Price matching and product differentiation strategies considering showrooming

Showrooming, a phenomenon in which customers use brick-and-mortar stores to assess products and then purchase them from online retailers (o-retailers) for lower prices, is considered a great threat to traditional retailers (t-retailers). To combat showrooming, many t-retailers have executed price matching which enables customers to pay o-retailers' lower prices for the identical product. To avoid direct competition with t-retailers who execute price matching, many o-retailers have begun to sell differentiated products from t-retailers, which weakens the information advantage to customers from practicing showrooming. Motivated by these observations, we construct a duopoly game, where a t-retailer and an o-retailer sell products in a same category, to study the profitabilities of product differentiation and price matching in the context of showrooming. The results show that in the scenario without price matching, the o-retailer is likely to benefit from product differentiation only when the o-retailer's differentiated product is more popular with customers than the t-retailer's product. However, in the price matching scenario, the o-retailer also has the opportunity to benefit from product differentiation when the o-retailer's differentiated product is less popular with customers than the t-retailer's product, and product differentiation can be a win-win strategy for the two retailers under certain conditions. Considering the o-retailer's product differentiation decision, the t-retailer is only likely to execute price matching if the non-digital attributes of the product category sold by two retailers are not very obvious.     

Time preference and international trade

We first consider a closed model, where households' time discount depends on externality in consumption. We can prove that there is a unique steady state, which is a saddle point. Then we extend the model to a two‐country world, and derive the condition on the effects of consumption externality under which there is a unique free trade steady state with saddle‐point stability.     

“倒挂型”土地利用总体规划的耕地保护效果评价

研究目的:揭示"倒挂型"土地利用总体规划与耕地保护效果之间的关系,以期为国土空间规划的编制提供参考。研究方法:倾向值匹配法。研究结果:(1)耕地"倒挂型"规划对耕地面积净变化的影响具有统计显著性,2006—2008年,耕保指标"倒挂型"规划使耕地面积净变化量平均减少2.270万hm2;(2)2010—2016年,耕保指标"倒挂型"规划使耕地面积净变化量平均减少0.496万hm2;(3)建设用地"倒挂型"规划对耕地面积净变化量并无显著影响。研究结论:在省级政府耕地保护责任目标考核的制度环境下,耕地"倒挂型"规划对地方政府形成一定压力,促使各级政府采取措施加以应对,从而使得"倒挂型"规划在一定程度上降低了耕地净减少量。     

“一带一路”倡议能否提升对外直接投资企业盈利能力？

基于2011-2015年A股上市公司与商务部《境外投资企业（机构）名录》数据，利用倾向得分匹配方法（PSM）和双重差分法（DID）考察“一带一路”倡议对中国对外直接投资企业盈利能力的影响及作用机制。研究发现：“一带一路”倡议显著提升了中国对沿线国家投资企业的盈利能力。机制检验表明，“一带一路”倡议能够通过推进中国与沿线国家间的经济融合和文化包容来提升对外投资企业的盈利能力，而政治互信在样本期内不显著。进一步研究发现，“一带一路”倡议对企业盈利能力的提升作用主要体现在国有企业、新兴优势产业以及投资目的为非邻近中国的“一带一路”国家上。     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Customer Experience in Online Shopping: A Structural Modeling Approach

Online retail's rapid growth in India has triggered both untapped growth opportunities and challenges to maintain consumer “stickiness” to retailer websites. In this context, online customer experience (OCE) has emerged as a strategic differentiator for sustainable competitive edge. However, there is a paucity of empirical research in this field. Therefore, drawing on extant literature and qualitative research of online shoppers, this study proposes an integrated model of OCE with antecedents, components, and outcome variables. Data for this study were collected from Indian online shoppers; the data are empirically tested along with the moderating effect of time availability on OCE. Results of the study largely support the model and contribute to knowledge creation on OCE in the Indian context and to strategy development for online retailers.     

Impacts of sustainability certification on farm income: Evidence from small-scale specialty green tea farmers in Vietnam

Sustainability certification for small-scale farms has gained considerable momentum in developing countries. However, evidence on the economic benefits of certification schemes in the context of domestically consumed foods and beverages is scarce. This paper addresses this gap by evaluating the impacts of sustainability standards on the selling prices, sales, costs and net income of small-scale specialty green tea farming in Vietnam. We analyze original survey data on 401 smallholder green tea farmers and employ propensity score matching to mitigate self-selection bias. Our estimates reveal that certified green tea producers are able to obtain a higher average selling price and sales value. In addition, although certification leads to a dramatic increase in the costs of hired labor, net farm income is still significantly improved.     

The German labor market during the Great Recession: Shocks and institutions

This paper analyzes Germany's unusual labor market experience during the Great Recession. We estimate a general equilibrium model with a detailed labor market block for post-unification Germany. This allows us to disentangle the role of institutions (short-time work, government spending rules) and shocks (aggregate, labor market, and policy shocks) and to perform counterfactual exercises. We identify positive labor market performance shocks (likely caused by labor market reforms) as the key driver for the “German labor market miracle” during the Great Recession.     

Does studying abroad enhance employability?

Over 200,000 European students study abroad under the Erasmus programme. It appears that studying abroad may impart skills which are helpful for professional careers after graduation. Approximately, 54% of European students report that participation in the Erasmus exchange programme helped them to obtain a first job; however, interestingly, those from Central and Eastern European countries report it much more often—in 73% of cases. The aim of this paper is to find out whether studying abroad enhances the employability of higher education graduates in Poland. Using the propensity score matching method and data from a nationwide tracer survey of Polish graduates, we find that Polish students who completed at least one semester of their studies abroad do not benefit in terms of a higher employment rate after graduation.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.