This paper comparatively studies people’s opinions on AI privacy between the US and China. Based on data collected from Twitter and Weibo, we perform text clustering and content analysis to classify opinion types and analyze the symptoms of opinion polarization and drivers by regression analysis. Results show that US people express more concerns about AI privacy, focusing more on privacy disclosure by AI applications. In contrast, Chinese people are more optimistic about AI’s role in promoting privacy protection. Security, economics, and application are driving factors leading to the polarization of the US people, while technologies and algorithms influence the polarization of Chinese people. This study offers methodological guidance for examining the relationship between AI and user privacy. It also guides the government agencies and other practitioners in developing the policies on AI regulations for privacy protection.

The paper describes a study that examines the factors affecting Chinese users’ online social media fatigue on WeChat from the perspective of perceived pressure. This study is based on the S-S-O (Stressor-Strain-Outcome) theoretical framework and uses the structural equation modeling to analyze the factors affecting Chinese users’ online social media fatigue on WeChat. The results show that information overload, privacy concerns and time cost produce a strong positive impact on social media fatigue intention while compulsive use have a negative impact on social media fatigue intention. Fear of missing out (FoMO) affects social media fatigue intention not significantly, but FoMO indirectly affects social media fatigue intention through compulsive use. Intrinsic motivation plays a negatively moderating role between social media fatigue intention and fatigue behavior while extrinsic motivation plays a positively moderating role between them.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs.

Deal-seeking behavior is booming over the last decade. This article aims to identify online deal-seeking resources, understand the current status of online deal-seeking and identify key insights and trends for understanding online deal seeker's behaviors. The authors first conducted an in-depth review of the relevant literature including white papers, survey reports, online news articles, as well as, numerous postings on multiple deal-seeking websites. Then, a blog mining approach is used to mine relevant blog posts they found from the Internet. The article identifies different types of online deal-seeking resources and summarizes key insights and trends for understanding online deal seeker's behaviors. Online deal seeking behavior is a subject that is not well explored. The article summarizes key insights and trends for understanding online deal seeker's behaviors. Information-seeking theories are used to help explain shopper's online deal-seeking behaviors.

This article reports on a longitudinal analysis of query logs of a web‐based case library system during an 8‐year period (from 2005 to 2012). The analysis studies 3 different information‐seeking approaches: keyword searching, browsing, and case‐based reasoning (CBR) searching provided by the system by examining the query logs that stretch over 8 years. The longitudinal dimension of this study offers unique possibilities to see how users used the 3 different approaches over time. Various user information‐seeking patterns and trends are identified through the query usage pattern analysis and session analysis. The study identified different user groups and found that a majority of the users tend to stick to their favorite information‐seeking approach to meet their immediate information needs and do not seem to care whether alternative search options will offer greater benefits. The study also found that return users used CBR searching much more frequently than 1‐time users and tend to use more query terms to look for information than 1‐time users.

A large amount of user-generated content is now freely available on social media sites. To increase their competitive advantage, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the content on their competitors’ social media sites. In this article, we describe a framework to integrate several techniques including quantitative analysis, text mining, and sentiment analysis for analyzing and comparing social media content from business competitors. Specifically, we conducted an in-depth case study which applies our developed framework to the analysis and comparison of social media content on the Facebook sites of the three largest drugstore chains in the United States: Walgreens, CVS, and Rite Aid. We found similarities and differences in the social media use among the three drugstore chains. We discuss the implications of our findings and provide recommendations to help companies develop their social media competitive analysis strategies.

As a new IT paradigm for users, cloud computing has the potential to transform the way that IT resources are utilized and consumed. Many multinational enterprises (MNEs) are interested in cloud computing but do not know how to adopt and implement cloud computing in their enterprise settings. In an effort to help MNEs understand cloud computing and develop successful enterprise adoption strategies for cloud computing, the authors propose a hybrid cloud model for MNEs and illustrate the utility of this model by using two case studies. Insights for adopting and implementing this model in international settings are provided as well.

Abstract A conceptually relaxed utilization of the variable “search experience” makes it difficult for researchers to perform meaningful cross‐study comparisons. The purpose of this study was to examine how search experience is defined and measured when used as a research variable. We implemented a qualitative analysis of 32 library and information science (LIS) research articles. We found that there was inconsistent terminology usage and measurements. Specifically, there were 21 unique labels to describe the search experience and 18 different measurements. The majority of the studies used a generic label “search experience” and relied on the reader to grasp specific context of the electronic information retrieval environment to which the variable applies from the description of the overall research design. In addition, there was a strong preference for measures that represented subjective self‐reporting about the level of exposure to some information retrieval system. It is evident that there is a need for articles to contain detailed definitions of search experience variables for readers to truly understand the findings.