Journal of Database Management

Journal Information
ISSN / EISSN : 1063-8016 / 1533-8010
Published by: IGI Global (10.4018)
Total articles ≅ 498
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Latest articles in this journal

Marcelo Fantinato, Sarajane Marques Peres, , Dickson K. W. Chiu, Patrick C. K. Hung
Published: 1 July 2021
Journal of Database Management, Volume 32, pp 95-119; https://doi.org/10.4018/jdm.2021070105

Abstract:
In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.
Yumeng Miao, Rong Du, Veda C. Storey
Published: 1 July 2021
Journal of Database Management, Volume 32, pp 29-45; https://doi.org/10.4018/jdm.2021070102

Abstract:
Developer creativity is vital for software companies to innovate and survive. Studies on social media have yielded mixed results about its impact on creativity due to the ubiquitous nature of social media. This research differentiates the effects of informational and socializing social media usage on both incremental and radical creativity and explore the moderating role of a developer's openness to experience. Based on a survey of software developers, the authors show that openness positively moderates the impact of informational social media usage on incremental and radical creativity and negatively moderates the impact of socializing social media usage on both types of creativity. There is a stronger positive moderation for the relationship between informational social media usage and radical creativity compared to incremental creativity. The results provide a foundation for understanding explanations of the paradoxical effect of social media usage on creativity.
, Alan R. Hevner
Published: 1 July 2021
Journal of Database Management, Volume 32, pp 1-28; https://doi.org/10.4018/jdm.2021070101

Abstract:
Online analytical processing (OLAP) engines display aggregated data to help business analysts compare data, observe trends, and make decisions. Issues of data quality and, in particular, issues with missing data impact the quality of the information. Key decision-makers who rely on these data typically make decisions based on what they assume to be all the available data. The authors investigate three approaches to dealing with missing data: 1) ignore missing data, 2) show missing data explicitly (e.g., as unknown data values), and 3) design mitigation algorithms for missing data (e.g., allocate missing data into known value categories). The authors evaluate the approach with focus groups and controlled experiments. When one tries to inform decision-makers using the approaches in the research, the authors find that they often alter their decisions and adjust their decision confidence: individual differences of tolerance for ambiguity and pre-existing omission bias in the decision context influence their decisions.
, Maya Daneva
Published: 1 July 2021
Journal of Database Management, Volume 32, pp 69-94; https://doi.org/10.4018/jdm.2021070104

Abstract:
Requirements engineering (RE) for startups has only recently become an area of intense exploration. This paper provides results of a qualitative study with 45 practitioners from four startup companies in four countries. This research was planned and executed using the design science research (DSR) methodology and yielded a descriptive framework that was subjected to a first evaluation in empirical settings. The authors found that practitioners in startups deploy rapid prototyping practices and user feedback but in a different way than the agile methods assume. This research concludes with discussion on validity threats and some implications for practice and research.
Fei Liu, Meiyun Zuo
Published: 1 July 2021
Journal of Database Management, Volume 32, pp 46-68; https://doi.org/10.4018/jdm.2021070103

Abstract:
The COVID-19 pandemic is an ongoing global pandemic, which has caused global social and economic disruption. In addition to physical illness, people have to endure the intrusion of rumors psychologically. Thus, it is critical to summarize the correlating infodemic, a significant part of COVID-19, to eventually defeat the epidemic. This article aims to mine the topic distribution and evolution patterns of online rumors by comparing and contrasting COVID-19 rumors from the two most popular rumor-refuting platforms—Jiaozhen in China and Full Fact in the United Kingdom (UK)—via a novel topic mining model, text clustering based on bidirectional encoder representations from transformers (BERT), and lifecycle theory. This comparison and contrast can enrich the research of infodemiology based on the spatio-temporal aspect, providing practical guidance for governments, rumor-refuting platforms, and individuals. The comparative study highlights the similarities and differences of online rumors about global public health emergencies across countries.
, Zuopeng (Justin) Zhang
Published: 1 April 2021
Journal of Database Management, Volume 32, pp 1-19; https://doi.org/10.4018/jdm.2021040101

