Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

大学科技园运作模式探讨

本文总结了国内外大学科技园的发展经验,认为在大学科技园发展到一定阶段以后必须及时建立企业化的运作模式。针对我国大学科技园存在的问题,讨论了如何将国外先进经验与我国实际情况结合,构建适应我国国情的大学科技园运作模式。     

Economics and underdetermination: a case study of urban land and housing economics

Underdetermination, associated with the Duhem–Quine thesis,is a familiar if under-researched theme in economics. In thelight of this, we examine the development of urban land andhousing economics. Through its Cartesian dualistic delineationof theory and data, the contemporary mainstream approach appearsunable to circumvent the problem of underdetermination. In effect,it employs the strong version of Duhem–Quine in its retentionof the assumption of a single, unitary competitive market (andassociated access–space trade-off). Conversely, we highlightthe affinity of Ely's (and the later Columbia School's) approachto pragmatists Dewey and Peirce, which provides a more fruitfulbasis for explanation.     

浅析"y=T"的合理性

在双层绕组中,线圈的节距y小于极距τ.这是因为正弦交流电会产生多次谐波,其中的5、7次谐波对电机转动力矩的影响最大,在现实生活中,为了有效地削弱磁场中的高次谐波,改善电动机运行性能,我们采用y=T,这是合理的.     

浅析我国食品冷藏物流的现状及对策

从分析冷藏物流的概念和我国冷藏物流现状入手，阐述了我国在冷藏物流上的特点和问题，并针对这些问题提出了我国发展冷藏物流的对策。     

土地增值税的税收筹划

税收筹划是指纳税人在税收法规允许的范围内，通过对经营、投资、理财活动的事先安排和筹划。达到减轻税负，实现税后收益最大化的目的。本文从利息支出扣除、合理定价、有关房产销售中代收费用、不同增值率的房产是否合并等四个方面并结合具体的案例对土地增值税的税收筹划进行了探讨。     

盾构机自动控制技术现状与展望

随着我国社会主义市场经济的迅速发展以及科技水平的不断提升,交通运输行业得到了蓬勃发展。当前,随着我国地下隧道等各项工程建设数量的不断上升,盾构机的重要性不言而喻。加强对盾构机自动控制技术的研究工作,不断解决该技术应用过程中存在的问题,才能有效地促进技术应用,为提升我国地下工程施工安全性能奠定技术支持。     

我国港机CAD的应用与发展

港口装卸机械产品计算机辅助设计简称港机ＣＡＤ，本文介绍了港机ＣＡＤ１５年来发展的历程和取得的成绩，指出了港机ＣＡＤ今后的研究方向与课题。     

广州-茂名铁路换碴施工技术

文章介绍了有关在广州-茂名铁路运用大型养路机械进行更换石碴施工的技术和经验。