信息不对称、分析师关注度与增发折价

以2006—2010年间实施新股增发的我国沪深A股上市公司为样本,研究分析师关注度对增发折价的影响。研究发现:分析师关注度较高的上市公司具有较低的投资者间估值分歧和增发折价;分析师能力和盈余预测分歧对增发折价没有显著影响;分析师关注度的提高给分析师能力强的公司带来的增发折价降低幅度大于给分析师能力不强的公司带来的效果。     

Online boundary work tactics: an affordance perspective

Studies of work/life balance have focussed on offline settings, and even though technology is considered as a boundary‐influencing feature, social media have not been the focus. Social technologies challenge the relationship between work and private life in new ways, due to their identified affordances: visibility, persistence, association and editability (Treem and Leonardi, 2012). In this paper, we present the results of a study of social media use (Facebook and Twitter) by employees of non‐governmental organisations (NGOs), and show how, through their affordances, these technologies influence the relationship and boundaries between work and non‐work, increasing visibility and reducing individual privacy. Consequently, we observe boundary work tactics that aim to protect private life from both public and professional scrutiny, in prohibitive, reactive or active ways. Our results call for organisations to develop explicit policies or guidelines for social media use, in both their own interests and those of their employees.     

The effects of online negative word-of-mouth on dissatisfied customers: A frustration–aggression perspective

Conceptualizing how customers construe online negative word-of-mouth (nWOM) following failure experiences remains unsettled, leaving providers with inconclusive recovery strategy programmes. This empirical study recognizes online nWOM as a co-created encounter between the complainant (i.e., the initiator of the online nWOM) and the recipient (i.e., the consumer who engages with the online nWOM), examining their idiosyncrasies to discern their understanding of the experience. It introduces frustration–aggression theory to online WOM literature, recognizing that it can support a higher-order understanding of phenomena. Through phenomenological hermeneutics, interviews and focus groups, data were collected from millennials in Albania and Kosovo that provided accounts of nuanced and distinctive online nWOM realities. The emerged insights extended extant theory to a three-fold online nWOM typology (i.e., lenient online nWOM, moderate online nWOM and severe online nWOM) recognizing the negative impact customers have on a provider, which is controlled by frustration–aggression tags. Frustration–aggression variations across online nWOM led to the construct of three types of customers that engage in online nWOM, namely tolerable online nWOM customers, rigorous online nWOM customers and confrontational online nWOM customers. Findings culminated with satisfactory recovery strategies aligned to customer inferences regardless of the nWOM context.     

President’s Tweets,US-China economic conflict and stock market Volatility: Evidence from China and G5 countries

This study provides empirical evidence that the tweets from US President Donald J. Trump influence the trading decisions of investors worldwide. We examine the effects of Trump’s tweets related to China on stock market volatility in China and the G5 countries. Our results show that Trump’s original tweets related to the US-China economic conflict expand volatility in stock markets worldwide, and the US-China trade friction intensifies this effect. Furthermore, Trump’s tweets with different sentiments have different impacts on the returns of global stock markets. Our findings confirm that international investors may make their investment decisions based on information conveyed in these tweets.     

Privacy rights in online interactions and litigation dynamics: A social custom view

We develop a social custom model where a population of social media users decide whether to remain online and accept the platform’s data-gathering policy or abandon the social media and litigate for privacy violations. By allowing the users’ concerns for informational security to co-evolve with the number of privacy-related trials, we find that the system may converge to multiple equilibria. When users put relative emphasis on the relational benefits of online interactions, privacy-related trials remain contained and the provider imposes no limitations to its data-gathering activities. Conversely, when users put relative emphasis on the privacy costs of web-mediated interactions, privacy-related trials become endemic and platforms modulate their data-gathering activities by mediating between profitability and the legal implications of their choice. We use these results to comment the recent shift in the users’ orientation towards online platforms and caution against the inability of institutions to keep up with the process of technological change.     