Abstract:
A new type of coronavirus (COVID-19), detected at the end of December 2019 in Wuhan, China, can pass from person to person, spreading very quickly. The COVID-19 outbreak has created stress among societies. This study aims to evaluate the usability of Google Trends data in predicting and modeling the COVID-19 outbreak and the attitudes of different societies to it by using an infodemiological method. The authors collected the search words related to coronavirus and their relative search volume (RSV) from 11 different countries affected by the COVID-19 outbreak from Google Trends. A positive correlation was found between the trend rate of the words searched on the internet and the number of COVID-19 cases in countries related to the COVID-19 outbreak (p<0.05). There was a significant difference between 11 country societies in the daily RSV for the COVID-19 outbreak (p<0.05). The Turkish, South Korean, Iranian, and Swiss society have searched more intensely on the internet for COVID-19 than others. The research shows that Google Trends data can be used to build the forecast model for case numbers in the COVID-19 outbreak. Besides, Google Trends data provides information about different societies' attitudes in the COVID-19 outbreak.
Xiaokang Song, , Yuxiang (Chris) Zhao, Hua Min, Qinghua Zhu
Published: 1 April 2021
Journal of Database Management, Volume 32, pp 20-35; https://doi.org/10.4018/jdm.2021040102

Abstract:
COVID-19 has brought a great impact on people's lives around the world. This paper aims to study the influencing factors of people's fear of missing out (FOMO) toward personal ICT use and its further impact on life satisfaction during the pandemic. A sample consisting of 318 participants was obtained by an online survey in China. Partial least squares structural equation modeling (PLS-SEM) was used for data analysis. The results suggested that people's anxiety and boredom brought by the pandemic are positively correlated with their FOMO. People with higher FOMO used personal ICTs more frequently for both social and process purposes. Furthermore, the social use of ICTs promoted people's life satisfaction, while the process use of ICTs had no significant effect on life satisfaction. Several theoretical and practical implications were discussed based on the results.
Wen-Lung Shiau, Keng Siau, Yuan Yu, Jia Guo
Published: 1 April 2021
Journal of Database Management, Volume 32, pp 67-75; https://doi.org/10.4018/jdm.2021040105

Abstract:
IS/IT plays an important role in our everyday life, especially in today's Internet era. This article discusses the roles of IS/IT in providing services and support on information gathering, analysis, and management during major public emergencies and pandemic crises such as the battle against the new coronavirus. The five selected papers in this special issue introduce advanced methods on data collection and social media user analysis to deal with the challenges brought by the COVID-19 pandemic. This paper also presents future research directions on the use of IS/IT in emergency and pandemic management such as IS control and governance, intelligent health care, enhancing people's lives and mental health, and knowledge management.
Chunnian Liu, Qi Tian, Mengqiu Chen
Published: 1 April 2021
Journal of Database Management, Volume 32, pp 76-91; https://doi.org/10.4018/jdm.20210401.oa1

Abstract:
The purpose of this paper is to explore the emotional composition, psychological characteristics, and the consistency between information behavior and attitude of social media users, and to provide reference for online public opinion monitoring, topic detection, and emotional situation evaluation. Based on big-five personality theory and self-difference theory, this paper takes 12,151 Twitter texts during Hurricane Maria as the analysis objects, extracts the personality characteristics of the texts based on convolution neural network, and analyzes the subjectivity and emotional polarity of the texts by Python. Based on the experimental results, this paper analyzes the psychological characteristics and information needs reflected by social media users' information behavior in disaster environment and further verifies and expounds the reasons for the inconsistent information behavior and attitude of social media users in disaster environments.
Milad Mirbabaie, Ireti Amojo, Stefan Stieglitz
Published: 1 April 2021
Journal of Database Management, Volume 32, pp 50-66; https://doi.org/10.4018/jdm.2021040104

Abstract:
This study focuses on Twitter affordances and sense-making outcomes during a single emergency situation. By using an interpretive affordance lens, this study aims to assess rumors as influencers of sense-making during the 2017 Manchester terrorist attack. The authors combined a quantitative network analysis with a qualitative content analysis to assess the role of rumors during the emergency management after the attack. This study provides argumentative grounds for the notion of sense-making as a consequence of affording social media and builds on prior research to place sense-making as a cognitive process within the affordance concept. The authors emphasize new potentials to prevent or control rumors on social media for practitioners and contribute insights to rumor research. Namely, the authors contribute a novel perspective of rumors and their role during emergency management on social media.
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