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.     

Drug testing and the law

This article examines judicial and administrative rulings and legislation involving or related to the issue of drug testing in the workplace. It discusses the rights of employees in the public and private sector: constitutional rights; federal, state, and local statutory rights; rights of unionized employees; and common law rights. It analyzes both the current state of the law and future directions the law may take, as courts and administrative agencies decide more cases and governmental bodies continue to pass drug testing legislation.     

死者发表权与隐私权的冲突与保护

死者发表权的行使既要从公益出发、鼓励作品传播,又要以个性为落脚点保护作者合法权益。死者发表权的代行者出于个人利益往往会侵犯死者的隐私权。隐私权是人身权,是精神权利;发表权是著作权,有一定的物质利益,所以,发表权的行使应受到限制,在合理推定死者生前意图的基础上,最大限度地保护死者的隐私权。     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.     

基于人文关怀的和谐旅游城市发展模式研究--以上海市为例

从人文关怀的角度出发,探讨了和谐旅游城市的内涵,认为和谐旅游城市发展模式是旅游城市的发展目标和高级阶段,并以上海市为例,提出了建设和谐旅游城市的对策与建议.     

Contagion or Competition: Going Concern Audit Opinions for Real Estate Firms

We examine whether intra–industry information transfers from going–concern audit opinion announcements create contagion or competitive stock price reactions for other real estate firms operating in the same line of business. Using returns from publicly-traded land subdivision/development firms and Real Estate Investment Trusts, we find modest evidence supporting a competitive effect among rival firms as a result of another real estate firm announcing the receipt of a Going Concern Opinion (GCO) from its independent auditors.     

Balance sheet conservatism and audit reporting conservatism

Financial reports are prepared on a going‐concern (GC) basis rather than a liquidation basis even when companies are highly distressed. This allows distressed companies to report book values of assets that greatly exceed their liquidation values, implying a lack of conservatism in the balance sheet. We argue that auditors issue going‐concern opinions in order to warn investors about this lack of balance sheet conservatism. This argument leads to two testable hypotheses. First, for companies that are at risk of bankruptcy, auditors are more likely to issue GC opinions when the book values of assets under the GC assumption are high relative to the expected liquidation values of assets (i.e., when the GC assumption causes the balance sheet to lack conservatism). Second, for companies that enter bankruptcy, the issuance of a prior GC opinion has predictive information content with respect to the wedge between the book values of assets and the future liquidation values of those same assets. Our results strongly support both hypotheses. The findings are important because they indicate that conservative audit reporting helps to compensate for a lack of conservatism in the balance sheet, which arises because the GC assumption permits the book values of assets to exceed their liquidation values.     

The Unintended Consequences of the Frequency of PCAOB Inspection

After more than 50 years of self‐regulation of the US auditing profession, the Sarbanes‐Oxley Act of 2002 (SOX) created the Public Company Accounting Oversight Board (PCAOB) as a quasi‐governmental entity with statutory authority to inspect accounting firms that audit public clients. The frequency of this inspection is annual or triennial, based upon the number of public clients the firm audits. We examine the effects of these two levels of inspection frequency on financial reporting quality and audit fees for clients of small and midsize public accounting firms. Our findings provide evidence of significantly higher audit quality and audit fees for clients of annually inspected firms relative to clients of triennially inspected firms. These findings are robust to auditor‐client alignment analyses, propensity score matching, time‐series analyses, examination of firms that have changed from triennial to annual inspection, and particular examination of firms with inspection deficiencies. Overall, our study suggests that the two‐tier frequency system of PCAOB inspection may have also resulted in two‐tier audit quality and audit fee systems for small and midsize public accounting firms, with more frequent inspection leading to more rigorous and informed auditor decisions. We discuss the implications of our results for the Board and the profession at large.     

Throwing It All Away: Exploring Affluent Consumers’ Attitudes Toward Wasting Edible Food

The skewed allocation of food across the world population is a major concern, as the result is that some of us live in abundant supply while others spend their life in constant scarcity. While researchers have studied a variety of issues related to the amounts of food being wasted, or the waste of edible food across consumers with different demographic characteristics, less is known about the drivers of attitudes toward food waste. Based on survey data from a sample of affluent Norwegian consumers, we test how subjective food knowledge, environmental concern, age, and income are related to consumer attitudes toward wasting edible food. We find that all variables except income drive consumer attitudes. Implications for theory and public policy are offered.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.