Search results

The purpose of this paper is to construct an indicator framework for evaluating open health data portals from the perspective of user experience (UX) to reduce users’ learning costs, save their time and energy and strengthen the emotional connection with users, thereby encouraging them to actively use open health data.

This study uses card sorting, Delphi and analytic hierarchy process to determine the weights of indicators for evaluating open health data portals. Then, this study uses a coding method to score, evaluate and compare the selection of more than 120 open health data portals supported by organizations in more than 100 countries or regions that are in the World's top confirmed cases of COVID-19 as released by the World Health Organization.

At present, open health data portals have shortcomings with regard to UX. Different types of open health data portals vary significantly in the dimensions of technical experience and functional experience, but the differences in the dimensions of aesthetic experience, emotional experience and content experience are not significant.

The constructed open health data portal evaluation indicator framework introduces users' actual application needs and proposes optimization suggestions for the portal to meet the needs of users to quickly obtain, reliable and accurate health data.

Currently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.

Based on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.

The results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.

Unlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

With the worldwide open government data (OGD) movement and frequent public health emergencies in recent years, academic research on OGD for public health emergencies has been growing. However, it is not fully understood how to promote OGD on public health emergencies. Therefore, this paper aims to explore the factors that influence OGD on public health emergencies.

The technology–organization–environment framework is applied to explore factors that influence OGD during COVID-19. It is argued that the effects of four key factors – technical capacity, organizational readiness, social attention and top-down pressure – are contingent on the severity of the pandemic. A unique data set was created by combining multiple data sources which include archival government data, a survey of 1,034 Chinese respondents during the COVID-19 outbreak and official COVID-19 reports.

The data analysis indicates that the four factors positively affect OGD, and pandemic severity strengthens the effects of technical capacity, organizational readiness and social attention on OGD.

This study provides theoretical insights regarding how to improve OGD during public health emergencies, which can guide government efforts in sharing data with the public when dealing with outbreak in the future.

Open data are characterized by a number of economic, environmental, technological, innovative, and social benefits. They are seen as a significant contributor to the city’s transformation into smart city. This is all the more so when the society is on the border of Society 5.0, that is, shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware, etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals (SDGs). Therefore, many agendas (sustainable development strategies, action plans) now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities toward smart cities, mapping them to the features of the Society 5.0. We believe that this trend develops a new form of society, which we refer to as “open data-driven society.” It forms a bridge from Society 4.0 to Society 5.0. This chapter attempts to identify the role of openness in promoting human-centric smart society, smart city, and smart living.

This paper aims to explore the open government data initiative in the Food Protection program area within the New York State’s Department of Health to assess the impacts of opening data in terms of data quality and public value. An ecosystem lens is used to explore the dynamics of actors and their interactions, the processes involved in the program and the consequences such interplay brought forth to data quality.

The data were collected through 15 semistructured interviews with multiple stakeholders from different sectors, such as county officials, administrators and technicians, food sanitarians, data journalists and restaurant owners. At the analysis stage, the ecosystem perspective helped to capture the big picture of the open data actor interrelationships within this community regarding the food service inspections datasets.

Prior research suggests that open data initiatives enhance data quality. However, this study shows how opening data can adversely affect the quality of data. Results are explained by competing dynamics and conflicting interests among open data actors, undermining the expected public value from open data initiatives.

The findings are in contrast with the mainstream open data literature and helps open data scholars to anticipate some currently unexpected results of open data initiatives. Limitations include potential biases associated to interpretation of interview data and that the results are based on a single case study.

This study makes governments and policymakers alert about the possibility of similar open data byproducts and unwanted outcomes and helps them to design more effective open data policies, hence gaining higher economic advantage while lowering costs of open data initiatives.

Detailed open data and open data case studies through the ecosystem perspective are still scarce and can enrich discussions about open data policy design and refinement in the public sector. The data used for this research are not used in any prior papers, and to the best of the authors’ knowledge, this is the first study to identify such adverse effects of data quality that have been reported.

This paper aims to examine the nature and sufficiency of descriptive information included in open datasets and the nature of comments and questions users write in relation to specific datasets. Open datasets are provided to facilitate civic engagement and government transparency. However, making the data available does not guarantee usage. This paper examined the nature of context-related information provided together with the datasets and identified the challenges users encounter while using the resources.

The authors extracted descriptive text provided together with (often at the top of) datasets (N = 216) and the nature of questions and comments users post in relation to the dataset. They then segmented text descriptions and user comments into “idea units” and applied open-coding with constant comparison method. This allowed them to come up with thematic issues that descriptions focus on and the challenges users encounter.

Results of the analysis revealed that context-related descriptions are limited and normative. Users are expected to figure out how to use the data. Analysis of user comments/questions revealed four areas of challenge they encounter: organization and accessibility of the data, clarity and completeness, usefulness and accuracy and language (spelling and grammar). Data providers can do more to address these issues.

The purpose of the study is to understand the nature of open data provision and suggest ways of making open data more accessible to “non expert users”. As such, it is not focused on generalizing about open data provision in various countries as such provision may be different based on jurisdiction.

The study provides insight about ways of organizing open dataset that the resource can be accessible by the general public. It also provides suggestions about how open data providers could consider users' perspectives including providing continuous support.

Research on open data often focuses on technological, policy and political perspectives. Arguably, this is the first study on analysis of context-related information in open-datasets. Datasets do not “speak for themselves” because they require context for analysis and interpretation. Understanding the nature of context-related information in open dataset is original idea.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

The purpose of this paper is to inspire research at the intersection of marketing and mental health. Marketing academics have much to offer – and much to learn from – research on consumer mental health. However, the context, terminology and setting may prove intimidating to marketing scholars unfamiliar with this vulnerable population. Here, experienced researchers offer guidance for conducting compelling research that not only applies marketing frameworks to the mental health industry but also uses this unique context to deepen our understanding of all consumers.

Common concerns about conducting marketing research in the area of mental health were circulated to researchers experienced working with vulnerable populations. Their thoughtful responses are reported here, organized around the research cycle.

Academics and practitioners offer insights into developing compelling research questions at the intersection of marketing and mental health, strategies to identify relevant populations to research and guidance for safe and ethical research design, conduct and publication.

To the best of the authors’ knowledge, this is the first instructional paper to provide practical advice to begin and maintain a successful research agenda at the intersection of mental health and marketing.