Data and information management最新文献

The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.

There are productivity differences among academic fields. Researchers who work in academic fields that have low productivity are pressured to publish more, and this policy may cause researchers to publish more in journals that have lenient standards and publish articles that are not necessarily valuable for their academic field. The problem is not solved by normalizing journals’ impact factors by the subjects because the normalized impact factors do not reflect the difficulty of publication in that subject. In this paper, we propose an evaluation method –Reference Group Similarity Index-that addresses the productivity differences issue. The method uses the publications of a reference group of departments that are believed to have the right publication incentives. Then, other departments are evaluated to the degree that their publications are similar to that of the reference group. We apply the method to the top 50 economics departments according to USNews rankings and show that the department rankings that we get from the Reference Group Similarity Index are largely consistent with the USNews Rankings.

This paper investigates applying AI models and topic modelling techniques to enhance computational literature reviews in business, management, and information systems. The study highlights the significance of impactful journals and emphasises the need for interdisciplinary and transdisciplinary research, especially in addressing AI's ethical and regulatory challenges. We demonstrate the effectiveness of combining machine learning and ChatGPT in the literature review process. Machine learning is used to identify research topics, and ChatGPT assists researchers in labelling the topics, generating content, and improving the efficiency of academic writing. By leveraging topic modelling techniques and ChatGPT, we uncover and label topics within the literature, shedding light on the thematic structure and content of the research field, allowing researchers to uncover meaningful insights, identify research gaps, and highlight rapidly expanding research areas. Additionally, we contribute to the literature review process by introducing a methodology that identifies impactful papers, helping to bridge the gap between computational literature reviews and traditional literature reviews.

Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers.

The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research.

The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI.

As digital transformation continues to advance, organisations are becoming increasingly aware of the benefits that modern technologies offer. However, with greater technology adoption comes a higher risk of cyber security threats and attacks. Therefore, there is a need for more advanced measures to protect against constantly evolving threats. One potential solution is the use of Artificial Intelligence (AI). The aim of this research paper was to conduct a systematic literature review (SLR) to assess the impact of AI-based technologies on organisational cyber security and determine their effectiveness compared to traditional cyber security approaches. The PRISMA flow diagram was used to guide the review process. Peer-reviewed articles from 2018 to 2023 were included from EBSCO Host, Google Scholar, Science Direct, ProQuest & SCOPUS and 73 remaining articles were synthesised.

The results revealed that AI can impact cybersecurity throughout it's entire life cycle, yielding benefits like automation, threat intelligence, and improved cyber defense. Nevertheless, it also brings challenges like adversarial attacks and the need for high-quality data, which could lead to the inefficiency of AI. These results affirm the positive influence of AI on cybersecurity, enhancing effectiveness and resilience. These findings provide a solid foundation for further research in the field of organisational cybersecurity. These results can help organisations make informed decisions on AI implementations by offering an impartial view of its impacts.

To enhance customer satisfaction and transform negative perceptions of online apparel shopping, fashion brands are increasingly adopting virtual fitting rooms. Despite clothing being the most frequently purchased item online, customers often struggle to find garments that fit their size and skin tone. Consequently, the clothing industry experiences a higher return rate compared to other e-commerce sectors. To address this issue, fashion brands prioritize the implementation of try-on technology, aiming to improve product visibility and provide sensory input. However, existing studies have yielded conflicting findings. This research aims to resolve this issue by organizing and categorizing the relevant literature. To collect related publications, scholarly databases such as Scopus, Emerald, Springer, Wiley, Science Direct, ProQuest, and IEEE Xplore were extensively searched. The investigation employed a systematic literature review strategy, with the search conducted from January 2005 to February 2023. Ultimately, eighty publications meeting the selection criteria were chosen for further review. The study classifies the literature into subfields based on publication year and region, thoroughly exploring various aspects of TOT, including theories, influencing factors, moderating, or mediating variables, outcomes, and notable findings. Based on the evaluation results, a conceptual model and research gap for TOT is proposed to guide future research in this domain, providing valuable insights for both the management and academic research communities.

Digital transformation has recently introduced itself as a groundbreaking phenomenon with profound impacts on societies, industries, businesses, and even individuals. Accordingly, several studies have attempted to give a literature review or analysis of digital transformation research during the last few years. However, most of them are domain-specific studies based on small data samples or subjective review methods, so we lack a general and robust understanding of the landscape of this field of research across different disciplines and domains. Taking a step toward filling this gap, the current study aims to shape an overall and reliable picture of the research realm on digital transformation. To the aim, a computational method namely topic modeling was applied to two big texts, one of which includes all digital transformation-related publications that were indexed in well-known Scopus and Web of Science databases (8639 documents), and the other one only contains studies that were published by high-quality JCR journals (1264 documents). As a result, 20 and 13 topics were respectively introduced as the underlying themes of the global trends and core knowledge in digital transformation research along with their temporal evolutionary paths throughout the recent years. Also, by comparing these two groups of topics, it was known that there are nine developing trends in this field of research that require more attention and advancements to establish themselves as the core knowledge of the field. Complementing the contributions of previous domain-specific or subjective reviews on digital transformation, this study tries to favor a better understanding of this scholarship through multidisciplinary and multidimensional analyses of digital transformation-related publications by using the topic modeling approach.