Data and information management最新文献

The Consumer Financial Protection Bureau (CFPB) is a government body responsible for safeguarding consumers from financial fraud and abuse. Managing customer complaints is one of the key tasks undertaken by the CFPB. However, the sheer volume of complaints received can overwhelm the bureau's resources, hindering prompt and efficient resolution. To address this challenge, we propose a novel approach called the Two-Stage Residual One-Dimensional Convolutional Neural Network (TSR1DCNN) to optimize the processing of consumer complaints at the CFPB. In this study, we conducted comprehensive experiments, including Ablation Experiment 1 (AE1) and Ablation Experiment 2 (AE2), to evaluate the effectiveness of our proposed TSR1DCNN model. AE1 involved removing the first Conv1D layer, while AE2 removed the Batch Normalization layer. These experiments allowed us to assess the impact of removing specific components on the overall performance of the model. Furthermore, we compared our TSR1DCNN model with other popular deep learning architectures, including 1DCNN, LSTM, and BLSTM, to provide a comprehensive analysis of our proposed approach. Using a dataset of 555,957 consumer complaints received by the CFPB, we trained and tested the TSR1DCNN model, as well as the ablated versions in AE1 and AE2, alongside the 1DCNN, LSTM, and BLSTM models. The results showed that the TSR1DCNN model achieved an impressive accuracy of 78.07% on the training set and 76.53% on the test set. In comparison, AE1 achieved an accuracy of 69.63% with a loss of 1.1207, while AE2 achieved an accuracy of 71.00% with a loss of 1.0583. The performance of the TSR1DCNN model outperformed the other deep learning architectures, including 1DCNN, LSTM, and BLSTM, indicating its superiority in handling consumer complaints effectively. These results demonstrate the superiority of the TSR1DCNN model over the ablated versions in AE1 and AE2, as well as its superiority over other commonly used deep learning architectures. By incorporating advanced neural network architectures such as 1DCNN, LSTM, and BLSTM, and considering the specific modules where our proposed method operates, we provide a promising solution for enhancing the efficiency and effectiveness of complaint-handling processes in organizations facing a large volume of complaints, such as the CFPB.

Passive radio frequency identification (P-RFID) is discussed previously for its sustainable usefulness in the construction industry. The subject of this research is the barriers to the implementation of passive radio frequency identification (P-RFID) technology for the digital transformation of small construction projects, and the interrelationships among these barriers. This study emphasizes notably smaller building projects. Considering technological, cultural, privacy, interest, volatility, and resource constraints that must be overcome to encourage the implementation of P-RFID in the construction business. A PLS-SEM analysis is also provided, showing how much technological, cultural, and privacy hurdles affect the P-RFID rollout. Most severely inhibiting adoption are technical obstacles, cultural ones, and privacy concerns. Findings stress the need of construction firms to adopt new technology to speed up project completion and keep up with rising demand. The use of P-RFID technology may improve the efficiency of project delivery processes and reduce the number of failed projects. Additionally, the research highlights the value of privacy as well as the need of making its protection a key priority in the development of P-RFID technology. According to the findings of the research, one of the most important things that has to be done in order to increase the usage of P-RFID in low-volume sustainable construction projects in Malaysia is to remove the barriers that prevent people from being interested in it.

Under the environment of open science, the transformation of scientific exchange and research paradigms urgently needs diversified and open scientific evaluation. This study aims to advance the construction of academic journals, reform the quantitative evaluation mechanism, and provide references for promoting the international discourse power of Chinese academic journals. It also aims to enrich and improve the indicator system and evaluation theory of the discourse power of academic journals with Chinese characteristics. Firstly, this paper analyzes the fundamental concerns of the discourse power evaluation of academic journals, drawing on the theory of evaluation science, discourse power theory, and communication theory. According to the characteristics and connotation of the evaluation indicators, the definition and formation process of academic journals' discourse power, this paper constructed. the evaluation indicator system. Secondly, this paper obtained 163,628 international citations and 211,150 domestic citations, and 111,496 altmetrics indicators data for 29,203 published papers from 238 Chinese English-language journals. Meanwhile, 12 scientometrics indicators and 14 altmetrics indicators are selected. Thirdly, descriptive statistical analysis and an overview indexed by international databases are carried out at the macro level. Then, correlation analysis, factor analysis, entropy weight method and TOPSIS method are adopted to evaluate and analyze the discourse influence and discourse leading in Chinese English-language journals by integrating scientometrics and altmetrics indicators. Finally, Two-dimensional and four quadrant method is used to evaluate academic journals' discourse power. At the same time, this paper compares and verifies the reliability of the evaluation results. The findings demonstrate that the research on the academic journals' discourse power evaluation based on the theory, method and application logic is practical, comprehensive and reliable. This paper also puts forward the countermeasures to enhance the discourse power of Chinese academic journals.

The proliferation of online reviews has provoked a substantial deal of academic research, providing proof of online reviews’ influence on purchase intention in consumer behavior. However, the current empirical research often yields inconsistent and inconclusive results. In order to bridge the gap in our understanding of the relationship between purchase intention and its determinants, a meta-analysis of 156 studies with 214 effect sizes and 69,006 observations was conducted. Additionally, two new factors - review content type and review format - were included in the study. The results showed that all antecedents significantly affect purchase intention, with review valence showing the most potent effect (r = 0.563). Furthermore, individualism, product type, and four study characteristics are important moderating factors. In conclusion, this study provides more reliable generalizations, and its findings are beneficial to scholars, managers, retailers, and website owners, for example, to help set the agenda for future research efforts and to provide practical guidance that could boost marketing activities.

