Shopping intention at AI-powered automated retail stores (AIPARS)

Artificial Intelligence (AI) is transforming the way retail stores operate. AI-Powered Automated Retail Stores are the next revolution in physical retail. Consumers are facing fully automated technology in these retail stores. Therefore, it is necessary to scrutinize the antecedents of consumers' intention to shop at AI-Powered Automated Retail Stores. This study delves into this area to find the predictors of consumers’ intention to shop at AI-Powered Automated Retail Stores. It extends the technology readiness and acceptance model by the addition of AI context-specific constructs such as Perceived Enjoyment, Customization and Interactivity from the present literature. The proposed model is tested by surveying 1250 consumers & the data is analyzed using the PLS-SEM technique and empirically validated. The outcome of the study reveals that Innovativeness and Optimism of consumers affect the perceived ease and perceived usefulness. Insecurity negatively affects the perceived usefulness of AI-powered automated retail stores. Perceived ease of use, perceived usefulness, perceived enjoyment, customization and interactivity are significant predictors of shopping intention of consumers in AI-powered automated stores. This research presents insightful academic and managerial implications in the domain of retailing and technology in retail.     

Taking superintelligence seriously: Superintelligence: Paths,dangers, strategies by Nick Bostrom (Oxford University Press, 2014)

A new book by Nick Bostrom, Superintelligence: Paths, Dangers, Strategies, is reviewed. Superintelligence explores the future of artificial intelligence and related technologies and the risks they may pose to human civilization. The book ably demonstrates the potential for serious thinking aimed at the long-term future. Bostrom succeeds in arguing that the development of superintelligent machines will, if not properly managed, create catastrophic risks to humanity. The book falls short in some respects, and some sections are more compelling and novel than others. Overall, however, Bostrom’s book succeeds in demolishing the “null hypothesis” according to which the possibility and risks of superintelligence can continue to be ignored, and is a must-read for those interested in the long-term future of humanity.     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

Modelling property values in Nigeria using artificial neural network

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.     

多元智能理论对独立学院英语教学改革的启示

多元智能理论是美国哈佛大学著名心理学家加德纳于1983年在其著作《智能的结构》中提出来的。该理论认为人类拥有多种相互独立的一些智能,而每个人都有着不同的智能组合。如果能够建立一种教育体系,开发学生多种不同的智能潜力,帮助他们发展其独特的智力特点,就会促进学生全面而又有个性的发展。就独立学院的英语教学改革而言,多元智能理论对当前教学环境的变革、对教学理念和教学内容的重新解读以及对教学评价的多样化思考都产生了深远的影响。     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

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.     

CRTSⅡ型板式无砟轨道用CA砂浆灌注施工关键技术

CA砂浆灌注质量控制对保证整个轨道板的施工质量非常关键。本文总体论述了CRTSⅡ型板式无砟轨道用CA砂浆灌注工艺,包括压紧装置的安装、封边、砂浆的运输、灌注、养生、缺陷的修补等相关工序的施工方法及关键技术,为确保CA砂浆灌注质量提供借鉴。     

我们为什么选择了社会主义市场经济

我们选择市场经济,是因为迄今为止的历史证明市场经济是可以富其国裕其民的。然而,我们所要的富裕,不是少数人的富裕,而是人民群众的共同富裕。不仅要富裕,而且必须是共同富裕的市场经济。因此,我们必须对市场经济进行改造,创造出我们自己的市场经济,这就是有中国特色的社会主义市场经济。该文遵循实事求是和历史唯物主义的原则,从市场经济旨在提高效率、社会主义则是坚持党的领导和社会公正、路径依赖和历史沿革体现了市场经济的中国特色等四个方面论证了中国共产党领导全国人民选择社会主义市场经济是一个具有历史必然性的伟大选择。这样的选择使得中国经济发展和人民富裕。     

考虑换乘费用的城市公共交通票价优化

换乘所产生的附加费用,如时间和票价等,导致配流影响因素产生变化,如果沿用传统方式将降低预测精度和可靠性。通过引入换乘次数和方式等因子计算出行等待、乘车、换乘及风险评估预留时间等,定义广义出行费用与计算方法;建立双层规划模型求解最优票价,最后通过算例分析弹性出行需求、换乘费用、票价之间的关系。计算结果表明,换乘费用对出行需求的影响小于票价优化对出行需求的影响,优化票价随换乘费用增加而加速降低,为公共交通票价优化提供研究依据。