Visitor perceptions of European Holocaust Heritage: A social media analysis

This study presents a netnographic discourse analysis of social media content generated around three high profile European Holocaust heritage sites: Ann Frank's House in Amsterdam, The Auschwitz-Birkenau Memorial and Museum in Poland, and the Jewish Museum in Berlin, Germany. It identifies four salient discourses under the headings of Holocaust heritage as social memory, reactions to Holocaust heritage, obligation and ritual, and transgressive visitor behaviour which frame the values, existential anxieties, emotions, priorities and expectations of visitors. The findings will be of interest to stakeholders involved in the planning and management of Holocaust heritage since they provide unique access to a synthesis of unmediated visitor feedback on European Holocaust heritage experiences.     

基于遥感数据的郑州市植被覆盖度变化研究

植被覆盖度是衡量某一地区植被覆盖状况的指标,某一地区的植被覆盖状况会直接影响到该地区的土壤、水分和气候等自然环境要素。随着中原经济区的快速发展,郑州市作为中原经济区的增长极,发展速度很快,城市扩张迅速。为了研究郑州市的快速发展给生态环境带来的影响,选取植被覆盖度这一指标为研究对象,利用不同时间阶段的遥感影像估算郑州市不同时期的植被覆盖度,通过分析植被覆盖度变化来研究郑州市的快速增长对植被覆盖度造成的影响。结论如下:(1)2000年郑州市的植被覆盖度主要以低植被覆盖为主,2014年以中低植被覆盖度为主,高植被和较高植被覆盖度略有提高;(2)从植被覆盖度变化来看,2000—2014年整个研究区植被覆盖度主要以轻微增加为主。     

Who is a tourist? Classifying international urban tourists using machine learning

A key issue in tourism management relates to the lack of consensus regarding a theoretical and practical definition of the term “tourist.” In turn, this results in a range of methods for counting tourists and measuring tourism. This paper presents a novel non-linear model for classifying international tourists in urban settings, based on machine learning classification methods. These methods utilize innovative feature engineering derived from photos posted on the Flickr social media platform combined with the specific urban destination street structure. The data science model that we developed for identifying international tourists produced an overall accuracy of 69% for Manhattan and 94% for Vienna and Prague, offering new tourism indicators such as repeat visits, travel distances, and short stays. The outcome of this study offers a better understanding of travel patterns among international tourists, which could improve international tourism management and promote a more practical and adaptable model for measuring and analyzing international tourism using machine learning and user-generated content.     

财经新媒体管理面临的新问题及建议

传统财经媒体逐渐从纸媒向网络转移,同时诞生了一批原生于网络的财经新媒体,从根本上改变了财经媒体的传播格局。2015年前后,伴随着多种功能和多种形态内容的不断推出,以个性化推荐、内容付费为代表的精细化运营逐渐成为财经新媒体的行业主调。从信息传递向信息服务拓展,导致财经新媒体具有多重身份,不同角色边界日益模糊;从单一文字内容向音视频多元形态探索,跨平台、多元方式传播给管理带来困难。鉴于上述情况,从严格分类管理、坚持资质准入、完善责任制度和保护数据安全等方面提出了财经新媒体的管理制度和建议。     

Measuring public opinion via digital footprints

Do digital traces accurately reflect individual preferences? Can signals from social media be used to measure public opinion? This paper provides evidence in favour of these hypotheses. We test a regression and post-stratification strategy that combines samples of digital traces with a stratification frame containing individual-level socio-economic data, in order to generate area forecasts of the outcome social phenomena of interest. In our example, we forecast the two-party vote of Democrats and Republicans in the 2018 Texas congressional district and Senate election. Our implementation assumes we can observe, and sample, individuals signaling their preference by favoring one virtual location over another; in our case, visiting Democrat versus Republican Facebook pages during the election campaign. Over the course of seven weeks preceding the mid-term elections we generate vote share forecasts which do not use any traditional survey data as input. Our results indicate that individuals leave digital traces that reflect their preferences.