Food scandals,media attention and habit persistence among desensitised meat consumers

Consumer reactions to food scandals and their resulting economic implication are well documented. However, studies have typically neglected the roles that consumption habits and media usage behaviours may play in explaining household’s response to food safety incidences. In this study we develop a model of heterogeneous media usage intensity, information impacts and decay over time to estimate household’s behavioural responses to the 2011 German Dioxin scandal. We are specifically interested in determining the degree of heterogeneity in household’s short-term adjustments demand patterns versus persisting long-term consumption habits of meat products (chicken and pork) directly affected by the incident. The empirical analysis employs detailed household-level retail scanner and media usage data collected by the GfK Consumer Scan panel for a total of 16,023 households over a period of 104 calendar weeks. Results of dynamic correlated random effect Tobit models indicate an important role of unobserved heterogeneity in explaining household responses during the food scandal. We find strong empirical evidence supporting our hypothesis that short-term marginal adjustments in demand and propensity to buy affected products triggered by the negative impact of household media exposure were over-compensated by habit persistence. The question of how consumption patterns evolve over time in the presence of food scandals is expected to be of interest for both policy makers and the food industry. The potential biases in the projection of economic impacts resulting from simplifying assumptions of household’s response patterns to a proliferating numbers of food safety incidences has implications for risk management and public policy.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

A model of West African millet prices in rural markets

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts.     

The long-term role of non-traditional banking in profitability and risk profiles: Evidence from a panel of U.S. banking institutions

The goal of this empirical study is to identify empirically and on a panel basis how non-traditional bank activities affect directly the profitability and risk profiles of the financial institutions involved in such activities. Through a dataset that covers 1725 U.S. financial institutions involved in non-traditional bank activities spanning the period 2000–2013 and the methodology of panel cointegration, the empirical findings document that non-traditional bank activities exert a positive effect on both the profitability and the insolvency risk. The results could be important for regulators given they could serve as a pre-warning signal that sends a clear message to regulators about the potential systemic risk that exists within the financial markets.     

Making sense of complex data using interactive data visualization

An important role for accountants today is to provide decision support to senior management by assisting them in the analysis of large, complex data sets. Interactive data visualization (IDV) facilitates this process by allowing users to navigate, select, and display data via an easy-to-use interface often used as a component of data analytics. Given the increasing popularity of IDV as a tool for making sense of complex data, it is important that accountants become familiar with and learn how to use this technology. This case provides a hands-on opportunity to organize complex accounting data to create IDVs for decision makers to use. Further, the case enables students to understand the potential impact of IDVs on preparers and users of accounting information. Students will assume the role of a division controller in a hypothetical company and create an IDV to assist the chief executive officer (CEO) in decision making.     

浅谈新形势下的高职院校设备管理工作

从设备的购置、转资、使用、维修和报废等5个环节入手,介绍了新形势下高职院校设备管理工作的一些经验做法,指出了加强设备管理人员培训的重要性。     

确权、交易、资产化：对大数据转为生产要素基础理论问题的再思考

大数据是数字经济的基础性、战略性资源,是后疫情时代经济发展的重要生产要素,但社会各界对大数据的理论认知仍落后于其应用实践,而且大数据确权、交易定价、资产化问题也存在诸多争议,这对大数据产业和数字经济的持续健康发展形成潜在隐患,也成为制约大数据向生产要素正常转化并参与社会大生产的关键。文章在对大数据概念、属性重新认识的基础上,综合财经、法律等理论观点和实操技巧,阐述了大数据如何从商品流通要素演变为社会生产要素的市场逻辑以及使用价值如何注入大数据的资产化过程,并从实践视角提出大数据确权的法理基础和大数据交易所生态下交易定价模型。同时提出以数据税来弥补大数据采集环节个人和企业放弃的小微权利,将私权转化为社会公共产品,为大数据产业和要素市场发展提供新的借鉴。     

移动网络数据中大数据处理的关键技术探索

最近几年以来,我国科技水平持续提升,计算机网络技术也逐渐在各行各业中获得了广泛运用,这也使得信息技术的深度融合发展受到一定的促进,数据规模愈发庞大,在此背景下,开始演变成大数据供不应求的状况.为此,国内陆续推出了一系列相关的管理法规与条例,同时扩大了对大数据的探究创新力度,有效提升了移动通信网络处理数据的速度,这同时促...     

大数据能力对农业科技企业创新的影响：一个有调节的中介效应模型

目前学者们较为关注大数据能力对于企业创新的影响研究,但大多笼统探究大数据能力对企业创新的直接影响,深入探究其中介机制和调节机制的研究尚未完善,立足于农业科技企业的研究更是缺乏。本文基于动态能力理论,深入探究大数据能力对农业科技企业创新的影响机制,以双元组织学习为中介变量,以市场导向为调节变量,建立可调节的中介模型。基于181家农业科技企业样本,结果表明：大数据能力对企业创新具有显著正向影响;探索式学习和利用式学习在大数据能力和企业创新间均具有正向中介作用;市场导向增强了利用式学习在大数据能力与企业创新之间的中介作用;而对探索式学习在大数据能力与企业创新之间的中介作用未发挥显著的调节作用。