Toward a new business model of retail industry: The role of brand experience and brand authenticity

While the customer-to-manufacturer (C2M) business model has received increasing attention as a new business model for e-commerce and retail industry, little is still known about it and the effect of its approach. This study aims to understand how brand-related stimuli in C2M environments affect customer responses as the worldwide COVID-19 pandemic. The outcomes reveal that the Sensory, affective, and intellectual aspects of brand experience positively influence brand authenticity. Brand authenticity has a positive effect on behavioral intention, such as reuse intention and word-of-mouth. Additionally, this research finds that social presence moderates the association between the sensory aspect of brand experience. Thus, this study can suggest a C2M business model as a means of sustainable operation of the retail industry to both researchers and practitioners in relation to the retail industry.     

Factors driving embodied carbon in international trade: a multiregional input–output gravity model

ABSTRACT

Concerns about the effects and consequences of climate change have notably increased in recent decades. Despite large advances in the understanding of this phenomenon, further research into the determinants of gas emissions is necessary, to shed light on the responsibilities of producers and consumers, and their potential contribution to mitigation strategies. This paper studies the trajectories and determinants of carbon embodied in world trade during a period of 15 years. Our methodology relies on a multiregional input–output model, environmentally extended. Drawing on data from the World Input–Output Database, we estimate embodied emissions in bilateral flows. Then, we assess the determinants of CO₂ emissions embodied in trade, combining input–output modelling with trade gravity panel data analysis. This paper offers a methodological approach that explains and quantifies the underlying factors of carbon trade, integrating the production and consumption perspectives and considering the geographical, structural and institutional context of countries.     

The impact of research collaboration on academic performance: An empirical analysis for some European countries

The aim of this paper is to investigate the impact of internal and external research collaborations on the scientific performance of academic institutions. The data are derived from the international SCOPUS database. We consider both quantity (the number of publications) and quality indicators (the field-weighted citation impact and the share of publications in the 10% most-cited articles) to evaluate universities' performance in some European countries (Germany, France, Italy, the UK and Russia). To this end, we develop a non-overlapping generations model to evidence the theoretical idea of research externalities between academic institutions. Moreover, we implement an empirical model to determine the extent to which the impact of internal and external collaborations on universities' performance is sensitive to the geographical dimension of the data.     

互联网金融规制与市场有效性改善*——来自中国网络借贷行业的证据

中国的互联网金融经历了一个先创新发展后监管规制的过程，本文通过对网络借贷行业的实证研究，分析监管规则的出台实施对新型互联网融资市场有效性的影响。实证结果表明，网络借贷平台资金的银行存管、持有ICP证等强制性规定缓解了投资者、平台企业与借款人之间的信息不对称，对克服早期平台间通过竞相承诺高回报率来吸引投资者导致的“柠檬市场”问题、对解决网贷市场上缺乏平台风险性高低有效信号的问题都起到了正向作用，有助于互联网融资市场的有效性提高。     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Impact of modern communication infrastructure on productivity,production structure and factor demands of US industries: Impact revisited

This study investigates the contribution of modern communication infrastructure characterized by high speed broadband access network on the productivity growth, production structure and factor demands for US industries and for the aggregate economy. To evaluate such contribution, we modify the traditional cost function by incorporating communication infrastructure as input in production process in conjunction with other public infrastructures. The network externality and spillover effect of broadband access technology are captured by introducing broadband penetration rate as a shift factor in industry level production function. Empirical results show that the increased use of modern communications infrastructure increases the productivity of all industries with wide variations across industries. Estimated impacts on input demands show that increase in use of communications infrastructure service saves labor and materials and increase the demand for private capital. Finally, aggregate social rate of return on such investment has been estimated for policy implications.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

我国M2现状及未来趋势浅析

对我国当前M2快速增长的原因,从经济发展、居民储蓄和外汇储备等三个方面进行深入分析,并在此基础上对未来发展趋势进行预测分析。     

Impact of state-dependent oil price on US stock returns using local projections

In this paper, we investigate the impact of oil prices on both aggregate and industry US real stock returns over the period 1973–2017. The empirical analysis contributes to the related literature introducing a state-dependent oil price (high and low) and the local projections approach. Our main finding is that, depending on the nature of the shock and industry, the negative effects of oil price shocks become exacerbated -and the positive effects get moderated- if oil prices are already high.     

What is happening in Paris? Airbnb,hotels, and the Parisian market: A case study

Airbnb has been the subject of heated discussions among tourism and hospitality scholars. In order to understand the true impact of P2Psharing on the tourism and hospitality landscape, it is important to understand that market in detail first. Therefore, in this paper, we explore the development of the Airbnb market over the last seven years in Paris, the most popular destination for Airbnb guests with more than 40,000 accommodation rentals. This research note is a summary of our findings on the Paris market. Our study shows different growth and seasonality patterns for Airbnb and hotels and dissimilarities in the geographical location of the offers. The findings indicate that the two products are not in direct competition and that their relationship might be more complex than previously thought. We trust that exploring the nature of the competitive relationship would be a valuable avenue for further investigation.