Electronic word-of-mouth (eWOM) plays a critical role in shaping consumer decisions and influencing product sales. While prior research has extensively examined the relationship between aggregate eWOM metrics, such as valence and volume, and sales, less is known about how aspect-level eWOM sentiment relates to overall ratings and market outcomes. This study leverages large language models (LLMs) to extract fine-grained aspect-specific sentiment from online reviews and examines the relationships among multi-aspect sentiment, overall ratings, and product sales. Using data from the movie industry, we develop an LLM-based framework to identify sentiment toward plot, cast, director, and audiovisual, and empirically test their associations with ratings and box office using ridge regression. The findings show that plot sentiment has the strongest positive association with box office, followed by cast and director sentiment. Audiovisual sentiment is positively associated with overall ratings, but its relationship with box office is more context-dependent. Overall ratings partially mediate the effects of plot, cast, and director sentiment, and overall ratings, sentiment dispersion, and cross-aspect sentiment interactions further condition aspect-level effects. This research contributes to the eWOM literature by offering a fine-grained understanding of how multi-aspect sentiment relates to market outcomes and provides practical insights for managing online movie reputation.

Purpose Considering the unique characteristics of equity crowdfunding platforms including the removal of stringent structural barriers (e.g. lack of co-location), high visibility and traceability of investor characteristics, large pool of available investors and simplified transaction process, the authors aim to examine how the two most prevalent mechanisms (i.e. homophily and repeated ties) unfold in this context by incorporating the contextual characteristics. The authors theorize an inverted U-shaped relationship between leader-backer similarity and the likelihood of co-investment in a syndicate on equity crowdfunding platforms. In addition, a leader–backer dyad is more likely to form new syndicates if the students have more prior co-investment ties. Design/methodology/approach The empirical study is based on data from the AngelList syndicate platform and a linear probability model (LPM) with fixed effects is adopted to estimate the syndicate formation. Findings The authors find that the similarity between a leader and a backer has an inverted U-shaped relationship with the leader and backer's likelihood of co-investment in a syndicate, which is different from the dominant homophily-based tie formation in venture capital (VC) syndicates and other digital platform contexts. Although equity crowdfunding platforms encourage the possibility of exploring new partners, investors are more likely to co-invest with others who have stronger prior ties. Originality/value This research theoretically contributes to the scant literature of equity crowdfunding syndicates by contextualizing two most prevalent mechanisms (i.e. homophily and repeated ties) driving tie formation in VC syndicates and digital platforms.

This study investigated the impacts of network structure on a venture capital (VC) alliance’s successful exit from an emerging market by empirically analyzing joint VC data in China. We find that, compared to a mature capital market, the mechanism not only has a certain commonality but also shows the emerging market’s particularities. From the commonality perspective, the mechanism has a positive effect on successful exit by obtaining heterogeneity information. These particularities are manifested in the following three aspects. First, the mechanism is not conducive to deepening the enterprise value chain to establish credibility by obtaining short-term cash during an initial public offering with the enhancement of the VC alliance’s intervention ability for enterprise development. In addition, a VC alliance’s independent judgment is bound by the VC market. Furthermore, the problem of over-trust in investees reduces the likelihood of a VC alliance’s successful exit. Therefore, we should pay more attention to the particularity of emerging markets such as China to improve the relevant management mechanism.

Prior literature recognized the importance of members' prior ties on venture capital (VC) syndicated investments and uncovered investors' different roles, in separate streams of research. This study bridges these two relevant but disjoint streams by examining how different roles' prior ties affect VC syndicated investments. We consider the difference between leaders and followers, as well as their prior ties to the VC network and to the target investee. Our empirical analysis of the Chinese VC market demonstrates that VC investors' prior ties have stronger influences for followers in VC syndicate formation than those for leaders. This research sheds lights on the understanding of different mechanisms of VC syndicate formation for different roles.

Celebrity firms can gain management, marketing, and financial benefits. Being popular on social media has been proven to have a similar effect. Based on nearly 5,000 actively traded US firms data, this paper discusses the factors that drive users to follow them. The factors were explored from two aspects: the firm's intrinsic characteristics and firm social media engagement. Our results show that there are "engaging traits" that attract users meaning that some firms are 'born-to-be' more popular than others. Luckily for those less attractive firms, they can take advantage of social media engagements to increase their popularity levels and to eventually enjoy marketing and management benefits, because social media engagements have stronger impacts on the level of firm celebrity. However, firm intrinsic characteristics, which have long been ignored by previous studies, might play a bigger role in the future with the diminishing marginal returns of social engagements.