The continued growth of e-commerce has seen several studies focusing on the business-to-consumer (B2C) e-commerce market, while research on the consumer-to-consumer (C2C) market is relatively limited. C2C e-commerce has also experienced significant growth in the last few years, specifically in the second-hand goods market. Consequently, trust remains a key determinant of e-commerce adoption; hence, understanding the elements that impact trust in the C2C market remains important.

This research aims to uncover factors influencing trust in the C2C e-commerce market and identify areas for future research. A literature review from five databases, focusing on peer-reviewed articles and empirical studies from 2013 to 2023 was conducted. The PRISMA flow diagram guided the process. A defined search criteria initially yielded 382 articles, but after thorough analysis, only 44 were suitable for synthesis and reporting.

The Mendeley referencing tool and MS Excel were instrumental in facilitating the analysis. The findings uncovered a persistent shortage of empirical studies within the realm of C2C e-commerce, particularly in the context of trust. The identified factors were categorised into those affecting buyers and sellers. Various elements, including seller reputation, product reviews, pricing, and the presence of profile photos, emerged as notable influencers on the buyer's side. On the other hand, aspects like structural assurance and website quality played a significant role in influencing trust in the platform from the buyer's perspective. Future C2C e-commerce research should diversify methods, contexts, and factors, like culture and reviews. The study's identified factors can guide researchers and aid practitioners in enhancing trust and C2C adoption.

Digital platform ecosystem governance refers to a platform owner's decisions and mechanisms that seek to influence complementors and users to build and sustain an ecosystem. The relevance of digital platform ecosystem governance is broadly acknowledged by researchers. However, the extant body of research is fragmented, and varied terminologies are employed, leading to challenges in identifying and recognizing results across different fields. This article provides a multidisciplinary and systematic literature review with the aim of consolidating knowledge on this important topic. Based on an analysis of 103 journal articles and conference papers, this review synthesizes the literature into a conceptual model with five building blocks of platform ecosystem governance. The model aims to create a robust foundation for researchers approaching the topic for the first time and conducting subsequent research. The conceptual model also offers practical guidance for governing ecosystems in a structured manner. Finally, this article provides a research agenda with five areas for future investigation.

The purpose of this paper is to systematically review research related to the value of new technologies in the context of performance measurement and management. The review synthesizes findings from previous studies to provide a comprehensive understanding of the current state-of-the-art of various novel technologies and how they assist performance measurement and management across various industries. Scopus was selected as the main electronic database for this study. As a principal result, we found that research related to the value of novel technologies in the context of performance measurement and management is formed around six research streams: automation techniques, predictive analytics, real-time data collection, visualization, collaboration tools, and blockchain technology. As a conclusion, the research presents that novel technologies can be used to track and monitor performance, automate processes, and streamline operations. The findings of this review will bring more coherent value to the current state of the literature and contribute to a diverse body of knowledge.

Due to digitalization resulting in artificial intelligence advice, there are increasing studies on advice taking, exploring individual and task-relevant factors associated with the acceptance of algorithm advice. However, to our notice, there are no reviews of studies on the acceptance of algorithm advice that focus explicitly on a task level that consider methodological features and provide a quantitative measure of algorithm acceptance. Our review closes these research gaps. We evaluated 44 studies, 122 tasks, and 89,751 participants. Our review shows that algorithm aversion is present in 75% of the 122 considered tasks. In addition, our quantified measures underscore some shortcomings by the underrepresented individual, task, or methodological characteristics—for example, the expertise of advice takers and longitudinal studies. Finally, we provide valuable recommendations to continue research on algorithm acceptance.

As one specific type of chatbot, Intelligent Customer Service Bot (ICSB) satisfies users by providing uninterrupted customer service and reducing response time. However, users still prefer to interact with humans rather than robots. In previous studies, scholars have focused more on enhancing the anthropomorphic characteristics of chatbot by treating anthropomorphic characteristics as a general concept. Few literatures have studied the influence of both external and internal features on user satisfaction and the possible interactive effects between the two dimensions of chatbot anthropomorphic cues. Therefore, this paper studies the main effects of internal anthropomorphic constructs (cognitive empathy and emotional empathy) and external anthropomorphic constructs (virtual appearance) of ICSB on user satisfaction, as well as the two-way and three-way interaction effects among them. With a scenario-based survey of 331 respondents, the results confirm the hypotheses about the main effects, the two-way and three-way interaction effects in the model.

Vaccine hesitancy is the delay or refusal of vaccination when vaccines are available. Over the last decade, many reports have suggested that the proliferation of vaccine disinformation and misinformation on social media has aggravated the vaccine-hesitancy problem. Access to vaccine dis(mis)information on social media is deemed partly responsible for the resurfacing of vaccine-preventable diseases (e.g., measles). Although studies have examined social media dis(mis)information, including that related to vaccines, the newsfeed algorithm, which determines the content social media users see, has received scant attention in the literature. We examine how people's perceptions of the fairness, accountability, and transparency (FAT) of the Facebook newsfeed algorithm influence their intention to vaccinate their children. We find that people's perceptions of the Facebook newsfeed algorithm's FAT increase their negative attitudes toward vaccination (fairness and transparency). However, they decrease users' perceptions of antivaccination norms on Facebook (fairness, accountability, and transparency). Negative attitudes toward vaccination decrease the intention to vaccinate, as do perceptions of Facebook antivaccination norms. Our findings demonstrate that to decrease the effectiveness of vaccine dis(mis)information, it is critical to educate the public about how social media newsfeed algorithms make content-display decisions.