Data and information management最新文献

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.

As the concept of the Metaverse rapidly spread, massively multiplayer online role-playing game (MMORPG), one of the Metaverse games, received public's attention again. Additionally, MMORPGs have garnered significant academic interest in multidisciplinary fields, including human-computer interaction, computer science, and psychology. This study provides a general overview of the knowledge structures on MMORPGs in various academic fields using a bibliometric approach. To this end, we collected articles on MMORPGs published between 2001 and 2021 from the Web of Science. Then, main research forces in the field of MMORPGs are identified by examining productive authors, institutions, and countries/regions. Additionally, we applied VOSviewer to conduct co-citation analyses for identifying highly cited authors, journals, and literatures on MMORPGs. The results revealed that the USA and South Korea are the most productive countries. In addition, players' motivation, players' demographics, in-game social interaction, and pathological usage are the main research topics in the field. We expect that our results will help researchers and stakeholders to gain a general understanding of the development of the MMORPG academic field.

E-commerce is a huge component of the digital economy as the business market landscape is now based on digital competition. E-commerce assists with national development through the creation of jobs and import tax. However, developing countries are lagging in e-commerce adoption due to internet challenges, trust, and security issues about online payment facilities. Despite these challenges, this study aimed at understanding the factors influencing e-commerce adoption in developing countries. The factors are built from the benefits and challenges of adoption. To address the research aim, the study identified the challenges and benefits of e-commerce adoption in developing countries using a systematic literature review methodology.

Systematic searches were carried out in 2021 in four databases (Ebsco host, ScienceDirect, SpringerLink, and Google Scholar) and Google Searches (for statistics). The included papers are 23 peer-reviewed studies and 2 reports published in English between 2014 and 2021. Thematic analysis was used to assess the papers and reports.

Our findings identified technology, environment, and customer trust as the main challenges affecting e-commerce adoption. The environment, organization, and performance are seen as benefits developing countries can leverage to boost e-commerce adoption. We found that all challenges and benefits are interconnected. However, e-commerce can only be adopted if those in management perceive and understand its benefits and values.

The research contributes to the theory by presenting a novel model of the factors influencing the adoption of e-commerce in developing countries and its ecosystem. Practically, having identified that the factors should be addressed collectively assists developing countries in identifying the strategies for eliminating e-commerce adoption challenges. The research contributes to SDG 8 and the body of knowledge on e-commerce 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.

Virtual reality (VR) has been increasingly introduced to digital museums to enable more immersive experiences. However, there is a lack of comprehensive evaluation for user experience in VR-based digital museums. A comprehensive framework for evaluating user experience of VR interfaces in digital museums was created. Based on this framework, the study conducted an experiment to investigate learning effectiveness in VR-based digital museums and compared user experience on desktop and mobile devices. A total of 45 participants were recruited. They were asked to visit a digital museum on either a desktop or a mobile device. After they completed their visit, they took a recognition test and a recall test to measure their effectiveness of learning. Their user experience was evaluated using a new scale, followed by an open-ended interview to collect their opinions and feedback. It was found that introducing VR to the digital museum helped increase users’ effectiveness of learning. Significant differences existed between the desktop and mobile VR interfaces in user experience. It means that previous research findings cannot be directly applied to the user experience of digital museums on mobile. These empirical evidences also suggest that the established framework is effective for evaluating the user experience in VR-based digital museums.

The success of rolling out online courses depends on users adopting this technology and understanding the fundamental difficulties confronting the existing online education delivery system. This research delves into the causes and effects of how well students adjust to using an online learning environment. The study also investigates whether gender categories of respondents moderates the relationship between adaption of online classes and perceived learning outcome. Using Smart PLS and SEM the results of a survey taken of 531 Indian students show that students' adaptability to online classrooms has a major impact on the knowledge they gain from these courses, with a slight moderating effect of respondents' gender on the knowledge they gain. The majority of respondents have a positive outlook on taking classes online. However, they also recognize that the digital divide is a significant barrier to their education. The findings are expected to be helpful in increasing awareness and facilitating quicker, more informed decision making by those responsible for shaping educational policy, curriculum, and technology.

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.