Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements

PurposeMarketing research mainly uses self-reported method to record respondents' perceptions of creativity, and while self-reported method has its own merits, there exists some critique, particularly in terms of its ability to adequately capture the influence of message appeal on creativity. This paper studies how viewers’ responses to message appeals in social media advertisement compare in terms of self-reported responses versus responses taken through a neurophysiological method of Electroencephalograph (EEG).MethodologyTwo social media advertisements are displayed through a laboratory experiment to 17 subjects observing the subjects' neurophysiological reactions as well as their self-reported responses with regard to the commercials’ emotional, informational, and brand-related content.FindingsResults show that neurophysiological method offers unique details about emotional appeal, which the self-reported method fails to reflect. Furthermore, the neurophysiological measure identifies differences across the two target commercials in the emotional content part, which again are not identified through the self-reported method.OriginalityThis paper advances advertising research in social media literature by comparing content evaluation within advertisement through neurophysiological and self-reported measure. These findings have implications for marketers to use and measure message appeals in advertisement on social media to influence consumer response.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Opportunities to reduce road traffic injury: new insights from the study of urban areas

Abstract

Over the past four decades considerable efforts have been taken to mitigate the growing burden of road injury. With increasing urbanisation along with global mobility that demands not only safe but equitable, efficient and clean (reduced carbon footprint) transport, the responses to dealing with the burgeoning road traffic injury in low- and middle-income countries has become increasingly complex. In this paper, we apply unique methods to identify important strategies that could be implemented to reduce road traffic injury in the Asia-Pacific region; a region comprising large middle-income countries (China and India) that are currently in the throes of rapid motorisation. Using a convolutional neural network approach, we clustered countries containing a total of 1632 cities from around the world into groups based on urban characteristics related to road and public transport infrastructure. We then analysed 20 countries (containing 689 cities) from the Asia-Pacific region and assessed the global burden of disease attributed to road traffic injury and these various urban characteristics. This study demonstrates the utility of employing image recognition methods to discover new insights that afford urban and transport planning opportunities to mitigate road traffic injury at a regional and global scale.     

Alternative farming and collective goals: Towards a powerful relationships for future food policies

Based on the significant amount of literature on the concept of spatial, ecological, and social embeddedness in the Alternative Food Networks (AFNs) research, this study describes the main dynamics related to the involvement of non-conventional farmers in their collective ideas and actions.With a comparison between two European regions: Sardinia and the Community of Madrid, the paper identifies different styles of behaviour among organic producers, whether or not supervised by a control body, and highlights the differences and similarities regarding their ideas about how alternative agriculture has an impact on the environment as well as society. Eco-economies and ecopreneurship are also considered. The aim is to see if, and to what extent, these ideas have a collective character, going beyond the limits of the single farm to reach a regional scale and, thus, if there is a basis for the development of future food-related planning policies. Through the information provided by semi-structured interviews, farmers have been divided into categories according to their degree of embeddedness, described through the main themes that emerged during the interviews. Every farmer has been included in only one category according to his or her main preference, which does not imply the ab-sence of a positive attitude towards other categories.The study aims to contribute to the understanding of how AFN values and methods can boost the improvement of biodiversity and landscape conservation through collective actions which have the power to boost and develop rural initiative at a regional scale.     

Interbank loans,collateral and modern monetary policy

This study develops a novel agent-based model of the interbank market with endogenous credit risk formation mechanisms. We allow banks to exchange funds through unsecured and secured transactions, which facilitates the flow of funds to the most profitable investment projects. Risk premiums result from banks׳ forecasting rules and depend on past performance of the benchmark risk factors and interest rates. Our model confirms basic stylized facts of the interbank interest rates and volumes. We also find that network structures within the secured market segment are characterized by the presence of dealer banks, while we do not observe similar patterns in the unsecured market. We perturb the model with exogenous shocks and policy scenarios which correspond to unconventional monetary policies.     

论高速铁路精密工程测量“三网合一”

客运专线铁路对测量精度要求较高,传统的分阶段布网方式和方法已不能满足现代高速铁路建设的需要。文章提出了＂三网合一＂的测量体系和布设原则及控制网的精度梯度等级,该体系的建立能统一高速铁路勘测控制网、施工控制网、运营维护控制网的起算基准和协调其精度,保证无碴轨道铁路工程的顺利实施。     

Economic corollaries of personalized recommendations

The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.     

央行和商业银行视角下存款准备金率的实证分析

本文分别从央行货币政策调控目标和商业银行对存款准备金率容忍度的视角出发,利用参数法和神经网络模型对存款准备金率进行实证分析。研究发现,央行近年来多次上调存款准备金率主要是为了对抗通货膨胀以及回收货币流动性。在不考虑银行容忍度下,参数模型给出的目标存款准备金率为23%,而在考虑了存款准备金率对银行的负面影响后,根据神经网络模型得出2011年上半年合理的存款准备金率应为21.34%,与当前21.5%的实际存款准备金率相符。说明央行在货币调控时考虑到了银行的容忍度,是符合宏观审慎性原则的。而模型的敏感性分析表明,未来存款准备金率仍旧存在上调的区间与上调的可能性。     

The power of market mood — Evidence from an emerging market

This article focuses on investor behavior and, consequently, the mood in the market. By using a self-organizing network we develop a model which tries to capture the market mood and serves as an indicator of the reasonableness of selling or purchasing securities. In this sense, the final result of this model is the same as in the model-type prediction of future stock prices, with the only exception being that one is not required to know the concrete future values of the selected security. This will indirectly support the hypothesis that psychological factors are an important (if not key) market driving force.     

Co‐movements in commodity prices: a note based on network analysis

This article analyses co‐movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co‐movement of the commodity prices in our time sample, however from mid‐2008 to the end of 2009 co‐movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co‐movement during the sampled period.