在媒介融合的行业背景下,数字出版产业链正在发生深刻变革.在分析媒介融合对数字出版产业链影响机制的基础上,梳理数字出版产业链在发展过程中的现存问题,探究媒介融合背景下数字出版产业链的变革方向,进而探讨数字出版产业链的重构路径:挖掘内容精髓,拓展媒介渠道,重塑产品价值链;跨品类、跨用户、跨场景拓展跨界企业链;培养与引进数字出版人才,完善人才供应链;整合优质资源、加强数字出版生产要素市场建设、健全数字出版市场监管体系,构建产业生态链.

Wearable device and BDA bring opportunities for healthcare sector.BDA adoption strategy is investigated by stylizing an analytical model.The effects of the privacy risk and healthcare efficiency are discussed.BDAs influence on social welfare is analytically pointed. This paper investigates the impact of big data and analytics (BDA) on health IT market competition as well as health IT providers optimal BDA adoption decisions. To capture the specific characteristics of BDA in healthcare, we simultaneously model BDAs healthcare efficiency and privacy risk from consumer perspective and BDAs benefit and cost from provider perspective in a stylized two-dimensional product differentiation framework. The results indicate firms optimal pricing strategies with the dynamic of BDAs efficiency and privacy risk. In addition, BDAs influence on firms outcomes and social welfare are analytically pointed. Theoretically, this study has potentials to provide foundations for future big data research by stylizing an analytical model to understand firms BDA adoption. Practically, insights for business managers on how to optimize strategies of BDA adoption, and for social planners on how to conduct better policies to improve healthcare service quality by promoting BDA adoption in healthcare, are derived.

[目的/意义]通过对国内外大数据背景下政府开放数据过程中出现的个人隐私现状进行分析,明晰目前的隐私保护挑战并归纳出合理的分析框架.[方法/过程]通过国内外的文献分析,针对大数据、政府数据开放和隐私保护等关键词研究当前热点趋势,并对重点模型进行深入逻辑分析.[结果/结论]在此基础上,推导出一个先导性理论框架,关注数据实践过程中利益、文化规范、隐私保护工具、技术和非政府实体参与等主体的关系,并针对这个模型提出下一步研究方向.

Social sentiment reflects grassroots views regarding stock trends and has played a leading role in stock movements. Previous studies have relied predominantly on statistical models, regression mode...

This paper investigates the impact of big data and analytics (BDA) on health IT market competition as well as health IT provider's optimal BDA adoption decisions. To capture the specific characteristics of BDA in healthcare, we simultaneously model BDA's healthcare efficiency and privacy risk from consumer perspective and BDA's benefit and cost from provider perspective in a stylized two-dimensional product differentiation framework. The results indicate firm's optimal pricing strategies with the dynamic of BDA's efficiency and privacy risk. In addition, BDA's influence on firms' outcomes and social welfare are analytically pointed. Theoretically, this study has potentials to provide foundations for future big data research by stylizing an analytical model to understand firm's BDA adoption. Practically, insights for business managers on how to optimize strategies of BDA adoption, and for social planners on how to conduct better policies to improve healthcare service quality by promoting BDA adoption in healthcare, are derived. (C) 2017 Published by Elsevier B.V.

Numerous studies have shown that social media marketing strategies have positive impacts on the longterm financial performance of firms. However, whether short-term marketing campaigns have any influence on firm revenue remains unknown. This paper examines data from Singles’ Day, the world’s largest shopping event, revealing that firms’ social media efforts have a positive impact on product sales. Furthermore, we find that the two social media effort measures generally thought to have positive impacts on a firm’s long-term financial performance, richness and intensity, have no significant influence on the success of a firm’s short-term marketing campaign. Instead, relevance shows significant and positive impacts. Moreover, we compare the effects of social media marketing yields from companyowned accounts with those of employee-owned accounts, finding that employee-owned accounts have better marketing effects than company-owned ones.

回顾互联网时代政府信息供给遇到的新挑战,包括互联网广泛应用、知识全球化和去垂直化、知识扩散和外溢加快等,分析当前政府信息供给的不足.通过综述国内外学术界对于开放式创新和政府信息供给的研究现状,认为封闭式的供给模式已不能满足政府转型发展的需要,如何在现有的信息服务基础上创建符合时代需求的政府信息供给机制成为下一步的研究方向.

Social sentiment reflects grassroots views regarding stock trends and has played a leading role in stock movements. Previous studies have relied predominantly on statistical models, regression models or vector-based predictive models to analyze the influence of social sentiment without considering other information sources or their intrinsic interactions. However, stock movements are in essence driven by various types of highly interrelated information sources including firm characteristics, social sentiment, and professional opinions. This paper describes the degree to which the problem arises in understanding the role of social sentiment in financial markets and proposes a novel intelligent stock analysis system to solve it. It first captures social sentiment and professional opinions from textual information in social media and financial news, respectively, and then represents the whole market information space consisting of these two information sources along with firm characteristics via tensors. Finally, a tensor-based learning algorithm is utilized to capture the interactions of these information sources on stock movements. Experiments performed on an entire year of data of China Securities Index (CSI 100) stocks demonstrate the effectiveness of the proposed intelligent system to study the role of social sentiment from the perspective of joint effects of multiple information sources compared with traditional vector-based systems.

