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.     

Effects of state-sponsored human capital investment on the selection of training type

Since the 1990s, firms in Japan have reduced their human capital investment in the workplace to minimize costs. Moreover, in response to the increase in the number of non-regular employees and turnover rates, workers need to have greater incentive to make the self-motivated investment in themselves for their self-protection. In this study, we first estimate the effects of workers’ self-motivated investment in themselves on wage rates. Next, we explore who is likely to participate in which training type and accordingly estimate the effects of the self-motivated investment on wage rates by training type. Our estimates controlling for individual-level fixed-effects indicate that the return is significantly positive and particularly high for practical training related to workers’ current jobs, and regular workers tend to self-select these higher-returns programs, while non-regular workers are more likely to enroll in lower-returns programs, such as schooling. This trend in investment in oneself could potentially increase the wage inequality between regular and non-regular workers through the self-selection of training types. Our estimates reveal that receiving the training and education benefit raises the likelihood for workers to participate in a high-return training program regardless of whether they are non-regular or regular workers. This suggests that government benefits on self-investment change workers’ self-selection of training type and serve to promote practical trainings that lead to high returns.     

数量驱动或创新驱动：异质型人力资本对经济增长的机制演进

基于1987-2017年中国省际面板数据，运用SYS-GMM模型研究异质型人力资本对经济增长的影响。在区分高级与基本人力资本基础上，基于招生率和收入维度，从全局和阶段差异视角剖析异质型人力资本对经济提速的机制演进。研究发现：全局而言，两种测度下基础和高级人力资本各自以数量及创新方式驱动经济增长，并呈现以数量驱动为主，数量、创新双驱动的增长模式；从演进视角看，两种测度均表明，高级人力资本创新驱动是新常态阶段经济增长的核心动力，但相比旧常态阶段，基础人力资本数量驱动日趋弱化并呈异质性；进一步研究表明，产业结构、城镇化及开放程度均对创新驱动有不同程度激励效应，而城镇化是挤出数量驱动的主要推手。     

The Importance of Tax Adjustments When Evaluating Wage Expectations*

Using elicited expectations of future gross salaries, we evaluate characteristics causing German students to make larger or smaller estimation errors. While students seem to underestimate actual salaries by 18 percent, we show that these errors are highly attributable to misconceptions of the progressive income tax. Developing a suitable adjustment procedure, we correct students’ estimates and find that errors decline by 12 percentage points. Conducting regression analyses, we reveal strong connections with students’ age, gender, work experience, secondary school track, and knowledge about student loans. These results change notably if not controlling for students’ misconceptions of the tax system.     

Designing awareness campaigns to counter human trafficking: An analytic approach

Human trafficking – the trade of persons for the gain of others – is a pervasive societal issue. Ongoing trafficking recruitment calls for targeted prevention strategies for individuals and communities at risk. Awareness campaigns, arguably the most common form of trafficking prevention, are intended to alert individuals to the tactics used by traffickers and provide them with practical strategies to avoid deception and exploitation. However there is a shortage of technical solutions regarding the analysis, impact assessment, and evaluation of alternatives related to implementing awareness campaigns. We present a resource allocation model that can be used to determine the allocation of funds that maximizes increases in trafficking awareness among at-risk populations. We apply this model to determine the allocation of a limited budget among different types of prevention programs in at-risk populations in Nepal. For each awareness campaign, we estimate a production function that relates the amount invested to the associated change in awareness. When allocating limited resources, decision makers must balance competing interests, and we illustrate the impact of three allocation policies on trafficking awareness. The model presented in this article is a first attempt to help decision makers evaluate how to allocate funds in the context of trafficking awareness.     

