j.jjimei.2024.100292","DOIUrl":"10.1016/j.jjimei.2024.100292","url":null,"abstract":"<div><div>In a Sustainable Supply Chain (SSC) context, information management offers a unique perspective on the digital economy and information management. Artificial intelligence (AI) is developing into a more robust digital field to facilitate quick information access and intelligent decisions in expanding commercial contexts. These days, Supply Chains (SC) would crumble without robust information systems. Applying AI and information management is crucial in determining the direction of sustainable supply chain management (SSCM). A systematic literature review (SLR) of the use of AI in SSCM is conducted in this research. The authors can identify crucial factors of the present literature using bibliometric and network analysis. AI is essential to the SSC to address sustainability challenges and manage the large volumes of data produced by numerous industrial processes. In the corpus of research that is already accessible, there is currently no comprehensive and bibliometric analysis of the potential for AI techniques for information management in SSC. Scientific publications were analysed from an objective point of view. Based on our results, we have drafted a proposal for an AI supply chain framework. Researchers, policymakers, and SCM practitioners may all benefit from the approach. This study is the first to analyse AI applications for information management in SSCM. In consideration of this, organizations are now exploring AI capabilities to improve operational efficiency and innovate their processes. This will assist industry people in understanding how AI methods support SC processes in their optimization to attain sustainability in SC practices.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100292"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358555","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}

引用次数: 0

How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms 如何利用人工智能预测运输股价？基于长短期记忆算法的实验结果

International Journal of Information Management Data Insights Pub Date : 2024-09-28 DOI: 10.1016/j.jjimei.2024.100293

Dinar Ajeng Kristiyanti, Willibrordus Bayu Nova Pramudya, Samuel Ady Sanjaya

{"title":"How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms","authors":"Dinar Ajeng Kristiyanti, Willibrordus Bayu Nova Pramudya, Samuel Ady Sanjaya","doi":"10.1016/j.jjimei.2024.100293","DOIUrl":"10.1016/j.jjimei.2024.100293","url":null,"abstract":"<div><div>Inflation growth in Indonesia and other countries impacts the currency value and investors' purchasing power, particularly in the transportation sector. This research explores the impact of inflation growth in Indonesia and comparable nations on currency valuation and the purchasing power of investors, with a focus on the transportation sector. Data collection was carried out from April to October 2023 by scraping stock data from several transportation stocks such as: AKSI.JK, CMPP.JK, SAFE.JK, SMDR.JK, TMAS.JK, and WEHA. The research primarily aims to forecast stock prices in Indonesia's transportation sector, utilizing data mining techniques within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes stages such as business understanding, data preparation, modeling, evaluation, and deployment. It employs the Long Short-Term Memory (LSTM) algorithm, assessing different hyperparameter activation functions (linear, ReLU, sigmoid, tanh) and optimizers (ADAM, ADAGRAD, NADAM, RMSPROP, ADADELTA, SGD, ADAMAX) to refine prediction accuracy. Findings demonstrate the ReLU activation function and ADAM optimizer's effectiveness, highlighted by evaluation metrics such as Mean Absolute Error (MAE) of 0.0092918, Mean Absolute Percentage Error (MAPE) of 0.06422, and R-Squared of 96 %. The study notably identifies significant growth in Temas (TMAS.JK) stock from April to October 2023, surpassing other sector stocks. Additionally, a web-based application for predicting transportation stock prices has been developed, facilitating user inputs like ticker, activation-optimizer choice, and date range.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100293"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358554","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}

引用次数: 0

SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services SAFERIDES：分散控制边缘计算在共享乘车监控服务中的应用

International Journal of Information Management Data Insights Pub Date : 2024-09-28 DOI: 10.1016/j.jjimei.2024.100282

Samaa Elnagar , Kweku Muata Osei Bryson , Manoj Thomas

{"title":"SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services","authors":"Samaa Elnagar , Kweku Muata Osei Bryson , Manoj Thomas","doi":"10.1016/j.jjimei.2024.100282","DOIUrl":"10.1016/j.jjimei.2024.100282","url":null,"abstract":"<div><div>Edge computing changed the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy breach. However, advances in deep learning enabled Internet of Things (IoTs) to onload tasks and run cognitive tasks locally. This research introduces a decentralized-control edge model where computation and decision-making are moved to the IoT level. The model aims at decreasing <em>communication and computation dependance</em> on the edge which affect <em>efficiency</em> and <em>latency</em>. The model also limits data transfer to the edge to avoid <em>security</em> and <em>privacy</em> risks. Decentralized control is a key to many business applications that prioritizes <em>safety, real-time response, and privacy</em> such as ridesharing monitoring and industrial operations. To examine the model, we developed <em>SAFERIDES,</em> a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current monitoring systems require costly infrastructure and continuous network connectivity. However, <em>SAFRIDES</em> uses optimized deep learning models that run locally on IoTs to detect and record violations in ridesharing. The system achieved the lowest latency among current solution, while minimizing data sharing and maintaining <em>privacy</em>. Moreover, decentralized edge computing empowers IoTs and upgrades their functionality from sensing to independent decision-making.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328244","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}

引用次数: 0

Blockchain: An opportunity to improve supply chains in the wake of digitalization 区块链：数字化后改善供应链的机遇

International Journal of Information Management Data Insights Pub Date : 2024-09-28 DOI: 10.1016/j.jjimei.2024.100290

Liliana Rivera , Valérie Gauthier-Umaña , Chetna Chauhan

{"title":"Blockchain: An opportunity to improve supply chains in the wake of digitalization","authors":"Liliana Rivera , Valérie Gauthier-Umaña , Chetna Chauhan","doi":"10.1016/j.jjimei.2024.100290","DOIUrl":"10.1016/j.jjimei.2024.100290","url":null,"abstract":"<div><div>Industry 4.0 technologies have created the opportunity to overcome inefficiencies along the supply chain by offering data transparency, tracing, and security. In this regard, the role of blockchain technology has garnered a lot of attention among practitioners as well as academia. Blockchain's decentralized and immutable nature ensures trustworthy data sharing, real-time tracking, and enhanced cybersecurity. However, adoption in emerging markets has not been as fast as in developed countries. There has been little clarity as to what the drivers and barriers to its adoption are, and what role governments and academia should play in the process. The present study addresses these issues using a qualitative study that utilizes data obtained from semi-structured interviews conducted with blockchain companies and supply chain companies in Latin America. Results show that the main barriers are 1) low knowledge about blockchain, 2) insufficient information, connectivity, and financial infrastructure, 3) lack of clear regulation, 4) a scarce presence of a local market for entrepreneurs to produce blockchain applications. It is also interesting to find that in contrast to lack of skilled manpower in many emerging economies globally, Latin America has skilled human capital for blockchain adoption. Thus, emerging markets need to foster collaborative work between companies, government, and academia to overcome these barriers and be able to enjoy blockchain benefits while increasing competitiveness in supply chains.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"4 2","pages":"Article 100290"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358553","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}

引用次数: 0