Big data analytics (BDA) has shown distinctive advantages on improving healthcare outcomes and reducing healthcare cost. While it also increases consumer’s privacy risk. We thus focus on the question that whether health IT providers should adopt BDA to increase healthcare efficiency in the presence of privacy concerns. We focus on the healthcare wearable device market due to its natural advantages and popularity in healthcare field. Since the consumers has various preferences on the products (horizontal differentiation) with different quality levels (vertical differentiation), we adopt a two-dimensional product differentiation model to investigate the effects of BDA’s efficiency-privacy tradeoff on the competition. Our results demonstrate that health IT providers should adopt BDA technology when their efficiency-privacy tradeoffs are large. Besides, when BDA cost on per unit benefit is small, or the investment has more unit benefit for itself than the rival, health IT providers also should adopt BDA. Our findings provide insights to business managers on how to optimize strategies of BDA adoption. Social planners are also guided to conduct better policies to improve health service quality by promoting BDA adoption in healthcare sector.

Stock return comovement analysis is important to financial analysts, decision makers, and academic researchers and has many financial implications, such as portfolio management, style investing, and market risk detecting. This paper proposes a novel model to both identify homogeneous stock groups and predict stock comovement with respect to firm-specific social media metrics. One of the innovations of the social media platform is that it breaks traditional media intermediation. A firm with an official Twitter account can publish information and interact with its users directly. Such direct information is largely reflected on firm-specific metrics, e.g., the firm's number of followers and number of tweets sent. To the best of our knowledge, this paper is the first to reveal the impact of social media metrics on stock return comovement studies. By analyzing samples from the NYSE and NASDAQ stock exchanges, we find that firms with official Twitter accounts have a much higher comovement than those without such accounts. Furthermore, we classify the former set of firms into homogeneous groups by their specific microblogging metrics. The results demonstrate that these metrics cannot only predict the comovement of stocks but also notably increase the accuracy of comovement predicting, compared with industry categories. (C) 2015 Elsevier Ltd. All rights reserved.

教材编写与传统文化教育的发展关系密切.传统文化教材的编写需要首先辨析取向与知识取向,并由此形成系统的、适合教材使用的知识体系,然后以教育内容和学生特点为基础,兼顾社会发展对教育的要求,完善教材内容设计和教材评价体系.这样,传统文化教材的编写水平才能逐渐满足受教育者的需要,为我国教育改革注入新的活力.

近20年来，读经教育在中国海峡两岸声名鹊起，读经的群体上至古稀的老人，下至咿呀学语的幼童；既有身价百亿的富人，也有衣食不继的穷人；既有政府高官，也有普通百姓；既有学校的学生，也有各行各业的人员，甚至走在城市的马路旁，乡村的街道旁，都会偶尔看到读经的人或者听到与读经教育有关的话题，甚至不少新闻报道提到连监狱的犯人也在读经。截至目前，读经群体的总人数可能已经过亿。

With technological advancements that cultivate vibrant creation, sharing, and collaboration among Web users, investors can rapidly obtain more valuable and timely information. Meanwhile, the adaption of user engagement in media effectively magnifies the information in the news. With such rapid information influx, investor decisions tend to be influenced by peer and public emotions. An effective methodology to quantitatively analyze the mechanism of information percolation and its degree of impact on stock markets has yet to be explored. In this article, we propose a quantitative media-aware trading strategy to investigate the media impact on stock markets. Our main findings are that (1) fundamental information of firm-specific news articles can enrich the knowledge of investors and affect their trading activities; (2) public sentiments cause emotional fluctuations in investors and intervene in their decision making; and (3) the media impact on firms varies according to firm characteristics and article content.

Stock return comovement analysis is important to financial analysts, decision makers, and academic researchers, in many financial implications, such as, portfolio management, style investing, and market risk detecting. This paper examines firms’ social media, in particular, microblogging metrics’ role on analyzing stock return comovement. Social media allows firms to proactively connect with public users, customers, suppliers, and other business partners. It also provides us a large-scale and free data set to automatically and quickly uncover firms’ social media metrics’ influence on financial outcomes. Most prior studies of social media metrics focused on overall firm metrics and their predictability on stock returns. However, the role of firms proactive activities has been omitted. This paper filled this gap by using cross-sectional data from the US and China to investigate on how firm-specific social media metrics make an impact to the stock return comovement. The results show starting with the four-digit Global Industry Classification Standard (GICS) system, the stock groups that are divided by firms’ effect microblogging metrics, have a higher comovement than six-digit GICS groups.