Data and information management最新文献

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.

This study delves into the wrong-way driving (WWD) research performed in the United States over the past five decades. The study employs a text network to summarize and synthesize major themes, data sources, and collaboration efforts from 123 previously published WWD articles collected from various online databases. The analysis shows that the majority of WWD studies were conducted between 2014 and 2020. Throughout the entire period, the primary focuses have been on WWD countermeasures, freeways, exit ramps, contributing factors, driver behavior, and detection systems. Over time, there has been a noticeable shift in the key themes, whereby earlier studies focused on freeways and driver behaviors, while the latter delved into exit ramps, limited access, and contributing factors among others. Most studies were from Florida, Texas, Illinois, and Alabama, with little collaboration of researchers across the States, except for Alabama and Illinois. Further, across the four States, there observed variations in the interests. Studies from Texas and Florida were centered on WWD countermeasures, detection systems, and the use of connected vehicles, while Illinois and Alabama investigated WWD crashes on exit ramps and interchanges. Despite the development and deployment of various countermeasures, there is still a need to develop crash modification factors to examine the effectiveness of the countermeasures. Furthermore, as technology continues to advance, connected and autonomous technology is expected to play a significant role in mitigating the WWD problem. The findings are essential for transportation agencies to evaluate the mitigation efforts and direct resources toward the right course of action.

Automated responses to questions for conversational agents, known as conversation passage retrieval, is challenging due to omissions and implied context in user queries. To help address this challenge, queries can be re-written using pre-trained sequence-to-sequence models based on contextual clues from the conversation's history to resolve ambiguities. In this research, we use the TREC conversational assistant (CAsT) 2020 dataset, selecting relevant single sentences from conversation history for query reformulation to improve system effectiveness and efficiency by avoiding topic drift. We propose a practical query selection method that measures clarity score to determine whether to use response sentences during reformulation. We further explore query reformulation as a binary term classification problem and the effects of rank fusion using multiple retrieval models. T5 and BERT retrievals are inventively combined to better represent user information need. Using multi-model fusion, our best system outperforms the best CAsT 2020 run, with an NDCG@3 of 0.537. The implication is that a more selective system that varies the use of responses depending on the query produces a more effective conversational reformulation system. Combining different retrieval results also proved effective in improving system recall.

Social media platforms, search engine transaction logs, Web portal transaction logs, large text corpora, and other data sources offer users a variety of invaluable data sources. Data generated based on social media platforms and other data sources have become important sources of big data for researchers in information science. Big data provide not only challenges but also opportunities for information science. Emerging big data trends inevitably have an impact on research methods in information science. The authors of this paper discuss the impact of big data on research methods in information science.

This paper addresses these challenges and opportunities through the lens of research methods, ranging from data processing, to sampling, to information visualization, to temporal analysis, to sentiment analysis, to correlation, to cause-effect relationship, to data accessibility, to data privacy, and data ethics issues.

The discussions on related aspects of research methods provoke a healthy reflection and debate for the information science research community, which can help researchers in the field produce sound research designs for big data-oriented research studies and assist information practitioners in solving problems they face in the big data age.

Research methods are fundamental and essential for any research studies. Big data analysis has a natural relationship with research methods. The paper discusses an emerging and important research topic big data from the unique angle of research methods.

This paper explores the influence of political leaders' populist communication styles on public adherence to government policies regarding COVID-19 containment. We adopt a mixed-methods approach that combines: theory building with a nested multicase study design for Study 1 and an empirical study in a natural setting for Study 2. Based on the results from Studies 1 and 2, we develop two propositions that we further explain theoretically: (P1) countries with political leaders associated with engaging or intimate populist communication styles (i.e., the UK, Canada, Australia, Singapore, and Ireland) exhibit better public adherence to their governments' COVID-19 movement restrictions than do countries with political leaders associated with communication styles that combine the champion of the people and engaging styles (i.e., the US); (P2) the country whose political leader is associated with a combination of engaging and intimate populist communication styles (i.e., Singapore) exhibits better public adherence to the government's COVID-19 movement restrictions than do countries whose political leaders adopted solely engaging or solely intimate styles, namely, the UK, Canada, Australia, and Ireland. This paper contributes to the research on political leadership in crises and populist political communication.

Scientific research serves as both the root and the driving force behind technological innovation. While the transformation of science to technology is more commonly observed in hard science disciplines, the application of knowledge from soft science disciplines to technological innovation has received less attention, so is the Library and Information Science (abbreviated as LIS). This study seeks to examine the role of LIS knowledge in bridging the link between science and technology, how the LIS knowledge contributes to patented inventions, and and discover the future trend of scientific and technological innovation in LIS. The study collected LIS papers cited by patents and the citing patents’ data to explore the relationship between LIS papers and their citing patents based on time, impact, and topic. Six hypotheses were developed to analyze the flow of knowledge between science and technology in LIS, providing insight into the characteristics of knowledge flow and transformation of this field into technology. The study found that the amount of LIS scientific knowledge transformed into technology applications has been increasing, and the average “paper-patent” transformation time lag per year has been getting longer, etc.. Additionally, the topics “Knowledge organization and strategic planning”, “Network service and reference consultation,” and “Library and public services” in the LIS have the potential to further contribute to the technology field.

Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals’ response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors.

Information science and data science are closely related, but the relationships and synergies between them may not be sufficiently addressed in educational curricula for information professionals. Consequently, information professionals are at risk of being perceived as having little relevance in data-intensive settings and may fail to demonstrate the unique contribution that they can make in such environments. The knowledge and skills that information professionals can bring relate to the social, cultural, and ethical dimensions of data that are essential to recognise for successful data governance. If information professionals are not actively engaged with data initiatives, then they may be ceding their professional jurisdiction to other occupations.

When there are differences in research objects and methodology between data science and information science, there are also linkages between data science and information science, based on the DIKW hierarchy to the concept chain, namely data – information – knowledge – wisdom. While knowledge metrics provides a quantitative linkage of data – information – knowledge – wisdom, information is the logarithm of data and knowledge is the logarithm of information, on which the mechanism of Brookes’ basic equation of information science is revealed. We suggest to maintain similar principles of data science and information science, including the principle of order, the principle of correlation, the principle of reorganized transformation, the principle of scatter distribution, the principle of logarithmic perspective, and the principle of least effort. Also, we extend to discuss a few issues on knowledge science.

Information Science has been evolving for almost a century and an allied field of study called data science is generating excitement and impact. This article provides a framework for advancing and distinguishing information science and data science. The terms ‘data’ and ‘information’ are compared with respect to the word ‘knowledge’, and the consequent areas of study and practice called data science and information science are then compared on factors such as degree of context, primary unit of interest, consideration of meaning, and attention to human impact.