Small system dynamics model for alleviating the general practitioners rural care gap in Ontario,Canada

The disproportionate concentration of healthcare professionals in urban areas is a concern in many countries, including Canada. A need to address this rural care gap has driven a large number of government led initiatives worldwide over the years. This paper presents a model that can be used as a tool to examine the efficacy of such policies on the workforce distribution in the long term. A small system dynamics model is employed to simulate the current and future distribution of general physicians at a jurisdictional level. The model represents the transition of general practitioners to provide insight into the dynamics of care provision over time. The movement, and competition, between rural and urban areas is modeled to enable detailed exploration of the ability for proposed measures to alleviate the care gap in the future. Among the tested policies are such commonly used initiatives as financial incentives to rural professionals, promotion of medical education in rural areas, expansion of rural education programs and the engagement of international medical graduates etc. We demonstrate how the model can be used as a tool to determine an efficient and well-chosen combination of policies which can help alleviate the rural care gap in the future, given that some policies are more effective than others alone but also combined with other initiatives. The presented small system dynamics model is tested on Canada's reality, but its simple nature lends itself to easy application to other countries that experience a similar problem.     

The impact of foreign direct investment on productivity: New evidence for developing countries

We provide new empirical evidence on the relationship between inward foreign direct investment (FDI) and total factor productivity (TFP) growth using cross-country data for 51 developing countries over the period 1984–2010. Our results suggest a weak direct effect of FDI on TFP growth but, after accounting for the roles of human capital and institutions as contingencies in the FDI-TFP growth relationship, we find a robust FDI-induced productivity growth response dependent on these ‘absorptive capacities’. However, the relevance of the human capital contingency effect diminishes when the effect of institutions is also considered, which suggests that improving institutions is relatively more important than human capital development for developing countries to realise productivity gains from FDI.     

The dynamic impact of intellectual capital on firm value: evidence from China

We examine the dynamic relationship between intellectual capital (measured by human capital and organizational capital) and firm value of Chinese listed firms. We do causality identification using system GMM and IV estimation, and find no significant relation between human capital and firm value, but organizational capital positively affects firm value with a lag. Our findings are robust to firms with different property rights, of different sizes, or in different industries, with the only exception of capital-intensive firms, in which human capital has a significant influence on firm value. The results imply that the improvement of organizational system plays a more important role in raising the value of a firm in a typical developing country, like China.     

“Data localization”: The internet in the balance

There is a steady global trend towards “Data Localization,” laws by which data is required to be maintained and processed within the geographic boundaries of its state of origin. This development has raised concerns about its possible adverse impacts on emerging data-intensive technologies such as Cloud services/E-commerce, Big Data, Artificial Intelligence and the Internet of Things (collectively, the Embedded Infosphere). The inability to reach an international agreement on rules for cross-border data flows may have significant adverse consequences for all future users of the Internet.The basis of Data Localization is grounded in two distinct but inter-related policy models: Data Sovereignty and Trans-Border Data Flows. These two concepts have different origins. “Data Sovereignty” is derived from the historic power of a state of absolute and exclusive control within its geographic borders. Policies behind TBDFs arose in Europe following World War II, primarily motivated by Nazi use of early proto-computers to help round up Jews and others. As they have evolved, TBDF policies have been directed primarily at protecting personal data and privacy.This article first examines the issues of: 1) “Information Sovereignty” and 2) TBDFs. It then describes the arguments for and against “Data Localization,” offers some examples of strong localization policies (Russia, China), and summarizes contesting policy proposals. It then contextualizes TBDF with issues of human rights (free flow of information) and privacy.While the utility of an international agreement on TBDFs is clear, the differences in approaches are tenacious. For the free-market developed world (e.g, EU, OECD), the path forward seems to lead through policy convergence to compatible rules, with differentiated levels of data protection and accountability. It is far from clear whether these rules will address, in a mandatory way, issues of the “free flow” of information in the human rights sense. At the same time, there are countries (e.g., BRICS), representing a majority of the world's population, in which political and cultural resistance will produce stringent Cyber Sovereignty and Data Localization policies with few if any human rights components.The article concludes that the more the Internet is “localized”, the more attenuated its benefits will become. The negative consequences of Data Localization will become increasingly obvious as new, data-intensive technologies become ubiquitous, creating a condition of “Data Dependence”. It is projected that in the future the nations with the least amount of Data Localization and the most open flow of information will be the most successful in benefiting from new data-intensive embedded, networked technologies. This will most likely be characterized by values adopted as policies and practices in the EU.