Lei Hou

Short videos have become a dominant format in digital commerce, enabling brands to engage consumers and drive purchases through dynamic and visually rich content. This highlights the need for a more nuanced understanding of visual framing strategies, that is, what elements are shown and how they are presented. Drawing on Cognitive Load Theory, this study explores the impact of visual compositional framing strategies and their dynamics on consumer engagement and sales. Applying a CNN-based deep learning model, 249,043 images (video frames) extracted from 3426 book-related short sales videos on Douyin are classified into one of three categories: functional, contextual, or social, according to the visual composition of the frame. Further econometric modeling reveals distinct effects of such framing categories: functional framing is positively associated with both engagement and sales, contextual framing relates to higher sales only, while social framing relates positively to engagement but negatively to sales. From a dynamic perspective, frequent transitions between framing types within a short video increase visual complexity, which reduces both engagement and sales and moderates the effects of specific framing strategies. These findings advance theoretical understanding of visual framing in dynamic media environments and offer practical insights for designing more effective short video content.

Introduction: Due to its effectiveness and various benefits, the use of online health consultation (OHC) has dramatically increased in recent years, especially since the outbreak of the COVID-19 pandemic. However, underlying mechanism whereby the pandemic impacted OHC usage is still unclear.Methods: Via an online survey (N=318), the present paper measures the users’ perceptions towards both offline and online services, their intention to switch to OHC, and the perceived pandemic risks. The relationships among these factors are conceptualized by the push-pull-mooring framework, and tested via structural equation modelling.Results: Dissatisfaction with offline service (process inefficiency and consultation anxiety), the attractiveness of OHC (perceived benefits and perceived ease of use), and users’ behavioral inertia (switching cost and habit) jointly influence the intention to switching to OHC. The significant role of the perceived pandemic risk of going to medical facilities is particularly addressed. On the one hand, the perceived pandemic risk is found with an indirect impact on the switching intention by enlarging the dissatisfaction with offline service and the attractiveness of OHC. On the other hand, a high perceived pandemic risk induces more actual switching behavior and also amplifies the transition from switching intention to behavior.Discussion: The study provides novel insights into the understanding of OHC usage in the post-pandemic era, and also informs medical facilities, OHC platforms, and policymakers on managing and balancing the online and offline healthcare provision.

Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.

Universities significantly empower the development of science and technology, and inter-university research collaborations have been one of the major approaches. Considering each university has its expertise regarding research topics in a given discipline, the present paper examines the specialization of university research and its impact on inter-university collaborations. Based on a keyword network constructed via research articles in Information Science and Library Science, 10 research topics are identified. Accordingly, the research topic diversity of a university and the research topic similarity between two universities are quantified. The universities with diverse research topics are found to be more collaborative. A further collaboration network analysis based on the Quadradic Assignment Procedure reveals the important role of research topic similarity on the closeness and impact of collaborations. The different research topic specializations largely prevent close collaborations between two universities, but on the other hand, have the potential to form a complementary combination of knowledge leading to more impactful research output. The analysis and results highlight the important role of research topic specialization and provide insights for the university- or department-level strategy for research collaborations.

Purpose: User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.Design/methodology/approach: Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.Findings: UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.Originality/value: The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

Purpose: Experienced reviewers in general can produce high-quality product reviews, and thereby get more helpful votes. This paper explores the question that whether the depth and width of the reviewers' experience distribution have effects on the helpfulness of their reviews.Design/methodology/approach: Adopting the restaurant review data from Yelp, the present paper classifies the restaurants in to different categories applying the Word2Vec technique, such as Asian or fast food. By evaluating the number of a user's historical reviews in a specific category, and the evenness of such distribution in different categories, the experience specialty and experience diversity are defined respectively.Findings: The analysis shows that users specialised in a given category can produce more helpful reviews in that category. The users with diverse historical experience, i.e. have posted reviews for many categories, also can produce helpful reviews. In addition, the experience diversity shows a positive moderation effect on the influence of experience specialty. Thus, users with diverse experience while specialized in a particular category are the source of most helpful reviews.Originality/value: While previous studies mostly consider the raw number of historical reviews as a reviewer's experience, we distinguish such experience by product category and focus on the width and depth of its distribution. The results not only shed lights on the mining of high-quality reviews and reviewers but also provide insights on the management of online review platforms and electronic marketing.