A Resource-Based Model of IT Usage in Shanghai Higher Education Institutions

On the basis of resource-based view, this paper analyzes the impacts of IT resource on different levels of IT usage in Shanghai higher education institutions. By analyzing the survey data from 40 Shanghai institutions, the study contributes several insights to China-context IT usage research and practice in higher education system. First of all, this study sheds lights on the impacts of IT resource on deep IT usage in Shanghai higher education system. Second, the findings suggest that organizational support has significant positive impact on higher education institutions’ managerial IT usage. The study is the first few attempts to explore the process model of IT usage in China higher education institutions

User Engagement with Mobile Technologies: A Multi-Dimensional Conceptualization of Technology Use

Our study conceptualizes user engagement – a form of technology use targeting the emerging ubiquitous mobile technology generation such as mobile health (mHealth) and social network applications. User engagement manifests in three dimensions, including behavioral, cognitive, and emotional engagement. We validated the measures (in both objective and subjective forms) for the three-dimension user engagement in two different mobile technology contexts, i.e., an e-nursing mobile application and a question-and-answer social network application. We further delineated the relationships among the three dimensions: 1) prior behavioral engagement contributed to both emotional and cognitive engagement, 2) emotional engagement lead to post behavioral engagement, and 3) emotional engagement, compared with prior behavioral engagement and cognitive engagement, exerted a stronger influence predicting post behavioral engagement. Our study enriches both technology use and engagement literature

Striking a Balance: Harnessing Both the Business and Informational Value of Online Reviews through Resource-matching

A majority of consumers now are getting used to consulting reviews before making any purchase decisions. Although we have witnessed fruitful studies in this stream of literature, there lacks sufficient knowledge regarding whether and how we can realize the information and business values simultaneously. We undertook to bridge this gap. Drawn from the cognitive tuning theory and resource-matching theory, we posit that review sentiment would intertwine with the information richness of a review to affect consumers’ judgment of review helpfulness and purchase decision. Our empirical results demonstrate that the information richness of a review, overall, moderates the U-shaped relationship between review sentiment and review helpfulness, as well as the inverted U-shaped relationship between review sentiment and consumer purchase likelihood. These findings unravel certain conditions under which increasing both purchases and review helpfulness could be achieved, which, therefore, offer non-trivial insights into business practice about review-featuring designs

Examining Drivers and Impacts of Informatization in Shanghai Manufacturing Firms

With careful theoretical development and empirical data examination, this paper investigates several key factors that influence the IT usage in Shanghai firms: technology resource, human resource and environment resource. On the basis of the resource-based view and the process model, the study imports government regulation policies, as well as e-government actions, as environmental resource to affect firms’ IT usage. By surveying 398 manufacturing firms in Shanghai and statistically analyzing the field data using structural equation modeling technique, the study contributes several insights to the IT usage in Chinese firms. First of all, this study sheds lights on the value creation process of firms’ informatization in Shanghai manufacturing industry and validates the route from IT investment to value realization. Second, the findings suggest that government promotion policies have significant impacts on manufacturing firms’ technology infrastructure and IT management decision. However, there is no evidence showing the government impact on firms’ IT usage level

Scientific Knowledge Communication in Online Q&A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contribution

With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before. However, those contributing high-quality professional scientific knowledge are confronted with the challenge of making their contributions popular, since non expert readers may not recognize the importance of their contributions given the massive amount of information available online. In this study, we show that non expert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts. However, we discovered that a salient discrepancy exists between the content non experts favor and the content they perceive as professional. In line with studies that have suggested that writing techniques play an important role in how expert content is received by lay persons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities. Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism. Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context. Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use. The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communitie

The Influence Of User Interaction And Participation In Social Media On The Consumption Intention Of Niche Products

The potential of social media in helping businesses generate commercial values has attracted significant attention from researchers and practitioners alike in recent years. An important characteristic differentiating social media from traditional media is the central role of user interaction and participation in generating content that makes the platform sustainable and potentially profitable. It has been noted that social media may be particularly apt in promoting the sales of niche products, due to the tendency of consumers to generate reviews and discuss about such products that raise awareness about them. In this study, we build on and extend the extant literature to investigate how patterns of social interaction among the consumers in discussing about a niche product influence the overall level of participation, which in turn enhances consumption intention of the product. Through analyzing data from a social media site that allows consumers to comment on and discuss about books of philosophical genre (a niche product type), we show that the level of user participation can indeed have a significant positive effect on consumption intention. Furthermore, inclusiveness and betweenness centralization structures may enhance the participation level, but out-degree centralization has a detrimental effect. Implications for research and practice are discussed

W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for multi-variable weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We pre-train W-MAE using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours and using only two years of data. Under the same training data conditions, we compare W-MAE with FourCastNet, and W-MAE outperforms FourCastNet in precipitation forecasting. In the setting where the training data is far less than that of FourCastNet, our model still performs much better in precipitation prediction (0.80 vs. 0.98). Additionally, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting (i.e., forecasting time ranges from 6 hours to one week), and the longer the prediction time, the more evident the performance advantage of W-MAE, further proving its robustness