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.     

External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Uncertainty,story-telling and transformative learning: An instructor’s experience of TEFI’s Walking Workshop in Nepal

ABSTRACT

This article recounts three stories from TEFI’s walking workshop in Nepal: the construction of a road through what was once a trekking path; a dance-floor encounter at a Himalayan party; and the arrival of one participant, fatigued by jet lag and disoriented by the new surroundings. These stories of confusion, discomfort and fear are linked by one common theme: the potential of uncertainty to foster deep reflection and nuanced conclusions. The premise that uncertainty is to be valued and even cultivated has been explored in educational theory, spiritual traditions, and research on transformative learning. These sources affirm the role of uncertainty in the process of knowledge creation. However, accepting this role can be challenging for educators because it requires they assume a new identity, one which they may perceive as being at odds with their status as “teacher” the identity of learner. One way for the educator to address this challenge may be through recounting their own stories of uncertainty. Stories of being immersed in unfamiliar situations that challenge, confuse and even frighten – stories, in other words of being a tourist – can foster reflection on an intellectual, emotional and spiritual level, engaging the “whole” person, and thus initiating the educator/learner’s transformative journey.     

Street-level new public governances in integrated services-as-a-system

The paper explores the emergence of street-level public service integration in eight cities, in five countries using a new framework: services-as-a-system to explore new public governances in health and social care integration. Data is analysed from eight cities in six countries (Australia, Canada, The Netherlands, Spain, UK, and USA) gathered in over 100 semi-structured interviews with key agents. We show that whilst culture and context shape the form and processes of governances there are underlying processual drivers of new public governances, in particular where users are involved in the co-design and co-production of integrated services.     

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.     

Firm-specific assets and the internationalization–performance relationship in the U.S. movie studio industry

We study the role that firm-specific assets (FSAs) play in the processes underlying the internationalization–performance relationship. International business scholars have begun studying the interrelationship between FSAs, internationalization, and performance; however, this research is still emergent, and has produced inconclusive results. We believe that this may be due, in part, to research designs involving the same FSAs across many industries, even though individual industries may rely on different FSAs in their internationalization strategies. We address this issue in a single industry study of U.S. movie studios, which typically rely on blockbuster production capabilities as FSAs in their internationalization efforts. We show how these FSAs co-evolve with firms’ degree of internationalization, and how each factor mediates the positive effect of the other on performance. Our results highlight the importance of studying specific industries and their salient FSAs when assessing their role of such resources in the internationalization–performance relationship.     

摩托车单向器自动装配机气动系统设计

针对当前摩托车单向器组件主要采用手工装配、装配效率低、随机性大、无法对装配过程实现有效管理控制的情况,为了易于对零件装配过程进行实时监测和提高生产效率,开发了一种智能摩托车单向器自动装配机。介绍了摩托车单向器自动装配机的上料系统、装配系统、检测系统和落料系统的机械结构,重点阐述了设备的气动系统设计,并进行了实验研究。结果表明,该设备可实现组成单向器的5种零件自动上料和装配,同时降低了操作人员的劳动强度,生产效率提高约50%。其气动系统噪音小、工作可靠,可应用于摩托车单向器的大批量生产。研究结果可为其他相似异形零件自动装配机的开发提供参考。     

The Right Choice at the Right Time: A Herding Experiment in Endogenous Time

Herding describes the phenomenon in decision-making where an economic agent disregards his own private information to follow the actions of his predecessors as in Banerjee (1992). With later decision-makers simply copying earlier decisions their private information cannot be inferred by other decision-makers and will be forever lost. There is some experimental evidence on simple sequential herding of this type in the literature, notably Anderson and Holt (1997). This paper differs by allowing subjects to delay their decision-making in order to benefit from observing others' actions as in more recent herding models such as Chamley and Gale (1994). The results in this paper suggest that subjects will indeed delay when their private information is not sufficiently strong. Despite this ability to wait, as predicted in the theoretical literature, cascades remained ubiquitous and more worrying still, reverse-cascades occurred in which incorrect decisions made by early decision-makers produced informational cascades on the wrong action. In an alternative design, informing subjects that they had made incorrect choices only made matters worse as subjects moved further away from rational behavior.